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<link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">查询 Hudi 数据集 | 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/0.5.0/querying_data"><meta data-react-helmet="true" name="docsearch:language" content="cn"><meta data-react-helmet="true" name="docsearch:version" content="0.5.0"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="docs-default-0.5.0"><meta data-react-helmet="true" property="og:title" content="查询 Hudi 数据集 | Apache Hudi"><meta data-react-helmet="true" name="description" content="从概念上讲，Hudi物理存储一次数据到DFS上，同时在其上提供三个逻辑视图，如之前所述。"><meta data-react-helmet="true" property="og:description" content="从概念上讲，Hudi物理存储一次数据到DFS上，同时在其上提供三个逻辑视图，如之前所述。"><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/0.5.0/querying_data"><link data-react-helmet="true" rel="alternate" href="https://hudi.apache.org/docs/0.5.0/querying_data" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://hudi.apache.org/cn/docs/0.5.0/querying_data" hreflang="cn"><link data-react-helmet="true" rel="alternate" href="https://hudi.apache.org/docs/0.5.0/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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class="theme-doc-version-banner alert alert--warning margin-bottom--md" role="alert"><div>This is documentation for <!-- -->Apache Hudi<!-- --> <b>0.5.0</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, which is no longer actively maintained.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: <!-- -->0.5.0</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>查询 Hudi 数据集</h1></header><p>从概念上讲，Hudi物理存储一次数据到DFS上，同时在其上提供三个逻辑视图，如<a href="/cn/docs/concepts#views">之前</a>所述。
数据集同步到Hive Metastore后，它将提供由Hudi的自定义输入格式支持的Hive外部表。一旦提供了适当的Hudi捆绑包，
就可以通过Hive、Spark和Presto之类的常用查询引擎来查询数据集。</p><p>具体来说，在写入过程中传递了两个由<a href="/cn/docs/configurations#TABLE_NAME_OPT_KEY">table name</a>命名的Hive表。
例如，如果<code>table name = hudi_tbl</code>，我们得到</p><ul><li><code>hudi_tbl</code> 实现了由 <code>HoodieParquetInputFormat</code> 支持的数据集的读优化视图，从而提供了纯列式数据。</li><li><code>hudi_tbl_rt</code> 实现了由 <code>HoodieParquetRealtimeInputFormat</code> 支持的数据集的实时视图，从而提供了基础数据和日志数据的合并视图。</li></ul><p>如概念部分所述，<a href="https://www.oreilly.com/ideas/ubers-case-for-incremental-processing-on-hadoop" target="_blank" rel="noopener noreferrer">增量处理</a>所需要的
一个关键原语是<code>增量拉取</code>（以从数据集中获取更改流/日志）。您可以增量提取Hudi数据集，这意味着自指定的即时时间起，
您可以只获得全部更新和新行。 这与插入更新一起使用，对于构建某些数据管道尤其有用，包括将1个或多个源Hudi表（数据流/事实）以增量方式拉出（流/事实）
并与其他表（数据集/维度）结合以<a href="/cn/docs/writing_data">写出增量</a>到目标Hudi数据集。增量视图是通过查询上表之一实现的，并具有特殊配置，
该特殊配置指示查询计划仅需要从数据集中获取增量数据。</p><p>接下来，我们将详细讨论在每个查询引擎上如何访问所有三个视图。</p><h2 class="anchor anchorWithStickyNavbar_y2LR" id="hive">Hive<a class="hash-link" href="#hive" title="Direct link to heading">​</a></h2><p>为了使Hive能够识别Hudi数据集并正确查询，
HiveServer2需要在其<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">辅助jars路径</a>中提供<code>hudi-hadoop-mr-bundle-x.y.z-SNAPSHOT.jar</code>。
这将确保输入格式类及其依赖项可用于查询计划和执行。</p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="hive-ro-view">读优化表<a class="hash-link" href="#hive-ro-view" title="Direct link to heading">​</a></h3><p>除了上述设置之外，对于beeline cli访问，还需要将<code>hive.input.format</code>变量设置为<code>org.apache.hudi.hadoop.HoodieParquetInputFormat</code>输入格式的完全限定路径名。
