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| <i class="fa fa-chevron-down"></i></a></li><div id=Migration class=collapse><ul class=sub-menu><li><a href=../table-migration/>Overview</a></li><li><a href=../hive-migration/>Hive Migration</a></li><li><a href=../delta-lake-migration/>Delta Lake Migration</a></li></ul></div><li><a href=https://iceberg.apache.org/docs/fd-update-javadocs/../../javadoc/latest><span>Javadoc</span></a></li><li><a target=_blank href=https://py.iceberg.apache.org/><span>PyIceberg</span></a></li></div></div><div id=content class=markdown-body><div class=margin-for-toc><h1 id=spark-structured-streaming>Spark Structured Streaming</h1><p>Iceberg uses Apache Spark’s DataSourceV2 API for data source and catalog implementations. Spark DSv2 is an evolving API |
| with different levels of support in Spark versions.</p><p>As of Spark 3, DataFrame reads and writes are supported.</p><h2 id=streaming-reads>Streaming Reads</h2><p>Iceberg supports processing incremental data in spark structured streaming jobs which starts from a historical timestamp:</p><div class=highlight><pre tabindex=0 style=color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-scala data-lang=scala><span style=display:flex><span><span style=color:#66d9ef>val</span> df <span style=color:#66d9ef>=</span> spark<span style=color:#f92672>.</span>readStream |
| </span></span><span style=display:flex><span> <span style=color:#f92672>.</span>format<span style=color:#f92672>(</span><span style=color:#e6db74>"iceberg"</span><span style=color:#f92672>)</span> |
| </span></span><span style=display:flex><span> <span style=color:#f92672>.</span>option<span style=color:#f92672>(</span><span style=color:#e6db74>"stream-from-timestamp"</span><span style=color:#f92672>,</span> <span style=color:#a6e22e>Long</span><span style=color:#f92672>.</span>toString<span style=color:#f92672>(</span>streamStartTimestamp<span style=color:#f92672>))</span> |
| </span></span><span style=display:flex><span> <span style=color:#f92672>.</span>load<span style=color:#f92672>(</span><span style=color:#e6db74>"database.table_name"</span><span style=color:#f92672>)</span> |
| </span></span></code></pre></div><div class=warning>Iceberg only supports reading data from append snapshots. Overwrite snapshots cannot be processed and will cause an exception by default. Overwrites may be ignored by setting <code>streaming-skip-overwrite-snapshots=true</code>. Similarly, delete snapshots will cause an exception by default, and deletes may be ignored by setting <code>streaming-skip-delete-snapshots=true</code>.</div><h2 id=streaming-writes>Streaming Writes</h2><p>To write values from streaming query to Iceberg table, use <code>DataStreamWriter</code>:</p><div class=highlight><pre tabindex=0 style=color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-scala data-lang=scala><span style=display:flex><span><span style=color:#66d9ef>val</span> tableIdentifier<span style=color:#66d9ef>:</span> <span style=color:#66d9ef>String</span> <span style=color:#f92672>=</span> <span style=color:#f92672>...</span> |
| </span></span><span style=display:flex><span>data<span style=color:#f92672>.</span>writeStream |
| </span></span><span style=display:flex><span> <span style=color:#f92672>.</span>format<span style=color:#f92672>(</span><span style=color:#e6db74>"iceberg"</span><span style=color:#f92672>)</span> |
| </span></span><span style=display:flex><span> <span style=color:#f92672>.</span>outputMode<span style=color:#f92672>(</span><span style=color:#e6db74>"append"</span><span style=color:#f92672>)</span> |
| </span></span><span style=display:flex><span> <span style=color:#f92672>.</span>trigger<span style=color:#f92672>(</span><span style=color:#a6e22e>Trigger</span><span style=color:#f92672>.</span><span style=color:#a6e22e>ProcessingTime</span><span style=color:#f92672>(</span><span style=color:#ae81ff>1</span><span style=color:#f92672>,</span> <span style=color:#a6e22e>TimeUnit</span><span style=color:#f92672>.</span><span style=color:#a6e22e>MINUTES</span><span style=color:#f92672>))</span> |
| </span></span><span style=display:flex><span> <span style=color:#f92672>.</span>option<span style=color:#f92672>(</span><span style=color:#e6db74>"path"</span><span style=color:#f92672>,</span> tableIdentifier<span style=color:#f92672>)</span> |
| </span></span><span style=display:flex><span> <span style=color:#f92672>.</span>option<span style=color:#f92672>(</span><span style=color:#e6db74>"checkpointLocation"</span><span style=color:#f92672>,</span> checkpointPath<span style=color:#f92672>)</span> |
| </span></span><span style=display:flex><span> <span style=color:#f92672>.</span>start<span style=color:#f92672>()</span> |
| </span></span></code></pre></div><p>The <code>tableIdentifier</code> can be:</p><ul><li>The fully-qualified path to a HDFS table, like <code>hdfs://nn:8020/path/to/table</code></li><li>A table name if the table is tracked by a catalog, like <code>database.table_name</code></li></ul><p>Iceberg doesn’t support “continuous processing”, as it doesn’t provide the interface to “commit” the output.</p><p>Iceberg supports <code>append</code> and <code>complete</code> output modes:</p><ul><li><code>append</code>: appends the rows of every micro-batch to the table</li><li><code>complete</code>: replaces the table contents every micro-batch</li></ul><p>The table should be created in prior to start the streaming query. Refer <a href=../spark-ddl/#create-table>SQL create table</a> |
| on Spark page to see how to create the Iceberg table.</p><h3 id=writing-against-partitioned-table>Writing against partitioned table</h3><p>Iceberg requires the data to be sorted according to the partition spec per task (Spark partition) in prior to write |
