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[SPARK-24499][SQL][DOC] Split the page of sql-programming-guide.html …
…to multiple separate pages ## What changes were proposed in this pull request? 1. Split the main page of sql-programming-guide into 7 parts: - Getting Started - Data Sources - Performance Turing - Distributed SQL Engine - PySpark Usage Guide for Pandas with Apache Arrow - Migration Guide - Reference 2. Add left menu for sql-programming-guide, keep first level index for each part in the menu.  ## How was this patch tested? Local test with jekyll build/serve. Closes #22746 from xuanyuanking/SPARK-24499. Authored-by: Yuanjian Li <xyliyuanjian@gmail.com> Signed-off-by: gatorsmile <gatorsmile@gmail.com>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,81 @@ | ||
| - text: Getting Started | ||
| url: sql-getting-started.html | ||
| subitems: | ||
| - text: "Starting Point: SparkSession" | ||
| url: sql-getting-started.html#starting-point-sparksession | ||
| - text: Creating DataFrames | ||
| url: sql-getting-started.html#creating-dataframes | ||
| - text: Untyped Dataset Operations (DataFrame operations) | ||
| url: sql-getting-started.html#untyped-dataset-operations-aka-dataframe-operations | ||
| - text: Running SQL Queries Programmatically | ||
| url: sql-getting-started.html#running-sql-queries-programmatically | ||
| - text: Global Temporary View | ||
| url: sql-getting-started.html#global-temporary-view | ||
| - text: Creating Datasets | ||
| url: sql-getting-started.html#creating-datasets | ||
| - text: Interoperating with RDDs | ||
| url: sql-getting-started.html#interoperating-with-rdds | ||
| - text: Aggregations | ||
| url: sql-getting-started.html#aggregations | ||
| - text: Data Sources | ||
| url: sql-data-sources.html | ||
| subitems: | ||
| - text: "Generic Load/Save Functions" | ||
| url: sql-data-sources-load-save-functions.html | ||
| - text: Parquet Files | ||
| url: sql-data-sources-parquet.html | ||
| - text: ORC Files | ||
| url: sql-data-sources-orc.html | ||
| - text: JSON Files | ||
| url: sql-data-sources-json.html | ||
| - text: Hive Tables | ||
| url: sql-data-sources-hive-tables.html | ||
| - text: JDBC To Other Databases | ||
| url: sql-data-sources-jdbc.html | ||
| - text: Avro Files | ||
| url: sql-data-sources-avro.html | ||
| - text: Troubleshooting | ||
| url: sql-data-sources-troubleshooting.html | ||
| - text: Performance Turing | ||
| url: sql-performance-turing.html | ||
| subitems: | ||
| - text: Caching Data In Memory | ||
| url: sql-performance-turing.html#caching-data-in-memory | ||
| - text: Other Configuration Options | ||
| url: sql-performance-turing.html#other-configuration-options | ||
| - text: Broadcast Hint for SQL Queries | ||
| url: sql-performance-turing.html#broadcast-hint-for-sql-queries | ||
| - text: Distributed SQL Engine | ||
| url: sql-distributed-sql-engine.html | ||
| subitems: | ||
| - text: "Running the Thrift JDBC/ODBC server" | ||
| url: sql-distributed-sql-engine.html#running-the-thrift-jdbcodbc-server | ||
| - text: Running the Spark SQL CLI | ||
| url: sql-distributed-sql-engine.html#running-the-spark-sql-cli | ||
| - text: PySpark Usage Guide for Pandas with Apache Arrow | ||
| url: sql-pyspark-pandas-with-arrow.html | ||
| subitems: | ||
| - text: Apache Arrow in Spark | ||
| url: sql-pyspark-pandas-with-arrow.html#apache-arrow-in-spark | ||
| - text: "Enabling for Conversion to/from Pandas" | ||
| url: sql-pyspark-pandas-with-arrow.html#enabling-for-conversion-tofrom-pandas | ||
| - text: "Pandas UDFs (a.k.a. Vectorized UDFs)" | ||
| url: sql-pyspark-pandas-with-arrow.html#pandas-udfs-aka-vectorized-udfs | ||
| - text: Usage Notes | ||
| url: sql-pyspark-pandas-with-arrow.html#usage-notes | ||
| - text: Migration Guide | ||
| url: sql-migration-guide.html | ||
| subitems: | ||
| - text: Spark SQL Upgrading Guide | ||
| url: sql-migration-guide-upgrade.html | ||
| - text: Compatibility with Apache Hive | ||
| url: sql-migration-guide-hive-compatibility.html | ||
| - text: Reference | ||
| url: sql-reference.html | ||
| subitems: | ||
| - text: Data Types | ||
| url: sql-reference.html#data-types | ||
| - text: NaN Semantics | ||
| url: sql-reference.html#nan-semantics | ||
| - text: Arithmetic operations | ||
| url: sql-reference.html#arithmetic-operations |
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| <div class="left-menu-wrapper"> | ||
| <div class="left-menu"> | ||
