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| <blockquote> |
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| <div class="section" id="google-cloud-dataflow-operators"> |
| <h1>Google Cloud Dataflow Operators<a class="headerlink" href="#google-cloud-dataflow-operators" title="Permalink to this heading">¶</a></h1> |
| <p><a class="reference external" href="https://cloud.google.com/dataflow/">Dataflow</a> is a managed service for |
| executing a wide variety of data processing patterns. These pipelines are created |
| using the Apache Beam programming model which allows for both batch and streaming processing.</p> |
| <div class="section" id="prerequisite-tasks"> |
| <h2>Prerequisite Tasks<a class="headerlink" href="#prerequisite-tasks" title="Permalink to this heading">¶</a></h2> |
| </div> |
| <div class="section" id="ways-to-run-a-data-pipeline"> |
| <h2>Ways to run a data pipeline<a class="headerlink" href="#ways-to-run-a-data-pipeline" title="Permalink to this heading">¶</a></h2> |
| <p>There are several ways to run a Dataflow pipeline depending on your environment, source files:</p> |
| <ul class="simple"> |
| <li><p><strong>Non-templated pipeline</strong>: Developer can run the pipeline as a local process on the Airflow worker |
| if you have a ‘<em>.jar’ file for Java or a ‘</em> .py` file for Python. This also means that the necessary system |
| dependencies must be installed on the worker. For Java, worker must have the JRE Runtime installed. |
| For Python, the Python interpreter. The runtime versions must be compatible with the pipeline versions. |
| This is the fastest way to start a pipeline, but because of its frequent problems with system dependencies, |
| it may cause problems. See: <a class="reference internal" href="#howto-operator-dataflowcreatejavajoboperator"><span class="std std-ref">Java SDK pipelines</span></a>, |
| <a class="reference internal" href="#howto-operator-dataflowcreatepythonjoboperator"><span class="std std-ref">Python SDK pipelines</span></a> for more detailed information.</p></li> |
| <li><p><strong>Templated pipeline</strong>: The programmer can make the pipeline independent of the environment by preparing |
| a template that will then be run on a machine managed by Google. This way, changes to the environment |
| won’t affect your pipeline. There are two types of the templates:</p> |
| <ul> |
| <li><p><strong>Classic templates</strong>. Developers run the pipeline and create a template. The Apache Beam SDK stages |
| files in Cloud Storage, creates a template file (similar to job request), |
| and saves the template file in Cloud Storage. See: <a class="reference internal" href="#howto-operator-dataflowtemplatedjobstartoperator"><span class="std std-ref">Templated jobs</span></a></p></li> |
| <li><p><strong>Flex Templates</strong>. Developers package the pipeline into a Docker image and then use the <code class="docutils literal notranslate"><span class="pre">gcloud</span></code> |
| command-line tool to build and save the Flex Template spec file in Cloud Storage. See: |
| <a class="reference internal" href="#howto-operator-dataflowstartflextemplateoperator"><span class="std std-ref">Templated jobs</span></a></p></li> |
| </ul> |
| </li> |
| <li><p><strong>SQL pipeline</strong>: Developer can write pipeline as SQL statement and then execute it in Dataflow. See: |
| <a class="reference internal" href="#howto-operator-dataflowstartsqljoboperator"><span class="std std-ref">Dataflow SQL</span></a></p></li> |
| </ul> |
| <p>It is a good idea to test your pipeline using the non-templated pipeline, |
| and then run the pipeline in production using the templates.</p> |
| <p>For details on the differences between the pipeline types, see |
| <a class="reference external" href="https://cloud.google.com/dataflow/docs/concepts/dataflow-templates">Dataflow templates</a> |
| in the Google Cloud documentation.</p> |
| </div> |
| <div class="section" id="starting-non-templated-pipeline"> |
| <h2>Starting Non-templated pipeline<a class="headerlink" href="#starting-non-templated-pipeline" title="Permalink to this heading">¶</a></h2> |
| <p>To create a new pipeline using the source file (JAR in Java or Python file) use |
| the create job operators. The source file can be located on GCS or on the local filesystem. |
| <a class="reference internal" href="../../_api/airflow/providers/google/cloud/operators/dataflow/index.html#airflow.providers.google.cloud.operators.dataflow.DataflowCreateJavaJobOperator" title="airflow.providers.google.cloud.operators.dataflow.DataflowCreateJavaJobOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataflowCreateJavaJobOperator</span></code></a> |
| or |
| <a class="reference internal" href="../../_api/airflow/providers/google/cloud/operators/dataflow/index.html#airflow.providers.google.cloud.operators.dataflow.DataflowCreatePythonJobOperator" title="airflow.providers.google.cloud.operators.dataflow.DataflowCreatePythonJobOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataflowCreatePythonJobOperator</span></code></a></p> |
| <div class="section" id="java-sdk-pipelines"> |
| <span id="howto-operator-dataflowcreatejavajoboperator"></span><h3>Java SDK pipelines<a class="headerlink" href="#java-sdk-pipelines" title="Permalink to this heading">¶</a></h3> |
| <p>For Java pipeline the <code class="docutils literal notranslate"><span class="pre">jar</span></code> argument must be specified for |
