Real-Time Analytics With Flink (Iceberg)

This guide will get you up and running with Apache Flink to do real-time analytics, covering some powerful features of Fluss, including integrating with Apache Iceberg. The guide is derived from TPC-H Q5.

For more information on working with Flink, refer to the Apache Flink Engine section.

Environment Setup

Prerequisites

Before proceeding with this guide, ensure that Docker and the Docker Compose plugin are installed on your machine. All commands were tested with Docker version 27.4.0 and Docker Compose version v2.30.3.

note

We encourage you to use a recent version of Docker and Compose v2 (however, Compose v1 might work with a few adaptions).

Starting required components

We will use docker compose to spin up the required components for this tutorial.

1. Create a working directory for this guide.

mkdir fluss-quickstart-flink-iceberg
cd fluss-quickstart-flink-iceberg

2. Create a lib directory and download the required Hadoop jar file:

mkdir lib
wget -O lib/hadoop-apache-3.3.5-2.jar https://repo1.maven.org/maven2/io/trino/hadoop/hadoop-apache/3.3.5-2/hadoop-apache-3.3.5-2.jar

This jar file provides Hadoop 3.3.5 dependencies required for Iceberg's Hadoop catalog integration.

info

The lib directory serves as a staging area for additional jars needed by the Fluss coordinator server. The docker-compose configuration (see step 3) mounts this directory and copies all jars to /opt/fluss/plugins/iceberg/ inside the coordinator container at startup.

You can add more jars to this lib directory based on your requirements:

• Cloud storage support: For AWS S3 integration with Iceberg, add the corresponding Iceberg bundle jars (e.g., iceberg-aws-bundle)
• Custom Hadoop configurations: Add jars for specific HDFS distributions or custom authentication mechanisms
• Other catalog backends: Add jars needed for alternative Iceberg catalog implementations (e.g., Rest, Hive, Glue)

Any jar placed in the lib directory will be automatically loaded by the Fluss coordinator server, making it available for Iceberg integration.

3. Create a docker-compose.yml file with the following content:

services:
  zookeeper:
    restart: always
    image: zookeeper:3.9.2

  coordinator-server:
    image: fluss/fluss:0.8-SNAPSHOT
    command: coordinatorServer
    depends_on:
      - zookeeper
    environment:
      - |
        FLUSS_PROPERTIES=
        zookeeper.address: zookeeper:2181
        bind.listeners: FLUSS://coordinator-server:9123
        remote.data.dir: /tmp/fluss/remote-data
        datalake.format: iceberg
        datalake.iceberg.type: hadoop
        datalake.iceberg.warehouse: /tmp/iceberg
    volumes:
      - shared-tmpfs:/tmp/iceberg
      - ./lib:/tmp/lib
    entrypoint: ["sh", "-c", "cp -v /tmp/lib/*.jar /opt/fluss/plugins/iceberg/ && exec /docker-entrypoint.sh coordinatorServer"]

  tablet-server:
    image: fluss/fluss:0.8-SNAPSHOT
    command: tabletServer
    depends_on:
      - coordinator-server
    environment:
      - |
        FLUSS_PROPERTIES=
        zookeeper.address: zookeeper:2181
        bind.listeners: FLUSS://tablet-server:9123
        data.dir: /tmp/fluss/data
        remote.data.dir: /tmp/fluss/remote-data
        kv.snapshot.interval: 0s
        datalake.format: iceberg
        datalake.iceberg.type: hadoop
        datalake.iceberg.warehouse: /tmp/iceberg
    volumes:
      - shared-tmpfs:/tmp/iceberg

  jobmanager:
    image: fluss/quickstart-flink:1.20-0.8-SNAPSHOT
    ports:
      - "8083:8081"
    command: jobmanager
    environment:
      - |
        FLINK_PROPERTIES=
        jobmanager.rpc.address: jobmanager
    volumes:
      - shared-tmpfs:/tmp/iceberg

  taskmanager:
    image: fluss/quickstart-flink:1.20-0.8-SNAPSHOT
    depends_on:
      - jobmanager
    command: taskmanager
    environment:
      - |
        FLINK_PROPERTIES=
        jobmanager.rpc.address: jobmanager
        taskmanager.numberOfTaskSlots: 10
        taskmanager.memory.process.size: 2048m
        taskmanager.memory.framework.off-heap.size: 256m
    volumes:
      - shared-tmpfs:/tmp/iceberg

volumes:
  shared-tmpfs:
    driver: local
    driver_opts:
      type: "tmpfs"
      device: "tmpfs"

The Docker Compose environment consists of the following containers:

• Fluss Cluster: a Fluss CoordinatorServer, a Fluss TabletServer and a ZooKeeper server.
• Flink Cluster: a Flink JobManager and a Flink TaskManager container to execute queries.

