Alternatives to Zabbix logo

Alternatives to Zabbix

Nagios, Graphite, Datadog, InfluxDB, and Prometheus are the most popular alternatives and competitors to Zabbix.
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What is Zabbix and what are its top alternatives?

Zabbix is a popular open-source monitoring solution that offers network and infrastructure monitoring capabilities. It provides real-time monitoring, alerting, and visualization features to help organizations monitor their IT environment efficiently. Key features include auto-discovery of devices, flexible alerting mechanisms, customizable dashboards, and support for various data sources. However, some limitations of Zabbix include a steep learning curve for beginners, complex configuration settings, and resource-intensive performance.

  1. Prometheus: Prometheus is a cloud-native monitoring solution that specializes in monitoring dynamic container environments. It offers powerful querying, alerting, and visualization functionalities. Pros include flexible data processing, multi-dimensional data model, and integrations with various exporters. Cons compared to Zabbix are the need for additional components for full functionality and a steeper learning curve.
  2. Nagios: Nagios is a widely-used monitoring tool that focuses on monitoring IT infrastructure components like servers, switches, and applications. It provides comprehensive monitoring capabilities, plugins for extending functionality, and strong notification features. Pros include a large community, extensive plugin ecosystem, and historical performance data. Cons include an outdated web interface and complex setup and configuration process.
  3. Grafana: Grafana is a visualization tool that works well with data sources like Prometheus, Graphite, and InfluxDB. It offers advanced visualization features, dashboard creation, and alerting capabilities. Pros include a user-friendly interface, extensive graphing options, and community-built dashboards. Cons compared to Zabbix are the lack of in-depth monitoring functionalities and the need for additional data sources.
  4. Icinga: Icinga is an open-source monitoring solution that focuses on extensibility and scalability. It provides monitoring for networks, servers, and services with features like reporting, graphing, and distributed monitoring. Pros include a modular design, REST API for integration, and strong community support. Cons compared to Zabbix are the complexity of setting up advanced monitoring configurations and the learning curve for beginners.
  5. Observium: Observium is a network monitoring tool that specializes in monitoring network devices like routers, switches, and firewalls. It offers automatic discovery, detailed network insights, and SNMP monitoring capabilities. Pros include a simple setup process, support for multiple network device vendors, and customizable dashboards. Cons compared to Zabbix are the limited support for non-network infrastructure monitoring and fewer alerting options.
  6. Netdata: Netdata is a real-time monitoring and performance optimization tool for servers, containers, and applications. It provides per-second metrics, customizable dashboards, and anomaly detection features. Pros include a lightweight agent, simple installation process, and cloud monitoring capabilities. Cons compared to Zabbix are the lack of long-term data storage and the focus on real-time monitoring rather than historical data analysis.
  7. Opsgenie: Opsgenie is an incident and alert management tool that helps teams respond to alerts and incidents effectively. It offers alert routing, on-call scheduling, and incident visualization features. Pros include integrations with monitoring tools, escalation policies, and mobile alerting options. Cons compared to Zabbix are the focus on incident response rather than monitoring and the additional cost for alert management capabilities.
  8. Zenoss: Zenoss is an enterprise monitoring solution that provides unified monitoring for networks, infrastructure, and applications. It offers automated discovery, event correlation, and performance analytics features. Pros include a single-pane-of-glass view, support for hybrid environments, and customizable reporting. Cons compared to Zabbix are the higher cost for enterprise features and the complexity of setting up advanced configurations.
  9. Checkmk: Checkmk is a monitoring tool that focuses on simplicity and ease of use. It offers monitoring for servers, networks, applications, and cloud environments with features like agent-based monitoring, automation, and reporting. Pros include a user-friendly web interface, pre-configured monitoring checks, and scalability for large environments. Cons compared to Zabbix are the lack of customization options and the reliance on predefined check plugins.
  10. Splunk: Splunk is a data analytics and monitoring platform that specializes in log monitoring and analysis. It offers real-time visibility, search capabilities, and machine learning features for troubleshooting and monitoring. Pros include advanced analytics capabilities, machine learning algorithms, and customizable dashboards. Cons compared to Zabbix are the high cost for enterprise features and the focus on log monitoring rather than infrastructure monitoring.

Top Alternatives to Zabbix

  • Nagios
    Nagios

    Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License. ...

  • Graphite
    Graphite

    Graphite does two things: 1) Store numeric time-series data and 2) Render graphs of this data on demand ...

  • Datadog
    Datadog

    Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog! ...

  • InfluxDB
    InfluxDB

    InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out. ...

  • Prometheus
    Prometheus

    Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true. ...

  • PRTG
    PRTG

    It can monitor and classify system conditions like bandwidth usage or uptime and collect statistics from miscellaneous hosts as switches, routers, servers and other devices and applications. ...