对于Tez，还需要将<code>hive.tez.input.format</code>设置为<code>org.apache.hadoop.hive.ql.io.HiveInputFormat</code>。</p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="hive-rt-view">实时表<a class="hash-link" href="#hive-rt-view" title="Direct link to heading">​</a></h3><p>除了在HiveServer2上安装Hive捆绑jars之外，还需要将其放在整个集群的hadoop/hive安装中，这样查询也可以使用自定义RecordReader。</p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="hive-incr-pull">增量拉取<a class="hash-link" href="#hive-incr-pull" title="Direct link to heading">​</a></h3><p><code>HiveIncrementalPuller</code>允许通过HiveQL从大型事实/维表中增量提取更改，
结合了Hive（可靠地处理复杂的SQL查询）和增量原语的好处（通过增量拉取而不是完全扫描来加快查询速度）。
该工具使用Hive JDBC运行hive查询并将其结果保存在临时表中，这个表可以被插入更新。
Upsert实用程序（<code>HoodieDeltaStreamer</code>）具有目录结构所需的所有状态，以了解目标表上的提交时间应为多少。
例如：<code>/app/incremental-hql/intermediate/{source_table_name}_temp/{last_commit_included}</code>。
已注册的Delta Hive表的格式为<code>{tmpdb}.{source_table}_{last_commit_included}</code>。</p><p>以下是HiveIncrementalPuller的配置选项</p><table><thead><tr><th><strong>配置</strong></th><th><strong>描述</strong></th><th><strong>默认值</strong></th></tr></thead><tbody><tr><td>hiveUrl</td><td>要连接的Hive Server 2的URL</td><td></td></tr><tr><td>hiveUser</td><td>Hive Server 2 用户名</td><td></td></tr><tr><td>hivePass</td><td>Hive Server 2 密码</td><td></td></tr><tr><td>queue</td><td>YARN 队列名称</td><td></td></tr><tr><td>tmp</td><td>DFS中存储临时增量数据的目录。目录结构将遵循约定。请参阅以下部分。</td><td></td></tr><tr><td>extractSQLFile</td><td>在源表上要执行的提取数据的SQL。提取的数据将是自特定时间点以来已更改的所有行。</td><td></td></tr><tr><td>sourceTable</td><td>源表名称。在Hive环境属性中需要设置。</td><td></td></tr><tr><td>targetTable</td><td>目标表名称。中间存储目录结构需要。</td><td></td></tr><tr><td>sourceDataPath</td><td>源DFS基本路径。这是读取Hudi元数据的地方。</td><td></td></tr><tr><td>targetDataPath</td><td>目标DFS基本路径。 这是计算fromCommitTime所必需的。 如果显式指定了fromCommitTime，则不需要设置这个参数。</td><td></td></tr><tr><td>tmpdb</td><td>用来创建中间临时增量表的数据库</td><td>hoodie_temp</td></tr><tr><td>fromCommitTime</td><td>这是最重要的参数。 这是从中提取更改的记录的时间点。</td><td></td></tr><tr><td>maxCommits</td><td>要包含在拉取中的提交数。将此设置为-1将包括从fromCommitTime开始的所有提交。将此设置为大于0的值，将包括在fromCommitTime之后仅更改指定提交次数的记录。如果您需要一次赶上两次提交，则可能需要这样做。</td><td>3</td></tr><tr><td>help</td><td>实用程序帮助</td><td></td></tr></tbody></table><p>设置fromCommitTime=0和maxCommits=-1将提取整个源数据集，可用于启动Backfill。
如果目标数据集是Hudi数据集，则该实用程序可以确定目标数据集是否没有提交或延迟超过24小时（这是可配置的），
它将自动使用Backfill配置，因为增量应用最近24小时的更改会比Backfill花费更多的时间。
该工具当前的局限性在于缺乏在混合模式（正常模式和增量模式）下自联接同一表的支持。</p><p><strong>关于使用Fetch任务执行的Hive查询的说明：</strong>
由于Fetch任务为每个分区调用InputFormat.listStatus()，每个listStatus()调用都会列出Hoodie元数据。
为了避免这种情况，如下操作可能是有用的，即使用Hive session属性对增量查询禁用Fetch任务：
<code>set hive.fetch.task.conversion = none;</code>。这将确保Hive查询使用Map Reduce执行，
合并分区（用逗号分隔），并且对所有这些分区仅调用一次InputFormat.listStatus()。</p><h2 class="anchor anchorWithStickyNavbar_y2LR" id="spark">Spark<a class="hash-link" href="#spark" title="Direct link to heading">​</a></h2><p>Spark可将Hudi jars和捆绑包轻松部署和管理到作业/笔记本中。简而言之，通过Spark有两种方法可以访问Hudi数据集。</p><ul><li><strong>Hudi DataSource</strong>：支持读取优化和增量拉取，类似于标准数据源（例如：<code>spark.read.parquet</code>）的工作方式。</li><li><strong>以Hive表读取</strong>：支持所有三个视图，包括实时视图，依赖于自定义的Hudi输入格式（再次类似Hive）。</li></ul><p>通常，您的spark作业需要依赖<code>hudi-spark</code>或<code>hudi-spark-bundle-x.y.z.jar</code>，
它们必须位于驱动程序和执行程序的类路径上（提示：使用<code>--jars</code>参数）。</p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="spark-ro-view">读优化表<a class="hash-link" href="#spark-ro-view" title="Direct link to heading">​</a></h3><p>要使用SparkSQL将RO表读取为Hive表，只需按如下所示将路径过滤器推入sparkContext。
