Alternatives to Citus logo

Alternatives to Citus

TimescaleDB, CockroachDB, Apache Aurora, Cassandra, and Vitess are the most popular alternatives and competitors to Citus.
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What is Citus and what are its top alternatives?

It's an extension to Postgres that distributes data and queries in a cluster of multiple machines. Its query engine parallelizes incoming SQL queries across these servers to enable human real-time (less than a second) responses on large datasets.
Citus is a tool in the Databases category of a tech stack.
Citus is an open source tool with 10.1K GitHub stars and 652 GitHub forks. Here’s a link to Citus's open source repository on GitHub

Top Alternatives to Citus

  • 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. ...

  • CockroachDB
    CockroachDB

    CockroachDB is distributed SQL database that can be deployed in serverless, dedicated, or on-prem. Elastic scale, multi-active availability for resilience, and low latency performance. ...

  • Apache Aurora
    Apache Aurora

    Apache Aurora is a service scheduler that runs on top of Mesos, enabling you to run long-running services that take advantage of Mesos' scalability, fault-tolerance, and resource isolation. ...

  • Cassandra
    Cassandra

    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL. ...

  • Vitess
    Vitess

    It is a database solution for deploying, scaling and managing large clusters of MySQL instances. It’s architected to run as effectively in a public or private cloud architecture as it does on dedicated hardware. It combines and extends many important MySQL features with the scalability of a NoSQL database. ...

  • Clickhouse
    Clickhouse

    It allows analysis of data that is updated in real time. It offers instant results in most cases: the data is processed faster than it takes to create a query. ...

  • JavaScript
    JavaScript

    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...

  • Git
    Git

    Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. ...

Citus alternatives & related 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
  • 9
    Open source
  • 8
    Easy Query Language
  • 7
    Time-series data analysis
  • 5
    Established postgresql API and support
  • 4
    Reliable
  • 2
    Paid support for automatic Retention Policy
  • 2
    Chunk-based compression
  • 2
    Postgres integration
  • 2
    High-performance
  • 2
    Fast and scalable
  • 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
CockroachDB logo

CockroachDB

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A distributed SQL database that scales fast, survives disaster, and thrives everywhere
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PROS OF COCKROACHDB
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    CONS OF COCKROACHDB
      Be the first to leave a con

      related CockroachDB posts

      Lucas Litton
      Founder & CEO at Macombey · | 1 upvote · 11.7K views

      Just like Go, our team. uses CockroachDB because of the speed and the great integrations with Go and Node.js.

      See more
      Apache Aurora logo

      Apache Aurora

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      An Apcahe Mesos framework for scheduling jobs, originally developed by Twitter
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      PROS OF APACHE AURORA
        Be the first to leave a pro
        CONS OF APACHE AURORA
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          related Apache Aurora posts

          Docker containers on Mesos run their microservices with consistent configurations at scale, along with Aurora for long-running services and cron jobs.

          See more
          Cassandra logo

          Cassandra

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          A partitioned row store. Rows are organized into tables with a required primary key.
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          PROS OF CASSANDRA
          • 119
            Distributed
          • 98
            High performance
          • 81
            High availability
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            Easy scalability
          • 53
            Replication
          • 26
            Reliable
          • 26
            Multi datacenter deployments
          • 10
            Schema optional
          • 9
            OLTP
          • 8
            Open source
          • 2
            Workload separation (via MDC)
          • 1
            Fast
          CONS OF CASSANDRA
          • 3
            Reliability of replication
          • 1
            Size
          • 1
            Updates

          related Cassandra posts

          Thierry Schellenbach
          Shared insights
          on
          GolangGolangPythonPythonCassandraCassandra
          at

          After years of optimizing our existing feed technology, we decided to make a larger leap with 2.0 of Stream. While the first iteration of Stream was powered by Python and Cassandra, for Stream 2.0 of our infrastructure we switched to Go.

          The main reason why we switched from Python to Go is performance. Certain features of Stream such as aggregation, ranking and serialization were very difficult to speed up using Python.

          We’ve been using Go since March 2017 and it’s been a great experience so far. Go has greatly increased the productivity of our development team. Not only has it improved the speed at which we develop, it’s also 30x faster for many components of Stream. Initially we struggled a bit with package management for Go. However, using Dep together with the VG package contributed to creating a great workflow.

          Go as a language is heavily focused on performance. The built-in PPROF tool is amazing for finding performance issues. Uber’s Go-Torch library is great for visualizing data from PPROF and will be bundled in PPROF in Go 1.10.

          The performance of Go greatly influenced our architecture in a positive way. With Python we often found ourselves delegating logic to the database layer purely for performance reasons. The high performance of Go gave us more flexibility in terms of architecture. This led to a huge simplification of our infrastructure and a dramatic improvement of latency. For instance, we saw a 10 to 1 reduction in web-server count thanks to the lower memory and CPU usage for the same number of requests.

