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From k...@apache.org
Subject [2/3] beam-site git commit: Regenerate website
Date Fri, 09 Jun 2017 17:43:51 GMT
http://git-wip-us.apache.org/repos/asf/beam-site/blob/5c993c61/content/contribute/runner-guide/index.html
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+<!DOCTYPE html>
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+
+    <div class="body__contained">
+      <h1 id="runner-authoring-guide">Runner Authoring Guide</h1>
+
+<p>This guide walks through how to implement a new runner. It is aimed at someone
+who has a data processing system and wants to use it to execute a Beam
+pipeline. The guide starts from the basics, to help you evaluate the work
+ahead. Then the sections become more and more detailed, to be a resource
+throughout the development of your runner.</p>
+
+<p>Topics covered:</p>
+
+<ul id="markdown-toc">
+  <li><a href="#basics-of-the-beam-model" id="markdown-toc-basics-of-the-beam-model">Basics of the Beam model</a>    <ul>
+      <li><a href="#pipeline" id="markdown-toc-pipeline">Pipeline</a></li>
+      <li><a href="#ptransforms" id="markdown-toc-ptransforms">PTransforms</a></li>
+      <li><a href="#pcollections" id="markdown-toc-pcollections">PCollections</a>        <ul>
+          <li><a href="#bounded-vs-unbounded" id="markdown-toc-bounded-vs-unbounded">Bounded vs Unbounded</a></li>
+          <li><a href="#timestamps" id="markdown-toc-timestamps">Timestamps</a></li>
+          <li><a href="#watermarks" id="markdown-toc-watermarks">Watermarks</a></li>
+          <li><a href="#windowed-elements" id="markdown-toc-windowed-elements">Windowed elements</a></li>
+          <li><a href="#coder" id="markdown-toc-coder">Coder</a></li>
+          <li><a href="#windowing-strategy" id="markdown-toc-windowing-strategy">Windowing Strategy</a></li>
+        </ul>
+      </li>
+      <li><a href="#user-defined-functions-udfs" id="markdown-toc-user-defined-functions-udfs">User-Defined Functions (UDFs)</a></li>
+      <li><a href="#runner" id="markdown-toc-runner">Runner</a></li>
+    </ul>
+  </li>
+  <li><a href="#implementing-the-beam-primitives" id="markdown-toc-implementing-the-beam-primitives">Implementing the Beam Primitives</a>    <ul>
+      <li><a href="#what-if-you-havent-implemented-some-of-these-features" id="markdown-toc-what-if-you-havent-implemented-some-of-these-features">What if you haven’t implemented some of these features?</a></li>
+      <li><a href="#implementing-the-pardo-primitive" id="markdown-toc-implementing-the-pardo-primitive">Implementing the ParDo primitive</a>        <ul>
+          <li><a href="#bundles" id="markdown-toc-bundles">Bundles</a></li>
+          <li><a href="#the-dofn-lifecycle" id="markdown-toc-the-dofn-lifecycle">The DoFn Lifecycle</a></li>
+          <li><a href="#dofnrunners" id="markdown-toc-dofnrunners">DoFnRunner(s)</a></li>
+          <li><a href="#side-inputs" id="markdown-toc-side-inputs">Side Inputs</a></li>
+          <li><a href="#state-and-timers" id="markdown-toc-state-and-timers">State and Timers</a></li>
+          <li><a href="#splittable-dofn" id="markdown-toc-splittable-dofn">Splittable DoFn</a></li>
+        </ul>
+      </li>
+      <li><a href="#implementing-the-groupbykey-and-window-primitive" id="markdown-toc-implementing-the-groupbykey-and-window-primitive">Implementing the GroupByKey (and window) primitive</a>        <ul>
+          <li><a href="#group-by-encoded-bytes" id="markdown-toc-group-by-encoded-bytes">Group By Encoded Bytes</a></li>
+          <li><a href="#window-merging" id="markdown-toc-window-merging">Window Merging</a></li>
+          <li><a href="#implementing-via-groupbykeyonly--groupalsobywindow" id="markdown-toc-implementing-via-groupbykeyonly--groupalsobywindow">Implementing via GroupByKeyOnly + GroupAlsoByWindow</a></li>
+          <li><a href="#dropping-late-data" id="markdown-toc-dropping-late-data">Dropping late data</a></li>
+          <li><a href="#triggering" id="markdown-toc-triggering">Triggering</a></li>
+          <li><a href="#timestampcombiner" id="markdown-toc-timestampcombiner">TimestampCombiner</a></li>
+        </ul>
+      </li>
+      <li><a href="#implementing-the-window-primitive" id="markdown-toc-implementing-the-window-primitive">Implementing the Window primitive</a></li>
+      <li><a href="#implementing-the-read-primitive" id="markdown-toc-implementing-the-read-primitive">Implementing the Read primitive</a>        <ul>
+          <li><a href="#reading-from-an-unboundedsource" id="markdown-toc-reading-from-an-unboundedsource">Reading from an UnboundedSource</a></li>
+          <li><a href="#reading-from-a-boundedsource" id="markdown-toc-reading-from-a-boundedsource">Reading from a BoundedSource</a></li>
+        </ul>
+      </li>
+      <li><a href="#implementing-the-flatten-primitive" id="markdown-toc-implementing-the-flatten-primitive">Implementing the Flatten primitive</a></li>
+      <li><a href="#special-mention-the-combine-composite" id="markdown-toc-special-mention-the-combine-composite">Special mention: the Combine composite</a></li>
+    </ul>
+  </li>
+  <li><a href="#working-with-pipelines" id="markdown-toc-working-with-pipelines">Working with pipelines</a>    <ul>
+      <li><a href="#traversing-a-pipeline" id="markdown-toc-traversing-a-pipeline">Traversing a pipeline</a></li>
+      <li><a href="#altering-a-pipeline" id="markdown-toc-altering-a-pipeline">Altering a pipeline</a></li>
+    </ul>
+  </li>
+  <li><a href="#testing-your-runner" id="markdown-toc-testing-your-runner">Testing your runner</a></li>
+  <li><a href="#integrating-your-runner-nicely-with-sdks" id="markdown-toc-integrating-your-runner-nicely-with-sdks">Integrating your runner nicely with SDKs</a>    <ul>
+      <li><a href="#integrating-with-the-java-sdk" id="markdown-toc-integrating-with-the-java-sdk">Integrating with the Java SDK</a>        <ul>
+          <li><a href="#allowing-users-to-pass-options-to-your-runner" id="markdown-toc-allowing-users-to-pass-options-to-your-runner">Allowing users to pass options to your runner</a></li>
+          <li><a href="#registering-your-runner-with-sdks-for-command-line-use" id="markdown-toc-registering-your-runner-with-sdks-for-command-line-use">Registering your runner with SDKs for command line use</a></li>
+        </ul>
+      </li>
+      <li><a href="#integrating-with-the-python-sdk" id="markdown-toc-integrating-with-the-python-sdk">Integrating with the Python SDK</a></li>
+    </ul>
+  </li>
+  <li><a href="#writing-an-sdk-independent-runner" id="markdown-toc-writing-an-sdk-independent-runner">Writing an SDK-independent runner</a>    <ul>
+      <li><a href="#the-fn-api" id="markdown-toc-the-fn-api">The Fn API</a></li>
+      <li><a href="#the-runner-api" id="markdown-toc-the-runner-api">The Runner API</a></li>
+    </ul>
+  </li>
+  <li><a href="#the-runner-api-protos" id="markdown-toc-the-runner-api-protos">The Runner API protos</a>    <ul>
+      <li><a href="#functionspec-proto" id="markdown-toc-functionspec-proto"><code class="highlighter-rouge">FunctionSpec</code> proto</a></li>
+      <li><a href="#sdkfunctionspec-proto" id="markdown-toc-sdkfunctionspec-proto"><code class="highlighter-rouge">SdkFunctionSpec</code> proto</a></li>
+      <li><a href="#primitive-transform-payload-protos" id="markdown-toc-primitive-transform-payload-protos">Primitive transform payload protos</a>        <ul>
+          <li><a href="#pardopayload-proto" id="markdown-toc-pardopayload-proto"><code class="highlighter-rouge">ParDoPayload</code> proto</a></li>
+          <li><a href="#readpayload-proto" id="markdown-toc-readpayload-proto"><code class="highlighter-rouge">ReadPayload</code> proto</a></li>
+          <li><a href="#windowintopayload-proto" id="markdown-toc-windowintopayload-proto"><code class="highlighter-rouge">WindowIntoPayload</code> proto</a></li>
+          <li><a href="#combinepayload-proto" id="markdown-toc-combinepayload-proto"><code class="highlighter-rouge">CombinePayload</code> proto</a></li>
+        </ul>
+      </li>
+      <li><a href="#ptransform-proto" id="markdown-toc-ptransform-proto"><code class="highlighter-rouge">PTransform</code> proto</a></li>
+      <li><a href="#pcollection-proto" id="markdown-toc-pcollection-proto"><code class="highlighter-rouge">PCollection</code> proto</a></li>
+      <li><a href="#coder-proto" id="markdown-toc-coder-proto"><code class="highlighter-rouge">Coder</code> proto</a></li>
+    </ul>
+  </li>
+  <li><a href="#the-runner-api-rpcs" id="markdown-toc-the-runner-api-rpcs">The Runner API RPCs</a>    <ul>
+      <li><a href="#pipelinerunnerrunpipeline-rpc" id="markdown-toc-pipelinerunnerrunpipeline-rpc"><code class="highlighter-rouge">PipelineRunner.run(Pipeline)</code> RPC</a></li>
+      <li><a href="#pipelineresult-aka-job-api" id="markdown-toc-pipelineresult-aka-job-api"><code class="highlighter-rouge">PipelineResult</code> aka “Job API”</a></li>
+    </ul>
+  </li>
+</ul>
+
+<h2 id="basics-of-the-beam-model">Basics of the Beam model</h2>
+
+<p>Suppose you have a data processing engine that can pretty easily process graphs
+of operations. You want to integrate it with the Beam ecosystem to get access
+to other languages, great event time processing, and a library of connectors.
