Filebeat vs Logstash: Key Differences for Your Logging Needs

When working with the Elastic Stack, you'll come across two key tools for handling logs: Filebeat and Logstash. Both serve similar purposes but in different ways.

Filebeat is perfect for quickly collecting logs and forwarding them, while Logstash excels at processing and enriching those logs. The best part? They can work together to streamline your logging setup.

In this blog, we’ll talk about the differences between Filebeat and Logstash, and help you figure out when and why to use each one.

What Are Filebeat and Logstash?

At the core of the Elastic Stack, Filebeat and Logstash both help you get log data from point A to point B, but their methods are quite different.

Filebeat is a lightweight, open-source log shipper that focuses on log forwarding. It’s designed to collect logs from your servers and systems and send them to Elasticsearch or Logstash with minimal overhead.

This tool is all about simplicity, speed, and efficiency, handling log shipping without performing complex transformations.

Logstash, on the other hand, is a data processing pipeline. It allows you to collect, parse, transform, and enrich log data before sending it to Elasticsearch or another destination.

If you need to do more than just ship logs—like filtering, enriching, or transforming the data—Logstash is the tool to use.

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Filebeat:

What is Filebeat?
Filebeat is a lightweight, fast log shipper designed for environments where you need to quickly forward logs with minimal resource usage. It’s particularly useful in high-performance setups like microservices or cloud-native environments where logs need to be shipped without overburdening system resources.

Key Features:

• Resource Efficiency: Filebeat is designed to collect and forward logs with minimal impact on CPU and memory, making it ideal for low-resource environments like edge devices or Kubernetes clusters.
• Simple Log Collection: If your primary goal is to collect logs and send them to Elasticsearch or Logstash without complex processing, Filebeat is your best choice.

Ideal Use Cases:

• Cloud-Native and Containerized Environments: Perfect for microservices architectures, especially in Kubernetes.
• Edge Devices: In resource-constrained environments, Filebeat ensures logs are forwarded with minimal overhead.

Logstash:

What is Logstash?
Logstash is a powerful, full-fledged data transformation tool designed for more complex log processing. It allows you to parse, enrich, and filter log data before it reaches its destination, making it perfect for customized log workflows.

Key Features:

• Processing Power: Logstash can handle log parsing, filtering, and enrichment, enabling sophisticated workflows. It supports multiple log formats (JSON, XML, plain text) and can standardize them before sending them to Elasticsearch.
• Customizable Pipelines: You can create pipelines that modify log data on the fly, adding custom fields, geo-location tags, or even user data.

Ideal Use Cases:

• Complex Log Processing: When logs require filtering or enrichment, such as adding geo-location data or custom fields.
• Centralized Log Management: In environments where logs are collected from various sources and need to be standardized before analysis.

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Performance Comparison: Filebeat vs. Logstash

• Filebeat: Lightweight and optimized for speed. Best for environments where you simply need to forward logs without any processing. It’s a low-overhead solution that works well for high-volume or high-performance environments.
• Logstash: Performs heavy-duty processing tasks. It’s ideal for parsing, filtering, and enriching logs, but it comes with a higher resource cost (CPU and memory usage).

When to Use Filebeat vs. Logstash

Use Filebeat when:

• You need a simple, fast-log shipper with low resource usage.
• Working in cloud-native environments or containerized apps (like Kubernetes).
• You need to forward logs quickly without any pre-processing.

Use Logstash when:

• Your logs need to be parsed, enriched, or filtered before sending them to Elasticsearch.
• You need to process logs from multiple sources with different formats and standardize them.
• You have a centralized log management setup and require complex data transformation.

Using Last9’s high cardinality workflows, we were able to accurately measure customer SLAs across dimensions, extract knowledge about our systems, and measure customer impact proactively. — Ranjeet Walunj, SVP Engineering, CleverTap

Filebeat vs. Logstash: Which One to Choose?

Ultimately, the choice depends on your specific needs:

• Choose Filebeat for a lightweight, efficient log shipper that simply forwards logs without extra overhead. It’s perfect for high-performance, low-resource environments like Kubernetes.
• Choose Logstash for advanced log processing, such as parsing, enriching, or filtering logs before sending them to storage or analysis systems. It’s the better option for enterprise-level applications where data consistency and preprocessing are important.

Combined Approach: You don’t always have to choose between the two. Many setups use Filebeat to collect and forward logs, then pass them to Logstash for any necessary processing. This combination helps optimize performance while still leveraging the powerful transformation capabilities of Logstash.

Final Thoughts

Understanding the differences between Filebeat and Logstash can help you pick the right tool for your logging setup.

If you're looking for an observability solution that brings together metrics, logs and traces in one place, all at a cost-effective price, Last9 could be just what you need.

Last9 is built for high-cardinality observability and integrates seamlessly with Prometheus and OpenTelemetry. It makes correlating metrics, logs, and traces easy, and lets you generate dynamic metrics in real-time while managing large data sets with Streaming Aggregations.

