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Flink via Iceberg


Detailed steps on how to set up Pyspark + Iceberg + Flink + Nessie with Python is available on Binder

In order to use Flink with Python API, you will need to make sure pyflink have access to all Hadoop JARs as mentioned in these docs. After that, you will need to make sure iceberg-flink-runtime is added to Flink. This can be done by adding the iceberg JAR to pyflink via env.add_jar, e.g: env.add_jars("file://path/to/jar/iceberg-flink-runtime-1.5.2.jar"). This can be shown below:

import os

from pyflink.datastream import StreamExecutionEnvironment

env = StreamExecutionEnvironment.get_execution_environment()
iceberg_flink_runtime_jar = os.path.join(os.getcwd(), "iceberg-flink-runtime-1.5.2.jar")


Once we have added iceberg-flink-runtime JAR to pyflink, we can then create StreamTableEnvironment and execute Flink SQL statements. This can be shown in the following example:

from pyflink.table import StreamTableEnvironment

table_env = StreamTableEnvironment.create(env)

        """CREATE CATALOG <catalog_name> WITH (
        'warehouse' = '/path/to/flink/warehouse')"""

With the above statement, we have created a Nessie catalog (via Iceberg) that Flink will use to manage the tables.

For more general information about Flink and Iceberg, refer to Iceberg and Flink documentation.


To use Nessie Catalog in Flink via Iceberg, we will need to create a catalog in Flink through CREATE CATALOG SQL statement (replace <catalog_name> with the name of your catalog), example:

        """CREATE CATALOG <catalog_name> WITH (
        'warehouse' = '/path/to/flink/warehouse')"""

The following properties are required in Flink when creating the Nessie Catalog:

  • type: This must be iceberg for iceberg table format.
  • catalog-impl: This must be org.apache.iceberg.nessie.NessieCatalog in order to tell Flink to use Nessie catalog implementation.
  • uri: The location of the Nessie server.
  • ref: The Nessie ref/branch we want to use.
  • warehouse: The location where to store Iceberg tables managed by Nessie catalog.
  • authentication.type: The authentication type to be used, please refer to the authentication docs for more info.

Create tables

To create tables in Flink that are managed by Nessie/Iceberg, you will need to specify the catalog name in addition to the database whenever you issue CREATE TABLE statement, e.g:

CREATE DATABASE `<catalog_name>`.`<database_name>`;

CREATE TABLE `<catalog_name>`.`<database_name>`.`<table_name>` (
    id BIGINT COMMENT 'unique id',
    data STRING

Reading tables

To read tables in Flink, this can be done with a typical SQL SELECT statement, however as the same with creating tables, you will need to make sure to specify the catalog name in addition to the database. e.g:

SELECT * FROM `<catalog_name>`.`<database_name>`.`<table_name>`;

As well, similar to Spark, you can read tables from specific branches or hashes from within a SELECT statement. The general pattern is <table_name>@<branch> or <table>#<hash> or <table>@<branch>#<hash> (e.g: salaries@main):

SELECT * FROM `<catalog_name>`.`<database_name>`.`<table_name>@<branch>`;
SELECT * FROM `<catalog_name>`.`<database_name>`.`<table_name>#<hash>`;
SELECT * FROM `<catalog_name>`.`<database_name>`.`<table_name>@<branch>#<hash>`;

Other DDL statements

To read and write into tables that are managed by Iceberg and Nessie, typical Flink SQL queries can be used. Refer to this documentation here for more information.