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logstash VS Apache Storm

Compare logstash VS Apache Storm and see what are their differences

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Contents:
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  3. » Reviews
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logstash logo logstash

logstash is a tool for managing events and logs.

Apache Storm logo Apache Storm

Apache Storm is a free and open source distributed realtime computation system.
  • logstash Landing page
    Landing page //
    2023-10-21
  • Apache Storm Landing page
    Landing page //
    2019-03-11

logstash features and specs

  • Flexible Data Collection
    Logstash supports a wide variety of inputs, filters, and outputs, enabling it to collect, process, and forward data from numerous sources with ease.
  • Real-Time Processing
    Logstash can process logs and event data in real-time, enabling quick aggregation, transformation, and forwarding for timely insights and actions.
  • Ecosystem Integration
    As part of the Elastic Stack, Logstash integrates seamlessly with Elasticsearch, Kibana, and Beats, providing a cohesive solution for data ingestion, storage, and visualization.
  • Built-In Plugins
    Logstash has a robust collection of built-in plugins for inputs, codecs, filters, and outputs, minimizing the need for custom development.
  • Scalability
    Logstash can be scaled horizontally by adding more instances, which allows it to handle higher data throughput as your needs grow.
  • Extensibility
    Logstash's plugin architecture allows for custom plugins to be developed, providing flexibility for specific use cases.

Possible disadvantages of logstash

  • Resource Intensive
    Logstash can be quite resource-heavy, consuming significant CPU and memory, which could lead to increased infrastructure costs.
  • Complex Configuration
    The configuration syntax can be complex and sometimes unintuitive, making it challenging for new users to set up and maintain.
  • Latency
    In certain scenarios, Logstash can introduce latency in data processing, which may not be suitable for all real-time applications.
  • Single Point of Failure
    If not properly architected with redundancy, Logstash can become a single point of failure in your data pipeline.
  • Limited Error Handling
    Logstash's error handling is not very robust, which can make it difficult to troubleshoot and resolve issues as they arise.
  • Learning Curve
    Due to its powerful features and flexibility, there is a steep learning curve associated with mastering Logstash.

Apache Storm features and specs

  • Real-Time Processing
    Apache Storm is designed for processing data in real-time, which makes it ideal for applications like fraud detection, recommendation systems, and monitoring tools.
  • Scalability
    Storm is capable of scaling horizontally, allowing it to handle increasing amounts of data by adding more nodes, making it suitable for large-scale applications.
  • Fault Tolerance
    Storm provides robust fault-tolerance mechanisms by rerouting tasks from failed nodes to operational ones, ensuring continuous processing.
  • Broad Language Support
    Apache Storm supports multiple programming languages, including Java, Python, and Ruby, allowing developers to use the language they are most comfortable with.
  • Open Source Community
    Being an Apache project, Storm benefits from a strong open-source community, which contributes to its development and offers abundant resources and support.

Possible disadvantages of Apache Storm

  • Complex Setup
    Setting up and configuring Apache Storm can be complex and time-consuming, requiring detailed knowledge of its architecture and the underlying infrastructure.
  • High Learning Curve
    The architecture and components of Storm can be difficult for new users to grasp, leading to a steeper learning curve compared to some other streaming platforms.
  • Maintenance Overhead
    Managing and maintaining a Storm cluster can require significant effort, including monitoring, troubleshooting, and scaling the infrastructure.
  • Error Handling
    While Storm is fault-tolerant, its error handling at the application level can sometimes be challenging, requiring careful design to manage failures effectively.
  • Resource Intensive
    Storm can be resource-intensive, particularly in terms of memory and CPU usage, which can lead to increased costs and necessitate powerful hardware.

logstash videos

+ Add

Visualizing Logs Using ElasticSearch, Logstash and Kibana

More videos:

  • Review - Security Onion with Elasticsearch, Logstash, and Kibana (ELK)

Apache Storm videos

+ Add

Apache Storm Tutorial For Beginners | Apache Storm Training | Apache Storm Example | Edureka

More videos:

  • Review - Developing Java Streaming Applications with Apache Storm
  • Review - Atom Text Editor Option - Real-Time Analytics with Apache Storm

Category Popularity

0-100% (relative to logstash and Apache Storm)

logstash

Apache Storm

Monitoring Tools
100 100%
Monitoring Tools
0% 0
Big Data
0 0%
Big Data
100% 100
Log Management
100 100%
Log Management
0% 0
Stream Processing
0 0%
Stream Processing
100% 100

