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<h1>Source code for pyspark.context</h1><div class="highlight"><pre>
<span></span><span class="c1">#</span>
<span class="c1"># Licensed to the Apache Software Foundation (ASF) under one or more</span>
<span class="c1"># contributor license agreements. See the NOTICE file distributed with</span>
<span class="c1"># this work for additional information regarding copyright ownership.</span>
<span class="c1"># The ASF licenses this file to You under the Apache License, Version 2.0</span>
<span class="c1"># (the &quot;License&quot;); you may not use this file except in compliance with</span>
<span class="c1"># the License. You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="c1">#</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">shutil</span>
<span class="kn">import</span> <span class="nn">signal</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">threading</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">import</span> <span class="nn">importlib</span>
<span class="kn">from</span> <span class="nn">threading</span> <span class="kn">import</span> <span class="n">RLock</span>
<span class="kn">from</span> <span class="nn">tempfile</span> <span class="kn">import</span> <span class="n">NamedTemporaryFile</span>
<span class="kn">from</span> <span class="nn">types</span> <span class="kn">import</span> <span class="n">TracebackType</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">Any</span><span class="p">,</span>
<span class="n">Callable</span><span class="p">,</span>
<span class="n">cast</span><span class="p">,</span>
<span class="n">ClassVar</span><span class="p">,</span>
<span class="n">Dict</span><span class="p">,</span>
<span class="n">Iterable</span><span class="p">,</span>
<span class="n">List</span><span class="p">,</span>
<span class="n">NoReturn</span><span class="p">,</span>
<span class="n">Optional</span><span class="p">,</span>
<span class="n">Sequence</span><span class="p">,</span>
<span class="n">Tuple</span><span class="p">,</span>
<span class="n">Type</span><span class="p">,</span>
<span class="n">TYPE_CHECKING</span><span class="p">,</span>
<span class="n">TypeVar</span><span class="p">,</span>
<span class="n">Set</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">py4j.java_collections</span> <span class="kn">import</span> <span class="n">JavaMap</span>
<span class="kn">from</span> <span class="nn">py4j.protocol</span> <span class="kn">import</span> <span class="n">Py4JError</span>
<span class="kn">from</span> <span class="nn">pyspark</span> <span class="kn">import</span> <span class="n">accumulators</span>
<span class="kn">from</span> <span class="nn">pyspark.accumulators</span> <span class="kn">import</span> <span class="n">Accumulator</span>
<span class="kn">from</span> <span class="nn">pyspark.broadcast</span> <span class="kn">import</span> <span class="n">Broadcast</span><span class="p">,</span> <span class="n">BroadcastPickleRegistry</span>
<span class="kn">from</span> <span class="nn">pyspark.conf</span> <span class="kn">import</span> <span class="n">SparkConf</span>
<span class="kn">from</span> <span class="nn">pyspark.files</span> <span class="kn">import</span> <span class="n">SparkFiles</span>
<span class="kn">from</span> <span class="nn">pyspark.java_gateway</span> <span class="kn">import</span> <span class="n">launch_gateway</span><span class="p">,</span> <span class="n">local_connect_and_auth</span>
<span class="kn">from</span> <span class="nn">pyspark.serializers</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">CPickleSerializer</span><span class="p">,</span>
<span class="n">BatchedSerializer</span><span class="p">,</span>
<span class="n">Serializer</span><span class="p">,</span>
<span class="n">UTF8Deserializer</span><span class="p">,</span>
<span class="n">PairDeserializer</span><span class="p">,</span>
<span class="n">AutoBatchedSerializer</span><span class="p">,</span>
<span class="n">NoOpSerializer</span><span class="p">,</span>
<span class="n">ChunkedStream</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">pyspark.storagelevel</span> <span class="kn">import</span> <span class="n">StorageLevel</span>
<span class="kn">from</span> <span class="nn">pyspark.resource.information</span> <span class="kn">import</span> <span class="n">ResourceInformation</span>
<span class="kn">from</span> <span class="nn">pyspark.rdd</span> <span class="kn">import</span> <span class="n">RDD</span><span class="p">,</span> <span class="n">_load_from_socket</span>
<span class="kn">from</span> <span class="nn">pyspark.taskcontext</span> <span class="kn">import</span> <span class="n">TaskContext</span>
<span class="kn">from</span> <span class="nn">pyspark.traceback_utils</span> <span class="kn">import</span> <span class="n">CallSite</span><span class="p">,</span> <span class="n">first_spark_call</span>
<span class="kn">from</span> <span class="nn">pyspark.status</span> <span class="kn">import</span> <span class="n">StatusTracker</span>
<span class="kn">from</span> <span class="nn">pyspark.profiler</span> <span class="kn">import</span> <span class="n">ProfilerCollector</span><span class="p">,</span> <span class="n">BasicProfiler</span><span class="p">,</span> <span class="n">UDFBasicProfiler</span><span class="p">,</span> <span class="n">MemoryProfiler</span>
<span class="kn">from</span> <span class="nn">pyspark.errors</span> <span class="kn">import</span> <span class="n">PySparkRuntimeError</span>
<span class="kn">from</span> <span class="nn">py4j.java_gateway</span> <span class="kn">import</span> <span class="n">is_instance_of</span><span class="p">,</span> <span class="n">JavaGateway</span><span class="p">,</span> <span class="n">JavaObject</span><span class="p">,</span> <span class="n">JVMView</span>
<span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">pyspark.accumulators</span> <span class="kn">import</span> <span class="n">AccumulatorParam</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;SparkContext&quot;</span><span class="p">]</span>
<span class="c1"># These are special default configs for PySpark, they will overwrite</span>
<span class="c1"># the default ones for Spark if they are not configured by user.</span>
<span class="n">DEFAULT_CONFIGS</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">&quot;spark.serializer.objectStreamReset&quot;</span><span class="p">:</span> <span class="mi">100</span><span class="p">,</span>
<span class="s2">&quot;spark.rdd.compress&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
<span class="p">}</span>
<span class="n">T</span> <span class="o">=</span> <span class="n">TypeVar</span><span class="p">(</span><span class="s2">&quot;T&quot;</span><span class="p">)</span>
<span class="n">U</span> <span class="o">=</span> <span class="n">TypeVar</span><span class="p">(</span><span class="s2">&quot;U&quot;</span><span class="p">)</span>
<div class="viewcode-block" id="SparkContext"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.html#pyspark.SparkContext">[docs]</a><span class="k">class</span> <span class="nc">SparkContext</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Main entry point for Spark functionality. A SparkContext represents the</span>
<span class="sd"> connection to a Spark cluster, and can be used to create :class:`RDD` and</span>
<span class="sd"> broadcast variables on that cluster.</span>
<span class="sd"> When you create a new SparkContext, at least the master and app name should</span>
<span class="sd"> be set, either through the named parameters here or through `conf`.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> master : str, optional</span>
<span class="sd"> Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]).</span>
<span class="sd"> appName : str, optional</span>
<span class="sd"> A name for your job, to display on the cluster web UI.</span>
<span class="sd"> sparkHome : str, optional</span>
<span class="sd"> Location where Spark is installed on cluster nodes.</span>
<span class="sd"> pyFiles : list, optional</span>
<span class="sd"> Collection of .zip or .py files to send to the cluster</span>
<span class="sd"> and add to PYTHONPATH. These can be paths on the local file</span>
<span class="sd"> system or HDFS, HTTP, HTTPS, or FTP URLs.</span>
<span class="sd"> environment : dict, optional</span>
<span class="sd"> A dictionary of environment variables to set on</span>
<span class="sd"> worker nodes.</span>
<span class="sd"> batchSize : int, optional, default 0</span>
<span class="sd"> The number of Python objects represented as a single</span>
<span class="sd"> Java object. Set 1 to disable batching, 0 to automatically choose</span>
<span class="sd"> the batch size based on object sizes, or -1 to use an unlimited</span>
<span class="sd"> batch size</span>
<span class="sd"> serializer : :class:`Serializer`, optional, default :class:`CPickleSerializer`</span>
<span class="sd"> The serializer for RDDs.</span>
<span class="sd"> conf : :class:`SparkConf`, optional</span>
<span class="sd"> An object setting Spark properties.</span>
<span class="sd"> gateway : class:`py4j.java_gateway.JavaGateway`, optional</span>
<span class="sd"> Use an existing gateway and JVM, otherwise a new JVM</span>
<span class="sd"> will be instantiated. This is only used internally.</span>
<span class="sd"> jsc : class:`py4j.java_gateway.JavaObject`, optional</span>
<span class="sd"> The JavaSparkContext instance. This is only used internally.</span>
<span class="sd"> profiler_cls : type, optional, default :class:`BasicProfiler`</span>
<span class="sd"> A class of custom Profiler used to do profiling</span>
<span class="sd"> udf_profiler_cls : type, optional, default :class:`UDFBasicProfiler`</span>
<span class="sd"> A class of custom Profiler used to do udf profiling</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> Only one :class:`SparkContext` should be active per JVM. You must `stop()`</span>
<span class="sd"> the active :class:`SparkContext` before creating a new one.</span>
<span class="sd"> :class:`SparkContext` instance is not supported to share across multiple</span>
<span class="sd"> processes out of the box, and PySpark does not guarantee multi-processing execution.</span>
<span class="sd"> Use threads instead for concurrent processing purpose.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.context import SparkContext</span>
<span class="sd"> &gt;&gt;&gt; sc = SparkContext(&#39;local&#39;, &#39;test&#39;)</span>
<span class="sd"> &gt;&gt;&gt; sc2 = SparkContext(&#39;local&#39;, &#39;test2&#39;) # doctest: +IGNORE_EXCEPTION_DETAIL</span>
<span class="sd"> Traceback (most recent call last):</span>
<span class="sd"> ...</span>
<span class="sd"> ValueError: ...</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">_gateway</span><span class="p">:</span> <span class="n">ClassVar</span><span class="p">[</span><span class="n">Optional</span><span class="p">[</span><span class="n">JavaGateway</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">_jvm</span><span class="p">:</span> <span class="n">ClassVar</span><span class="p">[</span><span class="n">Optional</span><span class="p">[</span><span class="n">JVMView</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">_next_accum_id</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">_active_spark_context</span><span class="p">:</span> <span class="n">ClassVar</span><span class="p">[</span><span class="n">Optional</span><span class="p">[</span><span class="s2">&quot;SparkContext&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">_lock</span> <span class="o">=</span> <span class="n">RLock</span><span class="p">()</span>
<span class="n">_python_includes</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span>
<span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>
<span class="p">]</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># zip and egg files that need to be added to PYTHONPATH</span>
<span class="n">serializer</span><span class="p">:</span> <span class="n">Serializer</span>
<span class="n">profiler_collector</span><span class="p">:</span> <span class="n">ProfilerCollector</span>
<span class="n">PACKAGE_EXTENSIONS</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;.zip&quot;</span><span class="p">,</span> <span class="s2">&quot;.egg&quot;</span><span class="p">,</span> <span class="s2">&quot;.jar&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">master</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">appName</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">sparkHome</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">pyFiles</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">environment</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">batchSize</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
<span class="n">serializer</span><span class="p">:</span> <span class="s2">&quot;Serializer&quot;</span> <span class="o">=</span> <span class="n">CPickleSerializer</span><span class="p">(),</span>
<span class="n">conf</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">SparkConf</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">gateway</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">JavaGateway</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">jsc</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">JavaObject</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">profiler_cls</span><span class="p">:</span> <span class="n">Type</span><span class="p">[</span><span class="n">BasicProfiler</span><span class="p">]</span> <span class="o">=</span> <span class="n">BasicProfiler</span><span class="p">,</span>
<span class="n">udf_profiler_cls</span><span class="p">:</span> <span class="n">Type</span><span class="p">[</span><span class="n">UDFBasicProfiler</span><span class="p">]</span> <span class="o">=</span> <span class="n">UDFBasicProfiler</span><span class="p">,</span>
<span class="n">memory_profiler_cls</span><span class="p">:</span> <span class="n">Type</span><span class="p">[</span><span class="n">MemoryProfiler</span><span class="p">]</span> <span class="o">=</span> <span class="n">MemoryProfiler</span><span class="p">,</span>
<span class="p">):</span>
<span class="k">if</span> <span class="s2">&quot;SPARK_CONNECT_MODE_ENABLED&quot;</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span> <span class="ow">and</span> <span class="s2">&quot;SPARK_LOCAL_REMOTE&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">:</span>
<span class="k">raise</span> <span class="n">PySparkRuntimeError</span><span class="p">(</span>
<span class="n">error_class</span><span class="o">=</span><span class="s2">&quot;CONTEXT_UNAVAILABLE_FOR_REMOTE_CLIENT&quot;</span><span class="p">,</span>
<span class="n">message_parameters</span><span class="o">=</span><span class="p">{},</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">conf</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">conf</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;spark.executor.allowSparkContext&quot;</span><span class="p">,</span> <span class="s2">&quot;false&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">!=</span> <span class="s2">&quot;true&quot;</span><span class="p">:</span>
<span class="c1"># In order to prevent SparkContext from being created in executors.</span>
<span class="n">SparkContext</span><span class="o">.</span><span class="n">_assert_on_driver</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_callsite</span> <span class="o">=</span> <span class="n">first_spark_call</span><span class="p">()</span> <span class="ow">or</span> <span class="n">CallSite</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="k">if</span> <span class="n">gateway</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">gateway</span><span class="o">.</span><span class="n">gateway_parameters</span><span class="o">.</span><span class="n">auth_token</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s2">&quot;You are trying to pass an insecure Py4j gateway to Spark. This&quot;</span>
<span class="s2">&quot; is not allowed as it is a security risk.&quot;</span>
<span class="p">)</span>
<span class="n">SparkContext</span><span class="o">.</span><span class="n">_ensure_initialized</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">gateway</span><span class="o">=</span><span class="n">gateway</span><span class="p">,</span> <span class="n">conf</span><span class="o">=</span><span class="n">conf</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_do_init</span><span class="p">(</span>
<span class="n">master</span><span class="p">,</span>
<span class="n">appName</span><span class="p">,</span>
<span class="n">sparkHome</span><span class="p">,</span>
<span class="n">pyFiles</span><span class="p">,</span>
<span class="n">environment</span><span class="p">,</span>
<span class="n">batchSize</span><span class="p">,</span>
<span class="n">serializer</span><span class="p">,</span>
<span class="n">conf</span><span class="p">,</span>
<span class="n">jsc</span><span class="p">,</span>
<span class="n">profiler_cls</span><span class="p">,</span>
<span class="n">udf_profiler_cls</span><span class="p">,</span>
<span class="n">memory_profiler_cls</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">except</span> <span class="ne">BaseException</span><span class="p">:</span>
<span class="c1"># If an error occurs, clean up in order to allow future SparkContext creation:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
<span class="k">raise</span>
<span class="k">def</span> <span class="nf">_do_init</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">master</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
<span class="n">appName</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
<span class="n">sparkHome</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
<span class="n">pyFiles</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]],</span>
<span class="n">environment</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]],</span>
<span class="n">batchSize</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="n">serializer</span><span class="p">:</span> <span class="n">Serializer</span><span class="p">,</span>
<span class="n">conf</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">SparkConf</span><span class="p">],</span>
<span class="n">jsc</span><span class="p">:</span> <span class="n">JavaObject</span><span class="p">,</span>
<span class="n">profiler_cls</span><span class="p">:</span> <span class="n">Type</span><span class="p">[</span><span class="n">BasicProfiler</span><span class="p">]</span> <span class="o">=</span> <span class="n">BasicProfiler</span><span class="p">,</span>
<span class="n">udf_profiler_cls</span><span class="p">:</span> <span class="n">Type</span><span class="p">[</span><span class="n">UDFBasicProfiler</span><span class="p">]</span> <span class="o">=</span> <span class="n">UDFBasicProfiler</span><span class="p">,</span>
<span class="n">memory_profiler_cls</span><span class="p">:</span> <span class="n">Type</span><span class="p">[</span><span class="n">MemoryProfiler</span><span class="p">]</span> <span class="o">=</span> <span class="n">MemoryProfiler</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">environment</span> <span class="o">=</span> <span class="n">environment</span> <span class="ow">or</span> <span class="p">{}</span>
<span class="c1"># java gateway must have been launched at this point.</span>
<span class="k">if</span> <span class="n">conf</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">conf</span><span class="o">.</span><span class="n">_jconf</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="c1"># conf has been initialized in JVM properly, so use conf directly. This represents the</span>
<span class="c1"># scenario that JVM has been launched before SparkConf is created (e.g. SparkContext is</span>
<span class="c1"># created and then stopped, and we create a new SparkConf and new SparkContext again)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_conf</span> <span class="o">=</span> <span class="n">conf</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_conf</span> <span class="o">=</span> <span class="n">SparkConf</span><span class="p">(</span><span class="n">_jvm</span><span class="o">=</span><span class="n">SparkContext</span><span class="o">.</span><span class="n">_jvm</span><span class="p">)</span>
<span class="k">if</span> <span class="n">conf</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">conf</span><span class="o">.</span><span class="n">getAll</span><span class="p">():</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_batchSize</span> <span class="o">=</span> <span class="n">batchSize</span> <span class="c1"># -1 represents an unlimited batch size</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_unbatched_serializer</span> <span class="o">=</span> <span class="n">serializer</span>
<span class="k">if</span> <span class="n">batchSize</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">serializer</span> <span class="o">=</span> <span class="n">AutoBatchedSerializer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_unbatched_serializer</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">serializer</span> <span class="o">=</span> <span class="n">BatchedSerializer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_unbatched_serializer</span><span class="p">,</span> <span class="n">batchSize</span><span class="p">)</span>
<span class="c1"># Set any parameters passed directly to us on the conf</span>
