Comparisons
Fluentd vs Logstash

Choosing the Right Log Collector in 2023: Fluentd vs Logstash

Better Stack Team
Updated on April 3, 2023

Logs provide valuable insights into the behavior and performance of applications and are essential for identifying and resolving problems, protecting sensitive information, and increasing efficiency. Collecting and analyzing these logs helps us understand and manage our applications more effectively.

Log collection can be challenging for organizations due to the large amount and quick rate at which log data is generated, the variety of data sources and formats, and the need to effectively process and retain the data. Log collectors help solve these problems by allowing organizations to efficiently gather, transform, and store log data from multiple sources, and handle and keep the data secure.

In this article, we will look at log collectors, contrast two different log collectors, FluentD and Logstash, and discuss which tool is a better option.

What are Log Collectors?

Log collectors are tools or software that are designed to capture log data from multiple sources and send it to a centralized location for storage and analysis.

The purpose of log collectors is to provide a centralized location for storing and accessing logs from various sources, simplifying the management and analysis of log data. In addition, they are capable of performing various operations on the data, such as filtering, formatting, and enhancing log data to make it more useful and meaningful.

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

FluentD

Fluentd is an open-source data-collecting tool developed by Treasure Data that is used to gather, analyze, and send log data from diverse sources to a central data store. It was developed in Ruby and leverages the streaming paradigm to handle data in real-time, making it suitable for processing large amounts of data.

Logstash

Logstash is an open-source data collection and processing tool that is part of the Elastic Stack, also known as the ELK Stack. The Elastic Stack is a collection of tools for collecting, storing, and analyzing data that includes Elasticsearch, Logstash, and Kibana.

It is used to collect, parse, and transform data from a wide range of sources and is also a powerful and feature-rich tool that is well-suited for advanced data processing tasks.

Contrasting FluentD with Logstash

In this section, we will see the differences between both tools based on various factors.

Platform Overview: Tie

Fluentd is an open-source log collector developed by Treasure Data and supported by the Cloud Native Computing Foundation. It is written in CRuby which stands for "Concurrent Ruby". CRuby is a version of Ruby that has been modified to allow multiple tasks to be performed concurrently using threads. Fluentd is cross-platform, running on Linux, MacOS, and Windows. It can be deployed as a standalone application or as a service and can run on physical servers, virtual machines, or in containerized environments such as Docker and Kubernetes.

Logstash is an open-source tool developed by Elastic that is written in JRuby, a version of Ruby implemented in Java. It is cross-platform, running on Linux, macOS, and Windows operating systems as well. Logstash can also be installed as a standalone application or service and can be used on physical servers, virtual machines, and containerized environments like Docker and Kubernetes.

This round is a tie, as both are open-source and cross-platform.

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 compared to Logstash because it has a simpler architecture and a smaller codebase.  Fluentd is also highly scalable and able to process large amounts of data efficiently.

Logstash is a data processing tool with a large codebase and a complex architecture. It is designed to handle high volumes of data and can process billions of events per day. Its memory usage varies based on configuration, plugins, data volume, and complexity. Logstash is scalable and includes features to optimize performance, like parallel processing and concurrent output plugins.

Both Fluentd and Logstash offer lightweight alternatives, Fluent Bit and Elastic Beats, which require fewer resources to run.

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, and also having lightweight alternatives.

Ecosystem and Plugins: Tie

Fluentd and Logstash both have rich ecosystems of plugins to extend their functionality. They both have a wide range of input and output plugins for collecting and sending data from/to various sources/destinations, including servers, applications, devices, files, databases, cloud services, messaging systems, and logging/monitoring systems.

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 wider range of input plugins available, including plugins for collecting data from cloud services and messaging systems, while Logstash has additional plugins for collecting data from social media platforms and security systems.

Fluentd has a decentralized approach to plugins, with a community of contributors developing and maintaining them 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.

Fluentd plugin architecture

Logstash, on the other hand, has a centralized repository where all of the plugins are managed by the Logstash team. The logstash-plugins GitHub repository currently contains around 199 plugins for Logstash.

Logstash plugin architecture

This round also ends as a tie.