+You need to know the core vocabulary:</p>
+
+<ul>
+  <li><a href="#pipeline"><em>Pipeline</em></a> - A pipeline is a graph of transformations that a user constructs
+that defines the data processing they want to do.</li>
+  <li><a href="#pcollections"><em>PCollection</em></a> - Data being processed in a pipeline is part of a PCollection.</li>
+  <li><a href="#ptransforms"><em>PTransforms</em></a> - The operations executed within a pipeline. These are best
+thought of as operations on PCollections.</li>
+  <li><em>SDK</em> - A language-specific library for pipeline authors (we often call them
+“users” even though we have many kinds of users) to build transforms,
+construct their pipelines and submit them to a runner</li>
+  <li><em>Runner</em> - You are going to write a piece of software called a runner that
+takes a Beam pipeline and executes it using the capabilities of your data
+processing engine.</li>
+</ul>
+
+<p>These concepts may be very similar to your processing engine’s concepts. Since
+Beam’s design is for cross-language operation and reusable libraries of
+transforms, there are some special features worth highlighting.</p>
+
+<h3 id="pipeline">Pipeline</h3>
+
+<p>A pipeline in Beam is a graph of PTransforms operating on PCollections. A
+pipeline is constructed by a user in their SDK of choice, and makes its way to
+your runner either via the SDK directly or via the Runner API’s (forthcoming)
+RPC interfaces.</p>
+
+<h3 id="ptransforms">PTransforms</h3>
+
+<p>In Beam, a PTransform can be one of the five primitives or it can be a
+composite transform encapsulating a subgraph. The primitives are:</p>
+
+<ul>
+  <li><a href="#implementing-the-read-primitive"><em>Read</em></a> - parallel connectors to external
+systems</li>
+  <li><a href="#implementing-the-pardo-primitive"><em>ParDo</em></a> - per element processing</li>
+  <li><a href="#implementing-the-groupbykey-and-window-primitive"><em>GroupByKey</em></a> - 
+aggregating elements per key and window</li>
+  <li><a href="#implementing-the-flatten-primitive"><em>Flatten</em></a> - union of PCollections</li>
+  <li><a href="#implementing-the-window-primitive"><em>Window</em></a> - set the windowing strategy
+for a PCollection</li>
+</ul>
+
+<p>When implementing a runner, these are the operations you need to implement.
+Composite transforms may or may not be important to your runner. If you expose
+a UI, maintaining some of the composite structure will make the pipeline easier
+for a user to understand. But the result of processing is not changed.</p>
+
+<h3 id="pcollections">PCollections</h3>
+
+<p>A PCollection is an unordered bag of elements. Your runner will be responsible
+for storing these elements.  There are some major aspects of a PCollection to
+note:</p>
+
+<h4 id="bounded-vs-unbounded">Bounded vs Unbounded</h4>
+
+<p>A PCollection may be bounded or unbounded.</p>
+
+<ul>
+  <li><em>Bounded</em> - it is finite and you know it, as in batch use cases</li>
+  <li><em>Unbounded</em> - it may be never end, you don’t know, as in streaming use cases</li>
+</ul>
+
+<p>These derive from the intuitions of batch and stream processing, but the two
+are unified in Beam and bounded and unbounded PCollections can coexist in the
+same pipeline. If your runner can only support bounded PCollections, you’ll
+need to reject pipelines that contain unbounded PCollections. If your
+runner is only really targeting streams, there are adapters in our support code
+to convert everything to APIs targeting unbounded data.</p>
+
+<h4 id="timestamps">Timestamps</h4>
+
+<p>Every element in a PCollection has a timestamp associated with it.</p>
+
+<p>When you execute a primitive connector to some storage system, that connector
+is responsible for providing initial timestamps.  Your runner will need to
+propagate and aggregate timestamps. If the timestamp is not important, as with
+certain batch processing jobs where elements do not denote events, they will be
+the minimum representable timestamp, often referred to colloquially as
+“negative infinity”.</p>
+
+<h4 id="watermarks">Watermarks</h4>
+
+<p>Every PCollection has to have a watermark that estimates how complete the
+PCollection is.</p>
+
+<p>The watermark is a guess that “we’ll never see an element with an earlier
+timestamp”. Sources of data are responsible for producing a watermark. Your
+runner needs to implement watermark propagation as PCollections are processed,
+merged, and partitioned.</p>
+
+<p>The contents of a PCollection are complete when a watermark advances to
+“infinity”. In this manner, you may discover that an unbounded PCollection is
+finite.</p>
+
+<h4 id="windowed-elements">Windowed elements</h4>
+
+<p>Every element in a PCollection resides in a window. No element resides in
+multiple windows (two elements can be equal except for their window, but they
+are not the same).</p>
+
+<p>When elements are read from the outside world they arrive in the global window.
+When they are written to the outside world, they are effectively placed back
+into the global window (any writing transform that doesn’t take this
+perspective probably risks data loss).</p>
+
+<p>A window has a maximum timestamp, and when the watermark exceeds this plus
+user-specified allowed lateness the window is expired. All data related
+to an expired window may be discarded at any time.</p>
+
+<h4 id="coder">Coder</h4>
+
+<p>Every PCollection has a coder, a specification of the binary format of the elements.</p>
+
+<p>In Beam, the user’s pipeline may be written in a language other than the
+language of the runner. There is no expectation that the runner can actually
+deserialize user data. So the Beam model operates principally on encoded data -
+“just bytes”. Each PCollection has a declared encoding for its elements, called
+a coder. A coder has a URN that identifies the encoding, and may have
+additional sub-coders (for example, a coder for lists may contain a coder for
+the elements of the list). Language-specific serialization techniques can, and
+frequently are used, but there are a few key formats - such as key-value pairs
+and timestamps - that are common so your runner can understand them.</p>
+
+<h4 id="windowing-strategy">Windowing Strategy</h4>
+
+<p>Every PCollection has a windowing strategy, a specification of essential
+information for grouping and triggering operations.</p>
+
+<p>The details will be discussed below when we discuss the
+<a href="#implementing-the-window-primitive">Window</a> primitive, which sets up the
+windowing strategy, and
+<a href="#implementing-the-groupbykey-and-window-primitive">GroupByKey</a> primitive,
+which has behavior governed by the windowing strategy.</p>
+
+<h3 id="user-defined-functions-udfs">User-Defined Functions (UDFs)</h3>
+
+<p>Beam has seven varieties of user-defined function (UDF). A Beam pipeline
+may contain UDFs written in a language other than your runner, or even multiple
+languages in the same pipeline (see the <a href="#the-runner-api">Runner API</a>) so the
+definitions are language-independent (see the <a href="#the-fn-api">Fn API</a>).</p>
+
+<p>The UDFs of Beam are:</p>
+
+<ul>
+  <li><em>DoFn</em> - per-element processing function (used in ParDo)</li>
+  <li><em>WindowFn</em> - places elements in windows and merges windows (used in Window
+and GroupByKey)</li>
+  <li><em>Source</em> - emits data read from external sources, including initial and
+dynamic splitting for parallelism (used in Read)</li>
+  <li><em>ViewFn</em> - adapts a materialized PCollection to a particular interface (used
+in side inputs)</li>
+  <li><em>WindowMappingFn</em> - maps one element’s window to another, and specifies
+bounds on how far in the past the result window will be (used in side
+inputs)</li>
+  <li><em>CombineFn</em> - associative and commutative aggregation (used in Combine and
+state)</li>
+  <li><em>Coder</em> - encodes user data; some coders have standard formats and are not really UDFs</li>
+</ul>
+
+<p>The various types of user-defined functions will be described further alongside
+the primitives that use them.</p>
+
+<h3 id="runner">Runner</h3>
+
+<p>The term “runner” is used for a couple of things. It generally refers to the
+software that takes a Beam pipeline and executes it somehow. Often, this is the
+translation code that you write. It usually also includes some customized
+operators for your data processing engine, and is sometimes used to refer to
+the full stack.</p>
+
+<p>A runner has just a single method <code class="highlighter-rouge">run(Pipeline)</code>. From here on, I will often
+use code font for proper nouns in our APIs, whether or not the identifiers
+match across all SDKs.</p>
+
+<p>The <code class="highlighter-rouge">run(Pipeline)</code> method should be asynchronous and results in a
+PipelineResult which generally will be a job descriptor for your data
+processing engine, provides methods for checking its status, canceling it, and
+waiting for it to terminate.</p>
+
+<h2 id="implementing-the-beam-primitives">Implementing the Beam Primitives</h2>
+
+<p>Aside from encoding and persisting data - which presumably your engine already
+does in some way or another - most of what you need to do is implement the Beam
+primitives. This section provides a detailed look at each primitive, covering
+what you need to know that might not be obvious and what support code is
+provided.</p>
+
+<p>The primitives are designed for the benefit of pipeline authors, not runner
+authors. Each represents a different conceptual mode of operation (external IO,
+element-wise, grouping, windowing, union) rather than a specific implementation
+decision.  The same primitive may require very different implementation based
+on how the user instantiates it. For example, a <code class="highlighter-rouge">ParDo</code> that uses state or
+timers may require key partitioning, a <code class="highlighter-rouge">GroupByKey</code> with speculative triggering
+may require a more costly or complex implementation, and <code class="highlighter-rouge">Read</code> is completely
+different for bounded and unbounded data.</p>
+
+<h3 id="what-if-you-havent-implemented-some-of-these-features">What if you haven’t implemented some of these features?</h3>
+
+<p>That’s OK! You don’t have to do it all at once, and there may even be features
+that don’t make sense for your runner to ever support.  We maintain a
+<a href="/documentation/runners/capability-matrix/">capability matrix</a> on the Beam site so you can tell
+users what you support. When you receive a <code class="highlighter-rouge">Pipeline</code>, you should traverse it
+and determine whether or not you can execute each <code class="highlighter-rouge">DoFn</code> that you find. If
+you cannot execute some <code class="highlighter-rouge">DoFn</code> in the pipeline (or if there is any other
+requirement that your runner lacks) you should reject the pipeline. In your
+native environment, this may look like throwing an
+<code class="highlighter-rouge">UnsupportedOperationException</code>.  The Runner API RPCs will make this explicit,
+for cross-language portability.</p>
+
+<h3 id="implementing-the-pardo-primitive">Implementing the ParDo primitive</h3>
+
+<p>The <code class="highlighter-rouge">ParDo</code> primitive describes element-wise transformation for a
+<code class="highlighter-rouge">PCollection</code>.  <code class="highlighter-rouge">ParDo</code> is the most complex primitive, because it is where any
+per-element processing is described. In addition to very simple operations like
+standard <code class="highlighter-rouge">map</code> or <code class="highlighter-rouge">flatMap</code> from functional programming, <code class="highlighter-rouge">ParDo</code> also supports
+multiple outputs, side inputs, initialization, flushing, teardown, and stateful
+processing.</p>
+
+<p>The UDF that is applied to each element is called a <code class="highlighter-rouge">DoFn</code>. The exact APIs for
+a <code class="highlighter-rouge">DoFn</code> can vary per language/SDK but generally follow the same pattern, so we
+can discuss it with pseudocode. I will also often refer to the Java support
+code, since I know it and most of our current and future runners are
+Java-based.</p>
+
+<h4 id="bundles">Bundles</h4>
+
+<p>For correctness, a <code class="highlighter-rouge">DoFn</code> <em>should</em> represent an element-wise function, but in
+fact is a long-lived object that processes elements in small groups called
+bundles.</p>
+
+<p>Your runner decides how many elements, and which elements, to include in a
+bundle, and can even decide dynamically in the middle of processing that the
+current bundle has “ended”. How a bundle is processed ties in with the rest of
+a DoFn’s lifecycle.</p>
+
+<p>It will generally improve throughput to make the largest bundles possible, so
+that initialization and finalization costs are amortized over many elements.
