Spark Enable Hive Support

Let’s discuss how to enable hive support in Spark pr PySpark to work with Hive in order to read and write. In this article, I will explain Spark configurations to enable hive support, and different ways to enable it.

1. Introduction

Apache Spark or PySpark has built-in support for interacting with Apache Hive. Hive is a data warehouse system for querying and managing large datasets. Enabling hive support, allows Spark to seamlessly integrate with existing Hive installations, and leverage Hive’s metadata and storage capabilities.

When using Spark with Hive, you can read and write data stored in Hive tables using Spark APIs. This allows you to take advantage of the performance optimizations and scalability benefits of Spark while still being able to leverage the features and benefits of Hive.

2. Spark Configurations for Hive Support

To use Spark with Hive, you need to configure Spark to use Hive’s metastore as its metadata repository, and also specify the location of the Hive configuration files. This can be done using the following Spark configuration properties:

spark.sql.catalogImplementation=hive
spark.sql.hive.metastore.version=<hive-version>
spark.sql.hive.metastore.jars=<hive-jars>
spark.hadoop.hive.metastore.uris=<hive-metastore-uri>

Once these configuration properties are set, you can interact with Hive tables using the Spark SQL API or the DataFrame API. It’s important to note that in addition to the built-in support for Hive, Spark also has its own native SQL engine and data sources, which can be used independently or in conjunction with Hive.

3. Spark manually Enable Hive Support

To enable Hive support in Apache Spark, you need to set the above-mentioned configuration properties when you create your SparkSession or SparkContext. Here are the basic steps to enable Hive support in Spark:

1. Set the spark.sql.catalogImplementation configuration property to hive. This tells Spark to use the Hive metastore as the metadata repository for Spark SQL.


// Spark manually Enable Hive Support
import org.apache.spark.sql.SparkSession

val spark = SparkSession.builder()
  .appName("MyApp")
  .config("spark.sql.catalogImplementation", "hive")
  .getOrCreate()

2. Set the location of the Hive configuration files. By default, Spark looks for the Hive configuration files in the $HIVE_HOME/conf directory. If your configuration files are located elsewhere, you can use the spark.hadoop.hive.metastore.uris configuration property to specify the URI of the metastore.


import org.apache.spark.sql.SparkSession

val spark = SparkSession.builder()
  .appName("MyApp")
  .config("spark.sql.catalogImplementation", "hive")
  .config("spark.hadoop.hive.metastore.uris", "thrift://my-hive-metastore:9083")
  .getOrCreate()

3. If your Hive version is different from the one that Spark is built against, you may also need to set the spark.sql.hive.metastore.version and spark.sql.hive.metastore.jars configuration properties to ensure compatibility. For example:


import org.apache.spark.sql.SparkSession

val spark = SparkSession.builder()
  .appName("MyApp")
  .config("spark.sql.catalogImplementation", "hive")
  .config("spark.sql.hive.metastore.version", "2.3.7")
  .config("spark.sql.hive.metastore.jars", "/path/to/hive-jars")
  .getOrCreate()

With these configuration properties set, you can now use Spark to interact with Hive tables and databases using the Spark SQL API or the DataFrame API.

4. Spark enableHiveSupport()

In Apache Spark or PySpark, the enableHiveSupport() method is used to enable Hive support in a SparkSession. This method sets the required configuration properties to use the Hive metastore as the metadata repository for Spark SQL and configures Spark to use the Hive execution engine for certain SQL operations.

Here is an example of how to create a SparkSession with Hive support using the enableHiveSupport() method:


// Spark enableHiveSupport()
import org.apache.spark.sql.SparkSession

val spark = SparkSession.builder()
  .appName("MySparkApp")
  .enableHiveSupport()
  .getOrCreate()

This method is a convenient way to enable Hive support in a SparkSession, as it sets all the required configuration properties automatically. When you call this method, Spark will:

Set the spark.sql.catalogImplementation configuration property to hive.
Set the location of the Hive configuration files to the default location (i.e. $HIVE_HOME/conf).
Configure Spark to use the Hive execution engine for certain SQL operations, such as JOIN, GROUP BY, and ORDER BY.

If you have a specific configuration that is different from the default, you can still set the necessary configuration properties manually as mentioned above instead of using enableHiveSupport().

Note: enableHiveSupport() is only available in Spark 2.x and later versions. Additionally, it’s recommended to use the latest version of Hive to ensure compatibility and performance. Finally, be aware that enabling Hive support may have some performance overhead, as Spark needs to coordinate with the Hive metastore service.

5. Conclusion

In Conclusion, With Hive support enabled, you can use Spark/PySpark SQL to execute queries against Hive tables, and you can use Spark’s DataFrame and Dataset APIs to read and write data from and to Hive tables. The enableHiveSupport() function also sets up the necessary configurations for using Hive features, so you don’t need to worry about configuring your SparkSession manually.

1. Introduction

2. Spark Configurations for Hive Support

3. Spark manually Enable Hive Support

4. Spark enableHiveSupport()

5. Conclusion

Related Articles

rimmalapudi