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| <link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">Querying Data | Apache Hudi</title><meta data-react-helmet="true" name="twitter:card" content="summary_large_image"><meta data-react-helmet="true" property="og:url" content="https://hudi.apache.org/cn/docs/0.10.0/querying_data"><meta data-react-helmet="true" name="docsearch:language" content="cn"><meta data-react-helmet="true" name="docsearch:version" content="0.10.0"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="docs-default-0.10.0"><meta data-react-helmet="true" property="og:title" content="Querying Data | Apache Hudi"><meta data-react-helmet="true" name="description" content="Conceptually, Hudi stores data physically once on DFS, while providing 3 different ways of querying, as explained before."><meta data-react-helmet="true" property="og:description" content="Conceptually, Hudi stores data physically once on DFS, while providing 3 different ways of querying, as explained before."><meta data-react-helmet="true" name="keywords" content="hudi,hive,spark,sql,presto"><link data-react-helmet="true" rel="icon" href="/cn/assets/images/favicon.ico"><link data-react-helmet="true" rel="canonical" href="https://hudi.apache.org/cn/docs/0.10.0/querying_data"><link data-react-helmet="true" rel="alternate" href="https://hudi.apache.org/docs/0.10.0/querying_data" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://hudi.apache.org/cn/docs/0.10.0/querying_data" hreflang="cn"><link data-react-helmet="true" rel="alternate" href="https://hudi.apache.org/docs/0.10.0/querying_data" hreflang="x-default"><link data-react-helmet="true" rel="preconnect" href="https://BH4D9OD16A-dsn.algolia.net" crossorigin="anonymous"><link rel="stylesheet" href="/cn/assets/css/styles.052701b1.css"> |
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href="/cn/docs/0.10.0/use_cases">Use Cases</a></li><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-1 menu__list-item"><a class="menu__link" href="/cn/docs/0.10.0/faq">FAQs</a></li><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-1 menu__list-item"><a class="menu__link" href="/cn/docs/0.10.0/privacy">Privacy Policy</a></li></ul></nav></div></aside><main class="docMainContainer_Q970"><div class="container padding-top--md padding-bottom--lg"><div class="row"><div class="col docItemCol_zHA2"><div class="theme-doc-version-banner alert alert--warning margin-bottom--md" role="alert"><div>This is documentation for <!-- -->Apache Hudi<!-- --> <b>0.10.0</b>, which is no longer actively maintained.</div><div class="margin-top--md">For up-to-date documentation, see the <b><a href="/cn/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: <!-- -->0.10.0</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 Data</h1></header><p>Conceptually, Hudi stores data physically once on DFS, while providing 3 different ways of querying, as explained <a href="/cn/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 PrestoDB.</p><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="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 tables can be queried via the Spark datasource with a simple <code>spark.read.parquet</code>. |
| See the <a href="/cn/docs/quick-start-guide">Spark Quick Start</a> for more examples of Spark datasource reading queries. </p><p>To setup Spark for querying Hudi, see the <a href="/cn/docs/0.10.0/query_engine_setup#Spark-DataSource">Query Engine Setup</a> page.</p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="spark-snap-query">Snapshot query<a class="hash-link" href="#spark-snap-query" title="Direct link to heading"></a></h3><p>Retrieve the data table at the present point in time.</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 hudiIncQueryDF = spark</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> .read()</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> .format("hudi")</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> .option(DataSourceReadOptions.QUERY_TYPE_OPT_KEY(), DataSourceReadOptions.QUERY_TYPE_SNAPSHOT_OPT_VAL())</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> .load(tablePath) </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-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.</p><p>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="/cn/docs/quick-start-guide#incremental-query">Incremental Queries</a> in the Spark quickstart. |
| Please refer to <a href="/cn/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><h3 class="anchor anchorWithStickyNavbar_y2LR" id="spark-sql">Spark SQL<a class="hash-link" href="#spark-sql" title="Direct link to heading"></a></h3><p>Once the Hudi tables have been registered to the Hive metastore, they can be queried using the Spark-Hive integration. |
| By default, Spark SQL will try to use its own parquet reader instead of Hive SerDe when reading from Hive metastore parquet tables. |
| The following are important settings to consider when querying COPY_ON_WRITE or MERGE_ON_READ tables. </p><h4 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></h4><p>For COPY_ON_WRITE tables, Spark's default parquet reader can be used to retain Sparks built-in optimizations for reading parquet files like vectorized reading on Hudi Hive tables. |