| <h3><a href="sql-programming-guide.html">Spark SQL Guide</a></h3> | ||
| {% include nav-left.html nav=include.nav-sql %} | ||
| </div> | ||
| </div> |
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| Original file line number | Diff line number | Diff line change |
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| --- | ||
| layout: global | ||
| title: Hive Tables | ||
| displayTitle: Hive Tables | ||
| --- | ||
|
|
||
| * Table of contents | ||
| {:toc} | ||
|
|
||
| Spark SQL also supports reading and writing data stored in [Apache Hive](http://hive.apache.org/). | ||
| However, since Hive has a large number of dependencies, these dependencies are not included in the | ||
| default Spark distribution. If Hive dependencies can be found on the classpath, Spark will load them | ||
| automatically. Note that these Hive dependencies must also be present on all of the worker nodes, as | ||
| they will need access to the Hive serialization and deserialization libraries (SerDes) in order to | ||
| access data stored in Hive. | ||
|
|
||
| Configuration of Hive is done by placing your `hive-site.xml`, `core-site.xml` (for security configuration), | ||
| and `hdfs-site.xml` (for HDFS configuration) file in `conf/`. | ||
|
|
||
| When working with Hive, one must instantiate `SparkSession` with Hive support, including | ||
| connectivity to a persistent Hive metastore, support for Hive serdes, and Hive user-defined functions. | ||
| Users who do not have an existing Hive deployment can still enable Hive support. When not configured | ||
| by the `hive-site.xml`, the context automatically creates `metastore_db` in the current directory and | ||
| creates a directory configured by `spark.sql.warehouse.dir`, which defaults to the directory | ||
| `spark-warehouse` in the current directory that the Spark application is started. Note that | ||
| the `hive.metastore.warehouse.dir` property in `hive-site.xml` is deprecated since Spark 2.0.0. | ||
| Instead, use `spark.sql.warehouse.dir` to specify the default location of database in warehouse. | ||
| You may need to grant write privilege to the user who starts the Spark application. | ||
|
|
||
| <div class="codetabs"> | ||
|
|
||
| <div data-lang="scala" markdown="1"> | ||
| {% include_example spark_hive scala/org/apache/spark/examples/sql/hive/SparkHiveExample.scala %} | ||
| </div> | ||
|
|
||
| <div data-lang="java" markdown="1"> | ||
| {% include_example spark_hive java/org/apache/spark/examples/sql/hive/JavaSparkHiveExample.java %} | ||
| </div> | ||
|
|
||
| <div data-lang="python" markdown="1"> | ||
| {% include_example spark_hive python/sql/hive.py %} | ||
| </div> | ||
|
|
||
| <div data-lang="r" markdown="1"> | ||
|
|
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| When working with Hive one must instantiate `SparkSession` with Hive support. This | ||
| adds support for finding tables in the MetaStore and writing queries using HiveQL. | ||
|
|
||
| {% include_example spark_hive r/RSparkSQLExample.R %} | ||
|
|
||
| </div> | ||
| </div> | ||
|
|
||
| ### Specifying storage format for Hive tables | ||
|
|
||
| When you create a Hive table, you need to define how this table should read/write data from/to file system, | ||
| i.e. the "input format" and "output format". You also need to define how this table should deserialize the data | ||
| to rows, or serialize rows to data, i.e. the "serde". The following options can be used to specify the storage | ||
| format("serde", "input format", "output format"), e.g. `CREATE TABLE src(id int) USING hive OPTIONS(fileFormat 'parquet')`. | ||
| By default, we will read the table files as plain text. Note that, Hive storage handler is not supported yet when | ||
| creating table, you can create a table using storage handler at Hive side, and use Spark SQL to read it. | ||
|
|
||
| <table class="table"> | ||
| <tr><th>Property Name</th><th>Meaning</th></tr> | ||
| <tr> | ||
| <td><code>fileFormat</code></td> | ||
| <td> | ||
| A fileFormat is kind of a package of storage format specifications, including "serde", "input format" and | ||
| "output format". Currently we support 6 fileFormats: 'sequencefile', 'rcfile', 'orc', 'parquet', 'textfile' and 'avro'. | ||
| </td> | ||
| </tr> | ||
|
|
||
| <tr> | ||
| <td><code>inputFormat, outputFormat</code></td> | ||
| <td> | ||
| These 2 options specify the name of a corresponding `InputFormat` and `OutputFormat` class as a string literal, | ||