+But if your data is arriving as a stream, then you will want to terminate a
+bundle in order to achieve appropriate latency, so bundles may be just a few
+elements.</p>
+
+<h4 id="the-dofn-lifecycle">The DoFn Lifecycle</h4>
+
+<p>While each language’s SDK is free to make different decisions, the Python and
+Java SDKs share an API with the following stages of a DoFn’s lifecycle.</p>
+
+<p>However, if you choose to execute a DoFn directly to improve performance or
+single-language simplicity, then your runner is responsible for implementing
+the following sequence:</p>
+
+<ul>
+  <li><em>Setup</em> - called once per DoFn instance before anything else; this has not been
+implemented in the Python SDK so the user can work around just with lazy
+initialization</li>
+  <li><em>StartBundle</em> - called once per bundle as initialization (actually, lazy
+initialization is almost always equivalent and more efficient, but this hook
+remains for simplicity for users)</li>
+  <li><em>ProcessElement</em> / <em>OnTimer</em> - called for each element and timer activation</li>
+  <li><em>FinishBundle</em> - essentially “flush”; required to be called before
+considering elements actually processed</li>
+  <li><em>Teardown</em> - release resources that were used across bundles; calling this
+can be best effort due to failures</li>
+</ul>
+
+<h4 id="dofnrunners">DoFnRunner(s)</h4>
+
+<p>This is a support class that has manifestations in both the Java codebase and
+the Python codebase.</p>
+
+<p><strong>Java</strong></p>
+
+<p>In Java, the <code class="highlighter-rouge">beam-runners-core-java</code> library provides an interface
+<code class="highlighter-rouge">DoFnRunner</code> for bundle processing, with implementations for many situations.</p>
+
+<div class="language-java no-toggle highlighter-rouge"><pre class="highlight"><code><span class="kd">interface</span> <span class="nc">DoFnRunner</span><span class="o">&lt;</span><span class="n">InputT</span><span class="o">,</span> <span class="n">OutputT</span><span class="o">&gt;</span> <span class="o">{</span>
+  <span class="kt">void</span> <span class="nf">startBundle</span><span class="o">();</span>
+  <span class="kt">void</span> <span class="nf">processElement</span><span class="o">(</span><span class="n">WindowedValue</span><span class="o">&lt;</span><span class="n">InputT</span><span class="o">&gt;</span> <span class="n">elem</span><span class="o">);</span>
+  <span class="kt">void</span> <span class="nf">onTimer</span><span class="o">(</span><span class="n">String</span> <span class="n">timerId</span><span class="o">,</span> <span class="n">BoundedWindow</span> <span class="n">window</span><span class="o">,</span> <span class="n">Instant</span> <span class="n">timestamp</span><span class="o">,</span> <span class="n">TimeDomain</span> <span class="n">timeDomain</span><span class="o">);</span>
+  <span class="kt">void</span> <span class="nf">finishBundle</span><span class="o">();</span>
+<span class="o">}</span>
+</code></pre>
+</div>
+
+<p>There are some implementations and variations of this for different scenarios:</p>
+
+<ul>
+  <li><a href="https://github.com/apache/beam/blob/master/runners/core-java/src/main/java/org/apache/beam/runners/core/SimpleDoFnRunner.java"><code class="highlighter-rouge">SimpleDoFnRunner</code></a> - 
+not actually simple at all; implements lots of the core functionality of
+<code class="highlighter-rouge">ParDo</code>. This is how most runners execute most <code class="highlighter-rouge">DoFns</code>.</li>
+  <li><a href="https://github.com/apache/beam/blob/master/runners/core-java/src/main/java/org/apache/beam/runners/core/LateDataDroppingDoFnRunner.java"><code class="highlighter-rouge">LateDataDroppingDoFnRunner</code></a> - 
+wraps a <code class="highlighter-rouge">DoFnRunner</code> and drops data from expired windows so the wrapped
+<code class="highlighter-rouge">DoFnRunner</code> doesn’t get any unpleasant surprises</li>
+  <li><a href="https://github.com/apache/beam/blob/master/runners/core-java/src/main/java/org/apache/beam/runners/core/StatefulDoFnRunner.java"><code class="highlighter-rouge">StatefulDoFnRunner</code></a> - 
+handles collecting expired state</li>
+  <li><a href="https://github.com/apache/beam/blob/master/runners/core-java/src/main/java/org/apache/beam/runners/core/PushbackSideInputDoFnRunner.java"><code class="highlighter-rouge">PushBackSideInputDoFnRunner</code></a> - 
+buffers input while waiting for side inputs to be ready</li>
+</ul>
+
+<p>These are all used heavily in implementations of Java runners. Invocations
+via the <a href="#the-fn-api">Fn API</a> may manifest as another implementation of
+<code class="highlighter-rouge">DoFnRunner</code> even though it will be doing far more than running a <code class="highlighter-rouge">DoFn</code>.</p>
+
+<p><strong>Python</strong></p>
+
+<p>See the <a href="https://beam.apache.org/documentation/sdks/pydoc/2.0.0/apache_beam.runners.html#apache_beam.runners.common.DoFnRunner">DoFnRunner pydoc</a>.</p>
+
+<h4 id="side-inputs">Side Inputs</h4>
+
+<p><em>Main design document:
+<a href="https://s.apache.org/beam-side-inputs-1-pager">https://s.apache.org/beam-side-inputs-1-pager</a></em></p>
+
+<p>A side input is a global view of a window of a <code class="highlighter-rouge">PCollection</code>. This distinguishes
+it from the main input, which is processed one element at a time. The SDK/user
+prepares a <code class="highlighter-rouge">PCollection</code> adequately, the runner materializes it, and then the
+runner feeds it to the <code class="highlighter-rouge">DoFn</code>. See the</p>
+
+<p>What you will need to implement is to inspect the materialization requested for
+the side input, and prepare it appropriately, and corresponding interactions
+when a <code class="highlighter-rouge">DoFn</code> reads the side inputs.</p>
+
+<p>The details and available support code vary by language.</p>
+
+<p><strong>Java</strong></p>
+
+<p>If you are using one of the above <code class="highlighter-rouge">DoFnRunner</code> classes, then the interface for
+letting them request side inputs is
+<a href="https://github.com/apache/beam/blob/master/runners/core-java/src/main/java/org/apache/beam/runners/core/SideInputReader.java"><code class="highlighter-rouge">SideInputReader</code></a>.
+It is a simple mapping from side input and window to a value. The <code class="highlighter-rouge">DoFnRunner</code>
+will perform a mapping with the
+<a href="https://github.com/apache/beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/windowing/WindowMappingFn.java"><code class="highlighter-rouge">WindowMappingFn</code></a>
+to request the appropriate window so you do not worry about invoking this UDF.
