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| <h1>Source code for apache_beam.transforms.util</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 "License"); 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 "AS IS" 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="sd">"""Simple utility PTransforms.</span> |
| <span class="sd">"""</span> |
| |
| <span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">absolute_import</span> |
| <span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">division</span> |
| |
| <span class="kn">import</span> <span class="nn">collections</span> |
| <span class="kn">import</span> <span class="nn">contextlib</span> |
| <span class="kn">import</span> <span class="nn">random</span> |
| <span class="kn">import</span> <span class="nn">time</span> |
| <span class="kn">from</span> <span class="nn">builtins</span> <span class="k">import</span> <span class="nb">object</span> |
| <span class="kn">from</span> <span class="nn">builtins</span> <span class="k">import</span> <span class="nb">range</span> |
| <span class="kn">from</span> <span class="nn">builtins</span> <span class="k">import</span> <span class="nb">zip</span> |
| |
| <span class="kn">from</span> <span class="nn">future.utils</span> <span class="k">import</span> <span class="n">itervalues</span> |
| |
| <span class="kn">from</span> <span class="nn">apache_beam</span> <span class="k">import</span> <span class="n">typehints</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.metrics</span> <span class="k">import</span> <span class="n">Metrics</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms</span> <span class="k">import</span> <span class="n">window</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms.core</span> <span class="k">import</span> <span class="n">CombinePerKey</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms.core</span> <span class="k">import</span> <span class="n">DoFn</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms.core</span> <span class="k">import</span> <span class="n">FlatMap</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms.core</span> <span class="k">import</span> <span class="n">Flatten</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms.core</span> <span class="k">import</span> <span class="n">GroupByKey</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms.core</span> <span class="k">import</span> <span class="n">Map</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms.core</span> <span class="k">import</span> <span class="n">ParDo</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms.core</span> <span class="k">import</span> <span class="n">Windowing</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms.ptransform</span> <span class="k">import</span> <span class="n">PTransform</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms.ptransform</span> <span class="k">import</span> <span class="n">ptransform_fn</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms.trigger</span> <span class="k">import</span> <span class="n">AccumulationMode</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms.trigger</span> <span class="k">import</span> <span class="n">AfterCount</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms.window</span> <span class="k">import</span> <span class="n">NonMergingWindowFn</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms.window</span> <span class="k">import</span> <span class="n">TimestampCombiner</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.transforms.window</span> <span class="k">import</span> <span class="n">TimestampedValue</span> |
| <span class="kn">from</span> <span class="nn">apache_beam.utils</span> <span class="k">import</span> <span class="n">windowed_value</span> |
| |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span> |
| <span class="s1">'BatchElements'</span><span class="p">,</span> |
| <span class="s1">'CoGroupByKey'</span><span class="p">,</span> |
| <span class="s1">'Keys'</span><span class="p">,</span> |
| <span class="s1">'KvSwap'</span><span class="p">,</span> |
| <span class="s1">'RemoveDuplicates'</span><span class="p">,</span> |
| <span class="s1">'Reshuffle'</span><span class="p">,</span> |
| <span class="s1">'Values'</span><span class="p">,</span> |
| <span class="p">]</span> |
| |
| <span class="n">K</span> <span class="o">=</span> <span class="n">typehints</span><span class="o">.</span><span class="n">TypeVariable</span><span class="p">(</span><span class="s1">'K'</span><span class="p">)</span> |
| <span class="n">V</span> <span class="o">=</span> <span class="n">typehints</span><span class="o">.</span><span class="n">TypeVariable</span><span class="p">(</span><span class="s1">'V'</span><span class="p">)</span> |
| <span class="n">T</span> <span class="o">=</span> <span class="n">typehints</span><span class="o">.</span><span class="n">TypeVariable</span><span class="p">(</span><span class="s1">'T'</span><span class="p">)</span> |
| |
| |
| <div class="viewcode-block" id="CoGroupByKey"><a class="viewcode-back" href="../../../apache_beam.transforms.util.html#apache_beam.transforms.util.CoGroupByKey">[docs]</a><span class="k">class</span> <span class="nc">CoGroupByKey</span><span class="p">(</span><span class="n">PTransform</span><span class="p">):</span> |
| <span class="sd">"""Groups results across several PCollections by key.</span> |
| |
| <span class="sd"> Given an input dict of serializable keys (called "tags") to 0 or more</span> |
| <span class="sd"> PCollections of (key, value) tuples, it creates a single output PCollection</span> |
| <span class="sd"> of (key, value) tuples whose keys are the unique input keys from all inputs,</span> |
| <span class="sd"> and whose values are dicts mapping each tag to an iterable of whatever values</span> |
| <span class="sd"> were under the key in the corresponding PCollection, in this manner::</span> |
| |
| <span class="sd"> ('some key', {'tag1': ['value 1 under "some key" in pcoll1',</span> |
| <span class="sd"> 'value 2 under "some key" in pcoll1',</span> |
| <span class="sd"> ...],</span> |
| <span class="sd"> 'tag2': ... ,</span> |
| <span class="sd"> ... })</span> |
| |
| <span class="sd"> For example, given:</span> |
| |
| <span class="sd"> {'tag1': pc1, 'tag2': pc2, 333: pc3}</span> |
| |
| <span class="sd"> where:</span> |
| <span class="sd"> pc1 = [(k1, v1)]</span> |
| <span class="sd"> pc2 = []</span> |
| <span class="sd"> pc3 = [(k1, v31), (k1, v32), (k2, v33)]</span> |
| |
| <span class="sd"> The output PCollection would be:</span> |
| |
| <span class="sd"> [(k1, {'tag1': [v1], 'tag2': [], 333: [v31, v32]}),</span> |
| <span class="sd"> (k2, {'tag1': [], 'tag2': [], 333: [v33]})]</span> |
| |
| <span class="sd"> CoGroupByKey also works for tuples, lists, or other flat iterables of</span> |
| <span class="sd"> PCollections, in which case the values of the resulting PCollections</span> |
| <span class="sd"> will be tuples whose nth value is the list of values from the nth</span> |
| <span class="sd"> PCollection---conceptually, the "tags" are the indices into the input.</span> |
| <span class="sd"> Thus, for this input::</span> |
| |
| <span class="sd"> (pc1, pc2, pc3)</span> |
| |
| <span class="sd"> the output would be::</span> |
| |
| <span class="sd"> [(k1, ([v1], [], [v31, v32]),</span> |
| <span class="sd"> (k2, ([], [], [v33]))]</span> |
| |
| <span class="sd"> Attributes:</span> |
| <span class="sd"> **kwargs: Accepts a single named argument "pipeline", which specifies the</span> |
| <span class="sd"> pipeline that "owns" this PTransform. Ordinarily CoGroupByKey can obtain</span> |
| <span class="sd"> this information from one of the input PCollections, but if there are none</span> |
| <span class="sd"> (or if there's a chance there may be none), this argument is the only way</span> |