<span class="k">if</span> <span class="n">master</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">setMaster</span><span class="p">(</span><span class="n">master</span><span class="p">)</span>
<span class="k">if</span> <span class="n">appName</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">setAppName</span><span class="p">(</span><span class="n">appName</span><span class="p">)</span>
<span class="k">if</span> <span class="n">sparkHome</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">setSparkHome</span><span class="p">(</span><span class="n">sparkHome</span><span class="p">)</span>
<span class="k">if</span> <span class="n">environment</span><span class="p">:</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">environment</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">setExecutorEnv</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">DEFAULT_CONFIGS</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">setIfMissing</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
<span class="c1"># Check that we have at least the required parameters</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">contains</span><span class="p">(</span><span class="s2">&quot;spark.master&quot;</span><span class="p">):</span>
<span class="k">raise</span> <span class="n">PySparkRuntimeError</span><span class="p">(</span>
<span class="n">error_class</span><span class="o">=</span><span class="s2">&quot;MASTER_URL_NOT_SET&quot;</span><span class="p">,</span>
<span class="n">message_parameters</span><span class="o">=</span><span class="p">{},</span>
<span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">contains</span><span class="p">(</span><span class="s2">&quot;spark.app.name&quot;</span><span class="p">):</span>
<span class="k">raise</span> <span class="n">PySparkRuntimeError</span><span class="p">(</span>
<span class="n">error_class</span><span class="o">=</span><span class="s2">&quot;APPLICATION_NAME_NOT_SET&quot;</span><span class="p">,</span>
<span class="n">message_parameters</span><span class="o">=</span><span class="p">{},</span>
<span class="p">)</span>
<span class="c1"># Read back our properties from the conf in case we loaded some of them from</span>
<span class="c1"># the classpath or an external config file</span>
<span class="bp">self</span><span class="o">.</span><span class="n">master</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;spark.master&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">appName</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;spark.app.name&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sparkHome</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;spark.home&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="k">for</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">getAll</span><span class="p">():</span>
<span class="k">if</span> <span class="n">k</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;spark.executorEnv.&quot;</span><span class="p">):</span>
<span class="n">varName</span> <span class="o">=</span> <span class="n">k</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="s2">&quot;spark.executorEnv.&quot;</span><span class="p">)</span> <span class="p">:]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">environment</span><span class="p">[</span><span class="n">varName</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
<span class="bp">self</span><span class="o">.</span><span class="n">environment</span><span class="p">[</span><span class="s2">&quot;PYTHONHASHSEED&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;PYTHONHASHSEED&quot;</span><span class="p">,</span> <span class="s2">&quot;0&quot;</span><span class="p">)</span>
<span class="c1"># Create the Java SparkContext through Py4J</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span> <span class="o">=</span> <span class="n">jsc</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_initialize_context</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">_jconf</span><span class="p">)</span>
<span class="c1"># Reset the SparkConf to the one actually used by the SparkContext in JVM.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_conf</span> <span class="o">=</span> <span class="n">SparkConf</span><span class="p">(</span><span class="n">_jconf</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">()</span><span class="o">.</span><span class="n">conf</span><span class="p">())</span>
<span class="c1"># Create a single Accumulator in Java that we&#39;ll send all our updates through;</span>
<span class="c1"># they will be passed back to us through a TCP server</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gateway</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">auth_token</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gateway</span><span class="o">.</span><span class="n">gateway_parameters</span><span class="o">.</span><span class="n">auth_token</span>
<span class="n">start_update_server</span> <span class="o">=</span> <span class="n">accumulators</span><span class="o">.</span><span class="n">_start_update_server</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_accumulatorServer</span> <span class="o">=</span> <span class="n">start_update_server</span><span class="p">(</span><span class="n">auth_token</span><span class="p">)</span>
<span class="p">(</span><span class="n">host</span><span class="p">,</span> <span class="n">port</span><span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_accumulatorServer</span><span class="o">.</span><span class="n">server_address</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_javaAccumulator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonAccumulatorV2</span><span class="p">(</span><span class="n">host</span><span class="p">,</span> <span class="n">port</span><span class="p">,</span> <span class="n">auth_token</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">()</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_javaAccumulator</span><span class="p">)</span>
<span class="c1"># If encryption is enabled, we need to setup a server in the jvm to read broadcast</span>
<span class="c1"># data via a socket.</span>
<span class="c1"># scala&#39;s mangled names w/ $ in them require special treatment.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_encryption_enabled</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonUtils</span><span class="o">.</span><span class="n">isEncryptionEnabled</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;SPARK_AUTH_SOCKET_TIMEOUT&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonUtils</span><span class="o">.</span><span class="n">getPythonAuthSocketTimeout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;SPARK_BUFFER_SIZE&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonUtils</span><span class="o">.</span><span class="n">getSparkBufferSize</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pythonExec</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;PYSPARK_PYTHON&quot;</span><span class="p">,</span> <span class="s2">&quot;python3&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pythonVer</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">%d</span><span class="s2">.</span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
<span class="c1"># Broadcast&#39;s __reduce__ method stores Broadcast instances here.</span>
<span class="c1"># This allows other code to determine which Broadcast instances have</span>
<span class="c1"># been pickled, so it can determine which Java broadcast objects to</span>
<span class="c1"># send.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_pickled_broadcast_vars</span> <span class="o">=</span> <span class="n">BroadcastPickleRegistry</span><span class="p">()</span>
<span class="n">SparkFiles</span><span class="o">.</span><span class="n">_sc</span> <span class="o">=</span> <span class="bp">self</span>
<span class="n">root_dir</span> <span class="o">=</span> <span class="n">SparkFiles</span><span class="o">.</span><span class="n">getRootDirectory</span><span class="p">()</span>
<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">root_dir</span><span class="p">)</span>
<span class="c1"># Deploy any code dependencies specified in the constructor</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_python_includes</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">pyFiles</span> <span class="ow">or</span> <span class="p">[]:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">addPyFile</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
<span class="c1"># Deploy code dependencies set by spark-submit; these will already have been added</span>
<span class="c1"># with SparkContext.addFile, so we just need to add them to the PYTHONPATH</span>
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;spark.submit.pyFiles&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">):</span>
<span class="k">if</span> <span class="n">path</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
<span class="p">(</span><span class="n">dirname</span><span class="p">,</span> <span class="n">filename</span><span class="p">)</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">filepath</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">SparkFiles</span><span class="o">.</span><span class="n">getRootDirectory</span><span class="p">(),</span> <span class="n">filename</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">filepath</span><span class="p">):</span>
<span class="c1"># In case of YARN with shell mode, &#39;spark.submit.pyFiles&#39; files are</span>
<span class="c1"># not added via SparkContext.addFile. Here we check if the file exists,</span>
<span class="c1"># try to copy and then add it to the path. See SPARK-21945.</span>
<span class="n">shutil</span><span class="o">.</span><span class="n">copyfile</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">filepath</span><span class="p">)</span>
<span class="k">if</span> <span class="n">filename</span><span class="p">[</span><span class="o">-</span><span class="mi">4</span><span class="p">:]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">PACKAGE_EXTENSIONS</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_python_includes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">filepath</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
<span class="s2">&quot;Failed to add file [</span><span class="si">%s</span><span class="s2">] specified in &#39;spark.submit.pyFiles&#39; to &quot;</span>
<span class="s2">&quot;Python path:</span><span class="se">\n</span><span class="s2"> </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2"> &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="p">)),</span>
<span class="ne">RuntimeWarning</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># Create a temporary directory inside spark.local.dir:</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">local_dir</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">org</span><span class="o">.</span><span class="n">apache</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">Utils</span><span class="o">.</span><span class="n">getLocalDir</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">()</span><span class="o">.</span><span class="n">conf</span><span class="p">())</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_temp_dir</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">org</span><span class="o">.</span><span class="n">apache</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">Utils</span><span class="o">.</span><span class="n">createTempDir</span><span class="p">(</span>
<span class="n">local_dir</span><span class="p">,</span> <span class="s2">&quot;pyspark&quot;</span>
<span class="p">)</span><span class="o">.</span><span class="n">getAbsolutePath</span><span class="p">()</span>
<span class="c1"># profiling stats collected for each PythonRDD</span>
<span class="k">if</span> <span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;spark.python.profile&quot;</span><span class="p">,</span> <span class="s2">&quot;false&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;true&quot;</span>
<span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;spark.python.profile.memory&quot;</span><span class="p">,</span> <span class="s2">&quot;false&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;true&quot;</span>
<span class="p">):</span>
<span class="n">dump_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;spark.python.profile.dump&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">profiler_collector</span> <span class="o">=</span> <span class="n">ProfilerCollector</span><span class="p">(</span>
<span class="n">profiler_cls</span><span class="p">,</span> <span class="n">udf_profiler_cls</span><span class="p">,</span> <span class="n">memory_profiler_cls</span><span class="p">,</span> <span class="n">dump_path</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">profiler_collector</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># type: ignore[assignment]</span>
<span class="c1"># create a signal handler which would be invoked on receiving SIGINT</span>
<span class="k">def</span> <span class="nf">signal_handler</span><span class="p">(</span><span class="n">signal</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">frame</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">NoReturn</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cancelAllJobs</span><span class="p">()</span>
<span class="k">raise</span> <span class="ne">KeyboardInterrupt</span><span class="p">()</span>
<span class="c1"># see http://stackoverflow.com/questions/23206787/</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span>
<span class="n">threading</span><span class="o">.</span><span class="n">current_thread</span><span class="p">(),</span> <span class="n">threading</span><span class="o">.</span><span class="n">_MainThread</span> <span class="c1"># type: ignore[attr-defined]</span>
<span class="p">):</span>
<span class="n">signal</span><span class="o">.</span><span class="n">signal</span><span class="p">(</span><span class="n">signal</span><span class="o">.</span><span class="n">SIGINT</span><span class="p">,</span> <span class="n">signal_handler</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="k">return</span> <span class="s2">&quot;&lt;SparkContext master=</span><span class="si">{master}</span><span class="s2"> appName=</span><span class="si">{appName}</span><span class="s2">&gt;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">master</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">master</span><span class="p">,</span>
<span class="n">appName</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">appName</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">def</span> <span class="nf">_repr_html_</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="k">return</span> <span class="s2">&quot;&quot;&quot;</span>
<span class="s2"> &lt;div&gt;</span>
<span class="s2"> &lt;p&gt;&lt;b&gt;SparkContext&lt;/b&gt;&lt;/p&gt;</span>
<span class="s2"> &lt;p&gt;&lt;a href=&quot;</span><span class="si">{sc.uiWebUrl}</span><span class="s2">&quot;&gt;Spark UI&lt;/a&gt;&lt;/p&gt;</span>
<span class="s2"> &lt;dl&gt;</span>
<span class="s2"> &lt;dt&gt;Version&lt;/dt&gt;</span>
<span class="s2"> &lt;dd&gt;&lt;code&gt;v</span><span class="si">{sc.version}</span><span class="s2">&lt;/code&gt;&lt;/dd&gt;</span>
<span class="s2"> &lt;dt&gt;Master&lt;/dt&gt;</span>
<span class="s2"> &lt;dd&gt;&lt;code&gt;</span><span class="si">{sc.master}</span><span class="s2">&lt;/code&gt;&lt;/dd&gt;</span>
<span class="s2"> &lt;dt&gt;AppName&lt;/dt&gt;</span>
<span class="s2"> &lt;dd&gt;&lt;code&gt;</span><span class="si">{sc.appName}</span><span class="s2">&lt;/code&gt;&lt;/dd&gt;</span>
<span class="s2"> &lt;/dl&gt;</span>
<span class="s2"> &lt;/div&gt;</span>
<span class="s2"> &quot;&quot;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">sc</span><span class="o">=</span><span class="bp">self</span>
<span class="p">)</span>
<span class="k">def</span> <span class="nf">_initialize_context</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">jconf</span><span class="p">:</span> <span class="n">JavaObject</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">JavaObject</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Initialize SparkContext in function to allow subclass specific initialization</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">JavaSparkContext</span><span class="p">(</span><span class="n">jconf</span><span class="p">)</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">_ensure_initialized</span><span class="p">(</span>
<span class="bp">cls</span><span class="p">,</span>
<span class="n">instance</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="s2">&quot;SparkContext&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">gateway</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">JavaGateway</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">conf</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">SparkConf</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Checks whether a SparkContext is initialized or not.</span>
<span class="sd"> Throws error if a SparkContext is already running.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">with</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_lock</span><span class="p">:</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_gateway</span><span class="p">:</span>
<span class="n">SparkContext</span><span class="o">.</span><span class="n">_gateway</span> <span class="o">=</span> <span class="n">gateway</span> <span class="ow">or</span> <span class="n">launch_gateway</span><span class="p">(</span><span class="n">conf</span><span class="p">)</span>
<span class="n">SparkContext</span><span class="o">.</span><span class="n">_jvm</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_gateway</span><span class="o">.</span><span class="n">jvm</span>
<span class="k">if</span> <span class="n">instance</span><span class="p">:</span>
<span class="k">if</span> <span class="p">(</span>
<span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="ow">and</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span> <span class="o">!=</span> <span class="n">instance</span>
<span class="p">):</span>
<span class="n">currentMaster</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span><span class="o">.</span><span class="n">master</span>
<span class="n">currentAppName</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span><span class="o">.</span><span class="n">appName</span>
<span class="n">callsite</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span><span class="o">.</span><span class="n">_callsite</span>
<span class="c1"># Raise error if there is already a running Spark context</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s2">&quot;Cannot run multiple SparkContexts at once; &quot;</span>
<span class="s2">&quot;existing SparkContext(app=</span><span class="si">%s</span><span class="s2">, master=</span><span class="si">%s</span><span class="s2">)&quot;</span>
<span class="s2">&quot; created by </span><span class="si">%s</span><span class="s2"> at </span><span class="si">%s</span><span class="s2">:</span><span class="si">%s</span><span class="s2"> &quot;</span>
<span class="o">%</span> <span class="p">(</span>
<span class="n">currentAppName</span><span class="p">,</span>
<span class="n">currentMaster</span><span class="p">,</span>
<span class="n">callsite</span><span class="o">.</span><span class="n">function</span><span class="p">,</span>
<span class="n">callsite</span><span class="o">.</span><span class="n">file</span><span class="p">,</span>
<span class="n">callsite</span><span class="o">.</span><span class="n">linenum</span><span class="p">,</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span> <span class="o">=</span> <span class="n">instance</span>
<span class="k">def</span> <span class="nf">__getnewargs__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">NoReturn</span><span class="p">:</span>
<span class="c1"># This method is called when attempting to pickle SparkContext, which is always an error:</span>
<span class="k">raise</span> <span class="n">PySparkRuntimeError</span><span class="p">(</span>
<span class="n">error_class</span><span class="o">=</span><span class="s2">&quot;CONTEXT_ONLY_VALID_ON_DRIVER&quot;</span><span class="p">,</span>
<span class="n">message_parameters</span><span class="o">=</span><span class="p">{},</span>
<span class="p">)</span>
<span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;SparkContext&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Enable &#39;with SparkContext(...) as sc: app(sc)&#39; syntax.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="nb">type</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Type</span><span class="p">[</span><span class="ne">BaseException</span><span class="p">]],</span>
<span class="n">value</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="ne">BaseException</span><span class="p">],</span>
<span class="n">trace</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">TracebackType</span><span class="p">],</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Enable &#39;with SparkContext(...) as sc: app&#39; syntax.</span>
<span class="sd"> Specifically stop the context on exit of the with block.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
