Fluentd vs Logstash: How to Choose in 2024

Logging plays a crucial role in gaining valuable insights into application behavior and performance. It is essential for identifying and resolving issues, understanding application usage patterns, and meeting compliance requirements. However, log collection and aggregation cam often pose significant challenges due to the sheer volume and rapid generation of log data, diverse data sources and formats, and the need for efficient processing and storage.

To address these challenges, log collectors come into play. They enable you to gather and transform data from multiple sources, and securely aggregate them in one place to ensure effective management of the data. In this article, we will explore log collectors in detail, focusing on a comparison between two popular options: Fluentd and Logstash. We will analyze the strengths and weaknesses of each tool to determine which one is the superior choice for you.

Let's get started!

What are log collectors?

Log collectors are software tools specifically designed to collect log data from multiple sources and transmit it to a central location for storage and analysis. They help streamline the process of aggregating log data from diverse sources, and they often contain a range of functionalities to manipulate the data, including filtering, formatting, and enriching log entries to enhance their usefulness and significance.

Although there are numerous log collectors in use, we will only be concentrating on comparing Fluentd and Logstash in this article, as previously stated.

What is Fluentd?

Fluentd, a powerful data-collecting tool developed by Treasure Data, is widely used gather, analyze, and transmit log data from various sources to a centralized data store. One of its key strengths lies in its pluggable architecture, which enables seamless integration with a wide range of data sources and outputs through an extensive library of over 500 community-contributed plugins.

Harnessing the streaming paradigm, Fluentd excels in real-time data processing, enabling it to handle and analyze substantial data volumes with remarkable efficiency and precision. Whether capturing logs from numerous servers or managing data streams from Internet of Things (IoT) devices, Fluentd's prowess in real-time processing makes it an ideal choice for demanding high-throughput scenarios.

What is Logstash?

Logstash, an essential component of the Elastic Stack (ELK Stack), is a robust and open-source tool designed for data collection and processing. It is often used alongside Beats to aggregate and centralize log data from diverse sources such as servers, applications, network devices, and cloud platforms.

With its extensive collection of over 50 plugins, a wide range of input options for gathering data is supported. This flexibility allows organizations to gather log data from virtually any system or application within their infrastructure. Through the use of plugins like grok, dissect, and mutate, Logstash can parse, dissect, and enrich the log data so that relevant fields are extracted and transformed into the desired format before the data is centralized into Elasticsearch or other log management systems.

Now that you have an idea of what problems Fluentd and Logstash solve, let us now delve into a comparative analysis of these two tools. We will evaluate them based on the following criteria:

1. Platform overview
2. Memory usage/performance
3. Ecosystem and plugins
4. Log parsing
5. Event routing
6. Transport
7. UI & UX design
8. Pricing

✔ - partial or limited feature

✔✔ - complete feature

1. Platform Overview: Tie

Fluentd is written using a combination of C and Ruby. Its core functionality and most plugins are written in Ruby, but certain performance-sensitive components, such as event buffering and low-level I/O operations, are implemented in C for improved efficiency and reduced overhead. These C extensions enhance Fluentd's performance and allow it to handle high-volume data streams more effectively.

Fluentd is a cross-platform tool that is compatible with Linux, macOS, and Windows. It offers flexible deployment options, allowing it to function as either a standalone service or integrated into existing infrastructure such as virtual machines or containers.

Logstash, on the other hand, is developed using a combination of JRuby and Java. While the core of Logstash is primarily written in Java, it leverages JRuby for specific aspects of its implementation, such as optimizing the points of contact between Ruby and Java. JRuby enables Logstash to use Java libraries, expanding its range of features and functionalities. This integration with Java brings performance benefits and facilitates the use of multithreading, allowing Logstash to efficiently process data.

Logstash is also compatible with Linux, macOS, and Windows-based servers. In addition to traditional installations, Logstash also supports containerized deployment through Docker, enabling easy management and portability across different environments.

