file.d is a blazing fast tool for building data pipelines: read, process, and output events. Primarily developed to read from files, but also supports numerous input/action/output plugins.
⚠ Although we use it in production,
it still isn't v1.0.0. Please, test your pipelines carefully on dev/stage environments.
file.d is an open-source project and contributions are very welcome!
Please make sure to read our contributing guide before creating an issue and opening a PR!
Well, we already have several similar tools: vector, filebeat, logstash, fluend-d, fluent-bit, etc.
Performance tests state that best ones achieve a throughput of roughly 100MB/sec. Guys, it's 2023 now. HDDs and NICs can handle the throughput of a few GB/sec and CPUs processes dozens of GB/sec. Are you sure 100MB/sec is what we deserve? Are you sure it is fast?
- Fast: more than 10x faster compared to similar tools
- Predictable: it uses pooling, so memory consumption is limited
- Reliable: doesn't lose data due to commitment mechanism
- Container / cloud / kubernetes native
- Simply configurable with YAML
- Prometheus-friendly: transform your events into metrics on any pipeline stage
- Vault-friendly: store sensitive info and get it for any pipeline parameter
- Well-tested and used in production to collect logs from Kubernetes cluster with 3000+ total CPU cores
On MacBook Pro 2017 with two physical cores file.d can achieve the following throughput:
- 1.7GB/s in
files > devnullcase - 1.0GB/s in
files > json decode > devnullcase
TBD: throughput on production servers.
Input: dmesg, fake, file, http, journalctl, k8s, kafka
Action: add_file_name, add_host, convert_date, convert_log_level, convert_utf8_bytes, debug, discard, flatten, join, join_template, json_decode, json_encode, json_extract, keep_fields, mask, modify, move, parse_es, parse_re2, remove_fields, rename, set_time, split, throttle
Output: clickhouse, devnull, elasticsearch, file, gelf, kafka, postgres, s3, splunk, stdout
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