In production environments you need full control of the data you're collecting. Filtering lets you alter the collected data before delivering it to a destination.

graph LR
    accTitle: Fluent Bit data pipeline
    accDescr: A diagram of the Fluent Bit data pipeline, which includes input, a parser, a filter, a buffer, routing, and various outputs.
    A[Input] --> B[Parser]
    B --> C[Filter]
    C --> D[Buffer]
    D --> E((Routing))
    E --> F[Output 1]
    E --> G[Output 2]
    E --> H[Output 3]
    style C stroke:darkred,stroke-width:2px;

Filtering is implemented through plugins. Each available filter can be used to match, exclude, or enrich your logs with specific metadata.

Fluent Bit support many filters. A common use case for filtering is Kubernetes deployments. Every pod log needs the proper metadata associated with it.

Like input plugins, filters run in an instance context, which has its own independent configuration. Configuration keys are often called properties.

For more details about the Filters available and their usage, see Filters.