          #DataStores #Databases

          See more
          Thierry Schellenbach
          Shared insights
          on
          RedisRedisCassandraCassandraRocksDBRocksDB
          at

          1.0 of Stream leveraged Cassandra for storing the feed. Cassandra is a common choice for building feeds. Instagram, for instance started, out with Redis but eventually switched to Cassandra to handle their rapid usage growth. Cassandra can handle write heavy workloads very efficiently.

          Cassandra is a great tool that allows you to scale write capacity simply by adding more nodes, though it is also very complex. This complexity made it hard to diagnose performance fluctuations. Even though we had years of experience with running Cassandra, it still felt like a bit of a black box. When building Stream 2.0 we decided to go for a different approach and build Keevo. Keevo is our in-house key-value store built upon RocksDB, gRPC and Raft.

          RocksDB is a highly performant embeddable database library developed and maintained by Facebook’s data engineering team. RocksDB started as a fork of Google’s LevelDB that introduced several performance improvements for SSD. Nowadays RocksDB is a project on its own and is under active development. It is written in C++ and it’s fast. Have a look at how this benchmark handles 7 million QPS. In terms of technology it’s much more simple than Cassandra.

          This translates into reduced maintenance overhead, improved performance and, most importantly, more consistent performance. It’s interesting to note that LinkedIn also uses RocksDB for their feed.

          #InMemoryDatabases #DataStores #Databases

          See more
          Vitess logo

          Vitess

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          A database clustering system for horizontal scaling of MySQL
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          PROS OF VITESS
            Be the first to leave a pro
            CONS OF VITESS
              Be the first to leave a con

              related Vitess posts

              Shared insights
              on
              MySQLMySQLVitessVitess
              at

              They're critical to the business data and operated by an ecosystem of tools. But once the tools have been used, it was important to verify that the data remains as expected at all times. Even with the best efforts to prevent errors, inconsistencies are bound to creep at any stage. In order to test the code in a comprehensive manner, Slack developed a structure known as a consistency check framework.

              This is a responsive and personalized framework that can meaningfully analyze and report on your data with a number of proactive and reactive benefits. This framework is important because it can help with repair and recovery from an outage or bug, it can help ensure effective data migration through scripts that test the code post-migration, and find bugs throughout the database. This framework helped prevent duplication and identifies the canonical code in each case, running as reusable code.

              The framework was created by creating generic versions of the scanning and reporting code and an interface for the checking code. The checks could be run from the command line and either a single team could be scanned or the whole system. The process was improved over time to further customize the checks and make them more specific. In order to make this framework accessible to everyone, a GUI was added and connected to the internal administrative system. The framework was also modified to include code that can fix certain problems, while others are left for manual intervention. For Slack, such a tool proved extremely beneficial in ensuring data integrity both internally and externally.

              See more
              Clickhouse logo

              Clickhouse

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              A column-oriented database management system
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              PROS OF CLICKHOUSE
              • 19
                Fast, very very fast
              • 11
                Good compression ratio
              • 6
                Horizontally scalable
              • 5
                Great CLI
              • 5
                Utilizes all CPU resources
              • 5
                RESTful
              • 4
                Buggy
              • 4
                Open-source
              • 4
                Great number of SQL functions
              • 3
                Server crashes its normal :(
              • 3
                Has no transactions
              • 2
                Flexible connection options
              • 2
                Highly available
              • 2
                ODBC
              • 2
                Flexible compression options
              • 1
                In IDEA data import via HTTP interface not working
              CONS OF CLICKHOUSE
              • 5
                Slow insert operations

