Logstash is an open-source server-side data processing pipeline that can simultaneously ingest data from multiple sources, transform it, and then send it to your favorite "repository".
Core Functions
Logstash's main functions include:
- Data Ingestion: Collect log and event data from various sources
- Data Transformation: Parse, filter, enrich, and normalize data
- Data Output: Send processed data to target storage systems
How It Works
Logstash uses a plugin-based architecture with three main components:
1. Input Plugins
Responsible for reading data from sources, common data sources include:
- Files (File)
- System logs (Syslog)
- Network protocols (HTTP, TCP, UDP)
- Message queues (Kafka, RabbitMQ)
- Databases (JDBC)
- Beats (Filebeat, Metricbeat, etc.)
2. Filter Plugins
Parse, filter, and transform data, commonly used filters include:
- grok: Parse unstructured data into structured format
- mutate: Rename, delete, replace fields
- date: Parse timestamps and convert to Logstash @timestamp field
- geoip: Add geographic location information based on IP address
- ruby: Use Ruby code for complex data transformations
3. Output Plugins
Send processed data to target systems, common output targets include:
- Elasticsearch
- File system
- Message queues
- Databases
- Monitoring systems
Typical Use Cases
- Log Aggregation: Collect various logs from distributed systems
- Log Analysis: Parse, filter, and structure logs
- Data Transformation: Convert data in different formats to a unified format
- Real-time Monitoring: Real-time processing and forwarding of monitoring data
- ELK Stack: Form a complete log analysis platform with Elasticsearch and Kibana
Advantages
- Flexibility: Supports multiple input and output formats
- Extensibility: Rich plugin ecosystem
- Real-time Processing: Supports streaming data processing
- Easy Configuration: Use simple configuration files to define data processing pipelines
- High Availability: Supports cluster deployment and load balancing