  • LibreNMS
    LibreNMS

    It is an auto-discovering PHP/MySQL/SNMP based network monitoring which includes support for a wide range of network hardware and operating systems including Cisco, Linux, FreeBSD, Juniper, Brocade, Foundry, HP and many more. ...

  • Grafana
    Grafana

    Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins. ...

Zabbix alternatives & related posts

Nagios logo

Nagios

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Complete monitoring and alerting for servers, switches, applications, and services
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PROS OF NAGIOS
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    The Most flexible monitoring system
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CONS OF NAGIOS
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    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 4.5M views

    Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

    By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

    To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

    https://eng.uber.com/m3/

    (GitHub : https://github.com/m3db/m3)

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    Shared insights
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    PrometheusPrometheusNagiosNagios

    I am new to DevOps and looking for training in DevOps. Some institutes are offering Nagios while some Prometheus in their syllabus. Please suggest which one is being used in the industry and which one should I learn.

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    Graphite logo

    Graphite

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    A highly scalable real-time graphing system
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    PROS OF GRAPHITE
    • 16
      Render any graph
    • 9
      Great functions to apply on timeseries
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      Well supported integrations
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      Includes event tracking
    • 3
      Rolling aggregation makes storage managable
    CONS OF GRAPHITE
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      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 4.5M views

      Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

      By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

      To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

      https://eng.uber.com/m3/

      (GitHub : https://github.com/m3db/m3)

      See more

      A huge part of our continuous deployment practices is to have granular alerting and monitoring across the platform. To do this, we run Sentry on-premise, inside our VPCs, for our event alerting, and we run an awesome observability and monitoring system consisting of StatsD, Graphite and Grafana. We have dashboards using this system to monitor our core subsystems so that we can know the health of any given subsystem at any moment. This system ties into our PagerDuty rotation, as well as alerts from some of our Amazon CloudWatch alarms (we’re looking to migrate all of these to our internal monitoring system soon).

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      Datadog logo

      Datadog

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      • 107
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        Powerful ui
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        Powerful integrations
      • 70
        Great value
      • 54
        Great visualization
      • 46
        Events + metrics = clarity
      • 41
        Notifications
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        Custom metrics
      • 39
        Flexibility
      • 19
        Free & paid plans
      • 16
        Great customer support
      • 15
        Makes my life easier
      • 10
        Adapts automatically as i scale up
      • 9
        Easy setup and plugins
      • 8
        Super easy and powerful
      • 7
        In-context collaboration
      • 7
        AWS support
      • 6
        Rich in features
      • 5
        Docker support
      • 4
        Cute logo
      • 4
        Source control and bug tracking
      • 4
        Monitor almost everything
      • 4
        Cost
      • 4
        Full visibility of applications
      • 4
        Simple, powerful, great for infra
      • 4
        Easy to Analyze
      • 4
        Best than others
      • 4
        Automation tools
      • 3
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      • 3
        Free setup
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        Good for Startups
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        Expensive
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      CONS OF DATADOG
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        Expensive
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        No errors exception tracking
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      • 1
        Complicated

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      Noah Zoschke
      Engineering Manager at Segment · | 30 upvotes · 272.5K views

      We just launched the Segment Config API (try it out for yourself here) — a set of public REST APIs that enable you to manage your Segment configuration. Behind the scenes the Config API is built with Go , GRPC and Envoy.

      At Segment, we build new services in Go by default. The language is simple so new team members quickly ramp up on a codebase. The tool chain is fast so developers get immediate feedback when they break code, tests or integrations with other systems. The runtime is fast so it performs great at scale.

      For the newest round of APIs we adopted the GRPC service #framework.

      The Protocol Buffer service definition language makes it easy to design type-safe and consistent APIs, thanks to ecosystem tools like the Google API Design Guide for API standards, uber/prototool for formatting and linting .protos and lyft/protoc-gen-validate for defining field validations, and grpc-gateway for defining REST mapping.

      With a well designed .proto, its easy to generate a Go server interface and a TypeScript client, providing type-safe RPC between languages.

      For the API gateway and RPC we adopted the Envoy service proxy.

      The internet-facing segmentapis.com endpoint is an Envoy front proxy that rate-limits and authenticates every request. It then transcodes a #REST / #JSON request to an upstream GRPC request. The upstream GRPC servers are running an Envoy sidecar configured for Datadog stats.

      The result is API #security , #reliability and consistent #observability through Envoy configuration, not code.

      We experimented with Swagger service definitions, but the spec is sprawling and the generated clients and server stubs leave a lot to be desired. GRPC and .proto and the Go implementation feels better designed and implemented. Thanks to the GRPC tooling and ecosystem you can generate Swagger from .protos, but it’s effectively impossible to go the other way.

