DOCSLIB.ORG

Scheduling and Resource Management

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Scheduling and Resource Management Linux Clusters Institute: Monitoring Steve Bird, Systems Administrator, Indiana University XCRI Engineer, XSEDE This document is a result of work by volunteer LCI instructors and is licensed under CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/). August 2019 1 Why monitor? August 2019 2 Service Level Agreement (SLA) • Which services must be provided by you? • Which services must be provided to you? • Regulatory requirements • Contractual requirements • Business requirements • Common Deliverables • Availability of services (Uptime) • mean time between failures (MTBF) • mean time to repair or mean time to recovery (MTTR) August 2019 3 Monitoring and Notification Basic Flow Metric Metric Automated Gathering of collection aggregation Metrics Metric Metric Evaluate Metrics against Transformation Analysis Requirements Presentation Notification Prepare and share metrics for Stakeholders August 2019 4 What to Collect (Metrics) • Overall cluster health • Storage • Queue size • Capacity • Jobs running • Degraded status • Jobs Queued • Connectivity • Overall network usage • Number of responding nodes • Security • Logs of everything • Individual node health • Load average • Power status • Memory used • Network bandwidth • Temperatures • CPU usage • Cold-aisle • Temperature • Switches exhausts • CPU temperatures August 2019 5 Metric Collection • Collection Tools (Common Tools) Collection tools already exist to • Ganglia capture most metrics. • Collectd • Perfmon No single tool will do everything • Performance Co-pilot (PCP) you need unless you write it • Nagios yourself • Unified Fabric Manager (UFM) • Cacti Try to avoid re-inventing the • Syslog wheel. • TACC stats • Scripts August 2019 6 Metric Aggregation • Aggregation Tools Metrics need to be gathered from • Ganglia all over the cluster to a single • Collectd place for analysis and storage • Performance Co-pilot (PCP) • Nagios Most metrics should transfer over • Unified Fabric Manager (UFM) the Management Ethernet to • Cacti avoid interference with Job • Syslog performance in Low Latency interconnect • Round Robin Database (RRD) August 2019 7 Metric Analysis and Transformation • Monitoring Conundrum • Data is useless unless we do something with it • We can collect much more data than we can analyze • We generally won’t know what data we need until we need it • Exception: Data we must provide for SLA requirements • Limited storage and processing capacity for metric analysis August 2019 8 Notification • Notification Tools Basic functionality of all alerts: • Nagios Red, Yellow, Green • Icinga • Zenoss Most notification tools are forks • Zabbix or clones of Nagios • PRTG • OpenNMS Notification tools can be passive • OP5 or active in querying the status of • Pandora FMS the cluster • Unified Fabric Manager (UFM) August 2019 9 Notification • Monitoring for known evil • Basis for all notifications • Only alert if something known bad happens • Metrics -> Notifications • Most tools will require extensive configuration to be useful • Most tools will have a way to query metrics and create alerts • Some tools, such as Nagios, have this entire process built in • Others will have ways to bolt on this functionality • Nagios can query Ganglia • Ganglia can query Nagios August 2019 10 How should we get notified? • Emergency • Fire and smoke exiting machine • Urgent: • Email or text or phone call • Define this carefully • Not-so urgent: • Web page updates • Especially helpful for historical data • Email (filtered) • End-user support requests August 2019 11 SLA based Alerts • Alerts on Deliverables • Availability of services (Uptime) • Example: Alert if less than 98% of batch nodes are online • mean time between failures (MTBF) • Example: Send email report of time between failures • mean time to repair or mean time to recovery (MTTR) • Example: Alert if a down node does not come online after 4 hours down August 2019 12 How often to alert? • SLA requirements • If your SLA requires it, you may get called off-hours or even on holidays • You will quickly get a feel for this • Too much info is often worse than too little info • The “urgent” – continually • The “not-so-urgent” – anywhere from a few times per day to once per week • There’s nothing wrong with trial and error • Consider aggregated reports for ‘not-so-urgent’ August 2019 13 Security Alerts • Securing the cluster • Security alerts may need to go to specific groups or people instead of normal operations • Regulations and Security rules may apply to cluster which must be enforced • Compliance to Regulations: Sarbanes Oxley, Fisma, HIPAA, etc. • NIST 800-171 (CUI – Controlled Unclassified Information) has specific reporting timeframes • Active response may need to be required such as blocking IPs • Security status updates • Alerts on security failures • sudo reports • Network login failures (e.g. fail2ban) • crontab failures • Logfile errors (customize to fit) August 2019 14 Log Management • Centralized Log Management • Make troubleshooting easier • Analysis of your