Apache Gravitino Trino connector - Iceberg catalog

Apache Iceberg is an open table format for huge analytic datasets. The Iceberg catalog allows Trino querying data stored in files written in Iceberg format, as defined in the Iceberg Table Spec. The catalog supports Apache Iceberg table spec versions 1 and 2.

Requirements

To use Iceberg, you need:

• Network access from the Trino coordinator and workers to the distributed object storage.
• Access to a Hive metastore service (HMS), an AWS Glue catalog, a JDBC catalog, a REST catalog, or a Nessie server.
• Data files stored in a supported file format. These can be configured using file format configuration properties per catalog:
  • ORC
  • Parquet (default)

Schema operations

Create a schema

Users can create a schema through Apache Gravitino Trino connector as follows:

CREATE SCHEMA catalog.schema_name

Table operations

Create table

The Apache Gravitino Trino connector currently supports basic Iceberg table creation statements, such as defining fields, allowing null values, and adding comments. The Apache Gravitino Trino connector does not support CREATE TABLE AS SELECT.

The following example shows how to create a table in the Iceberg catalog:

CREATE TABLE catalog.schema_name.table_name
(
  name varchar,
  salary int
)

Alter table

Support for the following alter table operations:

• Rename table
• Add a column
• Drop a column
• Rename a column
• Change a column type
• Set a table property

Select

The Apache Gravitino Trino connector supports most SELECT statements, allowing the execution of queries successfully. Currently, it doesn't support certain query optimizations, such as pushdown and pruning functionalities.

Table and Schema properties

Create a schema with properties

Iceberg schema does not support properties.

Create a table with properties

Users can use the following example to create a table with properties:

CREATE TABLE catalog.dbname.tablename
(
  name varchar,
  salary int
) WITH (
  KEY = 'VALUE',
  ...      
);

The following tables are the properties supported by the Iceberg table:

Property	Description	Default Value	Required	Reserved	Since Version
partitioning	Partition columns for the table	(none)	No	No	0.4.0
sorted_by	Sorted columns for the table	(none)	No	No	0.4.0

Reserved properties: A reserved property is one can't be set by users but can be read by users.

Basic usage examples

You need to do the following steps before you can use the Iceberg catalog in Trino through Apache Gravitino.

• Create a metalake and catalog in Apache Gravitino. Assuming that the metalake name is test and the catalog name is iceberg_test, then you can use the following code to create them in Apache Gravitino:

curl -X POST -H "Content-Type: application/json" \
-d '{
  "name": "test",
  "comment": "comment",
  "properties": {}
}' http://gravitino-host:8090/api/metalakes

curl -X POST -H "Content-Type: application/json" \
-d '{
  "name": "iceberg_test",
  "type": "RELATIONAL",
  "comment": "comment",
  "provider": "lakehouse-iceberg",
  "properties": {
    "uri": "thrift://hive-host:9083",
    "catalog-backend": "hive",
    "warehouse": "hdfs://hdfs-host:9000/user/iceberg/warehouse"
  }
}' http://gravitino-host:8090/api/metalakes/test/catalogs

For More information about the Iceberg catalog, please refer to Iceberg catalog.

• Set the value of configuration gravitino.metalake to the metalake you have created, named 'test', and start the Trino container.

Use the Trino CLI to connect to the Trino container and run a query.

Listing all Apache Gravitino managed catalogs:

SHOW CATALOGS;

The results are similar to:

    Catalog
----------------
 gravitino
 jmx
 system
 iceberg_test
(4 rows)

Query 20231017_082503_00018_6nt3n, FINISHED, 1 node

The gravitino catalog is a catalog defined By Trino catalog configuration. The iceberg_test catalog is the catalog created by you in Apache Gravitino. Other catalogs are regular user-configured Trino catalogs.

Creating tables and schemas

Create a new schema named database_01 in test.iceberg_test catalog.

CREATE SCHEMA iceberg_test.database_01;

Create a new table named table_01 in schema iceberg_test.database_01.

CREATE TABLE iceberg_test.database_01.table_01
(
name varchar,
salary int
) with (
  partitioning = ARRAY['salary'],
  sorted_by = ARRAY['name']
);

Writing data

Insert data into the table table_01:

INSERT INTO iceberg_test.database_01.table_01 (name, salary) VALUES ('ice', 12);

Insert data into the table table_01 from select:

INSERT INTO iceberg_test.database_01.table_01 (name, salary) SELECT * FROM iceberg_test.database_01.table_01;

Querying data

Query the table_01 table:

SELECT * FROM iceberg_test.database_01.table_01;

Modify a table

Add a new column age to the table_01 table:

ALTER TABLE iceberg_test.database_01.table_01 ADD COLUMN age int;

Drop a column age from the table_01 table:

ALTER TABLE iceberg_test.database_01.table_01 DROP COLUMN age;

Rename the table_01 table to table_02:

ALTER TABLE iceberg_test.database_01.table_01 RENAME TO iceberg_test.database_01.table_02;

Drop

Drop a schema:

DROP SCHEMA iceberg_test.database_01;

Drop a table:

DROP TABLE iceberg_test.database_01.table_01;

HDFS username and permissions

Before running any Insert statements for Iceberg tables in Trino, you must check that the user Trino is using to access HDFS has access to the warehouse directory. You can override this username by setting the HADOOP_USER_NAME system property in the Trino JVM config, replacing hdfs_user with the appropriate username:

-DHADOOP_USER_NAME=hdfs_user

S3

When using AWS S3 within the Iceberg catalog, users need to configure the Trino Iceberg connector's AWS S3-related properties in the catalog's properties. Please refer to the documentation of Hive connector with Amazon S3. These configurations must use the trino.bypass. prefix in the Iceberg catalog's attributes to be effective.

To create an Iceberg catalog with AWS S3 configuration in the Trino CLI, use the following command:

call gravitino.system.create_catalog(
    'gt_iceberg',
    'lakehouse-iceberg',
    map(
        array['uri', 'catalog-backend', 'warehouse',
          'trino.bypass.hive.s3.aws-access-key', 'trino.bypass.hive.s3.aws-secret-key', 'trino.bypass.hive.s3.region',
          's3-access-key-id', 's3-secret-access-key', 's3-region', 'io-impl'
        ],
        array['thrift://hive:9083', 'hive', 's3a://trino-test-ice/dw2',
        '<aws-access-key>', '<aws-secret-key>', '<region>',
        '<aws-access-key>', '<aws-secret-key>', '<region>', 'org.apache.iceberg.aws.s3.S3FileIO']
    )
);

• The configurations of trino.bypass.hive.s3.aws-access-key, trino.bypass.hive.s3.aws-secret-key, trino.bypass.hive.s3.region are the required the configurations for the Apache Gravitino Trino connector.
• The configurations of s3-access-key-id, s3-secret-access-key, io-impl and s3-region. are the required the configurations for the Apache Gravitino Iceberg catalog.
• The location specifies the storage path on AWS S3. Ensure that the specified directory exists on AWS S3 before proceeding.

Once the Iceberg catalog is successfully created, users can create schemas and tables as follows:

CREATE SCHEMA gt_iceberg.gt_db03;

CREATE TABLE gt_iceberg.gt_db03.tb01 (
    name varchar,
    salary int
);

After running the command, the tables are ready for data reading and writing operations on AWS S3.

note

TThe Iceberg catalog module in the Apache Gravitino server should add AWS S3 support. Please refer to Apache Gravitino Iceberg catalog.