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.
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
The classpath of kyuubi spark sql engine with Iceberg supported consists of
kyuubi-spark-sql-engine-1.8.0-SNAPSHOT_2.12.jar, the engine jar deployed with Kyuubi distributions
a copy of spark distribution
iceberg-spark-runtime-<spark.version>_<scala.version>-<iceberg.version>.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:
Put the Iceberg packages into
Please mind the compatibility of different Iceberg and Spark versions, which can be confirmed on the page of Iceberg multi engine support.
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
CREATE TABLE as a example,
CREATE TABLE foo ( id bigint COMMENT 'unique id', data string) USING iceberg;
SELECT as a example,
SELECT * FROM foo;
INSERT as a example,
INSERT INTO foo VALUES (1, 'a'), (2, 'b'), (3, 'c');
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;
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;
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);