alibaba/jstormArchived
Jstorm
jStorm is a distributed stream processing engine designed for executing low-latency computations on high-volume data streams using Apache Storm topologies. It functions as a real-time data analytics platform and distributed task orchestrator that manages complex data pipelines via directed acyclic graph execution.
The system provides a scalable framework for data pipeline management, incorporating backpressure-aware flow control to regulate ingestion rates and dynamic resource allocation to adjust computing resources based on real-time demand. It maintains compatibility with Apache Storm configurations for topology management and coordinates event-driven analytics infrastructure.
The platform covers a broad range of capabilities including high-throughput data ingestion and distributed pipeline orchestration. It ensures continuous processing through fault-tolerant task orchestration that detects component failures and restarts interrupted processes across server clusters.
Features
- Stream Processing Engines - Functions as a distributed engine that performs continuous computation on real-time data streams with low latency.
- Apache Storm Topology Management - Maintains compatibility with Apache Storm configurations for managing distributed data processing topologies.
- Data Pipeline Orchestration - Coordinates complex data flows across server clusters with automated fault tolerance and task recovery.
- Distributed Task Schedulers - Orchestrates and distributes complex data processing workflows across computing clusters for horizontal scaling.
- Real-time Analytics Platforms - Manages complex data pipelines and event-driven analytics through directed acyclic graph execution.
- Real-Time Data Streaming - Analyzes high-volume data streams with low latency to derive insights from events as they occur.
- Distributed Task Orchestrators - Provides an automated fault-tolerant manager that detects component failures and restarts processes across clusters.
- Fault Tolerance - Provides automated recovery mechanisms that detect component failures and restart interrupted processes.
- Directed Acyclic Graph Pipelines - Uses directed acyclic graphs to manage processing dependencies and define the flow of streaming data.
- Event Analytics - Builds scalable systems that trigger computations and analysis based on continuous streams of event data.
- High-Throughput Ingestion Pipelines - Manages the flow of massive datasets into processing engines using backpressure control.
- Production Data Pipeline Management - Offers a pipeline framework with backpressure-aware flow control and dynamic resource allocation.
- Dynamic Resource Allocators - Adjusts computing resources assigned to specific tasks based on real-time processing demand and load.
- Message-Passing Process Coordination - Uses an optimized inter-process communication layer to move data between distributed nodes with minimal latency.
- Backpressure Management - Implements systems for regulating data flow between producers and consumers to prevent resource exhaustion.
- Big Data Frameworks - Distributed and fault-tolerant real-time computation system.
- Infrastructure and Backend - Enterprise-grade stream computing engine.
Star history
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