configs VS fluent-bit

I was a long time pre-commit fan, just converted to https://trunk.io/ though

Trunk | https://trunk.io | Sr Full Stack / Sr C++ | Full-Time | Remote / Hybrid, SF

Trunk is an a16z funded dev tools startup, redefining software development at scale. We've built three products so far and have plans for more:

* Trunk Check: a universal linter/formatter, available as a CLI, VSCode extension, and CI check;

Trunk is also a command line tool, so you can run all these checks on CI, with the same versions of tools you use locally

Tests are a good way to assert an invariant that you expect of your codebase, but as with all things, resolving the error can get a bit tricky/frustrating.

The canonical example in my mind is any kind of autofix-able linter, where there's some kind of patch (or more nuanced autofix) that the linter can generate on-the-spot for you. With a sh_test construct (or any other unit test), you generally find yourself printing out some command that the user can run to fix things, which in a sufficiently large codebase can get really frustrating.

(My company - https://trunk.io - is actually building a universal linter as part of our product offering, and we already have a system to write custom linters with varying levels of sophistication that can plug into both your IDE and CI system.)

Fluentbit

$ docker run --rm fluent-bit-dummy WARNING: The requested image's platform (linux/amd64) does not match the detected host platform (linux/arm64/v8) and no specific platform was requested Fluent Bit v1.9.10 * Copyright (C) 2015-2022 The Fluent Bit Authors * Fluent Bit is a CNCF sub-project under the umbrella of Fluentd * https://fluentbit.io [2023/12/24 16:06:59] [ info] [fluent bit] version=1.9.10, commit=557c8336e7, pid=1 [2023/12/24 16:06:59] [ info] [storage] version=1.4.0, type=memory-only, sync=normal, checksum=disabled, max_chunks_up=128 [2023/12/24 16:06:59] [ info] [cmetrics] version=0.3.7 [2023/12/24 16:06:59] [ info] [output:stdout:stdout.0] worker #0 started [2023/12/24 16:06:59] [ info] [sp] stream processor started [0] dummy.0: [1703434019.553880465, {"message"=>"custom dummy"}] [0] dummy.0: [1703434020.555768799, {"message"=>"custom dummy"}] [0] dummy.0: [1703434021.550525174, {"message"=>"custom dummy"}] [0] dummy.0: [1703434022.551563050, {"message"=>"custom dummy"}] [0] dummy.0: [1703434023.551944509, {"message"=>"custom dummy"}] [0] dummy.0: [1703434024.550027843, {"message"=>"custom dummy"}] [0] dummy.0: [1703434025.550901801, {"message"=>"custom dummy"}] [0] dummy.0: [1703434026.549279385, {"message"=>"custom dummy"}] ^C[2023/12/24 16:07:08] [engine] caught signal (SIGINT) [0] dummy.0: [1703434027.549678344, {"message"=>"custom dummy"}] [2023/12/24 16:07:08] [ warn] [engine] service will shutdown in max 5 seconds [2023/12/24 16:07:08] [ info] [engine] service has stopped (0 pending tasks) [2023/12/24 16:07:08] [ info] [output:stdout:stdout.0] thread worker #0 stopping... [2023/12/24 16:07:08] [ info] [output:stdout:stdout.0] thread worker #0 stopped

> Libc is a lot more tricky about signals, since not all libc functions can be safely called from handlers.

And this is a huge thing. People do all kinds of operations in signal handlers completely oblivious to the pitfalls. Pitfalls which often do not manifest, making it a great "it works for me" territory.

I once raised a ticket on fluentbit[1] about it but they have abused signal handlers so thoroughly that I do not think they can mitigate the issue without a major rewriting of the signal and crash handling.

[1] https://github.com/fluent/fluent-bit/issues/4836

Fluentbit is Fluentd's lightweight alternative to itself.

https://fluentbit.io

From here, we can explore other developments and tutorials on Kubernetes, such as o11y or observability (PLG, ELK, ELF, TICK, Jaeger, Pyroscope), service mesh (Linkerd, Istio, NSM, Consul Connect, Cillium), and progressive delivery (ArgoCD, FluxCD, Spinnaker).

From this (https://github.com/fluent/fluent-bit/issues/723), I can see there is no grok support for fluent-bit.

apiVersion: v1 kind: ConfigMap metadata: name: fluent-bit-config namespace: newrelic labels: k8s-app: newrelic-logging data: # Configuration files: server, input, filters and output # ====================================================== fluent-bit.conf: | [SERVICE] Flush 1 Log_Level ${LOG_LEVEL} Daemon off Parsers_File parsers.conf HTTP_Server On HTTP_Listen 0.0.0.0 HTTP_Port 2020 @INCLUDE input-kubernetes.conf @INCLUDE output-newrelic.conf @INCLUDE filter-kubernetes.conf input-kubernetes.conf: | [INPUT] Name tail Tag kube.* Path ${PATH} Parser ${LOG_PARSER} DB /var/log/flb_kube.db Mem_Buf_Limit 7MB Skip_Long_Lines On Refresh_Interval 10 filter-kubernetes.conf: | [FILTER] Name multiline Match * multiline.parser multiline-regex [FILTER] Name record_modifier Match * Record cluster_name ${CLUSTER_NAME} [FILTER] Name kubernetes Match kube.* Kube_URL https://kubernetes.default.svc.cluster.local:443 Merge_Log Off output-newrelic.conf: | [OUTPUT] Name newrelic Match * licenseKey ${LICENSE_KEY} endpoint ${ENDPOINT} parsers.conf: | # Relevant parsers retrieved from: https://github.com/fluent/fluent-bit/blob/master/conf/parsers.conf [PARSER] Name docker Format json Time_Key time Time_Format %Y-%m-%dT%H:%M:%S.%L Time_Keep On [PARSER] Name cri Format regex Regex ^(?[^ ]+) (?stdout|stderr) (?[^ ]*) (?.*)$ Time_Key time Time_Format %Y-%m-%dT%H:%M:%S.%L%z [MULTILINE_PARSER] name multiline-regex key_content message type regex flush_timeout 1000 # # Regex rules for multiline parsing # --------------------------------- # # configuration hints: # # - first state always has the name: start_state # - every field in the rule must be inside double quotes # # rules | state name | regex pattern | next state # ------|---------------|--------------------------------|----------- rule "start_state" "/(Dec \d+ \d+\:\d+\:\d+)(.*)/" "cont" rule "cont" "/^\s+at.*/" "cont"

There are also log forwarding tools like promtail and fluentbit that can be used to both ship logs to something like Loki and produce metrics.

FluentBit, is a logging processor that can help you to push all of your application logs to a central location like an ElasticSearch or OpenSearch cluster.