elastic/logstash
Logstash
Logstash is a JVM-based event processor and extract, transform, load system designed for log data processing pipelines. It functions as a plugin-based data ingestor that collects, transforms, and delivers logs and event data from multiple sources to various destinations.
The system utilizes a modular architecture of interchangeable input, filter, and output components to handle real-time data ingestion and enterprise log aggregation. Users can extend the pipeline's functionality by developing custom plugins to support unique data sources or specific transformation logic.
The platform covers comprehensive data delivery, event transformation, and observability. It includes a REST management API for health monitoring and a hierarchical metric collection system to track component performance.
The project provides tools to build deployable packages and manage dependencies within its Java and Ruby-based execution environment.
Features
- Log Aggregators - Centralizes logs from distributed external systems into a unified processing pipeline for visibility and monitoring.
- Data Pipelines - Implements a modular system of interchangeable input, filter, and output plugins to ingest, transform, and deliver event data.
- Data Processing and ETL - Functions as an extract, transform, load system that filters and modifies data streams before storage.
- Data Ingestion Sources - Collects logs and events from diverse external sources including files and raw streams for pipeline processing.
- Data Destination Connectors - Routes processed events to target indices or external storage systems via destination connectors.
- Data Processing Pipelines - Provides systems and workflows for ingesting, transforming, and orchestrating high-throughput data processing tasks.
- Data Ingestion Pipelines - Implements a complete ETL workflow that automates the extraction, transformation, and loading of event data.
- Data Transformation - Implements tools for modifying, restructuring, and converting raw event data into desired formats and schemas.
- Plugin Development - Allows users to build custom input, filter, and output plugins to support unique data sources and transformation logic.
- Event Processing Runtimes - Runs as a high-performance data pipeline on the Java Virtual Machine for scalable event transformation and routing.
- Ruby Execution Engines - Uses a JRuby engine to execute Ruby-based configuration and plugins within the JVM for combined flexibility and performance.
- Pipeline Extenders - Allows the development of custom plugins to implement new ways of ingesting, transforming, and delivering data.
- Plugin Extenders - Supports extending the system by loading custom external libraries as plugins to augment ingestion and transformation logic.
- Plugin-Based Architectures - Employs a modular architecture that allows extending core functionality via interchangeable input, filter, and output plugins.
- Log Processing Pipelines - Ships a system for parsing, normalizing, and structuring unstructured log data before it is indexed.
- High-Volume Data Ingestion - Handles massive streams of event data from diverse origins for real-time analysis and delivery.
- REST Administrative APIs - Exposes system health and state through a dedicated REST API that separates administrative endpoints from the data path.
- Bytecode Compilation - Transforms pipeline configuration files into executable Java classes to minimize processing overhead.
- Data Component Plugins - Allows developers to create new data components using base classes to extend the processing system's capabilities.
- System Configuration Management - Provides a centralized configuration file to modify behavior and manage feature toggles for the processing environment.
- Management APIs - Exposes a RESTful management API for monitoring system health and extracting internal performance metrics.
- Metrics Collection - Provides a hierarchical namespace system for scraping and recording granular performance data across pipeline components.
- Data Collection Agents - Pipeline for processing and transporting logs and event data.
- Databases & Data - Data pipeline tool for collecting and transforming log information.
- DevOps Tools - Process, transport, and manage logs and event data.
- Observability and Monitoring - Pipeline for collecting and processing log data.
