Flink Table Store

Flink Table Store is a unified storage to build dynamic tables for both streaming and batch processing in Flink, supporting high-speed data ingestion and timely data query.

Tip

This article assumes that you have mastered the basic knowledge and operation of Flink Table Store. For the knowledge about Flink Table Store not mentioned in this article, you can obtain it from its Official Documentation.

By using kyuubi, we can run SQL queries towards Flink Table Store which is more convenient, easy to understand, and easy to expand than directly using spark to manipulate Flink Table Store.

Flink Table Store Integration

To enable the integration of kyuubi spark sql engine and Flink Table Store through Apache Spark Datasource V2 and Catalog APIs, you need to:

• Referencing the Flink Table Store dependencies

• Setting the spark extension and catalog configurations

Dependencies

The classpath of kyuubi spark sql engine with Flink Table Store supported consists of

1. kyuubi-spark-sql-engine-build/mvn: line 99: help:evaluate: command not found_2.12.jar, the engine jar deployed with Kyuubi distributions

2. a copy of spark distribution

3. flink-table-store-spark-<version>.jar (example: flink-table-store-spark-0.2.jar), which can be found in the Maven Central

In order to make the Flink Table Store packages visible for the runtime classpath of engines, we can use one of these methods:

1. Put the Flink Table Store packages into $SPARK_HOME/jars directly

2. Set spark.jars=/path/to/flink-table-store-spark

Warning

Please mind the compatibility of different Flink Table Store and Spark versions, which can be confirmed on the page of Flink Table Store multi engine support.

Configurations

To activate functionality of Flink Table Store, we can set the following configurations:

spark.sql.catalog.tablestore=org.apache.flink.table.store.spark.SparkCatalog
spark.sql.catalog.tablestore.warehouse=file:/tmp/warehouse

Flink Table Store Operations

Flink Table Store supports reading table store tables through Spark. A common scenario is to write data with Flink and read data with Spark. You can follow this document Flink Table Store Quick Start to write data to a table store table and then use kyuubi spark sql engine to query the table with the following SQL SELECT statement.

select * from table_store.default.word_count;