In summary, since both tools are open-source and cross-platform, this round can be considered a tie.

2. Memory Usage/Performance: Tie

Fluentd and Logstash are both designed to be lightweight and efficient with memory usage, however, they have different memory usage patterns which are dependent on the use case. Fluentd is generally considered to be more lightweight and resource-efficient than Logstash so it is more suitable for memory constrained environments.

In terms of performance, they are both capable of handling high data volumes and scaling appropriately. If you need lighter alternatives to both tools, you should check out Fluent Bit and Elastic Beats respectively.

Fluent Bit is a lightweight and flexible data collection and processing tool that is designed to be smaller and faster than Fluentd and is recommended when using small or embedded applications. It is written in C.

Elastic Beats is a collection of data shippers that sends data to Elasticsearch or Logstash. It is not a lightweight version of Logstash, but it is efficient for collecting and processing data from specific sources. It is useful for cases where only a specific type of data is needed and not the full range of Logstash's features.

This round ends in a tie for both tools, as they are capable of scaling, processing vast amounts of data, while also having lightweight alternatives.

3. Ecosystem and Plugins: Tie

Both Fluentd and Logstash boast rich ecosystems of plugins to extend their functionality. These plugins offer a diverse array of input and output options, enabling data collection from various sources and sending data to different destinations including servers, applications, devices, files, databases, cloud services, and many more.

The main difference between the Fluentd and Logstash plugin ecosystems is the specific plugins that are available for each tool, as well as the difference in how the plugins are managed.

Fluentd has a decentralized approach to plugins, with a community of contributors developing and maintaining them individually rather than hosting them in a single repository. This allows for a wide range of plugins to be available and makes Fluentd highly flexible and extensible for use in a variety of scenarios. You can look at the official GitHub repository for available plugins but there are many other repositories and resources available for finding and installing additional plugins.

Logstash, on the other hand, has a centralized repository where all of the plugins are managed by the Elastic team, and it currently contains over 250 plugins at the time of writing.

This round ends in a tie since both tools provide an extensive selection of plugins to extend their functionality.

4. Log Parsing: Fluentd wins

Fluentd is a powerful log collection tool that excels in parsing both structured a and unstructured logs. It provides a range of built-in parsers, including JSON, regex, and CSV, which are useful for parsing log data from a variety of sources.

In contrast, Logstash relies on plugins for log parsing. It offers a variety of input, output, and filtering plugins that allow you to extract specific fields or metadata from log messages using regular expressions or other techniques.

Both Fluentd and Logstash support custom parsing patterns and can automatically parse common log formats. However, Fluentd gains an advantage with its built-in parsers, which eliminate the need for additional plugins in most cases. This convenience makes Fluentd a favorable choice for log parsing.

5. Event Routing: Fluentd wins

Event routing involves directing events or data to some destination based on specific criteria. Both Fluentd and Logstash provide event routing capabilities, allowing for the collection, processing, and routing of data from various sources to different destinations based on specific criteria.

Fluentd uses tags to apply routing rules to events. This means events are routed to specific destinations based on the tags assigned to them. On the other hand, Logstash uses conditional statements, such as if-then-else statements, to control data flow through its pipelines. While providing greater flexibility, it may require more configuration and can be more intricate to set up and maintain compared to Fluentd's simpler tag-based method.

Therefore, Fluentd wins this round due to its straightforward tagging approach, making it easier to set up and manage event routing.

6. Transporting data: Fluentd wins

When it comes to transporting data, Fluentd offers a variety of output plugins that allow you to move collected data to various destinations, including local files, remote servers, cloud storage services, message queues, and more. It also features a flexible and configurable buffer system that can store events in memory, on disk, or in the cloud. This helps prevent data loss during restarts.

Logstash also uses output plugins for transporting data to different destinations, similar to Fluentd. However, it relies on an in-memory queue with a default capacity of 20 events to buffer data during collection and processing. This queue helps handle spikes in event volume and reduces the load on output destinations.