Note: The fluss/quickstart-flink image is based on flink:1.20.1-java17 and includes the fluss-flink, iceberg-flink and flink-connector-faker to simplify this guide.

3. To start all containers, run:

docker compose up -d

This command automatically starts all the containers defined in the Docker Compose configuration in detached mode.

Run

docker container ls -a

to check whether all containers are running properly.

You can also visit http://localhost:8083/ to see if Flink is running normally.

note

• If you want to additionally use an observability stack, follow one of the provided quickstart guides here and then continue with this guide.
• If you want to run with your own Flink environment, remember to download the fluss-flink connector jar, flink-connector-faker and iceberg-flink connector jar and then put them to FLINK_HOME/lib/.
• All the following commands involving docker compose should be executed in the created working directory that contains the docker-compose.yml file.

Congratulations, you are all set!

Enter into SQL-Client

First, use the following command to enter the Flink SQL CLI Container:

docker compose exec jobmanager ./sql-client

Note: To simplify this guide, three temporary tables have been pre-created with faker connector to generate data. You can view their schemas by running the following commands:

Flink SQL

SHOW CREATE TABLE source_customer;

Flink SQL

SHOW CREATE TABLE source_order;

Flink SQL

SHOW CREATE TABLE source_nation;

Create Fluss Tables

Create Fluss Catalog

Use the following SQL to create a Fluss catalog:

Flink SQL

CREATE CATALOG fluss_catalog WITH (
    'type' = 'fluss',
    'bootstrap.servers' = 'coordinator-server:9123'
);

Flink SQL

USE CATALOG fluss_catalog;

info

By default, catalog configurations are not persisted across Flink SQL client sessions. For further information how to store catalog configurations, see Flink's Catalog Store.

Create Tables

Running the following SQL to create Fluss tables to be used in this guide:

Flink SQL

CREATE TABLE fluss_order (
    `order_key` BIGINT,
    `cust_key` INT NOT NULL,
    `total_price` DECIMAL(15, 2),
    `order_date` DATE,
    `order_priority` STRING,
    `clerk` STRING,
    `ptime` AS PROCTIME(),
    PRIMARY KEY (`order_key`) NOT ENFORCED
);

Flink SQL

CREATE TABLE fluss_customer (
    `cust_key` INT NOT NULL,
    `name` STRING,
    `phone` STRING,
    `nation_key` INT NOT NULL,
    `acctbal` DECIMAL(15, 2),
    `mktsegment` STRING,
    PRIMARY KEY (`cust_key`) NOT ENFORCED
);

Flink SQL

CREATE TABLE fluss_nation (
  `nation_key` INT NOT NULL,
  `name`       STRING,
   PRIMARY KEY (`nation_key`) NOT ENFORCED
);

Flink SQL

CREATE TABLE enriched_orders (
    `order_key` BIGINT,
    `cust_key` INT NOT NULL,
    `total_price` DECIMAL(15, 2),
    `order_date` DATE,
    `order_priority` STRING,
    `clerk` STRING,
    `cust_name` STRING,
    `cust_phone` STRING,
    `cust_acctbal` DECIMAL(15, 2),
    `cust_mktsegment` STRING,
    `nation_name` STRING,
    PRIMARY KEY (`order_key`) NOT ENFORCED
);

Streaming into Fluss

First, run the following SQL to sync data from source tables to Fluss tables:

Flink SQL

EXECUTE STATEMENT SET
BEGIN
    INSERT INTO fluss_nation SELECT * FROM `default_catalog`.`default_database`.source_nation;
    INSERT INTO fluss_customer SELECT * FROM `default_catalog`.`default_database`.source_customer;
    INSERT INTO fluss_order SELECT * FROM `default_catalog`.`default_database`.source_order;
END;

Fluss primary-key tables support high QPS point lookup queries on primary keys. Performing a lookup join is really efficient and you can use it to enrich the fluss_orders table with information from the fluss_customer and fluss_nation primary-key tables.

