Alternatives to OpenTSDB logo

Alternatives to OpenTSDB

Prometheus, Druid, KairosDB, InfluxDB, and Graphite are the most popular alternatives and competitors to OpenTSDB.
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What is OpenTSDB and what are its top alternatives?

It is a distributed, scalable time series database to store, index & serve metrics collected from computer systems at a large scale. It can store and serve massive amounts of time series data without losing granularity.
OpenTSDB is a tool in the Databases category of a tech stack.
OpenTSDB is an open source tool with 4.7K GitHub stars and 1.3K GitHub forks. Here’s a link to OpenTSDB's open source repository on GitHub

Top Alternatives to OpenTSDB

  • 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. ...

  • Druid
    Druid

    Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations. ...

  • KairosDB
    KairosDB

    KairosDB is a fast distributed scalable time series database written on top of Cassandra. ...

  • 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. ...

  • Graphite
    Graphite

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

  • Elasticsearch
    Elasticsearch

    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack). ...

  • Blueflood
    Blueflood

    It is a high throughput, low latency, multi-tenant distributed metric processing system behind Rackspace Metrics, which is currently used in production by the Rackspace Monitoring team and Rackspace Public Cloud team to store metrics generated by their systems. ...

  • TimescaleDB
    TimescaleDB

    TimescaleDB: An open-source database built for analyzing time-series data with the power and convenience of SQL — on premise, at the edge, or in the cloud. ...

OpenTSDB alternatives & related posts

Prometheus logo

Prometheus

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3.4K
239
An open-source service monitoring system and time series database, developed by SoundCloud
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PROS OF PROMETHEUS
  • 47
    Powerful easy to use monitoring
  • 38
    Flexible query language
  • 32
    Dimensional data model
  • 27
    Alerts
  • 23
    Active and responsive community
  • 22
    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
  • 12
    Just for metrics
  • 6
    Bad UI
  • 6
    Needs monitoring to access metrics endpoints
  • 4
    Not easy to configure and use
  • 3
    Supports only active agents
  • 2
    Written in Go
  • 2
    TLS is quite difficult to understand
  • 2
    Requires multiple applications and tools
  • 1
    Single point of failure

related Prometheus posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 3.2M 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 · | 15 upvotes · 294K 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
Druid logo

Druid

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Fast column-oriented distributed data store
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PROS OF DRUID
  • 14
    Real Time Aggregations
  • 6
    Batch and Real-Time Ingestion
  • 4
    OLAP
  • 3
    OLAP + OLTP
  • 2
    Combining stream and historical analytics
  • 1
    OLTP
CONS OF DRUID
  • 3
    Limited sql support
  • 2
    Joins are not supported well
  • 1
    Complexity

related Druid posts

Umair Iftikhar
Technical Architect at ERP Studio · | 3 upvotes · 275.5K views

Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

See more
KairosDB logo

KairosDB

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Fast Time Series Database on Cassandra
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+ 1
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PROS OF KAIROSDB
  • 1
    As fast as your cassandra/scylla cluster go
  • 1
    Time-Series data analysis
  • 1
    Easy setup
  • 1
    Easy Rest API
  • 1
    Open source
CONS OF KAIROSDB
    Be the first to leave a con

    related KairosDB posts

    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
    • 54
      Time-series data analysis
    • 30
      Easy setup, no dependencies
    • 24
      Fast, scalable & open source
    • 21
      Open source
    • 19
      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
    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
    • 8
      Well supported integrations
    • 6
      Includes event tracking
    • 3
      Rolling aggregation makes storage managable
    CONS OF GRAPHITE
      Be the first to leave a con

      related Graphite posts

      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 3.2M 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
      Elasticsearch logo

      Elasticsearch

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      Open Source, Distributed, RESTful Search Engine
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      PROS OF ELASTICSEARCH
      • 323
        Powerful api
      • 314
        Great search engine
      • 230
        Open source
      • 214
        Restful
      • 199
        Near real-time search
      • 96
        Free
      • 83
        Search everything
      • 54
        Easy to get started
      • 45
        Analytics
      • 26
        Distributed
      • 6
        Fast search
      • 5
        More than a search engine
      • 3
        Easy to scale
      • 3
        Awesome, great tool
      • 3
        Great docs
      • 2
        Potato
      • 2
        Document Store
      • 2
        Great customer support
      • 2
        Intuitive API
      • 2
        Reliable
      • 2
        Nosql DB
      • 2
        Fast
      • 2
        Easy setup
      • 2
        Highly Available
      • 2
        Great piece of software
      • 1
        Ecosystem
      • 1
        Scalability
      • 1
        Not stable
      • 1
        Github
      • 1
        Elaticsearch
      • 1
        Actively developing
      • 1
        Responsive maintainers on GitHub
      • 1
        Easy to get hot data
      • 1
        Open
      • 0
        Community
      CONS OF ELASTICSEARCH
      • 7
        Resource hungry
      • 6
        Diffecult to get started
      • 5
        Expensive
      • 4
        Hard to keep stable at large scale

      related Elasticsearch posts

      Tim Abbott

      We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

      We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

      And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

      I can't recommend it highly enough.

      See more
      Tymoteusz Paul
      Devops guy at X20X Development LTD · | 23 upvotes · 5.4M 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
      Blueflood logo

      Blueflood

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      A distributed system designed to ingest and process time series data
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      PROS OF BLUEFLOOD
        Be the first to leave a pro
        CONS OF BLUEFLOOD
          Be the first to leave a con

          related Blueflood posts

          TimescaleDB logo

          TimescaleDB

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          Scalable and reliable time-series SQL database optimized for fast ingest and complex queries. Built on PostgreSQL.
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          PROS OF TIMESCALEDB
          • 8
            Open source
          • 7
            Easy Query Language
          • 6
            Time-series data analysis
          • 5
            Established postgresql API and support
          • 4
            Reliable
          • 2
            Chunk-based compression
          • 2
            Postgres integration
          • 2
            Paid support for automatic Retention Policy
          • 2
            Fast and scalable
          • 2
            High-performance
          • 1
            Case studies
          CONS OF TIMESCALEDB
          • 5
            Licensing issues when running on managed databases

          related TimescaleDB posts

          John Kodumal

          As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

          We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

          See more

          Hi, I need advice on which Database tool to use in the following scenario:

          I work with Cesium, and I need to save and load CZML snapshot and update objects for a recording program that saves files containing several entities (along with the time of the snapshot or update). I need to be able to easily load the files according to the corresponding timeline point (for example, if the update was recorded at 13:15, I should be able to easily load the update file when I click on the 13:15 point on the timeline). I should also be able to make geo-queries relatively easily.

          I am currently thinking about Elasticsearch or PostgreSQL, but I am open to suggestions. I tried looking into Time Series Databases like TimescaleDB but found that it is unnecessarily powerful than my needs since the update time is a simple variable.

          Thanks for your advice in advance!

          See more