+When using the Fn API, it will be the SDK harness that maps windows as well.</p>
+
+<p>A simple, but not necessarily optimal approach to building a
+<a href="https://github.com/apache/beam/blob/master/runners/core-java/src/main/java/org/apache/beam/runners/core/SideInputReader.java"><code class="highlighter-rouge">SideInputReader</code></a>
+is to use a state backend. In our Java support code, this is called
+<a href="https://github.com/apache/beam/blob/master/runners/core-java/src/main/java/org/apache/beam/runners/core/StateInternals.java"><code class="highlighter-rouge">StateInternals</code></a>
+and you can build a
+<a href="https://github.com/apache/beam/blob/master/runners/core-java/src/main/java/org/apache/beam/runners/core/SideInputHandler.java"><code class="highlighter-rouge">SideInputHandler</code></a>
+that will use your <code class="highlighter-rouge">StateInternals</code> to materialize a <code class="highlighter-rouge">PCollection</code> into the
+appropriate side input view and then yield the value when requested for a
+particular side input and window.</p>
+
+<p>When a side input is needed but the side input has no data associated with it
+for a given window, elements in that window must be deferred until the side
+input has some data. The aforementioned
+<a href="https://github.com/apache/beam/blob/master/runners/core-java/src/main/java/org/apache/beam/runners/core/PushbackSideInputDoFnRunner.java"><code class="highlighter-rouge">PushBackSideInputDoFnRunner</code></a>
+is used to implement this.</p>
+
+<p><strong>Python</strong></p>
+
+<p>In Python, <a href="https://beam.apache.org/documentation/sdks/pydoc/2.0.0/apache_beam.transforms.html#apache_beam.transforms.sideinputs.SideInputMap"><code class="highlighter-rouge">SideInputMap</code></a> maps
+windows to side input values. The <code class="highlighter-rouge">WindowMappingFn</code> manifests as a simple
+function. See
+<a href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/transforms/sideinputs.py">sideinputs.py</a>.</p>
+
+<h4 id="state-and-timers">State and Timers</h4>
+
+<p><em>Main design document: <a href="https://s.apache.org/beam-state">https://s.apache.org/beam-state</a></em></p>
+
+<p>When <code class="highlighter-rouge">ParDo</code> includes state and timers, its execution on your runner is usually
+very different. See the full details beyond those covered here.</p>
+
+<p>State and timers are partitioned per key and window. You may need or want to
+explicitly shuffle data to support this.</p>
+
+<p><strong>Java</strong></p>
+
+<p>We provide
+<a href="https://github.com/apache/beam/blob/master/runners/core-java/src/main/java/org/apache/beam/runners/core/StatefulDoFnRunner.java"><code class="highlighter-rouge">StatefulDoFnRunner</code></a>
+to help with state cleanup. The non-user-facing interface
+<a href="https://github.com/apache/beam/blob/master/runners/core-java/src/main/java/org/apache/beam/runners/core/StateInternals.java"><code class="highlighter-rouge">StateInternals</code></a>
+is what a runner generally implements, and then the Beam support code can use
+this to implement user-facing state.</p>
+
+<h4 id="splittable-dofn">Splittable DoFn</h4>
+
+<p><em>Main design document: <a href="https://s.apache.org/splittable-do-fn">https://s.apache.org/splittable-do-fn</a></em></p>
+
+<p>Splittable <code class="highlighter-rouge">DoFn</code> is a generalization and combination of <code class="highlighter-rouge">ParDo</code> and <code class="highlighter-rouge">Read</code>. It
+is per-element processing where each element the capabilities of being “split”
+in the same ways as a <code class="highlighter-rouge">BoundedSource</code> or <code class="highlighter-rouge">UnboundedSource</code>. This enables better
+performance for use cases such as a <code class="highlighter-rouge">PCollection</code> of names of large files where
+you want to read each of them. Previously they would have to be static data in
+the pipeline or be read in a non-splittable manner.</p>
+
+<p>This feature is still under development, but likely to become the new primitive
+for reading. It is best to be aware of it and follow developments.</p>
+
+<h3 id="implementing-the-groupbykey-and-window-primitive">Implementing the GroupByKey (and window) primitive</h3>
+
+<p>The <code class="highlighter-rouge">GroupByKey</code> operation (sometimes called GBK for short) groups a
+<code class="highlighter-rouge">PCollection</code> of key-value pairs by key and window, emitting results according
+to the <code class="highlighter-rouge">PCollection</code>’s triggering configuration.</p>
+
+<p>It is quite a bit more elaborate than simply colocating elements with the same
+key, and uses many fields from the <code class="highlighter-rouge">PCollection</code>’s windowing strategy.</p>
+
+<h4 id="group-by-encoded-bytes">Group By Encoded Bytes</h4>
+
+<p>For both the key and window, your runner sees them as “just bytes”. So you need
+to group in a way that is consistent with grouping by those bytes, even if you
+have some special knowledge of the types involved.</p>
+
+<p>The elements you are processing will be key-value pairs, and you’ll need to extract
+the keys. For this reason, the format of key-value pairs is standardized and
+shared across all SDKS. See either
+<a href="https://beam.apache.org/documentation/sdks/javadoc/2.0.0/org/apache/beam/sdk/coders/KvCoder.html"><code class="highlighter-rouge">KvCoder</code></a>
+in Java or
+<a href="https://beam.apache.org/documentation/sdks/pydoc/2.0.0/apache_beam.coders.html#apache_beam.coders.coders.TupleCoder.key_coder"><code class="highlighter-rouge">TupleCoder</code></a>
+in Python for documentation on the binary format.</p>
+
+<h4 id="window-merging">Window Merging</h4>
+
+<p>As well as grouping by key, your runner must group elements by their window. A
+<code class="highlighter-rouge">WindowFn</code> has the option of declaring that it merges windows on a per-key
+basis.  For example, session windows for the same key will be merged if they
+overlap. So your runner must invoke the merge method of the <code class="highlighter-rouge">WindowFn</code> during
+grouping.</p>
+
+<h4 id="implementing-via-groupbykeyonly--groupalsobywindow">Implementing via GroupByKeyOnly + GroupAlsoByWindow</h4>
+
+<p>The Java codebase includes support code for a particularly common way of
+implement the full <code class="highlighter-rouge">GroupByKey</code> operation: first group the keys, and then group
+by window. For merging windows, this is essentially required, since merging is
+per key.</p>
+
+<h4 id="dropping-late-data">Dropping late data</h4>
+
+<p><em>Main design document:
+<a href="https://s.apache.org/beam-lateness">https://s.apache.org/beam-lateness</a></em></p>
+
+<p>A window is expired in a <code class="highlighter-rouge">PCollection</code>  if the watermark of the input PCollection
+has exceeded the end of the window by at least the input <code class="highlighter-rouge">PCollection</code>’s
+allowed lateness.</p>
+
+<p>Data for an expired window can be dropped any time and should be dropped at a
+<code class="highlighter-rouge">GroupByKey</code>. If you are using <code class="highlighter-rouge">GroupAlsoByWindow</code>, then just before executing
+this transform. You may shuffle less data if you drop data prior to
+<code class="highlighter-rouge">GroupByKeyOnly</code>, but should only safely be done for non-merging windows, as a
+window that appears expired may merge to become not expired.</p>
+
+<h4 id="triggering">Triggering</h4>
+
+<p><em>Main design document:
+<a href="https://s.apache.org/beam-triggers">https://s.apache.org/beam-triggers</a></em></p>
+
+<p>The input <code class="highlighter-rouge">PCollection</code>’s trigger and accumulation mode specify when and how
+outputs should be emitted from the <code class="highlighter-rouge">GroupByKey</code> operation.</p>
+
+<p>In Java, there is a lot of support code for executing triggers in the
+<code class="highlighter-rouge">GroupAlsoByWindow</code> implementations, <code class="highlighter-rouge">ReduceFnRunner</code> (legacy name), and
+<code class="highlighter-rouge">TriggerStateMachine</code>, which is an obvious way of implementing all triggers as
+an event-driven machine over elements and timers.</p>
+
+<h4 id="timestampcombiner">TimestampCombiner</h4>
+
+<p>When an aggregated output is produced from multiple inputs, the <code class="highlighter-rouge">GroupByKey</code>
+operation has to choose a timestamp for the combination. To do so, first the
+WindowFn has a chance to shift timestamps - this is needed to ensure watermarks
+do not prevent progress of windows like sliding windows (the details are beyond
+this doc). Then, the shifted timestamps need to be combined - this is specified
+by a <code class="highlighter-rouge">TimestampCombiner</code>, which can either select the minimum or maximum of its
+inputs, or just ignore inputs and choose the end of the window.</p>
+
+<h3 id="implementing-the-window-primitive">Implementing the Window primitive</h3>
+
+<p>The window primitive applies a <code class="highlighter-rouge">WindowFn</code> UDF to place each input element into
+one or more windows of its output PCollection. Note that the primitive also
+generally configures other aspects of the windowing strategy for a <code class="highlighter-rouge">PCollection</code>,
+but the fully constructed graph that your runner receive will already have a
+complete windowing strategy for each <code class="highlighter-rouge">PCollection</code>.</p>
+
+<p>To implement this primitive, you need to invoke the provided WindowFn on each
+element, which will return some set of windows for that element to be a part of
+in the output <code class="highlighter-rouge">PCollection</code>.</p>
+
+<p><strong>Implementation considerations</strong></p>
+
+<p>A “window” is just a second grouping key that has a “maximum timestamp”. It can
+be any arbitrary user-defined type. The <code class="highlighter-rouge">WindowFn</code> provides the coder for the
+window type.</p>
+
+<p>Beam’s support code provides <code class="highlighter-rouge">WindowedValue</code> which is a compressed
+representation of an element in multiple windows. You may want to do use this,
+or your own compressed representation. Remember that it simply represents
