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rsyslog VS Apache Storm

Compare rsyslog VS Apache Storm and see what are their differences

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rsyslog logo rsyslog

Rsyslog is an enhanced syslogd supporting, among others, MySQL, PostgreSQL, failover log...

Apache Storm logo Apache Storm

Apache Storm is a free and open source distributed realtime computation system.
  • rsyslog Landing page
    Landing page //
    2023-10-01
  • Apache Storm Landing page
    Landing page //
    2019-03-11

rsyslog features and specs

  • High Performance
    Rsyslog is designed for high performance, capable of processing thousands of messages per second and efficiently handling large volumes of log data.
  • Modular Architecture
    Its modular architecture allows for the addition of various plugins and modules to extend functionality and customize the logging system as needed.
  • Advanced Filtering
    Rsyslog offers advanced filtering capabilities, using both simple and complex filters to fine-tune which logs are collected and where they are sent.
  • Network Support
    It has strong support for remote logging via protocols such as TCP, UDP, and RELP, making it a robust solution for centralized logging.
  • Reliability
    Features such as disk-assisted queues and failover actions ensure that log messages are not lost, improving overall reliability.
  • Compatibility
    Rsyslog is compatible with existing syslog implementations and can drop-in replace older syslog daemons without significant changes.
  • Open Source
    Being open-source software, it is freely available for use and modification, supported by an active community.

Possible disadvantages of rsyslog

  • Complex Configuration
    The configuration syntax of rsyslog can be complex and unintuitive, requiring a steep learning curve for beginners.
  • Documentation Quality
    While comprehensive, the documentation can sometimes be difficult to navigate and understand, which might pose challenges for new users.
  • Resource Consumption
    Although efficient, rsyslog can be resource-intensive in certain configurations, potentially impacting system performance if not properly optimized.
  • Dependency Management
    Managing dependencies for various modules and plugins can be cumbersome and may require additional effort to ensure compatibility.
  • Version Inconsistency
    Different distributions might include various versions of rsyslog, leading to inconsistencies in features and behaviors across environments.

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.

Analysis of rsyslog

Overall verdict

  • Yes, rsyslog is considered a good logging tool, especially for those who need a flexible and powerful solution. Its continuous development and extensive feature set make it a reliable choice for system administrators and IT professionals looking to maintain effective log management and monitoring systems.

Why this product is good

  • Rsyslog is a highly versatile and reliable logging tool that is widely used in UNIX and Linux environments for gathering log data from various sources, processing it, and forwarding it to specified destinations. It is known for its ease of configuration, scalability, and compatibility with different protocols and formats. Its ability to handle high log volumes and support for dynamic configurations make it a popular choice for both small and large scale operations.

Recommended for

    Rsyslog is recommended for system administrators, IT professionals, and DevOps engineers who require robust logging capabilities. It is particularly suitable for enterprises and organizations that need to process and analyze large volumes of log data, as well as those who leverage complex IT infrastructures where advanced log manipulation and forwarding are necessary.

rsyslog videos

[LINUX] #11 Rsyslog Server Log Analyzer e Mysql

More videos:

  • Review - Ubuntu: How can I configure logrotate without having `/etc/logrotate.d/rsyslog`?

Apache Storm videos

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 rsyslog and Apache Storm)
Monitoring Tools
100 100%
0% 0
Big Data
0 0%
100% 100
Log Management
100 100%
0% 0
Stream Processing
0 0%
100% 100

User comments

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Reviews

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

rsyslog Reviews

Best Log Management Tools: Useful Tools for Log Management, Monitoring, Analytics, and More
Rsyslog is a blazing-fast system built for log processing. It offers great performance benchmarks, tight security features, and a modular design for custom modifications. Rsyslog has grown from a singular logging system to be able to parse and sort logs from an extended range of sources, which it can then transform and provide an output to be used in dedicated log analysis...
Source: stackify.com

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...
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...

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.

rsyslog mentions (0)

We have not tracked any mentions of rsyslog yet. Tracking of rsyslog 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 rsyslog 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.

Wazuh - Open Source Host and Endpoint Security

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

logstash - logstash is a tool for managing events and logs.

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