kevinwallimann / hyperdrive-trigger

Event based workflow manager.

Hyperdrive-trigger

Build Status

develop

---

• What is Hyperdrive-trigger?
• Requirements
• How to build and run
  • Application properties
  • Embedded Tomcat
  • Docker image
  • Web Application Archive
• User Interface
• Development
• How to contribute

What is Hyperdrive-trigger?

Hyperdrive-trigger is a Event based workflow manager and scheduler.

A workflow is defined via the graphical interface and consists of three parts: details, sensor and jobs:

• Details - General workflow's information (workflow name, project name and whether is workflow active)
• Sensor - Definition of workflow trigger, describes when workflow will be executed. Sensors that could be used:
  • Kafka - waits for kafka message with specific message
  • Time - time based trigger, cron-quartz expression or user friendly time recurring definition can be used
  • Recurring - workflow is triggered always when previous execution is finished
• Jobs - list of jobs. Supported job types:
  • Generic Spark Job - spark job deployed to Apache Hadoop YARN
  • Generic Shell Job - job executing a user-defined shell script

The user interface provides a visualization of running workflows with the ability to monitor and troubleshoot executions.

Requirements

Tested with:

OpenJDK	1.8.0_25
Scala	2.11
Maven	3.5.4
PostgreSQL	12.2_1
Spark	2.4.4
Hadoop Yarn	2.6.4.0
Tomcat	9.0.24
Docker	2.3.0.5
NPM	6.14.4
Angular CLI	9.0.3

Note: Docker is not required.

How to build and run

Application properties

Adjusted application properties have to be provided. An application properties template file can be found at src/main/resources/application.properties. Application properties that can be adjusted:

# Version of application. 
[email protected]@
# Enviroment where application will be running
environment=Local

# How will users authenticate. Available options: inmemory, ldap
auth.mechanism=inmemory
# INMEMORY authentication: username and password defined here will be used for authentication.
auth.inmemory.user=user
auth.inmemory.password=password
# LDAP authentication: props template that has to be defined in case of LDAP authentication
auth.ad.domain=
auth.ad.server=
auth.ldap.search.base=
auth.ldap.search.filter=

# Unique app id, for easier application identification
appUniqueId=

# Core properties.
# How many threads to use for each part of the "scheduler".
# Heart beat interval in milliseconds.
# Lag threshold, before instance is deactivated by another instance.
scheduler.thread.pool.size=10
scheduler.sensors.thread.pool.size=20
scheduler.executors.thread.pool.size=30
scheduler.jobs.parallel.number=100
scheduler.heart.beat=5000
scheduler.lag.threshold=20000

#Kafka sensor properties. Not all are required. Adjust according to your use case.
kafkaSource.group.id=hyper_drive_${appUniqueId}
kafkaSource.key.deserializer=org.apache.kafka.common.serialization.StringDeserializer
kafkaSource.value.deserializer=org.apache.kafka.common.serialization.StringDeserializer
kafkaSource.poll.duration=500
kafkaSource.max.poll.records=3
kafkaSource.properties.enable.auto.commit=false
kafkaSource.properties.auto.offset.reset=latest
kafkaSource.properties.security.protocol=
kafkaSource.properties.ssl.truststore.location=
kafkaSource.properties.ssl.truststore.password=
kafkaSource.properties.ssl.keystore.location=
kafkaSource.properties.ssl.keystore.password=
kafkaSource.properties.ssl.key.password=
kafkaSource.properties.sasl.kerberos.service.name=
kafkaSource.properties.sasl.mechanism=
kafkaSource.properties.sasl.jaas.config=

#Spark yarn sink properties. Properties used to deploy and run Spark job in Yarn. Not all are required. Adjust according to your use case.
sparkYarnSink.hadoopResourceManagerUrlBase=
sparkYarnSink.hadoopConfDir=
sparkYarnSink.sparkHome=
sparkYarnSink.master=yarn
sparkYarnSink.submitTimeout=160000
sparkYarnSink.filesToDeploy=
sparkYarnSink.additionalConfs.spark.ui.port=
sparkYarnSink.additionalConfs.spark.executor.extraJavaOptions=
sparkYarnSink.additionalConfs.spark.driver.extraJavaOptions=
sparkYarnSink.additionalConfs.spark.driver.memory=2g
sparkYarnSink.additionalConfs.spark.executor.instances=2
sparkYarnSink.additionalConfs.spark.executor.cores=2
sparkYarnSink.additionalConfs.spark.executor.memory=2g
sparkYarnSink.additionalConfs.spark.yarn.keytab=
sparkYarnSink.additionalConfs.spark.yarn.principal=
sparkYarnSink.additionalConfs.spark.shuffle.service.enabled=true
sparkYarnSink.additionalConfs.spark.dynamicAllocation.enabled=true

#Postgresql properties for connection to trigger database
db.driver=org.postgresql.Driver
db.url=jdbc:postgresql://
db.user=
db.password=
db.keepAliveConnection=true
db.connectionPool=HikariCP
db.numThreads=4
db.skip.liquibase=false

Embedded Tomcat

For development purposes, hyperdrive-trigger can be executed as an application with an embedded tomcat. Please check out branch feature/embedded-tomcat-2 to use it.

To build an executable jar and execute it, use the following commands:

• Package jar: mvn clean package or without tests mvn clean package -DskipTests
• Execute it: java -jar ./target/hyperdrive-trigger-<VERSION>.jar

Access the application at

http://localhost:7123/#

For local and iterative front end development, the UI can be run using a live development server with the following commands:

• Install required packages: cd ui && npm install
• Start front end application: cd ui && ng serve or cd ui && npm start

Access the application at

http://localhost:4200/#

Docker image

For development purposes, hyperdrive-trigger can be executed as a docker image. The provided docker composition consists of one container for the hyperdrive-trigger application and a container for the Postgres database. Additionally, there will be a persistent volume for the database. It is currently not possible to start spark-jobs using this docker composition.

To create the docker image, build the application and create the image using the following command. Please replace <current_version> accordingly.

mvn clean package
docker build --build-arg WAR_FILE=target/hyperdrive-trigger-<current_version>.war -t absaoss/hyperdrive-trigger .

To start the application, type

docker-compose up

Access the application at

http://localhost:8082/hyperdrive_trigger

To stop the application, type

docker-compose down

This will stop the containers, but the database data will not be removed.

To remove database data, type

docker container prune
docker volume prune

This removes stopped containers and volumes that are not referenced by any containers.

Web Application Archive

Hyperdrive-trigger can be packaged as a Web Application Archive and executed in a web server.

• Without tests: mvn clean package -DskipTests
• With unit tests: mvn clean package

Liquibase

The liquibase maven plugin may be used to issue liquibase commands. To use it, copy /etc/liquibase/liquibase-maven-plugin.properties.template to /etc/liquibase/liquibase-maven-plugin.properties and modify it as needed.

Then, the liquibase maven plugin can be executed, e.g.

• mvn liquibase:status to view the status
• mvn liquibase:dropAll to drop all tables, views etc.
• mvn liquibase:update to apply all pending changesets

User Interface

• Workflows: Overview of all workflows.

• Show workflow

• Create workflow

• Workflow history comparison

• Runs: Overview of all workflow's runs.

• Run Detail

How to contribute

Please see our Contribution Guidelines.