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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-add-github-release/../../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=getting-started>Getting Started</h1><p>The latest version of Iceberg is <a href=../../../releases>1.4.0</a>.</p><p>Spark is currently the most feature-rich compute engine for Iceberg operations. |
| We recommend you to get started with Spark to understand Iceberg concepts and features with examples. |
| You can also view documentations of using Iceberg with other compute engine under the <a href=https://iceberg.apache.org/multi-engine-support>Multi-Engine Support</a> page.</p><h2 id=using-iceberg-in-spark-3>Using Iceberg in Spark 3</h2><p>To use Iceberg in a Spark shell, use the <code>--packages</code> option:</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-sh data-lang=sh><span style=display:flex><span>spark-shell --packages org.apache.iceberg:iceberg-spark-runtime-3.2_2.12:1.4.0 |
| </span></span></code></pre></div><div class=info>If you want to include Iceberg in your Spark installation, add the <a href="https://search.maven.org/remotecontent?filepath=org/apache/iceberg/iceberg-spark-runtime-3.2_2.12/1.4.0/iceberg-spark-runtime-3.2_2.12-1.4.0.jar"><code>iceberg-spark-runtime-3.2_2.12</code> Jar</a> to Spark’s <code>jars</code> folder.</div><h3 id=adding-catalogs>Adding catalogs</h3><p>Iceberg comes with <a href=../spark-configuration#catalogs>catalogs</a> that enable SQL commands to manage tables and load them by name. Catalogs are configured using properties under <code>spark.sql.catalog.(catalog_name)</code>.</p><p>This command creates a path-based catalog named <code>local</code> for tables under <code>$PWD/warehouse</code> and adds support for Iceberg tables to Spark’s built-in catalog:</p><div class=highlight><pre tabindex=0 style=color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-sh data-lang=sh><span style=display:flex><span>spark-sql --packages org.apache.iceberg:iceberg-spark-runtime-3.2_2.12:1.4.0<span style=color:#ae81ff>\ |
| </span></span></span><span style=display:flex><span><span style=color:#ae81ff></span> --conf spark.sql.extensions<span style=color:#f92672>=</span>org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions <span style=color:#ae81ff>\ |
| </span></span></span><span style=display:flex><span><span style=color:#ae81ff></span> --conf spark.sql.catalog.spark_catalog<span style=color:#f92672>=</span>org.apache.iceberg.spark.SparkSessionCatalog <span style=color:#ae81ff>\ |
| </span></span></span><span style=display:flex><span><span style=color:#ae81ff></span> --conf spark.sql.catalog.spark_catalog.type<span style=color:#f92672>=</span>hive <span style=color:#ae81ff>\ |
| </span></span></span><span style=display:flex><span><span style=color:#ae81ff></span> --conf spark.sql.catalog.local<span style=color:#f92672>=</span>org.apache.iceberg.spark.SparkCatalog <span style=color:#ae81ff>\ |
| </span></span></span><span style=display:flex><span><span style=color:#ae81ff></span> --conf spark.sql.catalog.local.type<span style=color:#f92672>=</span>hadoop <span style=color:#ae81ff>\ |
| </span></span></span><span style=display:flex><span><span style=color:#ae81ff></span> --conf spark.sql.catalog.local.warehouse<span style=color:#f92672>=</span>$PWD/warehouse |
| </span></span></code></pre></div><h3 id=creating-a-table>Creating a table</h3><p>To create your first Iceberg table in Spark, use the <code>spark-sql</code> shell or <code>spark.sql(...)</code> to run a <a href=../spark-ddl#create-table><code>CREATE TABLE</code></a> command:</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-sql data-lang=sql><span style=display:flex><span><span style=color:#75715e>-- local is the path-based catalog defined above |
