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

Logstash vs Splunk

OverviewComparisonAlternatives

Overview

Logstash
Logstash
Stacks12.3K
Followers8.8K
Votes103
GitHub Stars14.7K
Forks3.5K
Splunk
Splunk
Stacks772
Followers1.0K
Votes20

Logstash vs Splunk: What are the differences?

Introduction:

Logstash and Splunk are both popular tools used for data collection, analysis, and visualization. However, there are key differences between the two which make them suitable for different use cases.

  1. Data Storage: Logstash is an open-source tool that allows users to collect, parse, and store data. It offers various options for data storage, including Elasticsearch, Apache Kafka, and more. On the other hand, Splunk provides its own centralized data storage system, making it more suitable for organizations looking for a comprehensive data analytics platform.

  2. Data Collection: Logstash mainly focuses on collecting and parsing data from various sources, such as log files, databases, and APIs. It can be easily integrated with different data sources and allows users to customize data parsing and transformation. In contrast, Splunk offers a wider range of data collection methods, including forwarders, APIs, and connectors to common data sources like cloud platforms, making it more versatile for gathering data.

  3. Query and Search Capabilities: Both Logstash and Splunk provide search capabilities, but they differ in terms of ease of use and features. Logstash relies on Elasticsearch for search and querying, which offers powerful searching capabilities but requires some technical knowledge to configure and use effectively. On the other hand, Splunk's search and query language is more user-friendly and intuitive, allowing users to easily explore and analyze their data without extensive technical knowledge.

  4. Scalability and Performance: Logstash is known for its scalability and performance when it comes to handling large amounts of data. It can efficiently process and ingest huge volumes of data in real-time, making it suitable for big data processing. Splunk, on the other hand, may require additional resources and optimization to handle large-scale data processing, especially in distributed environments.

  5. Data Visualization and Dashboards: Splunk offers a wide range of data visualization options and pre-built dashboards. It provides users with interactive charts, graphs, and visual representations of data, allowing for easy understanding and analysis. Logstash, being primarily a data collection and processing tool, does not provide extensive built-in visualization capabilities, although it can be integrated with other visualization tools like Kibana.

  6. Pricing and Licensing: Another significant difference is the pricing and licensing model. Logstash is an open-source tool released under the Apache 2.0 license, making it free to use and modify. Splunk, on the other hand, has a commercial license and offers both free and paid versions. The paid versions of Splunk come with additional features, enterprise support, and scalability options but require a substantial investment.

In summary, Logstash is well-suited for organizations looking for an open-source, scalable, and customizable data collection and processing tool, while Splunk is a comprehensive data analytics platform with its own data storage and visualization capabilities, suitable for organizations with complex data analysis needs.

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Detailed Comparison

Logstash
Logstash
Splunk
Splunk

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

Centralize data processing of all types;Normalize varying schema and formats;Quickly extend to custom log formats;Easily add plugins for custom data source
Predict and prevent problems with one unified monitoring experience; Streamline your entire security stack with Splunk as the nerve center; Detect, investigate and diagnose problems easily with end-to-end observability
Statistics
GitHub Stars
14.7K
GitHub Stars
-
GitHub Forks
3.5K
GitHub Forks
-
Stacks
12.3K
Stacks
772
Followers
8.8K
Followers
1.0K
Votes
103
Votes
20
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
Pros
  • 3
    Alert system based on custom query results
  • 3
    API for searching logs, running reports
  • 2
    Query engine supports joining, aggregation, stats, etc
  • 2
    Splunk language supports string, date manip, math, etc
  • 2
    Custom log parsing as well as automatic parsing
Cons
  • 1
    Splunk query language rich so lots to learn
Integrations
Kibana
Kibana
Elasticsearch
Elasticsearch
Beats
Beats
No integrations available

What are some alternatives to Logstash, Splunk?

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.

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

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.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

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.

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