#Fluentd

[mp3monsterme]

Speeding Ruby

Development trends have shown a shift towards precompiled languages like Go and Rust away from interpreted or Just-In-Time (JIT) compiled languages like Java and Ruby as it removes the startup time of the language virtual machine and the JIT compiler as well as a smaller memory footprint. All desirable features when you’re scaling containerized solutions and percentage point savings can really…

[virtualizationhowto]

Best Open Source Log Management Tools in 2023

Best Open Source Log Management Tools in 2023 #homelab #OpenSourceLogManagement2023 #TopLogAnalysisTools #CentralizedLoggingSystems #LogstashVsSyslogng #BestLogCollectors #FluentDAndCloudServices #ManageLargeVolumesOfLogData #GrafanaRealtimeMonitoring

When monitoring, troubleshooting, and auditing in today’s IT infrastructure, logs provide the low-level messaging needed to trace down events happening in the environment. They can be an invaluable source of insights into performance, security events, and errors that may be occurring across on-premises, cloud, and hybrid systems. You don’t have to buy into a commercial solution to get started…

[kennak]

読み込むログの特性を理解してないとチューニングできないので、インフラの人よりアプリの人が読んだほうが良いんじゃないかなあ。そもそもなにをログに出すか、みたいな設計がね。

[B! fluentd] 「Fluentd実践入門」を10月8日に出版します - たごもりすメモ

[qcs01]

Best Practices for Red Hat OpenShift and Why QCS DC Labs Training is Key

Introduction: In today's fast-paced digital landscape, businesses are increasingly turning to containerization to streamline their development and deployment processes. Red Hat OpenShift has emerged as a leading platform for managing containerized applications, offering a robust set of tools and features for orchestrating, scaling, and securing containerized workloads. However, to truly leverage the power of OpenShift and ensure optimal performance, it's essential to adhere to best practices. In this blog post, we'll explore some of the key best practices for Red Hat OpenShift and discuss why choosing QCS DC Labs for training can be instrumental in mastering this powerful platform.

Best Practices for Red Hat OpenShift:

Proper Resource Allocation: One of the fundamental principles of optimizing OpenShift deployments is to ensure proper resource allocation. This involves accurately estimating the resource requirements of your applications and provisioning the appropriate amount of CPU, memory, and storage resources to avoid under-provisioning or over-provisioning.

Utilizing Persistent Storage: In many cases, applications deployed on OpenShift require access to persistent storage for storing data. It's essential to leverage OpenShift's persistent volume framework to provision and manage persistent storage resources efficiently, ensuring data durability and availability.

Implementing Security Controls: Security should be a top priority when deploying applications on OpenShift. Utilize OpenShift's built-in security features such as Role-Based Access Control (RBAC), Pod Security Policies (PSP), Network Policies, and Image Scanning to enforce least privilege access, restrict network traffic, and ensure the integrity of container images.

Monitoring and Logging: Effective monitoring and logging are essential for maintaining the health and performance of applications running on OpenShift. Configure monitoring tools like Prometheus and Grafana to collect and visualize metrics, set up centralized logging with tools like Elasticsearch and Fluentd to capture and analyze logs, and implement alerting mechanisms to promptly respond to issues.

Implementing CI/CD Pipelines: Embrace Continuous Integration and Continuous Delivery (CI/CD) practices to automate the deployment pipeline and streamline the release process. Utilize tools like Jenkins, GitLab CI, or Tekton to create CI/CD pipelines that automate building, testing, and deploying applications on OpenShift.

Why Choose QCS DC Labs for Training: QCS DC Labs stands out as a premier training provider for Red Hat OpenShift, offering comprehensive courses tailored to meet the needs of both beginners and experienced professionals. Here's why choosing QCS DC Labs for training is essential:

Expert Instructors: QCS DC Labs instructors are industry experts with extensive experience in deploying and managing containerized applications on OpenShift. They provide practical insights, real-world examples, and hands-on guidance to help participants master the intricacies of the platform.

Hands-on Labs: QCS DC Labs courses feature hands-on lab exercises that allow participants to apply theoretical concepts in a simulated environment. These labs provide invaluable hands-on experience, enabling participants to gain confidence and proficiency in working with OpenShift.

Comprehensive Curriculum: QCS DC Labs offers a comprehensive curriculum covering all aspects of Red Hat OpenShift, from basic concepts to advanced topics. Participants gain a deep understanding of OpenShift's architecture, features, best practices, and real-world use cases through structured lessons and practical exercises.

Flexibility and Convenience: QCS DC Labs offers flexible training options, including online, instructor-led courses, self-paced learning modules, and customized training programs tailored to meet specific organizational needs. Participants can choose the format that best suits their schedule and learning preferences.

