fluent-bit VS kubernetes

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.

Deploying AI models into production requires tools that can package applications and manage them at scale. Docker simplifies the deployment of AI applications by containerizing them, ensuring that the application runs smoothly in any environment. Kubernetes, an orchestration system for Docker containers, allows for the automated deployment, scaling, and management of containerized applications, essential for AI applications that need to scale across multiple servers or cloud environments.

To learn more, you can start by exploring the official Kubernetes documentation.

With the containerized Node.js/Express API, I could run multiple containers, scaling to handle more traffic. Using a tool called minikube, we can easily spin up a local Kubernetes cluster to horizontally scale Docker containers. It was possible to keep one shared instance of the database, and many APIs were routed with an internal Kubernetes load balancer.

This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities.

We closely monitor Kubernetes and cloud providers' updates by following official changelogsand using RSS feeds, allowing us to anticipate potential issues and adapt our infrastructure proactively.

"You are holding it wrong", huh?

From the homepage https://kubernetes.io/:

"Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications."

Do you see "not recommended for smaller-scale applications" anywhere? Including on the entire home page? Looking for "small", "big" and "large" also yields nothing.

Open Source and Cloud Computing: A Match Made in Heaven The cloud is accelerating OSS adoption. Cloud-native technologies like Kubernetes [https://kubernetes.io/] and Istio [https://istio.io/], both open-source projects, are revolutionizing how applications are built and deployed across cloud platforms.

If you do kubectl explain deployment than (surprise!) you'll get a description for extensions/v1beta1. Because kubectl explain works the same way, just like kubectl get:

Through the Fast Forward program, we give free services and support to open source projects and the nonprofits that support them. We support many of the world’s top programming languages (like Python, Rust, Ruby, and the wonderful Scratch), foundational technologies (cURL, the Linux kernel, Kubernetes, OpenStreetMap), and projects that make the internet better and more fun for everyone (Inkscape, Mastodon, Electronic Frontier Foundation, Terms of Service; Didn’t Read).

In this project, you will understand and get hands on experience around the entire concept around CI/CD from applications perspective. To fully gain real expertise around this idea, it is best to see it in action across different programming languages and from the platform perspective too. From the application perspective, we will be focusing on PHP here; there are more projects ahead that are based on Java, Node.js, .Net and Python. By the time you start working on Terraform, Docker and Kubernetes projects, you will get to see the platform perspective of CI/CD in action.