logs • Reduce the data loss risk • ELK stack (https://www.elastic.co): • Elasticse arch: Stores and indexes logs • Logsta sh: Collects and parses logs • Kibana : Virtualization and analysis. August 2019 15 Example: Nagios August 2019 16 Example: Nagios • Nagios/NRPE (Nagios Remote Plugin Executor) • Generic executable that runs “plugins” • Plugins can monitor just about anything you can think of monitoring • Even works with Windows • Nagios (http://www.nagios.org/) is by far the most common monitoring system August 2019 17 Example: Check_MK August 2019 18 Example: Check_MK • Check_MK (http://mathias-kettner.com/check_mk.html) • Much faster than Nagios • Graphing tools and web GUI • Agent-side and server-side checks • Easy install and configure with OMD • Used by CERN (and HCC). August 2019 19 Example: Ganglia August 2019 20 Example: Ganglia • Ganglia (http://ganglia.sourceforge.net/) - for historical and resource monitoring • Ours are public • RRD files give historical data (a.k.a. “lots of pretty graphs”) August 2019 21 Example: Grafana August 2019 22 Example: Grafana • Grafana (https://grafana.com) – General purpose dashboard • InfluxDB (https://www.influxdata.com) – Time series database • Collectd (https://collectd.org) – Collect metric and send to InfluxDB August 2019 23 Example: ELK stack August 2019 24 Monitoring Future • Large data analysis using machine learning • Noise reduction • Anomaly detection • Correlation • Examples: • Moogsoft (https://www.moogsoft.com) • Metricly (https://www.metricly.com/product) • Anodot (https://www.anodot.com) August 2019 25 Questions? August 2019 26.
Load more

Recommended publications
  • SQL Server 2017 on Linux Quick Start Guide | 4
    SQL Server 2017 on Linux Quick Start Guide Contents Who should read this guide? ........................................................................................................................ 4 Getting started with SQL Server on Linux ..................................................................................................... 5 Why SQL Server with Linux? ..................................................................................................................... 5 Supported platforms ................................................................................................................................. 5 Architectural changes ............................................................................................................................... 6 Comparing SQL on Windows vs. Linux ...................................................................................................... 6 SQL Server installation on Linux ................................................................................................................ 8 Installing SQL Server packages .................................................................................................................. 8 Configuration capabilities ....................................................................................................................... 11 Licensing .................................................................................................................................................. 12 Administering and
    [Show full text]
  • Latest Pgwatch2 Docker Image with Built-In Influxdb Metrics Storage DB
    pgwatch2 Sep 30, 2021 Contents: 1 Introduction 1 1.1 Quick start with Docker.........................................1 1.2 Typical “pull” architecture........................................2 1.3 Typical “push” architecture.......................................2 2 Project background 5 2.1 Project feedback.............................................5 3 List of main features 7 4 Advanced features 9 4.1 Patroni support..............................................9 4.2 Log parsing................................................ 10 4.3 PgBouncer support............................................ 10 4.4 Pgpool-II support............................................. 11 4.5 Prometheus scraping........................................... 12 4.6 AWS / Azure / GCE support....................................... 12 5 Components 13 5.1 The metrics gathering daemon...................................... 13 5.2 Configuration store............................................ 13 5.3 Metrics storage DB............................................ 14 5.4 The Web UI............................................... 14 5.5 Metrics representation.......................................... 14 5.6 Component diagram........................................... 15 5.7 Component reuse............................................. 15 6 Installation options 17 6.1 Config DB based operation....................................... 17 6.2 File based operation........................................... 17 6.3 Ad-hoc mode............................................... 17 6.4 Prometheus
    [Show full text]
  • Grafana and Mysql Benefits and Challenges 2 About Me