Conclusion: Red Hat OpenShift offers a powerful platform for deploying and managing containerized applications, but maximizing its potential requires adherence to best practices. By following best practices such as proper resource allocation, security controls, monitoring, and CI/CD implementation, organizations can ensure the efficiency, reliability, and security of their OpenShift deployments. Additionally, choosing QCS DC Labs for training provides participants with the knowledge, skills, and hands-on experience needed to become proficient in deploying and managing applications on Red Hat OpenShift.

For more details click www.qcsdclabs.com

[tumnikkeimatome]

Rubyで実装 プラグイン豊富なログ収集・転送ツール「Fluentd」rsyslogやlogstashとの比較・相互補完について解説

Fluentdの概要と特徴 Fluentdは、Treasure Data社の古橋貞之氏によって開発されたオープンソースのログ収集・転送ツールです。 2011年に初リリースされ、現在はCloud Native Computing Foundation…

[jvalentino2]

An example of how to run Elastic Search, FluentD, and Kibana with some same starting data via Docker Compose. The purpose is to demonstrate a common pattern for centralized logging.

[observabilityfeed]

Essential Open-Source Tools to Get You Started on Kubernetes Observability Journey

In today's fast-paced and dynamic world of container orchestration, Kubernetes has emerged as the go-to platform for managing and scaling applications. As your Kubernetes infrastructure grows, ensuring effective observability becomes paramount. Thankfully, the open-source community has unleashed a plethora of powerful tools to help you monitor and gain valuable insights into your Kubernetes clusters. In this article, we'll dive into the top open-source tools that will set you on the path to Kubernetes observability success.

Prometheus: The Mighty Monitoring Powerhouse When it comes to monitoring Kubernetes, Prometheus stands tall as the de facto solution. Designed specifically for containerized environments, Prometheus collects rich metrics about your Kubernetes resources, services, and applications. With its powerful querying language, flexible alerting capabilities, and extensive integrations with visualization tools like Grafana, Prometheus enables you to gain deep insights into the health and performance of your Kubernetes clusters.

Jaeger: Tracing Made Easier To truly understand the behavior and performance of your microservices running on Kubernetes, distributed tracing is essential. Jaeger steps in as the open-source tracing platform that seamlessly integrates with Kubernetes. By providing end-to-end transaction monitoring, Jaeger allows you to trace requests as they flow through your complex microservices architecture. With its intuitive UI and powerful query features, Jaeger helps you pinpoint bottlenecks, optimize latency, and deliver exceptional user experiences.

Fluentd: Centralized Logging Simplicity Managing and analyzing logs from multiple Kubernetes pods and containers can quickly become overwhelming. Enter Fluentd, an open-source log collector and forwarder. Fluentd aggregates logs from various sources, standardizes the format, and routes them to your preferred log management system or storage backend. With Fluentd, you can effortlessly centralize and analyze logs from your Kubernetes clusters, making troubleshooting and debugging a breeze.

Grafana: Visualizing Your Kubernetes Insights While Prometheus collects the metrics and Fluentd manages the logs, you need a powerful visualization tool to bring your Kubernetes observability to life. Grafana comes to the rescue as the go-to open-source solution for creating stunning dashboards and visualizations. With its extensive library of pre-built panels and an active community, Grafana empowers you to explore, analyze, and share your Kubernetes monitoring data with ease.

Thanos: Scaling Prometheus for the Big League As your Kubernetes deployment grows, so does the volume of metrics data that Prometheus collects. Thanos steps in as an open-source project that extends Prometheus, enabling seamless scalability and long-term storage of your monitoring data. By leveraging object storage like Amazon S3 or Google Cloud Storage, Thanos allows you to retain and query your metrics across multiple Prometheus instances, providing a scalable solution for your growing observability needs.

In Conclusion

With Kubernetes becoming the backbone of modern application deployments, observability is no longer optional but essential. By harnessing the power of open-source tools like Prometheus, Jaeger, Fluentd, Grafana, and Thanos, you can unlock the full potential of Kubernetes observability.

These tools empower you to monitor, trace, log, and visualize your Kubernetes clusters, ensuring optimal performance, efficient troubleshooting, and better user experiences. So, embrace the world of open-source observability tools and embark on a journey to conquer your Kubernetes infrastructure like a true tech pioneer.

[freedomson]

How to Parse Syslog Messages - Fluentd

https://docs.fluentd.org/how-to-guides/parse-syslogSent from my Q1 22

[taglineinfotech]

What are the top Devops tools?

A Devops engineer’s job is to maintain and monitor the software development lifecycle (SDLC) in an effective and efficient way by automating processes as much as possible. In order to do so, Devops professionals use many different types of tools that make their lives easier, but what are the most common Devops tools? This question will be answered in this blog post about the top Devops tools. It’ll cover such things as continuous integration, testing and deployment systems, monitoring tools, network administration software, and management solutions.