| <a class="reference internal" href="../../_api/airflow/providers/google/cloud/operators/dataflow/index.html#airflow.providers.google.cloud.operators.dataflow.DataflowCreateJavaJobOperator" title="airflow.providers.google.cloud.operators.dataflow.DataflowCreateJavaJobOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataflowCreateJavaJobOperator</span></code></a> |
| as it contains the pipeline to be executed on Dataflow. The JAR can be available on GCS that Airflow |
| has the ability to download or available on the local filesystem (provide the absolute path to it).</p> |
| <p>Here is an example of creating and running a pipeline in Java with jar stored on GCS:</p> |
| <div class="example-block-wrapper compound"> |
| <p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/providers/google/cloud/example_dags/example_dataflow.py</span><a class="example-header-button viewcode-button reference internal" href="../../_modules/airflow/providers/google/cloud/example_dags/example_dataflow.html"><span class="viewcode-link">[source]</span></a></p> |
| <div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">start_java_job</span> <span class="o">=</span> <span class="n">BeamRunJavaPipelineOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s2">"start-java-job"</span><span class="p">,</span> |
| <span class="n">jar</span><span class="o">=</span><span class="n">GCS_JAR</span><span class="p">,</span> |
| <span class="n">pipeline_options</span><span class="o">=</span><span class="p">{</span> |
| <span class="s1">'output'</span><span class="p">:</span> <span class="n">GCS_OUTPUT</span><span class="p">,</span> |
| <span class="p">},</span> |
| <span class="n">job_class</span><span class="o">=</span><span class="s1">'org.apache.beam.examples.WordCount'</span><span class="p">,</span> |
| <span class="n">dataflow_config</span><span class="o">=</span><span class="p">{</span> |
| <span class="s2">"check_if_running"</span><span class="p">:</span> <span class="n">CheckJobRunning</span><span class="o">.</span><span class="n">IgnoreJob</span><span class="p">,</span> |
| <span class="s2">"location"</span><span class="p">:</span> <span class="s1">'europe-west3'</span><span class="p">,</span> |
| <span class="s2">"poll_sleep"</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span> |
| <span class="p">},</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Here is an example of creating and running a pipeline in Java with jar stored on local file system:</p> |
| <div class="example-block-wrapper compound"> |
| <p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/providers/google/cloud/example_dags/example_dataflow.py</span><a class="example-header-button viewcode-button reference internal" href="../../_modules/airflow/providers/google/cloud/example_dags/example_dataflow.html"><span class="viewcode-link">[source]</span></a></p> |
| <div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">jar_to_local</span> <span class="o">=</span> <span class="n">GCSToLocalFilesystemOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s2">"jar-to-local"</span><span class="p">,</span> |
| <span class="n">bucket</span><span class="o">=</span><span class="n">GCS_JAR_BUCKET_NAME</span><span class="p">,</span> |
| <span class="n">object_name</span><span class="o">=</span><span class="n">GCS_JAR_OBJECT_NAME</span><span class="p">,</span> |
| <span class="n">filename</span><span class="o">=</span><span class="s2">"/tmp/dataflow-{{ ds_nodash }}.jar"</span><span class="p">,</span> |
| <span class="p">)</span> |
| |
| <span class="n">start_java_job_local</span> <span class="o">=</span> <span class="n">BeamRunJavaPipelineOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s2">"start-java-job-local"</span><span class="p">,</span> |
| <span class="n">jar</span><span class="o">=</span><span class="s2">"/tmp/dataflow-{{ ds_nodash }}.jar"</span><span class="p">,</span> |
| <span class="n">pipeline_options</span><span class="o">=</span><span class="p">{</span> |
| <span class="s1">'output'</span><span class="p">:</span> <span class="n">GCS_OUTPUT</span><span class="p">,</span> |
| <span class="p">},</span> |
| <span class="n">job_class</span><span class="o">=</span><span class="s1">'org.apache.beam.examples.WordCount'</span><span class="p">,</span> |
| <span class="n">dataflow_config</span><span class="o">=</span><span class="p">{</span> |
| <span class="s2">"check_if_running"</span><span class="p">:</span> <span class="n">CheckJobRunning</span><span class="o">.</span><span class="n">WaitForRun</span><span class="p">,</span> |
| <span class="s2">"location"</span><span class="p">:</span> <span class="s1">'europe-west3'</span><span class="p">,</span> |
| <span class="s2">"poll_sleep"</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span> |
| <span class="p">},</span> |
| <span class="p">)</span> |
| <span class="n">jar_to_local</span> <span class="o">>></span> <span class="n">start_java_job_local</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="python-sdk-pipelines"> |
| <span id="howto-operator-dataflowcreatepythonjoboperator"></span><h3>Python SDK pipelines<a class="headerlink" href="#python-sdk-pipelines" title="Permalink to this heading">¶</a></h3> |
| <p>The <code class="docutils literal notranslate"><span class="pre">py_file</span></code> argument must be specified for |