<div class="viewcode-block" id="SparkContext.getOrCreate"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.getOrCreate.html#pyspark.SparkContext.getOrCreate">[docs]</a> <span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">getOrCreate</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">conf</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">SparkConf</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;SparkContext&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get or instantiate a :class:`SparkContext` and register it as a singleton object.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> conf : :class:`SparkConf`, optional</span>
<span class="sd"> :class:`SparkConf` that will be used for initialization of the :class:`SparkContext`.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`SparkContext`</span>
<span class="sd"> current :class:`SparkContext`, or a new one if it wasn&#39;t created before the function</span>
<span class="sd"> call.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; SparkContext.getOrCreate()</span>
<span class="sd"> &lt;SparkContext ...&gt;</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">with</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_lock</span><span class="p">:</span>
<span class="k">if</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">SparkContext</span><span class="p">(</span><span class="n">conf</span><span class="o">=</span><span class="n">conf</span> <span class="ow">or</span> <span class="n">SparkConf</span><span class="p">())</span>
<span class="k">assert</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="k">return</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span></div>
<div class="viewcode-block" id="SparkContext.setLogLevel"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.setLogLevel.html#pyspark.SparkContext.setLogLevel">[docs]</a> <span class="k">def</span> <span class="nf">setLogLevel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">logLevel</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Control our logLevel. This overrides any user-defined log settings.</span>
<span class="sd"> Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, WARN</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> logLevel : str</span>
<span class="sd"> The desired log level as a string.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; sc.setLogLevel(&quot;WARN&quot;) # doctest :+SKIP</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">setLogLevel</span><span class="p">(</span><span class="n">logLevel</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.setSystemProperty"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.setSystemProperty.html#pyspark.SparkContext.setSystemProperty">[docs]</a> <span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">setSystemProperty</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">key</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Set a Java system property, such as `spark.executor.memory`. This must</span>
<span class="sd"> be invoked before instantiating :class:`SparkContext`.</span>
<span class="sd"> .. versionadded:: 0.9.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> key : str</span>
<span class="sd"> The key of a new Java system property.</span>
<span class="sd"> value : str</span>
<span class="sd"> The value of a new Java system property.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">SparkContext</span><span class="o">.</span><span class="n">_ensure_initialized</span><span class="p">()</span>
<span class="k">assert</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_jvm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">SparkContext</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">java</span><span class="o">.</span><span class="n">lang</span><span class="o">.</span><span class="n">System</span><span class="o">.</span><span class="n">setProperty</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span></div>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">version</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The version of Spark on which this application is running.</span>
<span class="sd"> .. versionadded:: 1.1.0</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; _ = sc.version</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">version</span><span class="p">()</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">applicationId</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A unique identifier for the Spark application.</span>
<span class="sd"> Its format depends on the scheduler implementation.</span>
<span class="sd"> * in case of local spark app something like &#39;local-1433865536131&#39;</span>
<span class="sd"> * in case of YARN something like &#39;application_1433865536131_34483&#39;</span>
<span class="sd"> .. versionadded:: 1.5.0</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; sc.applicationId # doctest: +ELLIPSIS</span>
<span class="sd"> &#39;local-...&#39;</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">()</span><span class="o">.</span><span class="n">applicationId</span><span class="p">()</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">uiWebUrl</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Return the URL of the SparkUI instance started by this :class:`SparkContext`</span>
<span class="sd"> .. versionadded:: 2.1.0</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> When the web ui is disabled, e.g., by ``spark.ui.enabled`` set to ``False``,</span>
<span class="sd"> it returns ``None``.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; sc.uiWebUrl</span>
<span class="sd"> &#39;http://...&#39;</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">jurl</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">()</span><span class="o">.</span><span class="n">uiWebUrl</span><span class="p">()</span>
<span class="k">return</span> <span class="n">jurl</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> <span class="k">if</span> <span class="n">jurl</span><span class="o">.</span><span class="n">nonEmpty</span><span class="p">()</span> <span class="k">else</span> <span class="kc">None</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">startTime</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Return the epoch time when the :class:`SparkContext` was started.</span>
<span class="sd"> .. versionadded:: 1.5.0</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; _ = sc.startTime</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">startTime</span><span class="p">()</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">defaultParallelism</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Default level of parallelism to use when not given by user (e.g. for reduce tasks)</span>
<span class="sd"> .. versionadded:: 0.7.0</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; sc.defaultParallelism &gt; 0</span>
<span class="sd"> True</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">()</span><span class="o">.</span><span class="n">defaultParallelism</span><span class="p">()</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">defaultMinPartitions</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Default min number of partitions for Hadoop RDDs when not given by user</span>
<span class="sd"> .. versionadded:: 1.1.0</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; sc.defaultMinPartitions &gt; 0</span>
<span class="sd"> True</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">()</span><span class="o">.</span><span class="n">defaultMinPartitions</span><span class="p">()</span>
<div class="viewcode-block" id="SparkContext.stop"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.stop.html#pyspark.SparkContext.stop">[docs]</a> <span class="k">def</span> <span class="nf">stop</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Shut down the :class:`SparkContext`.</span>
<span class="sd"> .. versionadded:: 0.7.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_jsc&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span>
<span class="k">try</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
<span class="k">except</span> <span class="n">Py4JError</span><span class="p">:</span>
<span class="c1"># Case: SPARK-18523</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
<span class="s2">&quot;Unable to cleanly shutdown Spark JVM process.&quot;</span>
<span class="s2">&quot; It is possible that the process has crashed,&quot;</span>
<span class="s2">&quot; been killed or may also be in a zombie state.&quot;</span><span class="p">,</span>
<span class="ne">RuntimeWarning</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">finally</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_accumulatorServer&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_accumulatorServer</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_accumulatorServer</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># type: ignore[assignment]</span>
<span class="k">with</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_lock</span><span class="p">:</span>
<span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span> <span class="o">=</span> <span class="kc">None</span></div>
<div class="viewcode-block" id="SparkContext.emptyRDD"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.emptyRDD.html#pyspark.SparkContext.emptyRDD">[docs]</a> <span class="k">def</span> <span class="nf">emptyRDD</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RDD</span><span class="p">[</span><span class="n">Any</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Create an :class:`RDD` that has no partitions or elements.</span>
<span class="sd"> .. versionadded:: 1.5.0</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`RDD`</span>
<span class="sd"> An empty RDD</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; sc.emptyRDD()</span>
<span class="sd"> EmptyRDD...</span>
<span class="sd"> &gt;&gt;&gt; sc.emptyRDD().count()</span>
<span class="sd"> 0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">RDD</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">emptyRDD</span><span class="p">(),</span> <span class="bp">self</span><span class="p">,</span> <span class="n">NoOpSerializer</span><span class="p">())</span></div>
<div class="viewcode-block" id="SparkContext.range"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.range.html#pyspark.SparkContext.range">[docs]</a> <span class="k">def</span> <span class="nf">range</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span> <span class="n">start</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">end</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">step</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">numSlices</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RDD</span><span class="p">[</span><span class="nb">int</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Create a new RDD of int containing elements from `start` to `end`</span>
<span class="sd"> (exclusive), increased by `step` every element. Can be called the same</span>
<span class="sd"> way as python&#39;s built-in range() function. If called with a single argument,</span>
<span class="sd"> the argument is interpreted as `end`, and `start` is set to 0.</span>
<span class="sd"> .. versionadded:: 1.5.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> start : int</span>
<span class="sd"> the start value</span>
<span class="sd"> end : int, optional</span>
<span class="sd"> the end value (exclusive)</span>
<span class="sd"> step : int, optional, default 1</span>
<span class="sd"> the incremental step</span>
<span class="sd"> numSlices : int, optional</span>
<span class="sd"> the number of partitions of the new RDD</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`RDD`</span>
<span class="sd"> An RDD of int</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`pyspark.sql.SparkSession.range`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; sc.range(5).collect()</span>
<span class="sd"> [0, 1, 2, 3, 4]</span>
<span class="sd"> &gt;&gt;&gt; sc.range(2, 4).collect()</span>
<span class="sd"> [2, 3]</span>
<span class="sd"> &gt;&gt;&gt; sc.range(1, 7, 2).collect()</span>
<span class="sd"> [1, 3, 5]</span>
<span class="sd"> Generate RDD with a negative step</span>
<span class="sd"> &gt;&gt;&gt; sc.range(5, 0, -1).collect()</span>
<span class="sd"> [5, 4, 3, 2, 1]</span>
<span class="sd"> &gt;&gt;&gt; sc.range(0, 5, -1).collect()</span>
<span class="sd"> []</span>
<span class="sd"> Control the number of partitions</span>
<span class="sd"> &gt;&gt;&gt; sc.range(5, numSlices=1).getNumPartitions()</span>
<span class="sd"> 1</span>
<span class="sd"> &gt;&gt;&gt; sc.range(5, numSlices=10).getNumPartitions()</span>
<span class="sd"> 10</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">end</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">end</span> <span class="o">=</span> <span class="n">start</span>
<span class="n">start</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">,</span> <span class="n">step</span><span class="p">),</span> <span class="n">numSlices</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.parallelize"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.parallelize.html#pyspark.SparkContext.parallelize">[docs]</a> <span class="k">def</span> <span class="nf">parallelize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">T</span><span class="p">],</span> <span class="n">numSlices</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RDD</span><span class="p">[</span><span class="n">T</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Distribute a local Python collection to form an RDD. Using range</span>
<span class="sd"> is recommended if the input represents a range for performance.</span>
<span class="sd"> .. versionadded:: 0.7.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> c : :class:`collections.abc.Iterable`</span>
<span class="sd"> iterable collection to distribute</span>
<span class="sd"> numSlices : int, optional</span>
<span class="sd"> the number of partitions of the new RDD</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`RDD`</span>
<span class="sd"> RDD representing distributed collection.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; sc.parallelize([0, 2, 3, 4, 6], 5).glom().collect()</span>
<span class="sd"> [[0], [2], [3], [4], [6]]</span>
<span class="sd"> &gt;&gt;&gt; sc.parallelize(range(0, 6, 2), 5).glom().collect()</span>
<span class="sd"> [[], [0], [], [2], [4]]</span>
<span class="sd"> Deal with a list of strings.</span>
<span class="sd"> &gt;&gt;&gt; strings = [&quot;a&quot;, &quot;b&quot;, &quot;c&quot;]</span>
<span class="sd"> &gt;&gt;&gt; sc.parallelize(strings, 2).glom().collect()</span>
<span class="sd"> [[&#39;a&#39;], [&#39;b&#39;, &#39;c&#39;]]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">numSlices</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">numSlices</span><span class="p">)</span> <span class="k">if</span> <span class="n">numSlices</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">defaultParallelism</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="nb">range</span><span class="p">):</span>
<span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
<span class="k">if</span> <span class="n">size</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([],</span> <span class="n">numSlices</span><span class="p">)</span>
<span class="n">step</span> <span class="o">=</span> <span class="n">c</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">c</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="n">size</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="mi">1</span> <span class="c1"># type: ignore[index]</span>
<span class="n">start0</span> <span class="o">=</span> <span class="n">c</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># type: ignore[index]</span>
<span class="k">def</span> <span class="nf">getStart</span><span class="p">(</span><span class="n">split</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">numSlices</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="k">return</span> <span class="n">start0</span> <span class="o">+</span> <span class="nb">int</span><span class="p">((</span><span class="n">split</span> <span class="o">*</span> <span class="n">size</span> <span class="o">/</span> <span class="n">numSlices</span><span class="p">))</span> <span class="o">*</span> <span class="n">step</span>
<span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">split</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">iterator</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">T</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">Iterable</span><span class="p">:</span>
<span class="c1"># it&#39;s an empty iterator here but we need this line for triggering the</span>
<span class="c1"># logic of signal handling in FramedSerializer.load_stream, for instance,</span>
<span class="c1"># SpecialLengths.END_OF_DATA_SECTION in _read_with_length. Since</span>
<span class="c1"># FramedSerializer.load_stream produces a generator, the control should</span>
<span class="c1"># at least be in that function once. Here we do it by explicitly converting</span>
<span class="c1"># the empty iterator to a list, thus make sure worker reuse takes effect.</span>
<span class="c1"># See more details in SPARK-26549.</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">iterator</span><span class="p">))</span> <span class="o">==</span> <span class="mi">0</span>
<span class="k">return</span> <span class="nb">range</span><span class="p">(</span><span class="n">getStart</span><span class="p">(</span><span class="n">split</span><span class="p">),</span> <span class="n">getStart</span><span class="p">(</span><span class="n">split</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">step</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([],</span> <span class="n">numSlices</span><span class="p">)</span><span class="o">.</span><span class="n">mapPartitionsWithIndex</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="c1"># Make sure we distribute data evenly if it&#39;s smaller than self.batchSize</span>
<span class="k">if</span> <span class="s2">&quot;__len__&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="nb">dir</span><span class="p">(</span><span class="n">c</span><span class="p">):</span>
<span class="n">c</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="c1"># Make it a list so we can compute its length</span>
<span class="n">batchSize</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span>
<span class="mi">1</span><span class="p">,</span> <span class="nb">min</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="o">//</span> <span class="n">numSlices</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batchSize</span> <span class="ow">or</span> <span class="mi">1024</span><span class="p">)</span> <span class="c1"># type: ignore[arg-type]</span>
<span class="p">)</span>
<span class="n">serializer</span> <span class="o">=</span> <span class="n">BatchedSerializer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_unbatched_serializer</span><span class="p">,</span> <span class="n">batchSize</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">reader_func</span><span class="p">(</span><span class="n">temp_filename</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">JavaObject</span><span class="p">:</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonRDD</span><span class="o">.</span><span class="n">readRDDFromFile</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="p">,</span> <span class="n">temp_filename</span><span class="p">,</span> <span class="n">numSlices</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">createRDDServer</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">JavaObject</span><span class="p">:</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonParallelizeServer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">(),</span> <span class="n">numSlices</span><span class="p">)</span>
<span class="n">jrdd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_serialize_to_jvm</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">serializer</span><span class="p">,</span> <span class="n">reader_func</span><span class="p">,</span> <span class="n">createRDDServer</span><span class="p">)</span>
<span class="k">return</span> <span class="n">RDD</span><span class="p">(</span><span class="n">jrdd</span><span class="p">,</span> <span class="bp">self</span><span class="p">,</span> <span class="n">serializer</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_serialize_to_jvm</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">data</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">T</span><span class="p">],</span>
<span class="n">serializer</span><span class="p">:</span> <span class="n">Serializer</span><span class="p">,</span>
<span class="n">reader_func</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span>
<span class="n">server_func</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">JavaObject</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Using Py4J to send a large dataset to the jvm is slow, so we use either a file</span>
<span class="sd"> or a socket if we have encryption enabled.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> data</span>
<span class="sd"> object to be serialized</span>
<span class="sd"> serializer : class:`pyspark.serializers.Serializer`</span>
<span class="sd"> reader_func : function</span>
<span class="sd"> A function which takes a filename and reads in the data in the jvm and</span>