+multiple elements at the same time; there is no such thing as an element “in
+multiple windows”.</p>
+
+<p>For values in the global window, you may want to use an even further compressed
+representation that doesn’t bother including the window at all.</p>
+
+<p>In the future, this primitive may be retired as it can be implemented as a
+ParDo if the capabilities of ParDo are enhanced to allow output to new windows.</p>
+
+<h3 id="implementing-the-read-primitive">Implementing the Read primitive</h3>
+
+<p>You implement this primitive to read data from an external system. The APIs are
+carefully crafted to enable efficient parallel execution. Reading from an
+<code class="highlighter-rouge">UnboundedSource</code> is a bit different than reading from a <code class="highlighter-rouge">BoundedSource</code>.</p>
+
+<h4 id="reading-from-an-unboundedsource">Reading from an UnboundedSource</h4>
+
+<p>An <code class="highlighter-rouge">UnboundedSource</code> is a source of potentially infinite data; you can think of
+it like a stream. The capabilities are:</p>
+
+<ul>
+  <li><code class="highlighter-rouge">split(int)</code> - your runner should call this to get the desired parallelism</li>
+  <li><code class="highlighter-rouge">createReader(...)</code> - call this to start reading elements; it is an enhanced iterator that also vends:</li>
+  <li>watermark (for this source) which you should propagate downstream
+timestamps, which you should associate with elements read</li>
+  <li>record identifiers, so you can dedup downstream if needed</li>
+  <li>progress indication of its backlog</li>
+  <li>checkpointing</li>
+  <li><code class="highlighter-rouge">requiresDeduping</code> - this indicates that there is some chance that the source
+may emit dupes; your runner should do its best to dedupe based on the
+identifier attached to emitted records</li>
+</ul>
+
+<p>An unbounded source has a custom type of checkpoints and an associated coder for serializing them.</p>
+
+<h4 id="reading-from-a-boundedsource">Reading from a BoundedSource</h4>
+
+<p>A <code class="highlighter-rouge">BoundedSource</code> is a source of data that you know is finite, such as a static
+collection of log files, or a database table. The capabilities are:</p>
+
+<ul>
+  <li><code class="highlighter-rouge">split(int)</code> - your runner should call this to get desired initial parallelism (but you can often steal work later)</li>
+  <li><code class="highlighter-rouge">getEstimatedSizeBytes(...)</code> - self explanatory</li>
+  <li><code class="highlighter-rouge">createReader(...)</code> - call this to start reading elements; it is an enhanced iterator, with also:</li>
+  <li>timestamps to associate with each element read</li>
+  <li><code class="highlighter-rouge">splitAtFraction</code> for dynamic splitting to enable work stealing, and other
+methods to support it - see the <a href="https://beam.apache.org/blog/2016/05/18/splitAtFraction-method.html">Beam blog post on dynamic work
+rebalancing</a></li>
+</ul>
+
+<p>The <code class="highlighter-rouge">BoundedSource</code> does not report a watermark currently. Most of the time, reading
+from a bounded source can be parallelized in ways that result in utterly out-of-order
+data, so a watermark is not terribly useful.
+Thus the watermark for the output <code class="highlighter-rouge">PCollection</code> from a bounded read should
+remain at the minimum timestamp throughout reading (otherwise data might get
+dropped) and advance to the maximum timestamp when all data is exhausted.</p>
+
+<h3 id="implementing-the-flatten-primitive">Implementing the Flatten primitive</h3>
+
+<p>This one is easy - take as input a finite set of <code class="highlighter-rouge">PCollections</code> and outputs their
+bag union, keeping windows intact.</p>
+
+<p>For this operation to make sense, it is the SDK’s responsibility to make sure
+the windowing strategies are compatible.</p>
+
+<p>Also note that there is no requirement that the coders for all the <code class="highlighter-rouge">PCollections</code>
+be the same. If your runner wants to require that (to avoid tedious
+re-encoding) you have to enforce it yourself. Or you could just implement the
+fast path as an optimization.</p>
+
+<h3 id="special-mention-the-combine-composite">Special mention: the Combine composite</h3>
+
+<p>A composite transform that is almost always treated specially by a runner is
+<code class="highlighter-rouge">Combine</code> (per key), which applies an associative and commutative operator to
+the elements of a <code class="highlighter-rouge">PCollection</code>. This composite is not a primitive. It is
+implemented in terms of <code class="highlighter-rouge">ParDo</code> and <code class="highlighter-rouge">GroupByKey</code>, so your runner will work
+without treating it - but it does carry additional information that you
+probably want to use for optimizations: the associative-commutative operator,
+known as a <code class="highlighter-rouge">CombineFn</code>.</p>
+
+<h2 id="working-with-pipelines">Working with pipelines</h2>
+
+<p>When you receive a pipeline from a user, you will need to translate it. This is
+a tour of the APIs that you’ll use to do it.</p>
+
+<h3 id="traversing-a-pipeline">Traversing a pipeline</h3>
+
+<p>Something you will likely do is to traverse a pipeline, probably to translate
+it into primitives for your engine. The general pattern is to write a visitor
+that builds a job specification as it walks the graph of <code class="highlighter-rouge">PTransforms</code>.</p>
+
+<p>The entry point for this in Java is
+<a href="https://beam.apache.org/documentation/sdks/javadoc/2.0.0/org/apache/beam/sdk/Pipeline.html#traverseTopologically-org.apache.beam.sdk.Pipeline.PipelineVisitor-"><code class="highlighter-rouge">Pipeline.traverseTopologically</code></a>
+and
+<a href="https://beam.apache.org/documentation/sdks/pydoc/2.0.0/apache_beam.html#apache_beam.pipeline.Pipeline.visit"><code class="highlighter-rouge">Pipeline.visit</code></a>
+in Python. See the generated documentation for details.</p>
+
+<h3 id="altering-a-pipeline">Altering a pipeline</h3>
+
+<p>Often, the best way to keep your
+translator simple will be to alter the pipeline prior to translation. Some
+alterations you might perform:</p>
+
+<ul>
+  <li>Elaboration of a Beam primitive into a composite transform that uses
+multiple runner-specific primitives</li>
+  <li>Optimization of a Beam composite into a specialized primitive for your
+runner</li>
+  <li>Replacement of a Beam composite with a different expansion more suitable for
+your runner</li>
+</ul>
+
+<p>The Java SDK and the “runners core construction” library (the artifact is
+<code class="highlighter-rouge">beam-runners-core-construction-java</code> and the namespaces is
+<code class="highlighter-rouge">org.apache.beam.runners.core.construction</code>) contain helper code for this sort
+of work. In Python, support code is still under development.</p>
+
+<p>All pipeline alteration is done via
+<a href="https://beam.apache.org/documentation/sdks/javadoc/2.0.0/org/apache/beam/sdk/Pipeline.html#replaceAll-java.util.List-"><code class="highlighter-rouge">Pipeline.replaceAll(PTransformOverride)</code></a>
+method. A
+<a href="https://github.com/apache/beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/runners/PTransformOverride.java"><code class="highlighter-rouge">PTransformOverride</code></a>
+is a pair of a
+<a href="https://github.com/apache/beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/runners/PTransformMatcher.java"><code class="highlighter-rouge">PTransformMatcher</code></a>
+to select transforms for replacement and a
+<a href="https://github.com/apache/beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/runners/PTransformOverrideFactory.java"><code class="highlighter-rouge">PTransformOverrideFactory</code></a>
+to produce the replacement. All <code class="highlighter-rouge">PTransformMatchers</code> that have been needed by
+runners to date are provided. Examples include: matching a specific class,
+matching a <code class="highlighter-rouge">ParDo</code> where the <code class="highlighter-rouge">DoFn</code> uses state or timers, etc.</p>
+
+<h2 id="testing-your-runner">Testing your runner</h2>
+
+<p>The Beam Java SDK and Python SDK have suites of runner validation tests. The
+configuration may evolve faster than this document, so check the configuration
+of other Beam runners. But be aware that we have tests and you can use them
+very easily!  To enable these tests in a Java-based runner using Maven, you
+scan the dependencies of the SDK for tests with the JUnit category
+<code class="highlighter-rouge">ValidatesRunner</code>.</p>
+
+<div class="language-xml no-toggle highlighter-rouge"><pre class="highlight"><code><span class="nt">&lt;plugin&gt;</span>
+  <span class="nt">&lt;groupId&gt;</span>org.apache.maven.plugins<span class="nt">&lt;/groupId&gt;</span>
+  <span class="nt">&lt;artifactId&gt;</span>maven-surefire-plugin<span class="nt">&lt;/artifactId&gt;</span>
+  <span class="nt">&lt;executions&gt;</span>
+    <span class="nt">&lt;execution&gt;</span>
+      <span class="nt">&lt;id&gt;</span>validates-runner-tests<span class="nt">&lt;/id&gt;</span>
+      <span class="nt">&lt;phase&gt;</span>integration-test<span class="nt">&lt;/phase&gt;</span>
+      <span class="nt">&lt;goals&gt;&lt;goal&gt;</span>test<span class="nt">&lt;/goal&gt;&lt;/goals&gt;</span>
+      <span class="nt">&lt;configuration&gt;</span>
+        <span class="nt">&lt;groups&gt;</span>org.apache.beam.sdk.testing.ValidatesRunner<span class="nt">&lt;/groups&gt;</span>
+        <span class="nt">&lt;dependenciesToScan&gt;</span>
+          <span class="nt">&lt;dependency&gt;</span>org.apache.beam:beam-sdks-java-core<span class="nt">&lt;/dependency&gt;</span>
+        <span class="nt">&lt;/dependenciesToScan&gt;</span>
+        <span class="nt">&lt;systemPropertyVariables&gt;</span>
+          <span class="nt">&lt;beamTestPipelineOptions&gt;</span>
+            [
+              "--runner=MyRunner",
+              … misc test options … 
+            ]
+          <span class="nt">&lt;/beamTestPipelineOptions&gt;</span>
+        <span class="nt">&lt;/systemPropertyVariables&gt;</span>
+      <span class="nt">&lt;/configuration&gt;</span>