对于Hudi表，该方法保留了Spark内置的读取Parquet文件的优化功能，例如进行矢量化读取。</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(&quot;mapreduce.input.pathFilter.class&quot;, 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><p>如果您希望通过数据源在DFS上使用全局路径，则只需执行以下类似操作即可得到Spark DataFrame。</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">Dataset&lt;Row&gt; hoodieROViewDF = spark.read().format(&quot;org.apache.hudi&quot;)</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">// pass any path glob, can include hudi &amp; non-hudi datasets</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">.load(&quot;/glob/path/pattern&quot;);</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-rt-view">实时表<a class="hash-link" href="#spark-rt-view" title="Direct link to heading">​</a></h3><p>当前，实时表只能在Spark中作为Hive表进行查询。为了做到这一点，设置<code>spark.sql.hive.convertMetastoreParquet = false</code>，
迫使Spark回退到使用Hive Serde读取数据（计划/执行仍然是Spark）。</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-shell --jars hudi-spark-bundle-x.y.z-SNAPSHOT.jar --driver-class-path /etc/hive/conf  --packages com.databricks:spark-avro_2.11:4.0.0 --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&gt; sqlContext.sql(&quot;select count(*) from hudi_rt where datestr = &#x27;2016-10-02&#x27;&quot;).show()</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-pull">增量拉取<a class="hash-link" href="#spark-incr-pull" title="Direct link to heading">​</a></h3><p><code>hudi-spark</code>模块提供了DataSource API，这是一种从Hudi数据集中提取数据并通过Spark处理数据的更优雅的方法。
如下所示是一个示例增量拉取，它将获取自<code>beginInstantTime</code>以来写入的所有记录。</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&lt;Row&gt; hoodieIncViewDF = spark.read()</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">     .format(&quot;org.apache.hudi&quot;)</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">     .option(DataSourceReadOptions.VIEW_TYPE_OPT_KEY(),</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">             DataSourceReadOptions.VIEW_TYPE_INCREMENTAL_OPT_VAL())</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">     .option(DataSourceReadOptions.BEGIN_INSTANTTIME_OPT_KEY(),</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">            &lt;beginInstantTime&gt;)</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">     .load(tablePath); // For incremental view, pass in the root/base path of dataset</span><br></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>请参阅<a href="/cn/docs/configurations#spark-datasource">设置</a>部分，以查看所有数据源选项。</p><p>另外，<code>HoodieReadClient</code>通过Hudi的隐式索引提供了以下功能。</p><table><thead><tr><th><strong>API</strong></th><th><strong>描述</strong></th></tr></thead><tbody><tr><td>read(keys)</td><td>使用Hudi自己的索通过快速查找将与键对应的数据作为DataFrame读出</td></tr><tr><td>filterExists()</td><td>从提供的RDD<!-- -->[HoodieRecord]<!-- -->中过滤出已经存在的记录。对删除重复数据有用</td></tr><tr><td>checkExists(keys)</td><td>检查提供的键是否存在于Hudi数据集中</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是一种常用的查询引擎，可提供交互式查询性能。 Hudi RO表可以在Presto中无缝查询。
这需要在整个安装过程中将<code>hudi-presto-bundle</code> jar放入<code>&lt;presto_install&gt;/plugin/hive-hadoop2/</code>中。</p></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-0.5.0/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/0.5.0/writing_data"><div 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