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Enterprise job scheduling middleware with distributed computing ability.
Apache DolphinScheduler is the modern data orchestration platform. Agile to create high performance workflow with low-code
DataX是阿里云DataWorks数据集成的开源版本。
Quick Overview
XXL-JOB is a distributed task scheduling platform with high reliability, high availability, and high performance. It supports both single-machine and distributed deployments, making it suitable for various scenarios from small-scale applications to large-scale systems.
Pros
- Easy to use with a user-friendly web interface for job management
- Supports multiple execution modes (BEAN, GLUE, HTTP, etc.)
- Highly scalable and fault-tolerant with distributed architecture
- Provides real-time monitoring and alerting features
Cons
- Primarily documented in Chinese, which may be challenging for non-Chinese speakers
- Steeper learning curve compared to simpler scheduling solutions
- Requires additional setup and maintenance for distributed deployments
- Limited built-in integrations with other popular tools and frameworks
Code Examples
- Defining a simple XXL-JOB executor:
@Component
public class SampleXxlJob {
@XxlJob("demoJobHandler")
public void demoJobHandler() throws Exception {
XxlJobHelper.log("XXL-JOB, Hello World.");
}
}
- Configuring XXL-JOB in Spring Boot application:
xxl:
job:
admin:
addresses: http://xxl-job-admin:8080/xxl-job-admin
executor:
appname: xxl-job-executor-sample
ip:
port: 9999
logpath: /data/applogs/xxl-job/jobhandler
logretentiondays: 30
- Manually triggering a job execution:
@Autowired
private XxlJobTemplate xxlJobTemplate;
public void triggerJob() {
ReturnT<String> result = xxlJobTemplate.triggerJob(1, "");
if (ReturnT.SUCCESS_CODE == result.getCode()) {
System.out.println("Job triggered successfully");
}
}
Getting Started
- Add XXL-JOB dependency to your project:
<dependency>
<groupId>com.xuxueli</groupId>
<artifactId>xxl-job-core</artifactId>
<version>2.3.1</version>
</dependency>
-
Configure XXL-JOB in your application.yml or application.properties file (see example in Code Examples section).
-
Create a job handler class and annotate methods with @XxlJob:
@Component
public class MyJobHandler {
@XxlJob("myJobHandler")
public void execute() throws Exception {
// Your job logic here
}
}
- Start your application and access the XXL-JOB admin console to configure and manage jobs.
Competitor Comparisons
Enterprise job scheduling middleware with distributed computing ability.
Pros of PowerJob
- More advanced features like workflow orchestration and distributed computing
- Better scalability for large-scale distributed systems
- More flexible job scheduling options, including cron expressions and custom algorithms
Cons of PowerJob
- Steeper learning curve due to more complex architecture
- Less mature community and ecosystem compared to xxl-job
- Potentially overkill for simpler scheduling needs
Code Comparison
PowerJob example:
JobInfo jobInfo = new JobInfo();
jobInfo.setJobName("HelloWorld");
jobInfo.setJobParams("params");
jobInfo.setTimeExpression("0 0 * * * ?");
jobInfo.setExecuteType(ExecuteType.STANDALONE);
jobClient.saveJob(jobInfo);
xxl-job example:
XxlJobInfo jobInfo = new XxlJobInfo();
jobInfo.setJobDesc("HelloWorld");
jobInfo.setExecutorParam("params");
jobInfo.setJobCron("0 0 * * * ?");
jobInfo.setExecutorHandler("demoJobHandler");
xxlJobService.add(jobInfo);
Both examples demonstrate job creation, but PowerJob offers more configuration options and flexibility in its API. xxl-job's approach is simpler and more straightforward, which may be preferable for basic scheduling needs. PowerJob's additional features cater to more complex scenarios, but may require more setup and understanding of its architecture.
Apache DolphinScheduler is the modern data orchestration platform. Agile to create high performance workflow with low-code
Pros of DolphinScheduler
- More comprehensive workflow management with support for complex DAGs
- Built-in support for multiple data sources and task types
- Stronger focus on distributed scheduling and high availability
Cons of DolphinScheduler
- Steeper learning curve due to more complex architecture
- Heavier resource requirements for deployment and operation
- Less straightforward setup process compared to XXL-Job
Code Comparison
DolphinScheduler task definition:
TaskDefinition taskDefinition = new TaskDefinition();
taskDefinition.setName("MyTask");
taskDefinition.setType("SHELL");
taskDefinition.setCode(1L);
taskDefinition.setTaskParams("{\"rawScript\":\"echo hello\"}");
XXL-Job task definition:
XxlJobInfo jobInfo = new XxlJobInfo();
jobInfo.setJobDesc("MyJob");
jobInfo.setJobGroup(1);
jobInfo.setExecutorHandler("demoJobHandler");
jobInfo.setJobCron("0 0 12 * * ?");
Both projects offer Java-based job scheduling solutions, but DolphinScheduler provides a more comprehensive workflow management system with support for complex task dependencies and various data sources. XXL-Job, on the other hand, offers a simpler and more lightweight approach to distributed job scheduling, making it easier to set up and integrate into existing projects. The choice between the two depends on the specific requirements of your project, such as the complexity of workflows, scalability needs, and available resources for deployment and maintenance.
DataX是阿里云DataWorks数据集成的开源版本。
Pros of DataX
- Specialized in data integration and synchronization across various data sources
- Supports a wide range of data sources, including relational databases, NoSQL databases, and file systems
- Highly scalable and designed for handling large-scale data transfer tasks
Cons of DataX
- Primarily focused on data transfer, lacking job scheduling and management features
- May require more setup and configuration for complex data integration scenarios
- Less suitable for general-purpose job scheduling and execution
Code Comparison
DataX (JSON configuration):
{
"job": {
"content": [
{
"reader": { "name": "mysqlreader", "parameter": { ... } },
"writer": { "name": "hdfswriter", "parameter": { ... } }
}
]
}
}
xxl-job (Java code):
@XxlJob("demoJobHandler")
public void demoJobHandler() throws Exception {
XxlJobHelper.log("XXL-JOB, Hello World.");
}
Summary
DataX excels in data integration and synchronization tasks, supporting various data sources and large-scale operations. However, it lacks job scheduling features and may require more setup for complex scenarios. xxl-job, on the other hand, is a more general-purpose job scheduling framework with easier setup but less specialized in data transfer operations.
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XXL-JOB
XXL-JOB, a distributed task scheduling framework.
-- Home Page --
Sponsor
XXL-JOB is an open source and free project, with its ongoing development made possible entirely by the support of these awesome backers.
XXL-JOB æ¯ä¸ä¸ªå¼æºä¸å 费项ç®ï¼å ¶æ£å¨è¿è¡çå¼åå®å ¨å¾çäºæ¯æè çæ¯æãå¼æºä¸æï¼åå¾èµå©é¡¹ç®å¼å
Introduction
XXL-JOB is a distributed task scheduling framework. It's core design goal is to develop quickly and learn simple, lightweight, and easy to expand. Now, it's already open source, and many companies use it in production environments, real "out-of-the-box".
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Documentation
Communication
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Contributing
Contributions are welcome! Open a pull request to fix a bug, or open an Issue to discuss a new feature or change.
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Copyright and License
This product is open source and free, and will continue to provide free community technical support. Individual or enterprise users are free to access and use.
- Licensed under the GNU General Public License (GPL) v3.
- Copyright (c) 2015-present, xuxueli.
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