              related Clickhouse posts

              JavaScript logo

              JavaScript

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              Lightweight, interpreted, object-oriented language with first-class functions
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              PROS OF JAVASCRIPT
              • 1.7K
                Can be used on frontend/backend
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                It's everywhere
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                Lots of great frameworks
              • 897
                Fast
              • 745
                Light weight
              • 425
                Flexible
              • 392
                You can't get a device today that doesn't run js
              • 286
                Non-blocking i/o
              • 237
                Ubiquitousness
              • 191
                Expressive
              • 55
                Extended functionality to web pages
              • 49
                Relatively easy language
              • 46
                Executed on the client side
              • 30
                Relatively fast to the end user
              • 25
                Pure Javascript
              • 21
                Functional programming
              • 15
                Async
              • 13
                Full-stack
              • 12
                Setup is easy
              • 12
                Future Language of The Web
              • 12
                Its everywhere
              • 11
                Because I love functions
              • 11
                JavaScript is the New PHP
              • 10
                Like it or not, JS is part of the web standard
              • 9
                Expansive community
              • 9
                Everyone use it
              • 9
                Can be used in backend, frontend and DB
              • 9
                Easy
              • 8
                Most Popular Language in the World
              • 8
                Powerful
              • 8
                Can be used both as frontend and backend as well
              • 8
                For the good parts
              • 8
                No need to use PHP
              • 8
                Easy to hire developers
              • 7
                Agile, packages simple to use
              • 7
                Love-hate relationship
              • 7
                Photoshop has 3 JS runtimes built in
              • 7
                Evolution of C
              • 7
                It's fun
              • 7
                Hard not to use
              • 7
                Versitile
              • 7
                Its fun and fast
              • 7
                Nice
              • 7
                Popularized Class-Less Architecture & Lambdas
              • 7
                Supports lambdas and closures
              • 6
                It let's me use Babel & Typescript
              • 6
                Can be used on frontend/backend/Mobile/create PRO Ui
              • 6
                1.6K Can be used on frontend/backend
              • 6
                Client side JS uses the visitors CPU to save Server Res
              • 6
                Easy to make something
              • 5
                Clojurescript
              • 5
                Promise relationship
              • 5
                Stockholm Syndrome
              • 5
                Function expressions are useful for callbacks
              • 5
                Scope manipulation
              • 5
                Everywhere
              • 5
                Client processing
              • 5
                What to add
              • 4
                Because it is so simple and lightweight
              • 4
                Only Programming language on browser
              • 1
                Test
              • 1
                Hard to learn
              • 1
                Test2
              • 1
                Not the best
              • 1
                Easy to understand
              • 1
                Subskill #4
              • 1
                Easy to learn
              • 0
                Hard 彤
              CONS OF JAVASCRIPT
              • 22
                A constant moving target, too much churn
              • 20
                Horribly inconsistent
              • 15
                Javascript is the New PHP
              • 9
                No ability to monitor memory utilitization
              • 8
                Shows Zero output in case of ANY error
              • 7
                Thinks strange results are better than errors
              • 6
                Can be ugly
              • 3
                No GitHub
              • 2
                Slow
              • 0
                HORRIBLE DOCUMENTS, faulty code, repo has bugs

              related JavaScript posts

              Zach Holman

              Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.

              But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.

              But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.

              Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.

              See more
              Conor Myhrvold
              Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 11.2M views

              How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

              Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

              Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

              https://eng.uber.com/distributed-tracing/

              (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

              Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

              See more
              Git logo

              Git

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              6.6K
              Fast, scalable, distributed revision control system
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              PROS OF GIT
              • 1.4K
                Distributed version control system
              • 1.1K
                Efficient branching and merging
              • 959
                Fast
              • 845
                Open source
              • 726
                Better than svn
              • 368
                Great command-line application
              • 306
                Simple
              • 291
                Free
              • 232
                Easy to use
              • 222
                Does not require server
              • 27
                Distributed
              • 22
                Small & Fast
              • 18
                Feature based workflow
              • 15
                Staging Area
              • 13
                Most wide-spread VSC
              • 11
                Role-based codelines
              • 11
                Disposable Experimentation
              • 7
                Frictionless Context Switching
              • 6
                Data Assurance
              • 5
                Efficient
              • 4
                Just awesome
              • 3
                Github integration
              • 3
                Easy branching and merging
              • 2
                Compatible
              • 2
                Flexible
              • 2
                Possible to lose history and commits
              • 1
                Rebase supported natively; reflog; access to plumbing
              • 1
                Light
              • 1
                Team Integration
              • 1
                Fast, scalable, distributed revision control system
              • 1
                Easy
              • 1
                Flexible, easy, Safe, and fast
              • 1
                CLI is great, but the GUI tools are awesome
              • 1
                It's what you do
              • 0
                Phinx
              CONS OF GIT
              • 16
                Hard to learn
              • 11
                Inconsistent command line interface
              • 9
                Easy to lose uncommitted work
              • 7
                Worst documentation ever possibly made
              • 5
                Awful merge handling
              • 3
                Unexistent preventive security flows
              • 3
                Rebase hell
              • 2
                When --force is disabled, cannot rebase
              • 2
                Ironically even die-hard supporters screw up badly
              • 1
                Doesn't scale for big data

              related Git posts

              Simon Reymann
              Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 10M views

              Our whole DevOps stack consists of the following tools:

              • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
              • Respectively Git as revision control system
              • SourceTree as Git GUI
              • Visual Studio Code as IDE
              • CircleCI for continuous integration (automatize development process)
              • Prettier / TSLint / ESLint as code linter
              • SonarQube as quality gate
              • Docker as container management (incl. Docker Compose for multi-container application management)
              • VirtualBox for operating system simulation tests
              • Kubernetes as cluster management for docker containers
              • Heroku for deploying in test environments
              • nginx as web server (preferably used as facade server in production environment)
              • SSLMate (using OpenSSL) for certificate management
              • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
              • PostgreSQL as preferred database system
              • Redis as preferred in-memory database/store (great for caching)

              The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

              • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
              • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
              • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
              • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
              • Scalability: All-in-one framework for distributed systems.
              • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
              See more
              Tymoteusz Paul
              Devops guy at X20X Development LTD · | 23 upvotes · 8.9M 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