Alternatives to RethinkDB logo

Alternatives to RethinkDB

MongoDB, CouchDB, CockroachDB, Couchbase, and Firebase are the most popular alternatives and competitors to RethinkDB.
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What is RethinkDB and what are its top alternatives?

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.
RethinkDB is a tool in the Databases category of a tech stack.
RethinkDB is an open source tool with 26.6K GitHub stars and 1.9K GitHub forks. Here’s a link to RethinkDB's open source repository on GitHub

Top Alternatives to RethinkDB

  • MongoDB
    MongoDB

    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding. ...

  • CouchDB
    CouchDB

    Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript. ...

  • 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. ...

  • Couchbase
    Couchbase

    Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands. ...

  • Firebase
    Firebase

    Firebase is a cloud service designed to power real-time, collaborative applications. Simply add the Firebase library to your application to gain access to a shared data structure; any changes you make to that data are automatically synchronized with the Firebase cloud and with other clients within milliseconds. ...

  • Redis
    Redis

    Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams. ...

  • 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. ...

  • PostgreSQL
    PostgreSQL

    PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions. ...

RethinkDB alternatives & related posts

MongoDB logo

MongoDB

91.9K
79.3K
4.1K
The database for giant ideas
91.9K
79.3K
+ 1
4.1K
PROS OF MONGODB
  • 827
    Document-oriented storage
  • 593
    No sql
  • 553
    Ease of use
  • 464
    Fast
  • 410
    High performance
  • 257
    Free
  • 218
    Open source
  • 180
    Flexible
  • 145
    Replication & high availability
  • 112
    Easy to maintain
  • 42
    Querying
  • 39
    Easy scalability
  • 38
    Auto-sharding
  • 37
    High availability
  • 31
    Map/reduce
  • 27
    Document database
  • 25
    Easy setup
  • 25
    Full index support
  • 16
    Reliable
  • 15
    Fast in-place updates
  • 14
    Agile programming, flexible, fast
  • 12
    No database migrations
  • 8
    Easy integration with Node.Js
  • 8
    Enterprise
  • 6
    Enterprise Support
  • 5
    Great NoSQL DB
  • 4
    Support for many languages through different drivers
  • 3
    Schemaless
  • 3
    Aggregation Framework
  • 3
    Drivers support is good
  • 2
    Fast
  • 2
    Managed service
  • 2
    Easy to Scale
  • 2
    Awesome
  • 2
    Consistent
  • 1
    Good GUI
  • 1
    Acid Compliant
CONS OF MONGODB
  • 6
    Very slowly for connected models that require joins
  • 3
    Not acid compliant
  • 1
    Proprietary query language

related MongoDB posts

Shared insights
on
Node.jsNode.jsGraphQLGraphQLMongoDBMongoDB

I just finished the very first version of my new hobby project: #MovieGeeks. It is a minimalist online movie catalog for you to save the movies you want to see and for rating the movies you already saw. This is just the beginning as I am planning to add more features on the lines of sharing and discovery

For the #BackEnd I decided to use Node.js , GraphQL and MongoDB:

  1. Node.js has a huge community so it will always be a safe choice in terms of libraries and finding solutions to problems you may have

  2. GraphQL because I needed to improve my skills with it and because I was never comfortable with the usual REST approach. I believe GraphQL is a better option as it feels more natural to write apis, it improves the development velocity, by definition it fixes the over-fetching and under-fetching problem that is so common on REST apis, and on top of that, the community is getting bigger and bigger.

  3. MongoDB was my choice for the database as I already have a lot of experience working on it and because, despite of some bad reputation it has acquired in the last months, I still believe it is a powerful database for at least a very long list of use cases such as the one I needed for my website

See more
Vaibhav Taunk
Team Lead at Technovert · | 31 upvotes · 3.9M views

I am starting to become a full-stack developer, by choosing and learning .NET Core for API Development, Angular CLI / React for UI Development, MongoDB for database, as it a NoSQL DB and Flutter / React Native for Mobile App Development. Using Postman, Markdown and Visual Studio Code for development.