<span class="sd"> returns a JavaRDD. Only used when encryption is disabled.</span>
<span class="sd"> server_func : function</span>
<span class="sd"> A function which creates a SocketAuthServer in the JVM to</span>
<span class="sd"> accept the serialized data, for use when encryption is enabled.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_encryption_enabled</span><span class="p">:</span>
<span class="c1"># with encryption, we open a server in java and send the data directly</span>
<span class="n">server</span> <span class="o">=</span> <span class="n">server_func</span><span class="p">()</span>
<span class="p">(</span><span class="n">sock_file</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="o">=</span> <span class="n">local_connect_and_auth</span><span class="p">(</span><span class="n">server</span><span class="o">.</span><span class="n">port</span><span class="p">(),</span> <span class="n">server</span><span class="o">.</span><span class="n">secret</span><span class="p">())</span>
<span class="n">chunked_out</span> <span class="o">=</span> <span class="n">ChunkedStream</span><span class="p">(</span><span class="n">sock_file</span><span class="p">,</span> <span class="mi">8192</span><span class="p">)</span>
<span class="n">serializer</span><span class="o">.</span><span class="n">dump_stream</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">chunked_out</span><span class="p">)</span>
<span class="n">chunked_out</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="c1"># this call will block until the server has read all the data and processed it (or</span>
<span class="c1"># throws an exception)</span>
<span class="n">r</span> <span class="o">=</span> <span class="n">server</span><span class="o">.</span><span class="n">getResult</span><span class="p">()</span>
<span class="k">return</span> <span class="n">r</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># without encryption, we serialize to a file, and we read the file in java and</span>
<span class="c1"># parallelize from there.</span>
<span class="n">tempFile</span> <span class="o">=</span> <span class="n">NamedTemporaryFile</span><span class="p">(</span><span class="n">delete</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="nb">dir</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_temp_dir</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">serializer</span><span class="o">.</span><span class="n">dump_stream</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">tempFile</span><span class="p">)</span>
<span class="k">finally</span><span class="p">:</span>
<span class="n">tempFile</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="k">return</span> <span class="n">reader_func</span><span class="p">(</span><span class="n">tempFile</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
<span class="k">finally</span><span class="p">:</span>
<span class="c1"># we eagerly reads the file so we can delete right after.</span>
<span class="n">os</span><span class="o">.</span><span class="n">unlink</span><span class="p">(</span><span class="n">tempFile</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
<div class="viewcode-block" id="SparkContext.pickleFile"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.pickleFile.html#pyspark.SparkContext.pickleFile">[docs]</a> <span class="k">def</span> <span class="nf">pickleFile</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">minPartitions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RDD</span><span class="p">[</span><span class="n">Any</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Load an RDD previously saved using :meth:`RDD.saveAsPickleFile` method.</span>
<span class="sd"> .. versionadded:: 1.1.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> directory to the input data files, the path can be comma separated</span>
<span class="sd"> paths as a list of inputs</span>
<span class="sd"> minPartitions : int, optional</span>
<span class="sd"> suggested minimum number of partitions for the resulting RDD</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`RDD`</span>
<span class="sd"> RDD representing unpickled data from the file(s).</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`RDD.saveAsPickleFile`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import os</span>
<span class="sd"> &gt;&gt;&gt; import tempfile</span>
<span class="sd"> &gt;&gt;&gt; with tempfile.TemporaryDirectory() as d:</span>
<span class="sd"> ... # Write a temporary pickled file</span>
<span class="sd"> ... path1 = os.path.join(d, &quot;pickled1&quot;)</span>
<span class="sd"> ... sc.parallelize(range(10)).saveAsPickleFile(path1, 3)</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Write another temporary pickled file</span>
<span class="sd"> ... path2 = os.path.join(d, &quot;pickled2&quot;)</span>
<span class="sd"> ... sc.parallelize(range(-10, -5)).saveAsPickleFile(path2, 3)</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Load picked file</span>
<span class="sd"> ... collected1 = sorted(sc.pickleFile(path1, 3).collect())</span>
<span class="sd"> ... collected2 = sorted(sc.pickleFile(path2, 4).collect())</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Load two picked files together</span>
<span class="sd"> ... collected3 = sorted(sc.pickleFile(&#39;{},{}&#39;.format(path1, path2), 5).collect())</span>
<span class="sd"> &gt;&gt;&gt; collected1</span>
<span class="sd"> [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</span>
<span class="sd"> &gt;&gt;&gt; collected2</span>
<span class="sd"> [-10, -9, -8, -7, -6]</span>
<span class="sd"> &gt;&gt;&gt; collected3</span>
<span class="sd"> [-10, -9, -8, -7, -6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">minPartitions</span> <span class="o">=</span> <span class="n">minPartitions</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">defaultMinPartitions</span>
<span class="k">return</span> <span class="n">RDD</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">objectFile</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">minPartitions</span><span class="p">),</span> <span class="bp">self</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.textFile"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.textFile.html#pyspark.SparkContext.textFile">[docs]</a> <span class="k">def</span> <span class="nf">textFile</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">minPartitions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">use_unicode</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RDD</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Read a text file from HDFS, a local file system (available on all</span>
<span class="sd"> nodes), or any Hadoop-supported file system URI, and return it as an</span>
<span class="sd"> RDD of Strings. The text files must be encoded as UTF-8.</span>
<span class="sd"> .. versionadded:: 0.7.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> directory to the input data files, the path can be comma separated</span>
<span class="sd"> paths as a list of inputs</span>
<span class="sd"> minPartitions : int, optional</span>
<span class="sd"> suggested minimum number of partitions for the resulting RDD</span>
<span class="sd"> use_unicode : bool, default True</span>
<span class="sd"> If `use_unicode` is False, the strings will be kept as `str` (encoding</span>
<span class="sd"> as `utf-8`), which is faster and smaller than unicode.</span>
<span class="sd"> .. versionadded:: 1.2.0</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`RDD`</span>
<span class="sd"> RDD representing text data from the file(s).</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`RDD.saveAsTextFile`</span>
<span class="sd"> :meth:`SparkContext.wholeTextFiles`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import os</span>
<span class="sd"> &gt;&gt;&gt; import tempfile</span>
<span class="sd"> &gt;&gt;&gt; with tempfile.TemporaryDirectory() as d:</span>
<span class="sd"> ... path1 = os.path.join(d, &quot;text1&quot;)</span>
<span class="sd"> ... path2 = os.path.join(d, &quot;text2&quot;)</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Write a temporary text file</span>
<span class="sd"> ... sc.parallelize([&quot;x&quot;, &quot;y&quot;, &quot;z&quot;]).saveAsTextFile(path1)</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Write another temporary text file</span>
<span class="sd"> ... sc.parallelize([&quot;aa&quot;, &quot;bb&quot;, &quot;cc&quot;]).saveAsTextFile(path2)</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Load text file</span>
<span class="sd"> ... collected1 = sorted(sc.textFile(path1, 3).collect())</span>
<span class="sd"> ... collected2 = sorted(sc.textFile(path2, 4).collect())</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Load two text files together</span>
<span class="sd"> ... collected3 = sorted(sc.textFile(&#39;{},{}&#39;.format(path1, path2), 5).collect())</span>
<span class="sd"> &gt;&gt;&gt; collected1</span>
<span class="sd"> [&#39;x&#39;, &#39;y&#39;, &#39;z&#39;]</span>
<span class="sd"> &gt;&gt;&gt; collected2</span>
<span class="sd"> [&#39;aa&#39;, &#39;bb&#39;, &#39;cc&#39;]</span>
<span class="sd"> &gt;&gt;&gt; collected3</span>
<span class="sd"> [&#39;aa&#39;, &#39;bb&#39;, &#39;cc&#39;, &#39;x&#39;, &#39;y&#39;, &#39;z&#39;]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">minPartitions</span> <span class="o">=</span> <span class="n">minPartitions</span> <span class="ow">or</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">defaultParallelism</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="k">return</span> <span class="n">RDD</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">textFile</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">minPartitions</span><span class="p">),</span> <span class="bp">self</span><span class="p">,</span> <span class="n">UTF8Deserializer</span><span class="p">(</span><span class="n">use_unicode</span><span class="p">))</span></div>
<div class="viewcode-block" id="SparkContext.wholeTextFiles"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.wholeTextFiles.html#pyspark.SparkContext.wholeTextFiles">[docs]</a> <span class="k">def</span> <span class="nf">wholeTextFiles</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">minPartitions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">use_unicode</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RDD</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Read a directory of text files from HDFS, a local file system</span>
<span class="sd"> (available on all nodes), or any Hadoop-supported file system</span>
<span class="sd"> URI. Each file is read as a single record and returned in a</span>
<span class="sd"> key-value pair, where the key is the path of each file, the</span>
<span class="sd"> value is the content of each file.</span>
<span class="sd"> The text files must be encoded as UTF-8.</span>
<span class="sd"> .. versionadded:: 1.0.0</span>
<span class="sd"> For example, if you have the following files:</span>
<span class="sd"> .. code-block:: text</span>
<span class="sd"> hdfs://a-hdfs-path/part-00000</span>
<span class="sd"> hdfs://a-hdfs-path/part-00001</span>
<span class="sd"> ...</span>
<span class="sd"> hdfs://a-hdfs-path/part-nnnnn</span>
<span class="sd"> Do ``rdd = sparkContext.wholeTextFiles(&quot;hdfs://a-hdfs-path&quot;)``,</span>
<span class="sd"> then ``rdd`` contains:</span>
<span class="sd"> .. code-block:: text</span>
<span class="sd"> (a-hdfs-path/part-00000, its content)</span>
<span class="sd"> (a-hdfs-path/part-00001, its content)</span>
<span class="sd"> ...</span>
<span class="sd"> (a-hdfs-path/part-nnnnn, its content)</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str</span>
<span class="sd"> directory to the input data files, the path can be comma separated</span>
<span class="sd"> paths as a list of inputs</span>
<span class="sd"> minPartitions : int, optional</span>
<span class="sd"> suggested minimum number of partitions for the resulting RDD</span>
<span class="sd"> use_unicode : bool, default True</span>
<span class="sd"> If `use_unicode` is False, the strings will be kept as `str` (encoding</span>
<span class="sd"> as `utf-8`), which is faster and smaller than unicode.</span>
<span class="sd"> .. versionadded:: 1.2.0</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`RDD`</span>
<span class="sd"> RDD representing path-content pairs from the file(s).</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> Small files are preferred, as each file will be loaded fully in memory.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`RDD.saveAsTextFile`</span>
<span class="sd"> :meth:`SparkContext.textFile`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import os</span>
<span class="sd"> &gt;&gt;&gt; import tempfile</span>
<span class="sd"> &gt;&gt;&gt; with tempfile.TemporaryDirectory() as d:</span>
<span class="sd"> ... # Write a temporary text file</span>
<span class="sd"> ... with open(os.path.join(d, &quot;1.txt&quot;), &quot;w&quot;) as f:</span>
<span class="sd"> ... _ = f.write(&quot;123&quot;)</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Write another temporary text file</span>
<span class="sd"> ... with open(os.path.join(d, &quot;2.txt&quot;), &quot;w&quot;) as f:</span>
<span class="sd"> ... _ = f.write(&quot;xyz&quot;)</span>
<span class="sd"> ...</span>
<span class="sd"> ... collected = sorted(sc.wholeTextFiles(d).collect())</span>
<span class="sd"> &gt;&gt;&gt; collected</span>
<span class="sd"> [(&#39;.../1.txt&#39;, &#39;123&#39;), (&#39;.../2.txt&#39;, &#39;xyz&#39;)]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">minPartitions</span> <span class="o">=</span> <span class="n">minPartitions</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">defaultMinPartitions</span>
<span class="k">return</span> <span class="n">RDD</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">wholeTextFiles</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">minPartitions</span><span class="p">),</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">PairDeserializer</span><span class="p">(</span><span class="n">UTF8Deserializer</span><span class="p">(</span><span class="n">use_unicode</span><span class="p">),</span> <span class="n">UTF8Deserializer</span><span class="p">(</span><span class="n">use_unicode</span><span class="p">)),</span>
<span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.binaryFiles"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.binaryFiles.html#pyspark.SparkContext.binaryFiles">[docs]</a> <span class="k">def</span> <span class="nf">binaryFiles</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">minPartitions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RDD</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bytes</span><span class="p">]]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Read a directory of binary files from HDFS, a local file system</span>
<span class="sd"> (available on all nodes), or any Hadoop-supported file system URI</span>
<span class="sd"> as a byte array. Each file is read as a single record and returned</span>
<span class="sd"> in a key-value pair, where the key is the path of each file, the</span>
<span class="sd"> value is the content of each file.</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str</span>
<span class="sd"> directory to the input data files, the path can be comma separated</span>
<span class="sd"> paths as a list of inputs</span>
<span class="sd"> minPartitions : int, optional</span>
<span class="sd"> suggested minimum number of partitions for the resulting RDD</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`RDD`</span>
<span class="sd"> RDD representing path-content pairs from the file(s).</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> Small files are preferred, large file is also allowable, but may cause bad performance.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.binaryRecords`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import os</span>
<span class="sd"> &gt;&gt;&gt; import tempfile</span>
<span class="sd"> &gt;&gt;&gt; with tempfile.TemporaryDirectory() as d:</span>
<span class="sd"> ... # Write a temporary binary file</span>
<span class="sd"> ... with open(os.path.join(d, &quot;1.bin&quot;), &quot;wb&quot;) as f1:</span>
<span class="sd"> ... _ = f1.write(b&quot;binary data I&quot;)</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Write another temporary binary file</span>
<span class="sd"> ... with open(os.path.join(d, &quot;2.bin&quot;), &quot;wb&quot;) as f2:</span>
<span class="sd"> ... _ = f2.write(b&quot;binary data II&quot;)</span>
<span class="sd"> ...</span>
<span class="sd"> ... collected = sorted(sc.binaryFiles(d).collect())</span>
<span class="sd"> &gt;&gt;&gt; collected</span>
<span class="sd"> [(&#39;.../1.bin&#39;, b&#39;binary data I&#39;), (&#39;.../2.bin&#39;, b&#39;binary data II&#39;)]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">minPartitions</span> <span class="o">=</span> <span class="n">minPartitions</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">defaultMinPartitions</span>
<span class="k">return</span> <span class="n">RDD</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">binaryFiles</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">minPartitions</span><span class="p">),</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">PairDeserializer</span><span class="p">(</span><span class="n">UTF8Deserializer</span><span class="p">(),</span> <span class="n">NoOpSerializer</span><span class="p">()),</span>
<span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.binaryRecords"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.binaryRecords.html#pyspark.SparkContext.binaryRecords">[docs]</a> <span class="k">def</span> <span class="nf">binaryRecords</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">recordLength</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RDD</span><span class="p">[</span><span class="nb">bytes</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Load data from a flat binary file, assuming each record is a set of numbers</span>
<span class="sd"> with the specified numerical format (see ByteBuffer), and the number of</span>
<span class="sd"> bytes per record is constant.</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str</span>
<span class="sd"> Directory to the input data files</span>
<span class="sd"> recordLength : int</span>
<span class="sd"> The length at which to split the records</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`RDD`</span>
<span class="sd"> RDD of data with values, represented as byte arrays</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.binaryFiles`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import os</span>
<span class="sd"> &gt;&gt;&gt; import tempfile</span>
<span class="sd"> &gt;&gt;&gt; with tempfile.TemporaryDirectory() as d:</span>
<span class="sd"> ... # Write a temporary file</span>
<span class="sd"> ... with open(os.path.join(d, &quot;1.bin&quot;), &quot;w&quot;) as f:</span>
<span class="sd"> ... for i in range(3):</span>
<span class="sd"> ... _ = f.write(&quot;%04d&quot; % i)</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Write another file</span>
<span class="sd"> ... with open(os.path.join(d, &quot;2.bin&quot;), &quot;w&quot;) as f:</span>
<span class="sd"> ... for i in [-1, -2, -10]:</span>
<span class="sd"> ... _ = f.write(&quot;%04d&quot; % i)</span>
<span class="sd"> ...</span>
<span class="sd"> ... collected = sorted(sc.binaryRecords(d, 4).collect())</span>
<span class="sd"> &gt;&gt;&gt; collected</span>
<span class="sd"> [b&#39;-001&#39;, b&#39;-002&#39;, b&#39;-010&#39;, b&#39;0000&#39;, b&#39;0001&#39;, b&#39;0002&#39;]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">RDD</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">binaryRecords</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">recordLength</span><span class="p">),</span> <span class="bp">self</span><span class="p">,</span> <span class="n">NoOpSerializer</span><span class="p">())</span></div>
<span class="k">def</span> <span class="nf">_dictToJavaMap</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">d</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]])</span> <span class="o">-&gt;</span> <span class="n">JavaMap</span><span class="p">:</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">jm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">java</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">HashMap</span><span class="p">()</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">d</span><span class="p">:</span>
<span class="n">d</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">jm</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
<span class="k">return</span> <span class="n">jm</span>
<div class="viewcode-block" id="SparkContext.sequenceFile"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.sequenceFile.html#pyspark.SparkContext.sequenceFile">[docs]</a> <span class="k">def</span> <span class="nf">sequenceFile</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">keyClass</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">valueClass</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">keyConverter</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">valueConverter</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">minSplits</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">batchSize</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RDD</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">T</span><span class="p">,</span> <span class="n">U</span><span class="p">]]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Read a Hadoop SequenceFile with arbitrary key and value Writable class from HDFS,</span>
