xuxueli/xxl-job

Features

• Job Schedulers - Provides a centralized job scheduler to orchestrate background tasks and automate execution based on time-based schedules.
• Distributed Task Schedulers - Serves as a centralized system for managing and triggering timed jobs across a distributed cluster of worker nodes.
• Scheduler-Worker Topologies - Employs a central scheduling server to manage task distribution while remote executor nodes handle the actual job execution.
• Job State Persistence - Persists job metadata and execution logs in a relational database to ensure reliability across system failures.
• Relational Database Persistence - Utilizes relational database persistence to maintain consistent state for job configurations and execution history.
• Cron Scheduling - Provides a web-based interface for defining and managing task execution using cron expressions.
• Cron Trigger Management - Provides a system for creating and managing cron-based triggers to automate task execution timing.
• Management Interfaces - Offers a dedicated web interface for creating and updating scheduled tasks without modifying source code.
• Multi-Strategy Task Triggers - Executes jobs using multiple timing patterns such as cron expressions, fixed intervals, or external API events.
• Distributed Job Orchestration - Provides a framework for linking dependent tasks and managing complex job lifecycles across a distributed environment.
• Distributed Task Orchestrators - Coordinates and scales parallel task execution and dependencies across distributed computing resources.
• HTTP Task Dispatching - Communicates with distributed executors via a web interface to trigger tasks and receive real-time status updates.
• Job Dependency Coordination - Manages complex asynchronous workflows through parent-child job dependencies and execution lifecycles.
• Job Sharding - Implements techniques for splitting a single logical task into parallel shards for distributed execution across a cluster.
• Data Partitioning - Splits workloads across multiple executors by assigning unique indices to nodes for parallel data processing.
• Task-Based Shard Broadcasting - Broadcasts tasks across multiple cluster executors so each instance processes a unique subset of a larger dataset.
• Online Scripting Environments - Provides a web-based editor to write and deploy task scripts without needing to restart active services.
• Task Dependency Management - Defines execution order and prerequisites for tasks, ensuring child processes start only after parents complete successfully.
• Distributed Job Executors - Integrates with external distributed executors to enable cross-language task execution via a standardized interface.
• Capacity Scaling - Supports increasing or decreasing the number of executor nodes to meet task resource demands dynamically.
• Distributed Execution Gateways - Enables triggering operations on third-party executors written in different programming languages via a standardized web interface.
• Cluster Load Balancing - Distributes computational workloads across cluster nodes using specific routing algorithms to maximize hardware utilization.
• Asynchronous Task Execution - Implements mechanisms for executing long-running operations with real-time progress polling and result retrieval from remote executors.
• Failure Handling Policies - Implements failure handling policies including automated retries and error alerts when tasks fail.
• Clustered Task Distribution - Distributes scheduled jobs across a cluster of executors using routing strategies to ensure high availability.
• Remote Executor Frameworks - Provides a system for triggering logic on external servers through a web interface with real-time execution monitoring.
• Task Status Monitors - Provides real-time tracking of execution status and remote log viewing for troubleshooting failures.
• Job Status Monitoring - Tracks real-time execution status and provides rolling logs from remote executors to verify task completion.

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