| <span class="sd"> to provide pipeline information, and should be considered mandatory.</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">CoGroupByKey</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">pipeline</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'pipeline'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">kwargs</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Unexpected keyword arguments: </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="nb">list</span><span class="p">(</span><span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span> |
| |
| <span class="k">def</span> <span class="nf">_extract_input_pvalues</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pvalueish</span><span class="p">):</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="c1"># If this works, it's a dict.</span> |
| <span class="k">return</span> <span class="n">pvalueish</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">itervalues</span><span class="p">(</span><span class="n">pvalueish</span><span class="p">))</span> |
| <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span> |
| <span class="n">pcolls</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">pvalueish</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">pcolls</span><span class="p">,</span> <span class="n">pcolls</span> |
| |
| <div class="viewcode-block" id="CoGroupByKey.expand"><a class="viewcode-back" href="../../../apache_beam.transforms.util.html#apache_beam.transforms.util.CoGroupByKey.expand">[docs]</a> <span class="k">def</span> <span class="nf">expand</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pcolls</span><span class="p">):</span> |
| <span class="sd">"""Performs CoGroupByKey on argument pcolls; see class docstring."""</span> |
| <span class="c1"># For associating values in K-V pairs with the PCollections they came from.</span> |
| <span class="k">def</span> <span class="nf">_pair_tag_with_value</span><span class="p">(</span><span class="n">key_value</span><span class="p">,</span> <span class="n">tag</span><span class="p">):</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="o">=</span> <span class="n">key_value</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="p">(</span><span class="n">tag</span><span class="p">,</span> <span class="n">value</span><span class="p">))</span> |
| |
| <span class="c1"># Creates the key, value pairs for the output PCollection. Values are either</span> |
| <span class="c1"># lists or dicts (per the class docstring), initialized by the result of</span> |
| <span class="c1"># result_ctor(result_ctor_arg).</span> |
| <span class="k">def</span> <span class="nf">_merge_tagged_vals_under_key</span><span class="p">(</span><span class="n">key_grouped</span><span class="p">,</span> <span class="n">result_ctor</span><span class="p">,</span> |
| <span class="n">result_ctor_arg</span><span class="p">):</span> |
| <span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">grouped</span><span class="p">)</span> <span class="o">=</span> <span class="n">key_grouped</span> |
| <span class="n">result_value</span> <span class="o">=</span> <span class="n">result_ctor</span><span class="p">(</span><span class="n">result_ctor_arg</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">tag</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">grouped</span><span class="p">:</span> |
| <span class="n">result_value</span><span class="p">[</span><span class="n">tag</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">result_value</span><span class="p">)</span> |
| |
| <span class="k">try</span><span class="p">:</span> |
| <span class="c1"># If pcolls is a dict, we turn it into (tag, pcoll) pairs for use in the</span> |
| <span class="c1"># general-purpose code below. The result value constructor creates dicts</span> |
| <span class="c1"># whose keys are the tags.</span> |
| <span class="n">result_ctor_arg</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">pcolls</span><span class="p">)</span> |
| <span class="n">result_ctor</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">tags</span><span class="p">:</span> <span class="nb">dict</span><span class="p">((</span><span class="n">tag</span><span class="p">,</span> <span class="p">[])</span> <span class="k">for</span> <span class="n">tag</span> <span class="ow">in</span> <span class="n">tags</span><span class="p">)</span> |
| <span class="n">pcolls</span> <span class="o">=</span> <span class="n">pcolls</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> |
| <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span> |
| <span class="c1"># Otherwise, pcolls is a list/tuple, so we turn it into (index, pcoll)</span> |
| <span class="c1"># pairs. The result value constructor makes tuples with len(pcolls) slots.</span> |
| <span class="n">pcolls</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">pcolls</span><span class="p">))</span> |
| <span class="n">result_ctor_arg</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">pcolls</span><span class="p">)</span> |
| <span class="n">result_ctor</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">size</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">([]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">size</span><span class="p">))</span> |
| |
| <span class="c1"># Check input PCollections for PCollection-ness, and that they all belong</span> |
| <span class="c1"># to the same pipeline.</span> |
| <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">pcoll</span> <span class="ow">in</span> <span class="n">pcolls</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_check_pcollection</span><span class="p">(</span><span class="n">pcoll</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pipeline</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">pcoll</span><span class="o">.</span><span class="n">pipeline</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">pipeline</span> |
| |
| <span class="k">return</span> <span class="p">([</span><span class="n">pcoll</span> <span class="o">|</span> <span class="s1">'pair_with_</span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="n">tag</span> <span class="o">>></span> <span class="n">Map</span><span class="p">(</span><span class="n">_pair_tag_with_value</span><span class="p">,</span> <span class="n">tag</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">tag</span><span class="p">,</span> <span class="n">pcoll</span> <span class="ow">in</span> <span class="n">pcolls</span><span class="p">]</span> |
| <span class="o">|</span> <span class="n">Flatten</span><span class="p">(</span><span class="n">pipeline</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pipeline</span><span class="p">)</span> |
| <span class="o">|</span> <span class="n">GroupByKey</span><span class="p">()</span> |
| <span class="o">|</span> <span class="n">Map</span><span class="p">(</span><span class="n">_merge_tagged_vals_under_key</span><span class="p">,</span> <span class="n">result_ctor</span><span class="p">,</span> <span class="n">result_ctor_arg</span><span class="p">))</span></div></div> |
| |
| |
| <div class="viewcode-block" id="Keys"><a class="viewcode-back" href="../../../apache_beam.transforms.util.html#apache_beam.transforms.util.Keys">[docs]</a><span class="k">def</span> <span class="nf">Keys</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s1">'Keys'</span><span class="p">):</span> <span class="c1"># pylint: disable=invalid-name</span> |
| <span class="sd">"""Produces a PCollection of first elements of 2-tuples in a PCollection."""</span> |
| <span class="k">return</span> <span class="n">label</span> <span class="o">>></span> <span class="n">Map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">k_v</span><span class="p">:</span> <span class="n">k_v</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span></div> |
| |
| |
