Hive Warehouse Connector for accessing Apache Spark data
The Hive Warehouse Connector (HWC) is a Spark library/plugin that is launched with the Spark app. You use the Hive Warehouse Connector API to access any managed Hive table from Spark. You must use low-latency analytical processing (LLAP) in HiveServer Interactive to read ACID, or other Hive-managed tables, from Spark.
In HDP 3.0 and later, Spark and Hive use independent catalogs for accessing SparkSQL or Hive tables on the same or different platforms. A table created by Spark resides in the Spark catalog. A table created by Hive resides in the Hive catalog. Databases fall under the catalog namespace, similar to how tables belong to a database namespace. Although independent, these tables interoperate and you can see Spark tables in the Hive catalog, but only when using the Hive Warehouse Connector.
You can use the Hive Warehouse Connector (HWC) API to access any type of table in the Hive catalog from Spark. When you use SparkSQL, standard Spark APIs access tables in the Spark catalog.
Using HWC, you can export tables and extracts from the Spark catalog to Hive and from the Hive catalog to Spark. You export tables and extracts from the Spark catalog to Hive by reading them using Spark APIs and writing them to the Hive catalog using the HWC. You must use low-latency analytical processing (LLAP) in HiveServer Interactive to read ACID, or other Hive-managed tables, from Spark. You do not need LLAP to write to ACID, or other managed tables, from Spark. You do not need HWC to access external tables from Spark.
Using the HWC, you can read and write Apache Spark DataFrames and Streaming DataFrames. Apache Ranger and the HiveWarehouseConnector library provide row and column, fine-grained access to the data.
- HWC supports tables in ORC format only.
- The spark thrift server is not supported.
- Table stats are not generated when you write a DataFrame to Hive.
Supported applications and operations
- Spark shell
- The spark-submit script
- Describing a table
- Creating a table for ORC-formatted data
- Selecting Hive data and retrieving a DataFrame
- Writing a DataFrame to Hive in batch
- Executing a Hive update statement
- Reading Hive table data, transforming it in Spark, and writing it to a new Hive table
- Writing a DataFrame or Spark stream to Hive using HiveStreaming