Alternatives to StatsD logo

Alternatives to StatsD

collectd, Prometheus, InfluxDB, Telegraf, and Logstash are the most popular alternatives and competitors to StatsD.
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What is StatsD and what are its top alternatives?

It is a network daemon that runs on the Node.js platform and listens for statistics, like counters and timers, sent over UDP or TCP and sends aggregates to one or more pluggable backend services (e.g., Graphite).
StatsD is a tool in the Monitoring Tools category of a tech stack.
StatsD is an open source tool with 16.2K GitHub stars and 2K GitHub forks. Here’s a link to StatsD's open source repository on GitHub

Top Alternatives to StatsD

  • collectd

    collectd

    collectd gathers statistics about the system it is running on and stores this information. Those statistics can then be used to find current performance bottlenecks (i.e. performance analysis) and predict future system load (i.e. capacity planning). Or if you just want pretty graphs of your private server and are fed up with some homegrown solution you're at the right place, too. ...

  • 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. ...

  • 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. ...

  • Telegraf

    Telegraf

    It is an agent for collecting, processing, aggregating, and writing metrics. Design goals are to have a minimal memory footprint with a plugin system so that developers in the community can easily add support for collecting metrics. ...

  • Logstash

    Logstash

    Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana. ...

  • 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! ...

  • Fluentd

    Fluentd

    Fluentd collects events from various data sources and writes them to files, RDBMS, NoSQL, IaaS, SaaS, Hadoop and so on. Fluentd helps you unify your logging infrastructure. ...

StatsD alternatives & related posts

collectd logo

collectd

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System and applications metrics collector
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PROS OF COLLECTD
  • 1
    Open Source
  • 1
    KISS
  • 1
    Modular, plugins
CONS OF COLLECTD
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    related collectd posts

    Łukasz Korecki
    CTO & Co-founder at EnjoyHQ · | 7 upvotes · 249.1K views

    We use collectd because of it's low footprint and great capabilities. We use it to monitor our Google Compute Engine machines. More interestingly we setup collectd as StatsD replacement - all our Clojure services push application-level metrics using our own metrics library and collectd pushes them to Stackdriver

    See more
    Prometheus logo

    Prometheus

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    3K
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    An open-source service monitoring system and time series database, developed by SoundCloud
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    PROS OF PROMETHEUS
    • 46
      Powerful easy to use monitoring
    • 38
      Flexible query language
    • 32
      Dimensional data model
    • 27
      Alerts
    • 22
      Active and responsive community
    • 21
      Extensive integrations
    • 19
      Easy to setup
    • 12
      Beautiful Model and Query language
    • 7
      Easy to extend
    • 6
      Nice
    • 3
      Written in Go
    • 2
      Good for experimentation
    • 1
      Easy for monitoring
    CONS OF PROMETHEUS
    • 11
      Just for metrics
    • 6
      Bad UI
    • 6
      Needs monitoring to access metrics endpoints
    • 3
      Not easy to configure and use
    • 3
      Supports only active agents
    • 2
      Written in Go
    • 2
      Requires multiple applications and tools
    • 2
      TLS is quite difficult to understand

    related Prometheus posts

    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 2.9M 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
    Matt Menzenski
    Senior Software Engineering Manager at PayIt · | 13 upvotes · 85.8K 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
    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
    • 51
      Time-series data analysis
    • 28
      Easy setup, no dependencies
    • 24
      Fast, scalable & open source
    • 21
      Open source
    • 18
      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
      HA or Clustering is only in paid version

    related InfluxDB posts

    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.

    See more
    Telegraf logo

    Telegraf

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    The plugin-driven server agent for collecting & reporting metrics
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    PROS OF TELEGRAF
    • 5
      Cohesioned stack for monitoring
    • 4
      One agent can work as multiple exporter with min hndlng
    • 2
      Open Source
    • 2
      Metrics
    • 1
      Supports custom plugins in any language
    • 1
      Many hundreds of plugins
    CONS OF TELEGRAF
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      related Telegraf posts

      Logstash logo

      Logstash

      8.2K
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      Collect, Parse, & Enrich Data
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      PROS OF LOGSTASH
      • 68
        Free
      • 18
        Easy but powerful filtering
      • 12
        Scalable
      • 2
        Kibana provides machine learning based analytics to log
      • 1
        Great to meet GDPR goals
      • 1
        Well Documented
      CONS OF LOGSTASH
      • 3
        Memory-intensive
      • 1
        Documentation difficult to use

      related Logstash posts

      Tymoteusz Paul
      Devops guy at X20X Development LTD · | 23 upvotes · 4.7M views

      Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

      It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

      I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

      We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

      If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

      The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

      Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

      See more
      Tanya Bragin
      Product Lead, Observability at Elastic · | 10 upvotes · 615.2K views

      ELK Stack (Elasticsearch, Logstash, Kibana) is widely known as the de facto way to centralize logs from operational systems. The assumption is that Elasticsearch (a "search engine") is a good place to put text-based logs for the purposes of free-text search. And indeed, simply searching text-based logs for the word "error" or filtering logs based on a set of a well-known tags is extremely powerful, and is often where most users start.

      See more
      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
      • 7
        Well supported integrations
      • 5
        Includes event tracking
      • 2
        Rolling aggregation makes storage managable
      CONS OF GRAPHITE
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        related Graphite posts

        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 2.9M 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).

        See more
        Datadog logo

        Datadog

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        Unify logs, metrics, and traces from across your distributed infrastructure.
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        PROS OF DATADOG
        • 133
          Monitoring for many apps (databases, web servers, etc)
        • 104
          Easy setup
        • 84
          Powerful ui
        • 81
          Powerful integrations
        • 68
          Great value
        • 52
          Great visualization
        • 44
          Events + metrics = clarity
        • 40
          Custom metrics
        • 39
          Notifications
        • 37
          Flexibility
        • 17
          Free & paid plans
        • 14
          Great customer support
        • 13
          Makes my life easier
        • 8
          Adapts automatically as i scale up
        • 8
          Easy setup and plugins
        • 6
          Super easy and powerful
        • 5
          In-context collaboration
        • 5
          AWS support
        • 4
          Rich in features
        • 3
          Docker support
        • 3
          Cost
        • 3
          Best than others
        • 2
          Source control and bug tracking
        • 2
          Easy to Analyze
        • 2
          Expensive
        • 2
          Cute logo
        • 2
          Good for Startups
        • 2
          Free setup
        • 2
          Monitor almost everything
        • 2
          Automation tools
        • 2
          Simple, powerful, great for infra
        • 2
          Full visibility of applications
        • 1
          Best in the field
        CONS OF DATADOG
        • 16
          Expensive
        • 3
          No errors exception tracking
        • 2
          External Network Goes Down You Wont Be Logging
        • 1
          Complicated

        related Datadog posts

        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.

        See more

        We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.

        We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.

        We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.

        You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?

        See more
        Fluentd logo

        Fluentd

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        Unified logging layer
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        PROS OF FLUENTD
        • 10
          Open-source
        • 9
          Great for Kubernetes node container log forwarding
        • 9
          Lightweight
        • 7
          Easy
        CONS OF FLUENTD
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          related Fluentd posts