Filebeat and Logstash, both developed by Elastic, are integral components of the Elastic Stack, each serving as log collectors with distinct features and functionalities. Logstash, an original component of the ELK Stack (Elasticsearch, Logstash, Kibana), was developed to efficiently collect a large volume of logs from multiple sources and dispatch them to various destinations.
While Logstash gained popularity, it faced challenges due to resource-intensive operations, particularly on systems with limited resources. To address this, Elastic introduced Filebeat as part of the Beats family, offering a lightweight alternative to Logstash. This addition led to the rebranding of the ELK Stack to the Elastic Stack to better encompass the growing suite of tools, including Beats. Filebeat was intended to complement Logstash but evolved into an independent log collector over time.
As a result, deciding between these two log collectors can be a daunting task, given their unique strengths and functions. This article will compare Filebeat and Logstash, explore their pros and cons, and provide insights on when to choose one over the other.
Let's get started!
What is Filebeat?
Filebeat is a free and open-source log collector developed by Elastic, which is part of the beats family within the Elastic Stack. This suite of tools is essential for collecting and shipping various types of data like logs, metrics, and network information. Filebeat was initially designed to work mainly with Logstash, but over time, Filebeat has grown beyond that with Elastic's continual updates to its log processing abilities.
Further, Filebeat has various built-in inputs and outputs, catering to different sources and destinations. If these built-in options do not meet specific requirements, it also allows for creating custom plugins. Filebeat also includes internal modules for collecting and parsing logs from widely used tools such as NGINX, Apache, system logs, and MySQL.
Filebeat is valued for its lightweight design, reliability, and robustness. It supports encrypted data transmission and uses a backpressure-sensitive protocol, which is useful when handling large volumes of data. This feature enables it to adjust its data transmission rate, preventing overload at the destination.
What is Logstash?
Logstash is a free and open-source data pipeline tool created by Elastic. It's designed to efficiently collect, process, and send logs to various destinations. Known for its flexibility, Logstash offers multiple inputs, filters, and outputs, making it adaptable for different log processing needs. It excels in filtering, parsing, and transforming logs, providing advanced log processing capabilities.
As a key component of the Elastic Stack, Logstash works seamlessly with other tools like Beats, Elasticsearch, and Kibana. It pulls data from various sources and pushes it to Elasticsearch, which is then forwarded to Kibana for analysis and visualization.
Logstash has over 200 plugins and offers an API for creating custom plugins for specific user needs. A notable feature of Logstash is its reliability, underpinned by a persistent queue that holds log events as they are transmitted. In scenarios where events fail to be sent, Logstash can reroute them to another queue for further inspection and reprocessing.
Now that we have a basic idea of Filebeat and Logstash let's compare these two tools. Our comparison will focus on the following key criteria:
|Windows, macOS, Linux
|Windows, macOS, Linux
|Utilizes significant memory
|Ecosystem and plugins
|Less than 60 plugins
|Over 200 plugins
|Has built-in parsers and modules
|Has more powerful parsers
|No event routing support
|Uses conditional statements to route logs
|Buffers log events and has persistent queues
|Buffers log events and has persistent queues
|UI & UX design
|No UI but can be integrated with Kibana
|No UI but can be integrated with Kibana
1. Supported platforms: Tie
Filebeat is developed using Go, a modern language renowned for creating high-performance networking and infrastructure programs. Its design allows Filebeat to gather, process, and forward logs with a low memory footprint. Additionally, its compatibility spans various platforms, including Linux, Windows, MacOS, and even containerized environments.
On the other hand, Logstash is built with JRuby, a high-performance implementation of the Ruby programming language in Java. To run Logstash, the Java Virtual Machine (JVM) is required. The JVM's cross-platform compatibility ensures that Logstash can operate on various systems, including Linux, Windows, and MacOS. This makes Logstash equally versatile in terms of platform support.
Considering the supported platforms, both Filebeat and Logstash demonstrate strong adaptability, making them excellent choices for cross-platform support.
2. Memory Usage/Performance: Filebeat wins
Filebeat is designed to focus on lightweight efficiency, allowing it to handle significant data volumes while maintaining minimal memory consumption. An individual instance of Filebeat typically operates on less than 2MB of memory and uses under 30% of the CPU.This remarkable efficiency can be attributed mainly to its development in Go, as mentioned earlier.
Moreover, Filebeat includes load balancing and failover capabilities, which are crucial for ensuring consistent log data retrieval and forwarding, particularly in high-traffic scenarios.
By contrast, Logstash requires a considerably higher memory footprint. To run effectively, it needs a host with at least 2GB of memory, and the recommended memory allocation is around 4GB, according to the official documentation. This increased demand stems from Logstash's reliance on the Java Virtual Machine (JVM) and its sophisticated log processing features, which naturally consume more resources. While Logstash does incorporate load balancing and failover features for reliable log handling, its higher resource requirements make it less suitable for environments where memory and CPU usage are critical constraints.