    Grafana and MySQL Benefits and Challenges 2 About me Philip Wernersbach Software Engineer Ingram Content Group https://github.com/philip-wernersbach https://www.linkedin.com/in/pwernersbach 3 • I work in Ingram Content Group’s Automated Print On Demand division • We have an automated process in which publishers (independent or corporate) request books via a website, and we automatically print, bind, and ship those books to them • This process involves lots of hardware devices and software components 4 The Problem 5 The Problem “How do we aggregate and track metrics from our hardware and software sources, and display those data points in a graph format to the end user?” à Grafana! 6 Which data store should we use with Grafana? ▸ Out of the box, Grafana supports Elasticsearch, Graphite, InfluxDB, KairosDB, OpenTSDB 7 Which data store should we use with Grafana? ▸ We compared the options and tried InfluxDB ▸ There were several sticking points with InfluxDB, both technical and organizational, that caused us to rule it out 8 Which data store should we use with Grafana? ▸ We already have a MySQL cluster deployed, System Administrators and Operations know how to manage it ▸ Decided to go with MySQL as a data store for Grafana 9 The Solution: Ingram Content’s Grafana-MySQL Integration 10 ▸ Written in Nim ▸ Emulates an InfluxDB server ▸ Connects to an existing The Integration MySQL server ▸ Protocol compatible with InfluxDB 0.9.3 ▸ Acts as a proxy that converts the InfluxDB protocol to the MySQL protocol and vice- versa 11 Grafana The Integration
    [Show full text]
  • Monitoring Container Environment with Prometheus and Grafana
    Matti Holopainen Monitoring Container Environment with Prometheus and Grafana Metropolia University of Applied Sciences Bachelor of Engineering Information and Communication Technology Bachelor’s Thesis 3.5.2021 Abstract Tekijä Matti Holopainen Otsikko Monitoring Container Environment with Prometheus and Grafana Sivumäärä Aika 50 sivua 3.5.2021 Tutkinto Insinööri (AMK) Tutkinto-ohjelma Tieto- ja viestintätekniikka Ammatillinen pääaine Ohjelmistotuotanto Ohjaajat Nina Simola, Projektipäällikkö Auvo Häkkinen, Yliopettaja Insinöörityön tavoitteena oli oppia pystyttämään monitorointijärjestelmä konttiympäristön re- surssien käytön seuraamista, monitorointia ja analysoimista varten. Tavoitteena oli helpot- taa monitorointijärjestelmän käyttöönottoa. Työ tehtiin käytettävien ohjelmistojen dokumen- taation ja käytännön tekemisellä opittujen asioiden pohjalta. Insinöörityön alussa käytiin läpi työssä käytettyjä teknologioita. Tämän jälkeen käytiin läpi monitorointi järjestelmän konfiguraatio ja käyttöönotto. Seuraavaksi tutustuttiin PromQL-ha- kukieleen, jonka jälkeen näytettiin kuinka pystyttää valvontamonitori ja hälytykset sähköpos- timuistutuksella. Työn lopussa käydään läpi kuinka monitorointijärjestelmässä saatua dataa analysoidaan ja mietitään miten monitorointijärjestelmää voisi parantaa. Keywords Monitorointi, Kontti, Prometheus, Grafana, Docker Abstract Author Matti Holopainen Title Monitoring Container Environment with Prometheus and Grafana Number of Pages Date 50 pages 3.5.2021 Degree Bachelor of Engineering Degree Programme Information
    [Show full text]
  • Eventindex Monitoring
    BigData tools for the monitoring of the ATLAS EventIndex Evgeny Alexandrov1, Andrei Kazymov1, Fedor Prokoshin2, on behalf of the ATLAS collaboration 1Joint Institute for Nuclear Research, Dubna, Russia. 2Centro Científico Tecnológico de Valparaíso-CCTVal, Universidad Técnica Federico Santa María. GRID Conference at JINR 12 September 2018 Introduction • The EventIndex is the complete catalogue of all ATLAS events, keeping the references to all files that contain a given event in any processing stage. • The ATLAS EventIndex collects event information from data both at CERN and Grid sites. • It uses the Hadoop system to store the results, and web services to access them. • Its successful operation depends on a number of different components. • Each component has completely different sets of parameters and states and requires a special approach. Monitoring Components Prodsys EIOracle ??? Event Picking Tests XML ORACLE? Old Monitoring System based on Kibana Disadvantages of Kibana Slow dashboard retrieving time: - for two days period: 15 seconds; - for 7 days period: 1 minute 30 seconds; - for a longer periods: it may take tens of minutes and eventually get stuck. Not very comfortable way of editing the dashboard’s page Grafana Grafana is one of the most popular packages for visualizing monitoring data. Uses modern technologies: - back-end is written using Go programming language; - front-end is written on typescript and uses angular approach. The following datasources are officially supported: Graphite InfluxDB MySQL Elasticsearch OpenTSDB Postgres CloudWatch Prometheus Microsoft SQL Server (MSSQL) InfluxDB InfluxDB is InfluxData's open source time series database designed to handle high write and query loads. Uses modern technologies: - it is written on GO; - It has the possibility of working in cluster mode; - availability of libraries for a large number of programming languages (Python, JavaScript, PHP, Haskell and others); - SQL-like query language, with which you can perform various operations with time series (merging, splitting).
