The bare metal platform consists of a cluster of 6 nodes and is solely used for this experiment. We performed benchmarking performance experiments on a bare metal (BM) server. Vector supports logs and metrics, making it easy to collect and process all your observability data. Vector: Built-in Rust, Vector is fast, memory efficient, and designed to handle the most demanding workloads. Fluent Bit is written in C language and designed with performance in mind: high throughput with low CPU and Memory usage. It collects log data at the cluster level from different nodes and can forward it to various types of sinks.įluent Bit: This is another open-source collector type that collects any data like metrics and logs from different sources, enriches them with filters, and sends them to multiple destinations. Fluentd allows unifying data collection and consumption for better use and understanding of data. It is written primarily in C with a thin-Ruby wrapper that gives users flexibility. Fluentd treats logs as JSON, a popular, machine-readable format. Let’s consider the log collectors we are comparing:įluentd: This is an open-source data collector for the unified logging layer. Since logs are critical, it is very important for collectors to work as efficiently as possible. In such cases, some percentage of logs are missed. Log sources generate logs with different rates and it is likely the cumulative volume is higher than collectors’ capacity to process them. These logs then are sent to multiple types of persistent storage. Collectors may perform transformations operation, which includes enriching entries with meta-data, parsing, filtering, and many more. Log collectors consume logs from several sources (e.g. We will compare the performance of log collectors Fluentd, Fluent Bit, and Vector based on log-collection rate, CPU, and memory.Īs shown in Figure 1. Log collection is expensive in terms of resources like CPU, memory, and storage and it demands more resources in cloud environments where many micro-services are deployed emitting logs constantly. Logs is a primary data source that can be collected, transformed, and stored for observability. Observability is an area that helps us understand the internal workings of a system using Logs, Metrics, and Traces. Organizations are seriously impacted when critical applications are unavailable it becomes imperative to quickly resolve issues. Many modern applications are deployed as micro-services in cloud environments like OpenShift or Kubernetes. The quote above is relevant in many situations including log collector performance benchmarking, which is the theme of this article. “If you can’t measure it, you can’t improve it.” – Peter Drucker Who is the winner - Comparing Vector, Fluent Bit, Fluentd performance
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