In summary, for environments where performance and scalability are paramount, and memory usage is a crucial concern, Filebeat is the more advantageous option due to its lower memory footprint and efficient resource utilization.
3. Ecosystem and Plugins: Logstash wins
Filebeat, with its focus on being lightweight and efficient, still offers a substantial library of over 60 plugins. These plugins cover a variety of inputs and outputs, including AWS S3, Kafka, Redis, and File. The comprehensive details of these plugins can be found on their detailed inputs and outputs pages in the documentation. For those skilled in Go, Filebeat also allows the creation of custom plugins, providing flexibility for specific use cases. Additionally, Filebeat's modules simplify the process of reading logs from popular tools like MySQL or Nginx, enhancing its ease of use.
On the other hand, Logstash boasts a rich ecosystem of plugins, surpassing 200 in number and categorized into input, output, filter, and codec. Most of these plugins are built-in, forming the foundation of Logstash's advanced log processing capabilities. Among the widely used plugins are the Grok filter plugin, which parses logs using regular expressions, and the GeoIP filter, which produces geographical information for IP addresses.
Creating custom plugins for Logstash is straightforward, and the documentation provides comprehensive guidance.
Due to its extensive plugin ecosystem, Logstash emerges as a clear winner for scenarios requiring advanced data processing.
4. Log parsing: Logstash wins
In terms of log parsing, both Filebeat and Logstash display advanced log parsing capabilities, integrating built-in plugins capable of handling both structured and unstructured logs.
Filebeat enhances its parsing capabilities through the use of available processors, capable of parsing standard formats such as JSON and CSV. Additionally, internal modules enable Filebeat to parse prevalent formats from sources like Nginx, MySQL, or Apache.
Similarly, Logstash offers robust parsers and built-in capabilities. What distinguishes Logstash is the inclusion of Grok, which uses regular expressions to match log events against specific patterns. With over 200 predefined patterns, Grok enables the parsing of structured and unstructured logs from diverse sources such as MongoDB, Postgres, and AWS and offers flexibility in defining patterns for parsing custom log formats.
When it comes to log parsing, while Filebeat is suitably equipped to handle standard log formats, Logstash stands out with powerful parsing capabilities.
5. Event routing: Logstash wins
Event routing is a process where log events are directed to specific destinations based on criteria or the content within each log event. This means you can set up rules to determine where each log should be sent based on the data it contains. For instance, you might configure the log collector to send all log events with an HTTP status code of 200 to a remote location, while those with status code 400 are written to a specific file.
Filebeat, while efficient in log collection, is not designed with event routing. It typically forwards all collected logs to a single endpoint, usually Logstash, aligning with its original design purpose. Therefore, in scenarios requiring the distribution of logs from multiple sources to varied destinations, Filebeat's functionality is limited.
On the other hand, Logstash demonstrates superior proficiency in event routing.
It supports routing log events to multiple destinations using conditional
if-then-else. These conditions allow Logstash to assess log
events against specific criteria and direct them accordingly.
Given the significance of event routing in customizing log distribution to specific needs, Logstash outperforms Filebeat in this area.
6. Transporting data: Tie
In transporting data, Filebeat and Logstash are on par, each offering effective output plugins for collecting logs from various sources and delivering them to multiple destinations, including cloud storage, Kafka, AWS, and local files.
A key feature of both tools is their use of in-memory queues to buffer log events. This capability is crucial in managing spikes in log data and temporarily storing logs to prevent overloading outputs with data. The in-memory queues also play a role in resending log events that might have failed to transmit due to issues with the output destination.
In unexpected interruptions or premature exits, there's a risk of losing the log events stored in these in-memory queues. However, Filebeat and Logstash mitigate this risk by offering the option to configure a disk queue for persistent data storage. This added layer of resilience ensures that the tools can resume their operations seamlessly, picking up from where they left off in the event of a sudden shutdown.
It's important to note that despite Logstash and Filebeat having persistent queues, occasional failures may occur, especially when handling high volumes of data. To mitigate this, it is advisable to use a dedicated tool such as Kafka to offload the load from the log collectors. The tool serves as a temporary data persistence layer, adding extra resilience and stability to the log management infrastructure.
Considering their capabilities in data transportation, neither Filebeat nor Logstash holds a distinct advantage over the other. Both can reliably transport and persist data.
When should you use Filebeat or Logstash?
Deciding between Filebeat and Logstash hinges on your specific log management needs.
