fluent-bit VS helm

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.

Applying Kubernetes manifests individually is problematic because files can get overlooked. Packaging your applications as Helm charts lets you version your manifests and easily repeat deployments into different environments. Helm tracks the state of each deployment as a "release" in your cluster.

helm

Explanation here: https://github.com/helm/helm/issues/12681#issuecomment-19593...

Looks like it's a bug in Helm, but actually isn't Helm's fault, the issue was introduced by Fedora Linux.

Helm (Get from here https://helm.sh/)

It’s also well understood that having a k8s cluster is not enough to make developers able to host their services - you need a devops team to work with them, using tools like delivery pipelines, Helm, kustomize, infra as code, service mesh, ingress, secrets management, key management - the list goes on! Developer Portals like Backstage, Port and Cortex have started to emerge to help manage some of this complexity.

Kubernetes orchestrates deployments and manages resources through yaml configuration files. While Kubernetes supports a wide array of resources and configurations, our aim in this tutorial is to maintain simplicity. For the sake of clarity and ease of understanding, we will use yaml configurations with hardcoded values. This method simplifies the learning process but isn’t ideal for production environments due to the need for manual updates with each new deployment. Although there are methods to streamline and automate this process, such as using Helm charts or bash scripts, we’ll not delve into those techniques to keep the tutorial manageable and avoid fatigue — you might be quite tired by that point!

Helm is a package manager that automates Kubernetes applications' creation, packaging, configuration, and deployment by combining your configuration files into a single reusable package. This eliminates the requirement to create the mentioned Kubernetes resources by ourselves since they have been implemented within the Helm chart. All we need to do is configure it as needed to match our requirements. From the public Helm chart repository, we can get the charts for common software packages like Consul, Jenkins SonarQube, etc. We can also create our own Helm charts for our custom applications so that we don’t need to repeat ourselves and simplify deployments.

We can search for charts https://helm.sh/ . Charts can be pulled(downloaded) and optionally unpacked(untar).

Generally I felt as if I was diving in the deepest of waters without the correct equipement and that was horrifying. Unfortunately to me, I had to dive even deeper before getting equiped with tools like ArgoCD, and k8slens. I had to start working with... HELM.

Within the architecture of Cyclops, a central component is the Helm engine. Helm is very popular within the Kubernetes community; chances are you have already run into it. The popularity of Helm plays to Cyclops's strength because of its straightforward integration.