| If using the default parquet reader, a path filter needs to be pushed into sparkContext as follows.</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><h4 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></h4><p>No special configurations are needed for querying MERGE_ON_READ tables with Hudi version 0.9.0+</p><p>If you are querying MERGE_ON_READ tables using Hudi version <= 0.8.0, you need to turn off the SparkSQL default parquet reader by setting: <code>spark.sql.hive.convertMetastoreParquet=false</code>.</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</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><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>Note: COPY_ON_WRITE tables can also still be read if you turn off the default parquet reader.</p></div></div><h2 class="anchor anchorWithStickyNavbar_y2LR" id="flink-sql">Flink SQL<a class="hash-link" href="#flink-sql" title="Direct link to heading"></a></h2><p>Once the flink Hudi tables have been registered to the Flink catalog, it can be queried using the Flink SQL. It supports all query types across both Hudi table types, |
| relying on the custom Hudi input formats again like Hive. Typically notebook users and Flink SQL CLI users leverage flink sql for querying Hudi tables. Please add hudi-flink-bundle as described in the <a href="/cn/docs/flink-quick-start-guide">Flink Quickstart</a>.</p><p>By default, Flink SQL will try to use its own parquet reader instead of Hive SerDe when reading from Hive metastore parquet tables.</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 comment" style="color:rgb(98, 114, 164)"># HADOOP_HOME is your hadoop root directory after unpack the binary package.</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token builtin class-name" style="color:rgb(189, 147, 249)">export</span><span class="token plain"> </span><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">HADOOP_CLASSPATH</span><span class="token operator">=</span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">`</span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">$HADOOP_HOME/bin/hadoop classpath</span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">`</span><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">./bin/sql-client.sh embedded -j </span><span class="token punctuation" style="color:rgb(248, 248, 242)">..</span><span class="token plain">./hudi-flink-bundle_2.1?-*.*.*.jar shell</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="codeBlockContainer_J+bg language-sql theme-code-block"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token comment" style="color:rgb(98, 114, 164)">-- this defines a COPY_ON_WRITE table named 't1'</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">CREATE</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">TABLE</span><span class="token plain"> t1</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"> uuid </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">VARCHAR</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">20</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token comment" style="color:rgb(98, 114, 164)">-- you can use 'PRIMARY KEY NOT ENFORCED' syntax to specify the field as record key</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> name </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">VARCHAR</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">10</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</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"> age </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">INT</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"> ts </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">TIMESTAMP</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">3</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</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"> </span><span class="token identifier punctuation" style="color:rgb(248, 248, 242)">`</span><span class="token identifier">partition</span><span class="token identifier punctuation" style="color:rgb(248, 248, 242)">`</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">VARCHAR</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">20</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"></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">PARTITIONED </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">BY</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token identifier punctuation" style="color:rgb(248, 248, 242)">`</span><span class="token identifier">partition</span><span class="token identifier punctuation" style="color:rgb(248, 248, 242)">`</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"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WITH</span><span class="token plain"> </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"> </span><span class="token string" style="color:rgb(255, 121, 198)">'connector'</span><span class="token plain"> </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'hudi'</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"> </span><span class="token string" style="color:rgb(255, 121, 198)">'path'</span><span class="token