Filebeat is an ideal choice when your primary requirement is to gather logs from various sources and direct them to a single destination like Elasticsearch or cloud storage. Other cases when it is ideal are when your logs need basic parsing, such as processing Nginx logs or managing syslog inputs that don't require complex processing.
On the other hand, if you're working with semi-structured or unstructured logs that need complex manipulations, enrichment, or filtering, then Logstash is more suitable. It's also the go-to tool when your objective is to route log events to multiple destinations.
A more effective strategy is using Filebeat and Logstash together, particularly for managing logs from diverse sources. Typically, you employ Filebeat to collect logs due to its efficiency. The collected logs are then forwarded to Logstash for more sophisticated processing. Logstash can be configured to distribute logs to various destinations or forward them to Elasticsearch for indexing and then to Kibana for visualization. This combined approach effectively uses the strengths of both Filebeat and Logstash.
For a more scalable setup, see the following diagram:
Let's look at a practical example using the Elastic Stack and consider how Filebeat and Logstash can work together to manage and process logs.
Suppose you have Nginx logs in the following format:
203.0.113.1 - - [14/Jan/2022:08:30:45 +0000] "GET /example-page HTTP/1.1" 200 1024 "-" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36"
Here's how you can handle these logs with Filebeat and Logstash:
Using Filebeat: First, employ Filebeat to collect logs from Nginx. Filebeat can be configured with the Nginx module, which is designed to parse Nginx logs.
Forwarding to Logstash: After collection and initial parsing, configure Filebeat to send these logs to Logstash.
Processing in Logstash: In Logstash, you can perform advanced manipulations on the logs. This includes extracting specific fields, formatting dates, redacting IP addresses, renaming fields, and other required transformations.
Sending to Elasticsearch and Kibana: Once processed, Logstash forwards the logs to Elasticsearch for indexing. After indexing, the data can be visualized and analyzed in Kibana.
This workflow demonstrates how Filebeat and Logstash can work seamlessly together. This setup is handy for handling logs requiring simple and complex handling and visualization processes.
Aggregating your logs with Better Stack
While the typical setup involves configuring Logstash or Filebeat to send logs to Elasticsearch and Kibana, this process can be complex and time-consuming. Opting for a cloud-based log management solution is a practical alternative for simplicity and ease of management. This is where Better Stack comes into play.
Better Stack, a cloud log management tool, excels in aggregating logs from log collectors like Filebeat and Logstash. It offers many advanced features for parsing, analyzing, filtering, and visualizing logs. Better Stack efficiently manages large volumes of data, enabling quicker insights.
Once Better Stack receives logs from Filebeat or Logstash, they are displayed on its Live Tail page. This feature offers real-time visibility into your log streams, allowing you to monitor ongoing activities as they happen:
To further analyze and understand your logs, Better Stack enables you to create customized dashboards. These dashboards visualize the log data, facilitating easier monitoring and quicker identification of trends or issues:
Moreover, Better Stack allows for the setting up custom alerts and notifications. This feature is crucial for staying informed about specific events or anomalies in your log data. You can choose your preferred channels for these notifications, such as phone, email, or webhooks, ensuring that you receive timely alerts most conveniently:
Despite its powerful features, Better Stack maintains an easy-to-use interface and remains cost-effective, making it accessible to many users.
While Filebeat and Logstash are proficient at collecting logs, Better Stack enhances their functionality by offering an integrated log management solution. This combination provides a comprehensive approach to log management, from collection and transport to analysis and monitoring.
In this article, we compared Filebeat and Logstash, showcasing how each tool addresses distinct requirements.
Filebeat is a lightweight option, ideal for environments with limited resources and basic log parsing needs. Conversely, Logstash is tailored for scenarios that demand advanced log processing.
Using both tools together can also be strategic to get the best of both worlds. Using Filebeat to collect logs and forwarding them to Logstash for advanced transformations provides a balanced approach.
While Filebeat and Logstash are prominent, alternative log collectors are worth exploring in our comprehensive guide on log shippers.
Thanks for reading, and happy logging!
Learn how to use Filebeat to collect, process, and ship log data at scale, and improve your observability and troubleshooting capabilitiesGuides
Learn how Logstash streamlines the collection, processing, and shipping of log data at scale, boosting observability and troubleshooting capabilities.Guides
The Top 6 Log Shippers Explained
This article will help you choose the right log shipper for your needs, and will also provide a comparison of the top 6 log shippers on the marketGuides
Make your mark
Join the writer's program
Are you a developer and love writing and sharing your knowledge with the world? Join our guest writing program and get paid for writing amazing technical guides. We'll get them to the right readers that will appreciate them.Write for us
Build on top of Better Stack
Write a script, app or project on top of Better Stack and share it with the world. Make a public repository and share it with us at our firstname.lastname@example.org
or submit a pull request and help us build better products for everyone.
See the full list of amazing projects on github