User comments

Share your experience with using logstash and Apache Storm. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare logstash and Apache Storm

logstash Reviews

10 Best Open Source ETL Tools for Data Integration
A free and open source ETL tool, Logstash collects data from several sources, performs a transformation process, and sends the output back to your choice of data warehouse. It consists of pre-built filters and more than a hundred plugins to carry out the data process operations. No matter the format or the complexity of data, Logstash dynamically ingests, transforms, and...
Source: testsigma.com
11 Best FREE Open-Source ETL Tools in 2024
Logstash is an Open-Source Data Pipeline that extracts data from multiple data sources and transforms the source data and events and loads them into ElasticSearch, a JSON-based search, and analytics engine. It is part of the ELK Stack. The “E” stands for ElasticSearch and the “K” stands for Kibana, a Data Visualization engine.
Source: hevodata.com
10 Best Linux Monitoring Tools and Software to Improve Server Performance [2022 Comparison]
Lastly, the Elastic Stack (ELK Stack) is a well-known tool for Linux performance monitoring. It’s composed of Elasticsearch (full-text search), Logstash (a log aggregator), Kibana (visualization via graphs and charts), and Beats (lightweight metrics collectors and shippers).
Source: sematext.com
Top 10 Popular Open-Source ETL Tools for 2021
Logstash is an Open-Source Data Pipeline that extracts data from multiple data sources and transforms the source data and events and loads them into ElasticSearch, a JSON-based search, and analytics engine. It is part of the ELK Stack. The “E” stands for ElasticSearch and the “K” stands for Kibana, a Data Visualization engine.
Source: hevodata.com
Top ETL Tools For 2021...And The Case For Saying "No" To ETL
Logstash is an open source data processing pipeline that ingests data from multiple sources simultaneously, transforming the source data and store events into ElasticSearch by default. Logstash is part of an ELK stack. The E stands for Elasticsearch, a JSON-based search and analytics engine, and the K stands for Kibana, which enables data visualization.
Source: blog.panoply.io

Apache Storm Reviews

Top 15 Kafka Alternatives Popular In 2021
Apache Storm is a recognized, distributed, open-source real-time computational system. It is free, simple to use, and helps in easily and accurately processing multiple data streams in real-time. Because of its simplicity, it can be utilized with any programming language and that is one reason it is a developer’s preferred choice. It is fast, scalable, and integrates well...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Storm is an open-source distributed real-time computational system for processing data streams. Similar to what Hadoop does for batch processing, Apache Storm does for unbounded streams of data in a reliable manner. Built by Twitter, Apache Storm specifically aims at the transformation of data streams. Storm has many use cases like real-time analytics, online machine...
Source: www.analyticsinsight.net

Social recommendations and mentions

Based on our record, Apache Storm seems to be more popular. It has been mentiond 11 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

logstash mentions (0)

We have not tracked any mentions of logstash yet. Tracking of logstash recommendations started around Mar 2021.

Apache Storm mentions (11)

  • Data Engineering and DataOps: A Beginner's Guide to Building Data Solutions and Solving Real-World Challenges
    There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS. - Source: dev.to / over 2 years ago
  • Real Time Data Infra Stack
    Although this article lists a lot of targets for technical selection, there are definitely others that I haven't listed, which may be either outdated, less-used options such as Apache Storm or out of my radar from the beginning, like JAVA ecosystem. - Source: dev.to / over 2 years ago
  • In One Minute : Hadoop
    Storm, a system for real-time and stream processing. - Source: dev.to / over 2 years ago
  • Elon Musk reportedly wants to fire 75% of Twitter’s employees
    Google has scaled well and has helped others scale, Twitter has always been behind by years. I think the only thing they did well was Twitter Storm, now taken up by Apache Foundation. Source: over 2 years ago
  • Spark for beginners - and you
    Streaming: Sparks Streamings's latency is at least 500ms, since it operates on micro-batches of records, instead of processing one record at a time. Native streaming tools like Storm, Apex or Flink might be better for low-latency applications. - Source: dev.to / over 3 years ago
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What are some alternatives?

When comparing logstash and Apache Storm, you can also consider the following products

Fluentd - Fluentd is a cross platform open source data collection solution originally developed at Treasure Data.

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Splunk - Splunk's operational intelligence platform helps unearth intelligent insights from machine data.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

Datadog - See metrics from all of your apps, tools & services in one place with Datadog's cloud monitoring as a service solution. Try it for free.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

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