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id: ‘quick-start’ title: ‘Quick Start’ sidebar_position: 3

How to use

The installation of the one-stop platform streampark-console has been introduced in detail in the previous chapter. In this chapter, let‘s see how to quickly deploy and run a job with streampark-console. The official structure and specification) and projects developed with streampark are well supported. Let’s use streampark-quickstart to quickly start the journey of streampark-console

streampark-quickstart is a sample program for developing Flink by StreamPark. For details, please refer to:

• Github: https://github.com/apache/streampark-quickstart.git

Deploy DataStream tasks

The following example demonstrates how to deploy a DataStream application

Deploy the FlinkSql task

The following example demonstrates how to deploy a FlinkSql application

• The flink sql used in the project demonstration is as follows

CREATE TABLE user_log (
    user_id VARCHAR,
    item_id VARCHAR,
    category_id VARCHAR,
    behavior VARCHAR,
    ts TIMESTAMP(3)
 ) WITH (
'connector' = 'kafka', -- Using the kafka connector
'properties.group.id' = 'group01' ,
'topic' = 'user_behavior',  -- kafka topic
'properties.bootstrap.servers'='kafka-1:9092,kafka-2:9092,kafka-3:9092',
'scan.startup.mode' = 'earliest-offset', -- Read from start offset
'format' = 'json'  -- The data source format is json
 );

CREATE TABLE pvuv_sink (
    dt VARCHAR,
    pv BIGINT,
    uv BIGINT,
    PRIMARY KEY (dt,pv,uv) NOT ENFORCED
 ) WITH (
'connector' = 'jdbc', -- using jdbc connector
'url' = 'jdbc:mysql://test-mysql:3306/test', -- jdbc url
'table-name' = 'pvuv_sink', -- Table Name
'username' = 'root', -- username
'password' = '123456', --password
'sink.buffer-flush.max-rows' = '1' -- Default 5000, changed to 1 for demonstration
 );

INSERT INTO pvuv_sink
SELECT
  DATE_FORMAT(ts, 'yyyy-MM-dd HH:00') dt,
  COUNT(*) AS pv,
  COUNT(DISTINCT user_id) AS uv
FROM user_log
GROUP BY DATE_FORMAT(ts, 'yyyy-MM-dd HH:00');

• The maven dependencies are used as follows

<dependency>
    <groupId>mysql</groupId>
    <artifactId>mysql-connector-java</artifactId>
    <version>5.1.48</version>
</dependency>

<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-sql-connector-kafka_2.11</artifactId>
    <version>1.14.6</version>
</dependency>

<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-connector-jdbc_2.11</artifactId>
    <version>1.14.6</version>
</dependency>

• The data sent by Kafka simulation is as follows

{"user_id": "543462", "item_id":"1715", "category_id": "1464116", "behavior": "pv", "ts":"2021-02-01 01:00:00"}
{"user_id": "662867", "item_id":"2244074","category_id":"1575622","behavior": "pv", "ts":"2021-02-01 01:00:00"}
{"user_id": "662867", "item_id":"2244074","category_id":"1575622","behavior": "pv", "ts":"2021-02-01 01:00:00"}
{"user_id": "662867", "item_id":"2244074","category_id":"1575622","behavior": "learning flink", "ts":"2021-02-01 01:00:00"}

Task start process

The task startup flow chart is as follows

Regarding the concept of the project, Development Mode, savepoint, NoteBook, custom jar management, task release, task recovery, parameter configuration, parameter comparison, multi-version management and more tutorials and documents will be continuously updated. ..