    [Show full text]
  • Monitoring with Influxdb and Grafana
    Monitoring with InfluxDB and Grafana Andrew Lahiff STFC RAL ! HEPiX 2015 Fall Workshop, BNL Introduction Monitoring at RAL • Like many (most?) sites, we use Ganglia • have ~89000 individual metrics • What’s wrong with Ganglia? Problems with ganglia • Plots look very dated Problems with ganglia • Difficult & time-consuming to make custom plots • currently use long, complex, messy Perl scripts • e.g. HTCondor monitoring > 2000 lines Problems with ganglia • Difficult & time-consuming to make custom plots • Ganglia UI for making customised plots is restricted & doesn’t give good results Problems with ganglia • Ganglia server has demanding host requirements • e.g. we store all rrds in a RAM disk • have problems if trying to use a VM • Doesn’t handle dynamic resources well • Occasional problems with gmond using too much memory, affecting other processes on machines • Not really suitable for Ceph monitoring A possible alternative • InfluxDB + Grafana • InfluxDB is a time-series database • Grafana is a metrics dashboard • originally a fork of Kibana • can make plots of data from InfluxDB, Graphite, others… • Very easy to make (nice) plots • Easy to install InfluxDB • Time series database written in Go • No external dependencies • SQL-like query language • Distributed • can be run as a single node • can be run as a cluster for redundancy & performance (not suitable for production use yet) • Data can be written in using REST, or an API (e.g. Python) • or from collectd or graphite InfluxDB • Data organised by time series, grouped together into databases • Time series have zero to many points • Each point consists of: • time - the timestamp • a measurement (e.g.
    [Show full text]
  • Solution Deploying Elasticsearch on Kubernetes Using
    Solution Guide Deploying Elasticsearch on Kubernetes using OpenEBS Contents Part 1 - Before starting Part 2 - Preconfiguration Part 3 - Getting Started with OpenEBS Part 4 - Installing Kudo Operator Part 5 - Installing ElasticSearch using Kudo Part 6 - Installing Kibana Part 7 - Installing Fluentd-ES Part 8 - Monitoring Elastic search 01 www.mayadata.io Overview Elasticsearch is a distributed, free and open search and analytics engine for all types of data, including textual, numerical, geospatial, structured, and unstructured. Elasticsearch is built on Apache Lucene and was first released in 2010 by Elasticsearch N.V. (now known as Elastic). Known for its simple REST APIs, distributed nature, speed, and scalability, Elasticsearch is the central component of the Elastic Stack, a set of free and open tools for data ingestion, enrichment, storage, analysis, and visualization. Commonly referred to as the ELK Stack (after Elasticsearch, Logstash, and Kibana), the Elastic Stack now includes a rich collection of lightweight shipping agents known as Beats for sending data to Elasticsearch.[1]. This guide explains the basic installation for Elasticsearch operators on OpenEBS Local PV devices using KUDO. We will be installing Fluentd and Kibana to form the EFK stack. The guide will also provide a way to monitor the health of Elasticsearch using Prometheus and Grafana. Before starting You require an existing Kubernetes cluster. Kubernetes provides platform abstraction, cloud-native software runs, and behaves the same way on a managed Kubernetes service like AWS EKS, Google Cloud GKE, Microsoft AKS, DigitalOcean Kubernetes Service, or self-managed based on Red Hat OpenShift and Rancher. You can also use kubeadm, kubespray, minikube.