| e.g. `org.apache.hadoop.hive.ql.io.orc.OrcInputFormat`. These 2 options must be appeared in pair, and you can not | ||
| specify them if you already specified the `fileFormat` option. | ||
| </td> | ||
| </tr> | ||
|
|
||
| <tr> | ||
| <td><code>serde</code></td> | ||
| <td> | ||
| This option specifies the name of a serde class. When the `fileFormat` option is specified, do not specify this option | ||
| if the given `fileFormat` already include the information of serde. Currently "sequencefile", "textfile" and "rcfile" | ||
| don't include the serde information and you can use this option with these 3 fileFormats. | ||
| </td> | ||
| </tr> | ||
|
|
||
| <tr> | ||
| <td><code>fieldDelim, escapeDelim, collectionDelim, mapkeyDelim, lineDelim</code></td> | ||
| <td> | ||
| These options can only be used with "textfile" fileFormat. They define how to read delimited files into rows. | ||
| </td> | ||
| </tr> | ||
| </table> | ||
|
|
||
| All other properties defined with `OPTIONS` will be regarded as Hive serde properties. | ||
|
|
||
| ### Interacting with Different Versions of Hive Metastore | ||
|
|
||
| One of the most important pieces of Spark SQL's Hive support is interaction with Hive metastore, | ||
| which enables Spark SQL to access metadata of Hive tables. Starting from Spark 1.4.0, a single binary | ||
| build of Spark SQL can be used to query different versions of Hive metastores, using the configuration described below. | ||
| Note that independent of the version of Hive that is being used to talk to the metastore, internally Spark SQL | ||
| will compile against Hive 1.2.1 and use those classes for internal execution (serdes, UDFs, UDAFs, etc). | ||
|
|
||
| The following options can be used to configure the version of Hive that is used to retrieve metadata: | ||
|
|
||
| <table class="table"> | ||
| <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr> | ||
| <tr> | ||
| <td><code>spark.sql.hive.metastore.version</code></td> | ||
| <td><code>1.2.1</code></td> | ||
| <td> | ||
| Version of the Hive metastore. Available | ||
| options are <code>0.12.0</code> through <code>2.3.3</code>. | ||
| </td> | ||
| </tr> | ||
| <tr> | ||
| <td><code>spark.sql.hive.metastore.jars</code></td> | ||
| <td><code>builtin</code></td> | ||
| <td> | ||
| Location of the jars that should be used to instantiate the HiveMetastoreClient. This | ||
| property can be one of three options: | ||
| <ol> | ||
| <li><code>builtin</code></li> | ||
| Use Hive 1.2.1, which is bundled with the Spark assembly when <code>-Phive</code> is | ||
| enabled. When this option is chosen, <code>spark.sql.hive.metastore.version</code> must be | ||
| either <code>1.2.1</code> or not defined. | ||
| <li><code>maven</code></li> | ||
| Use Hive jars of specified version downloaded from Maven repositories. This configuration | ||
| is not generally recommended for production deployments. | ||
| <li>A classpath in the standard format for the JVM. This classpath must include all of Hive | ||
| and its dependencies, including the correct version of Hadoop. These jars only need to be | ||
| present on the driver, but if you are running in yarn cluster mode then you must ensure | ||
| they are packaged with your application.</li> | ||
| </ol> | ||
| </td> | ||
| </tr> | ||
| <tr> | ||
| <td><code>spark.sql.hive.metastore.sharedPrefixes</code></td> | ||
| <td><code>com.mysql.jdbc,<br/>org.postgresql,<br/>com.microsoft.sqlserver,<br/>oracle.jdbc</code></td> | ||
| <td> | ||
| <p> | ||
| A comma-separated list of class prefixes that should be loaded using the classloader that is | ||
| shared between Spark SQL and a specific version of Hive. An example of classes that should | ||
| be shared is JDBC drivers that are needed to talk to the metastore. Other classes that need | ||
| to be shared are those that interact with classes that are already shared. For example, | ||
| custom appenders that are used by log4j. | ||
| </p> | ||
| </td> | ||
| </tr> | ||
| <tr> | ||
| <td><code>spark.sql.hive.metastore.barrierPrefixes</code></td> | ||
| <td><code>(empty)</code></td> | ||
| <td> | ||
| <p> | ||
| A comma separated list of class prefixes that should explicitly be reloaded for each version | ||
| of Hive that Spark SQL is communicating with. For example, Hive UDFs that are declared in a | ||
| prefix that typically would be shared (i.e. <code>org.apache.spark.*</code>). | ||
| </p> | ||
| </td> | ||
| </tr> | ||
| </table> |
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