Spark connector Iceberg catalog

The Apache Gravitino Spark connector offers the capability to read and write Iceberg tables, with the metadata managed by the Gravitino server. To enable the use of the Iceberg catalog within the Spark connector, you must set the configuration spark.sql.gravitino.enableIcebergSupport to true and download Iceberg Spark runtime jar to Spark classpath.

Capabilities

Support DML and DDL operations:

• CREATE TABLE

Doesn't support distribution and sort orders.

• DROP TABLE
• ALTER TABLE
• INSERT INTO&OVERWRITE
• SELECT
• MERGE INTO
• DELETE FROM
• UPDATE
• CALL
• TIME TRAVEL QUERY
• DESCRIBE TABLE

Not supported operations:

• View operations.
• Metadata tables, like:
  • {iceberg_catalog}.{iceberg_database}.{iceberg_table}.snapshots
• Other Iceberg extension SQLs, like:
  • ALTER TABLE prod.db.sample ADD PARTITION FIELD xx
  • ALTER TABLE ... WRITE ORDERED BY
  • ALTER TABLE prod.db.sample CREATE BRANCH branchName
  • ALTER TABLE prod.db.sample CREATE TAG tagName
• AtomicCreateTableAsSelect&AtomicReplaceTableAsSelect

SQL example

-- Suppose iceberg_a is the Iceberg catalog name managed by Gravitino
USE iceberg_a;

CREATE DATABASE IF NOT EXISTS mydatabase;
USE mydatabase;

CREATE TABLE IF NOT EXISTS employee (
  id bigint,
  name string,
  department string,
  hire_date timestamp
) USING iceberg
PARTITIONED BY (days(hire_date));
DESC TABLE EXTENDED employee;

INSERT INTO employee
VALUES
(1, 'Alice', 'Engineering', TIMESTAMP '2021-01-01 09:00:00'),
(2, 'Bob', 'Marketing', TIMESTAMP '2021-02-01 10:30:00'),
(3, 'Charlie', 'Sales', TIMESTAMP '2021-03-01 08:45:00');

SELECT * FROM employee WHERE date(hire_date) = '2021-01-01';

UPDATE employee SET department = 'Jenny' WHERE id = 1;

DELETE FROM employee WHERE id < 2;

MERGE INTO employee
USING (SELECT 4 as id, 'David' as name, 'Engineering' as department, TIMESTAMP '2021-04-01 09:00:00' as hire_date) as new_employee
ON employee.id = new_employee.id
WHEN MATCHED THEN UPDATE SET *
WHEN NOT MATCHED THEN INSERT *;

MERGE INTO employee
USING (SELECT 4 as id, 'David' as name, 'Engineering' as department, TIMESTAMP '2021-04-01 09:00:00' as hire_date) as new_employee
ON employee.id = new_employee.id
WHEN MATCHED THEN DELETE
WHEN NOT MATCHED THEN INSERT *;

-- Suppose that the first snapshotId of employee is 1L and the second snapshotId is 2L
-- Rollback the snapshot for iceberg_a.mydatabase.employee to 1L
CALL iceberg_a.system.rollback_to_snapshot('iceberg_a.mydatabase.employee', 1);
-- Set the snapshot for iceberg_a.mydatabase.employee to 2L
CALL iceberg_a.system.set_current_snapshot('iceberg_a.mydatabase.employee', 2);

-- Suppose that the commit timestamp of the first snapshot is older than '2024-05-27 01:01:00'
-- Time travel to '2024-05-27 01:01:00'
SELECT * FROM employee TIMESTAMP AS OF '2024-05-27 01:01:00';
SELECT * FROM employee FOR SYSTEM_TIME AS OF '2024-05-27 01:01:00';

-- Show the details of employee, such as schema and reserved properties(like location, current-snapshot-id, provider, format, format-version, etc)
DESC EXTENDED employee;

For more details about CALL, please refer to the Spark Procedures description in Iceberg official document.

Catalog properties

Gravitino spark connector will transform below property names which are defined in catalog properties to Spark Iceberg connector configuration.

Gravitino catalog property name	Spark Iceberg connector configuration	Description	Since Version
catalog-backend	type	Catalog backend type	0.5.0
uri	uri	Catalog backend uri	0.5.0
warehouse	warehouse	Catalog backend warehouse	0.5.0
jdbc-user	jdbc.user	JDBC user name	0.5.0
jdbc-password	jdbc.password	JDBC password	0.5.0
io-impl	io-impl	The io implementation for FileIO in Iceberg.	0.6.0
s3-endpoint	s3.endpoint	An alternative endpoint of the S3 service, This could be used for S3FileIO with any s3-compatible object storage service that has a different endpoint, or access a private S3 endpoint in a virtual private cloud.	0.6.0
s3-region	client.region	The region of the S3 service, like us-west-2.	0.6.0

Gravitino catalog property names with the prefix spark.bypass. are passed to Spark Iceberg connector. For example, using spark.bypass.clients to pass the clients to the Spark Iceberg connector.

info

Iceberg catalog property cache-enabled is setting to false internally and not allowed to change.

Storage

S3

You need to add s3 secret to the Spark configuration using spark.sql.catalog.${iceberg_catalog_name}.s3.access-key-id and spark.sql.catalog.${iceberg_catalog_name}.s3.secret-access-key. Additionally, download the Iceberg AWS bundle and place it in the classpath of Spark.