| <a class="reference internal" href="../../_api/airflow/providers/google/cloud/operators/dataflow/index.html#airflow.providers.google.cloud.operators.dataflow.DataflowCreatePythonJobOperator" title="airflow.providers.google.cloud.operators.dataflow.DataflowCreatePythonJobOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataflowCreatePythonJobOperator</span></code></a> |
| as it contains the pipeline to be executed on Dataflow. The Python file can be available on GCS that Airflow |
| has the ability to download or available on the local filesystem (provide the absolute path to it).</p> |
| <p>The <code class="docutils literal notranslate"><span class="pre">py_interpreter</span></code> argument specifies the Python version to be used when executing the pipeline, the default |
| is <code class="docutils literal notranslate"><span class="pre">python3`.</span> <span class="pre">If</span> <span class="pre">your</span> <span class="pre">Airflow</span> <span class="pre">instance</span> <span class="pre">is</span> <span class="pre">running</span> <span class="pre">on</span> <span class="pre">Python</span> <span class="pre">2</span> <span class="pre">-</span> <span class="pre">specify</span> <span class="pre">``python2</span></code> and ensure your <code class="docutils literal notranslate"><span class="pre">py_file</span></code> is |
| in Python 2. For best results, use Python 3.</p> |
| <p>If <code class="docutils literal notranslate"><span class="pre">py_requirements</span></code> argument is specified a temporary Python virtual environment with specified requirements will be created |
| and within it pipeline will run.</p> |
| <p>The <code class="docutils literal notranslate"><span class="pre">py_system_site_packages</span></code> argument specifies whether or not all the Python packages from your Airflow instance, |
| will be accessible within virtual environment (if <code class="docutils literal notranslate"><span class="pre">py_requirements</span></code> argument is specified), |
| recommend avoiding unless the Dataflow job requires it.</p> |
| <div class="example-block-wrapper compound"> |
| <p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/providers/google/cloud/example_dags/example_dataflow.py</span><a class="example-header-button viewcode-button reference internal" href="../../_modules/airflow/providers/google/cloud/example_dags/example_dataflow.html"><span class="viewcode-link">[source]</span></a></p> |
| <div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">start_python_job</span> <span class="o">=</span> <span class="n">BeamRunPythonPipelineOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s2">"start-python-job"</span><span class="p">,</span> |
| <span class="n">py_file</span><span class="o">=</span><span class="n">GCS_PYTHON</span><span class="p">,</span> |
| <span class="n">py_options</span><span class="o">=</span><span class="p">[],</span> |
| <span class="n">pipeline_options</span><span class="o">=</span><span class="p">{</span> |
| <span class="s1">'output'</span><span class="p">:</span> <span class="n">GCS_OUTPUT</span><span class="p">,</span> |
| <span class="p">},</span> |
| <span class="n">py_requirements</span><span class="o">=</span><span class="p">[</span><span class="s1">'apache-beam[gcp]==2.21.0'</span><span class="p">],</span> |
| <span class="n">py_interpreter</span><span class="o">=</span><span class="s1">'python3'</span><span class="p">,</span> |
| <span class="n">py_system_site_packages</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">dataflow_config</span><span class="o">=</span><span class="p">{</span><span class="s1">'location'</span><span class="p">:</span> <span class="s1">'europe-west3'</span><span class="p">},</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="execution-models"> |
| <h2>Execution models<a class="headerlink" href="#execution-models" title="Permalink to this heading">¶</a></h2> |
| <p>Dataflow has multiple options of executing pipelines. It can be done in the following modes: |
| batch asynchronously (fire and forget), batch blocking (wait until completion), or streaming (run indefinitely). |
| In Airflow it is best practice to use asynchronous batch pipelines or streams and use sensors to listen for expected job state.</p> |
| <p>By default <a class="reference internal" href="../../_api/airflow/providers/google/cloud/operators/dataflow/index.html#airflow.providers.google.cloud.operators.dataflow.DataflowCreateJavaJobOperator" title="airflow.providers.google.cloud.operators.dataflow.DataflowCreateJavaJobOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataflowCreateJavaJobOperator</span></code></a>, |
| <a class="reference internal" href="../../_api/airflow/providers/google/cloud/operators/dataflow/index.html#airflow.providers.google.cloud.operators.dataflow.DataflowCreatePythonJobOperator" title="airflow.providers.google.cloud.operators.dataflow.DataflowCreatePythonJobOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataflowCreatePythonJobOperator</span></code></a>, |
| <a class="reference internal" href="../../_api/airflow/providers/google/cloud/operators/dataflow/index.html#airflow.providers.google.cloud.operators.dataflow.DataflowTemplatedJobStartOperator" title="airflow.providers.google.cloud.operators.dataflow.DataflowTemplatedJobStartOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataflowTemplatedJobStartOperator</span></code></a> and |