+    <span class="nt">&lt;/execution&gt;</span>
+  <span class="nt">&lt;/executions&gt;</span>
+<span class="nt">&lt;/plugin&gt;</span>
+</code></pre>
+</div>
+
+<p>Enable these tests in other languages is unexplored.</p>
+
+<h2 id="integrating-your-runner-nicely-with-sdks">Integrating your runner nicely with SDKs</h2>
+
+<p>Whether or not your runner is based in the same language as an SDK (such as
+Java), you will want to provide a shim to invoke it from another SDK if you
+want the users of that SDK (such as Python) to use it.</p>
+
+<h3 id="integrating-with-the-java-sdk">Integrating with the Java SDK</h3>
+
+<h4 id="allowing-users-to-pass-options-to-your-runner">Allowing users to pass options to your runner</h4>
+
+<p>The mechanism for configuration is
+<a href="https://beam.apache.org/documentation/sdks/javadoc/2.0.0/org/apache/beam/sdk/options/PipelineOptions.html"><code class="highlighter-rouge">PipelineOptions</code></a>,
+an interface that works completely differently than normal Java objects. Forget
+what you know, and follow the rules, and <code class="highlighter-rouge">PipelineOptions</code> will treat you well.</p>
+
+<p>You must implement a sub-interface for your runner with getters and setters
+with matching names, like so:</p>
+
+<div class="language-java no-toggle highlighter-rouge"><pre class="highlight"><code><span class="kd">public</span> <span class="kd">interface</span> <span class="nc">MyRunnerOptions</span> <span class="kd">extends</span> <span class="n">PipelineOptions</span> <span class="o">{</span>
+  <span class="nd">@Description</span><span class="o">(</span><span class="s">"The Foo to use with MyRunner"</span><span class="o">)</span>
+  <span class="nd">@Required</span>
+  <span class="kd">public</span> <span class="n">Foo</span> <span class="nf">getMyRequiredFoo</span><span class="o">();</span>
+  <span class="kd">public</span> <span class="kt">void</span> <span class="nf">setMyRequiredFoo</span><span class="o">(</span><span class="n">Foo</span> <span class="n">newValue</span><span class="o">);</span>
+ 
+  <span class="nd">@Description</span><span class="o">(</span><span class="s">"Enable Baz; on by default"</span><span class="o">)</span>
+  <span class="nd">@Default</span><span class="o">.</span><span class="na">Boolean</span><span class="o">(</span><span class="kc">true</span><span class="o">)</span>
+  <span class="kd">public</span> <span class="n">Boolean</span> <span class="nf">isBazEnabled</span><span class="o">();</span>
+  <span class="kd">public</span> <span class="kt">void</span> <span class="nf">setBazEnabled</span><span class="o">(</span><span class="n">Boolean</span> <span class="n">newValue</span><span class="o">);</span>
+<span class="o">}</span>
+</code></pre>
+</div>
+
+<p>You can set up defaults, etc. See the javadoc for details.  When your runner is
+instantiated with a <code class="highlighter-rouge">PipelineOptions</code> object, you access your interface by
+<code class="highlighter-rouge">options.as(MyRunnerOptions.class)</code>.</p>
+
+<p>To make these options available on the command line, you register your options
+with a <code class="highlighter-rouge">PipelineOptionsRegistrar</code>. It is easy if you use <code class="highlighter-rouge">@AutoService</code>:</p>
+
+<div class="language-java no-toggle highlighter-rouge"><pre class="highlight"><code><span class="nd">@AutoService</span><span class="o">(</span><span class="n">PipelineOptionsRegistrar</span><span class="o">.</span><span class="na">class</span><span class="o">)</span>
+<span class="kd">public</span> <span class="kd">static</span> <span class="kd">class</span> <span class="nc">MyOptionsRegistrar</span> <span class="kd">implements</span> <span class="n">PipelineOptionsRegistrar</span> <span class="o">{</span>
+  <span class="nd">@Override</span>
+  <span class="kd">public</span> <span class="n">Iterable</span><span class="o">&lt;</span><span class="n">Class</span><span class="o">&lt;?</span> <span class="kd">extends</span> <span class="n">PipelineOptions</span><span class="o">&gt;&gt;</span> <span class="nf">getPipelineOptions</span><span class="o">()</span> <span class="o">{</span>
+    <span class="k">return</span> <span class="n">ImmutableList</span><span class="o">.&lt;</span><span class="n">Class</span><span class="o">&lt;?</span> <span class="kd">extends</span> <span class="n">PipelineOptions</span><span class="o">&gt;&gt;</span><span class="n">of</span><span class="o">(</span><span class="n">MyRunnerOptions</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
+  <span class="o">}</span>
+<span class="o">}</span>
+</code></pre>
+</div>
+
+<h4 id="registering-your-runner-with-sdks-for-command-line-use">Registering your runner with SDKs for command line use</h4>
+
+<p>To make your runner available on the command line, you register your options
+with a <code class="highlighter-rouge">PipelineRunnerRegistrar</code>. It is easy if you use <code class="highlighter-rouge">@AutoService</code>:</p>
+
+<div class="language-java no-toggle highlighter-rouge"><pre class="highlight"><code><span class="nd">@AutoService</span><span class="o">(</span><span class="n">PipelineRunnerRegistrar</span><span class="o">.</span><span class="na">class</span><span class="o">)</span>
+<span class="kd">public</span> <span class="kd">static</span> <span class="kd">class</span> <span class="nc">MyRunnerRegistrar</span> <span class="kd">implements</span> <span class="n">PipelineRunnerRegistrar</span> <span class="o">{</span>
+  <span class="nd">@Override</span>
+  <span class="kd">public</span> <span class="n">Iterable</span><span class="o">&lt;</span><span class="n">Class</span><span class="o">&lt;?</span> <span class="kd">extends</span> <span class="n">PipelineRunner</span><span class="o">&gt;&gt;</span> <span class="nf">getPipelineRunners</span><span class="o">()</span> <span class="o">{</span>
+    <span class="k">return</span> <span class="n">ImmutableList</span><span class="o">.&lt;</span><span class="n">Class</span><span class="o">&lt;?</span> <span class="kd">extends</span> <span class="n">PipelineRunner</span><span class="o">&gt;&gt;</span><span class="n">of</span><span class="o">(</span><span class="n">MyRunner</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
+  <span class="o">}</span>
+<span class="o">}</span>
+</code></pre>
+</div>
+
+<h3 id="integrating-with-the-python-sdk">Integrating with the Python SDK</h3>
+
+<p>In the Python SDK the registration of the code is not automatic. So there are
+few things to keep in mind when creating a new runner.</p>
+
+<p>Any dependencies on packages for the new runner should be options so create a
+new target in <code class="highlighter-rouge">extra_requires</code> in <code class="highlighter-rouge">setup.py</code> that is needed for the new runner.</p>
+
+<p>All runner code should go in it’s own package in <code class="highlighter-rouge">apache_beam/runners</code> directory.</p>
+
+<p>Register the new runner in the <code class="highlighter-rouge">create_runner</code> function of <code class="highlighter-rouge">runner.py</code> so that the
+partial name is matched with the correct class to be used.</p>
+
+<h2 id="writing-an-sdk-independent-runner">Writing an SDK-independent runner</h2>
+
+<p>There are two aspects to making your runner SDK-independent, able to run
+pipelines written in other languages: The Fn API and the Runner API.</p>
+
+<h3 id="the-fn-api">The Fn API</h3>
+
+<p><em>Design documents:</em></p>
+
+<ul>
+  <li><em><a href="https://s.apache.org/beam-fn-api">https://s.apache.org/beam-fn-api</a></em></li>
+  <li><em><a href="https://s.apache.org/beam-fn-api-processing-a-bundle">https://s.apache.org/beam-fn-api-processing-a-bundle</a></em></li>
+  <li><em><a href="https://s.apache.org/beam-fn-api-send-and-receive-data">https://s.apache.org/beam-fn-api-send-and-receive-data</a></em></li>
+</ul>
+
+<p>To run a user’s pipeline, you need to be able to invoke their UDFs.  The Fn API
+is an RPC interface for the standard UDFs of Beam, implemented using protocol
+buffers over gRPC.</p>
+
+<p>The Fn API includes:</p>
+
+<ul>
+  <li>APIs for registering a subgraph of UDFs</li>
+  <li>APIs for streaming elements of a bundle</li>
+  <li>Shared data formats (key-value pairs, timestamps, iterables, etc)</li>
+</ul>
+
+<p>You are fully welcome to <em>also</em> use the SDK for your language for utility code,
+or provide optimized implementations of bundle processing for same-language
+UDFs.</p>
+
+<h3 id="the-runner-api">The Runner API</h3>
+
+<p>The Runner API is an SDK-independent schema for a pipeline along with RPC
+interfaces for launching a pipeline and checking the status of a job. The RPC
+interfaces are still in development so for now we focus on the SDK-agnostic
+representation of a pipeline. By examining a pipeline only through Runner API
+interfaces, you remove your runner’s dependence on the SDK for its language for
+pipeline analysis and job translation.</p>
+
+<p>To execute such an SDK-independent pipeline, you will need to support the Fn
+API. UDFs are embedded in the pipeline as a specification of the function
+(often just opaque serialized bytes for a particular language) plus a
+specification of an environment that can execute it (essentially a particular
+SDK). So far, this specification is expected to be a URI for a Docker container
+hosting the SDK’s Fn API harness.</p>
+
+<p>You are fully welcome to <em>also</em> use the SDK for your language, which may offer
+useful utility code.</p>
+
+<p>The language-independent definition of a pipeline is described via a protocol
+buffers schema, covered below for reference. But your runner <em>should not</em>
+directly manipulate protobuf messages.  Instead, the Beam codebase provides
+utilities for working with pipelines so that you don’t need to be aware of
+whether or not the pipeline has ever been serialized or transmitted, or what
+language it may have been written in to begin with.</p>
+
+<p><strong>Java</strong></p>
+
+<p>If your runner is Java-based, the tools to interact with pipelines in an
+SDK-agnostic manner are in the <code class="highlighter-rouge">beam-runners-core-construction-java</code>
+artifact, in the <code class="highlighter-rouge">org.apache.beam.runners.core.construction</code> namespace.