Log Parsing: Fluentd wins

Fluentd comes with built-in parsers like JSON, regex, and CSV for parsing log data. These parsers can be useful for tasks like parsing log messages, extracting metadata, and aggregating data.

On the other hand, Logstash relies on plugins for log parsing. These plugins include input, output, and filtering plugins that extract specific fields or metadata from log messages using regular expressions or other techniques. They can be useful for tasks like parsing log messages, extracting metadata, and aggregating data.

Fluentd's built-in parsers are a convenient option for log parsing, as they do not require external plugins. However, Logstash's plugin-based approach offers more options and customization when parsing log data.

Logstash Parsing

Fluentd wins this round because of its built-in parsers.

Event Routing: Fluentd wins

Event routing involves directing events or data based on specific criteria. Both Fluentd and Logstash have the ability to route events by collecting data from various sources, processing it, and sending it to various destinations.

Fluentd uses tags to apply routing rules to events. These rules can modify events and route them to specific outputs based on their tags, which allows for flexible control of data flow. This feature is useful because it allows you to easily direct the flow of data based on the tags assigned to events.

Logstash uses conditional statements, such as if-then-else statements, to control data flow/route events through its pipelines. This allows for more flexible event routing using complex criteria defined by regular expressions and conditional statements. However, this approach may require more configuration and may be more complex to set up and maintain compared to Fluentd's tagging method for routing events.

Fluentd also wins this round because of its simplicity.

Transport: Fluentd wins

Both Fluentd and Logstash have mechanisms for buffering and transporting data, but the specific approach and features available may differ depending on the tool you are using.

Fluentd features a built-in configurable buffer system that enables you to store events in memory, on disk, or in a cloud storage service. Since this buffer system is durable, it can hold events during restarts and contribute to preventing data loss. To ensure the best performance, the buffer system may need more tuning and maintenance as compared to Logstash's in-memory queue.

Logstash lacks an in-built buffer system for data transport and relies on an in-memory queue with a default capacity of 20 events to buffer data as it is collected and processed. This queue helps smooth out spikes in event volume and reduce the load on output destinations. However, the queue is not persistent and will be lost if Logstash restarts, so Logstash relies on external queues like Redis or Kafka for persistence across restarts.

Fluentd wins again.

UI & UX: Fluentd

Both Fluentd and Logstash focus on functionality and efficiency in their user interfaces (UIs) 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.

Pricing: Tie

Both Fluentd and Logstash are open-source tools, which means that they are available for free to download and use.

The developers of FluentD, Treasure Data, offer a free, open-source version of Fluentd under Apache License 2.0. Fluentd is developed and maintained by a community of volunteers, and there are no subscription fees or costs associated with using the software.

However, if you want to use Fluentd as part of a larger logging and data analysis solution, you may need to purchase additional software or services. For example, if you want to store and analyze the data collected by Fluentd using Elasticsearch, you may need to purchase a subscription to Elastic Stack. Similarly, if you want to use Fluentd with other tools or services, you may need to pay for those as well.

The developers of Logstash, Elastic, offer a free, open-source version of Logstash, as well as paid versions with additional features and support options.

Elastic pricing

This round is a tie, as both tools have free and paid versions.

Conclusion

Fluentd and Logstash are two of the most popular open-source log processing and analysis tools available. Fluentd is known for its low memory footprint and high performance, making it a popular choice for log collection and analysis in high-volume, real-time environments. Logstash is a more feature-rich tool with a wider range of log processing capabilities, including more complex log transformations and a robust web interface, Kibana, for log management and analysis. However, for organizations seeking a solution that provides real-time log analysis and management capabilities without sacrificing ease of use, Logtail is the best option.

Unlike its counterparts, Logtail offers a modern and intuitive web interface that simplifies the management of logs, enabling real-time log collection, alerting, and visualization with just a few clicks. Say goodbye to manual backups, scaling, and disaster recovery, as Logtail takes care of all these for you as a cloud-based solution. And with real-time log collection, alerting, and visualization, you can stay ahead of any potential issues and make informed decisions in real-time.

So, if you're looking for a log collection and analysis tool that combines the best of both worlds - ease of use and real-time analysis capabilities - Logtail is an option that you simply cannot afford to overlook.

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