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class="margin-top--md">For up-to-date documentation, see the <b><a href="/docs/querying_data">latest version</a></b> (<!-- -->0.14.1<!-- -->).</div></div><div class="docItemContainer_oiyr"><article><span class="theme-doc-version-badge badge badge--secondary">Version: <!-- -->0.5.3</span><div class="tocCollapsible_aw-L theme-doc-toc-mobile tocMobile_Tx6Y"><button type="button" class="clean-btn tocCollapsibleButton_zr6a">On this page</button></div><div class="theme-doc-markdown markdown"><header><h1>Querying Hudi Tables</h1></header><p>Conceptually, Hudi stores data physically once on DFS, while providing 3 different ways of querying, as explained <a href="/docs/concepts#query-types">before</a>. |
| Once the table is synced to the Hive metastore, it provides external Hive tables backed by Hudi's custom inputformats. Once the proper hudi |
| bundle has been installed, the table can be queried by popular query engines like Hive, Spark SQL, Spark Datasource API and Presto.</p><p>Specifically, following Hive tables are registered based off <a href="/docs/configurations#TABLE_NAME_OPT_KEY">table name</a> |
| and <a href="/docs/configurations#TABLE_TYPE_OPT_KEY">table type</a> configs passed during write. </p><p>If <code>table name = hudi_trips</code> and <code>table type = COPY_ON_WRITE</code>, then we get: </p><ul><li><code>hudi_trips</code> supports snapshot query and incremental query on the table backed by <code>HoodieParquetInputFormat</code>, exposing purely columnar data.</li></ul><p>If <code>table name = hudi_trips</code> and <code>table type = MERGE_ON_READ</code>, then we get:</p><ul><li><code>hudi_trips_rt</code> supports snapshot query and incremental query (providing near-real time data) on the table backed by <code>HoodieParquetRealtimeInputFormat</code>, exposing merged view of base and log data.</li><li><code>hudi_trips_ro</code> supports read optimized query on the table backed by <code>HoodieParquetInputFormat</code>, exposing purely columnar data stored in base files.</li></ul><p>As discussed in the concepts section, the one key capability needed for <a href="https://www.oreilly.com/ideas/ubers-case-for-incremental-processing-on-hadoop" target="_blank" rel="noopener noreferrer">incrementally processing</a>, |
| is obtaining a change stream/log from a table. Hudi tables can be queried incrementally, which means you can get ALL and ONLY the updated & new rows |
| since a specified instant time. This, together with upserts, is particularly useful for building data pipelines where 1 or more source Hudi tables are incrementally queried (streams/facts), |
| joined with other tables (tables/dimensions), to <a href="/docs/writing_data">write out deltas</a> to a target Hudi table. Incremental queries are realized by querying one of the tables above, |
| with special configurations that indicates to query planning that only incremental data needs to be fetched out of the table. </p><h2 class="anchor anchorWithStickyNavbar_y2LR" id="support-matrix">Support Matrix<a class="hash-link" href="#support-matrix" title="Direct link to heading"></a></h2><p>Following tables show whether a given query is supported on specific query engine.</p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="copy-on-write-tables">Copy-On-Write tables<a class="hash-link" href="#copy-on-write-tables" title="Direct link to heading"></a></h3><table><thead><tr><th>Query Engine</th><th>Snapshot Queries</th><th>Incremental Queries</th></tr></thead><tbody><tr><td><strong>Hive</strong></td><td>Y</td><td>Y</td></tr><tr><td><strong>Spark SQL</strong></td><td>Y</td><td>Y</td></tr><tr><td><strong>Spark Datasource</strong></td><td>Y</td><td>Y</td></tr><tr><td><strong>Presto</strong></td><td>Y</td><td>N</td></tr><tr><td><strong>Impala</strong></td><td>Y</td><td>N</td></tr></tbody></table><p>Note that <code>Read Optimized</code> queries are not applicable for COPY_ON_WRITE tables.</p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="merge-on-read-tables">Merge-On-Read tables<a class="hash-link" href="#merge-on-read-tables" title="Direct link to heading"></a></h3><table><thead><tr><th>Query Engine</th><th>Snapshot Queries</th><th>Incremental Queries</th><th>Read Optimized Queries</th></tr></thead><tbody><tr><td><strong>Hive</strong></td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td><strong>Spark SQL</strong></td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td><strong>Spark Datasource</strong></td><td>N</td><td>N</td><td>Y</td></tr><tr><td><strong>Presto</strong></td><td>N</td><td>N</td><td>Y</td></tr><tr><td><strong>Impala</strong></td><td>N</td><td>N</td><td>Y</td></tr></tbody></table><p>In sections, below we will discuss specific setup to access different query types from different query engines. </p><h2 class="anchor anchorWithStickyNavbar_y2LR" id="hive">Hive<a class="hash-link" href="#hive" title="Direct link to heading"></a></h2><p>In order for Hive to recognize Hudi tables and query correctly, </p><ul><li>the HiveServer2 needs to be provided with the <code>hudi-hadoop-mr-bundle-x.y.z-SNAPSHOT.jar</code> in its <a href="https://www.cloudera.com/documentation/enterprise/5-6-x/topics/cm_mc_hive_udf#concept_nc3_mms_lr" target="_blank" rel="noopener noreferrer">aux jars path</a>. This will ensure the input format |
