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  1. Stackups
  2. DevOps
  3. Log Management
  4. Log Management
  5. Filebeat vs Logstash

Filebeat vs Logstash

OverviewComparisonAlternatives

Overview

Logstash
Logstash
Stacks12.3K
Followers8.8K
Votes103
GitHub Stars14.7K
Forks3.5K
Filebeat
Filebeat
Stacks133
Followers252
Votes0

Filebeat vs Logstash: What are the differences?

Introduction

This Markdown code provides a comparison between Filebeat and Logstash, two popular open-source data collection and processing tools.

  1. Ease of Use: Filebeat is a lightweight log shipper that is easy to set up and configure. It is designed to ship log files from various sources to Elasticsearch or Logstash. On the other hand, Logstash is a more powerful and flexible tool that allows for complex event processing, including filtering, transforming, and enriching data. It requires more configuration and knowledge to set up and manage compared to Filebeat.

  2. Performance: Filebeat is optimized for high-performance log collection and shipping. It is lightweight and has a low resource footprint, making it suitable for low-latency use cases. Logstash, on the other hand, provides more advanced processing capabilities but has a higher resource requirement. It may introduce additional latency, especially when dealing with complex pipelines or large volumes of data.

  3. Data Transformation: Logstash provides a wide range of plugins and filters to manipulate data during the ingestion process. It can parse various formats like JSON, CSV, and XML, and perform operations like field mapping, data enrichment, and conditional filtering. Filebeat, on the other hand, focuses mainly on log collection and shipping, offering limited data manipulation capabilities. It can, however, extract fields from log lines using regular expressions.

  4. Scalability: Filebeat is a lightweight and horizontally scalable tool that can be configured to ship logs from multiple sources to Elasticsearch or Logstash. It allows for easy distribution of the workload across multiple instances. Logstash, with its more advanced processing capabilities, can handle complex data pipelines and transformations. However, it requires more resources and management overhead, making it more suitable for medium to large-scale deployments.

  5. Plugins and Integrations: Logstash has a vast ecosystem of plugins that extend its functionality, allowing integration with various data sources, transformation tools, and output destinations. It offers a wide range of input, codec, filter, and output plugins. Filebeat, on the other hand, has a more limited plugin ecosystem, offering fewer options for data manipulation and integration. It is primarily focused on log shipping.

  6. Community and Support: Both Filebeat and Logstash are open-source projects supported by a large community of users and developers. They have active online communities, documentation, and forums for support. However, due to its wider adoption and longer history, Logstash has a larger community, more extensive documentation resources, and a broader range of community-contributed plugins and integrations.

In Summary, Filebeat and Logstash have distinct differences in terms of ease of use, performance, data transformation capabilities, scalability, plugin and integration options, and community support. Choosing the right tool depends on the specific requirements of the use case, considering factors such as resource constraints, complexity of data processing, and the need for extensibility.

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Detailed Comparison

Logstash
Logstash
Filebeat
Filebeat

Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana.

It helps you keep the simple things simple by offering a lightweight way to forward and centralize logs and files.

Centralize data processing of all types;Normalize varying schema and formats;Quickly extend to custom log formats;Easily add plugins for custom data source
-
Statistics
GitHub Stars
14.7K
GitHub Stars
-
GitHub Forks
3.5K
GitHub Forks
-
Stacks
12.3K
Stacks
133
Followers
8.8K
Followers
252
Votes
103
Votes
0
Pros & Cons
Pros
  • 69
    Free
  • 18
    Easy but powerful filtering
  • 12
    Scalable
  • 2
    Kibana provides machine learning based analytics to log
  • 1
    Well Documented
Cons
  • 4
    Memory-intensive
  • 1
    Documentation difficult to use
No community feedback yet
Integrations
Kibana
Kibana
Elasticsearch
Elasticsearch
Beats
Beats
No integrations available

What are some alternatives to Logstash, Filebeat?

Papertrail

Papertrail

Papertrail helps detect, resolve, and avoid infrastructure problems using log messages. Papertrail's practicality comes from our own experience as sysadmins, developers, and entrepreneurs.

Logmatic

Logmatic

Get a clear overview of what is happening across your distributed environments, and spot the needle in the haystack in no time. Build dynamic analyses and identify improvements for your software, your user experience and your business.

Loggly

Loggly

It is a SaaS solution to manage your log data. There is nothing to install and updates are automatically applied to your Loggly subdomain.

Logentries

Logentries

Logentries makes machine-generated log data easily accessible to IT operations, development, and business analysis teams of all sizes. With the broadest platform support and an open API, Logentries brings the value of log-level data to any system, to any team member, and to a community of more than 25,000 worldwide users.

Graylog

Graylog

Centralize and aggregate all your log files for 100% visibility. Use our powerful query language to search through terabytes of log data to discover and analyze important information.

Sematext

Sematext

Sematext pulls together performance monitoring, logs, user experience and synthetic monitoring that tools organizations need to troubleshoot performance issues faster.

Fluentd

Fluentd

Fluentd collects events from various data sources and writes them to files, RDBMS, NoSQL, IaaS, SaaS, Hadoop and so on. Fluentd helps you unify your logging infrastructure.

ELK

ELK

It is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch.

Sumo Logic

Sumo Logic

Cloud-based machine data analytics platform that enables companies to proactively identify availability and performance issues in their infrastructure, improve their security posture and enhance application rollouts. Companies using Sumo Logic reduce their mean-time-to-resolution by 50% and can save hundreds of thousands of dollars, annually. Customers include Netflix, Medallia, Orange, and GoGo Inflight.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

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