+The utilities are named consistently, like so:</p>
+
+<ul>
+  <li><code class="highlighter-rouge">PTransformTranslation</code> - registry of known transforms and standard URNs</li>
+  <li><code class="highlighter-rouge">ParDoTranslation</code> - utilities for working with <code class="highlighter-rouge">ParDo</code> in a
+language-independent manner</li>
+  <li><code class="highlighter-rouge">WindowIntoTranslation</code> - same for <code class="highlighter-rouge">Window</code></li>
+  <li><code class="highlighter-rouge">FlattenTranslation</code> - same for <code class="highlighter-rouge">Flatten</code></li>
+  <li><code class="highlighter-rouge">WindowingStrategyTranslation</code> - same for windowing strategies</li>
+  <li><code class="highlighter-rouge">CoderTranslation</code> - same for coders</li>
+  <li>… etc, etc …</li>
+</ul>
+
+<p>By inspecting transforms only through these classes, your runner will not
+depend on the particulars of the Java SDK.</p>
+
+<h2 id="the-runner-api-protos">The Runner API protos</h2>
+
+<p><a href="https://github.com/apache/beam/blob/master/sdks/common/runner-api/src/main/proto/beam_runner_api.proto">The Runner
+API</a>
+refers to a specific manifestation of the concepts in the Beam model, as a
+protocol buffers schema.  Even though you should not manipulate these messages
+directly, it can be helpful to know the canonical data that makes up a
+pipeline.</p>
+
+<p>Most of the API is exactly the same as the high-level description; you can get
+started implementing a runner without understanding all the low-level details.</p>
+
+<p>The most important takeaway of the Runner API for you is that it is a
+language-independent definition of a Beam pipeline. You will probably always
+interact via a particular SDK’s support code wrapping these definitions with
+sensible idiomatic APIs, but always be aware that this is the specification and
+any other data is not necessarily inherent to the pipeline, but may be
+SDK-specific enrichments (or bugs!).</p>
+
+<p>The UDFs in the pipeline may be written for any Beam SDK, or even multiple in
+the same pipeline. So this is where we will start, taking a bottom-up approach
+to understanding the protocol buffers definitions for UDFs before going back to
+the higher-level, mostly obvious, record definitions.</p>
+
+<h3 id="functionspec-proto"><code class="highlighter-rouge">FunctionSpec</code> proto</h3>
+
+<p>The heart of cross-language portability is the <code class="highlighter-rouge">FunctionSpec</code>. This is a
+language-independent specification of a function, in the usual programming
+sense that includes side effects, etc.</p>
+
+<div class="language-proto no-toggle highlighter-rouge"><pre class="highlight"><code><span class="kd">message</span> <span class="nc">FunctionSpec</span> <span class="p">{</span>
+  <span class="kt">string</span> <span class="n">urn</span><span class="p">;</span>
+  <span class="n">google.protobuf.Any</span> <span class="n">parameter</span><span class="p">;</span>
+<span class="p">}</span>
+</code></pre>
+</div>
+
+<p>A <code class="highlighter-rouge">FunctionSpec</code> includes a URN identifying the function as well as an arbitrary
+fixed parameter. For example the (hypothetical) “max” CombineFn might have the
+URN <code class="highlighter-rouge">urn:beam:combinefn:max:0.1</code> and a parameter that indicates by what
+comparison to take the max.</p>
+
+<p>For most UDFs in a pipeline constructed using a particular language’s SDK, the
+URN will indicate that the SDK must interpret it, for example
+<code class="highlighter-rouge">urn:beam:dofn:javasdk:0.1</code> or <code class="highlighter-rouge">urn:beam:dofn:pythonsdk:0.1</code>. The parameter
+will contain serialized code, such as a Java-serialized <code class="highlighter-rouge">DoFn</code> or a Python
+pickled <code class="highlighter-rouge">DoFn</code>.</p>
+
+<p>A <code class="highlighter-rouge">FunctionSpec</code> is not only for UDFs. It is just a generic way to name/specify
+any function. It is also used as the specification for a <code class="highlighter-rouge">PTransform</code>. But when
+used in a <code class="highlighter-rouge">PTransform</code> it describes a function from <code class="highlighter-rouge">PCollection</code> to <code class="highlighter-rouge">PCollection</code>
+and cannot be specific to an SDK because the runner is in charge of evaluating
+transforms and producing <code class="highlighter-rouge">PCollections</code>.</p>
+
+<h3 id="sdkfunctionspec-proto"><code class="highlighter-rouge">SdkFunctionSpec</code> proto</h3>
+
+<p>When a <code class="highlighter-rouge">FunctionSpec</code> represents a UDF, in general only the SDK that serialized
+it will be guaranteed to understand it. So in that case, it will always come
+with an environment that can understand and execute the function. This is
+represented by the <code class="highlighter-rouge">SdkFunctionSpec</code>.</p>
+
+<div class="language-proto no-toggle highlighter-rouge"><pre class="highlight"><code><span class="kd">message</span> <span class="nc">SdkFunctionSpec</span> <span class="p">{</span>
+  <span class="n">FunctionSpec</span> <span class="n">spec</span><span class="p">;</span>
+  <span class="kt">bytes</span> <span class="n">environment_id</span><span class="p">;</span>
+<span class="p">}</span>
+</code></pre>
+</div>
+
+<p>In the Runner API, many objects are stored by reference. Here in the
+<code class="highlighter-rouge">environment_id</code> is a pointer, local to the pipeline and just made up by the
+SDK that serialized it, that can be dereferenced to yield the actual
+environment proto.</p>
+
+<p>Thus far, an environment is expected to be a Docker container specification for
+an SDK harness that can execute the specified UDF.</p>
+
+<h3 id="primitive-transform-payload-protos">Primitive transform payload protos</h3>
+
+<p>The payload for the primitive transforms are just proto serializations of their
+specifications. Rather than reproduce their full code here, I will just
+highlight the important pieces to show how they fit together.</p>
+
+<p>It is worth emphasizing again that while you probably will not interact
+directly with these payloads, they are the only data that is inherently part of
+the transform.</p>
+
+<h4 id="pardopayload-proto"><code class="highlighter-rouge">ParDoPayload</code> proto</h4>
+
+<p>A <code class="highlighter-rouge">ParDo</code> transform carries its <code class="highlighter-rouge">DoFn</code> in an <code class="highlighter-rouge">SdkFunctionSpec</code> and then
+provides language-independent specifications for its other features - side
+inputs, state declarations, timer declarations, etc.</p>
+
+<div class="language-proto no-toggle highlighter-rouge"><pre class="highlight"><code><span class="kd">message</span> <span class="nc">ParDoPayload</span> <span class="p">{</span>
+  <span class="n">SdkFunctionSpec</span> <span class="n">do_fn</span><span class="p">;</span>
+  <span class="n">map</span><span class="o">&lt;</span><span class="kt">string</span><span class="p">,</span> <span class="n">SideInput</span><span class="err">&gt;</span> <span class="n">side_inputs</span><span class="p">;</span>
+  <span class="n">map</span><span class="o">&lt;</span><span class="kt">string</span><span class="p">,</span> <span class="n">StateSpec</span><span class="err">&gt;</span> <span class="n">state_specs</span><span class="p">;</span>
+  <span class="n">map</span><span class="o">&lt;</span><span class="kt">string</span><span class="p">,</span> <span class="n">TimerSpec</span><span class="err">&gt;</span> <span class="n">timer_specs</span><span class="p">;</span>
+  <span class="o">...</span>
+<span class="p">}</span>
+</code></pre>
+</div>
+
+<h4 id="readpayload-proto"><code class="highlighter-rouge">ReadPayload</code> proto</h4>
+
+<p>A <code class="highlighter-rouge">Read</code> transform carries an <code class="highlighter-rouge">SdkFunctionSpec</code> for its <code class="highlighter-rouge">Source</code> UDF.</p>
+
+<div class="language-proto no-toggle highlighter-rouge"><pre class="highlight"><code><span class="kd">message</span> <span class="nc">ReadPayload</span> <span class="p">{</span>
+  <span class="n">SdkFunctionSpec</span> <span class="n">source</span><span class="p">;</span>
+  <span class="o">...</span>
+<span class="p">}</span>
+</code></pre>
+</div>
+
+<h4 id="windowintopayload-proto"><code class="highlighter-rouge">WindowIntoPayload</code> proto</h4>
+
+<p>A <code class="highlighter-rouge">Window</code> transform carries an <code class="highlighter-rouge">SdkFunctionSpec</code> for its <code class="highlighter-rouge">WindowFn</code> UDF. It is
+part of the Fn API that the runner passes this UDF along and tells the SDK
+harness to use it to assign windows (as opposed to merging).</p>
+
+<div class="language-proto no-toggle highlighter-rouge"><pre class="highlight"><code><span class="kd">message</span> <span class="nc">WindowIntoPayload</span> <span class="p">{</span>
+  <span class="n">SdkFunctionSpec</span> <span class="n">window_fn</span><span class="p">;</span>
+  <span class="o">...</span>
+<span class="p">}</span>
+</code></pre>
+</div>
+
+<h4 id="combinepayload-proto"><code class="highlighter-rouge">CombinePayload</code> proto</h4>
+
+<p><code class="highlighter-rouge">Combine</code> is not a primitive. But non-primitives are perfectly able to carry
+additional information for better optimization. The most important thing that a
+<code class="highlighter-rouge">Combine</code> transform carries is the <code class="highlighter-rouge">CombineFn</code> in an <code class="highlighter-rouge">SdkFunctionSpec</code> record.