Notably, the in-memory queue used by Logstash is not persistent, meaning that queued events are lost during restarts. To ensure persistence, Logstash relies on external queues like Redis or Kafka.

Fluentd wins this round since it doesn't require additional tools to achieve persistence.

7. User interface: Fluentd wins

Both Fluentd and Logstash focus on functionality and efficiency in their user interfaces rather than aesthetics. Fluentd has a simpler and more streamlined UI, with a web-based dashboard for viewing logs and metrics and a simple configuration file syntax for setting up data collection.

Logstash itself does not have a built-in graphical user interface (GUI) or web-based interface for configuring and monitoring pipelines. Instead, Logstash is typically configured using a text-based configuration file that defines the inputs, filters, and outputs for the pipeline.

However, Logstash can be integrated with other tools that provide a GUI for pipeline management and monitoring. For example, the Elastic Stack (which includes Logstash, Elasticsearch, and Kibana) provides a centralized platform for data ingestion, storage, and analysis, with Kibana serving as the primary GUI for managing and monitoring Logstash pipelines.

In terms of user experience (UX), Fluentd is a good choice for those new to data collection and processing tools or for organizations that need to quickly set up and start collecting data, while Logstash is better suited for more advanced data processing tasks.

Fluentd wins this round.

8. Pricing: Tie

The developers of Fluentd provide a free and open-source version under the Apache License 2.0. This means that there are no subscription fees or costs associated with using the tool. However, if you intend to incorporate Fluentd into a comprehensive logging and data analysis solution, additional software or services may need to be purchased. For example, if you wish to store and analyze data collected by Fluentd using Better Stack, a subscription may be required.

Logstash also offers a free and open-source version, along with paid versions that provide additional features and support options. Elastic (the company behind Logstash) provides different pricing tiers for their commercial offerings, allowing users to choose the level of support and features that best suit their requirements.

This round is considered a tie since both tools offer open-source versions at no cost while providing paid options for additional features and support.

Aggregating your logs with Better Stack

Fluentd and Logstash are both effective at collecting and forwarding logs, but managing and processing logs becomes increasingly challenging as the log volume grows. To address this challenge, integrating a reliable log management tool is essential.

Better Stack provides a powerful cloud-based log management tool that excels at aggregating logs from Fluentd and Logstash. It offers advanced features such as log parsing, filtering, and correlation, making it a suitable choice for efficiently managing large volumes of logs.

Better Stack offers effortless integration with various popular tools, including Slack, Amazon S3, and others. It also enables email notifications and webhooks, allowing users to receive log notifications and alerts directly in their preferred communication channels.

These seamless integrations help to simplify the log management process and enable quick issue resolution. With Better Stack's versatile notification options, users can easily stay on top of any critical events related to their system logs, no matter where they are or what communication channel they prefer.

One of the key advantages of Better Stack is its ability to notify users of any irregular volumes of logs generated by their services or any other specified conditions, which can be immensely helpful in detecting problems and fixing them quickly.

Another advantage of Better Stack is its affordability. It is more cost-effective when compared to other log management solutions such as the Elastic Stack, which utilizes Logstash as its log collector. With Better Stack, you can enjoy a reliable and robust log management solution without breaking the bank.

Final thoughts

Fluentd and Logstash are two of the most popular open-source log processing and analysis tools available. The former is known for its low memory footprint and high performance, making it a popular choice for log collection and analysis in real-time environments. Logstash, on the other hand, offers a wider range of log processing capabilities and first-class integration with the rest of the Elastic Stack for log management and analysis.

The correct choice between the two would depend on your specific requirements, such as the available resources, desired performance levels, and the complexity of data processing tasks. We hope that is comparison has given you an idea of what tool is better for your needs. However, we recommend that you carefully evaluate and benchmark both tools before making a final decision.

Thanks for reading, and happy logging!

Article by

Daniel Favour

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