<span class="sd"> a local file system (available on all nodes), or any Hadoop-supported file system URI.</span>
<span class="sd"> The mechanism is as follows:</span>
<span class="sd"> 1. A Java RDD is created from the SequenceFile or other InputFormat, and the key</span>
<span class="sd"> and value Writable classes</span>
<span class="sd"> 2. Serialization is attempted via Pickle pickling</span>
<span class="sd"> 3. If this fails, the fallback is to call &#39;toString&#39; on each key and value</span>
<span class="sd"> 4. :class:`CPickleSerializer` is used to deserialize pickled objects on the Python side</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str</span>
<span class="sd"> path to sequencefile</span>
<span class="sd"> keyClass: str, optional</span>
<span class="sd"> fully qualified classname of key Writable class (e.g. &quot;org.apache.hadoop.io.Text&quot;)</span>
<span class="sd"> valueClass : str, optional</span>
<span class="sd"> fully qualified classname of value Writable class</span>
<span class="sd"> (e.g. &quot;org.apache.hadoop.io.LongWritable&quot;)</span>
<span class="sd"> keyConverter : str, optional</span>
<span class="sd"> fully qualified name of a function returning key WritableConverter</span>
<span class="sd"> valueConverter : str, optional</span>
<span class="sd"> fully qualifiedname of a function returning value WritableConverter</span>
<span class="sd"> minSplits : int, optional</span>
<span class="sd"> minimum splits in dataset (default min(2, sc.defaultParallelism))</span>
<span class="sd"> batchSize : int, optional, default 0</span>
<span class="sd"> The number of Python objects represented as a single</span>
<span class="sd"> Java object. (default 0, choose batchSize automatically)</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`RDD`</span>
<span class="sd"> RDD of tuples of key and corresponding value</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`RDD.saveAsSequenceFile`</span>
<span class="sd"> :meth:`RDD.saveAsNewAPIHadoopFile`</span>
<span class="sd"> :meth:`RDD.saveAsHadoopFile`</span>
<span class="sd"> :meth:`SparkContext.newAPIHadoopFile`</span>
<span class="sd"> :meth:`SparkContext.hadoopFile`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import os</span>
<span class="sd"> &gt;&gt;&gt; import tempfile</span>
<span class="sd"> Set the class of output format</span>
<span class="sd"> &gt;&gt;&gt; output_format_class = &quot;org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat&quot;</span>
<span class="sd"> &gt;&gt;&gt; with tempfile.TemporaryDirectory() as d:</span>
<span class="sd"> ... path = os.path.join(d, &quot;hadoop_file&quot;)</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Write a temporary Hadoop file</span>
<span class="sd"> ... rdd = sc.parallelize([(1, {3.0: &quot;bb&quot;}), (2, {1.0: &quot;aa&quot;}), (3, {2.0: &quot;dd&quot;})])</span>
<span class="sd"> ... rdd.saveAsNewAPIHadoopFile(path, output_format_class)</span>
<span class="sd"> ...</span>
<span class="sd"> ... collected = sorted(sc.sequenceFile(path).collect())</span>
<span class="sd"> &gt;&gt;&gt; collected</span>
<span class="sd"> [(1, {3.0: &#39;bb&#39;}), (2, {1.0: &#39;aa&#39;}), (3, {2.0: &#39;dd&#39;})]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">minSplits</span> <span class="o">=</span> <span class="n">minSplits</span> <span class="ow">or</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">defaultParallelism</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">jrdd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonRDD</span><span class="o">.</span><span class="n">sequenceFile</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="p">,</span>
<span class="n">path</span><span class="p">,</span>
<span class="n">keyClass</span><span class="p">,</span>
<span class="n">valueClass</span><span class="p">,</span>
<span class="n">keyConverter</span><span class="p">,</span>
<span class="n">valueConverter</span><span class="p">,</span>
<span class="n">minSplits</span><span class="p">,</span>
<span class="n">batchSize</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">RDD</span><span class="p">(</span><span class="n">jrdd</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.newAPIHadoopFile"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.newAPIHadoopFile.html#pyspark.SparkContext.newAPIHadoopFile">[docs]</a> <span class="k">def</span> <span class="nf">newAPIHadoopFile</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">inputFormatClass</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">keyClass</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">valueClass</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">keyConverter</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">valueConverter</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">conf</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">batchSize</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RDD</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">T</span><span class="p">,</span> <span class="n">U</span><span class="p">]]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Read a &#39;new API&#39; Hadoop InputFormat with arbitrary key and value class from HDFS,</span>
<span class="sd"> a local file system (available on all nodes), or any Hadoop-supported file system URI.</span>
<span class="sd"> The mechanism is the same as for meth:`SparkContext.sequenceFile`.</span>
<span class="sd"> A Hadoop configuration can be passed in as a Python dict. This will be converted into a</span>
<span class="sd"> Configuration in Java</span>
<span class="sd"> .. versionadded:: 1.1.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str</span>
<span class="sd"> path to Hadoop file</span>
<span class="sd"> inputFormatClass : str</span>
<span class="sd"> fully qualified classname of Hadoop InputFormat</span>
<span class="sd"> (e.g. &quot;org.apache.hadoop.mapreduce.lib.input.TextInputFormat&quot;)</span>
<span class="sd"> keyClass : str</span>
<span class="sd"> fully qualified classname of key Writable class</span>
<span class="sd"> (e.g. &quot;org.apache.hadoop.io.Text&quot;)</span>
<span class="sd"> valueClass : str</span>
<span class="sd"> fully qualified classname of value Writable class</span>
<span class="sd"> (e.g. &quot;org.apache.hadoop.io.LongWritable&quot;)</span>
<span class="sd"> keyConverter : str, optional</span>
<span class="sd"> fully qualified name of a function returning key WritableConverter</span>
<span class="sd"> None by default</span>
<span class="sd"> valueConverter : str, optional</span>
<span class="sd"> fully qualified name of a function returning value WritableConverter</span>
<span class="sd"> None by default</span>
<span class="sd"> conf : dict, optional</span>
<span class="sd"> Hadoop configuration, passed in as a dict</span>
<span class="sd"> None by default</span>
<span class="sd"> batchSize : int, optional, default 0</span>
<span class="sd"> The number of Python objects represented as a single</span>
<span class="sd"> Java object. (default 0, choose batchSize automatically)</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`RDD`</span>
<span class="sd"> RDD of tuples of key and corresponding value</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`RDD.saveAsSequenceFile`</span>
<span class="sd"> :meth:`RDD.saveAsNewAPIHadoopFile`</span>
<span class="sd"> :meth:`RDD.saveAsHadoopFile`</span>
<span class="sd"> :meth:`SparkContext.sequenceFile`</span>
<span class="sd"> :meth:`SparkContext.hadoopFile`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import os</span>
<span class="sd"> &gt;&gt;&gt; import tempfile</span>
<span class="sd"> Set the related classes</span>
<span class="sd"> &gt;&gt;&gt; output_format_class = &quot;org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat&quot;</span>
<span class="sd"> &gt;&gt;&gt; input_format_class = &quot;org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat&quot;</span>
<span class="sd"> &gt;&gt;&gt; key_class = &quot;org.apache.hadoop.io.IntWritable&quot;</span>
<span class="sd"> &gt;&gt;&gt; value_class = &quot;org.apache.hadoop.io.Text&quot;</span>
<span class="sd"> &gt;&gt;&gt; with tempfile.TemporaryDirectory() as d:</span>
<span class="sd"> ... path = os.path.join(d, &quot;new_hadoop_file&quot;)</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Write a temporary Hadoop file</span>
<span class="sd"> ... rdd = sc.parallelize([(1, &quot;&quot;), (1, &quot;a&quot;), (3, &quot;x&quot;)])</span>
<span class="sd"> ... rdd.saveAsNewAPIHadoopFile(path, output_format_class, key_class, value_class)</span>
<span class="sd"> ...</span>
<span class="sd"> ... loaded = sc.newAPIHadoopFile(path, input_format_class, key_class, value_class)</span>
<span class="sd"> ... collected = sorted(loaded.collect())</span>
<span class="sd"> &gt;&gt;&gt; collected</span>
<span class="sd"> [(1, &#39;&#39;), (1, &#39;a&#39;), (3, &#39;x&#39;)]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">jconf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dictToJavaMap</span><span class="p">(</span><span class="n">conf</span><span class="p">)</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">jrdd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonRDD</span><span class="o">.</span><span class="n">newAPIHadoopFile</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="p">,</span>
<span class="n">path</span><span class="p">,</span>
<span class="n">inputFormatClass</span><span class="p">,</span>
<span class="n">keyClass</span><span class="p">,</span>
<span class="n">valueClass</span><span class="p">,</span>
<span class="n">keyConverter</span><span class="p">,</span>
<span class="n">valueConverter</span><span class="p">,</span>
<span class="n">jconf</span><span class="p">,</span>
<span class="n">batchSize</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">RDD</span><span class="p">(</span><span class="n">jrdd</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.newAPIHadoopRDD"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.newAPIHadoopRDD.html#pyspark.SparkContext.newAPIHadoopRDD">[docs]</a> <span class="k">def</span> <span class="nf">newAPIHadoopRDD</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">inputFormatClass</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">keyClass</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">valueClass</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">keyConverter</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">valueConverter</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">conf</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">batchSize</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RDD</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">T</span><span class="p">,</span> <span class="n">U</span><span class="p">]]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Read a &#39;new API&#39; Hadoop InputFormat with arbitrary key and value class, from an arbitrary</span>
<span class="sd"> Hadoop configuration, which is passed in as a Python dict.</span>
<span class="sd"> This will be converted into a Configuration in Java.</span>
<span class="sd"> The mechanism is the same as for meth:`SparkContext.sequenceFile`.</span>
<span class="sd"> .. versionadded:: 1.1.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> inputFormatClass : str</span>
<span class="sd"> fully qualified classname of Hadoop InputFormat</span>
<span class="sd"> (e.g. &quot;org.apache.hadoop.mapreduce.lib.input.TextInputFormat&quot;)</span>
<span class="sd"> keyClass : str</span>
<span class="sd"> fully qualified classname of key Writable class (e.g. &quot;org.apache.hadoop.io.Text&quot;)</span>
<span class="sd"> valueClass : str</span>
<span class="sd"> fully qualified classname of value Writable class</span>
<span class="sd"> (e.g. &quot;org.apache.hadoop.io.LongWritable&quot;)</span>
<span class="sd"> keyConverter : str, optional</span>
<span class="sd"> fully qualified name of a function returning key WritableConverter</span>
<span class="sd"> (None by default)</span>
<span class="sd"> valueConverter : str, optional</span>
<span class="sd"> fully qualified name of a function returning value WritableConverter</span>
<span class="sd"> (None by default)</span>
<span class="sd"> conf : dict, optional</span>
<span class="sd"> Hadoop configuration, passed in as a dict (None by default)</span>
<span class="sd"> batchSize : int, optional, default 0</span>
<span class="sd"> The number of Python objects represented as a single</span>
<span class="sd"> Java object. (default 0, choose batchSize automatically)</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`RDD`</span>
<span class="sd"> RDD of tuples of key and corresponding value</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`RDD.saveAsNewAPIHadoopDataset`</span>
<span class="sd"> :meth:`RDD.saveAsHadoopDataset`</span>
<span class="sd"> :meth:`SparkContext.hadoopRDD`</span>
<span class="sd"> :meth:`SparkContext.hadoopFile`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import os</span>
<span class="sd"> &gt;&gt;&gt; import tempfile</span>
<span class="sd"> Set the related classes</span>
<span class="sd"> &gt;&gt;&gt; output_format_class = &quot;org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat&quot;</span>
<span class="sd"> &gt;&gt;&gt; input_format_class = &quot;org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat&quot;</span>
<span class="sd"> &gt;&gt;&gt; key_class = &quot;org.apache.hadoop.io.IntWritable&quot;</span>
<span class="sd"> &gt;&gt;&gt; value_class = &quot;org.apache.hadoop.io.Text&quot;</span>
<span class="sd"> &gt;&gt;&gt; with tempfile.TemporaryDirectory() as d:</span>
<span class="sd"> ... path = os.path.join(d, &quot;new_hadoop_file&quot;)</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Create the conf for writing</span>
<span class="sd"> ... write_conf = {</span>
<span class="sd"> ... &quot;mapreduce.job.outputformat.class&quot;: (output_format_class),</span>
<span class="sd"> ... &quot;mapreduce.job.output.key.class&quot;: key_class,</span>
<span class="sd"> ... &quot;mapreduce.job.output.value.class&quot;: value_class,</span>
<span class="sd"> ... &quot;mapreduce.output.fileoutputformat.outputdir&quot;: path,</span>
<span class="sd"> ... }</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Write a temporary Hadoop file</span>
<span class="sd"> ... rdd = sc.parallelize([(1, &quot;&quot;), (1, &quot;a&quot;), (3, &quot;x&quot;)])</span>
<span class="sd"> ... rdd.saveAsNewAPIHadoopDataset(conf=write_conf)</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Create the conf for reading</span>
<span class="sd"> ... read_conf = {&quot;mapreduce.input.fileinputformat.inputdir&quot;: path}</span>
<span class="sd"> ...</span>
<span class="sd"> ... loaded = sc.newAPIHadoopRDD(input_format_class,</span>
<span class="sd"> ... key_class, value_class, conf=read_conf)</span>
<span class="sd"> ... collected = sorted(loaded.collect())</span>
<span class="sd"> &gt;&gt;&gt; collected</span>
<span class="sd"> [(1, &#39;&#39;), (1, &#39;a&#39;), (3, &#39;x&#39;)]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">jconf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dictToJavaMap</span><span class="p">(</span><span class="n">conf</span><span class="p">)</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">jrdd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonRDD</span><span class="o">.</span><span class="n">newAPIHadoopRDD</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="p">,</span>
<span class="n">inputFormatClass</span><span class="p">,</span>
<span class="n">keyClass</span><span class="p">,</span>
<span class="n">valueClass</span><span class="p">,</span>
<span class="n">keyConverter</span><span class="p">,</span>
<span class="n">valueConverter</span><span class="p">,</span>
<span class="n">jconf</span><span class="p">,</span>
<span class="n">batchSize</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">RDD</span><span class="p">(</span><span class="n">jrdd</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.hadoopFile"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.hadoopFile.html#pyspark.SparkContext.hadoopFile">[docs]</a> <span class="k">def</span> <span class="nf">hadoopFile</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">inputFormatClass</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">keyClass</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">valueClass</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">keyConverter</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">valueConverter</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">conf</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">batchSize</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RDD</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">T</span><span class="p">,</span> <span class="n">U</span><span class="p">]]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Read an &#39;old&#39; Hadoop InputFormat with arbitrary key and value class from HDFS,</span>
<span class="sd"> a local file system (available on all nodes), or any Hadoop-supported file system URI.</span>
<span class="sd"> The mechanism is the same as for meth:`SparkContext.sequenceFile`.</span>
<span class="sd"> .. versionadded:: 1.1.0</span>
<span class="sd"> A Hadoop configuration can be passed in as a Python dict. This will be converted into a</span>
<span class="sd"> Configuration in Java.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str</span>
<span class="sd"> path to Hadoop file</span>
<span class="sd"> inputFormatClass : str</span>
<span class="sd"> fully qualified classname of Hadoop InputFormat</span>
<span class="sd"> (e.g. &quot;org.apache.hadoop.mapreduce.lib.input.TextInputFormat&quot;)</span>
<span class="sd"> keyClass : str</span>
<span class="sd"> fully qualified classname of key Writable class (e.g. &quot;org.apache.hadoop.io.Text&quot;)</span>
<span class="sd"> valueClass : str</span>
<span class="sd"> fully qualified classname of value Writable class</span>
<span class="sd"> (e.g. &quot;org.apache.hadoop.io.LongWritable&quot;)</span>
<span class="sd"> keyConverter : str, optional</span>
<span class="sd"> fully qualified name of a function returning key WritableConverter</span>
<span class="sd"> valueConverter : str, optional</span>
<span class="sd"> fully qualified name of a function returning value WritableConverter</span>
<span class="sd"> conf : dict, optional</span>
<span class="sd"> Hadoop configuration, passed in as a dict</span>
<span class="sd"> batchSize : int, optional, default 0</span>
<span class="sd"> The number of Python objects represented as a single</span>
<span class="sd"> Java object. (default 0, choose batchSize automatically)</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`RDD`</span>
<span class="sd"> RDD of tuples of key and corresponding value</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`RDD.saveAsSequenceFile`</span>
<span class="sd"> :meth:`RDD.saveAsNewAPIHadoopFile`</span>
<span class="sd"> :meth:`RDD.saveAsHadoopFile`</span>
<span class="sd"> :meth:`SparkContext.newAPIHadoopFile`</span>
<span class="sd"> :meth:`SparkContext.hadoopRDD`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import os</span>
<span class="sd"> &gt;&gt;&gt; import tempfile</span>
<span class="sd"> Set the related classes</span>
<span class="sd"> &gt;&gt;&gt; output_format_class = &quot;org.apache.hadoop.mapred.TextOutputFormat&quot;</span>
<span class="sd"> &gt;&gt;&gt; input_format_class = &quot;org.apache.hadoop.mapred.TextInputFormat&quot;</span>
<span class="sd"> &gt;&gt;&gt; key_class = &quot;org.apache.hadoop.io.IntWritable&quot;</span>
<span class="sd"> &gt;&gt;&gt; value_class = &quot;org.apache.hadoop.io.Text&quot;</span>
<span class="sd"> &gt;&gt;&gt; with tempfile.TemporaryDirectory() as d:</span>
<span class="sd"> ... path = os.path.join(d, &quot;old_hadoop_file&quot;)</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Write a temporary Hadoop file</span>
<span class="sd"> ... rdd = sc.parallelize([(1, &quot;&quot;), (1, &quot;a&quot;), (3, &quot;x&quot;)])</span>
<span class="sd"> ... rdd.saveAsHadoopFile(path, output_format_class, key_class, value_class)</span>
<span class="sd"> ...</span>
<span class="sd"> ... loaded = sc.hadoopFile(path, input_format_class, key_class, value_class)</span>
<span class="sd"> ... collected = sorted(loaded.collect())</span>
<span class="sd"> &gt;&gt;&gt; collected</span>
<span class="sd"> [(0, &#39;1\\t&#39;), (0, &#39;1\\ta&#39;), (0, &#39;3\\tx&#39;)]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">jconf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dictToJavaMap</span><span class="p">(</span><span class="n">conf</span><span class="p">)</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">jrdd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonRDD</span><span class="o">.</span><span class="n">hadoopFile</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="p">,</span>
<span class="n">path</span><span class="p">,</span>
<span class="n">inputFormatClass</span><span class="p">,</span>
<span class="n">keyClass</span><span class="p">,</span>
<span class="n">valueClass</span><span class="p">,</span>
<span class="n">keyConverter</span><span class="p">,</span>