+In order to effectively carry out the optimizations desired, it is also
+necessary to know the coder for intermediate accumulations, so it also carries
+a reference to this coder.</p>
+
+<div class="language-proto no-toggle highlighter-rouge"><pre class="highlight"><code><span class="kd">message</span> <span class="nc">CombinePayload</span> <span class="p">{</span>
+  <span class="n">SdkFunctionSpec</span> <span class="n">combine_fn</span><span class="p">;</span>
+  <span class="kt">string</span> <span class="n">accumulator_coder_id</span><span class="p">;</span>
+  <span class="o">...</span>
+<span class="p">}</span>
+</code></pre>
+</div>
+
+<h3 id="ptransform-proto"><code class="highlighter-rouge">PTransform</code> proto</h3>
+
+<p>A <code class="highlighter-rouge">PTransform</code> is a function from <code class="highlighter-rouge">PCollection</code> to <code class="highlighter-rouge">PCollection</code>. This is
+represented in the proto using a FunctionSpec. Note that this is not an
+<code class="highlighter-rouge">SdkFunctionSpec</code>, since it is the runner that observes these. They will never
+be passed back to an SDK harness; they do not represent a UDF.</p>
+
+<div class="language-proto no-toggle highlighter-rouge"><pre class="highlight"><code><span class="kd">message</span> <span class="nc">PTransform</span> <span class="p">{</span>
+  <span class="n">FunctionSpec</span> <span class="n">spec</span><span class="p">;</span>
+  <span class="k">repeated</span> <span class="kt">string</span> <span class="n">subtransforms</span><span class="p">;</span>
+ 
+  <span class="c1">// Maps from local string names to PCollection ids
+</span>  <span class="n">map</span><span class="o">&lt;</span><span class="kt">string</span><span class="p">,</span> <span class="kt">bytes</span><span class="err">&gt;</span> <span class="n">inputs</span><span class="p">;</span>
+  <span class="n">map</span><span class="o">&lt;</span><span class="kt">string</span><span class="p">,</span> <span class="kt">bytes</span><span class="err">&gt;</span> <span class="n">outputs</span><span class="p">;</span>
+  <span class="o">...</span>
+<span class="p">}</span>
+</code></pre>
+</div>
+
+<p>A <code class="highlighter-rouge">PTransform</code> may have subtransforms if it is a composite, in which case the
+<code class="highlighter-rouge">FunctionSpec</code> may be omitted since the subtransforms define its behavior.</p>
+
+<p>The input and output <code class="highlighter-rouge">PCollections</code> are unordered and referred to by a local
+name. The SDK decides what this name is, since it will likely be embedded in
+serialized UDFs.</p>
+
+<h3 id="pcollection-proto"><code class="highlighter-rouge">PCollection</code> proto</h3>
+
+<p>A <code class="highlighter-rouge">PCollection</code> just stores a coder, windowing strategy, and whether or not it
+is bounded.</p>
+
+<div class="language-proto no-toggle highlighter-rouge"><pre class="highlight"><code><span class="kd">message</span> <span class="nc">PCollection</span> <span class="p">{</span>
+  <span class="kt">string</span> <span class="n">coder_id</span><span class="p">;</span>
+  <span class="n">IsBounded</span> <span class="n">is_bounded</span><span class="p">;</span>
+  <span class="kt">string</span> <span class="n">windowing_strategy_id</span><span class="p">;</span>
+  <span class="o">...</span>
+<span class="p">}</span>
+</code></pre>
+</div>
+
+<h3 id="coder-proto"><code class="highlighter-rouge">Coder</code> proto</h3>
+
+<p>This is a very interesting proto. A coder is a parameterized function that may
+only be understood by a particular SDK, hence an <code class="highlighter-rouge">SdkFunctionSpec</code>, but also
+may have component coders that fully define it. For example, a <code class="highlighter-rouge">ListCoder</code> is
+only a meta-format, while <code class="highlighter-rouge">ListCoder(VarIntCoder)</code> is a fully specified format.</p>
+
+<div class="language-proto no-toggle highlighter-rouge"><pre class="highlight"><code><span class="kd">message</span> <span class="nc">Coder</span> <span class="p">{</span>
+  <span class="n">SdkFunctionSpec</span> <span class="n">spec</span><span class="p">;</span>
+  <span class="k">repeated</span> <span class="kt">string</span> <span class="n">component_coder_ids</span><span class="p">;</span>
+<span class="p">}</span>
+</code></pre>
+</div>
+
+<h2 id="the-runner-api-rpcs">The Runner API RPCs</h2>
+
+<p>While your language’s SDK will probably insulate you from touching the Runner
+API protos directly, you may need to implement adapters for your runner, to
+expose it to another language. So this section covers proto that you will
+possibly interact with quite directly.</p>
+
+<p>The specific manner in which the existing runner method calls will be expressed
+as RPCs is not implemented as proto yet. This RPC layer is to enable, for
+example, building a pipeline using the Python SDK and launching it on a runner
+that is written in Java. It is expected that a small Python shim will
+communicate with a Java process or service hosting the Runner API.</p>
+
+<p>The RPCs themselves will necessarily follow the existing APIs of PipelineRunner
+and PipelineResult, but altered to be the minimal backend channel, versus a
+rich and convenient API.</p>
+
+<h3 id="pipelinerunnerrunpipeline-rpc"><code class="highlighter-rouge">PipelineRunner.run(Pipeline)</code> RPC</h3>
+
+<p>This will take the same form, but <code class="highlighter-rouge">PipelineOptions</code> will have to be serialized
+to JSON (or a proto <code class="highlighter-rouge">Struct</code>) and passed along.</p>
+
+<div class="language-proto no-toggle highlighter-rouge"><pre class="highlight"><code><span class="kd">message</span> <span class="nc">RunPipelineRequest</span> <span class="p">{</span>
+  <span class="n">Pipeline</span> <span class="n">pipeline</span><span class="p">;</span>
+  <span class="n">Struct</span> <span class="n">pipeline_options</span><span class="p">;</span>
+<span class="p">}</span>
+</code></pre>
+</div>
+
+<div class="language-proto no-toggle highlighter-rouge"><pre class="highlight"><code><span class="kd">message</span> <span class="nc">RunPipelineResponse</span> <span class="p">{</span>
+  <span class="kt">bytes</span> <span class="n">pipeline_id</span><span class="p">;</span>
+
+  <span class="c1">// TODO: protocol for rejecting pipelines that cannot be executed
+</span>  <span class="c1">// by this runner. May just be REJECTED job state with error message.
+</span> 
+  <span class="c1">// totally opaque to the SDK; for the shim to interpret
+</span>  <span class="n">Any</span> <span class="n">contents</span><span class="p">;</span>
+<span class="p">}</span>
+</code></pre>
+</div>
+
+<h3 id="pipelineresult-aka-job-api"><code class="highlighter-rouge">PipelineResult</code> aka “Job API”</h3>
+
+<p>The two core pieces of functionality in this API today are getting the state of
+a job and canceling the job. It is very much likely to evolve, for example to
+be generalized to support draining a job (stop reading input and let watermarks
+go to infinity). Today, verifying our test framework benefits (but does not
+depend upon wholly) querying metrics over this channel.</p>
+
+<div class="language-proto no-toggle highlighter-rouge"><pre class="highlight"><code><span class="kd">message</span> <span class="nc">CancelPipelineRequest</span> <span class="p">{</span>
+  <span class="kt">bytes</span> <span class="n">pipeline_id</span><span class="p">;</span>
+  <span class="o">...</span>
+<span class="p">}</span>
+ 
+<span class="kd">message</span> <span class="nc">GetStateRequest</span> <span class="p">{</span>
+  <span class="kt">bytes</span> <span class="n">pipeline_id</span><span class="p">;</span>
+  <span class="o">...</span>
+<span class="p">}</span>
+ 
+<span class="kd">message</span> <span class="nc">GetStateResponse</span> <span class="p">{</span>
+  <span class="n">JobState</span> <span class="n">state</span><span class="p">;</span>
+  <span class="o">...</span>
+<span class="p">}</span>
+ 
+<span class="kd">enum</span> <span class="n">JobState</span> <span class="p">{</span>
+  <span class="o">...</span>
+<span class="p">}</span>
+</code></pre>
+</div>
+
+
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