| classes with its dependencies are available for query planning & execution. </li><li>For MERGE_ON_READ tables, additionally the bundle needs to be put on the hadoop/hive installation across the cluster, so that queries can pick up the custom RecordReader as well.</li></ul><p>In addition to setup above, for beeline cli access, the <code>hive.input.format</code> variable needs to be set to the fully qualified path name of the |
| inputformat <code>org.apache.hudi.hadoop.HoodieParquetInputFormat</code>. For Tez, additionally the <code>hive.tez.input.format</code> needs to be set |
| to <code>org.apache.hadoop.hive.ql.io.HiveInputFormat</code>. Then proceed to query the table like any other Hive table.</p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="incremental-query">Incremental query<a class="hash-link" href="#incremental-query" title="Direct link to heading"></a></h3><p><code>HiveIncrementalPuller</code> allows incrementally extracting changes from large fact/dimension tables via HiveQL, combining the benefits of Hive (reliably process complex SQL queries) and |
| incremental primitives (speed up querying tables incrementally instead of scanning fully). The tool uses Hive JDBC to run the hive query and saves its results in a temp table. |
| that can later be upserted. Upsert utility (<code>HoodieDeltaStreamer</code>) has all the state it needs from the directory structure to know what should be the commit time on the target table. |
| e.g: <code>/app/incremental-hql/intermediate/{source_table_name}_temp/{last_commit_included}</code>.The Delta Hive table registered will be of the form <code>{tmpdb}.{source_table}_{last_commit_included}</code>.</p><p>The following are the configuration options for HiveIncrementalPuller</p><table><thead><tr><th><strong>Config</strong></th><th><strong>Description</strong></th><th><strong>Default</strong></th></tr></thead><tbody><tr><td>hiveUrl</td><td>Hive Server 2 URL to connect to</td><td></td></tr><tr><td>hiveUser</td><td>Hive Server 2 Username</td><td></td></tr><tr><td>hivePass</td><td>Hive Server 2 Password</td><td></td></tr><tr><td>queue</td><td>YARN Queue name</td><td></td></tr><tr><td>tmp</td><td>Directory where the temporary delta data is stored in DFS. The directory structure will follow conventions. Please see the below section.</td><td></td></tr><tr><td>extractSQLFile</td><td>The SQL to execute on the source table to extract the data. The data extracted will be all the rows that changed since a particular point in time.</td><td></td></tr><tr><td>sourceTable</td><td>Source Table Name. Needed to set hive environment properties.</td><td></td></tr><tr><td>sourceDb</td><td>Source DB name. Needed to set hive environment properties.</td><td></td></tr><tr><td>targetTable</td><td>Target Table Name. Needed for the intermediate storage directory structure.</td><td></td></tr><tr><td>targetDb</td><td>Target table's DB name.</td><td></td></tr><tr><td>tmpdb</td><td>The database to which the intermediate temp delta table will be created</td><td>hoodie_temp</td></tr><tr><td>fromCommitTime</td><td>This is the most important parameter. This is the point in time from which the changed records are queried from.</td><td></td></tr><tr><td>maxCommits</td><td>Number of commits to include in the query. Setting this to -1 will include all the commits from fromCommitTime. Setting this to a value > 0, will include records that ONLY changed in the specified number of commits after fromCommitTime. This may be needed if you need to catch up say 2 commits at a time.</td><td>3</td></tr><tr><td>help</td><td>Utility Help</td><td></td></tr></tbody></table><p>Setting fromCommitTime=0 and maxCommits=-1 will fetch the entire source table and can be used to initiate backfills. If the target table is a Hudi table, |
| then the utility can determine if the target table has no commits or is behind more than 24 hour (this is configurable), |
| it will automatically use the backfill configuration, since applying the last 24 hours incrementally could take more time than doing a backfill. The current limitation of the tool |
| is the lack of support for self-joining the same table in mixed mode (snapshot and incremental modes).</p><p><strong>NOTE on Hive incremental queries that are executed using Fetch task:</strong> |
