Apache Iceberg is an open table format for huge analytic datasets. Iceberg adds tables to compute engines including Spark, Trino, PrestoDB, Flink, Hive and Impala using a high-performance table format that works just like a SQL table.

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

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

Iceberg Integration

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

• Referencing the Iceberg dependencies
• Setting the spark extension and catalog configurations

Dependencies

The classpath of kyuubi spark sql engine with Iceberg supported consists of

1. kyuubi-spark-sql-engine-1.9.1_2.12.jar, the engine jar deployed with Kyuubi distributions
2. a copy of spark distribution
3. iceberg-spark-runtime-_-.jar (example: iceberg-spark-runtime-3.2_2.12-0.14.0.jar), which can be found in the Maven Central

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

1. Put the Iceberg packages into $SPARK_HOME/jars directly
2. Set spark.jars=/path/to/iceberg-spark-runtime

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

Configurations

To activate functionality of Iceberg, we can set the following configurations:

spark.sql.catalog.spark_catalog=org.apache.iceberg.spark.SparkCatalog
spark.sql.catalog.spark_catalog.type=hive
spark.sql.catalog.spark_catalog.uri=thrift://metastore-host:port
spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions

Iceberg Operations

Taking CREATE TABLE as a example,

CREATE TABLE foo (
  id bigint COMMENT 'unique id',
  data string)
USING iceberg;

Taking SELECT as a example,

SELECT * FROM foo;

Taking INSERT as a example,

INSERT INTO foo VALUES (1, 'a'), (2, 'b'), (3, 'c');

Taking UPDATE as a example, Spark 3.1 added support for UPDATE queries that update matching rows in tables.

UPDATE foo SET data = 'd', id = 4 WHERE id >= 3 and id < 4;

Taking DELETE FROM as a example, Spark 3 added support for DELETE FROM queries to remove data from tables.

DELETE FROM foo WHERE id >= 1 and id < 2;

Taking MERGE INTO as a example,

MERGE INTO target_table t
USING source_table s
ON t.id = s.id
WHEN MATCHED AND s.opType = 'delete' THEN DELETE
WHEN MATCHED AND s.opType = 'update' THEN UPDATE SET id = s.id, data = s.data
WHEN NOT MATCHED AND s.opType = 'insert' THEN INSERT (id, data) VALUES (s.id, s.data);