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class="menu__link header-linkedin-link" aria-label="Hudi Linkedin Page"><div class="labelWrapperDropdown_Mqbj"></div></a></li></ul></div><div class="navbar-sidebar__item menu"><button type="button" class="clean-btn navbar-sidebar__back">← Back to main menu</button></div></div></div></nav><div class="main-wrapper blog-wrapper blog-post-page"><div class="container margin-vert--lg"><div class="row"><main class="col col--9 col--offset-2" itemscope="" itemtype="http://schema.org/Blog"><article itemprop="blogPost" itemscope="" itemtype="http://schema.org/BlogPosting"><header class="postHeader_Ipb1"><div><h1 class="blogPostTitle_RC3s" itemprop="headline"><h1 class="blogPostPageTitle_bKZt" itemprop="headline">Asynchronous Clustering using Hudi</h1></h1><div class="blogInfo_1FPd margin-top--sm margin-bottom--sm"><div class="blogPostText_jBA8 row"><time datetime="2021-08-23T00:00:00.000Z" itemprop="datePublished">August 23, 2021</time><div><div><div><a itemprop="url"><span class="blogPostAuthorsList_dlEG" itemprop="name">codope</span></a></div></div></div></div><div class="blogPostData_A2Le">7 min read</div></div></div><ul class="authorTimeTags_oN88 padding--none margin-left--sm tagsWrapperPostPage_VdId"><li class="tag_MgfY tagPostPage_gnvv"><a class="tag_WK-t tagRegular_LXbV" href="/blog/tags/design">design</a></li><li class="tag_MgfY tagPostPage_gnvv"><a class="tag_WK-t tagRegular_LXbV" href="/blog/tags/clustering">clustering</a></li><li class="tag_MgfY tagPostPage_gnvv"><a class="tag_WK-t tagRegular_LXbV" href="/blog/tags/apache-hudi">apache hudi</a></li></ul></header><div class="markdown" itemprop="articleBody"><p>In one of the <a href="/blog/2021/01/27/hudi-clustering-intro">previous blog</a> posts, we introduced a new |
| kind of table service called clustering to reorganize data for improved query performance without compromising on |
| ingestion speed. We learnt how to setup inline clustering. In this post, we will discuss what has changed since then and |
| see how asynchronous clustering can be setup using HoodieClusteringJob as well as DeltaStreamer utility.</p><h2 class="anchor anchorWithStickyNavbar_y2LR" id="introduction">Introduction<a class="hash-link" href="#introduction" title="Direct link to heading"></a></h2><p>On a high level, clustering creates a plan based on a configurable strategy, groups eligible files based on specific |
| criteria and then executes the plan. Hudi supports <a href="https://hudi.apache.org/docs/concurrency_control#enabling-multi-writing" target="_blank" rel="noopener noreferrer">multi-writers</a> which provides |
| snapshot isolation between multiple table services, thus allowing writers to continue with ingestion while clustering |
| runs in the background. For a more detailed overview of the clustering architecture please check out the previous blog |
| post.</p><h2 class="anchor anchorWithStickyNavbar_y2LR" id="clustering-strategies">Clustering Strategies<a class="hash-link" href="#clustering-strategies" title="Direct link to heading"></a></h2><p>As mentioned before, clustering plan as well as execution depends on configurable strategy. These strategies can be |
| broadly classified into three types: clustering plan strategy, execution strategy and update strategy.</p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="plan-strategy">Plan Strategy<a class="hash-link" href="#plan-strategy" title="Direct link to heading"></a></h3><p>This strategy comes into play while creating clustering plan. It helps to decide what file groups should be clustered. |
| Let's look at different plan strategies that are available with Hudi. Note that these strategies are easily pluggable |
| using this <a href="/docs/next/configurations#hoodieclusteringplanstrategyclass">config</a>.</p><ol><li><code>SparkSizeBasedClusteringPlanStrategy</code>: It selects file slices based on |
| the <a href="/docs/next/configurations/#hoodieclusteringplanstrategysmallfilelimit">small file limit</a> |
| of base files and creates clustering groups upto max file size allowed per group. The max size can be specified using |
| this <a href="/docs/next/configurations/#hoodieclusteringplanstrategymaxbytespergroup">config</a>. This |
| strategy is useful for stitching together medium-sized files into larger ones to reduce lot of files spread across |
| cold partitions.</li><li><code>SparkRecentDaysClusteringPlanStrategy</code>: It looks back previous 'N' days partitions and creates a plan that will |
| cluster the 'small' file slices within those partitions. This is the default strategy. It could be useful when the |
| workload is predictable and data is partitioned by time.</li><li><code>SparkSelectedPartitionsClusteringPlanStrategy</code>: In case you want to cluster only specific partitions within a range, |
| no matter how old or new are those partitions, then this strategy could be useful. To use this strategy, one needs |
