Beats vs Logstash: What are the differences?

Introduction

In this article, we will explore the key differences between Beats and Logstash for processing and shipping log data in a centralized logging system.

1. Data Collection: Beats are lightweight data shippers that can be installed on remote servers to collect and send log files or other data to a centralized location. They are designed to be efficient and low overhead, making them suitable for limited-resource devices or environments. On the other hand, Logstash is a more powerful and flexible data collection and processing pipeline. It supports a wide range of inputs, filters, and outputs, allowing for complex data transformations and enrichment during the collection process.

2. Data Transformation: While Beats focus on efficiently collecting and shipping log data, Logstash provides powerful data transformation capabilities. Logstash allows for applying filters to log events, such as parsing and extracting specific fields, applying data manipulation functions, or enriching data with additional metadata. These transformations can be particularly useful to normalize and structure log data before storing or further processing it.

3. Plugin Ecosystem: Logstash has a vast plugin ecosystem that provides a wide range of input, filter, and output plugins, allowing for seamless integration with various systems and services. This extensive plugin support enables Logstash to handle diverse data sources and destination requirements. Beats, on the other hand, have a more limited set of input and output plugins, primarily focused on shipping data to Elasticsearch or Logstash.

4. Scalability: Both Beats and Logstash can scale to accommodate large log volumes. However, there is a difference in how they achieve scalability. Beats rely on lightweight shippers that can be distributed across multiple servers to collect and send data concurrently. Each Beat instance can be configured to handle a specific type of data, enabling parallel processing and horizontal scaling. In contrast, Logstash can leverage multiple instances running on separate machines, forming a pipeline within a centralized logging cluster to handle larger workloads.

5. Ease of Setup and Configuration: Beats are designed to be easy to install and configure with minimal effort. They have a simple configuration model that allows users to specify inputs and outputs, making it straightforward to start collecting and shipping log data. Logstash, on the other hand, has a more complex setup and configuration process due to its extensive capabilities. It requires defining pipelines with inputs, filters, and outputs in a configuration file, making it more suitable for scenarios that require advanced data processing and transformation.

6. Performance Overhead: Due to their lightweight nature, Beats have lower performance overhead compared to Logstash. They are optimized for minimal resource utilization and can be deployed on resource-constrained systems without significant impact. Logstash, on the other hand, is more resource-intensive due to its wider range of capabilities and flexibility. It may require more memory, CPU, and disk space to handle larger workloads efficiently.

In summary, Beats are lightweight data shippers designed for efficient log data collection and simple deployment, while Logstash offers powerful data transformation capabilities, a vast plugin ecosystem, and more advanced data processing and transformation functionalities.