| <a class="reference internal" href="../../_api/airflow/providers/google/cloud/operators/dataflow/index.html#airflow.providers.google.cloud.operators.dataflow.DataflowStartFlexTemplateOperator" title="airflow.providers.google.cloud.operators.dataflow.DataflowStartFlexTemplateOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataflowStartFlexTemplateOperator</span></code></a> |
| have argument <code class="docutils literal notranslate"><span class="pre">wait_until_finished</span></code> set to <code class="docutils literal notranslate"><span class="pre">None</span></code> which cause different behaviour depends on the type of pipeline:</p> |
| <ul class="simple"> |
| <li><p>for the streaming pipeline, wait for jobs to start,</p></li> |
| <li><p>for the batch pipeline, wait for the jobs to complete.</p></li> |
| </ul> |
| <p>If <code class="docutils literal notranslate"><span class="pre">wait_until_finished</span></code> is set to <code class="docutils literal notranslate"><span class="pre">True</span></code> operator will always wait for end of pipeline execution. |
| If set to <code class="docutils literal notranslate"><span class="pre">False</span></code> only submits the jobs.</p> |
| <p>See: <a class="reference external" href="https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#configuring-pipelineoptions-for-execution-on-the-cloud-dataflow-service">Configuring PipelineOptions for execution on the Cloud Dataflow service</a></p> |
| <div class="section" id="asynchronous-execution"> |
| <h3>Asynchronous execution<a class="headerlink" href="#asynchronous-execution" title="Permalink to this heading">¶</a></h3> |
| <p>Dataflow batch jobs are by default asynchronous - however this is dependent on the application code (contained in the JAR |
| or Python file) and how it is written. In order for the Dataflow job to execute asynchronously, ensure the |
| pipeline objects are not being waited upon (not calling <code class="docutils literal notranslate"><span class="pre">waitUntilFinish</span></code> or <code class="docutils literal notranslate"><span class="pre">wait_until_finish</span></code> on the |
| <code class="docutils literal notranslate"><span class="pre">PipelineResult</span></code> in your application code).</p> |
| <div class="example-block-wrapper compound"> |
| <p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/providers/google/cloud/example_dags/example_dataflow.py</span><a class="example-header-button viewcode-button reference internal" href="../../_modules/airflow/providers/google/cloud/example_dags/example_dataflow.html"><span class="viewcode-link">[source]</span></a></p> |
| <div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">start_python_job_async</span> <span class="o">=</span> <span class="n">BeamRunPythonPipelineOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s2">"start-python-job-async"</span><span class="p">,</span> |
| <span class="n">runner</span><span class="o">=</span><span class="s2">"DataflowRunner"</span><span class="p">,</span> |
| <span class="n">py_file</span><span class="o">=</span><span class="n">GCS_PYTHON</span><span class="p">,</span> |
| <span class="n">py_options</span><span class="o">=</span><span class="p">[],</span> |
| <span class="n">pipeline_options</span><span class="o">=</span><span class="p">{</span> |
| <span class="s1">'output'</span><span class="p">:</span> <span class="n">GCS_OUTPUT</span><span class="p">,</span> |
| <span class="p">},</span> |
| <span class="n">py_requirements</span><span class="o">=</span><span class="p">[</span><span class="s1">'apache-beam[gcp]==2.25.0'</span><span class="p">],</span> |
| <span class="n">py_interpreter</span><span class="o">=</span><span class="s1">'python3'</span><span class="p">,</span> |
| <span class="n">py_system_site_packages</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">dataflow_config</span><span class="o">=</span><span class="p">{</span> |
| <span class="s2">"job_name"</span><span class="p">:</span> <span class="s2">"start-python-job-async"</span><span class="p">,</span> |
| <span class="s2">"location"</span><span class="p">:</span> <span class="s1">'europe-west3'</span><span class="p">,</span> |
| <span class="s2">"wait_until_finished"</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span> |
| <span class="p">},</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="blocking-execution"> |
| <h3>Blocking execution<a class="headerlink" href="#blocking-execution" title="Permalink to this heading">¶</a></h3> |
| <p>In order for a Dataflow job to execute and wait until completion, ensure the pipeline objects are waited upon |
| in the application code. This can be done for the Java SDK by calling <code class="docutils literal notranslate"><span class="pre">waitUntilFinish</span></code> on the <code class="docutils literal notranslate"><span class="pre">PipelineResult</span></code> |
| returned from <code class="docutils literal notranslate"><span class="pre">pipeline.run()</span></code> or for the Python SDK by calling <code class="docutils literal notranslate"><span class="pre">wait_until_finish</span></code> on the <code class="docutils literal notranslate"><span class="pre">PipelineResult</span></code> |
| returned from <code class="docutils literal notranslate"><span class="pre">pipeline.run()</span></code>.</p> |
| <p>Blocking jobs should be avoided as there is a background process that occurs when run on Airflow. This process is |
| continuously being run to wait for the Dataflow job to be completed and increases the consumption of resources by |