| to set below two configs additionally (both begin and end partitions are inclusive):</li></ol><div class="codeBlockContainer_J+bg theme-code-block"><div class="codeBlockContent_csEI"><pre tabindex="0" class="prism-code language-undefined codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">hoodie.clustering.plan.strategy.cluster.begin.partition</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">hoodie.clustering.plan.strategy.cluster.end.partition</span><br></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><div class="admonition admonition-note alert alert--secondary"><div class="admonition-heading"><h5><span class="admonition-icon"><svg xmlns="http://www.w3.org/2000/svg" width="14" height="16" viewBox="0 0 14 16"><path fill-rule="evenodd" d="M6.3 5.69a.942.942 0 0 1-.28-.7c0-.28.09-.52.28-.7.19-.18.42-.28.7-.28.28 0 .52.09.7.28.18.19.28.42.28.7 0 .28-.09.52-.28.7a1 1 0 0 1-.7.3c-.28 0-.52-.11-.7-.3zM8 7.99c-.02-.25-.11-.48-.31-.69-.2-.19-.42-.3-.69-.31H6c-.27.02-.48.13-.69.31-.2.2-.3.44-.31.69h1v3c.02.27.11.5.31.69.2.2.42.31.69.31h1c.27 0 .48-.11.69-.31.2-.19.3-.42.31-.69H8V7.98v.01zM7 2.3c-3.14 0-5.7 2.54-5.7 5.68 0 3.14 2.56 5.7 5.7 5.7s5.7-2.55 5.7-5.7c0-3.15-2.56-5.69-5.7-5.69v.01zM7 .98c3.86 0 7 3.14 7 7s-3.14 7-7 7-7-3.12-7-7 3.14-7 7-7z"></path></svg></span>note</h5></div><div class="admonition-content"><p>All the strategies are partition-aware and the latter two are still bound by the size limits of the first strategy.</p></div></div><h3 class="anchor anchorWithStickyNavbar_y2LR" id="execution-strategy">Execution Strategy<a class="hash-link" href="#execution-strategy" title="Direct link to heading"></a></h3><p>After building the clustering groups in the planning phase, Hudi applies execution strategy, for each group, primarily |
| based on sort columns and size. The strategy can be specified using this <a href="/docs/next/configurations/#hoodieclusteringexecutionstrategyclass">config</a>.</p><p><code>SparkSortAndSizeExecutionStrategy</code> is the default strategy. Users can specify the columns to sort the data by, when |
| clustering using |
| this <a href="/docs/next/configurations/#hoodieclusteringplanstrategysortcolumns">config</a>. Apart from |
| that, we can also set <a href="/docs/next/configurations/#hoodieparquetmaxfilesize">max file size</a> |
| for the parquet files produced due to clustering. The strategy uses bulk insert to write data into new files, in which |
| case, Hudi implicitly uses a partitioner that does sorting based on specified columns. In this way, the strategy changes |
| the data layout in a way that not only improves query performance but also balance rewrite overhead automatically.</p><p>Now this strategy can be executed either as a single spark job or multiple jobs depending on number of clustering groups |
| created in the planning phase. By default, Hudi will submit multiple spark jobs and union the results. In case you want |
| to force Hudi to use single spark job, set the execution strategy |
| class <a href="/docs/next/configurations/#hoodieclusteringexecutionstrategyclass">config</a> |
| to <code>SingleSparkJobExecutionStrategy</code>.</p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="update-strategy">Update Strategy<a class="hash-link" href="#update-strategy" title="Direct link to heading"></a></h3><p>Currently, clustering can only be scheduled for tables/partitions not receiving any concurrent updates. By default, |
| the <a href="/docs/next/configurations/#hoodieclusteringupdatesstrategy">config for update strategy</a> is |
| set to <strong><em>SparkRejectUpdateStrategy</em></strong>. If some file group has updates during clustering then it will reject updates and |
| throw an exception. However, in some use-cases updates are very sparse and do not touch most file groups. The default |
| strategy to simply reject updates does not seem fair. In such use-cases, users can set the config to <strong><em>SparkAllowUpdateStrategy</em></strong>.</p><p>We discussed the critical strategy configurations. All other configurations related to clustering are |
| listed <a href="/docs/next/configurations/#Clustering-Configs">here</a>. Out of this list, a few |
| configurations that will be very useful are:</p><table><thead><tr><th>Config key</th><th>Remarks</th><th>Default</th></tr></thead><tbody><tr><td><code>hoodie.clustering.async.enabled</code></td><td>Enable running of clustering service, asynchronously as writes happen on the table.</td><td>False</td></tr><tr><td><code>hoodie.clustering.async.max.commits</code></td><td>Control frequency of async clustering by specifying after how many commits clustering should be triggered.</td><td>4</td></tr><tr><td><code>hoodie.clustering.preserve.commit.metadata</code></td><td>When rewriting data, preserves existing _hoodie_commit_time. This means users can run incremental queries on clustered data without any side-effects.</td><td>False</td></tr></tbody></table><h2 class="anchor anchorWithStickyNavbar_y2LR" id="asynchronous-clustering">Asynchronous Clustering<a class="hash-link" href="#asynchronous-clustering" title="Direct link to heading"></a></h2><p>Previously, we have seen how users |
| can <a href="/blog/2021/01/27/hudi-clustering-intro#setting-up-clustering">setup inline clustering</a>. |
| Additionally, users can |
| leverage <a href="https://cwiki.apache.org/confluence/display/HUDI/RFC+-+19+Clustering+data+for+freshness+and+query+performance#RFC19Clusteringdataforfreshnessandqueryperformance-SetupforAsyncclusteringJob" target="_blank" rel="noopener noreferrer">HoodieClusteringJob</a> |
| to setup 2-step asynchronous clustering.</p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="hoodieclusteringjob">HoodieClusteringJob<a class="hash-link" href="#hoodieclusteringjob" title="Direct link to heading"></a></h3><p>With the release of Hudi version 0.9.0, we can schedule as well as execute clustering in the same step. We just need to |
