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Real-Time Analytics With Flink

This guide will get you up and running with Apache Flink to do real-time analytics, covering some powerful features of Fluss. 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
cd fluss-quickstart-flink
  1. Create a lib directory and download the required jar files. You can adjust the Flink version as needed. Please make sure to download the compatible versions of fluss-flink connector jar and flink-connector-faker
export FLINK_VERSION="1.20"
mkdir lib
wget -O lib/flink-faker-0.5.3.jar https://github.com/knaufk/flink-faker/releases/download/v0.5.3/flink-faker-0.5.3.jar
wget -O "lib/fluss-flink-${FLINK_VERSION}-0.9-SNAPSHOT.jar" "https://repo1.maven.org/maven2/org/apache/fluss/fluss-flink-${FLINK_VERSION}/0.9-SNAPSHOT/fluss-flink-${FLINK_VERSION}-0.9-SNAPSHOT.jar"
  1. Create a docker-compose.yml file with the following content:
services:
  #begin Fluss cluster
  coordinator-server:
    image: apache/fluss:0.9-SNAPSHOT
    command: coordinatorServer
    depends_on:
      - zookeeper
    environment:
      - |
        FLUSS_PROPERTIES=
        zookeeper.address: zookeeper:2181
        bind.listeners: FLUSS://coordinator-server:9123
        remote.data.dir: /tmp/fluss/remote-data
  tablet-server:
    image: apache/fluss:0.9-SNAPSHOT
    command: tabletServer
    depends_on:
      - coordinator-server
    environment:
      - |
        FLUSS_PROPERTIES=
        zookeeper.address: zookeeper:2181
        bind.listeners: FLUSS://tablet-server:9123
        data.dir: /tmp/fluss/data
        remote.data.dir: /tmp/fluss/remote-data
        kv.snapshot.interval: 0s
  zookeeper:
    restart: always
    image: zookeeper:3.9.2
  #end
  #begin Flink cluster
  jobmanager:
    image: flink:${FLINK_VERSION}
    ports:
      - "8083:8081"
    environment:
      - |
        FLINK_PROPERTIES=
        jobmanager.rpc.address: jobmanager
    entrypoint: ["sh", "-c", "cp -v /tmp/lib/*.jar /opt/flink/lib && exec /docker-entrypoint.sh jobmanager"]
    volumes:
      - ./lib:/tmp/lib
  taskmanager:
    image: flink:${FLINK_VERSION}
    depends_on:
      - jobmanager
    environment:
      - |
        FLINK_PROPERTIES=
        jobmanager.rpc.address: jobmanager
        taskmanager.numberOfTaskSlots: 10
        taskmanager.memory.process.size: 2048m
        taskmanager.memory.framework.off-heap.size: 256m
    entrypoint: ["sh", "-c", "cp -v /tmp/lib/*.jar /opt/flink/lib && exec /docker-entrypoint.sh taskmanager"]
    volumes:
      - ./lib:/tmp/lib
  sql-client:
    image: flink:${FLINK_VERSION}
    depends_on:
      - jobmanager
    environment:
      - |
        FLINK_PROPERTIES=
        jobmanager.rpc.address: jobmanager
        rest.address: jobmanager    
    entrypoint: ["sh", "-c", "cp -v /tmp/lib/*.jar /opt/flink/lib && exec /docker-entrypoint.sh bin/sql-client.sh"]
    volumes:
      - ./lib:/tmp/lib
  #end

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, a Flink TaskManager, and a Flink SQL client container to execute queries.
  1. 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.
  • 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 run sql-client

To simplify this guide, we will create three temporary tables with faker connector to generate data:

Flink SQL
CREATE TEMPORARY TABLE source_order (
    `order_key` BIGINT,
    `cust_key` INT,
    `total_price` DECIMAL(15, 2),
    `order_date` DATE,
    `order_priority` STRING,
    `clerk` STRING
) WITH (
  'connector' = 'faker',
  'rows-per-second' = '10',
  'number-of-rows' = '10000',
  'fields.order_key.expression' = '#{number.numberBetween ''0'',''100000000''}',
  'fields.cust_key.expression' = '#{number.numberBetween ''0'',''20''}',
  'fields.total_price.expression' = '#{number.randomDouble ''3'',''1'',''1000''}',
  'fields.order_date.expression' = '#{date.past ''100'' ''DAYS''}',
  'fields.order_priority.expression' = '#{regexify ''(low|medium|high){1}''}',
  'fields.clerk.expression' = '#{regexify ''(Clerk1|Clerk2|Clerk3|Clerk4){1}''}'
);
Flink SQL
CREATE TEMPORARY TABLE source_customer (
    `cust_key` INT,
    `name` STRING,
    `phone` STRING,
    `nation_key` INT NOT NULL,
    `acctbal` DECIMAL(15, 2),
    `mktsegment` STRING,
    PRIMARY KEY (`cust_key`) NOT ENFORCED
) WITH (
  'connector' = 'faker',
  'number-of-rows' = '200',
  'fields.cust_key.expression' = '#{number.numberBetween ''0'',''20''}',
  'fields.name.expression' = '#{funnyName.name}',
  'fields.nation_key.expression' = '#{number.numberBetween ''1'',''5''}',
  'fields.phone.expression' = '#{phoneNumber.cellPhone}',
  'fields.acctbal.expression' = '#{number.randomDouble ''3'',''1'',''1000''}',
  'fields.mktsegment.expression' = '#{regexify ''(AUTOMOBILE|BUILDING|FURNITURE|MACHINERY|HOUSEHOLD){1}''}'
);
Flink SQL
CREATE TEMPORARY TABLE `source_nation` (
  `nation_key` INT NOT NULL,
  `name`       STRING,
   PRIMARY KEY (`nation_key`) NOT ENFORCED
) WITH (
  'connector' = 'faker',
  'number-of-rows' = '100',
  'fields.nation_key.expression' = '#{number.numberBetween ''1'',''5''}',
  'fields.name.expression' = '#{regexify ''(CANADA|JORDAN|CHINA|UNITED|INDIA){1}''}'
);
Flink SQL
-- drop records silently if a null value would have to be inserted into a NOT NULL column
SET 'table.exec.sink.not-null-enforcer'='DROP';

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
-- execute DML job synchronously
SET 'table.dml-sync' = 'true';
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;

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, 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.