| <div class="viewcode-block" id="Values"><a class="viewcode-back" href="../../../apache_beam.transforms.util.html#apache_beam.transforms.util.Values">[docs]</a><span class="k">def</span> <span class="nf">Values</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s1">'Values'</span><span class="p">):</span> <span class="c1"># pylint: disable=invalid-name</span> |
| <span class="sd">"""Produces a PCollection of second elements of 2-tuples in a PCollection."""</span> |
| <span class="k">return</span> <span class="n">label</span> <span class="o">>></span> <span class="n">Map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">k_v1</span><span class="p">:</span> <span class="n">k_v1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span></div> |
| |
| |
| <div class="viewcode-block" id="KvSwap"><a class="viewcode-back" href="../../../apache_beam.transforms.util.html#apache_beam.transforms.util.KvSwap">[docs]</a><span class="k">def</span> <span class="nf">KvSwap</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s1">'KvSwap'</span><span class="p">):</span> <span class="c1"># pylint: disable=invalid-name</span> |
| <span class="sd">"""Produces a PCollection reversing 2-tuples in a PCollection."""</span> |
| <span class="k">return</span> <span class="n">label</span> <span class="o">>></span> <span class="n">Map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">k_v2</span><span class="p">:</span> <span class="p">(</span><span class="n">k_v2</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">k_v2</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span></div> |
| |
| |
| <span class="nd">@ptransform_fn</span> |
| <span class="k">def</span> <span class="nf">RemoveDuplicates</span><span class="p">(</span><span class="n">pcoll</span><span class="p">):</span> <span class="c1"># pylint: disable=invalid-name</span> |
| <span class="sd">"""Produces a PCollection containing the unique elements of a PCollection."""</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">pcoll</span> |
| <span class="o">|</span> <span class="s1">'ToPairs'</span> <span class="o">>></span> <span class="n">Map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="kc">None</span><span class="p">))</span> |
| <span class="o">|</span> <span class="s1">'Group'</span> <span class="o">>></span> <span class="n">CombinePerKey</span><span class="p">(</span><span class="k">lambda</span> <span class="n">vs</span><span class="p">:</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="o">|</span> <span class="s1">'RemoveDuplicates'</span> <span class="o">>></span> <span class="n">Keys</span><span class="p">())</span> |
| |
| |
| <span class="k">class</span> <span class="nc">_BatchSizeEstimator</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="sd">"""Estimates the best size for batches given historical timing.</span> |
| <span class="sd"> """</span> |
| |
| <span class="n">_MAX_DATA_POINTS</span> <span class="o">=</span> <span class="mi">100</span> |
| <span class="n">_MAX_GROWTH_FACTOR</span> <span class="o">=</span> <span class="mi">2</span> |
| |
| <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> |
| <span class="n">min_batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> |
| <span class="n">max_batch_size</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> |
| <span class="n">target_batch_overhead</span><span class="o">=.</span><span class="mi">1</span><span class="p">,</span> |
| <span class="n">target_batch_duration_secs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> |
| <span class="n">variance</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> |
| <span class="n">clock</span><span class="o">=</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">min_batch_size</span> <span class="o">></span> <span class="n">max_batch_size</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Minimum (</span><span class="si">%s</span><span class="s2">) must not be greater than maximum (</span><span class="si">%s</span><span class="s2">)"</span> <span class="o">%</span> <span class="p">(</span> |
| <span class="n">min_batch_size</span><span class="p">,</span> <span class="n">max_batch_size</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">target_batch_overhead</span> <span class="ow">and</span> <span class="ow">not</span> <span class="mi">0</span> <span class="o"><</span> <span class="n">target_batch_overhead</span> <span class="o"><=</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"target_batch_overhead (</span><span class="si">%s</span><span class="s2">) must be between 0 and 1"</span> <span class="o">%</span> <span class="p">(</span> |
| <span class="n">target_batch_overhead</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">target_batch_duration_secs</span> <span class="ow">and</span> <span class="n">target_batch_duration_secs</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"target_batch_duration_secs (</span><span class="si">%s</span><span class="s2">) must be positive"</span> <span class="o">%</span> <span class="p">(</span> |
| <span class="n">target_batch_duration_secs</span><span class="p">))</span> |
| <span class="k">if</span> <span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">target_batch_overhead</span><span class="p">,</span> <span class="n">target_batch_duration_secs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"At least one of target_batch_overhead or "</span> |
| <span class="s2">"target_batch_duration_secs must be positive."</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_min_batch_size</span> <span class="o">=</span> <span class="n">min_batch_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_max_batch_size</span> <span class="o">=</span> <span class="n">max_batch_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_target_batch_overhead</span> <span class="o">=</span> <span class="n">target_batch_overhead</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_target_batch_duration_secs</span> <span class="o">=</span> <span class="n">target_batch_duration_secs</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_variance</span> <span class="o">=</span> <span class="n">variance</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_clock</span> <span class="o">=</span> <span class="n">clock</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_ignore_next_timing</span> <span class="o">=</span> <span class="kc">False</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_size_distribution</span> <span class="o">=</span> <span class="n">Metrics</span><span class="o">.</span><span class="n">distribution</span><span class="p">(</span> |
| <span class="s1">'BatchElements'</span><span class="p">,</span> <span class="s1">'batch_size'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_time_distribution</span> <span class="o">=</span> <span class="n">Metrics</span><span class="o">.</span><span class="n">distribution</span><span class="p">(</span> |
| <span class="s1">'BatchElements'</span><span class="p">,</span> <span class="s1">'msec_per_batch'</span><span class="p">)</span> |
| <span class="c1"># Beam distributions only accept integer values, so we use this to</span> |
| <span class="c1"># accumulate under-reported values until they add up to whole milliseconds.</span> |
| <span class="c1"># (Milliseconds are chosen because that's conventionally used elsewhere in</span> |
| <span class="c1"># profiling-style counters.)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_remainder_msecs</span> <span class="o">=</span> <span class="mi">0</span> |
| |
| <span class="k">def</span> <span class="nf">ignore_next_timing</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Call to indicate the next timing should be ignored.</span> |
| |
| <span class="sd"> For example, the first emit of a ParDo operation is known to be anomalous</span> |
| <span class="sd"> due to setup that may occur.</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_ignore_next_timing</span> <span class="o">=</span> <span class="kc">False</span> |