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      Robert Zuber

      Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

      We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

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      InfluxDB logo

      InfluxDB

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      An open-source distributed time series database with no external dependencies
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      PROS OF INFLUXDB
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        Time-series data analysis
      • 30
        Easy setup, no dependencies
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        Fast, scalable & open source
      • 21
        Open source
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        Real-time analytics
      • 6
        Continuous Query support
      • 5
        Easy Query Language
      • 4
        HTTP API
      • 4
        Out-of-the-box, automatic Retention Policy
      • 1
        Offers Enterprise version
      • 1
        Free Open Source version
      CONS OF INFLUXDB
      • 4
        Instability
      • 1
        Proprietary query language
      • 1
        HA or Clustering is only in paid version

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      Hi everyone. I'm trying to create my personal syslog monitoring.

      1. To get the logs, I have uncertainty to choose the way: 1.1 Use Logstash like a TCP server. 1.2 Implement a Go TCP server.

      2. To store and plot data. 2.1 Use Elasticsearch tools. 2.2 Use InfluxDB and Grafana.

      I would like to know... Which is a cheaper and scalable solution?

      Or even if there is a better way to do it.

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      InfluxDBInfluxDBJSONJSON

      Hi all, I am trying to decide on a database for time-series data. The data could be tracking some simple series like statistics over time or could be a nested JSON (multi-level nested). I have been experimenting with InfluxDB for the former case of a simple list of variables over time. The continuous queries are powerful too. But for the latter case, where InfluxDB requires to flatten out a nested JSON before saving it into the database the complexity arises. The nested JSON could be objects or a list of objects and objects under objects in which a complete flattening doesn't leave the data in a state for the queries I'm thinking.

      [ 
        { "timestamp": "2021-09-06T12:51:00Z",
          "name": "Name1",
          "books": [
              { "title": "Book1", "page": 100 },
              { "title": "Book2", "page": 280 },
          ]
        },
       { "timestamp": "2021-09-06T12:52:00Z",
         "name": "Name2",
         "books": [
             { "title": "Book1", "page": 320},
             { "title": "Book2", "page": 530 },
             { "title": "Book3", "page": 150 },
         ]
       }
      ]
      

      Sample query: With a time range, for name xyz, find all the book title for which # of page < 400.

      If I flatten it completely, it will result in fields like books_0_title, books_0_page, books_1_title, books_1_page, ... And by losing the nested context it will be hard to return one field (title) where some condition for another field (page) satisfies.

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      Prometheus logo

      Prometheus

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      PROS OF PROMETHEUS
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        Flexible query language
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        Dimensional data model
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        Alerts
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        Active and responsive community
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        Extensive integrations
      • 19
        Easy to setup
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        Beautiful Model and Query language
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        Easy to extend
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        Nice
      • 3
        Written in Go
      • 2
        Good for experimentation
      • 1
        Easy for monitoring
      CONS OF PROMETHEUS
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        Just for metrics
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        Bad UI
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        Not easy to configure and use
      • 3
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        Written in Go
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        TLS is quite difficult to understand
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        Requires multiple applications and tools
      • 1
        Single point of failure

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      Senior Software Engineering Manager at PayIt · | 16 upvotes · 1M views

      Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.

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      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 4.5M views

      Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

      By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

      To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

      https://eng.uber.com/m3/

      (GitHub : https://github.com/m3db/m3)

      See more
      PRTG logo

      PRTG

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      A powerful & easy network monitoring software
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      PROS OF PRTG
        Be the first to leave a pro
        CONS OF PRTG
        • 1
          Poor search capabilities
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          Graphs are static
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          Running on windows

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        LibreNMS logo

        LibreNMS

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        Opensource Auto-discoverying network monitoring system
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            Grafana logo

            Grafana

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            Open source Graphite & InfluxDB Dashboard and Graph Editor
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            PROS OF GRAFANA
            • 89
              Beautiful
            • 68
              Graphs are interactive
            • 57
              Free
            • 56
              Easy
            • 34
              Nicer than the Graphite web interface
            • 26
              Many integrations
            • 18
              Can build dashboards
            • 10
              Easy to specify time window
            • 10
              Can collaborate on dashboards
            • 9
              Dashboards contain number tiles
            • 5
              Open Source
            • 5
              Integration with InfluxDB
            • 5
              Click and drag to zoom in
            • 4
              Authentification and users management
            • 4
              Threshold limits in graphs
            • 3
              Alerts
            • 3
              It is open to cloud watch and many database
            • 3
              Simple and native support to Prometheus
            • 2
              Great community support
            • 2
              You can use this for development to check memcache
            • 2
              You can visualize real time data to put alerts
            • 0
              Grapsh as code
            • 0
              Plugin visualizationa
            CONS OF GRAFANA
            • 1
              No interactive query builder

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            Senior Software Engineering Manager at PayIt · | 16 upvotes · 1M views

            Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.

            See more
            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 4.5M views

            Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

            By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

            To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

            https://eng.uber.com/m3/

            (GitHub : https://github.com/m3db/m3)

            See more