Flink SQL

INSERT INTO enriched_orders
SELECT o.order_key,
       o.cust_key,
       o.total_price,
       o.order_date,
       o.order_priority,
       o.clerk,
       c.name,
       c.phone,
       c.acctbal,
       c.mktsegment,
       n.name
FROM fluss_order o
LEFT JOIN fluss_customer FOR SYSTEM_TIME AS OF `o`.`ptime` AS `c`
    ON o.cust_key = c.cust_key
LEFT JOIN fluss_nation FOR SYSTEM_TIME AS OF `o`.`ptime` AS `n`
    ON c.nation_key = n.nation_key;

Run Ad-hoc Queries on Fluss Tables

You can now perform real-time analytics directly on Fluss tables. For instance, to calculate the number of orders placed by a specific customer, you can execute the following SQL query to obtain instant, real-time results.

Flink SQL

-- use tableau result mode
SET 'sql-client.execution.result-mode' = 'tableau';

Flink SQL

-- switch to batch mode
SET 'execution.runtime-mode' = 'batch';

Flink SQL

-- use limit to query the enriched_orders table
SELECT * FROM enriched_orders LIMIT 2;

Sample Output

+-----------+----------+-------------+------------+----------------+--------+------------+----------------+--------------+-----------------+-------------+
| order_key | cust_key | total_price | order_date | order_priority |  clerk |  cust_name |     cust_phone | cust_acctbal | cust_mktsegment | nation_name |
+-----------+----------+-------------+------------+----------------+--------+------------+----------------+--------------+-----------------+-------------+
|  23199744 |        9 |      266.44 | 2024-08-29 |           high | Clerk1 |   Joe King |   908.207.8513 |       124.28 |       FURNITURE |      JORDAN |
|  10715776 |        2 |      924.43 | 2024-11-04 |         medium | Clerk3 | Rita Booke | (925) 775-0717 |       172.39 |       FURNITURE |      UNITED |
+-----------+----------+-------------+------------+----------------+--------+------------+----------------+--------------+-----------------+-------------+

If you are interested in a specific customer, you can retrieve their details by performing a lookup on the cust_key.

Flink SQL

-- lookup by primary key
SELECT * FROM fluss_customer WHERE `cust_key` = 1;

Sample Output

+----------+---------------+--------------+------------+---------+------------+
| cust_key |          name |        phone | nation_key | acctbal | mktsegment |
+----------+---------------+--------------+------------+---------+------------+
|        1 | Al K. Seltzer | 817-617-7960 |          1 |  533.41 | AUTOMOBILE |
+----------+---------------+--------------+------------+---------+------------+

Note: Overall the query results are returned really fast, as Fluss enables efficient primary key lookups for tables with defined primary keys.

Update/Delete rows on Fluss Tables

You can use UPDATE and DELETE statements to update/delete rows on Fluss tables.

Update

Flink SQL

-- update by primary key
UPDATE fluss_customer SET `name` = 'fluss_updated' WHERE `cust_key` = 1;

Then you can lookup the specific row:

Flink SQL

SELECT * FROM fluss_customer WHERE `cust_key` = 1;

Sample Output

+----------+---------------+--------------+------------+---------+------------+
| cust_key |          name |        phone | nation_key | acctbal | mktsegment |
+----------+---------------+--------------+------------+---------+------------+
|        1 | fluss_updated | 817-617-7960 |          1 |  533.41 | AUTOMOBILE |
+----------+---------------+--------------+------------+---------+------------+

Notice that the name column has been updated to fluss_updated.

Delete

Flink SQL

DELETE FROM fluss_customer WHERE `cust_key` = 1;

The following SQL query should return an empty result.

Flink SQL

SELECT * FROM fluss_customer WHERE `cust_key` = 1;

Integrate with Iceberg

Start the Lakehouse Tiering Service

To integrate with Apache Iceberg, you need to start the Lakehouse Tiering Service. Open a new terminal, navigate to the fluss-quickstart-flink-iceberg directory, and execute the following command within this directory to start the service:

docker compose exec jobmanager \
    /opt/flink/bin/flink run \
    /opt/flink/opt/fluss-flink-tiering-0.8-SNAPSHOT.jar \
    --fluss.bootstrap.servers coordinator-server:9123 \
    --datalake.format iceberg \
    --datalake.iceberg.type hadoop \
    --datalake.iceberg.warehouse /tmp/iceberg

You should see a Flink Job to tier data from Fluss to Iceberg running in the Flink Web UI.

Streaming into Fluss datalake-enabled tables

By default, tables are created with data lake integration disabled, meaning the Lakehouse Tiering Service will not tier the table's data to the data lake.

