Alternatives to RRDtool logo

Alternatives to RRDtool

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

RRDtool lets you log and analyze the data you gather from all kinds of data-sources (DS). The data analysis part of RRDtool is based on the ability to quickly generate graphical representations of the data values collected over a definable time period.
RRDtool is a tool in the Monitoring Tools category of a tech stack.
RRDtool is an open source tool with 785 GitHub stars and 232 GitHub forks. Here’s a link to RRDtool's open source repository on GitHub

Top Alternatives to RRDtool

  • 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. ...

  • Zabbix
    Zabbix

    Zabbix is a mature and effortless enterprise-class open source monitoring solution for network monitoring and application monitoring of millions of metrics. ...

  • Graphite
    Graphite

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

  • 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. ...

  • MySQL
    MySQL

    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software. ...

  • 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. ...

  • Kibana
    Kibana

    Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch. ...

  • Nagios
    Nagios

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

RRDtool alternatives & related posts

InfluxDB logo

InfluxDB

934
1K
167
An open-source distributed time series database with no external dependencies
934
1K
+ 1
167
PROS OF INFLUXDB
  • 53
    Time-series data analysis
  • 29
    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
Zabbix logo

Zabbix

575
827
62
Track, record, alert and visualize performance and availability of IT resources
575
827
+ 1
62
PROS OF ZABBIX
  • 18
    Free
  • 9
    Alerts
  • 5
    Service/node/network discovery
  • 4
    Templates
  • 4
    Base metrics from the box
  • 3
    Multi-dashboards
  • 3
    SMS/Email/Messenger alerts
  • 2
    Grafana plugin available
  • 2
    Supports Graphs ans screens
  • 2
    Support proxies (for monitoring remote branches)
  • 1
    Perform website checking (response time, loading, ...)
  • 1
    API available for creating own apps
  • 1
    Templates free available (Zabbix Share)
  • 1
    Works with multiple databases
  • 1
    Advanced integrations
  • 1
    Supports multiple protocols/agents
  • 1
    Complete Logs Report
  • 1
    Open source
  • 1
    Supports large variety of Operating Systems
  • 1
    Supports JMX (Java, Tomcat, Jboss, ...)
CONS OF ZABBIX
  • 5
    The UI is in PHP
  • 2
    Puppet module is sluggish

related Zabbix posts

Shared insights
on
DatadogDatadogZabbixZabbixCentreonCentreon

My team is divided on using Centreon or Zabbix for enterprise monitoring and alert automation. Can someone let us know which one is better? There is one more tool called Datadog that we are using for cloud assets. Of course, Datadog presents us with huge bills. So we want to have a comparative study. Suggestions and advice are welcome. Thanks!

See more
Graphite logo

Graphite

377
389
39
A highly scalable real-time graphing system
377
389
+ 1
39
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
    Be the first to leave a con

    related Graphite posts

    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 3.1M 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
    Prometheus logo

    Prometheus

    3K
    3.2K
    238
    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
    • 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 · | 14 upvotes · 3.1M 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 · | 14 upvotes · 209.8K 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
    MySQL logo

    MySQL

    96.2K
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    The world's most popular open source database
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    PROS OF MYSQL
    • 795
      Sql
    • 674
      Free
    • 557
      Easy
    • 527
      Widely used
    • 485
      Open source
    • 180
      High availability
    • 160
      Cross-platform support
    • 104
      Great community
    • 78
      Secure
    • 75
      Full-text indexing and searching
    • 25
      Fast, open, available
    • 15
      SSL support
    • 14
      Reliable
    • 13
      Robust
    • 8
      Enterprise Version
    • 7
      Easy to set up on all platforms
    • 2
      NoSQL access to JSON data type
    • 1
      Relational database
    • 1
      Easy, light, scalable
    • 1
      Sequel Pro (best SQL GUI)
    • 1
      Replica Support
    CONS OF MYSQL
    • 14
      Owned by a company with their own agenda
    • 1
      Can't roll back schema changes

    related MySQL 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
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 21 upvotes · 1.2M views

    Our most popular (& controversial!) article to date on the Uber Engineering blog in 3+ yrs. Why we moved from PostgreSQL to MySQL. In essence, it was due to a variety of limitations of Postgres at the time. Fun fact -- earlier in Uber's history we'd actually moved from MySQL to Postgres before switching back for good, & though we published the article in Summer 2016 we haven't looked back since:

    The early architecture of Uber consisted of a monolithic backend application written in Python that used Postgres for data persistence. Since that time, the architecture of Uber has changed significantly, to a model of microservices and new data platforms. Specifically, in many of the cases where we previously used Postgres, we now use Schemaless, a novel database sharding layer built on top of MySQL (https://eng.uber.com/schemaless-part-one/). In this article, we’ll explore some of the drawbacks we found with Postgres and explain the decision to build Schemaless and other backend services on top of MySQL:

    https://eng.uber.com/mysql-migration/

    See more
    Grafana logo

    Grafana

    12.9K
    10.2K
    402
    Open source Graphite & InfluxDB Dashboard and Graph Editor
    12.9K
    10.2K
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    PROS OF GRAFANA
    • 84
      Beautiful
    • 67
      Graphs are interactive
    • 57
      Free
    • 56
      Easy
    • 33
      Nicer than the Graphite web interface
    • 24
      Many integrations
    • 17
      Can build dashboards
    • 10
      Easy to specify time window
    • 9
      Dashboards contain number tiles
    • 8
      Can collaborate on dashboards
    • 5
      Open Source
    • 5
      Click and drag to zoom in
    • 5
      Integration with InfluxDB
    • 4
      Authentification and users management
    • 4
      Threshold limits in graphs
    • 3
      It is open to cloud watch and many database
    • 3
      Simple and native support to Prometheus
    • 2
      Great community support
    • 2
      Alerts
    • 2
      You can visualize real time data to put alerts
    • 2
      You can use this for development to check memcache
    • 0
      Grapsh as code
    • 0
      Plugin visualizationa
    CONS OF GRAFANA
    • 1
      No interactive query builder

    related Grafana posts

    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 3.1M 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 · | 14 upvotes · 209.8K 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
    Kibana logo

    Kibana

    16.7K
    13.1K
    257
    Visualize your Elasticsearch data and navigate the Elastic Stack
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    PROS OF KIBANA
    • 88
      Easy to setup
    • 62
      Free
    • 45
      Can search text
    • 21
      Has pie chart
    • 13
      X-axis is not restricted to timestamp
    • 8
      Easy queries and is a good way to view logs
    • 6
      Supports Plugins
    • 3
      Dev Tools
    • 3
      More "user-friendly"
    • 3
      Can build dashboards
    • 2
      Easy to drill-down
    • 2
      Out-of-Box Dashboards/Analytics for Metrics/Heartbeat
    • 1
      Up and running
    CONS OF KIBANA
    • 5
      Unintuituve
    • 3
      Elasticsearch is huge
    • 3
      Works on top of elastic only
    • 2
      Hardweight UI

    related Kibana posts

    Tymoteusz Paul
    Devops guy at X20X Development LTD · | 23 upvotes · 5.1M 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
    Patrick Sun
    Software Engineer at Stitch Fix · | 11 upvotes · 507.2K views

    Elasticsearch's built-in visualization tool, Kibana, is robust and the appropriate tool in many cases. However, it is geared specifically towards log exploration and time-series data, and we felt that its steep learning curve would impede adoption rate among data scientists accustomed to writing SQL. The solution was to create something that would replicate some of Kibana's essential functionality while hiding Elasticsearch's complexity behind SQL-esque labels and terminology ("table" instead of "index", "group by" instead of "sub-aggregation") in the UI.

    Elasticsearch's API is really well-suited for aggregating time-series data, indexing arbitrary data without defining a schema, and creating dashboards. For the purpose of a data exploration backend, Elasticsearch fits the bill really well. Users can send an HTTP request with aggregations and sub-aggregations to an index with millions of documents and get a response within seconds, thus allowing them to rapidly iterate through their data.

    See more
    Nagios logo

    Nagios

    803
    957
    102
    Complete monitoring and alerting for servers, switches, applications, and services
    803
    957
    + 1
    102
    PROS OF NAGIOS
    • 53
      It just works
    • 28
      The standard
    • 12
      Customizable
    • 8
      The Most flexible monitoring system
    • 1
      Huge stack of free checks/plugins to choose from
    CONS OF NAGIOS
      Be the first to leave a con

      related Nagios posts

      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 3.1M 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
      Shared insights
      on
      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.

      See more