| </span></span></span><span style=display:flex><span><span style=color:#75715e></span><span style=color:#66d9ef>CREATE</span> <span style=color:#66d9ef>TABLE</span> <span style=color:#66d9ef>local</span>.db.<span style=color:#66d9ef>table</span> (id bigint, <span style=color:#66d9ef>data</span> string) <span style=color:#66d9ef>USING</span> iceberg |
| </span></span></code></pre></div><p>Iceberg catalogs support the full range of SQL DDL commands, including:</p><ul><li><a href=../spark-ddl#create-table><code>CREATE TABLE ... PARTITIONED BY</code></a></li><li><a href=../spark-ddl#create-table--as-select><code>CREATE TABLE ... AS SELECT</code></a></li><li><a href=../spark-ddl#alter-table><code>ALTER TABLE</code></a></li><li><a href=../spark-ddl#drop-table><code>DROP TABLE</code></a></li></ul><h3 id=writing>Writing</h3><p>Once your table is created, insert data using <a href=../spark-writes#insert-into><code>INSERT INTO</code></a>:</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-sql data-lang=sql><span style=display:flex><span><span style=color:#66d9ef>INSERT</span> <span style=color:#66d9ef>INTO</span> <span style=color:#66d9ef>local</span>.db.<span style=color:#66d9ef>table</span> <span style=color:#66d9ef>VALUES</span> (<span style=color:#ae81ff>1</span>, <span style=color:#e6db74>'a'</span>), (<span style=color:#ae81ff>2</span>, <span style=color:#e6db74>'b'</span>), (<span style=color:#ae81ff>3</span>, <span style=color:#e6db74>'c'</span>); |
| </span></span><span style=display:flex><span><span style=color:#66d9ef>INSERT</span> <span style=color:#66d9ef>INTO</span> <span style=color:#66d9ef>local</span>.db.<span style=color:#66d9ef>table</span> <span style=color:#66d9ef>SELECT</span> id, <span style=color:#66d9ef>data</span> <span style=color:#66d9ef>FROM</span> <span style=color:#66d9ef>source</span> <span style=color:#66d9ef>WHERE</span> <span style=color:#66d9ef>length</span>(<span style=color:#66d9ef>data</span>) <span style=color:#f92672>=</span> <span style=color:#ae81ff>1</span>; |
| </span></span></code></pre></div><p>Iceberg also adds row-level SQL updates to Spark, <a href=../spark-writes#merge-into><code>MERGE INTO</code></a> and <a href=../spark-writes#delete-from><code>DELETE FROM</code></a>:</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-sql data-lang=sql><span style=display:flex><span>MERGE <span style=color:#66d9ef>INTO</span> <span style=color:#66d9ef>local</span>.db.target t <span style=color:#66d9ef>USING</span> (<span style=color:#66d9ef>SELECT</span> <span style=color:#f92672>*</span> <span style=color:#66d9ef>FROM</span> updates) u <span style=color:#66d9ef>ON</span> t.id <span style=color:#f92672>=</span> u.id |
| </span></span><span style=display:flex><span><span style=color:#66d9ef>WHEN</span> MATCHED <span style=color:#66d9ef>THEN</span> <span style=color:#66d9ef>UPDATE</span> <span style=color:#66d9ef>SET</span> t.<span style=color:#66d9ef>count</span> <span style=color:#f92672>=</span> t.<span style=color:#66d9ef>count</span> <span style=color:#f92672>+</span> u.<span style=color:#66d9ef>count</span> |
| </span></span><span style=display:flex><span><span style=color:#66d9ef>WHEN</span> <span style=color:#66d9ef>NOT</span> MATCHED <span style=color:#66d9ef>THEN</span> <span style=color:#66d9ef>INSERT</span> <span style=color:#f92672>*</span> |
| </span></span></code></pre></div><p>Iceberg supports writing DataFrames using the new <a href=../spark-writes#writing-with-dataframes>v2 DataFrame write API</a>:</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>spark<span style=color:#f92672>.</span>table<span style=color:#f92672>(</span><span style=color:#e6db74>"source"</span><span style=color:#f92672>).</span>select<span style=color:#f92672>(</span><span style=color:#e6db74>"id"</span><span style=color:#f92672>,</span> <span style=color:#e6db74>"data"</span><span style=color:#f92672>)</span> |
| </span></span><span style=display:flex><span> <span style=color:#f92672>.</span>writeTo<span style=color:#f92672>(</span><span style=color:#e6db74>"local.db.table"</span><span style=color:#f92672>).</span>append<span style=color:#f92672>()</span> |
