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Platform Deployment

The overall component stack structure of StreamPark is as follows. It consists of two major parts: streampark-core and streampark-console. streampark-console is a very important module, positioned as a integrated real-time data platform, streaming data warehouse Platform, Low Code, Flink & Spark task hosting platform, can better manage Flink tasks, integrate project compilation, publishing, parameter configuration, startup, savepoint, flame graph ( flame graph ), Flink SQL, monitoring and many other functions are integrated into one, which greatly simplifies the daily operation and maintenance of Flink tasks and integrates many best practices. Its ultimate goal is to create a one-stop big data solution that integrates real-time data warehouses and batches

StreamPark Archite

streampark-console provides an out-of-the-box installation package. Before installation, there are some requirements for the environment. The specific requirements are as follows:

Environmental requirements

OSLinuxUnSupport Windows
Flink1.12.0+Flink version >= 1.12
Hadoop2+Optional, If on yarn, hadoop envs is required.

At present, StreamPark has released tasks for Flink, and supports both Flink on YARN and Flink on Kubernetes modes.


To use Flink on YARN, you need to install and configure Hadoop-related environment variables in the deployed cluster. For example, if you installed the hadoop environment based on CDH, Related environment variables can refer to the following configuration:

export HADOOP_HOME=/opt/cloudera/parcels/CDH/lib/hadoop #hadoop installation manual
export HADOOP_CONF_DIR=/etc/hadoop/conf
export HIVE_HOME=$HADOOP_HOME/../hive
export HBASE_HOME=$HADOOP_HOME/../hbase
export HADOOP_HDFS_HOME=$HADOOP_HOME/../hadoop-hdfs
export HADOOP_MAPRED_HOME=$HADOOP_HOME/../hadoop-mapreduce
export HADOOP_YARN_HOME=$HADOOP_HOME/../hadoop-yarn


Using Flink on Kubernetes requires additional deployment/or use of an existing Kubernetes cluster, please refer to the entry: StreamPark Flink-K8s Integration Support.

Build & Deploy

You can directly download the compiled release package (recommended), or you can choose to manually compile and install. The manual compilation and installation steps are as follows:

Environmental requirements

install streampark

download streampark release package, unpacking as follows

├── bin
│ ├── //startup script
│ ├── //Scripts related to java environment variables (internal use, users do not need to pay attention)
│ ├── //stop script
│ ├── //Internally uses a script that parses yaml parameters (for internal use, users don't need to pay attention)
├── conf
│ ├── application.yaml //Project configuration file (be careful not to change the name)
│ ├── flink-application.template //flink configuration template (for internal use, users don't need to pay attention)
│ ├── logback-spring.xml //logback
│ └── ...
├── lib
│ └── *.jar //Project jar package
├── client
│ └── streampark-flink-sqlclient-2.0.0.jar //Flink SQl submit related functions (for internal use, users do not need to pay attention)
├── script
│ ├── schema
│ │ ├── mysql-schema.sql // mysql ddl
│ │ └── pgsql-schema.sql // pgsql ddl
│ ├── data
│ │ ├── mysql-data.sql // mysql init data
│ │ └── pgsql-data.sql // pgsql init data
│ ├── upgrade
│ │ ├── 1.2.3.sql // 1.2.3 upgrade sql
│ │ └── 2.0.0.sql // 2.0.0 upgrade sql
│ │ ...
├── logs // log dir
├── temp // temp dir, don't remove
Initialize table structure

In the installation process of versions before 1.2.1, there is no need to manually initialize data, just set the database information, and some column operations such as table creation and data initialization will be automatically completed. Versions after 1.2.1 (included) are not included. Automatic table creation and upgrade requires the user to manually execute ddl for initialization. The ddl description is as follows:

├── script
│ ├── schema
│ │ ├── mysql-schema.sql // mysql ddl
│ │ └── pgsql-schema.sql // pgsql ddl
│ ├── data
│ │ ├── mysql-data.sql // mysql init data
│ │ └── pgsql-data.sql // pgsql init data
│ ├── upgrade
│ │ ├── 1.2.3.sql // 1.2.3 upgrade sql
│ │ └── 2.0.0.sql // 2.0.0 upgrade sql
│ │ ...
├── logs // log dir
├── temp // temp dir, don't remove

If streampark first installation, need to connect to the corresponding database client to execute the script under the schema and data directories in sequence, and if it is an upgrade, execute under upgrade sql file of the corresponding version number.

Modify the configuration

The installation and unpacking have been completed, and the next step is to prepare the data-related work

Create a new database streampark

Make sure to create a new database streampark in mysql that the deployment machine can connect to

Modify connection information

Go to conf, modify conf/application.yml, find the spring item, find the configuration, and modify it to the corresponding information, as follows

spring: mysql #[h2,pgsql,mysql] StreamPark
devtools.restart.enabled: false
mvc.pathmatch.matching-strategy: ant_path_matcher
enabled: true
max-file-size: 500MB
max-request-size: 500MB
aop.proxy-target-class: true
messages.encoding: utf-8
date-format: yyyy-MM-dd HH:mm:ss
time-zone: GMT+8
allow-circular-references: true
banner-mode: off

After modify conf/application.yml, then modify the config/application-mysql.yml to change the config information of database as follows:

Tips: Because of license incompatibility between Apache project and mysql jdbc driver, so you should download mysql jdbc driver by yourself and put it in $STREAMPARK_HOME/lib

username: root
password: xxxx
driver-class-name: com.mysql.cj.jdbc.Driver
url: jdbc:mysql://localhost:3306/streampark?useSSL=false&useUnicode=true&characterEncoding=UTF-8&allowPublicKeyRetrieval=false&useJDBCCompliantTimezoneShift=true&useLegacyDatetimeCode=false&serverTimezone=GMT%2B8
Modify workspace

Go to conf, modify conf/application.yml, find the item streampark, find the workspace configuration, and change it to a directory that the user has permission to.

# HADOOP_USER_NAME If it is on yarn mode ( yarn-prejob | yarn-application | yarn-session), you need to configure hadoop-user-name
hadoop-user-name: hdfs
# Local workspace, used to store project source code, build directory, etc.
local: /opt/streampark_workspace # A local workspace directory (very important), users can change the directory by themselves, it is recommended to put it in other places separately to store the project source code, the built directory, etc.
remote: hdfs:///streampark # support hdfs:///streampark/ 、 /streampark 、hdfs://host:ip/streampark/

Enter bin and directly execute to start the project. The default port is 10000, if there is no accident, it will start successfully

cd streampark-console-service-2.0.0/bin

Relevant logs will be output to streampark-console-service-1.0.0/logs/streampark.out

login system

After the above steps, even if the deployment is completed, you can directly log in to the system

StreamPark Login


Default password: admin / streampark

System Configuration

After entering the system, the first thing to do is to modify the system configuration. Under the menu/StreamPark/Setting, the operation interface is as follows:

StreamPark Settings

The main configuration items are divided into the following categories

  • System Setting
  • Alert Setting
  • Flink Home
  • Flink Cluster

System Setting

The current system configuration includes:

  • Maven Setting
  • Docker Setting
  • Sender Email Setting
  • Ingress Setting

Alert Setting

The configuration related to Alert Email is to configure the information of the sender's email. For the specific configuration, please refer to the relevant mailbox information and documents for configuration.

The global Flink Home is configured here. This is the only place in the system to specify the Flink environment, which will apply to all jobs.


Special Note: The minimum supported Flink version is 1.12.0, and later versions are supported

The cluster modes currently supported by Flink include:

  • Standalone
  • Yarn Session
  • Kubernetes Session