| Airflow in doing so.</p> |
| </div> |
| <div class="section" id="streaming-execution"> |
| <h3>Streaming execution<a class="headerlink" href="#streaming-execution" title="Permalink to this heading">¶</a></h3> |
| <p>To execute a streaming Dataflow job, ensure the streaming option is set (for Python) or read from an unbounded data |
| source, such as Pub/Sub, in your pipeline (for Java).</p> |
| <p>Setting argument <code class="docutils literal notranslate"><span class="pre">drain_pipeline</span></code> to <code class="docutils literal notranslate"><span class="pre">True</span></code> allows to stop streaming job by draining it |
| instead of canceling during killing task instance.</p> |
| <p>See the <a class="reference external" href="https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline">Stopping a running pipeline</a>.</p> |
| </div> |
| <div class="section" id="templated-jobs"> |
| <span id="howto-operator-dataflowstartflextemplateoperator"></span><span id="howto-operator-dataflowtemplatedjobstartoperator"></span><h3>Templated jobs<a class="headerlink" href="#templated-jobs" title="Permalink to this heading">¶</a></h3> |
| <p>Templates give the ability to stage a pipeline on Cloud Storage and run it from there. This |
| provides flexibility in the development workflow as it separates the development of a pipeline |
| from the staging and execution steps. There are two types of templates for Dataflow: Classic and Flex. |
| See the <a class="reference external" href="https://cloud.google.com/dataflow/docs/concepts/dataflow-templates">official documentation for Dataflow templates</a> for more information.</p> |
| <p>Here is an example of running Classic template with |
| <a class="reference internal" href="../../_api/airflow/providers/google/cloud/operators/dataflow/index.html#airflow.providers.google.cloud.operators.dataflow.DataflowTemplatedJobStartOperator" title="airflow.providers.google.cloud.operators.dataflow.DataflowTemplatedJobStartOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataflowTemplatedJobStartOperator</span></code></a>:</p> |
| <div class="example-block-wrapper compound"> |
| <p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/providers/google/cloud/example_dags/example_dataflow.py</span><a class="example-header-button viewcode-button reference internal" href="../../_modules/airflow/providers/google/cloud/example_dags/example_dataflow.html"><span class="viewcode-link">[source]</span></a></p> |
| <div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">start_template_job</span> <span class="o">=</span> <span class="n">DataflowTemplatedJobStartOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s2">"start-template-job"</span><span class="p">,</span> |
| <span class="n">template</span><span class="o">=</span><span class="s1">'gs://dataflow-templates/latest/Word_Count'</span><span class="p">,</span> |
| <span class="n">parameters</span><span class="o">=</span><span class="p">{</span><span class="s1">'inputFile'</span><span class="p">:</span> <span class="s2">"gs://dataflow-samples/shakespeare/kinglear.txt"</span><span class="p">,</span> <span class="s1">'output'</span><span class="p">:</span> <span class="n">GCS_OUTPUT</span><span class="p">},</span> |
| <span class="n">location</span><span class="o">=</span><span class="s1">'europe-west3'</span><span class="p">,</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>See the <a class="reference external" href="https://cloud.google.com/dataflow/docs/guides/templates/provided-templates">list of Google-provided templates that can be used with this operator</a>.</p> |
| <p>Here is an example of running Flex template with |
| <a class="reference internal" href="../../_api/airflow/providers/google/cloud/operators/dataflow/index.html#airflow.providers.google.cloud.operators.dataflow.DataflowStartFlexTemplateOperator" title="airflow.providers.google.cloud.operators.dataflow.DataflowStartFlexTemplateOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataflowStartFlexTemplateOperator</span></code></a>:</p> |
| <div class="example-block-wrapper compound"> |
| <p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/providers/google/cloud/example_dags/example_dataflow_flex_template.py</span><a class="example-header-button viewcode-button reference internal" href="../../_modules/airflow/providers/google/cloud/example_dags/example_dataflow_flex_template.html"><span class="viewcode-link">[source]</span></a></p> |
| <div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">start_flex_template</span> <span class="o">=</span> <span class="n">DataflowStartFlexTemplateOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s2">"start_flex_template_streaming_beam_sql"</span><span class="p">,</span> |
| <span class="n">body</span><span class="o">=</span><span class="p">{</span> |
| <span class="s2">"launchParameter"</span><span class="p">:</span> <span class="p">{</span> |
| <span class="s2">"containerSpecGcsPath"</span><span class="p">:</span> <span class="n">GCS_FLEX_TEMPLATE_TEMPLATE_PATH</span><span class="p">,</span> |
| <span class="s2">"jobName"</span><span class="p">:</span> <span class="n">DATAFLOW_FLEX_TEMPLATE_JOB_NAME</span><span class="p">,</span> |
| <span class="s2">"parameters"</span><span class="p">:</span> <span class="p">{</span> |
| <span class="s2">"inputSubscription"</span><span class="p">:</span> <span class="n">PUBSUB_FLEX_TEMPLATE_SUBSCRIPTION</span><span class="p">,</span> |