plain"> </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'table_base+path'</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</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" style="display:inline-block"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token comment" style="color:rgb(98, 114, 164)">-- query the data</span><span class="token plain"></span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">select</span><span class="token plain"> </span><span class="token operator">*</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">from</span><span class="token plain"> t1 </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">where</span><span class="token plain"> </span><span class="token identifier punctuation" style="color:rgb(248, 248, 242)">`</span><span class="token identifier">partition</span><span class="token identifier punctuation" style="color:rgb(248, 248, 242)">`</span><span class="token plain"> </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'par1'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">;</span><br></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>Flink's built-in support parquet is used for both COPY_ON_WRITE and MERGE_ON_READ tables, |
| additionally partition prune is applied by Flink engine internally if a partition path is specified |
| in the filter. Filters push down is not supported yet (already on the roadmap).</p><p>For MERGE_ON_READ table, in order to query hudi table as a streaming, you need to add option <code>'read.streaming.enabled' = 'true'</code>, |
| when querying the table, a Flink streaming pipeline starts and never ends until the user cancel the job manually. |
| You can specify the start commit with option <code>read.streaming.start-commit</code> and source monitoring interval with option |
| <code>read.streaming.check-interval</code>.</p><h2 class="anchor anchorWithStickyNavbar_y2LR" id="hive">Hive<a class="hash-link" href="#hive" title="Direct link to heading"></a></h2><p>To setup Hive for querying Hudi, see the <a href="/cn/docs/0.10.0/query_engine_setup#hive">Query Engine Setup</a> page.</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="prestodb">PrestoDB<a class="hash-link" href="#prestodb" title="Direct link to heading"></a></h2><p>To setup PrestoDB for querying Hudi, see the <a href="/cn/docs/0.10.0/query_engine_setup#PrestoDB">Query Engine Setup</a> page.</p><h2 class="anchor anchorWithStickyNavbar_y2LR" id="trino">Trino<a class="hash-link" href="#trino" title="Direct link to heading"></a></h2><p>To setup Trino for querying Hudi, see the <a href="/cn/docs/0.10.0/query_engine_setup#Trino">Query Engine Setup</a> page.</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><h2 class="anchor anchorWithStickyNavbar_y2LR" id="redshift-spectrum">Redshift Spectrum<a class="hash-link" href="#redshift-spectrum" title="Direct link to heading"></a></h2><p>To set up Redshift Spectrum for querying Hudi, see the <a href="/cn/docs/0.10.0/query_engine_setup#redshift-spectrum">Query Engine Setup</a> page.</p><h2 class="anchor anchorWithStickyNavbar_y2LR" id="doris">Doris<a class="hash-link" href="#doris" title="Direct link to heading"></a></h2><p>To set up Doris for querying Hudi, see the <a href="/cn/docs/0.10.0/query_engine_setup#doris">Query Engine Setup</a> page.</p><h2 class="anchor anchorWithStickyNavbar_y2LR" id="starrocks">StarRocks<a class="hash-link" href="#starrocks" title="Direct link to heading"></a></h2><p>To set up StarRocks for querying Hudi, see the <a href="/cn/docs/0.10.0/query_engine_setup#starrocks">Query Engine Setup</a> page.</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-1">Copy-On-Write tables<a class="hash-link" href="#copy-on-write-tables-1" 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>Flink SQL</strong></td><td>Y</td><td>N</td></tr><tr><td><strong>PrestoDB</strong></td><td>Y</td><td>N</td></tr><tr><td><strong>Trino</strong></td><td>Y</td><td>N</td></tr><tr><td><strong>Impala</strong></td><td>Y</td><td>N</td></tr><tr><td><strong>Redshift Spectrum</strong></td><td>Y</td><td>N</td></tr><tr><td><strong>Doris</strong></td><td>Y</td><td>N</td></tr><tr><td><strong>StarRocks</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-1">Merge-On-Read tables<a class="hash-link" href="#merge-on-read-tables-1" 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>Y</td><td>Y</td><td>Y</td></tr><tr><td><strong>Flink SQL</strong></td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td><strong>PrestoDB</strong></td><td>Y</td><td>N</td><td>Y</td></tr><tr><td><strong>Trino</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></div><footer class="theme-doc-footer docusaurus-mt-lg"><div class="theme-doc-footer-edit-meta-row row"><div class="col"><a href="https://github.com/apache/hudi/tree/asf-site/website/versioned_docs/version-0.10.0/querying_data.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><div class="col lastUpdated_mt2f"></div></div></footer></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages navigation"><div class="pagination-nav__item"><a class="pagination-nav__link" href="/cn/docs/0.10.0/hoodie_deltastreamer"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Streaming Ingestion</div></a></div><div class="pagination-nav__item pagination-nav__item--next"><a class="pagination-nav__link" 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