| Since Fetch tasks invoke InputFormat.listStatus() per partition, Hoodie metadata can be listed in |
| every such listStatus() call. In order to avoid this, it might be useful to disable fetch tasks |
| using the hive session property for incremental queries: <code>set hive.fetch.task.conversion=none;</code> This |
| would ensure Map Reduce execution is chosen for a Hive query, which combines partitions (comma |
| separated) and calls InputFormat.listStatus() only once with all those partitions.</p><h2 class="anchor anchorWithStickyNavbar_y2LR" id="spark-sql">Spark SQL<a class="hash-link" href="#spark-sql" title="Direct link to heading"></a></h2><p>Once the Hudi tables have been registered to the Hive metastore, it can be queried using the Spark-Hive integration. It supports all query types across both Hudi table types, |
| relying on the custom Hudi input formats again like Hive. Typically notebook users and spark-shell users leverage spark sql for querying Hudi tables. Please add hudi-spark-bundle as described above via --jars or --packages.</p><p>By default, Spark SQL will try to use its own parquet reader instead of Hive SerDe when reading from Hive metastore parquet tables. However, for MERGE_ON_READ tables which has |
| both parquet and avro data, this default setting needs to be turned off using set <code>spark.sql.hive.convertMetastoreParquet=false</code>. |
| This will force Spark to fallback to using the Hive Serde to read the data (planning/executions is still Spark). </p><div class="codeBlockContainer_J+bg language-java theme-code-block"><div class="codeBlockContent_csEI java"><pre tabindex="0" class="prism-code language-java 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-shell --driver-class-path /etc/hive/conf --packages org.apache.hudi:hudi-spark-bundle_2.11:0.5.3,org.apache.spark:spark-avro_2.11:2.4.4 --conf spark.sql.hive.convertMetastoreParquet=false --num-executors 10 --driver-memory 7g --executor-memory 2g --master yarn-client</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">scala> sqlContext.sql("select count(*) from hudi_trips_mor_rt where datestr = '2016-10-02'").show()</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">scala> sqlContext.sql("select count(*) from hudi_trips_mor_rt where datestr = '2016-10-02'").show()</span><br></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>For COPY_ON_WRITE tables, either Hive SerDe can be used by turning off <code>spark.sql.hive.convertMetastoreParquet=false</code> as described above or Spark's built in support can be leveraged. |
| If using spark's built in support, additionally a path filter needs to be pushed into sparkContext as follows. This method retains Spark built-in optimizations for reading parquet files like vectorized reading on Hudi Hive tables.</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">spark.sparkContext.hadoopConfiguration.setClass("mapreduce.input.pathFilter.class", classOf[org.apache.hudi.hadoop.HoodieROTablePathFilter], classOf[org.apache.hadoop.fs.PathFilter]);</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="spark-datasource">Spark Datasource<a class="hash-link" href="#spark-datasource" title="Direct link to heading"></a></h2><p>The Spark Datasource API is a popular way of authoring Spark ETL pipelines. Hudi COPY_ON_WRITE tables can be queried via Spark datasource similar to how standard |
| datasources work (e.g: <code>spark.read.parquet</code>). Both snapshot querying and incremental querying are supported here. Typically spark jobs require adding <code>--jars <path to jar>/hudi-spark-bundle_2.11-<hudi version>.jar</code> to classpath of drivers |
| and executors. Alternatively, hudi-spark-bundle can also fetched via the <code>--packages</code> options (e.g: <code>--packages org.apache.hudi:hudi-spark-bundle_2.11:0.5.3</code>).</p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="spark-incr-query">Incremental query<a class="hash-link" href="#spark-incr-query" title="Direct link to heading"></a></h3><p>Of special interest to spark pipelines, is Hudi's ability to support incremental queries, like below. A sample incremental query, that will obtain all records written since <code>beginInstantTime</code>, looks like below. |
| Thanks to Hudi's support for record level change streams, these incremental pipelines often offer 10x efficiency over batch counterparts, by only processing the changed records. |