| |
| <span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span> |
| <span class="k">def</span> <span class="nf">record_time</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span> |
| <span class="n">start</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clock</span><span class="p">()</span> |
| <span class="k">yield</span> |
| <span class="n">elapsed</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clock</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span> |
| <span class="n">elapsed_msec</span> <span class="o">=</span> <span class="mf">1e3</span> <span class="o">*</span> <span class="n">elapsed</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_remainder_msecs</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_size_distribution</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_time_distribution</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">elapsed_msec</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_remainder_msecs</span> <span class="o">=</span> <span class="n">elapsed_msec</span> <span class="o">-</span> <span class="nb">int</span><span class="p">(</span><span class="n">elapsed_msec</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ignore_next_timing</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_ignore_next_timing</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">elapsed</span><span class="p">))</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">)</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_MAX_DATA_POINTS</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_thin_data</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">_thin_data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="c1"># Make sure we don't change the parity of len(self._data)</span> |
| <span class="c1"># As it's used below to alternate jitter.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">randrange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">)</span> <span class="o">//</span> <span class="mi">4</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">randrange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">))</span> |
| |
| <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">linear_regression_no_numpy</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">):</span> |
| <span class="c1"># Least squares fit for y = a + bx over all points.</span> |
| <span class="n">n</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">xs</span><span class="p">))</span> |
| <span class="n">xbar</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">xs</span><span class="p">)</span> <span class="o">/</span> <span class="n">n</span> |
| <span class="n">ybar</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">ys</span><span class="p">)</span> <span class="o">/</span> <span class="n">n</span> |
| <span class="n">b</span> <span class="o">=</span> <span class="p">(</span><span class="nb">sum</span><span class="p">([(</span><span class="n">x</span> <span class="o">-</span> <span class="n">xbar</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">y</span> <span class="o">-</span> <span class="n">ybar</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">)])</span> |
| <span class="o">/</span> <span class="nb">sum</span><span class="p">([(</span><span class="n">x</span> <span class="o">-</span> <span class="n">xbar</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">xs</span><span class="p">]))</span> |
| <span class="n">a</span> <span class="o">=</span> <span class="n">ybar</span> <span class="o">-</span> <span class="n">b</span> <span class="o">*</span> <span class="n">xbar</span> |
| <span class="k">return</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span> |
| |
| <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">linear_regression_numpy</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">):</span> |
| <span class="c1"># pylint: disable=wrong-import-order, wrong-import-position</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| <span class="kn">from</span> <span class="nn">numpy</span> <span class="k">import</span> <span class="nb">sum</span> |
| <span class="n">xs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span> |
| <span class="n">ys</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">ys</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span> |
| |
| <span class="c1"># First do a simple least squares fit for y = a + bx over all points.</span> |
| <span class="n">b</span><span class="p">,</span> <span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">polyfit</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> |
| |
| <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">xs</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">n</span> <span class="o"><</span> <span class="mi">10</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># Refine this by throwing out outliers, according to Cook's distance.</span> |
| <span class="c1"># https://en.wikipedia.org/wiki/Cook%27s_distance</span> |
| <span class="n">sum_x</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">xs</span><span class="p">)</span> |
| <span class="n">sum_x2</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">xs</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> |
| <span class="n">errs</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span> <span class="o">*</span> <span class="n">xs</span> <span class="o">-</span> <span class="n">ys</span> |
| <span class="n">s2</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">errs</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">s2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="c1"># It's an exact fit!</span> |
| <span class="k">return</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span> |
| <span class="n">h</span> <span class="o">=</span> <span class="p">(</span><span class="n">sum_x2</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">sum_x</span> <span class="o">*</span> <span class="n">xs</span> <span class="o">+</span> <span class="n">n</span> <span class="o">*</span> <span class="n">xs</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">n</span> <span class="o">*</span> <span class="n">sum_x2</span> <span class="o">-</span> <span class="n">sum_x</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> |
| <span class="n">cook_ds</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">/</span> <span class="n">s2</span> <span class="o">*</span> <span class="n">errs</span><span class="o">**</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">h</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">h</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> |
| |
| <span class="c1"># Re-compute the regression, excluding those points with Cook's distance</span> |
| <span class="c1"># greater than 0.5, and weighting by the inverse of x to give a more</span> |
| <span class="c1"># stable y-intercept (as small batches have relatively more information</span> |
| <span class="c1"># about the fixed overhead).</span> |
| <span class="n">weight</span> <span class="o">=</span> <span class="p">(</span><span class="n">cook_ds</span> <span class="o"><=</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">/</span> <span class="n">xs</span> |
| <span class="n">b</span><span class="p">,</span> <span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">polyfit</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">w</span><span class="o">=</span><span class="n">weight</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span> |
| |
| <span class="k">try</span><span class="p">:</span> |
| <span class="c1"># pylint: disable=wrong-import-order, wrong-import-position</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| <span class="n">linear_regression</span> <span class="o">=</span> <span class="n">linear_regression_numpy</span> |