Configuration management tools

Configuration management is a crucial part of any successful Devops process. With configuration management, you can set up standards for how your servers should be configured. Plus, it allows you to automate the deployment of new software, and have that software installed consistently across all your servers. There are many great configuration management tools on the market today including Chef, Puppet, Ansible and Saltstack.

Continuous integration tools

Continuous integration, or CI, is a term used in software development to describe a system that monitors changes in code and automatically runs tests to verify that any new code does not break existing features. There are various CI tools available for use by developers, including Jenkins, Travis CI and TeamCity. These systems usually have dashboards where users can track builds as they happen.

Continuous delivery and deployment tools

There is a wide variety of continuous delivery and deployment tools, but these five tend to be the most popular: Jenkins, Travis, CircleCI, ConcurrenTree CI and GitLab.

Jenkins is a java-based open-source tool that has been around for over 20 years. It's often called the Swiss army knife of continuous integration. Developers can use it to automatically test code they've written by integrating with other software.

Monitoring and logging tools

Monitoring and logging is an essential part of any service deployment. In order to monitor, one must have some way to measure and record information about a system. Logging provides a mechanism for capturing this data over time, which can be used for debugging and security analysis. One of the most common methods for logging is syslog, but there are many other options available depending on your requirements, such as fluentd or logstash .

Testing tools

Ansible, Chef and Puppet make up three of the most popular configuration management tools. Jenkins, a continuous integration tool, is also a very common Devops tool. Ansible is an open-source automation engine that can be used to execute commands on many devices at once.

Conclusion -

To save time, avoid headaches, and make informed decisions as you hire Devops developers, it's helpful to know what tools they'll be using. With that in mind, here is a list of common Devops tools: ● Jenkins - A Continuous Integration tool (automated). ● Puppet - an automation tool for testing and deployment. ● Docker - an open source project for virtualization. ● Saltstack - another configuration management system to control software.

[mp3monsterme]

Demo Fluentd using Ubuntu with optional inclusion of OpenSearch and OCI Log Analytics

Demo Fluentd using Ubuntu with optional inclusion of OpenSearch and OCI Log Analytics

One of the areas I present publicly is the use of Fluentd. including the use of distributed and multiple nodes. As many events have been virtual it has been easy to demo everything from my desktop – everything is set up so I can demo things very easily. While doing this all on one machine does point to how compact and efficient Fluentd is as I can run multiple instances concurrently it does…

[kaobei-engineer]

#純靠北工程師6j8

----------

最近看到有些人覺得DevOps工程師薪水高便想轉職，並不是不能轉職，只是轉職之前要問一下自己，你的武器儲備庫足夠嗎？你有信心自己能一直學下去嗎？那些三大雲的架構怎樣做HA, cost optimization, high performance, landing zone先不說只是入門的基本，然後那些工具像是CI/CD pipeline, Docker, k8s, helm chart, terraform, terragrunt, ansible, ArgoCD也都只是基本工具方便你維護 萬一公司沒資源請devsecops那恭喜你還要多兼任一個職位，那你便需要在CI/CD pipeline套上SAST, SCA, DAST, IAST之類的東西 你以為只要弄完就好嗎？不好意思因為你是devops也包含ops的工作，那些弄好的server像是gitlab, ELK, sonarqube, registry platform你都需要定期更新和維護 要說到怎樣更好的維護，那你便需要先學點監控的工具像是grafana, thanos, prometheus, loki, fluentd, alert manager 當然，上面的東西都是一些很基本的，平日花最多的時間就是處理軟件工程師們的需求，像是他們想要各種各樣的database, 想要做queuing你都得幫他們想辦法，沒錯，也就是說那些mongodb, mysql, postgresql, mariadb, dynamodb/cloud SQL都要懂一點，自己架還是用雲端的，怎樣設定都要會一點。當然上面有提到監控，也就是說如果你需要監控db裡面的field再給警告，那會一點query syntax也是在所難免。甚麼？你說軟件工程師想用微服務架構？那你就要學懂service mesh怎麼玩，恭喜你可以有機會接觸istio, consul, linkerd, envoy等的產品 身為一個devops工程師，程式語言方面略懂shell script/python/go是基本，雖然有很多工具，但還是免不了要客製化一些功能，不過不用寫很深的algo, 程度在leetcode的medium就差不多 如果公司不是全部在用雲端，還有部份放在自己DC的設備，那恭喜你還有機會要懂firewall, switch, router甚至乎要管理整個DC的環境(濕度、溫度等等)，不過幸好devops的工具夠多，某些品牌你還可以用terraform/ansible做管理，沒有的話…嗯，那就再學那套品牌的cli吧。對了，因為是hybrid的架構，所以怎樣弄tunnel連接上雲也是要做的 當然還有其他的鬼故事，像是公司還沒請SRE，需要你星期六日都幫忙on-call，或是說公司沒請Automation QA需要你幫忙寫test case 不過有一點確實沒錯，就薪水很高啊，甚至比軟件工程師的team lead高也不奇怪，再高級一點可以當架構師薪水更高，前提是你有把握一直學下去…