| specify the <code>—mode</code> or <code>-m</code> option. There are three modes:</p><ol><li><code>schedule</code>: Make a clustering plan. This gives an instant which can be passed in execute mode.</li><li><code>execute</code>: Execute a clustering plan at given instant which means --instant-time is required here.</li><li><code>scheduleAndExecute</code>: Make a clustering plan first and execute that plan immediately.</li></ol><p>Note that to run this job while the original writer is still running, please enable multi-writing:</p><div class="codeBlockContainer_J+bg theme-code-block"><div class="codeBlockContent_csEI"><pre tabindex="0" class="prism-code language-undefined codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">hoodie.write.concurrency.mode=optimistic_concurrency_control</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">hoodie.write.lock.provider=org.apache.hudi.client.transaction.lock.ZookeeperBasedLockProvider</span><br></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>A sample spark-submit command to setup HoodieClusteringJob is as below:</p><div class="codeBlockContainer_J+bg language-bash theme-code-block"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">spark-submit </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">--class org.apache.hudi.utilities.HoodieClusteringJob </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">/path/to/hudi-utilities-bundle/target/hudi-utilities-bundle_2.12-0.9.0-SNAPSHOT.jar </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">--props /path/to/config/clusteringjob.properties </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">--mode scheduleAndExecute </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">--base-path /path/to/hudi_table/basePath </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">--table-name hudi_table_schedule_clustering </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">--spark-memory 1g</span><br></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>A sample <code>clusteringjob.properties</code> file:</p><div class="codeBlockContainer_J+bg theme-code-block"><div class="codeBlockContent_csEI"><pre tabindex="0" class="prism-code language-undefined codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">hoodie.clustering.async.enabled=true</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">hoodie.clustering.async.max.commits=4</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">hoodie.clustering.plan.strategy.target.file.max.bytes=1073741824</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">hoodie.clustering.plan.strategy.small.file.limit=629145600</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">hoodie.clustering.execution.strategy.class=org.apache.hudi.client.clustering.run.strategy.SparkSortAndSizeExecutionStrategy</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">hoodie.clustering.plan.strategy.sort.columns=column1,column2</span><br></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h3 class="anchor anchorWithStickyNavbar_y2LR" id="hoodiedeltastreamer">HoodieDeltaStreamer<a class="hash-link" href="#hoodiedeltastreamer" title="Direct link to heading"></a></h3><p>This brings us to our users' favorite utility in Hudi. Now, we can trigger asynchronous clustering with DeltaStreamer. |
| Just set the <code>hoodie.clustering.async.enabled</code> config to true and specify other clustering config in properties file |
| whose location can be pased as <code>—props</code> when starting the deltastreamer (just like in the case of HoodieClusteringJob).</p><p>A sample spark-submit command to setup HoodieDeltaStreamer is as below:</p><div class="codeBlockContainer_J+bg language-bash theme-code-block"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">spark-submit </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">--class org.apache.hudi.utilities.deltastreamer.HoodieDeltaStreamer </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">/path/to/hudi-utilities-bundle/target/hudi-utilities-bundle_2.12-0.9.0-SNAPSHOT.jar </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">--props /path/to/config/clustering_kafka.properties </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">--schemaprovider-class org.apache.hudi.utilities.schema.SchemaRegistryProvider </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">--source-class org.apache.hudi.utilities.sources.AvroKafkaSource </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">--source-ordering-field impresssiontime </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">--table-type COPY_ON_WRITE </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">--target-base-path /path/to/hudi_table/basePath </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">--target-table impressions_cow_cluster </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">--op INSERT </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">--hoodie-conf hoodie.clustering.async.enabled</span><span class="token operator">=</span><span class="token plain">true </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">--continuous</span><br></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h3 class="anchor anchorWithStickyNavbar_y2LR" id="spark-structured-streaming">Spark Structured Streaming<a class="hash-link" href="#spark-structured-streaming" title="Direct link to heading"></a></h3><p>We can also enable asynchronous clustering with Spark structured streaming sink as shown below. </p><div class="codeBlockContainer_J+bg language-scala theme-code-block"><div class="codeBlockContent_csEI scala"><pre tabindex="0" class="prism-code language-scala codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">val