| <span class="s2">"outputTable"</span><span class="p">:</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">GCP_PROJECT_ID</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="n">BQ_FLEX_TEMPLATE_DATASET</span><span class="si">}</span><span class="s2">.streaming_beam_sql"</span><span class="p">,</span> |
| <span class="p">},</span> |
| <span class="p">}</span> |
| <span class="p">},</span> |
| <span class="n">do_xcom_push</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">location</span><span class="o">=</span><span class="n">BQ_FLEX_TEMPLATE_LOCATION</span><span class="p">,</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="dataflow-sql"> |
| <span id="howto-operator-dataflowstartsqljoboperator"></span><h3>Dataflow SQL<a class="headerlink" href="#dataflow-sql" title="Permalink to this heading">¶</a></h3> |
| <p>Dataflow SQL supports a variant of the ZetaSQL query syntax and includes additional streaming |
| extensions for running Dataflow streaming jobs.</p> |
| <p>Here is an example of running Dataflow SQL job with |
| <a class="reference internal" href="../../_api/airflow/providers/google/cloud/operators/dataflow/index.html#airflow.providers.google.cloud.operators.dataflow.DataflowStartSqlJobOperator" title="airflow.providers.google.cloud.operators.dataflow.DataflowStartSqlJobOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataflowStartSqlJobOperator</span></code></a>:</p> |
| <div class="example-block-wrapper compound"> |
| <p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/providers/google/cloud/example_dags/example_dataflow_sql.py</span><a class="example-header-button viewcode-button reference internal" href="../../_modules/airflow/providers/google/cloud/example_dags/example_dataflow_sql.html"><span class="viewcode-link">[source]</span></a></p> |
| <div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">start_sql</span> <span class="o">=</span> <span class="n">DataflowStartSqlJobOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s2">"start_sql_query"</span><span class="p">,</span> |
| <span class="n">job_name</span><span class="o">=</span><span class="n">DATAFLOW_SQL_JOB_NAME</span><span class="p">,</span> |
| <span class="n">query</span><span class="o">=</span><span class="sa">f</span><span class="s2">"""</span> |
| <span class="s2"> SELECT</span> |
| <span class="s2"> sales_region as sales_region,</span> |
| <span class="s2"> count(state_id) as count_state</span> |
| <span class="s2"> FROM</span> |
| <span class="s2"> bigquery.table.`</span><span class="si">{</span><span class="n">GCP_PROJECT_ID</span><span class="si">}</span><span class="s2">`.`</span><span class="si">{</span><span class="n">BQ_SQL_DATASET</span><span class="si">}</span><span class="s2">`.`</span><span class="si">{</span><span class="n">BQ_SQL_TABLE_INPUT</span><span class="si">}</span><span class="s2">`</span> |
| <span class="s2"> WHERE state_id >= @state_id_min</span> |
| <span class="s2"> GROUP BY sales_region;</span> |
| <span class="s2"> """</span><span class="p">,</span> |
| <span class="n">options</span><span class="o">=</span><span class="p">{</span> |
| <span class="s2">"bigquery-project"</span><span class="p">:</span> <span class="n">GCP_PROJECT_ID</span><span class="p">,</span> |
| <span class="s2">"bigquery-dataset"</span><span class="p">:</span> <span class="n">BQ_SQL_DATASET</span><span class="p">,</span> |
| <span class="s2">"bigquery-table"</span><span class="p">:</span> <span class="n">BQ_SQL_TABLE_OUTPUT</span><span class="p">,</span> |
| <span class="s2">"bigquery-write-disposition"</span><span class="p">:</span> <span class="s2">"write-truncate"</span><span class="p">,</span> |
| <span class="s2">"parameter"</span><span class="p">:</span> <span class="s2">"state_id_min:INT64:2"</span><span class="p">,</span> |
| <span class="p">},</span> |
| <span class="n">location</span><span class="o">=</span><span class="n">DATAFLOW_SQL_LOCATION</span><span class="p">,</span> |
| <span class="n">do_xcom_push</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="admonition warning"> |
| <p class="admonition-title">Warning</p> |
| <p>This operator requires <code class="docutils literal notranslate"><span class="pre">gcloud</span></code> command (Google Cloud SDK) must be installed on the Airflow worker |
| <<a class="reference external" href="https://cloud.google.com/sdk/docs/install">https://cloud.google.com/sdk/docs/install</a>>`__</p> |
| </div> |
| <p>See the <a class="reference external" href="https://cloud.google.com/dataflow/docs/reference/sql">Dataflow SQL reference</a>.</p> |
| </div> |
| </div> |
| <div class="section" id="sensors"> |
| <span id="howto-operator-dataflowjobautoscalingeventssensor"></span><span id="howto-operator-dataflowjobmessagessensor"></span><span id="howto-operator-dataflowjobmetricssensor"></span><span id="howto-operator-dataflowjobstatussensor"></span><h2>Sensors<a class="headerlink" href="#sensors" title="Permalink to this heading">¶</a></h2> |
| <p>When job is triggered asynchronously sensors may be used to run checks for specific job properties.</p> |
| <p><a class="reference internal" href="../../_api/airflow/providers/google/cloud/sensors/dataflow/index.html#airflow.providers.google.cloud.sensors.dataflow.DataflowJobStatusSensor" title="airflow.providers.google.cloud.sensors.dataflow.DataflowJobStatusSensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataflowJobStatusSensor</span></code></a>.</p> |
