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<h1>Source code for pyspark.sql.observation</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">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Optional</span>
<span class="kn">from</span> <span class="nn">py4j.java_gateway</span> <span class="kn">import</span> <span class="n">JavaObject</span><span class="p">,</span> <span class="n">JVMView</span>
<span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">column</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.column</span> <span class="kn">import</span> <span class="n">Column</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.dataframe</span> <span class="kn">import</span> <span class="n">DataFrame</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.utils</span> <span class="kn">import</span> <span class="n">try_remote_observation</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Observation&quot;</span><span class="p">]</span>
<div class="viewcode-block" id="Observation"><a class="viewcode-back" href="../../../reference/pyspark.sql/api/pyspark.sql.Observation.html#pyspark.sql.Observation">[docs]</a><span class="k">class</span> <span class="nc">Observation</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Class to observe (named) metrics on a :class:`DataFrame`.</span>
<span class="sd"> Metrics are aggregation expressions, which are applied to the DataFrame while it is being</span>
<span class="sd"> processed by an action.</span>
<span class="sd"> The metrics have the following guarantees:</span>
<span class="sd"> - It will compute the defined aggregates (metrics) on all the data that is flowing through</span>
<span class="sd"> the Dataset during the action.</span>
<span class="sd"> - It will report the value of the defined aggregate columns as soon as we reach the end of</span>
<span class="sd"> the action.</span>
<span class="sd"> The metrics columns must either contain a literal (e.g. lit(42)), or should contain one or</span>
<span class="sd"> more aggregate functions (e.g. sum(a) or sum(a + b) + avg(c) - lit(1)). Expressions that</span>
<span class="sd"> contain references to the input Dataset&#39;s columns must always be wrapped in an aggregate</span>
<span class="sd"> function.</span>
<span class="sd"> An Observation instance collects the metrics while the first action is executed. Subsequent</span>
<span class="sd"> actions do not modify the metrics returned by `Observation.get`. Retrieval of the metric via</span>
<span class="sd"> `Observation.get` blocks until the first action has finished and metrics become available.</span>
<span class="sd"> .. versionadded:: 3.3.0</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> This class does not support streaming datasets.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.sql.functions import col, count, lit, max</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.sql import Observation</span>
<span class="sd"> &gt;&gt;&gt; df = spark.createDataFrame([[&quot;Alice&quot;, 2], [&quot;Bob&quot;, 5]], [&quot;name&quot;, &quot;age&quot;])</span>
<span class="sd"> &gt;&gt;&gt; observation = Observation(&quot;my metrics&quot;)</span>
<span class="sd"> &gt;&gt;&gt; observed_df = df.observe(observation, count(lit(1)).alias(&quot;count&quot;), max(col(&quot;age&quot;)))</span>
<span class="sd"> &gt;&gt;&gt; observed_df.count()</span>
<span class="sd"> 2</span>
<span class="sd"> &gt;&gt;&gt; observation.get</span>
<span class="sd"> {&#39;count&#39;: 2, &#39;max(age)&#39;: 5}</span>
<span class="sd"> &quot;&quot;&quot;</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">name</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="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Constructs a named or unnamed Observation instance.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str, optional</span>
<span class="sd"> default is a random UUID string. This is the name of the Observation and the metric.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</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="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;name should be a string&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;name should not be empty&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">=</span> <span class="n">name</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jvm</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="bp">self</span><span class="o">.</span><span class="n">_jo</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="nd">@try_remote_observation</span>
<span class="k">def</span> <span class="nf">_on</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="o">*</span><span class="n">exprs</span><span class="p">:</span> <span class="n">Column</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Attaches this observation to the given :class:`DataFrame` to observe aggregations.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> df : :class:`DataFrame`</span>
<span class="sd"> the :class:`DataFrame` to be observed</span>
<span class="sd"> exprs : list of :class:`Column`</span>
<span class="sd"> column expressions (:class:`Column`).</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`DataFrame`</span>
<span class="sd"> the observed :class:`DataFrame`.</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">_jo</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;an Observation can be used with a DataFrame only once&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jvm</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">_jvm</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">cls</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">sql</span><span class="o">.</span><span class="n">Observation</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jo</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_name</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">cls</span><span class="p">()</span>
<span class="n">observed_df</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jo</span><span class="o">.</span><span class="n">on</span><span class="p">(</span>
<span class="n">df</span><span class="o">.</span><span class="n">_jdf</span><span class="p">,</span> <span class="n">exprs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">_jc</span><span class="p">,</span> <span class="n">column</span><span class="o">.</span><span class="n">_to_seq</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">_sc</span><span class="p">,</span> <span class="p">[</span><span class="n">c</span><span class="o">.</span><span class="n">_jc</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">exprs</span><span class="p">[</span><span class="mi">1</span><span class="p">:]])</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">DataFrame</span><span class="p">(</span><span class="n">observed_df</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">sparkSession</span><span class="p">)</span>
<span class="c1"># Note that decorated property only works with Python 3.9+ which Spark Connect requires.</span>
<span class="nd">@property</span>
<span class="nd">@try_remote_observation</span>
<span class="k">def</span> <span class="nf">get</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">Any</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Get the observed metrics.</span>
<span class="sd"> Waits until the observed dataset finishes its first action. Only the result of the</span>
<span class="sd"> first action is available. Subsequent actions do not modify the result.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> dict</span>
<span class="sd"> the observed metrics</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">_jo</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;call DataFrame.observe&quot;</span>
<span class="n">jmap</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jo</span><span class="o">.</span><span class="n">getAsJava</span><span class="p">()</span>
<span class="c1"># return a pure Python dict, not jmap which is a py4j JavaMap</span>
<span class="k">return</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</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">jmap</span><span class="o">.</span><span class="n">items</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">import</span> <span class="nn">sys</span>
<span class="kn">from</span> <span class="nn">pyspark.context</span> <span class="kn">import</span> <span class="n">SparkContext</span>
<span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SparkSession</span>
<span class="kn">import</span> <span class="nn">pyspark.sql.observation</span>
<span class="n">globs</span> <span class="o">=</span> <span class="n">pyspark</span><span class="o">.</span><span class="n">sql</span><span class="o">.</span><span class="n">observation</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">sc</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;PythonTest&quot;</span><span class="p">)</span>
<span class="n">globs</span><span class="p">[</span><span class="s2">&quot;spark&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">SparkSession</span><span class="p">(</span><span class="n">sc</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">pyspark</span><span class="o">.</span><span class="n">sql</span><span class="o">.</span><span class="n">observation</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">sc</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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