github.com/junegunn/fzf

Using fzf as a Go library is straightforward when you need interactive selection capabilities. The package spawns the fzf binary as a subprocess, which means you're dealing with process management rather than native Go code. In production, this works reliably but requires the fzf binary to be present in PATH or explicitly configured. Memory footprint is reasonable since filtering happens in a separate process.

Error handling is practical - failed spawns and pipe errors surface clearly, though you'll need to implement your own retry logic for transient failures. The library doesn't include connection pooling concepts since each invocation is stateless. Timeout behavior requires wrapping calls with context.Context yourself. Performance is excellent for interactive use cases (sub-100ms response times typical), but spawning processes repeatedly in tight loops adds overhead.

Configuration is flexible through command-line flag building, but breaking changes between fzf binary versions can bite you if you're not pinning versions. Observability is limited - you get stdout/stderr but no structured logging hooks. For production services doing batch processing, consider alternatives, but for CLI tools and interactive workflows, it's a solid choice.

fzf is primarily a command-line tool with Go bindings for embedding fuzzy finding into applications. From a security perspective, it's refreshingly simple—mostly a search algorithm with no network calls, no external dependencies beyond the standard library, and no crypto/TLS concerns. The attack surface is minimal, which is exactly what you want.

The main security consideration is input handling. When embedding fzf, you're responsible for sanitizing what gets passed to it and how results are used. The library doesn't provide built-in input validation or escaping utilities, so shell injection risks exist if you pipe user input directly to commands based on fzf selections. Error messages are straightforward and don't leak sensitive information, but they also don't help catch injection attempts.

Dependency supply chain risk is low—the Go module has zero external dependencies. Updates are regular but CVE response isn't really applicable since the threat model is narrow. The code is clean and auditable, following secure-by-default principles for what it does, though you'll need to layer your own security controls around user input.

Using fzf as a Go library is straightforward for integrating interactive fuzzy finding into CLI tools. The API is minimal—you typically spawn the fzf binary as a subprocess, pipe candidates through stdin, and read the selection from stdout. This approach works reliably but means you're managing external process lifecycle rather than using native Go code.

Performance is excellent for the end-user experience with sub-millisecond search updates even on large datasets (100k+ items). However, the subprocess model adds ~50-100ms startup overhead per invocation, which matters if you're calling fzf repeatedly in tight loops. Memory usage is reasonable, typically under 100MB even with massive lists, though it scales linearly with input size. No connection pooling concerns since it's stateless, but you need proper cleanup of file descriptors and process handles.

Error handling requires wrapping standard exec patterns—parsing exit codes to distinguish user cancellation (exit 130) from actual errors. Timeout control is manual via context.Context. Logging integration is limited; fzf outputs to stderr which you'll need to capture separately. The preview feature can spawn additional subprocesses, so watch your ulimits under concurrent load.