| <div class="example-block-wrapper compound"> |
| <p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/providers/google/cloud/example_dags/example_dataflow.py</span><a class="example-header-button viewcode-button reference internal" href="../../_modules/airflow/providers/google/cloud/example_dags/example_dataflow.html"><span class="viewcode-link">[source]</span></a></p> |
| <div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">wait_for_python_job_async_done</span> <span class="o">=</span> <span class="n">DataflowJobStatusSensor</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s2">"wait-for-python-job-async-done"</span><span class="p">,</span> |
| <span class="n">job_id</span><span class="o">=</span><span class="s2">"{{task_instance.xcom_pull('start-python-job-async')['dataflow_job_id']}}"</span><span class="p">,</span> |
| <span class="n">expected_statuses</span><span class="o">=</span><span class="p">{</span><span class="n">DataflowJobStatus</span><span class="o">.</span><span class="n">JOB_STATE_DONE</span><span class="p">},</span> |
| <span class="n">location</span><span class="o">=</span><span class="s1">'europe-west3'</span><span class="p">,</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p><a class="reference internal" href="../../_api/airflow/providers/google/cloud/sensors/dataflow/index.html#airflow.providers.google.cloud.sensors.dataflow.DataflowJobMetricsSensor" title="airflow.providers.google.cloud.sensors.dataflow.DataflowJobMetricsSensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataflowJobMetricsSensor</span></code></a>.</p> |
| <div class="example-block-wrapper compound"> |
| <p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/providers/google/cloud/example_dags/example_dataflow.py</span><a class="example-header-button viewcode-button reference internal" href="../../_modules/airflow/providers/google/cloud/example_dags/example_dataflow.html"><span class="viewcode-link">[source]</span></a></p> |
| <div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">check_metric_scalar_gte</span><span class="p">(</span><span class="n">metric_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="n">Callable</span><span class="p">:</span> |
| <span class="sd">"""Check is metric greater than equals to given value."""</span> |
| |
| <span class="k">def</span> <span class="nf">callback</span><span class="p">(</span><span class="n">metrics</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">dict</span><span class="p">])</span> <span class="o">-></span> <span class="nb">bool</span><span class="p">:</span> |
| <span class="n">dag_native_python_async</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Looking for '</span><span class="si">%s</span><span class="s2">' >= </span><span class="si">%d</span><span class="s2">"</span><span class="p">,</span> <span class="n">metric_name</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">metrics</span><span class="p">:</span> |
| <span class="n">context</span> <span class="o">=</span> <span class="n">metric</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"name"</span><span class="p">,</span> <span class="p">{})</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"context"</span><span class="p">,</span> <span class="p">{})</span> |
| <span class="n">original_name</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"original_name"</span><span class="p">,</span> <span class="s2">""</span><span class="p">)</span> |
| <span class="n">tentative</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"tentative"</span><span class="p">,</span> <span class="s2">""</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">original_name</span> <span class="o">==</span> <span class="s2">"Service-cpu_num_seconds"</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">tentative</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">metric</span><span class="p">[</span><span class="s2">"scalar"</span><span class="p">]</span> <span class="o">>=</span> <span class="n">value</span> |
| <span class="k">raise</span> <span class="n">AirflowException</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Metric '</span><span class="si">{</span><span class="n">metric_name</span><span class="si">}</span><span class="s2">' not found in metrics"</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">callback</span> |
| |
| <span class="n">wait_for_python_job_async_metric</span> <span class="o">=</span> <span class="n">DataflowJobMetricsSensor</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s2">"wait-for-python-job-async-metric"</span><span class="p">,</span> |
| <span class="n">job_id</span><span class="o">=</span><span class="s2">"{{task_instance.xcom_pull('start-python-job-async')['dataflow_job_id']}}"</span><span class="p">,</span> |
| <span class="n">location</span><span class="o">=</span><span class="s1">'europe-west3'</span><span class="p">,</span> |
| <span class="n">callback</span><span class="o">=</span><span class="n">check_metric_scalar_gte</span><span class="p">(</span><span class="n">metric_name</span><span class="o">=</span><span class="s2">"Service-cpu_num_seconds"</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="mi">100</span><span class="p">),</span> |
