Hive Tables

  • Table of contents {:toc}

Spark SQL also supports reading and writing data stored in Apache Hive. 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.

{% include_example spark_hive scala/org/apache/spark/examples/sql/hive/SparkHiveExample.scala %}
{% include_example spark_hive java/org/apache/spark/examples/sql/hive/ %}
{% include_example spark_hive python/sql/ %}
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 %}

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.

Property Name Meaning
fileFormat 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'.
inputFormat, outputFormat These 2 options specify the name of a corresponding InputFormat and OutputFormat class as a string literal, e.g. These 2 options must be appeared in a pair, and you can not specify them if you already specified the fileFormat option.
serde 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.
fieldDelim, escapeDelim, collectionDelim, mapkeyDelim, lineDelim These options can only be used with "textfile" fileFormat. They define how to read delimited files into rows.

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 built-in Hive 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:

Property Name Default Meaning
spark.sql.hive.metastore.version 2.3.6 Version of the Hive metastore. Available options are 0.12.0 through 2.3.6 and 3.0.0 through 3.1.2.
spark.sql.hive.metastore.jars builtin Location of the jars that should be used to instantiate the HiveMetastoreClient. This property can be one of three options:
  1. builtin
  2. Use Hive 2.3.6, which is bundled with the Spark assembly when -Phive is enabled. When this option is chosen, spark.sql.hive.metastore.version must be either 2.3.6 or not defined.
  3. maven
  4. Use Hive jars of specified version downloaded from Maven repositories. This configuration is not generally recommended for production deployments.
  5. 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.
spark.sql.hive.metastore.sharedPrefixes com.mysql.jdbc,

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.

spark.sql.hive.metastore.barrierPrefixes (empty)

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. org.apache.spark.*).