| <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> |
| <span class="n">linear_regression</span> <span class="o">=</span> <span class="n">linear_regression_no_numpy</span> |
| |
| <span class="k">def</span> <span class="nf">next_batch_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_min_batch_size</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_max_batch_size</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_min_batch_size</span> |
| <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">)</span> <span class="o"><</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_min_batch_size</span> |
| <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">)</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span> |
| <span class="c1"># Force some variety so we have distinct batch sizes on which to do</span> |
| <span class="c1"># linear regression below.</span> |
| <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span> |
| <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_max_batch_size</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_min_batch_size</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_MAX_GROWTH_FACTOR</span><span class="p">),</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_min_batch_size</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span> |
| |
| <span class="c1"># There tends to be a lot of noise in the top quantile, which also</span> |
| <span class="c1"># has outsided influence in the regression. If we have enough data,</span> |
| <span class="c1"># Simply declare the top 20% to be outliers.</span> |
| <span class="n">trimmed_data</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">)[:</span><span class="nb">max</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">)</span> <span class="o">*</span> <span class="mi">4</span> <span class="o">//</span> <span class="mi">5</span><span class="p">)]</span> |
| |
| <span class="c1"># Linear regression for y = a + bx, where x is batch size and y is time.</span> |
| <span class="n">xs</span><span class="p">,</span> <span class="n">ys</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">trimmed_data</span><span class="p">)</span> |
| <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear_regression</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">)</span> |
| |
| <span class="c1"># Avoid nonsensical or division-by-zero errors below due to noise.</span> |
| <span class="n">a</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="mf">1e-10</span><span class="p">)</span> |
| <span class="n">b</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="mf">1e-20</span><span class="p">)</span> |
| |
| <span class="n">last_batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">cap</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">last_batch_size</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_MAX_GROWTH_FACTOR</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_max_batch_size</span><span class="p">)</span> |
| |
| <span class="n">target</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_max_batch_size</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_target_batch_duration_secs</span><span class="p">:</span> |
| <span class="c1"># Solution to a + b*x = self._target_batch_duration_secs.</span> |
| <span class="n">target</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_target_batch_duration_secs</span> <span class="o">-</span> <span class="n">a</span><span class="p">)</span> <span class="o">/</span> <span class="n">b</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_target_batch_overhead</span><span class="p">:</span> |
| <span class="c1"># Solution to a / (a + b*x) = self._target_batch_overhead.</span> |
| <span class="n">target</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="p">(</span><span class="n">a</span> <span class="o">/</span> <span class="n">b</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">_target_batch_overhead</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span> |
| |
| <span class="c1"># Avoid getting stuck at a single batch size (especially the minimal</span> |
| <span class="c1"># batch size) which would not allow us to extrapolate to other batch</span> |
| <span class="c1"># sizes.</span> |
| <span class="c1"># Jitter alternates between 0 and 1.</span> |
| <span class="n">jitter</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">)</span> <span class="o">%</span> <span class="mi">2</span> |
| <span class="c1"># Smear our samples across a range centered at the target.</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">)</span> <span class="o">></span> <span class="mi">10</span><span class="p">:</span> |
| <span class="n">target</span> <span class="o">+=</span> <span class="nb">int</span><span class="p">(</span><span class="n">target</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_variance</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o">-</span> <span class="o">.</span><span class="mi">5</span><span class="p">))</span> |
| |
| <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_min_batch_size</span> <span class="o">+</span> <span class="n">jitter</span><span class="p">,</span> <span class="nb">min</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">cap</span><span class="p">)))</span> |
| |
| |
| <span class="k">class</span> <span class="nc">_GlobalWindowsBatchingDoFn</span><span class="p">(</span><span class="n">DoFn</span><span class="p">):</span> |
| <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size_estimator</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span> <span class="o">=</span> <span class="n">batch_size_estimator</span> |
| |
| <span class="k">def</span> <span class="nf">start_bundle</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batch</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span><span class="o">.</span><span class="n">next_batch_size</span><span class="p">()</span> |
| <span class="c1"># The first emit often involves non-trivial setup.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span><span class="o">.</span><span class="n">ignore_next_timing</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">element</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batch</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">element</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_batch</span><span class="p">)</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size</span><span class="p">:</span> |
| <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span><span class="o">.</span><span class="n">record_time</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_batch_size</span><span class="p">):</span> |
| <span class="k">yield</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batch</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span><span class="o">.</span><span class="n">next_batch_size</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">finish_bundle</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch</span><span class="p">:</span> |
| <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span><span class="o">.</span><span class="n">record_time</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_batch_size</span><span class="p">):</span> |
| <span class="k">yield</span> <span class="n">window</span><span class="o">.</span><span class="n">GlobalWindows</span><span class="o">.</span><span class="n">windowed_value</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_batch</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batch</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span><span class="o">.</span><span class="n">next_batch_size</span><span class="p">()</span> |
| |
| |
| <span class="k">class</span> <span class="nc">_WindowAwareBatchingDoFn</span><span class="p">(</span><span class="n">DoFn</span><span class="p">):</span> |