| <span class="n">fail_on_terminal_state</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p><a class="reference internal" href="../../_api/airflow/providers/google/cloud/sensors/dataflow/index.html#airflow.providers.google.cloud.sensors.dataflow.DataflowJobMessagesSensor" title="airflow.providers.google.cloud.sensors.dataflow.DataflowJobMessagesSensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataflowJobMessagesSensor</span></code></a>.</p> |
| <div class="example-block-wrapper compound"> |
| <p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/providers/google/cloud/example_dags/example_dataflow.py</span><a class="example-header-button viewcode-button reference internal" href="../../_modules/airflow/providers/google/cloud/example_dags/example_dataflow.html"><span class="viewcode-link">[source]</span></a></p> |
| <div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">check_message</span><span class="p">(</span><span class="n">messages</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">dict</span><span class="p">])</span> <span class="o">-></span> <span class="nb">bool</span><span class="p">:</span> |
| <span class="sd">"""Check message"""</span> |
| <span class="k">for</span> <span class="n">message</span> <span class="ow">in</span> <span class="n">messages</span><span class="p">:</span> |
| <span class="k">if</span> <span class="s2">"Adding workflow start and stop steps."</span> <span class="ow">in</span> <span class="n">message</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"messageText"</span><span class="p">,</span> <span class="s2">""</span><span class="p">):</span> |
| <span class="k">return</span> <span class="kc">True</span> |
| <span class="k">return</span> <span class="kc">False</span> |
| |
| <span class="n">wait_for_python_job_async_message</span> <span class="o">=</span> <span class="n">DataflowJobMessagesSensor</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s2">"wait-for-python-job-async-message"</span><span class="p">,</span> |
| <span class="n">job_id</span><span class="o">=</span><span class="s2">"{{task_instance.xcom_pull('start-python-job-async')['dataflow_job_id']}}"</span><span class="p">,</span> |
| <span class="n">location</span><span class="o">=</span><span class="s1">'europe-west3'</span><span class="p">,</span> |
| <span class="n">callback</span><span class="o">=</span><span class="n">check_message</span><span class="p">,</span> |
| <span class="n">fail_on_terminal_state</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p><a class="reference internal" href="../../_api/airflow/providers/google/cloud/sensors/dataflow/index.html#airflow.providers.google.cloud.sensors.dataflow.DataflowJobAutoScalingEventsSensor" title="airflow.providers.google.cloud.sensors.dataflow.DataflowJobAutoScalingEventsSensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataflowJobAutoScalingEventsSensor</span></code></a>.</p> |
| <div class="example-block-wrapper compound"> |
| <p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/providers/google/cloud/example_dags/example_dataflow.py</span><a class="example-header-button viewcode-button reference internal" href="../../_modules/airflow/providers/google/cloud/example_dags/example_dataflow.html"><span class="viewcode-link">[source]</span></a></p> |
| <div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">check_autoscaling_event</span><span class="p">(</span><span class="n">autoscaling_events</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">dict</span><span class="p">])</span> <span class="o">-></span> <span class="nb">bool</span><span class="p">:</span> |
| <span class="sd">"""Check autoscaling event"""</span> |
| <span class="k">for</span> <span class="n">autoscaling_event</span> <span class="ow">in</span> <span class="n">autoscaling_events</span><span class="p">:</span> |
| <span class="k">if</span> <span class="s2">"Worker pool started."</span> <span class="ow">in</span> <span class="n">autoscaling_event</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"description"</span><span class="p">,</span> <span class="p">{})</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"messageText"</span><span class="p">,</span> <span class="s2">""</span><span class="p">):</span> |
| <span class="k">return</span> <span class="kc">True</span> |
| <span class="k">return</span> <span class="kc">False</span> |
| |
| <span class="n">wait_for_python_job_async_autoscaling_event</span> <span class="o">=</span> <span class="n">DataflowJobAutoScalingEventsSensor</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s2">"wait-for-python-job-async-autoscaling-event"</span><span class="p">,</span> |
| <span class="n">job_id</span><span class="o">=</span><span class="s2">"{{task_instance.xcom_pull('start-python-job-async')['dataflow_job_id']}}"</span><span class="p">,</span> |
| <span class="n">location</span><span class="o">=</span><span class="s1">'europe-west3'</span><span class="p">,</span> |
| <span class="n">callback</span><span class="o">=</span><span class="n">check_autoscaling_event</span><span class="p">,</span> |
| <span class="n">fail_on_terminal_state</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="reference"> |
| <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this heading">¶</a></h2> |
| <p>For further information, look at:</p> |
| <ul class="simple"> |
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| <li><p><a class="reference external" href="https://cloud.google.com/dataflow/docs/guides/using-monitoring-intf/">Dataflow Monitoring Interface</a></p></li> |
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