See more
CouchDB logo

CouchDB

498
573
139
HTTP + JSON document database with Map Reduce views and peer-based replication
498
573
+ 1
139
PROS OF COUCHDB
  • 43
    JSON
  • 30
    Open source
  • 18
    Highly available
  • 12
    Partition tolerant
  • 11
    Eventual consistency
  • 7
    Sync
  • 5
    REST API
  • 4
    Attachments mechanism to docs
  • 4
    Multi master replication
  • 3
    Changes feed
  • 1
    REST interface
  • 1
    js- and erlang-views
CONS OF COUCHDB
    Be the first to leave a con

    related CouchDB posts

    Jonathan Pugh
    Software Engineer / Project Manager / Technical Architect · | 25 upvotes · 2.9M views

    I needed to choose a full stack of tools for cross platform mobile application design & development. After much research and trying different tools, these are what I came up with that work for me today:

    For the client coding I chose Framework7 because of its performance, easy learning curve, and very well designed, beautiful UI widgets. I think it's perfect for solo development or small teams. I didn't like React Native. It felt heavy to me and rigid. Framework7 allows the use of #CSS3, which I think is the best technology to come out of the #WWW movement. No other tech has been able to allow designers and developers to develop such flexible, high performance, customisable user interface elements that are highly responsive and hardware accelerated before. Now #CSS3 includes variables and flexboxes it is truly a powerful language and there is no longer a need for preprocessors such as #SCSS / #Sass / #less. React Native contains a very limited interpretation of #CSS3 which I found very frustrating after using #CSS3 for some years already and knowing its powerful features. The other very nice feature of Framework7 is that you can even build for the browser if you want your app to be available for desktop web browsers. The latest release also includes the ability to build for #Electron so you can have MacOS, Windows and Linux desktop apps. This is not possible with React Native yet.

    Framework7 runs on top of Apache Cordova. Cordova and webviews have been slated as being slow in the past. Having a game developer background I found the tweeks to make it run as smooth as silk. One of those tweeks is to use WKWebView. Another important one was using srcset on images.

    I use #Template7 for the for the templating system which is a no-nonsense mobile-centric #HandleBars style extensible templating system. It's easy to write custom helpers for, is fast and has a small footprint. I'm not forced into a new paradigm or learning some new syntax. It operates with standard JavaScript, HTML5 and CSS 3. It's written by the developer of Framework7 and so dovetails with it as expected.

    I configured TypeScript to work with the latest version of Framework7. I consider TypeScript to be one of the best creations to come out of Microsoft in some time. They must have an amazing team working on it. It's very powerful and flexible. It helps you catch a lot of bugs and also provides code completion in supporting IDEs. So for my IDE I use Visual Studio Code which is a blazingly fast and silky smooth editor that integrates seamlessly with TypeScript for the ultimate type checking setup (both products are produced by Microsoft).

    I use Webpack and Babel to compile the JavaScript. TypeScript can compile to JavaScript directly but Babel offers a few more options and polyfills so you can use the latest (and even prerelease) JavaScript features today and compile to be backwards compatible with virtually any browser. My favorite recent addition is "optional chaining" which greatly simplifies and increases readability of a number of sections of my code dealing with getting and setting data in nested objects.

    I use some Ruby scripts to process images with ImageMagick and pngquant to optimise for size and even auto insert responsive image code into the HTML5. Ruby is the ultimate cross platform scripting language. Even as your scripts become large, Ruby allows you to refactor your code easily and make it Object Oriented if necessary. I find it the quickest and easiest way to maintain certain aspects of my build process.

    For the user interface design and prototyping I use Figma. Figma has an almost identical user interface to #Sketch but has the added advantage of being cross platform (MacOS and Windows). Its real-time collaboration features are outstanding and I use them a often as I work mostly on remote projects. Clients can collaborate in real-time and see changes I make as I make them. The clickable prototyping features in Figma are also very well designed and mean I can send clickable prototypes to clients to try user interface updates as they are made and get immediate feedback. I'm currently also evaluating the latest version of #AdobeXD as an alternative to Figma as it has the very cool auto-animate feature. It doesn't have real-time collaboration yet, but I heard it is proposed for 2019.

    For the UI icons I use Font Awesome Pro. They have the largest selection and best looking icons you can find on the internet with several variations in styles so you can find most of the icons you want for standard projects.