| |
| <span class="n">_MAX_LIVE_WINDOWS</span> <span class="o">=</span> <span class="mi">10</span> |
| |
| <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size_estimator</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span> <span class="o">=</span> <span class="n">batch_size_estimator</span> |
| |
| <span class="k">def</span> <span class="nf">start_bundle</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batches</span> <span class="o">=</span> <span class="n">collections</span><span class="o">.</span><span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span><span class="o">.</span><span class="n">next_batch_size</span><span class="p">()</span> |
| <span class="c1"># The first emit often involves non-trivial setup.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span><span class="o">.</span><span class="n">ignore_next_timing</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">element</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="n">DoFn</span><span class="o">.</span><span class="n">WindowParam</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batches</span><span class="p">[</span><span class="n">window</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">element</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_batches</span><span class="p">[</span><span class="n">window</span><span class="p">])</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size</span><span class="p">:</span> |
| <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span><span class="o">.</span><span class="n">record_time</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_batch_size</span><span class="p">):</span> |
| <span class="k">yield</span> <span class="n">windowed_value</span><span class="o">.</span><span class="n">WindowedValue</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batches</span><span class="p">[</span><span class="n">window</span><span class="p">],</span> <span class="n">window</span><span class="o">.</span><span class="n">max_timestamp</span><span class="p">(),</span> <span class="p">(</span><span class="n">window</span><span class="p">,))</span> |
| <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batches</span><span class="p">[</span><span class="n">window</span><span class="p">]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span><span class="o">.</span><span class="n">next_batch_size</span><span class="p">()</span> |
| <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_batches</span><span class="p">)</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">_MAX_LIVE_WINDOWS</span><span class="p">:</span> |
| <span class="n">window</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batches</span><span class="o">.</span><span class="n">items</span><span class="p">(),</span> |
| <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">window_batch</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">window_batch</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> |
| <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span><span class="o">.</span><span class="n">record_time</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_batch_size</span><span class="p">):</span> |
| <span class="k">yield</span> <span class="n">windowed_value</span><span class="o">.</span><span class="n">WindowedValue</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batches</span><span class="p">[</span><span class="n">window</span><span class="p">],</span> <span class="n">window</span><span class="o">.</span><span class="n">max_timestamp</span><span class="p">(),</span> <span class="p">(</span><span class="n">window</span><span class="p">,))</span> |
| <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batches</span><span class="p">[</span><span class="n">window</span><span class="p">]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span><span class="o">.</span><span class="n">next_batch_size</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">finish_bundle</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">window</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batches</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="k">if</span> <span class="n">batch</span><span class="p">:</span> |
| <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span><span class="o">.</span><span class="n">record_time</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_batch_size</span><span class="p">):</span> |
| <span class="k">yield</span> <span class="n">windowed_value</span><span class="o">.</span><span class="n">WindowedValue</span><span class="p">(</span> |
| <span class="n">batch</span><span class="p">,</span> <span class="n">window</span><span class="o">.</span><span class="n">max_timestamp</span><span class="p">(),</span> <span class="p">(</span><span class="n">window</span><span class="p">,))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batches</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span><span class="o">.</span><span class="n">next_batch_size</span><span class="p">()</span> |
| |
| |
| <div class="viewcode-block" id="BatchElements"><a class="viewcode-back" href="../../../apache_beam.transforms.util.html#apache_beam.transforms.util.BatchElements">[docs]</a><span class="nd">@typehints</span><span class="o">.</span><span class="n">with_input_types</span><span class="p">(</span><span class="n">T</span><span class="p">)</span> |
| <span class="nd">@typehints</span><span class="o">.</span><span class="n">with_output_types</span><span class="p">(</span><span class="n">typehints</span><span class="o">.</span><span class="n">List</span><span class="p">[</span><span class="n">T</span><span class="p">])</span> |
| <span class="k">class</span> <span class="nc">BatchElements</span><span class="p">(</span><span class="n">PTransform</span><span class="p">):</span> |
| <span class="sd">"""A Transform that batches elements for amortized processing.</span> |
| |
| <span class="sd"> This transform is designed to precede operations whose processing cost</span> |
| <span class="sd"> is of the form</span> |
| |
| <span class="sd"> time = fixed_cost + num_elements * per_element_cost</span> |
| |
| <span class="sd"> where the per element cost is (often significantly) smaller than the fixed</span> |
| <span class="sd"> cost and could be amortized over multiple elements. It consumes a PCollection</span> |
| <span class="sd"> of element type T and produces a PCollection of element type List[T].</span> |
| |
| <span class="sd"> This transform attempts to find the best batch size between the minimim</span> |
| <span class="sd"> and maximum parameters by profiling the time taken by (fused) downstream</span> |
| <span class="sd"> operations. For a fixed batch size, set the min and max to be equal.</span> |
| |
| <span class="sd"> Elements are batched per-window and batches emitted in the window</span> |
| <span class="sd"> corresponding to its contents.</span> |
| |
| <span class="sd"> Args:</span> |
| <span class="sd"> min_batch_size: (optional) the smallest number of elements per batch</span> |
| <span class="sd"> max_batch_size: (optional) the largest number of elements per batch</span> |
| <span class="sd"> target_batch_overhead: (optional) a target for fixed_cost / time,</span> |
| <span class="sd"> as used in the formula above</span> |
| <span class="sd"> target_batch_duration_secs: (optional) a target for total time per bundle,</span> |
| <span class="sd"> in seconds</span> |
| <span class="sd"> variance: (optional) the permitted (relative) amount of deviation from the</span> |
| <span class="sd"> (estimated) ideal batch size used to produce a wider base for</span> |
| <span class="sd"> linear interpolation</span> |
| <span class="sd"> clock: (optional) an alternative to time.time for measuring the cost of</span> |