    [Show full text]
  • The Open-‐Source Monitoring Landscape
    The Open-Source Monitoring Landscape Michael Merideth Sr. Director of IT, VictorOps [email protected], @vo_mike My History and Background • Working in IT since the mid 90’s • Mostly at startups in the Colorado Front Range area • Along for the ride during the “dot com boom” • Build my career using open-source tools Since the 90’s now, there’s been a sharp division in tools and methodology between the enterprise space and the startup and small business communi;es. Obviously, smaller businesses, especially in the tech sector, were early and eager adopters of open- source technology, and much quicker to learn to rely on open-source tool chains in business-cri;cal applica;ons. Up at the enterprise level, at the public companies, they’re only now fully-embracing Linux as a business-cri;cal tool, and I think that’s largely because “the enterprise” is star;ng to be defined by companies that either came up in the dot com era, like Google, or built the dot com era, like Sun, or Dell, or let’s say RedHat. So, the “enterprise” had their toolchain, built on commercial solu;ons like HPUX and OpenView and so on, and the startup community, the “dot com” community had a completely different toolchain, based on Linux, based on open standards and protocols, built with open-source components like GNU, and Apache, and ISC Bind and so on. I’m lucky enough that I’ve been able to spend my career in the startup sphere, working with that open-source toolchain. I started working in IT in the mid 90’s in the Colorado front range, and I’ve spent my ;me since then working for and consul;ng at early-stage startups and other “non enterprise” shops.
    [Show full text]
  • Grafana and Prometheus Just Married & PMM Was Born to Monitor Your
    23.11.2020 Grafana and Prometheus just married & PMM was born to monitor your DBs 1 Who we are The Company > Founded in 2010 > More than 70 specialists > Specialized in the Middleware Infrastructure > The invisible part of IT > Customers in Switzerland and all over Europe Our Offer > Consulting > Service Level Agreements (SLA) > Trainings > License Management Grafana and Prometheus just married & PMM was born to monitor your DBs 19.11.2020 Page 2 2 23.11.2020 About me Elisa Usai Delivery Manager & Consultant +41 78 638 09 78 elisa.usai[at]dbi-services.com Elisa Usai elisetta1984 Grafana and Prometheus just married & PMM was born to monitor your DBs 19.11.2020 Page 3 3 Agenda Grafana and Prometheus just married & PMM was born to monitor your DBs 19.11.2020 Page 4 4 23.11.2020 Agenda 1.Introduction 2.Percona Monitoring and Management (PMM) tool 3.Monitor your DBs with PMM 4.Conclusion Grafana and Prometheus just married & PMM was born to monitor your DBs 19.11.2020 Page 5 5 Introduction 1 > Monitor a DB 2 > Prometheus > Grafana > The benefits of a marriage 3 4 Grafana and Prometheus just married & PMM was born to monitor your DBs 19.11.2020 Page 6 6 23.11.2020 Introduction Monitor a DB Why is the monitoring so essential? > Services Health > System Optimization > Performance Problems > Business Process Improvement > Capacity Planning What could we monitor? > Databases, Instances > Hosts > CPU, Memory, I/O, Network > Storage, Filesystems > Application Grafana and Prometheus just married & PMM was born to monitor your DBs19.11.2020 Page 7
    [Show full text]
  • Monitoring Open Source Databases with Icinga
    PGConf EU | Warsaw | 26.10.2017 Monitoring Open Source Databases with Icinga • Blerim Sheqa • Product Manager • Working @netways • @bobapple Introduction to Icinga2 Quick Poll Icinga is a scalable and extensible monitoring system which checks the availability of your resources, notifies users of outages and provides extensive metricsmetrics. • Multithreaded Perfdata Graphite Livestatus • Modular Features • Zone support Notify InfluxDB • Secure Agent Checker Gelf IDO • No Nagios® • Availability and scaling zones • Automatic redistribution of checks • Zones for multitenancymultitenancy environments Monitoring Databases • MySQLish • PostgreSQL • MongoDB • Firebird • SQLite • check_mongodb_py • connect • last_flush_time • connections • replset_state • • replicaton_lag index_miss_ratio • collections • memory • database_size • memory_mapped • database_indexes • lock • replica_primary • flushing https://github.com/mzupan/nagios-plugin-mongodb • check_postgres.pl • archive_ready • disabled_triggers • autovac_freeze • disk_space • fsm_pages • backends • prepared_txns • bloat • query_runtime • checkpoint • query_time • cluster_id • replicate_row • commitratio • same_schema • connection • sequence • custom_query • settings_checksum https://bucardo.org/check_postgres/check_postgres.pl.html • check_mysql_health • connection-time • bufferpool-wait-free • uptime • log-waits • threads-connected • tablecache-hitrate • threadcache-hitrate • table-lock-contention • • qcache-hitrate index-usage • tmp-disk-tables • qcache-lowmem-prunes • slow-queries • bufferpool-hitrate