| against partitioned table. For batch queries you’re encouraged to do explicit sort to fulfill the requirement |
| (see <a href=../spark-writes/#writing-to-partitioned-tables>here</a>), but the approach would bring additional latency as |
| repartition and sort are considered as heavy operations for streaming workload. To avoid additional latency, you can |
| enable fanout writer to eliminate the requirement.</p><div class=highlight><pre tabindex=0 style=color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-scala data-lang=scala><span style=display:flex><span><span style=color:#66d9ef>val</span> tableIdentifier<span style=color:#66d9ef>:</span> <span style=color:#66d9ef>String</span> <span style=color:#f92672>=</span> <span style=color:#f92672>...</span> |
| </span></span><span style=display:flex><span>data<span style=color:#f92672>.</span>writeStream |
| </span></span><span style=display:flex><span> <span style=color:#f92672>.</span>format<span style=color:#f92672>(</span><span style=color:#e6db74>"iceberg"</span><span style=color:#f92672>)</span> |
| </span></span><span style=display:flex><span> <span style=color:#f92672>.</span>outputMode<span style=color:#f92672>(</span><span style=color:#e6db74>"append"</span><span style=color:#f92672>)</span> |
| </span></span><span style=display:flex><span> <span style=color:#f92672>.</span>trigger<span style=color:#f92672>(</span><span style=color:#a6e22e>Trigger</span><span style=color:#f92672>.</span><span style=color:#a6e22e>ProcessingTime</span><span style=color:#f92672>(</span><span style=color:#ae81ff>1</span><span style=color:#f92672>,</span> <span style=color:#a6e22e>TimeUnit</span><span style=color:#f92672>.</span><span style=color:#a6e22e>MINUTES</span><span style=color:#f92672>))</span> |
| </span></span><span style=display:flex><span> <span style=color:#f92672>.</span>option<span style=color:#f92672>(</span><span style=color:#e6db74>"path"</span><span style=color:#f92672>,</span> tableIdentifier<span style=color:#f92672>)</span> |
| </span></span><span style=display:flex><span> <span style=color:#f92672>.</span>option<span style=color:#f92672>(</span><span style=color:#e6db74>"fanout-enabled"</span><span style=color:#f92672>,</span> <span style=color:#e6db74>"true"</span><span style=color:#f92672>)</span> |
| </span></span><span style=display:flex><span> <span style=color:#f92672>.</span>option<span style=color:#f92672>(</span><span style=color:#e6db74>"checkpointLocation"</span><span style=color:#f92672>,</span> checkpointPath<span style=color:#f92672>)</span> |
| </span></span><span style=display:flex><span> <span style=color:#f92672>.</span>start<span style=color:#f92672>()</span> |
| </span></span></code></pre></div><p>Fanout writer opens the files per partition value and doesn’t close these files till write task is finished. |
| This functionality is discouraged for batch query, as explicit sort against output rows isn’t expensive for batch workload.</p><h2 id=maintenance-for-streaming-tables>Maintenance for streaming tables</h2><p>Streaming queries can create new table versions quickly, which creates lots of table metadata to track those versions. |
| Maintaining metadata by tuning the rate of commits, expiring old snapshots, and automatically cleaning up metadata files |
| is highly recommended.</p><h3 id=tune-the-rate-of-commits>Tune the rate of commits</h3><p>Having high rate of commits would produce lots of data files, manifests, and snapshots which leads the table hard |
| to maintain. We encourage having trigger interval 1 minute at minimum, and increase the interval if needed.</p><p>The triggers section in <a href=https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#triggers>Structured Streaming Programming Guide</a> |
| documents how to configure the interval.</p><h3 id=expire-old-snapshots>Expire old snapshots</h3><p>Each micro-batch written to a table produces a new snapshot, which are tracked in table metadata until they are expired to remove the metadata and any data files that are no longer needed. Snapshots accumulate quickly with frequent commits, so it is highly recommended that tables written by streaming queries are <a href=../maintenance#expire-snapshots>regularly maintained</a>.</p><h3 id=compacting-data-files>Compacting data files</h3><p>The amount of data written in a micro batch is typically small, which can cause the table metadata to track lots of small files. <a href=../maintenance#compact-data-files>Compacting small files into larger files</a> reduces the metadata needed by the table, and increases query efficiency.</p><h3 id=rewrite-manifests>Rewrite manifests</h3><p>To optimize write latency on streaming workload, Iceberg may write the new snapshot with a “fast” append that does not automatically compact manifests. |
| This could lead lots of small manifest files. Manifests can be <a href=../maintenance#rewrite-manifests>rewritten to optimize queries and to compact</a>.</p></div><div id=toc class=markdown-body><div id=full><nav id=TableOfContents><ul><li><a href=#streaming-reads>Streaming Reads</a></li><li><a href=#streaming-writes>Streaming Writes</a><ul><li><a href=#writing-against-partitioned-table>Writing against partitioned table</a></li></ul></li><li><a href=#maintenance-for-streaming-tables>Maintenance for streaming tables</a><ul><li><a href=#tune-the-rate-of-commits>Tune the rate of commits</a></li><li><a href=#expire-old-snapshots>Expire old snapshots</a></li><li><a href=#compacting-data-files>Compacting data files</a></li><li><a href=#rewrite-manifests>Rewrite manifests</a></li></ul></li></ul></nav></div></div></div></div></section></body><script src=https://iceberg.apache.org/docs/fd-update-javadocs//js/jquery-1.11.0.js></script> |
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