| The following snippet shows how to obtain all records changed after <code>beginInstantTime</code> and run some SQL on them.</p><div class="codeBlockContainer_J+bg language-java theme-code-block"><div class="codeBlockContent_csEI java"><pre tabindex="0" class="prism-code language-java 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"> Dataset<Row> hudiIncQueryDF = spark.read()</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"> .option(DataSourceReadOptions.QUERY_TYPE_OPT_KEY(), DataSourceReadOptions.QUERY_TYPE_INCREMENTAL_OPT_VAL())</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> .option(DataSourceReadOptions.BEGIN_INSTANTTIME_OPT_KEY(), <beginInstantTime>)</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> .option(DataSourceReadOptions.INCR_PATH_GLOB_OPT_KEY(), "/year=2020/month=*/day=*") // Optional, use glob pattern if querying certain partitions</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> .load(tablePath); // For incremental query, pass in the root/base path of table</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">hudiIncQueryDF.createOrReplaceTempView("hudi_trips_incremental")</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">spark.sql("select `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts from hudi_trips_incremental where fare > 20.0").show()</span><br></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>For examples, refer to <a href="/docs/quick-start-guide#setup-spark-shell">Setup spark-shell in quickstart</a>. |
| Please refer to <a href="/docs/configurations#spark-datasource">configurations</a> section, to view all datasource options.</p><p>Additionally, <code>HoodieReadClient</code> offers the following functionality using Hudi's implicit indexing.</p><table><thead><tr><th><strong>API</strong></th><th><strong>Description</strong></th></tr></thead><tbody><tr><td>read(keys)</td><td>Read out the data corresponding to the keys as a DataFrame, using Hudi's own index for faster lookup</td></tr><tr><td>filterExists()</td><td>Filter out already existing records from the provided <code>RDD[HoodieRecord]</code>. Useful for de-duplication</td></tr><tr><td>checkExists(keys)</td><td>Check if the provided keys exist in a Hudi table</td></tr></tbody></table><h2 class="anchor anchorWithStickyNavbar_y2LR" id="presto">Presto<a class="hash-link" href="#presto" title="Direct link to heading"></a></h2><p>Presto is a popular query engine, providing interactive query performance. Presto currently supports snapshot queries on COPY_ON_WRITE and read optimized queries |
| on MERGE_ON_READ Hudi tables. This requires the <code>hudi-presto-bundle</code> jar to be placed into <code><presto_install>/plugin/hive-hadoop2/</code>, across the installation.</p><h2 class="anchor anchorWithStickyNavbar_y2LR" id="impala-34-or-later">Impala (3.4 or later)<a class="hash-link" href="#impala-34-or-later" title="Direct link to heading"></a></h2><h3 class="anchor anchorWithStickyNavbar_y2LR" id="snapshot-query">Snapshot Query<a class="hash-link" href="#snapshot-query" title="Direct link to heading"></a></h3><p>Impala is able to query Hudi Copy-on-write table as an <a href="https://docs.cloudera.com/documentation/enterprise/6/6.3/topics/impala_tables#external_tables" target="_blank" rel="noopener noreferrer">EXTERNAL TABLE</a> on HDFS. </p><p>To create a Hudi read optimized table on Impala:</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">CREATE EXTERNAL TABLE database.table_name</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">LIKE PARQUET '/path/to/load/xxx.parquet'</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">STORED AS HUDIPARQUET</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">LOCATION '/path/to/load';</span><br></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>Impala is able to take advantage of the physical partition structure to improve the query performance. |
| To create a partitioned table, the folder should follow the naming convention like <code>year=2020/month=1</code>. |
| Impala use <code>=</code> to separate partition name and partition value.<br> |
| <!-- -->To create a partitioned Hudi read optimized table on Impala:</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">CREATE EXTERNAL TABLE database.table_name</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">LIKE PARQUET '/path/to/load/xxx.parquet'</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">PARTITION BY (year int, month int, day int)</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">STORED AS HUDIPARQUET</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">LOCATION '/path/to/load';</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">ALTER TABLE database.table_name RECOVER PARTITIONS;</span><br></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>After Hudi made a new commit, refresh the Impala table to get the latest results.</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">REFRESH database.table_name</span><br></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></div><footer class="theme-doc-footer docusaurus-mt-lg"><div class="theme-doc-footer-edit-meta-row row"><div class="col"><a 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