<span class="n">valueConverter</span><span class="p">,</span>
<span class="n">jconf</span><span class="p">,</span>
<span class="n">batchSize</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">RDD</span><span class="p">(</span><span class="n">jrdd</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.hadoopRDD"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.hadoopRDD.html#pyspark.SparkContext.hadoopRDD">[docs]</a> <span class="k">def</span> <span class="nf">hadoopRDD</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">inputFormatClass</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">keyClass</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">valueClass</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">keyConverter</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">valueConverter</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">conf</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">batchSize</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RDD</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">T</span><span class="p">,</span> <span class="n">U</span><span class="p">]]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Read an &#39;old&#39; Hadoop InputFormat with arbitrary key and value class, from an arbitrary</span>
<span class="sd"> Hadoop configuration, which is passed in as a Python dict.</span>
<span class="sd"> This will be converted into a Configuration in Java.</span>
<span class="sd"> The mechanism is the same as for meth:`SparkContext.sequenceFile`.</span>
<span class="sd"> .. versionadded:: 1.1.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> inputFormatClass : str</span>
<span class="sd"> fully qualified classname of Hadoop InputFormat</span>
<span class="sd"> (e.g. &quot;org.apache.hadoop.mapreduce.lib.input.TextInputFormat&quot;)</span>
<span class="sd"> keyClass : str</span>
<span class="sd"> fully qualified classname of key Writable class (e.g. &quot;org.apache.hadoop.io.Text&quot;)</span>
<span class="sd"> valueClass : str</span>
<span class="sd"> fully qualified classname of value Writable class</span>
<span class="sd"> (e.g. &quot;org.apache.hadoop.io.LongWritable&quot;)</span>
<span class="sd"> keyConverter : str, optional</span>
<span class="sd"> fully qualified name of a function returning key WritableConverter</span>
<span class="sd"> valueConverter : str, optional</span>
<span class="sd"> fully qualified name of a function returning value WritableConverter</span>
<span class="sd"> conf : dict, optional</span>
<span class="sd"> Hadoop configuration, passed in as a dict</span>
<span class="sd"> batchSize : int, optional, default 0</span>
<span class="sd"> The number of Python objects represented as a single</span>
<span class="sd"> Java object. (default 0, choose batchSize automatically)</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`RDD`</span>
<span class="sd"> RDD of tuples of key and corresponding value</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`RDD.saveAsNewAPIHadoopDataset`</span>
<span class="sd"> :meth:`RDD.saveAsHadoopDataset`</span>
<span class="sd"> :meth:`SparkContext.newAPIHadoopRDD`</span>
<span class="sd"> :meth:`SparkContext.hadoopFile`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import os</span>
<span class="sd"> &gt;&gt;&gt; import tempfile</span>
<span class="sd"> Set the related classes</span>
<span class="sd"> &gt;&gt;&gt; output_format_class = &quot;org.apache.hadoop.mapred.TextOutputFormat&quot;</span>
<span class="sd"> &gt;&gt;&gt; input_format_class = &quot;org.apache.hadoop.mapred.TextInputFormat&quot;</span>
<span class="sd"> &gt;&gt;&gt; key_class = &quot;org.apache.hadoop.io.IntWritable&quot;</span>
<span class="sd"> &gt;&gt;&gt; value_class = &quot;org.apache.hadoop.io.Text&quot;</span>
<span class="sd"> &gt;&gt;&gt; with tempfile.TemporaryDirectory() as d:</span>
<span class="sd"> ... path = os.path.join(d, &quot;old_hadoop_file&quot;)</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Create the conf for writing</span>
<span class="sd"> ... write_conf = {</span>
<span class="sd"> ... &quot;mapred.output.format.class&quot;: output_format_class,</span>
<span class="sd"> ... &quot;mapreduce.job.output.key.class&quot;: key_class,</span>
<span class="sd"> ... &quot;mapreduce.job.output.value.class&quot;: value_class,</span>
<span class="sd"> ... &quot;mapreduce.output.fileoutputformat.outputdir&quot;: path,</span>
<span class="sd"> ... }</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Write a temporary Hadoop file</span>
<span class="sd"> ... rdd = sc.parallelize([(1, &quot;&quot;), (1, &quot;a&quot;), (3, &quot;x&quot;)])</span>
<span class="sd"> ... rdd.saveAsHadoopDataset(conf=write_conf)</span>
<span class="sd"> ...</span>
<span class="sd"> ... # Create the conf for reading</span>
<span class="sd"> ... read_conf = {&quot;mapreduce.input.fileinputformat.inputdir&quot;: path}</span>
<span class="sd"> ...</span>
<span class="sd"> ... loaded = sc.hadoopRDD(input_format_class, key_class, value_class, conf=read_conf)</span>
<span class="sd"> ... collected = sorted(loaded.collect())</span>
<span class="sd"> &gt;&gt;&gt; collected</span>
<span class="sd"> [(0, &#39;1\\t&#39;), (0, &#39;1\\ta&#39;), (0, &#39;3\\tx&#39;)]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">jconf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dictToJavaMap</span><span class="p">(</span><span class="n">conf</span><span class="p">)</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">jrdd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonRDD</span><span class="o">.</span><span class="n">hadoopRDD</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="p">,</span>
<span class="n">inputFormatClass</span><span class="p">,</span>
<span class="n">keyClass</span><span class="p">,</span>
<span class="n">valueClass</span><span class="p">,</span>
<span class="n">keyConverter</span><span class="p">,</span>
<span class="n">valueConverter</span><span class="p">,</span>
<span class="n">jconf</span><span class="p">,</span>
<span class="n">batchSize</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">RDD</span><span class="p">(</span><span class="n">jrdd</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_checkpointFile</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">input_deserializer</span><span class="p">:</span> <span class="n">PairDeserializer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RDD</span><span class="p">:</span>
<span class="n">jrdd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">checkpointFile</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
<span class="k">return</span> <span class="n">RDD</span><span class="p">(</span><span class="n">jrdd</span><span class="p">,</span> <span class="bp">self</span><span class="p">,</span> <span class="n">input_deserializer</span><span class="p">)</span>
<div class="viewcode-block" id="SparkContext.union"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.union.html#pyspark.SparkContext.union">[docs]</a> <span class="k">def</span> <span class="nf">union</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rdds</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">RDD</span><span class="p">[</span><span class="n">T</span><span class="p">]])</span> <span class="o">-&gt;</span> <span class="n">RDD</span><span class="p">[</span><span class="n">T</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Build the union of a list of RDDs.</span>
<span class="sd"> This supports unions() of RDDs with different serialized formats,</span>
<span class="sd"> although this forces them to be reserialized using the default</span>
<span class="sd"> serializer:</span>
<span class="sd"> .. versionadded:: 0.7.0</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`RDD.union`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import os</span>
<span class="sd"> &gt;&gt;&gt; import tempfile</span>
<span class="sd"> &gt;&gt;&gt; with tempfile.TemporaryDirectory() as d:</span>
<span class="sd"> ... # generate a text RDD</span>
<span class="sd"> ... with open(os.path.join(d, &quot;union-text.txt&quot;), &quot;w&quot;) as f:</span>
<span class="sd"> ... _ = f.write(&quot;Hello&quot;)</span>
<span class="sd"> ... text_rdd = sc.textFile(d)</span>
<span class="sd"> ...</span>
<span class="sd"> ... # generate another RDD</span>
<span class="sd"> ... parallelized = sc.parallelize([&quot;World!&quot;])</span>
<span class="sd"> ...</span>
<span class="sd"> ... unioned = sorted(sc.union([text_rdd, parallelized]).collect())</span>
<span class="sd"> &gt;&gt;&gt; unioned</span>
<span class="sd"> [&#39;Hello&#39;, &#39;World!&#39;]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">first_jrdd_deserializer</span> <span class="o">=</span> <span class="n">rdds</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">_jrdd_deserializer</span>
<span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">_jrdd_deserializer</span> <span class="o">!=</span> <span class="n">first_jrdd_deserializer</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">rdds</span><span class="p">):</span>
<span class="n">rdds</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">_reserialize</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">rdds</span><span class="p">]</span>
<span class="n">gw</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_gateway</span>
<span class="k">assert</span> <span class="n">gw</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">jvm</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_jvm</span>
<span class="k">assert</span> <span class="n">jvm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">jrdd_cls</span> <span class="o">=</span> <span class="n">jvm</span><span class="o">.</span><span class="n">org</span><span class="o">.</span><span class="n">apache</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">api</span><span class="o">.</span><span class="n">java</span><span class="o">.</span><span class="n">JavaRDD</span>
<span class="n">jpair_rdd_cls</span> <span class="o">=</span> <span class="n">jvm</span><span class="o">.</span><span class="n">org</span><span class="o">.</span><span class="n">apache</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">api</span><span class="o">.</span><span class="n">java</span><span class="o">.</span><span class="n">JavaPairRDD</span>
<span class="n">jdouble_rdd_cls</span> <span class="o">=</span> <span class="n">jvm</span><span class="o">.</span><span class="n">org</span><span class="o">.</span><span class="n">apache</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">api</span><span class="o">.</span><span class="n">java</span><span class="o">.</span><span class="n">JavaDoubleRDD</span>
<span class="k">if</span> <span class="n">is_instance_of</span><span class="p">(</span><span class="n">gw</span><span class="p">,</span> <span class="n">rdds</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">_jrdd</span><span class="p">,</span> <span class="n">jrdd_cls</span><span class="p">):</span>
<span class="bp">cls</span> <span class="o">=</span> <span class="n">jrdd_cls</span>
<span class="k">elif</span> <span class="n">is_instance_of</span><span class="p">(</span><span class="n">gw</span><span class="p">,</span> <span class="n">rdds</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">_jrdd</span><span class="p">,</span> <span class="n">jpair_rdd_cls</span><span class="p">):</span>
<span class="bp">cls</span> <span class="o">=</span> <span class="n">jpair_rdd_cls</span>
<span class="k">elif</span> <span class="n">is_instance_of</span><span class="p">(</span><span class="n">gw</span><span class="p">,</span> <span class="n">rdds</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">_jrdd</span><span class="p">,</span> <span class="n">jdouble_rdd_cls</span><span class="p">):</span>
<span class="bp">cls</span> <span class="o">=</span> <span class="n">jdouble_rdd_cls</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">cls_name</span> <span class="o">=</span> <span class="n">rdds</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">_jrdd</span><span class="o">.</span><span class="n">getClass</span><span class="p">()</span><span class="o">.</span><span class="n">getCanonicalName</span><span class="p">()</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Unsupported Java RDD class </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">cls_name</span><span class="p">)</span>
<span class="n">jrdds</span> <span class="o">=</span> <span class="n">gw</span><span class="o">.</span><span class="n">new_array</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">rdds</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">rdds</span><span class="p">)):</span>
<span class="n">jrdds</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">rdds</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">_jrdd</span>
<span class="k">return</span> <span class="n">RDD</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">jrdds</span><span class="p">),</span> <span class="bp">self</span><span class="p">,</span> <span class="n">rdds</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">_jrdd_deserializer</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.broadcast"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.broadcast.html#pyspark.SparkContext.broadcast">[docs]</a> <span class="k">def</span> <span class="nf">broadcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="n">T</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;Broadcast[T]&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Broadcast a read-only variable to the cluster, returning a :class:`Broadcast`</span>
<span class="sd"> object for reading it in distributed functions. The variable will</span>
<span class="sd"> be sent to each cluster only once.</span>
<span class="sd"> .. versionadded:: 0.7.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> value : T</span>
<span class="sd"> value to broadcast to the Spark nodes</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`Broadcast`</span>
<span class="sd"> :class:`Broadcast` object, a read-only variable cached on each machine</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; mapping = {1: 10001, 2: 10002}</span>
<span class="sd"> &gt;&gt;&gt; bc = sc.broadcast(mapping)</span>
<span class="sd"> &gt;&gt;&gt; rdd = sc.range(5)</span>
<span class="sd"> &gt;&gt;&gt; rdd2 = rdd.map(lambda i: bc.value[i] if i in bc.value else -1)</span>
<span class="sd"> &gt;&gt;&gt; rdd2.collect()</span>
<span class="sd"> [-1, 10001, 10002, -1, -1]</span>
<span class="sd"> &gt;&gt;&gt; bc.destroy()</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">Broadcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pickled_broadcast_vars</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.accumulator"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.accumulator.html#pyspark.SparkContext.accumulator">[docs]</a> <span class="k">def</span> <span class="nf">accumulator</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="n">T</span><span class="p">,</span> <span class="n">accum_param</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="s2">&quot;AccumulatorParam[T]&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;Accumulator[T]&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Create an :class:`Accumulator` with the given initial value, using a given</span>
<span class="sd"> :class:`AccumulatorParam` helper object to define how to add values of the</span>
<span class="sd"> data type if provided. Default AccumulatorParams are used for integers</span>
<span class="sd"> and floating-point numbers if you do not provide one. For other types,</span>
<span class="sd"> a custom AccumulatorParam can be used.</span>
<span class="sd"> .. versionadded:: 0.7.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> value : T</span>
<span class="sd"> initialized value</span>
<span class="sd"> accum_param : :class:`pyspark.AccumulatorParam`, optional</span>
<span class="sd"> helper object to define how to add values</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`Accumulator`</span>
<span class="sd"> `Accumulator` object, a shared variable that can be accumulated</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; acc = sc.accumulator(9)</span>
<span class="sd"> &gt;&gt;&gt; acc.value</span>
<span class="sd"> 9</span>
<span class="sd"> &gt;&gt;&gt; acc += 1</span>
<span class="sd"> &gt;&gt;&gt; acc.value</span>
<span class="sd"> 10</span>
<span class="sd"> Accumulator object can be accumulated in RDD operations:</span>
<span class="sd"> &gt;&gt;&gt; rdd = sc.range(5)</span>
<span class="sd"> &gt;&gt;&gt; def f(x):</span>
<span class="sd"> ... global acc</span>
<span class="sd"> ... acc += 1</span>
<span class="sd"> ...</span>
<span class="sd"> &gt;&gt;&gt; rdd.foreach(f)</span>
<span class="sd"> &gt;&gt;&gt; acc.value</span>
<span class="sd"> 15</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">accum_param</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
<span class="n">accum_param</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="s2">&quot;AccumulatorParam[T]&quot;</span><span class="p">,</span> <span class="n">accumulators</span><span class="o">.</span><span class="n">INT_ACCUMULATOR_PARAM</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">float</span><span class="p">):</span>
<span class="n">accum_param</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="s2">&quot;AccumulatorParam[T]&quot;</span><span class="p">,</span> <span class="n">accumulators</span><span class="o">.</span><span class="n">FLOAT_ACCUMULATOR_PARAM</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">complex</span><span class="p">):</span>
<span class="n">accum_param</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="s2">&quot;AccumulatorParam[T]&quot;</span><span class="p">,</span> <span class="n">accumulators</span><span class="o">.</span><span class="n">COMPLEX_ACCUMULATOR_PARAM</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;No default accumulator param for type </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="nb">type</span><span class="p">(</span><span class="n">value</span><span class="p">))</span>
<span class="n">SparkContext</span><span class="o">.</span><span class="n">_next_accum_id</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">Accumulator</span><span class="p">(</span><span class="n">SparkContext</span><span class="o">.</span><span class="n">_next_accum_id</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">accum_param</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.addFile"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.addFile.html#pyspark.SparkContext.addFile">[docs]</a> <span class="k">def</span> <span class="nf">addFile</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">recursive</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Add a file to be downloaded with this Spark job on every node.</span>
<span class="sd"> The `path` passed can be either a local file, a file in HDFS</span>
<span class="sd"> (or other Hadoop-supported filesystems), or an HTTP, HTTPS or</span>
<span class="sd"> FTP URI.</span>
<span class="sd"> To access the file in Spark jobs, use :meth:`SparkFiles.get` with the</span>
<span class="sd"> filename to find its download location.</span>
<span class="sd"> A directory can be given if the recursive option is set to True.</span>
<span class="sd"> Currently directories are only supported for Hadoop-supported filesystems.</span>
<span class="sd"> .. versionadded:: 0.7.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str</span>
<span class="sd"> can be either a local file, a file in HDFS (or other Hadoop-supported</span>
<span class="sd"> filesystems), or an HTTP, HTTPS or FTP URI. To access the file in Spark jobs,</span>
<span class="sd"> use :meth:`SparkFiles.get` to find its download location.</span>
<span class="sd"> recursive : bool, default False</span>
<span class="sd"> whether to recursively add files in the input directory</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.listFiles`</span>
<span class="sd"> :meth:`SparkContext.addPyFile`</span>
<span class="sd"> :meth:`SparkFiles.get`</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> A path can be added only once. Subsequent additions of the same path are ignored.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import os</span>
<span class="sd"> &gt;&gt;&gt; import tempfile</span>
<span class="sd"> &gt;&gt;&gt; from pyspark import SparkFiles</span>
<span class="sd"> &gt;&gt;&gt; with tempfile.TemporaryDirectory() as d:</span>
<span class="sd"> ... path1 = os.path.join(d, &quot;test1.txt&quot;)</span>
<span class="sd"> ... with open(path1, &quot;w&quot;) as f:</span>
<span class="sd"> ... _ = f.write(&quot;100&quot;)</span>
<span class="sd"> ...</span>
<span class="sd"> ... path2 = os.path.join(d, &quot;test2.txt&quot;)</span>
<span class="sd"> ... with open(path2, &quot;w&quot;) as f:</span>
<span class="sd"> ... _ = f.write(&quot;200&quot;)</span>
<span class="sd"> ...</span>
<span class="sd"> ... sc.addFile(path1)</span>
<span class="sd"> ... file_list1 = sorted(sc.listFiles)</span>
<span class="sd"> ...</span>
<span class="sd"> ... sc.addFile(path2)</span>
<span class="sd"> ... file_list2 = sorted(sc.listFiles)</span>
<span class="sd"> ...</span>
<span class="sd"> ... # add path2 twice, this addition will be ignored</span>
<span class="sd"> ... sc.addFile(path2)</span>
<span class="sd"> ... file_list3 = sorted(sc.listFiles)</span>
<span class="sd"> ...</span>