    For the backend I was using the #GraphCool Framework. As I later found out, #GraphQL still has some way to go in order to provide the full power of a mature graph query language so later in my project I ripped out #GraphCool and replaced it with CouchDB and Pouchdb. Primarily so I could provide good offline app support. CouchDB with Pouchdb is very flexible and efficient combination and overcomes some of the restrictions I found in #GraphQL and hence #GraphCool also. The most impressive and important feature of CouchDB is its replication. You can configure it in various ways for backups, fault tolerance, caching or conditional merging of databases. CouchDB and Pouchdb even supports storing, retrieving and serving binary or image data or other mime types. This removes a level of complexity usually present in database implementations where binary or image data is usually referenced through an #HTML5 link. With CouchDB and Pouchdb apps can operate offline and sync later, very efficiently, when the network connection is good.

    I use PhoneGap when testing the app. It auto-reloads your app when its code is changed and you can also install it on Android phones to preview your app instantly. iOS is a bit more tricky cause of Apple's policies so it's not available on the App Store, but you can build it and install it yourself to your device.

    So that's my latest mobile stack. What tools do you use? Have you tried these ones?

    See more
    Gabriel Pa

    We implemented our first large scale EPR application from naologic.com using CouchDB .

    Very fast, replication works great, doesn't consume much RAM, queries are blazing fast but we found a problem: the queries were very hard to write, it took a long time to figure out the API, we had to go and write our own @nodejs library to make it work properly.

    It lost most of its support. Since then, we migrated to Couchbase and the learning curve was steep but all worth it. Memcached indexing out of the box, full text search works great.

    See more
    CockroachDB logo

    CockroachDB

    208
    331
    0
    A distributed SQL database that scales fast, survives disaster, and thrives everywhere
    208
    331
    + 1
    0
    PROS OF COCKROACHDB
      Be the first to leave a pro
      CONS OF COCKROACHDB
        Be the first to leave a con

        related CockroachDB posts

        Lucas Litton
        Founder & CEO at Macombey · | 1 upvote · 11K views

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

        See more
        Couchbase logo

        Couchbase

        471
        600
        110
        Document-Oriented NoSQL Database
        471
        600
        + 1
        110
        PROS OF COUCHBASE
        • 18
          High performance
        • 18
          Flexible data model, easy scalability, extremely fast
        • 9
          Mobile app support
        • 7
          You can query it with Ansi-92 SQL
        • 6
          All nodes can be read/write
        • 5
          Equal nodes in cluster, allowing fast, flexible changes
        • 5
          Both a key-value store and document (JSON) db
        • 5
          Open source, community and enterprise editions
        • 4
          Automatic configuration of sharding
        • 4
          Local cache capability
        • 3
          Easy setup
        • 3
          Linearly scalable, useful to large number of tps
        • 3
          Easy cluster administration
        • 3
          Cross data center replication
        • 3
          SDKs in popular programming languages
        • 3
          Elasticsearch connector
        • 3
          Web based management, query and monitoring panel
        • 2
          Map reduce views
        • 2
          DBaaS available
        • 2
          NoSQL
        • 1
          Buckets, Scopes, Collections & Documents
        • 1
          FTS + SQL together
        CONS OF COUCHBASE
        • 3
          Terrible query language

        related Couchbase posts

        Gabriel Pa

        We implemented our first large scale EPR application from naologic.com using CouchDB .

        Very fast, replication works great, doesn't consume much RAM, queries are blazing fast but we found a problem: the queries were very hard to write, it took a long time to figure out the API, we had to go and write our own @nodejs library to make it work properly.

        It lost most of its support. Since then, we migrated to Couchbase and the learning curve was steep but all worth it. Memcached indexing out of the box, full text search works great.

        See more
        Ilias Mentzelos
        Software Engineer at Plum Fintech · | 9 upvotes · 232.5K views
        Shared insights
        on
        MongoDBMongoDBCouchbaseCouchbase

        Hey, we want to build a referral campaign mechanism that will probably contain millions of records within the next few years. We want fast read access based on IDs or some indexes, and isolation is crucial as some listeners will try to update the same document at the same time. What's your suggestion between Couchbase and MongoDB? Thanks!

