PowerJob/PowerJob
PowerJob
Enterprise job scheduling middleware with distributed computing ability.
Features
- Distributed Task Schedulers - Orchestrates and executes scheduled tasks across a cluster of machines with fault tolerance and load balancing.
- Scheduler-Worker Topologies - Separates scheduling servers from task execution workers to enable horizontal scaling and fault isolation.
- Multi-Language Task Processors - Ships a pluggable processor framework supporting Java, Shell, Python, and HTTP-based task logic.
- Cron Scheduling - Triggers jobs using CRON expressions with fixed-rate and fixed-delay options for precise timing.
- Custom Scheduling Policies - Triggers tasks using CRON expressions, fixed-rate, fixed-delay, or custom scheduling policies for precise timing.
- Multi-Strategy Schedulers - Triggers tasks using CRON, fixed-rate, fixed-delay, or custom scheduling policies for precise timing.
- Horizontal Scaling Deployments - Adds more server and worker nodes to distribute load and eliminate single points of failure.
- High-Availability Task Schedulers - Scales server and worker nodes horizontally to eliminate single points of failure in production scheduling.
- Horizontal Scaling Job Schedulers - Adds server and worker nodes to distribute load and eliminate single points of failure for high availability.
- Polyglot Task Processors - Implements job logic in Java, Shell, or Python with HTTP-based scheduling for polyglot environments.
- Distributed - Provides enterprise-grade middleware for scheduling and orchestrating distributed batch jobs with workflow and fault-tolerance support.
- DAG Workflow Executions - Models complex job pipelines as directed acyclic graphs to enforce execution order and data dependencies.
- Directed Acyclic Graph Engines - Models job dependencies as a DAG to enforce execution order and data flow between tasks.
- Distributed Job Processor Frameworks - Ships a multi-language processor framework supporting Java, Shell, Python, and HTTP-based task logic.
- Polyglot Task Processors - Implements task logic in Java, Shell, or Python with HTTP-based scheduling for polyglot environments.
- Distributed Execution Modes - Provides standalone, broadcast, Map, and MapReduce execution modes for distributed job workloads.
- Multi-Mode Executors - Supports standalone, broadcast, Map, and MapReduce execution patterns for diverse workload distribution.
- Retry Policies - Automatically re-executes failed tasks using configurable retry counts and delay intervals.
- Task Retry Policies - Re-attempts failed executions based on configurable retry policies to ensure tasks complete successfully.
- Web-Based Task Dashboards - Includes a web dashboard for viewing execution status, logs, and managing job configurations.
- Web-Based Dashboards - Provides a front-end interface to view execution status, logs, and manage job configurations.
- Java 项目 - Listed in the “Java 项目” section of the Great Open Source Project awesome list.
Star history
How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
