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<!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta http-equiv=x-ua-compatible content="IE=edge"><meta name=viewport content="width=device-width,initial-scale=1"><title>When in processing time?</title><meta name=description content="Apache Beam is an open source, unified model and set of language-specific SDKs for defining and executing data processing workflows, and also data ingestion and integration flows, supporting Enterprise Integration Patterns (EIPs) and Domain Specific Languages (DSLs). Dataflow pipelines simplify the mechanics of large-scale batch and streaming data processing and can run on a number of runtimes like Apache Flink, Apache Spark, and Google Cloud Dataflow (a cloud service). Beam also brings DSL in different languages, allowing users to easily implement their data integration processes."><link href="https://fonts.googleapis.com/css?family=Roboto:100,300,400,500,700" rel=stylesheet><link rel=preload href=/scss/main.min.408fddfe3e8a45f87a5a8c9a839d77db667c1c534e5e5cd0d957ffc3dd6c14cf.css as=style><link href=/scss/main.min.408fddfe3e8a45f87a5a8c9a839d77db667c1c534e5e5cd0d957ffc3dd6c14cf.css rel=stylesheet integrity><script src=https://code.jquery.com/jquery-2.2.4.min.js></script><style>.body__contained img{max-width:100%}</style><script type=text/javascript src=/js/bootstrap.min.2979f9a6e32fc42c3e7406339ee9fe76b31d1b52059776a02b4a7fa6a4fd280a.js defer></script>
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back to collapsed details</a><h4>When in processing time?</h4><div class=table-container><div class="table-left big-left"><table><tr><th></th></tr><tr><th>Configurable triggering</th></tr><tr><th>Event-time triggers</th></tr><tr><th>Processing-time triggers</th></tr><tr><th>Count triggers</th></tr><tr><th>Composite triggers</th></tr><tr><th>Allowed lateness</th></tr><tr><th>Timers</th></tr></table></div><div class="table-right big-right"><div id=when-in-processing-time class="table-headers big-headers"><table><tr><th>Google Cloud Dataflow</th><th>Apache Flink</th><th>Apache Spark (RDD/DStream based)</th><th>Apache Spark Structured Streaming (Dataset based)</th><th>Apache Samza</th><th>Apache Nemo</th><th>Hazelcast Jet</th><th>Twister2</th><th>Python Direct FnRunner</th><th>Go Direct Runner</th></tr></table></div><div id=when-in-processing-time class="table-center big-center"><table><tr><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br>Fully supported in streaming mode. In batch mode, intermediate trigger firings are effectively meaningless.</td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#f9f9f9;border-color:#d8d8d8><b><p>Partially : fully supported in batch mode</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td></tr><tr><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : yes in streaming, fixed granularity in batch</p></b><br>Fully supported in streaming mode. In batch mode, currently watermark progress jumps from the beginning of time to the end of time once the input has been fully consumed, thus no additional triggering granularity is available.</td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#f9f9f9;border-color:#d8d8d8><b><p>Partially : fully supported in batch mode</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td></tr><tr><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : yes in streaming, fixed granularity in batch</p></b><br>Fully supported in streaming mode. In batch mode, from the perspective of triggers, processing time currently jumps from the beginning of time to the end of time once the input has been fully consumed, thus no additional triggering granularity is available.</td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : This is Spark streaming's native model</p></b><br>Spark processes streams in micro-batches. The micro-batch size is actually a pre-set, fixed, time interval. Currently, the runner takes the first window size in the pipeline and sets it's size as the batch interval. Any following window operations will be considered processing time windows and will affect triggering.</td><td style=background-color:#f9f9f9;border-color:#d8d8d8><b><p>Partially : fully supported in batch mode</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td></tr><tr><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br>Fully supported in streaming mode. In batch mode, elements are processed in the largest bundles possible, so count-based triggers are effectively meaningless.</td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#f9f9f9;border-color:#d8d8d8><b><p>Partially : fully supported in batch mode</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td></tr><tr><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#f9f9f9;border-color:#d8d8d8><b><p>Partially : fully supported in batch mode</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#f9f9f9;border-color:#d8d8d8><b><p>Partially :</p></b><br></td></tr><tr><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br>Fully supported in streaming mode. In batch mode no data is ever late.</td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#e1e0e0;border-color:#bcbcbc><b><p>No</p></b><br></td><td style=background-color:#e1e0e0;border-color:#bcbcbc><b><p>No : no streaming support in the runner</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#fff;border-color:#f6f6f6><b><p>Yes : fully supported</p></b><br></td><td style=background-color:#f9f9f9;border-color:#d8d8d8><b><p>Partially :</p></b><br></td></tr><tr><td style=background-color:#f9f9f9;border-color:#d8d8d8><b><p>Partially : non-merging windows</p></b><br>Dataflow supports timers in non-merging windows.</td><td style=background-color:#f9f9f9;border-color:#d8d8d8><b><p>Partially : non-merging windows</p></b><br>The Flink Runner supports timers in non-merging windows.</td><td style=background-color:#f9f9f9;border-color:#d8d8d8><b><p>Partially : fully supported in batch mode</p></b><br></td><td style=background-color:#e1e0e0;border-color:#bcbcbc><b><p>No : not implemented</p></b><br></td><td style=background-color:#f9f9f9;border-color:#d8d8d8><b><p>Partially : non-merging windows</p></b><br>The Samza Runner supports timers in non-merging windows.</td><td style=background-color:#e1e0e0;border-color:#bcbcbc><b><p>No : not implemented</p></b><br></td><td style=background-color:#f9f9f9;border-color:#d8d8d8><b><p>Partially : non-merging windows</p></b><br></td><td style=background-color:#f9f9f9;border-color:#d8d8d8><b><p>Partially :</p></b><br></td></tr></table></div></div></div><script>$(".table-headers").bind("wheel",function(){$("#"+this.id+".table-center").scrollLeft($(this).scrollLeft())}),$(".table-center").bind("wheel",function(){$("#"+this.id+".table-headers").scrollLeft($(this).scrollLeft())})</script><div class=feedback><p class=update>Last updated on 2024/05/03</p><h3>Have you found everything you were looking for?</h3><p class=description>Was it all useful and clear? 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