| <span class="sd"> donwstream operations (mostly for testing)</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> |
| <span class="n">min_batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> |
| <span class="n">max_batch_size</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> |
| <span class="n">target_batch_overhead</span><span class="o">=.</span><span class="mi">05</span><span class="p">,</span> |
| <span class="n">target_batch_duration_secs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> |
| <span class="n">variance</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> |
| <span class="n">clock</span><span class="o">=</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span> <span class="o">=</span> <span class="n">_BatchSizeEstimator</span><span class="p">(</span> |
| <span class="n">min_batch_size</span><span class="o">=</span><span class="n">min_batch_size</span><span class="p">,</span> |
| <span class="n">max_batch_size</span><span class="o">=</span><span class="n">max_batch_size</span><span class="p">,</span> |
| <span class="n">target_batch_overhead</span><span class="o">=</span><span class="n">target_batch_overhead</span><span class="p">,</span> |
| <span class="n">target_batch_duration_secs</span><span class="o">=</span><span class="n">target_batch_duration_secs</span><span class="p">,</span> |
| <span class="n">variance</span><span class="o">=</span><span class="n">variance</span><span class="p">,</span> |
| <span class="n">clock</span><span class="o">=</span><span class="n">clock</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="BatchElements.expand"><a class="viewcode-back" href="../../../apache_beam.transforms.util.html#apache_beam.transforms.util.BatchElements.expand">[docs]</a> <span class="k">def</span> <span class="nf">expand</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pcoll</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">pcoll</span><span class="o">.</span><span class="n">pipeline</span><span class="o">.</span><span class="n">runner</span><span class="p">,</span> <span class="s1">'is_streaming'</span><span class="p">,</span> <span class="kc">False</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"Requires stateful processing (BEAM-2687)"</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">pcoll</span><span class="o">.</span><span class="n">windowing</span><span class="o">.</span><span class="n">is_default</span><span class="p">():</span> |
| <span class="c1"># This is the same logic as _GlobalWindowsBatchingDoFn, but optimized</span> |
| <span class="c1"># for that simpler case.</span> |
| <span class="k">return</span> <span class="n">pcoll</span> <span class="o">|</span> <span class="n">ParDo</span><span class="p">(</span><span class="n">_GlobalWindowsBatchingDoFn</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">pcoll</span> <span class="o">|</span> <span class="n">ParDo</span><span class="p">(</span><span class="n">_WindowAwareBatchingDoFn</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_batch_size_estimator</span><span class="p">))</span></div></div> |
| |
| |
| <span class="k">class</span> <span class="nc">_IdentityWindowFn</span><span class="p">(</span><span class="n">NonMergingWindowFn</span><span class="p">):</span> |
| <span class="sd">"""Windowing function that preserves existing windows.</span> |
| |
| <span class="sd"> To be used internally with the Reshuffle transform.</span> |
| <span class="sd"> Will raise an exception when used after DoFns that return TimestampedValue</span> |
| <span class="sd"> elements.</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">window_coder</span><span class="p">):</span> |
| <span class="sd">"""Create a new WindowFn with compatible coder.</span> |
| <span class="sd"> To be applied to PCollections with windows that are compatible with the</span> |
| <span class="sd"> given coder.</span> |
| |
| <span class="sd"> Arguments:</span> |
| <span class="sd"> window_coder: coders.Coder object to be used on windows.</span> |
| <span class="sd"> """</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">_IdentityWindowFn</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">window_coder</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="s1">'window_coder should not be None'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_window_coder</span> <span class="o">=</span> <span class="n">window_coder</span> |
| |
| <span class="k">def</span> <span class="nf">assign</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">assign_context</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">assign_context</span><span class="o">.</span><span class="n">window</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="s1">'assign_context.window should not be None. '</span> |
| <span class="s1">'This might be due to a DoFn returning a TimestampedValue.'</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">[</span><span class="n">assign_context</span><span class="o">.</span><span class="n">window</span><span class="p">]</span> |
| |
| <span class="k">def</span> <span class="nf">get_window_coder</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_window_coder</span> |
| |
| |
| <span class="nd">@typehints</span><span class="o">.</span><span class="n">with_input_types</span><span class="p">(</span><span class="n">typehints</span><span class="o">.</span><span class="n">KV</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="nd">@typehints</span><span class="o">.</span><span class="n">with_output_types</span><span class="p">(</span><span class="n">typehints</span><span class="o">.</span><span class="n">KV</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="k">class</span> <span class="nc">ReshufflePerKey</span><span class="p">(</span><span class="n">PTransform</span><span class="p">):</span> |
| <span class="sd">"""PTransform that returns a PCollection equivalent to its input,</span> |
| <span class="sd"> but operationally provides some of the side effects of a GroupByKey,</span> |
| <span class="sd"> in particular preventing fusion of the surrounding transforms,</span> |
| <span class="sd"> checkpointing, and deduplication by id.</span> |
| |
| <span class="sd"> ReshufflePerKey is experimental. No backwards compatibility guarantees.</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="nf">expand</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pcoll</span><span class="p">):</span> |
| <span class="n">windowing_saved</span> <span class="o">=</span> <span class="n">pcoll</span><span class="o">.</span><span class="n">windowing</span> |
| <span class="k">if</span> <span class="n">windowing_saved</span><span class="o">.</span><span class="n">is_default</span><span class="p">():</span> |
| <span class="c1"># In this (common) case we can use a trivial trigger driver</span> |
| <span class="c1"># and avoid the (expensive) window param.</span> |
| <span class="n">globally_windowed</span> <span class="o">=</span> <span class="n">window</span><span class="o">.</span><span class="n">GlobalWindows</span><span class="o">.</span><span class="n">windowed_value</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span> |
| <span class="n">window_fn</span> <span class="o">=</span> <span class="n">window</span><span class="o">.</span><span class="n">GlobalWindows</span><span class="p">()</span> |
| <span class="n">MIN_TIMESTAMP</span> <span class="o">=</span> <span class="n">window</span><span class="o">.</span><span class="n">MIN_TIMESTAMP</span> |
| |
| <span class="k">def</span> <span class="nf">reify_timestamps</span><span class="p">(</span><span class="n">element</span><span class="p">,</span> <span class="n">timestamp</span><span class="o">=</span><span class="n">DoFn</span><span class="o">.</span><span class="n">TimestampParam</span><span class="p">):</span> |
| <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">element</span> |