----------

💖 純靠北工程師 官方 Discord 歡迎在這找到你的同溫層！

👉 https://discord.gg/tPhnrs2

----------

💖 全平台留言、文章詳細內容

👉 https://init.engineer/cards/show/8468

[leanesch]

Kubernetes must know:

First thing to know is that Kubernetes has many competitors such as Docker Swarm, Zookeeper, Nomad etc.. and Kubernetes is not the solution for every architecture so please define your requirements and check other alternatives first before starting with Kuberenetes as it can be complex or not really that beneficial in your case and that an easier orchestrator can do the job.

If you are using a cloud provider, and you want a managed kubernetes service, you can check EKS for AWS, GCP for Google Cloud or AKS for Azure.

Make sure to have proper monitoring and alerting for your cluster as this enables more visibility and eases the management of containerized infrastructure by tracking utilization of cluster resources including memory, CPU, storage and networking performance. It is also recommended to monitor pods and applications in the cluster. The most common tools used for Kubernetes monitoring are ELK/EFK, datadog, Prometheus and Grafana which will be my topic for the next article, etc..

Please make sure to backup your cluster’s etcd data regularly.

In order to ensure that your kubernetes cluster resources are only accessed by certain people, it's recommended to use RBAC in your cluster in order to build roles with the right access.

Scalability and what's more important than scalability, 3 types we must know and include in our cluster architecture are Cluster autoscaler, HPA and VPA.

Resource management is important as well, setting and rightsizing cluster resources requests and limits will help avoiding issues like OOM and Pod eviction and saves you money!

You may want to check Kubernetes CIS Benchmark which is a set of recommendations for configuring Kubernetes to support a strong security posture, you can take a look at this article to learn more about it.

Try to always get the latest Kubernetes stable GA version for newer functionalities and if using cloud, for supported versions.

Scan containers for security vulnerabilities is very important as well, here we can talk about tools like Kube Hunter, Kube Bench etc..

Make use of Admission controllers when possible as they intercept and process requests to the Kubernetes API prior to persistence of the object, but after the request is authenticated and authorized, which is used when you have a set of constraints/behavior to be checked before a resource is deployed. It can also block vulnerable images from being deployed.

Speaking about Admission controller, you can also enforce policies in Kubernetes using a tool like OPA which lets you define sets of security and compliance policies as code.

Using a tool like Falco for auditing the cluster, this is a nice way to log and monitor real time activities and interactions with the API.

Another thing to take a look at is how to handle logging of applications running in containers (I recommend checking logging agents such fluentd/fluentbit) and especially how to setup Log rotation to reduce the storage growth and avoid performance issues.

In case you have multiple microservices running in the cluster, you can also implement a service mesh solution in order to have a reliable and secure architecture and other features such as encryption, authentication, authorization, routing between services and versions and load balancing. One of the famous service mesh solutions is Istio. You can take a look at this article for more details about service mesh.

One of the most important production ready clusters features is to have a backup&restore solution and especially a solution to take snapshots of your cluster’s Persistent Volumes. There are multiple tools to do this that you might check and benchmark like velero, portworx etc..

You can use quotas and limit ranges to control the amount of resources in a namespace for multi-tenancy.

For multi cluster management, you can check Rancher, weave Flux, Lens etc..

[datamattsson]

Got logs?

[daagencyde]

Einfache Protokollierung mit Elastic Cloud Kubernetes und Fluentd - da Agency

★ From da Agency Tech Blog ★

Einfache Protokollierung mit Elastic Cloud Kubernetes und Fluentd

Bei Kubernauts sind wir stets darauf bedacht, robuste, skalierbare und beobachtbare Umgebungen einzurichten. Daher ist eine einheitliche Ereignisprotokollierung eine wesentliche Säule. Dieser Beitrag könnte ein Startpunkt für Sie sein, um Ihre Protokollspeicherung und -verfolgung zu zentralisieren. Unser Kubernautic Cloudless Service mit Rancher ist ein solcher Anwendungsfall. Wir betreiben mehrere Cluster mit noch mehr Nodes. Um

Read more: https://www.da-agency.de/pressemitteilung/einfache-protokollierung-mit-elastic-cloud-kubernetes-und-fluentd/

[sybrenbolandit]

Are you sick of going into a Kubernetes cluster to look at the logs of an application? Do you want a clear overview of the access logs over several pods? Use these tools for a fluent log experience.