<span class="sd"> ... def func(iterator):</span>
<span class="sd"> ... with open(SparkFiles.get(&quot;test1.txt&quot;)) as f:</span>
<span class="sd"> ... mul = int(f.readline())</span>
<span class="sd"> ... return [x * mul for x in iterator]</span>
<span class="sd"> ...</span>
<span class="sd"> ... collected = sc.parallelize([1, 2, 3, 4]).mapPartitions(func).collect()</span>
<span class="sd"> &gt;&gt;&gt; file_list1</span>
<span class="sd"> [&#39;file:/.../test1.txt&#39;]</span>
<span class="sd"> &gt;&gt;&gt; file_list2</span>
<span class="sd"> [&#39;file:/.../test1.txt&#39;, &#39;file:/.../test2.txt&#39;]</span>
<span class="sd"> &gt;&gt;&gt; file_list3</span>
<span class="sd"> [&#39;file:/.../test1.txt&#39;, &#39;file:/.../test2.txt&#39;]</span>
<span class="sd"> &gt;&gt;&gt; collected</span>
<span class="sd"> [100, 200, 300, 400]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">()</span><span class="o">.</span><span class="n">addFile</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">recursive</span><span class="p">)</span></div>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">listFiles</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Returns a list of file paths that are added to resources.</span>
<span class="sd"> .. versionadded:: 3.4.0</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.addFile`</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">scala</span><span class="o">.</span><span class="n">collection</span><span class="o">.</span><span class="n">JavaConverters</span><span class="o">.</span><span class="n">seqAsJavaList</span><span class="p">(</span> <span class="c1"># type: ignore[union-attr]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">()</span><span class="o">.</span><span class="n">listFiles</span><span class="p">()</span>
<span class="p">)</span>
<span class="p">)</span>
<div class="viewcode-block" id="SparkContext.addPyFile"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.addPyFile.html#pyspark.SparkContext.addPyFile">[docs]</a> <span class="k">def</span> <span class="nf">addPyFile</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Add a .py or .zip dependency for all tasks to be executed on this</span>
<span class="sd"> SparkContext in the future. The `path` passed can be either a local</span>
<span class="sd"> file, a file in HDFS (or other Hadoop-supported filesystems), or an</span>
<span class="sd"> HTTP, HTTPS or FTP URI.</span>
<span class="sd"> .. versionadded:: 0.7.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str</span>
<span class="sd"> can be either a .py file or .zip dependency.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.addFile`</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> A path can be added only once. Subsequent additions of the same path are ignored.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">addFile</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
<span class="p">(</span><span class="n">dirname</span><span class="p">,</span> <span class="n">filename</span><span class="p">)</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> <span class="c1"># dirname may be directory or HDFS/S3 prefix</span>
<span class="k">if</span> <span class="n">filename</span><span class="p">[</span><span class="o">-</span><span class="mi">4</span><span class="p">:]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">PACKAGE_EXTENSIONS</span><span class="p">:</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_python_includes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_python_includes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
<span class="c1"># for tests in local mode</span>
<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">SparkFiles</span><span class="o">.</span><span class="n">getRootDirectory</span><span class="p">(),</span> <span class="n">filename</span><span class="p">))</span>
<span class="n">importlib</span><span class="o">.</span><span class="n">invalidate_caches</span><span class="p">()</span></div>
<div class="viewcode-block" id="SparkContext.addArchive"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.addArchive.html#pyspark.SparkContext.addArchive">[docs]</a> <span class="k">def</span> <span class="nf">addArchive</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Add an archive to be downloaded with this Spark job on every node.</span>
<span class="sd"> The `path` passed can be either a local file, a file in HDFS</span>
<span class="sd"> (or other Hadoop-supported filesystems), or an HTTP, HTTPS or</span>
<span class="sd"> FTP URI.</span>
<span class="sd"> To access the file in Spark jobs, use :meth:`SparkFiles.get` with the</span>
<span class="sd"> filename to find its download/unpacked location. The given path should</span>
<span class="sd"> be one of .zip, .tar, .tar.gz, .tgz and .jar.</span>
<span class="sd"> .. versionadded:: 3.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str</span>
<span class="sd"> can be either a local file, a file in HDFS (or other Hadoop-supported</span>
<span class="sd"> filesystems), or an HTTP, HTTPS or FTP URI. To access the file in Spark jobs,</span>
<span class="sd"> use :meth:`SparkFiles.get` to find its download location.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.listArchives`</span>
<span class="sd"> :meth:`SparkFiles.get`</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> A path can be added only once. Subsequent additions of the same path are ignored.</span>
<span class="sd"> This API is experimental.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> Creates a zipped file that contains a text file written &#39;100&#39;.</span>
<span class="sd"> &gt;&gt;&gt; import os</span>
<span class="sd"> &gt;&gt;&gt; import tempfile</span>
<span class="sd"> &gt;&gt;&gt; import zipfile</span>
<span class="sd"> &gt;&gt;&gt; from pyspark import SparkFiles</span>
<span class="sd"> &gt;&gt;&gt; with tempfile.TemporaryDirectory() as d:</span>
<span class="sd"> ... path = os.path.join(d, &quot;test.txt&quot;)</span>
<span class="sd"> ... with open(path, &quot;w&quot;) as f:</span>
<span class="sd"> ... _ = f.write(&quot;100&quot;)</span>
<span class="sd"> ...</span>
<span class="sd"> ... zip_path1 = os.path.join(d, &quot;test1.zip&quot;)</span>
<span class="sd"> ... with zipfile.ZipFile(zip_path1, &quot;w&quot;, zipfile.ZIP_DEFLATED) as z:</span>
<span class="sd"> ... z.write(path, os.path.basename(path))</span>
<span class="sd"> ...</span>
<span class="sd"> ... zip_path2 = os.path.join(d, &quot;test2.zip&quot;)</span>
<span class="sd"> ... with zipfile.ZipFile(zip_path2, &quot;w&quot;, zipfile.ZIP_DEFLATED) as z:</span>
<span class="sd"> ... z.write(path, os.path.basename(path))</span>
<span class="sd"> ...</span>
<span class="sd"> ... sc.addArchive(zip_path1)</span>
<span class="sd"> ... arch_list1 = sorted(sc.listArchives)</span>
<span class="sd"> ...</span>
<span class="sd"> ... sc.addArchive(zip_path2)</span>
<span class="sd"> ... arch_list2 = sorted(sc.listArchives)</span>
<span class="sd"> ...</span>
<span class="sd"> ... # add zip_path2 twice, this addition will be ignored</span>
<span class="sd"> ... sc.addArchive(zip_path2)</span>
<span class="sd"> ... arch_list3 = sorted(sc.listArchives)</span>
<span class="sd"> ...</span>
<span class="sd"> ... def func(iterator):</span>
<span class="sd"> ... with open(&quot;%s/test.txt&quot; % SparkFiles.get(&quot;test1.zip&quot;)) as f:</span>
<span class="sd"> ... mul = int(f.readline())</span>
<span class="sd"> ... return [x * mul for x in iterator]</span>
<span class="sd"> ...</span>
<span class="sd"> ... collected = sc.parallelize([1, 2, 3, 4]).mapPartitions(func).collect()</span>
<span class="sd"> &gt;&gt;&gt; arch_list1</span>
<span class="sd"> [&#39;file:/.../test1.zip&#39;]</span>
<span class="sd"> &gt;&gt;&gt; arch_list2</span>
<span class="sd"> [&#39;file:/.../test1.zip&#39;, &#39;file:/.../test2.zip&#39;]</span>
<span class="sd"> &gt;&gt;&gt; arch_list3</span>
<span class="sd"> [&#39;file:/.../test1.zip&#39;, &#39;file:/.../test2.zip&#39;]</span>
<span class="sd"> &gt;&gt;&gt; collected</span>
<span class="sd"> [100, 200, 300, 400]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">()</span><span class="o">.</span><span class="n">addArchive</span><span class="p">(</span><span class="n">path</span><span class="p">)</span></div>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">listArchives</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Returns a list of archive paths that are added to resources.</span>
<span class="sd"> .. versionadded:: 3.4.0</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.addArchive`</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">scala</span><span class="o">.</span><span class="n">collection</span><span class="o">.</span><span class="n">JavaConverters</span><span class="o">.</span><span class="n">seqAsJavaList</span><span class="p">(</span> <span class="c1"># type: ignore[union-attr]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">()</span><span class="o">.</span><span class="n">listArchives</span><span class="p">()</span>
<span class="p">)</span>
<span class="p">)</span>
<div class="viewcode-block" id="SparkContext.setCheckpointDir"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.setCheckpointDir.html#pyspark.SparkContext.setCheckpointDir">[docs]</a> <span class="k">def</span> <span class="nf">setCheckpointDir</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dirName</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Set the directory under which RDDs are going to be checkpointed. The</span>
<span class="sd"> directory must be an HDFS path if running on a cluster.</span>
<span class="sd"> .. versionadded:: 0.7.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> dirName : str</span>
<span class="sd"> path to the directory where checkpoint files will be stored</span>
<span class="sd"> (must be HDFS path if running in cluster)</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.getCheckpointDir`</span>
<span class="sd"> :meth:`RDD.checkpoint`</span>
<span class="sd"> :meth:`RDD.getCheckpointFile`</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">()</span><span class="o">.</span><span class="n">setCheckpointDir</span><span class="p">(</span><span class="n">dirName</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.getCheckpointDir"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.getCheckpointDir.html#pyspark.SparkContext.getCheckpointDir">[docs]</a> <span class="k">def</span> <span class="nf">getCheckpointDir</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return the directory where RDDs are checkpointed. Returns None if no</span>
<span class="sd"> checkpoint directory has been set.</span>
<span class="sd"> .. versionadded:: 3.1.0</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.setCheckpointDir`</span>
<span class="sd"> :meth:`RDD.checkpoint`</span>
<span class="sd"> :meth:`RDD.getCheckpointFile`</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">()</span><span class="o">.</span><span class="n">getCheckpointDir</span><span class="p">()</span><span class="o">.</span><span class="n">isEmpty</span><span class="p">():</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">()</span><span class="o">.</span><span class="n">getCheckpointDir</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="k">return</span> <span class="kc">None</span></div>
<span class="k">def</span> <span class="nf">_getJavaStorageLevel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">storageLevel</span><span class="p">:</span> <span class="n">StorageLevel</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">JavaObject</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a Java StorageLevel based on a pyspark.StorageLevel.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">storageLevel</span><span class="p">,</span> <span class="n">StorageLevel</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;storageLevel must be of type pyspark.StorageLevel&quot;</span><span class="p">)</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">newStorageLevel</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">org</span><span class="o">.</span><span class="n">apache</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">storage</span><span class="o">.</span><span class="n">StorageLevel</span>
<span class="k">return</span> <span class="n">newStorageLevel</span><span class="p">(</span>
<span class="n">storageLevel</span><span class="o">.</span><span class="n">useDisk</span><span class="p">,</span>
<span class="n">storageLevel</span><span class="o">.</span><span class="n">useMemory</span><span class="p">,</span>
<span class="n">storageLevel</span><span class="o">.</span><span class="n">useOffHeap</span><span class="p">,</span>
<span class="n">storageLevel</span><span class="o">.</span><span class="n">deserialized</span><span class="p">,</span>
<span class="n">storageLevel</span><span class="o">.</span><span class="n">replication</span><span class="p">,</span>
<span class="p">)</span>
<div class="viewcode-block" id="SparkContext.setJobGroup"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.setJobGroup.html#pyspark.SparkContext.setJobGroup">[docs]</a> <span class="k">def</span> <span class="nf">setJobGroup</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">groupId</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">description</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">interruptOnCancel</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Assigns a group ID to all the jobs started by this thread until the group ID is set to a</span>
<span class="sd"> different value or cleared.</span>
<span class="sd"> Often, a unit of execution in an application consists of multiple Spark actions or jobs.</span>
<span class="sd"> Application programmers can use this method to group all those jobs together and give a</span>
<span class="sd"> group description. Once set, the Spark web UI will associate such jobs with this group.</span>
<span class="sd"> The application can use :meth:`SparkContext.cancelJobGroup` to cancel all</span>
<span class="sd"> running jobs in this group.</span>
<span class="sd"> .. versionadded:: 1.0.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> groupId : str</span>
<span class="sd"> The group ID to assign.</span>
<span class="sd"> description : str</span>
<span class="sd"> The description to set for the job group.</span>
<span class="sd"> interruptOnCancel : bool, optional, default False</span>
<span class="sd"> whether to interrupt jobs on job cancellation.</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> If interruptOnCancel is set to true for the job group, then job cancellation will result</span>
<span class="sd"> in Thread.interrupt() being called on the job&#39;s executor threads. This is useful to help</span>
<span class="sd"> ensure that the tasks are actually stopped in a timely manner, but is off by default due</span>
<span class="sd"> to HDFS-1208, where HDFS may respond to Thread.interrupt() by marking nodes as dead.</span>
<span class="sd"> If you run jobs in parallel, use :class:`pyspark.InheritableThread` for thread</span>
<span class="sd"> local inheritance.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.cancelJobGroup`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import threading</span>
<span class="sd"> &gt;&gt;&gt; from time import sleep</span>
<span class="sd"> &gt;&gt;&gt; from pyspark import InheritableThread</span>
<span class="sd"> &gt;&gt;&gt; result = &quot;Not Set&quot;</span>
<span class="sd"> &gt;&gt;&gt; lock = threading.Lock()</span>
<span class="sd"> &gt;&gt;&gt; def map_func(x):</span>
<span class="sd"> ... sleep(100)</span>
<span class="sd"> ... raise RuntimeError(&quot;Task should have been cancelled&quot;)</span>
<span class="sd"> ...</span>
<span class="sd"> &gt;&gt;&gt; def start_job(x):</span>
<span class="sd"> ... global result</span>
<span class="sd"> ... try:</span>
<span class="sd"> ... sc.setJobGroup(&quot;job_to_cancel&quot;, &quot;some description&quot;)</span>
<span class="sd"> ... result = sc.parallelize(range(x)).map(map_func).collect()</span>
<span class="sd"> ... except Exception as e:</span>
<span class="sd"> ... result = &quot;Cancelled&quot;</span>
<span class="sd"> ... lock.release()</span>
<span class="sd"> ...</span>
<span class="sd"> &gt;&gt;&gt; def stop_job():</span>
<span class="sd"> ... sleep(5)</span>
<span class="sd"> ... sc.cancelJobGroup(&quot;job_to_cancel&quot;)</span>
<span class="sd"> ...</span>
<span class="sd"> &gt;&gt;&gt; suppress = lock.acquire()</span>
<span class="sd"> &gt;&gt;&gt; suppress = InheritableThread(target=start_job, args=(10,)).start()</span>
<span class="sd"> &gt;&gt;&gt; suppress = InheritableThread(target=stop_job).start()</span>
<span class="sd"> &gt;&gt;&gt; suppress = lock.acquire()</span>
<span class="sd"> &gt;&gt;&gt; print(result)</span>
<span class="sd"> Cancelled</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">setJobGroup</span><span class="p">(</span><span class="n">groupId</span><span class="p">,</span> <span class="n">description</span><span class="p">,</span> <span class="n">interruptOnCancel</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.setInterruptOnCancel"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.setInterruptOnCancel.html#pyspark.SparkContext.setInterruptOnCancel">[docs]</a> <span class="k">def</span> <span class="nf">setInterruptOnCancel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">interruptOnCancel</span><span class="p">:</span> <span class="nb">bool</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Set the behavior of job cancellation from jobs started in this thread.</span>
<span class="sd"> .. versionadded:: 3.5.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> interruptOnCancel : bool</span>
<span class="sd"> If true, then job cancellation will result in ``Thread.interrupt()``</span>
<span class="sd"> being called on the job&#39;s executor threads. This is useful to help ensure that</span>
<span class="sd"> the tasks are actually stopped in a timely manner, but is off by default due to</span>
<span class="sd"> HDFS-1208, where HDFS may respond to ``Thread.interrupt()`` by marking nodes as dead.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.addJobTag`</span>
<span class="sd"> :meth:`SparkContext.removeJobTag`</span>
<span class="sd"> :meth:`SparkContext.cancelAllJobs`</span>
<span class="sd"> :meth:`SparkContext.cancelJobGroup`</span>
<span class="sd"> :meth:`SparkContext.cancelJobsWithTag`</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">setInterruptOnCancel</span><span class="p">(</span><span class="n">interruptOnCancel</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.addJobTag"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.addJobTag.html#pyspark.SparkContext.addJobTag">[docs]</a> <span class="k">def</span> <span class="nf">addJobTag</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tag</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Add a tag to be assigned to all the jobs started by this thread.</span>
<span class="sd"> .. versionadded:: 3.5.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> tag : str</span>
<span class="sd"> The tag to be added. Cannot contain &#39;,&#39; (comma) character.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.removeJobTag`</span>
<span class="sd"> :meth:`SparkContext.getJobTags`</span>
<span class="sd"> :meth:`SparkContext.clearJobTags`</span>
<span class="sd"> :meth:`SparkContext.cancelJobsWithTag`</span>
<span class="sd"> :meth:`SparkContext.setInterruptOnCancel`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import threading</span>
<span class="sd"> &gt;&gt;&gt; from time import sleep</span>
<span class="sd"> &gt;&gt;&gt; from pyspark import InheritableThread</span>
<span class="sd"> &gt;&gt;&gt; sc.setInterruptOnCancel(interruptOnCancel=True)</span>
<span class="sd"> &gt;&gt;&gt; result = &quot;Not Set&quot;</span>
<span class="sd"> &gt;&gt;&gt; lock = threading.Lock()</span>
<span class="sd"> &gt;&gt;&gt; def map_func(x):</span>
<span class="sd"> ... sleep(100)</span>
<span class="sd"> ... raise RuntimeError(&quot;Task should have been cancelled&quot;)</span>
<span class="sd"> ...</span>
<span class="sd"> &gt;&gt;&gt; def start_job(x):</span>
<span class="sd"> ... global result</span>
<span class="sd"> ... try:</span>
<span class="sd"> ... sc.addJobTag(&quot;job_to_cancel&quot;)</span>
<span class="sd"> ... result = sc.parallelize(range(x)).map(map_func).collect()</span>
<span class="sd"> ... except Exception as e:</span>
<span class="sd"> ... result = &quot;Cancelled&quot;</span>
<span class="sd"> ... lock.release()</span>
<span class="sd"> ...</span>
<span class="sd"> &gt;&gt;&gt; def stop_job():</span>
<span class="sd"> ... sleep(5)</span>
<span class="sd"> ... sc.cancelJobsWithTag(&quot;job_to_cancel&quot;)</span>
<span class="sd"> ...</span>
<span class="sd"> &gt;&gt;&gt; suppress = lock.acquire()</span>
<span class="sd"> &gt;&gt;&gt; suppress = InheritableThread(target=start_job, args=(10,)).start()</span>
<span class="sd"> &gt;&gt;&gt; suppress = InheritableThread(target=stop_job).start()</span>
<span class="sd"> &gt;&gt;&gt; suppress = lock.acquire()</span>
<span class="sd"> &gt;&gt;&gt; print(result)</span>
<span class="sd"> Cancelled</span>