To enable lakehouse functionality as a tiered storage solution for a table, you must create the table with the configuration option table.datalake.enabled = true. Return to the SQL client and execute the following SQL statement to create a table with data lake integration enabled:

Flink SQL

CREATE TABLE datalake_enriched_orders (
    `order_key` BIGINT,
    `cust_key` INT NOT NULL,
    `total_price` DECIMAL(15, 2),
    `order_date` DATE,
    `order_priority` STRING,
    `clerk` STRING,
    `cust_name` STRING,
    `cust_phone` STRING,
    `cust_acctbal` DECIMAL(15, 2),
    `cust_mktsegment` STRING,
    `nation_name` STRING,
    PRIMARY KEY (`order_key`) NOT ENFORCED
) WITH (
    'table.datalake.enabled' = 'true',
    'table.datalake.freshness' = '30s'
);

Next, perform streaming data writing into the datalake-enabled table, datalake_enriched_orders:

Flink SQL

-- switch to streaming mode
SET 'execution.runtime-mode' = 'streaming';

Flink SQL

-- insert tuples into datalake_enriched_orders
INSERT INTO datalake_enriched_orders
SELECT o.order_key,
       o.cust_key,
       o.total_price,
       o.order_date,
       o.order_priority,
       o.clerk,
       c.name,
       c.phone,
       c.acctbal,
       c.mktsegment,
       n.name
FROM fluss_order o
       LEFT JOIN fluss_customer FOR SYSTEM_TIME AS OF `o`.`ptime` AS `c`
                 ON o.cust_key = c.cust_key
       LEFT JOIN fluss_nation FOR SYSTEM_TIME AS OF `o`.`ptime` AS `n`
                 ON c.nation_key = n.nation_key;

Real-Time Analytics on Fluss datalake-enabled Tables

The data for the datalake_enriched_orders table is stored in Fluss (for real-time data) and Iceberg (for historical data).

When querying the datalake_enriched_orders table, Fluss uses a union operation that combines data from both Fluss and Iceberg to provide a complete result set -- combines real-time and historical data.

If you wish to query only the data stored in Iceberg—offering high-performance access without the overhead of unioning data—you can use the datalake_enriched_orders$lake table by appending the $lake suffix. This approach also enables all the optimizations and features of a Flink Iceberg table source, including system table such as datalake_enriched_orders$lake$snapshots.

Flink SQL

-- switch to batch mode
SET 'execution.runtime-mode' = 'batch';

Flink SQL

-- query snapshots in iceberg
SELECT snapshot_id, operation FROM datalake_enriched_orders$lake$snapshots;

Sample Output:

+-------------+--------------------+
| snapshot_id |          operation |
+-------------+--------------------+
|           1 |             append |
+-------------+--------------------+

Note: Make sure to wait for the configured datalake.freshness (~30s) to complete before querying the snapshots, otherwise the result will be empty.

Run the following SQL to do analytics on Iceberg data:

Flink SQL

-- to sum prices of all orders in iceberg
SELECT sum(total_price) as sum_price FROM datalake_enriched_orders$lake;

Sample Output:

+------------+
|  sum_price |
+------------+
| 1669519.92 |
+------------+

To achieve results with sub-second data freshness, you can query the table directly, which seamlessly unifies data from both Fluss and Iceberg:

Flink SQL

-- to sum prices of all orders (combining fluss and iceberg data)
SELECT sum(total_price) as sum_price FROM datalake_enriched_orders;

Sample Output:

+------------+
|  sum_price |
+------------+
| 1777908.36 |
+------------+

You can execute the real-time analytics query multiple times, and the results will vary with each run as new data is continuously written to Fluss in real-time.

Finally, you can use the following command to view the files stored in Iceberg:

docker compose exec taskmanager tree /tmp/iceberg/fluss.db

Sample Output:

/tmp/iceberg/fluss.db
└── datalake_enriched_orders
    ├── data
    │   └── 00000-0-abc123.parquet
    └── metadata
        ├── snap-1234567890123456789-1-abc123.avro
        └── v1.metadata.json

The files adhere to Iceberg's standard format, enabling seamless querying with other engines such as Spark and Trino.

Clean up

After finishing the tutorial, run exit to exit Flink SQL CLI Container and then run

docker compose down -v

to stop all containers.

Learn more

Now that you're up and running with Fluss and Flink with Iceberg, check out the Apache Flink Engine docs to learn more features with Flink or this guide to learn how to set up an observability stack for Fluss and Flink.