Plus, it’s available on AWS and GCP, so you can bring your own cloud.

Schedule a demo or try it for free to learn more!

FAQs

Can I use Filebeat and Logstash together?
Yes, Filebeat and Logstash can work together in a logging pipeline. You can use Filebeat to collect and forward logs to Logstash, where you can perform more complex processing, such as filtering or enriching the data. This combination allows you to optimize performance by leveraging Filebeat's lightweight nature for log forwarding and Logstash's flexibility for data transformation.

Which is more resource-intensive: Filebeat or Logstash?
Logstash tends to be more resource-intensive than Filebeat. This is because Logstash performs more complex tasks such as parsing, filtering, and enriching logs. Filebeat, on the other hand, is a lightweight tool primarily designed to forward logs with minimal resource usage, making it ideal for environments with limited resources or high-performance requirements.

How do I decide whether to use Filebeat or Logstash for my logs?
Use Filebeat if you need a simple, fast log shipper with minimal overhead. It’s perfect for environments where logs need to be collected and forwarded without much processing.
Use Logstash if you require more complex log processing, such as filtering, enrichment, or format conversion. Logstash is also useful when dealing with logs from diverse sources in different formats.

Can Filebeat perform data processing like Logstash?
Filebeat can perform basic log parsing and filtering (e.g., through modules or ingest pipelines in Elasticsearch), but it is not as powerful as Logstash when it comes to complex data transformation. If you need advanced processing (such as adding geo-location tags or custom fields), Logstash is the better choice.

How do I install Filebeat and Logstash?
Filebeat: Installation can be done via the official Elastic website or through package managers like APT or YUM. It's a straightforward installation, especially in cloud-native or containerized environments.
Logstash: Logstash also follows a similar installation process, available through Elastic’s website or package managers. However, setting up Logstash usually involves more configuration, especially when defining custom pipelines for processing logs.

Can I configure Filebeat to process logs like Logstash?
Filebeat offers some basic log processing features through modules and Elasticsearch ingest pipelines, but it lacks the extensive processing capabilities of Logstash. While you can perform simple parsing, filtering, or transformation tasks with Filebeat, for advanced tasks, Logstash remains the go-to tool.

Is Filebeat easier to set up than Logstash?
Yes, Filebeat is easier to set up because it focuses on log forwarding. It requires minimal configuration and works right out of the box with default settings. Logstash, in contrast, requires more configuration, especially when setting up complex pipelines to process data.

Can Filebeat send logs to multiple destinations?
Yes, Filebeat can send logs to multiple destinations, including Elasticsearch, Logstash, or even third-party services like Kafka. However, it will only forward the logs without performing any transformation on them unless configured to use an ingest pipeline in Elasticsearch.

What happens if Filebeat fails to send logs?
Filebeat has built-in buffering and retry mechanisms to ensure logs are not lost in case of failure. If it cannot send logs to its destination, it will retry based on the configured backoff settings, ensuring reliability in cases of temporary network or server issues.

Can I use Filebeat and Logstash together in a Kubernetes environment?
Yes, both tools can be used in Kubernetes environments, often together. Filebeat is deployed as a DaemonSet to collect logs from containers, while Logstash can be used to process logs from various sources before sending them to Elasticsearch. This combination helps optimize resource usage and processing flexibility.

How do I monitor the performance of Filebeat and Logstash?
Filebeat: You can monitor Filebeat’s performance using the built-in monitoring features in Elasticsearch or by using Metricbeat. Filebeat exposes metrics like event count, memory usage, and throughput, which can be used to track performance.
Logstash: Logstash also has monitoring options, including the ability to track throughput, queue sizes, and processing times through X-Pack Monitoring in the Elastic Stack. Logs from Logstash can also be pushed to Elasticsearch for further analysis.

Does Filebeat support log rotation?
Yes, Filebeat supports log rotation. It can be configured to handle rotating log files and will continue reading from the newly created log files as they are generated. This feature is particularly useful for applications or systems that generate large amounts of log data and rotate logs frequently.

Can Logstash process real-time log data?
Yes, Logstash can process real-time log data, but it’s more suited for batch processing and centralized logging pipelines. If you're dealing with real-time logs that require high throughput and low latency, Filebeat is the more suitable tool for quickly forwarding the data without much delay.

Which tool is easier to scale: Filebeat or Logstash?
Filebeat is easier to scale due to its lightweight nature. It can be deployed in large numbers across distributed environments without significant resource overhead. Logstash, while scalable, may require more resources and careful configuration when scaling for large deployments, especially when processing a high volume of logs.

Can Filebeat parse logs?
Filebeat has limited parsing capabilities compared to Logstash. It can parse simple log formats using modules or Elasticsearch ingest pipelines, but for more complex parsing and data enrichment, Logstash is required.