<span class="sd"> &gt;&gt;&gt; sc.clearJobTags()</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">addJobTag</span><span class="p">(</span><span class="n">tag</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.removeJobTag"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.removeJobTag.html#pyspark.SparkContext.removeJobTag">[docs]</a> <span class="k">def</span> <span class="nf">removeJobTag</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tag</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Remove a tag previously added to be assigned to all the jobs started by this thread.</span>
<span class="sd"> Noop if such a tag was not added earlier.</span>
<span class="sd"> .. versionadded:: 3.5.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> tag : str</span>
<span class="sd"> The tag to be removed. Cannot contain &#39;,&#39; (comma) character.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.addJobTag`</span>
<span class="sd"> :meth:`SparkContext.getJobTags`</span>
<span class="sd"> :meth:`SparkContext.clearJobTags`</span>
<span class="sd"> :meth:`SparkContext.cancelJobsWithTag`</span>
<span class="sd"> :meth:`SparkContext.setInterruptOnCancel`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; sc.addJobTag(&quot;job_to_cancel1&quot;)</span>
<span class="sd"> &gt;&gt;&gt; sc.addJobTag(&quot;job_to_cancel2&quot;)</span>
<span class="sd"> &gt;&gt;&gt; sc.getJobTags()</span>
<span class="sd"> {&#39;job_to_cancel1&#39;, &#39;job_to_cancel2&#39;}</span>
<span class="sd"> &gt;&gt;&gt; sc.removeJobTag(&quot;job_to_cancel1&quot;)</span>
<span class="sd"> &gt;&gt;&gt; sc.getJobTags()</span>
<span class="sd"> {&#39;job_to_cancel2&#39;}</span>
<span class="sd"> &gt;&gt;&gt; sc.clearJobTags()</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">removeJobTag</span><span class="p">(</span><span class="n">tag</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.getJobTags"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.getJobTags.html#pyspark.SparkContext.getJobTags">[docs]</a> <span class="k">def</span> <span class="nf">getJobTags</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Set</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get the tags that are currently set to be assigned to all the jobs started by this thread.</span>
<span class="sd"> .. versionadded:: 3.5.0</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> set of str</span>
<span class="sd"> the tags that are currently set to be assigned to all the jobs started by this thread.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.addJobTag`</span>
<span class="sd"> :meth:`SparkContext.removeJobTag`</span>
<span class="sd"> :meth:`SparkContext.clearJobTags`</span>
<span class="sd"> :meth:`SparkContext.cancelJobsWithTag`</span>
<span class="sd"> :meth:`SparkContext.setInterruptOnCancel`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; sc.addJobTag(&quot;job_to_cancel&quot;)</span>
<span class="sd"> &gt;&gt;&gt; sc.getJobTags()</span>
<span class="sd"> {&#39;job_to_cancel&#39;}</span>
<span class="sd"> &gt;&gt;&gt; sc.clearJobTags()</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">getJobTags</span><span class="p">()</span></div>
<div class="viewcode-block" id="SparkContext.clearJobTags"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.clearJobTags.html#pyspark.SparkContext.clearJobTags">[docs]</a> <span class="k">def</span> <span class="nf">clearJobTags</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Clear the current thread&#39;s job tags.</span>
<span class="sd"> .. versionadded:: 3.5.0</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.addJobTag`</span>
<span class="sd"> :meth:`SparkContext.removeJobTag`</span>
<span class="sd"> :meth:`SparkContext.getJobTags`</span>
<span class="sd"> :meth:`SparkContext.cancelJobsWithTag`</span>
<span class="sd"> :meth:`SparkContext.setInterruptOnCancel`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; sc.addJobTag(&quot;job_to_cancel&quot;)</span>
<span class="sd"> &gt;&gt;&gt; sc.clearJobTags()</span>
<span class="sd"> &gt;&gt;&gt; sc.getJobTags()</span>
<span class="sd"> set()</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">clearJobTags</span><span class="p">()</span></div>
<div class="viewcode-block" id="SparkContext.setLocalProperty"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.setLocalProperty.html#pyspark.SparkContext.setLocalProperty">[docs]</a> <span class="k">def</span> <span class="nf">setLocalProperty</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Set a local property that affects jobs submitted from this thread, such as the</span>
<span class="sd"> Spark fair scheduler pool.</span>
<span class="sd"> .. versionadded:: 1.0.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> key : str</span>
<span class="sd"> The key of the local property to set.</span>
<span class="sd"> value : str</span>
<span class="sd"> The value of the local property to set.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.getLocalProperty`</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> If you run jobs in parallel, use :class:`pyspark.InheritableThread` for thread</span>
<span class="sd"> local inheritance.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">setLocalProperty</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.getLocalProperty"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.getLocalProperty.html#pyspark.SparkContext.getLocalProperty">[docs]</a> <span class="k">def</span> <span class="nf">getLocalProperty</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get a local property set in this thread, or null if it is missing. See</span>
<span class="sd"> :meth:`setLocalProperty`.</span>
<span class="sd"> .. versionadded:: 1.0.0</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.setLocalProperty`</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">getLocalProperty</span><span class="p">(</span><span class="n">key</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.setJobDescription"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.setJobDescription.html#pyspark.SparkContext.setJobDescription">[docs]</a> <span class="k">def</span> <span class="nf">setJobDescription</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Set a human readable description of the current job.</span>
<span class="sd"> .. versionadded:: 2.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> value : str</span>
<span class="sd"> The job description to set.</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> If you run jobs in parallel, use :class:`pyspark.InheritableThread` for thread</span>
<span class="sd"> local inheritance.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">setJobDescription</span><span class="p">(</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.sparkUser"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.sparkUser.html#pyspark.SparkContext.sparkUser">[docs]</a> <span class="k">def</span> <span class="nf">sparkUser</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get SPARK_USER for user who is running SparkContext.</span>
<span class="sd"> .. versionadded:: 1.0.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">()</span><span class="o">.</span><span class="n">sparkUser</span><span class="p">()</span></div>
<div class="viewcode-block" id="SparkContext.cancelJobGroup"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.cancelJobGroup.html#pyspark.SparkContext.cancelJobGroup">[docs]</a> <span class="k">def</span> <span class="nf">cancelJobGroup</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">groupId</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Cancel active jobs for the specified group. See :meth:`SparkContext.setJobGroup`.</span>
<span class="sd"> for more information.</span>
<span class="sd"> .. versionadded:: 1.1.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> groupId : str</span>
<span class="sd"> The group ID to cancel the job.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.setJobGroup`</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">()</span><span class="o">.</span><span class="n">cancelJobGroup</span><span class="p">(</span><span class="n">groupId</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.cancelJobsWithTag"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.cancelJobsWithTag.html#pyspark.SparkContext.cancelJobsWithTag">[docs]</a> <span class="k">def</span> <span class="nf">cancelJobsWithTag</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tag</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Cancel active jobs that have the specified tag. See</span>
<span class="sd"> :meth:`SparkContext.addJobTag`.</span>
<span class="sd"> .. versionadded:: 3.5.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> tag : str</span>
<span class="sd"> The tag to be cancelled. Cannot contain &#39;,&#39; (comma) character.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.addJobTag`</span>
<span class="sd"> :meth:`SparkContext.removeJobTag`</span>
<span class="sd"> :meth:`SparkContext.getJobTags`</span>
<span class="sd"> :meth:`SparkContext.clearJobTags`</span>
<span class="sd"> :meth:`SparkContext.setInterruptOnCancel`</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">cancelJobsWithTag</span><span class="p">(</span><span class="n">tag</span><span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.cancelAllJobs"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.cancelAllJobs.html#pyspark.SparkContext.cancelAllJobs">[docs]</a> <span class="k">def</span> <span class="nf">cancelAllJobs</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Cancel all jobs that have been scheduled or are running.</span>
<span class="sd"> .. versionadded:: 1.1.0</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.cancelJobGroup`</span>
<span class="sd"> :meth:`SparkContext.cancelJobsWithTag`</span>
<span class="sd"> :meth:`SparkContext.runJob`</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">()</span><span class="o">.</span><span class="n">cancelAllJobs</span><span class="p">()</span></div>
<div class="viewcode-block" id="SparkContext.statusTracker"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.statusTracker.html#pyspark.SparkContext.statusTracker">[docs]</a> <span class="k">def</span> <span class="nf">statusTracker</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">StatusTracker</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return :class:`StatusTracker` object</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">StatusTracker</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">statusTracker</span><span class="p">())</span></div>
<div class="viewcode-block" id="SparkContext.runJob"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.runJob.html#pyspark.SparkContext.runJob">[docs]</a> <span class="k">def</span> <span class="nf">runJob</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">rdd</span><span class="p">:</span> <span class="n">RDD</span><span class="p">[</span><span class="n">T</span><span class="p">],</span>
<span class="n">partitionFunc</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="n">Iterable</span><span class="p">[</span><span class="n">T</span><span class="p">]],</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">U</span><span class="p">]],</span>
<span class="n">partitions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">allowLocal</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="n">U</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Executes the given partitionFunc on the specified set of partitions,</span>
<span class="sd"> returning the result as an array of elements.</span>
<span class="sd"> If &#39;partitions&#39; is not specified, this will run over all partitions.</span>
<span class="sd"> .. versionadded:: 1.1.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> rdd : :class:`RDD`</span>
<span class="sd"> target RDD to run tasks on</span>
<span class="sd"> partitionFunc : function</span>
<span class="sd"> a function to run on each partition of the RDD</span>
<span class="sd"> partitions : list, optional</span>
<span class="sd"> set of partitions to run on; some jobs may not want to compute on all</span>
<span class="sd"> partitions of the target RDD, e.g. for operations like `first`</span>
<span class="sd"> allowLocal : bool, default False</span>
<span class="sd"> this parameter takes no effect</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> list</span>
<span class="sd"> results of specified partitions</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.cancelAllJobs`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; myRDD = sc.parallelize(range(6), 3)</span>
<span class="sd"> &gt;&gt;&gt; sc.runJob(myRDD, lambda part: [x * x for x in part])</span>
<span class="sd"> [0, 1, 4, 9, 16, 25]</span>
<span class="sd"> &gt;&gt;&gt; myRDD = sc.parallelize(range(6), 3)</span>
<span class="sd"> &gt;&gt;&gt; sc.runJob(myRDD, lambda part: [x * x for x in part], [0, 2], True)</span>
<span class="sd"> [0, 1, 16, 25]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">partitions</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">partitions</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">rdd</span><span class="o">.</span><span class="n">_jrdd</span><span class="o">.</span><span class="n">partitions</span><span class="p">()</span><span class="o">.</span><span class="n">size</span><span class="p">()))</span>
<span class="c1"># Implementation note: This is implemented as a mapPartitions followed</span>
<span class="c1"># by runJob() in order to avoid having to pass a Python lambda into</span>
<span class="c1"># SparkContext#runJob.</span>
<span class="n">mappedRDD</span> <span class="o">=</span> <span class="n">rdd</span><span class="o">.</span><span class="n">mapPartitions</span><span class="p">(</span><span class="n">partitionFunc</span><span class="p">)</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">sock_info</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonRDD</span><span class="o">.</span><span class="n">runJob</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">(),</span> <span class="n">mappedRDD</span><span class="o">.</span><span class="n">_jrdd</span><span class="p">,</span> <span class="n">partitions</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="n">_load_from_socket</span><span class="p">(</span><span class="n">sock_info</span><span class="p">,</span> <span class="n">mappedRDD</span><span class="o">.</span><span class="n">_jrdd_deserializer</span><span class="p">))</span></div>
<div class="viewcode-block" id="SparkContext.show_profiles"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.show_profiles.html#pyspark.SparkContext.show_profiles">[docs]</a> <span class="k">def</span> <span class="nf">show_profiles</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Print the profile stats to stdout</span>
<span class="sd"> .. versionadded:: 1.2.0</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.dump_profiles`</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiler_collector</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">profiler_collector</span><span class="o">.</span><span class="n">show_profiles</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="n">PySparkRuntimeError</span><span class="p">(</span>
<span class="n">error_class</span><span class="o">=</span><span class="s2">&quot;INCORRECT_CONF_FOR_PROFILE&quot;</span><span class="p">,</span>
<span class="n">message_parameters</span><span class="o">=</span><span class="p">{},</span>
<span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.dump_profiles"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.dump_profiles.html#pyspark.SparkContext.dump_profiles">[docs]</a> <span class="k">def</span> <span class="nf">dump_profiles</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Dump the profile stats into directory `path`</span>
<span class="sd"> .. versionadded:: 1.2.0</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> :meth:`SparkContext.show_profiles`</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiler_collector</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">profiler_collector</span><span class="o">.</span><span class="n">dump_profiles</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="n">PySparkRuntimeError</span><span class="p">(</span>
<span class="n">error_class</span><span class="o">=</span><span class="s2">&quot;INCORRECT_CONF_FOR_PROFILE&quot;</span><span class="p">,</span>
<span class="n">message_parameters</span><span class="o">=</span><span class="p">{},</span>
<span class="p">)</span></div>
<div class="viewcode-block" id="SparkContext.getConf"><a class="viewcode-back" href="../../reference/api/pyspark.SparkContext.getConf.html#pyspark.SparkContext.getConf">[docs]</a> <span class="k">def</span> <span class="nf">getConf</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">SparkConf</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Return a copy of this SparkContext&#39;s configuration :class:`SparkConf`.</span>
<span class="sd"> .. versionadded:: 2.1.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">conf</span> <span class="o">=</span> <span class="n">SparkConf</span><span class="p">()</span>
<span class="n">conf</span><span class="o">.</span><span class="n">setAll</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_conf</span><span class="o">.</span><span class="n">getAll</span><span class="p">())</span>
<span class="k">return</span> <span class="n">conf</span></div>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">resources</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">ResourceInformation</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return the resource information of this :class:`SparkContext`.</span>
<span class="sd"> A resource could be a GPU, FPGA, etc.</span>
<span class="sd"> .. versionadded:: 3.0.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">resources</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">jresources</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">resources</span><span class="p">()</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">jresources</span><span class="p">:</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">jresources</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">.</span><span class="n">name</span><span class="p">()</span>
<span class="n">jaddresses</span> <span class="o">=</span> <span class="n">jresources</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">.</span><span class="n">addresses</span><span class="p">()</span>
<span class="n">addrs</span> <span class="o">=</span> <span class="p">[</span><span class="n">addr</span> <span class="k">for</span> <span class="n">addr</span> <span class="ow">in</span> <span class="n">jaddresses</span><span class="p">]</span>
<span class="n">resources</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">ResourceInformation</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">addrs</span><span class="p">)</span>
<span class="k">return</span> <span class="n">resources</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">_assert_on_driver</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Called to ensure that SparkContext is created only on the Driver.</span>
<span class="sd"> Throws an exception if a SparkContext is about to be created in executors.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">TaskContext</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="n">PySparkRuntimeError</span><span class="p">(</span>
<span class="n">error_class</span><span class="o">=</span><span class="s2">&quot;CONTEXT_ONLY_VALID_ON_DRIVER&quot;</span><span class="p">,</span>
<span class="n">message_parameters</span><span class="o">=</span><span class="p">{},</span>
<span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_test</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">doctest</span>
<span class="kn">from</span> <span class="nn">pyspark</span> <span class="kn">import</span> <span class="n">SparkConf</span>
<span class="n">globs</span> <span class="o">=</span> <span class="nb">globals</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">conf</span> <span class="o">=</span> <span class="n">SparkConf</span><span class="p">()</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="s2">&quot;spark.ui.enabled&quot;</span><span class="p">,</span> <span class="s2">&quot;True&quot;</span><span class="p">)</span>
<span class="n">globs</span><span class="p">[</span><span class="s2">&quot;sc&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="p">(</span><span class="s2">&quot;local[4]&quot;</span><span class="p">,</span> <span class="s2">&quot;context tests&quot;</span><span class="p">,</span> <span class="n">conf</span><span class="o">=</span><span class="n">conf</span><span class="p">)</span>
<span class="p">(</span><span class="n">failure_count</span><span class="p">,</span> <span class="n">test_count</span><span class="p">)</span> <span class="o">=</span> <span class="n">doctest</span><span class="o">.</span><span class="n">testmod</span><span class="p">(</span><span class="n">globs</span><span class="o">=</span><span class="n">globs</span><span class="p">,</span> <span class="n">optionflags</span><span class="o">=</span><span class="n">doctest</span><span class="o">.</span><span class="n">ELLIPSIS</span><span class="p">)</span>
<span class="n">globs</span><span class="p">[</span><span class="s2">&quot;sc&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
<span class="k">if</span> <span class="n">failure_count</span><span class="p">:</span>
<span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
<span class="n">_test</span><span class="p">()</span>
</pre></div>
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