| <span class="k">if</span> <span class="n">timestamp</span> <span class="o">==</span> <span class="n">MIN_TIMESTAMP</span><span class="p">:</span> |
| <span class="n">timestamp</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">return</span> <span class="n">key</span><span class="p">,</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">timestamp</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">restore_timestamps</span><span class="p">(</span><span class="n">element</span><span class="p">):</span> |
| <span class="n">key</span><span class="p">,</span> <span class="n">values</span> <span class="o">=</span> <span class="n">element</span> |
| <span class="k">return</span> <span class="p">[</span> |
| <span class="n">globally_windowed</span><span class="o">.</span><span class="n">with_value</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">if</span> <span class="n">timestamp</span> <span class="ow">is</span> <span class="kc">None</span> |
| <span class="k">else</span> <span class="n">window</span><span class="o">.</span><span class="n">GlobalWindows</span><span class="o">.</span><span class="n">windowed_value</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="n">timestamp</span><span class="p">)</span> |
| <span class="k">for</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">timestamp</span><span class="p">)</span> <span class="ow">in</span> <span class="n">values</span><span class="p">]</span> |
| |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># The linter is confused.</span> |
| <span class="c1"># hash(1) is used to force "runtime" selection of _IdentityWindowFn</span> |
| <span class="c1"># pylint: disable=abstract-class-instantiated</span> |
| <span class="bp">cls</span> <span class="o">=</span> <span class="nb">hash</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="ow">and</span> <span class="n">_IdentityWindowFn</span> |
| <span class="n">window_fn</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">(</span> |
| <span class="n">windowing_saved</span><span class="o">.</span><span class="n">windowfn</span><span class="o">.</span><span class="n">get_window_coder</span><span class="p">())</span> |
| |
| <span class="k">def</span> <span class="nf">reify_timestamps</span><span class="p">(</span><span class="n">element</span><span class="p">,</span> <span class="n">timestamp</span><span class="o">=</span><span class="n">DoFn</span><span class="o">.</span><span class="n">TimestampParam</span><span class="p">):</span> |
| <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">element</span> |
| <span class="k">return</span> <span class="n">key</span><span class="p">,</span> <span class="n">TimestampedValue</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">timestamp</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">restore_timestamps</span><span class="p">(</span><span class="n">element</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="n">DoFn</span><span class="o">.</span><span class="n">WindowParam</span><span class="p">):</span> |
| <span class="c1"># Pass the current window since _IdentityWindowFn wouldn't know how</span> |
| <span class="c1"># to generate it.</span> |
| <span class="n">key</span><span class="p">,</span> <span class="n">values</span> <span class="o">=</span> <span class="n">element</span> |
| <span class="k">return</span> <span class="p">[</span> |
| <span class="n">windowed_value</span><span class="o">.</span><span class="n">WindowedValue</span><span class="p">(</span> |
| <span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="o">.</span><span class="n">value</span><span class="p">),</span> <span class="n">value</span><span class="o">.</span><span class="n">timestamp</span><span class="p">,</span> <span class="p">[</span><span class="n">window</span><span class="p">])</span> |
| <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">values</span><span class="p">]</span> |
| |
| <span class="n">ungrouped</span> <span class="o">=</span> <span class="n">pcoll</span> <span class="o">|</span> <span class="n">Map</span><span class="p">(</span><span class="n">reify_timestamps</span><span class="p">)</span> |
| <span class="n">ungrouped</span><span class="o">.</span><span class="n">_windowing</span> <span class="o">=</span> <span class="n">Windowing</span><span class="p">(</span> |
| <span class="n">window_fn</span><span class="p">,</span> |
| <span class="n">triggerfn</span><span class="o">=</span><span class="n">AfterCount</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> |
| <span class="n">accumulation_mode</span><span class="o">=</span><span class="n">AccumulationMode</span><span class="o">.</span><span class="n">DISCARDING</span><span class="p">,</span> |
| <span class="n">timestamp_combiner</span><span class="o">=</span><span class="n">TimestampCombiner</span><span class="o">.</span><span class="n">OUTPUT_AT_EARLIEST</span><span class="p">)</span> |
| <span class="n">result</span> <span class="o">=</span> <span class="p">(</span><span class="n">ungrouped</span> |
| <span class="o">|</span> <span class="n">GroupByKey</span><span class="p">()</span> |
| <span class="o">|</span> <span class="n">FlatMap</span><span class="p">(</span><span class="n">restore_timestamps</span><span class="p">))</span> |
| <span class="n">result</span><span class="o">.</span><span class="n">_windowing</span> <span class="o">=</span> <span class="n">windowing_saved</span> |
| <span class="k">return</span> <span class="n">result</span> |
| |
| |
| <div class="viewcode-block" id="Reshuffle"><a class="viewcode-back" href="../../../apache_beam.transforms.util.html#apache_beam.transforms.util.Reshuffle">[docs]</a><span class="nd">@typehints</span><span class="o">.</span><span class="n">with_input_types</span><span class="p">(</span><span class="n">T</span><span class="p">)</span> |
| <span class="nd">@typehints</span><span class="o">.</span><span class="n">with_output_types</span><span class="p">(</span><span class="n">T</span><span class="p">)</span> |
| <span class="k">class</span> <span class="nc">Reshuffle</span><span class="p">(</span><span class="n">PTransform</span><span class="p">):</span> |
| <span class="sd">"""PTransform that returns a PCollection equivalent to its input,</span> |
| <span class="sd"> but operationally provides some of the side effects of a GroupByKey,</span> |
| <span class="sd"> in particular preventing fusion of the surrounding transforms,</span> |
| <span class="sd"> checkpointing, and deduplication by id.</span> |
| |
| <span class="sd"> Reshuffle adds a temporary random key to each element, performs a</span> |
| <span class="sd"> ReshufflePerKey, and finally removes the temporary key.</span> |
| |
| <span class="sd"> Reshuffle is experimental. No backwards compatibility guarantees.</span> |
| <span class="sd"> """</span> |
| |
| <div class="viewcode-block" id="Reshuffle.expand"><a class="viewcode-back" href="../../../apache_beam.transforms.util.html#apache_beam.transforms.util.Reshuffle.expand">[docs]</a> <span class="k">def</span> <span class="nf">expand</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pcoll</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">pcoll</span> |
| <span class="o">|</span> <span class="s1">'AddRandomKeys'</span> <span class="o">>></span> <span class="n">Map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">getrandbits</span><span class="p">(</span><span class="mi">32</span><span class="p">),</span> <span class="n">t</span><span class="p">))</span> |
| <span class="o">|</span> <span class="n">ReshufflePerKey</span><span class="p">()</span> |
| <span class="o">|</span> <span class="s1">'RemoveRandomKeys'</span> <span class="o">>></span> <span class="n">Map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="n">t</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span></div></div> |
| </pre></div> |
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