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Posted to commits@iceberg.apache.org by bl...@apache.org on 2021/01/28 17:32:44 UTC
[iceberg] branch asf-site updated: Deployed 2025fdbd5 with MkDocs
version: 1.0.4
This is an automated email from the ASF dual-hosted git repository.
blue pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/iceberg.git
The following commit(s) were added to refs/heads/asf-site by this push:
new a99d651 Deployed 2025fdbd5 with MkDocs version: 1.0.4
a99d651 is described below
commit a99d651c7965d8570719e1ab8accc3f5b99f922a
Author: Ryan Blue <bl...@apache.org>
AuthorDate: Thu Jan 28 09:32:28 2021 -0800
Deployed 2025fdbd5 with MkDocs version: 1.0.4
---
getting-started/index.html | 30 ++++++++++++++++++------------
sitemap.xml.gz | Bin 229 -> 229 bytes
2 files changed, 18 insertions(+), 12 deletions(-)
diff --git a/getting-started/index.html b/getting-started/index.html
index 7ef8565..66c9710 100644
--- a/getting-started/index.html
+++ b/getting-started/index.html
@@ -431,7 +431,7 @@
<p>If you want to include Iceberg in your Spark installation, add the <a href="https://search.maven.org/remotecontent?filepath=org/apache/iceberg/iceberg-spark3-runtime/0.11.0/iceberg-spark3-runtime-0.11.0.jar"><code>iceberg-spark3-runtime</code> Jar</a> to Spark’s <code>jars</code> folder.</p>
</div>
<h3 id="adding-catalogs">Adding catalogs<a class="headerlink" href="#adding-catalogs" title="Permanent link">¶</a></h3>
-<p>Iceberg comes with <a href="../spark/#configuring-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>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>
<pre><code class="sh">spark-sql --packages org.apache.iceberg:iceberg-spark3-runtime:0.11.0 \
--conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions \
@@ -443,31 +443,31 @@
</code></pre>
<h3 id="creating-a-table">Creating a table<a class="headerlink" href="#creating-a-table" title="Permanent link">¶</a></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/#create-table"><code>CREATE TABLE</code></a> command:</p>
+<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>
<pre><code class="sql">-- local is the path-based catalog defined above
CREATE TABLE local.db.table (id bigint, data string) USING iceberg
</code></pre>
<p>Iceberg catalogs support the full range of SQL DDL commands, including:</p>
<ul>
-<li><a href="../spark/#create-table"><code>CREATE TABLE ... PARTITIONED BY</code></a></li>
-<li><a href="../spark/#create-table-as-select"><code>CREATE TABLE ... AS SELECT</code></a></li>
-<li><a href="../spark/#alter-table"><code>ALTER TABLE</code></a></li>
-<li><a href="../spark/#drop-table"><code>DROP TABLE</code></a></li>
+<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<a class="headerlink" href="#writing" title="Permanent link">¶</a></h3>
-<p>Once your table is created, insert data using <a href="../spark/#insert-into"><code>INSERT INTO</code></a>:</p>
+<p>Once your table is created, insert data using <a href="../spark-writes/#insert-into"><code>INSERT INTO</code></a>:</p>
<pre><code class="sql">INSERT INTO local.db.table VALUES (1, 'a'), (2, 'b'), (3, 'c');
INSERT INTO local.db.table SELECT id, data FROM source WHERE length(data) = 1;
</code></pre>
-<p>Iceberg also adds row-level SQL updates to Spark, <a href="../spark/#merge-into"><code>MERGE INTO</code></a> and <a href="../spark/#delete-from"><code>DELETE FROM</code></a>:</p>
+<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>
<pre><code class="sql">MERGE INTO local.db.target t USING (SELECT * FROM updates) u ON t.id = u.id
WHEN MATCHED THEN UPDATE SET t.count = t.count + u.count
WHEN NOT MATCHED THEN INSERT *
</code></pre>
-<p>Iceberg supports writing DataFrames using the new <a href="../spark/#writing-with-dataframes">v2 DataFrame write API</a>:</p>
+<p>Iceberg supports writing DataFrames using the new <a href="../spark-writes/#writing-with-dataframes">v2 DataFrame write API</a>:</p>
<pre><code class="scala">spark.table("source").select("id", "data")
.writeTo("local.db.table").append()
</code></pre>
@@ -480,7 +480,7 @@ FROM local.db.table
GROUP BY data
</code></pre>
-<p>SQL is also the recommended way to <a href="../spark/#inspecting-tables">inspect tables</a>. To view all of the snapshots in a table, use the <code>snapshots</code> metadata table:</p>
+<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>
<pre><code class="sql">SELECT * FROM local.db.table.snapshots
</code></pre>
@@ -494,13 +494,19 @@ GROUP BY data
+-------------------------+----------------+-----------+-----------+----------------------------------------------------+-----+
</code></pre>
-<p><a href="../spark/#querying-with-dataframes">DataFrame reads</a> are supported and can now reference tables by name using <code>spark.table</code>:</p>
+<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>
<pre><code class="scala">val df = spark.table("local.db.table")
df.count()
</code></pre>
<h3 id="next-steps">Next steps<a class="headerlink" href="#next-steps" title="Permanent link">¶</a></h3>
-<p>Next, you can learn more about <a href="../spark/">Iceberg tables in Spark</a>, or about the <a href="../api/">Iceberg Table API</a>.</p></div>
+<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>
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