    [Show full text]
  • GARR Cloud Monitoring
    GARR Cloud monitoring Claudio Pisa - GARR EAPConnect Workshop II 2019-11-22 Rome Monitoring - day 0 (one year ago) ● Different monitoring systems: ○ Nagios ■ OpenStack services ○ Zabbix ■ Ceph ■ Hardware sensors ● Some systems not monitored Claudio Pisa // EAPConnect Workshop II 2019 - Rome // 2019-11-22 2 Architecture - day 0 Cloud Nagios Nagios Web Infrastructural interface services Cloud Zabbix Web Infrastructure Zabbix interface + Ceph Claudio Pisa // EAPConnect Workshop II 2019 - Rome // 2019-11-22 3 Architecture - day 0 Cloud Nagios Nagios Web Infrastructural interface services Cloud Zabbix Web Infrastructure Zabbix interface + Ceph Dell Chassis Web Bare Metal Dell tools interface GINS GINS Web Network (SNMP) interface Claudio Pisa // EAPConnect Workshop II 2019 - Rome // 2019-11-22 4 Objective Objective: State of the art: build a unified Grafana comprehensive dashboard Claudio Pisa // OpenInfra Days 2019 - Rome // 2019-10-03 5 Considerations ● Existing monitoring systems seem to be doing their job well ○ the right tool for the right job ● What is missing is just a single viewpoint ● Grafana seems to have what we need Claudio Pisa // EAPConnect Workshop II 2019 - Rome // 2019-11-22 6 Grafana ● Grafana is a platform for data visualization, querying and alerting ● Several pluggable data sources: ○ Zabbix ○ PNP (Nagios) ○ Prometheus ○ Gnocchi ○ Monasca ○ JSON (general purpose) ○ MySQL / PostgreSQL (general purpose) ● Data from heterogeneous sources can be mixed in the same dashboard Claudio Pisa // EAPConnect Workshop II 2019 - Rome
    [Show full text]
  • Training Telegraf, Influxdb, Grafana
    Telegraf, InfluxDB, Grafana Training Still Using MRTG? n Simple all in one SNMP monitoring software n Send SNMP requests n Store replies into text-based database n Generate images and HTML pages n Measures two values (input / output) n Collects data every five minutes n Static pages n RRDTools, Cacti 2 2019/11/15 Presented by Warren Chang Why should not use MRTG anymore n Pull-based n Mainly SNMP, 2-D data n Not scalable n Static image, web page n Five minutes interval n Difficult to customize n No modern alert mechanism n No distributed databases 3 2019/11/15 Presented by Warren Chang What we need n Collect data n Store and process data n Visualize data n Monitoring and alert n Telemetry data more than SNMP n What is telemetry data? n Getting more important n Big Data to AI 4 2019/11/15 Presented by Warren Chang Modern Data Monitoring and Processing Model 5 2019/11/28 Presented by Warren Chang Modern Data Monitoring and Processing Model Telegraf Grafana InfluxDB Kapacitor InfluxDB Prometheus 6 2019/11/28 Presented by Warren Chang TICK Architecture 7 2019/11/28 Presented by Warren Chang Products Telegraf InfluxDB Chronograf Kapacitor Agents for collecting Streaming data and reporting metrics Time Series Database Data visualization processing enging and events Graphite Grafana Logstash Kafka Prometheus Kibana Prometheus Grafana OpenTSDB Datadog Fluentd Prometheus Elasticsearch Splunk 8 2019/11/28 Presented by Warren Chang Why InfluxDB? source: https://db-engines.com/en/ranking/time+series+dbms 9 2019/11/28 Presented by Warren Chang Why InfluxDB, Telegraf, Grafana InfluxDB Telgraf Grafana n High performance, written in Go n High performance, written in Go n Rich data sources support n Native HTTP API n Collect and send almost all kinds n InfluxDB, Prometheus, MySQL of data n Powerful SQL-like language n Templating n 200+ input, output plugins n Supports logs n Alerts n Down sampling n Plugin, App 10 2019/11/28 Presented by Warren Chang Time Series Data Tags Measurement Stock_Price Name=Apple Inc.
    [Show full text]