Logstash vs Riemann

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Logstash

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Riemann

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Logstash vs Riemann: What are the differences?

Introduction

Logstash and Riemann are two popular tools used in the field of data processing and monitoring. Although both tools share some similarities, they also have key differences that set them apart.

  1. Integration: Logstash is primarily used for log data collection, parsing, and transformation before sending it to a centralized source like Elasticsearch. On the other hand, Riemann is a stream processing system that focuses on monitoring and alerting in real-time, making it more suitable for monitoring performance metrics and events.

  2. Processing: In Logstash, data processing is done through a series of filters and plugins that are executed sequentially. Riemann, however, uses a stream processing language that allows for more complex event processing and manipulation, giving users greater flexibility in defining monitoring logic.

  3. Scalability: Logstash does provide some scalability options through features like multiple pipelines and load balancing, but it is more suitable for small to medium-sized deployments. Riemann, on the other hand, is designed to handle high-throughput real-time data processing with its distributed architecture and built-in clustering capabilities.

  4. Use Cases: Logstash is commonly used in log management and analytics scenarios, where parsing and enriching log data are essential. Riemann is more focused on system monitoring, anomaly detection, and real-time event processing, making it an ideal choice for environments that require immediate action based on critical events.

  5. Data Sources: Logstash is typically used for ingesting log files and structured data from various sources, with built-in support for popular input formats like JSON, CSV, and syslog. Riemann, on the other hand, collects metrics and events from different sources such as servers, applications, and network devices, providing real-time insights into system performance.

  6. Alerting Mechanisms: While Logstash can be integrated with external systems for alerting purposes, its primary focus is on data collection and processing. In contrast, Riemann includes a powerful alerting engine that allows users to define complex alerting rules based on real-time data streams, enabling proactive monitoring and notification.

In Summary, Logstash and Riemann each offer unique capabilities for data processing and monitoring tasks, with Logstash focusing on log management and data transformation, while Riemann excels in real-time event processing and system monitoring.

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Pros of Logstash
Pros of Riemann
  • 69
    Free
  • 18
    Easy but powerful filtering
  • 12
    Scalable
  • 2
    Kibana provides machine learning based analytics to log
  • 1
    Great to meet GDPR goals
  • 1
    Well Documented
  • 5
    Sophisticated stream processing DSL
  • 4
    Clojure-based stream processing

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Cons of Logstash
Cons of Riemann
  • 4
    Memory-intensive
  • 1
    Documentation difficult to use
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    What is Logstash?

    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.

    What is Riemann?

    Riemann aggregates events from your servers and applications with a powerful stream processing language. Send an email for every exception in your app. Track the latency distribution of your web app. See the top processes on any host, by memory and CPU.

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    What companies use Logstash?
    What companies use Riemann?
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    What tools integrate with Logstash?
    What tools integrate with Riemann?

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    Blog Posts

    May 21 2019 at 12:20AM

    Elastic

    ElasticsearchKibanaLogstash+4
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    5298
    GitHubPythonReact+42
    49
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    GitHubMySQLSlack+44
    109
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    What are some alternatives to Logstash and Riemann?
    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.
    Splunk
    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
    Kafka
    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
    Beats
    Beats is the platform for single-purpose data shippers. They send data from hundreds or thousands of machines and systems to Logstash or Elasticsearch.
    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.
    See all alternatives