commonOpts = Map(</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> "hoodie.insert.shuffle.parallelism" -> "4",</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> "hoodie.upsert.shuffle.parallelism" -> "4",</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> DataSourceWriteOptions.RECORDKEY_FIELD.key -> "_row_key",</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> DataSourceWriteOptions.PARTITIONPATH_FIELD.key -> "partition",</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> DataSourceWriteOptions.PRECOMBINE_FIELD.key -> "timestamp",</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> HoodieWriteConfig.TBL_NAME.key -> "hoodie_test"</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">)</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain" style="display:inline-block"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">def getAsyncClusteringOpts(isAsyncClustering: String, </span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> clusteringNumCommit: String, </span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> executionStrategy: String):Map[String, String] = {</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> commonOpts + (DataSourceWriteOptions.ASYNC_CLUSTERING_ENABLE.key -> isAsyncClustering,</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> HoodieClusteringConfig.ASYNC_CLUSTERING_MAX_COMMITS.key -> clusteringNumCommit,</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> HoodieClusteringConfig.EXECUTION_STRATEGY_CLASS_NAME.key -> executionStrategy</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> )</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">}</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain" style="display:inline-block"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">def initStreamingWriteFuture(hudiOptions: Map[String, String]): Future[Unit] = {</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> val streamingInput = // define the source of streaming</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> Future {</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> println("streaming starting")</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> streamingInput</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> .writeStream</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> .format("org.apache.hudi")</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> .options(hudiOptions)</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> .option("checkpointLocation", basePath + "/checkpoint")</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> .mode(Append)</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> .start()</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> .awaitTermination(10000)</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> println("streaming ends")</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> }</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">}</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain" style="display:inline-block"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">def structuredStreamingWithClustering(): Unit = {</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> val df = //generate data frame</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> val hudiOptions = getClusteringOpts("true", "1", "org.apache.hudi.client.clustering.run.strategy.SparkSortAndSizeExecutionStrategy")</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> val f1 = initStreamingWriteFuture(hudiOptions)</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> Await.result(f1, Duration.Inf)</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">}</span><br></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h2 class="anchor anchorWithStickyNavbar_y2LR" id="conclusion-and-future-work">Conclusion and Future Work<a class="hash-link" href="#conclusion-and-future-work" title="Direct link to heading"></a></h2><p>In this post, we discussed different clustering strategies and how to setup asynchronous clustering. The story is not |
| over yet and future work entails:</p><ul><li>Support clustering with updates.</li><li>CLI tools to support clustering.</li></ul><p>Please follow this <a href="https://issues.apache.org/jira/browse/HUDI-1042" target="_blank" rel="noopener noreferrer">JIRA</a> to learn more about active development on |
| this issue. We look forward to contributions from the community. Hope you enjoyed this post. Put your Hudi on and keep |
| streaming!</p></div><footer0 class="row docusaurus-mt-lg blogPostDetailsFull_2lop"><div class="col margin-top--sm"><a href="https://github.com/apache/hudi/edit/asf-site/website/blog/blog/2021-08-23-async-clustering.md" target="_blank" rel="noreferrer noopener" class="theme-edit-this-page"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" class="iconEdit_mS5F" aria-hidden="true"><g><path d="m34.5 11.7l-3 3.1-6.3-6.3 3.1-3q0.5-0.5 1.2-0.5t1.1 0.5l3.9 3.9q0.5 0.4 0.5 1.1t-0.5 1.2z m-29.5 17.1l18.4-18.5 6.3 6.3-18.4 18.4h-6.3v-6.2z"></path></g></svg>Edit this page</a></div></footer0></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Blog post page navigation"><div class="pagination-nav__item"><a class="pagination-nav__link" href="/blog/2021/09/01/building-eb-level-data-lake-using-hudi-at-bytedance"><div class="pagination-nav__sublabel">Newer Post</div><div class="pagination-nav__label">« <!-- -->Building an ExaByte-level Data Lake Using Apache Hudi at 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