What is LogStash?

Logstash is an open-source data processing pipeline tool that ingests, transforms, and ships data from various sources to various destinations. Part of the Elastic Stack, it supports real-time data collection and transformation using a pluggable architecture with input, filter, and output plugins. Commonly used for centralized logging, data transformation, and real-time analytics, Logstash enables workflows and immediate insights by feeding data into systems like Elasticsearch or PubNub Iluminate for analysis.

Primary functions:

1. Ingestion: Logstash can collect and aggregate data from multiple sources in real-time. It supports a wide range of input sources including log files, databases, message queues, and various cloud services.

2. Transformation: Once the data is ingested, Logstash allows you to parse and transform it using a variety of filters. You can use these filters to clean, enrich, and modify the data before it is sent to the final destination. Common transformations include parsing unstructured log data, adding geographic information, or anonymizing sensitive information.

3. Output: After processing, Logstash can ship the data to various destinations such as Elasticsearch (for storage and search), various databases, or other systems and services.

Typical use cases for Logstash include:

1. Centralized logging: Aggregating and processing logs from various applications and systems.

2. Data Transformation: Cleaning and enriching data before analysis or storage.

3. Real-Time Analytics: Feeding data into Elasticsearch for real-time analytics with Kibana.

How LogStash work?

Logstash works by utilizing a pipeline architecture that processes data through three main stages: input, filter, and output. Here's a step-by-step explanation of how it operates:

  1. Input Stage:

    • Logstash collects data from various sources using input plugins. These sources can include log files, databases, message queues, cloud services, and more.

    • Each input plugin is configured to capture data from a specific source. For example, the file input plugin reads data from log files, while the jdbc input plugin reads from databases.

  2. Filter Stage:

    • Once data is ingested, it passes through a series of filters for processing. Filters allow you to parse, clean, enrich, and transform the data.

    • Common filter plugins include:

      • grok for parsing unstructured log data.

      • mutate for modifying fields (e.g., renaming, removing).

      • date for parsing timestamps.

      • geoip for adding geographic information based on IP addresses.

    • Filters can be chained together to perform complex transformations.

  3. Output Stage:

    • After filtering, the processed data is sent to one or more destinations using output plugins.

    • Destinations can include Elasticsearch (for storage and search), databases, messaging systems, and other services.

    • For example, the elasticsearch output plugin sends data to an Elasticsearch cluster, while the stdout plugin outputs data to the console for debugging.

Example Pipeline Configuration

A simple Logstash pipeline configuration might look like this:

input {

  file {

    path => "/var/log/syslog"

    start_position => "beginning"



filter {

  grok {

    match => { "message" => "%{SYSLOGTIMESTAMP:timestamp} %{SYSLOGHOST:hostname} %{DATA:program}: %{GREEDYDATA:logmessage}" }


  date {

    match => [ "timestamp", "MMM dd HH:mm:ss", "MMM d HH:mm:ss" ]



output {

  elasticsearch {

    hosts => ["localhost:9200"]

    index => "syslog-%{+YYYY.MM.dd}"


  stdout { codec => rubydebug }


Workflow Process:

  1. Ingestion: The file input plugin reads data from the /var/log/syslog file.

  2. Parsing and Transformation: The grok filter parses the log message, extracting fields like timestamp, hostname, program, and logmessage. The date filter parses the timestamp to ensure it is in a standard format.

  3. Output: The processed data is sent to an Elasticsearch instance and also printed to the console in a readable format using the stdout output plugin.

Key Points

  • Pluggable Architecture: Logstash uses a wide range of plugins for input, filtering, and output, making it highly extensible.

  • Real-Time Processing: Designed to handle data in real-time, enabling immediate analysis and insights.

  • Pipeline Configuration: Defined using a configuration file, allowing for the specification of complex data processing workflows.

Logstash’s ability to process and route data from various sources to multiple destinations makes it a versatile tool for data management and analytics pipelines.