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Monitoring Reactive Architecture Like Never Before / 今までになかったリアクティブアーキテクチャの監視 by Sahil Sawhney

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on June 28th at ScalaMatsuri 2019.
http://2019.scalamatsuri.org/index_en.html

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Monitoring Reactive Architecture Like Never Before / 今までになかったリアクティブアーキテクチャの監視 by Sahil Sawhney

  1. 1. A Reactive Platform to monitor Reactive Application Sahil Sawhney Lead DevOps Consultant +91-9871211045 www.knoldus.com 今までになかったリアクティブアーキテクチャの監視
  2. 2. Agenda What Is Reactive Monitoring Reactive Monitoring Challenges PremonR: The Solution Demonstration リアクティブな監視の課題から、PremonR というソリューションに ついて、デモを交えて紹介します。 Monitoring Needs Of The Hour
  3. 3. Monitoring Needs Of The Hour 顧客にとって本当に重要なことを計測するとはどういうことだろう どんなに注意深く、誠実であっても、問題は起こる。監視されてなければ、リリースさ れたとは言えない。 “Traditional metrics of cpu and memory usage don’t matter to your customers. How ’bout measuring what really matters to your customers?” “No matter how careful or good you are, sh!t will happen.” “If it isn’t monitored, it isn’t production!” “DevOps simply adds the idea that small, cross-functional teams should own the entire delivery process from concept through user feedback and production monitoring.” “Application up, monitoring applied, alerting all set. Now relax until things go down”
  4. 4. What is Reactive Monitoring? ❑ Applications whose foundation is laid on Reactive Manifesto accounts for being Reactive Applications. ❑ But can any monitoring pipeline ensure that its worthy enough to monitor your reactive fleet? リアクティブな監視とは? 監視パイプラインによってリアクティブなサービス群を監視することが十分に価値ある ことは確かめられるだろうか?
  5. 5. Reactive Monitoring: Enterprise Challenges リアクティブな監視: エンタープライズ領域での挑戦
  6. 6. Challenges Moving and decentralized component Lack of visibility across the enterprise Lack of democracy in monitoring, due to cost of acquiring commercial tools Insufficient, unmanaged alerting rules Alerting of the mishaps after they have occured. Lack of persistence of custom visualizations & alert rules Dedicated environment (prod, beta) based classification of viz + dashboard Keeping up with evolving applications & tools 様々な難しさ。流動的な、分散コンポーネント。不十分で管理されて いないアラートルール。問題の発生後に発報するアラートなど。
  7. 7. Premonition Based Monitoring & Alerting Platform PreMonR 予測ベースの監視、アラートのプラットホーム
  8. 8. In comes PreMonR With years of experience in Reactive stack; Knoldus compiles all its learning into a Premonition based Reactive Monitoring and Alerting Platform. The fabrication of such a tool was based on three driving forces: Driving Forces Monitor the reactive applications The monitoring platform must itself be Reactive Containing the mishaps before they turn into reality リアクティブ・アプリケーションの監視 / 監視プラットフォーム自体 がリアクティブ / 問題が実際に問題となる前に抑制する
  9. 9. Features of PreMonR Based on a reactive monitoring pipeline. Highly available monitoring platform. One Subscription all solution Centralized insights of your distributed platform Prebuilt fleet of dashboards & alerting rules as per the project stack. Customize as per your appetite Specialized for distributed environments like Kubernetes and DCOS Real time monitoring and premonition based alerting リアクティブなモニタリングパイプライン上に構築。 リアルタイムな監視と、予測ベースのアラート。高可用性。k8s などの分散環境に特化 。
  10. 10. PreMonR Architecture
  11. 11. PreMonR Architecture 1. Extractor extracts metrics from the underlying infrastructure. 2. Collector collects logs of the application as well as infrastructure. 3. Shipper exports the extracted metrics to transformer in case there are some transformations that must be applied to collected data. 4. Transformer transforms the input logs and metrics as per the use case. 5. Data Backend stores the metrics and logs aggregated by Extractore and Collectors 6. Premonition engine apply Machine Learning algorithm to detect anomalies and facilitate proactive alerting. 7. Visualizer is the UI where all logs and Dashboards could be visualized. 8. Alerter fires alerts in case of threshold breaches. Metrics Extractor Data Backend Alerter Visualizer Logs Collector PagerDuty Email Slack System logs(syslog, journald) Application logs(log4j, log4net) Server Logs(Apache, Nginx) Platform Logs(AWS, Baremetal) Cluster Logs(K8S, Mesos) System metrics (CPU, memory, disk) Infrastructure metrics (AWS, Gcloud, Baremetal) Application Agents (APM, error tracking) TransformerShipper PreMonition Analytics Engine インフラからメトリクスを収集、アプリケーションからのログ収集、メトリクスの変換 、ストア、機械学習による予測や異常検知、ダッシュボード、アラート
  12. 12. ● Lagom Metrics ● Spring-Boot Metrics ● Akka Metrics ● Play Metrics ● Application Logs ● System Resource Metrics ● Cassandra metrics ● Dgraph metrics ● Elasticsearch Metrics ● Kafka Metrics ● Anything That Gives Metrics Monitoring Application Monitoring Infrastructure Monitoring What Can I Monitor ? モニタリングできるもの
  13. 13. The PremonR Effect Centralized monitoring and alerting tool for cluster health, log analysis. High availability, persistence of Dashboards and alert rules. Clean and convenient setup procedure Automatically discover, configure and customize all relevant metrics. Easily Adaptable/customizable solution based on BELK Visualize the health and topology of their distributed applications in real-time Data science-driven anomaly detection quickly detects the tentative problems. Optimized thresholds ensuring real time alerting. 適切なメトリクスを自動で発見、設定、カスタマイズ。分散アプリケーションの健全性 、トポロジーをリアルタイムに可視化 機械学習ドリブンな異常検知。BELK 上に構築されている。
  14. 14. Demonstration (デモンストレーション)
  15. 15. Demo Architecture PreMonR Engine Kubernetes Visualization & Alerting
  16. 16. www.knoldus.com +(91) 987-121-1045 sahil.sawhney@knoldus.in @Knolspeak Thank You (ありがとうございます) Stay in Touch

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