| </span></span></code></pre></div><p>The old <code>write</code> API is supported, but <em>not</em> recommended.</p><h3 id=reading>Reading</h3><p>To read with SQL, use the an Iceberg table name in a <code>SELECT</code> query:</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-sql data-lang=sql><span style=display:flex><span><span style=color:#66d9ef>SELECT</span> <span style=color:#66d9ef>count</span>(<span style=color:#ae81ff>1</span>) <span style=color:#66d9ef>as</span> <span style=color:#66d9ef>count</span>, <span style=color:#66d9ef>data</span> |
| </span></span><span style=display:flex><span><span style=color:#66d9ef>FROM</span> <span style=color:#66d9ef>local</span>.db.<span style=color:#66d9ef>table</span> |
| </span></span><span style=display:flex><span><span style=color:#66d9ef>GROUP</span> <span style=color:#66d9ef>BY</span> <span style=color:#66d9ef>data</span> |
| </span></span></code></pre></div><p>SQL is also the recommended way to <a href=../spark-queries#inspecting-tables>inspect tables</a>. To view all of the snapshots in a table, use the <code>snapshots</code> metadata table:</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-sql data-lang=sql><span style=display:flex><span><span style=color:#66d9ef>SELECT</span> <span style=color:#f92672>*</span> <span style=color:#66d9ef>FROM</span> <span style=color:#66d9ef>local</span>.db.<span style=color:#66d9ef>table</span>.snapshots |
| </span></span></code></pre></div><pre tabindex=0><code>+-------------------------+----------------+-----------+-----------+----------------------------------------------------+-----+ |
| | committed_at | snapshot_id | parent_id | operation | manifest_list | ... | |
| +-------------------------+----------------+-----------+-----------+----------------------------------------------------+-----+ |
| | 2019-02-08 03:29:51.215 | 57897183625154 | null | append | s3://.../table/metadata/snap-57897183625154-1.avro | ... | |
| | | | | | | ... | |
| | | | | | | ... | |
| | ... | ... | ... | ... | ... | ... | |
| +-------------------------+----------------+-----------+-----------+----------------------------------------------------+-----+ |
| </code></pre><p><a href=../spark-queries#querying-with-dataframes>DataFrame reads</a> are supported and can now reference tables by name using <code>spark.table</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> df <span style=color:#66d9ef>=</span> spark<span style=color:#f92672>.</span>table<span style=color:#f92672>(</span><span style=color:#e6db74>"local.db.table"</span><span style=color:#f92672>)</span> |
| </span></span><span style=display:flex><span>df<span style=color:#f92672>.</span>count<span style=color:#f92672>()</span> |
| </span></span></code></pre></div><h3 id=next-steps>Next steps</h3><p>Next, you can learn more about Iceberg tables in Spark:</p><ul><li><a href=../spark-ddl>DDL commands</a>: <code>CREATE</code>, <code>ALTER</code>, and <code>DROP</code></li><li><a href=../spark-queries>Querying data</a>: <code>SELECT</code> queries and metadata tables</li><li><a href=../spark-writes>Writing data</a>: <code>INSERT INTO</code> and <code>MERGE INTO</code></li><li><a href=../spark-procedures>Maintaining tables</a> with stored procedures</li></ul></div><div id=toc class=markdown-body><div id=full><nav id=TableOfContents><ul><li><a href=#using-iceberg-in-spark-3>Using Iceberg in Spark 3</a><ul><li><a href=#adding-catalogs>Adding catalogs</a></li><li><a href=#creating-a-table>Creating a table</a></li><li><a href=#writing>Writing</a></li><li><a href=#reading>Reading</a></li><li><a href=#next-steps>Next steps</a></li></ul></li></ul></nav></div></div></div></div></section></body><script src=https://iceberg.apache.org/docs/fd-add-github-release//js/jquery-1.11.0.js></script> |
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