        See more
        Firebase logo

        Firebase

        40.2K
        34.5K
        2K
        The Realtime App Platform
        40.2K
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        PROS OF FIREBASE
        • 371
          Realtime backend made easy
        • 270
          Fast and responsive
        • 242
          Easy setup
        • 215
          Real-time
        • 191
          JSON
        • 134
          Free
        • 128
          Backed by google
        • 83
          Angular adaptor
        • 68
          Reliable
        • 36
          Great customer support
        • 32
          Great documentation
        • 25
          Real-time synchronization
        • 21
          Mobile friendly
        • 18
          Rapid prototyping
        • 14
          Great security
        • 12
          Automatic scaling
        • 11
          Freakingly awesome
        • 8
          Chat
        • 8
          Angularfire is an amazing addition!
        • 8
          Super fast development
        • 6
          Built in user auth/oauth
        • 6
          Firebase hosting
        • 6
          Ios adaptor
        • 6
          Awesome next-gen backend
        • 4
          Speed of light
        • 4
          Very easy to use
        • 3
          Great
        • 3
          It's made development super fast
        • 3
          Brilliant for startups
        • 2
          Free hosting
        • 2
          Cloud functions
        • 2
          JS Offline and Sync suport
        • 2
          Low battery consumption
        • 2
          .net
        • 2
          The concurrent updates create a great experience
        • 2
          Push notification
        • 2
          I can quickly create static web apps with no backend
        • 2
          Great all-round functionality
        • 2
          Free authentication solution
        • 1
          Easy Reactjs integration
        • 1
          Google's support
        • 1
          Free SSL
        • 1
          CDN & cache out of the box
        • 1
          Easy to use
        • 1
          Large
        • 1
          Faster workflow
        • 1
          Serverless
        • 1
          Good Free Limits
        • 1
          Simple and easy
        CONS OF FIREBASE
        • 31
          Can become expensive
        • 16
          No open source, you depend on external company
        • 15
          Scalability is not infinite
        • 9
          Not Flexible Enough
        • 7
          Cant filter queries
        • 3
          Very unstable server
        • 3
          No Relational Data
        • 2
          Too many errors
        • 2
          No offline sync

        related Firebase posts

        Johnny Bell

        I was building a personal project that I needed to store items in a real time database. I am more comfortable with my Frontend skills than my backend so I didn't want to spend time building out anything in Ruby or Go.

        I stumbled on Firebase by #Google, and it was really all I needed. It had realtime data, an area for storing file uploads and best of all for the amount of data I needed it was free!

        I built out my application using tools I was familiar with, React for the framework, Redux.js to manage my state across components, and styled-components for the styling.

        Now as this was a project I was just working on in my free time for fun I didn't really want to pay for hosting. I did some research and I found Netlify. I had actually seen them at #ReactRally the year before and deployed a Gatsby site to Netlify already.

        Netlify was very easy to setup and link to my GitHub account you select a repo and pretty much with very little configuration you have a live site that will deploy every time you push to master.

        With the selection of these tools I was able to build out my application, connect it to a realtime database, and deploy to a live environment all with $0 spent.

        If you're looking to build out a small app I suggest giving these tools a go as you can get your idea out into the real world for absolutely no cost.

        See more
        Collins Ogbuzuru
        Front-end dev at Evolve credit · | 16 upvotes · 12.7K views

        Your tech stack is solid for building a real-time messaging project.

        React and React Native are excellent choices for the frontend, especially if you want to have both web and mobile versions of your application share code.

        ExpressJS is an unopinionated framework that affords you the flexibility to use it's features at your term, which is a good start. However, I would recommend you explore Sails.js as well. Sails.js is built on top of Express.js and it provides additional features out of the box, especially the Websocket integration that your project requires.

        Don't forget to set up Graphql codegen, this would improve your dev experience (Add Typescript, if you can too).

        I don't know much about databases but you might want to consider using NO-SQL. I used Firebase real-time db and aws dynamo db on a few of my personal projects and I love they're easy to work with and offer more flexibility for a chat application.

        See more
        Redis logo

        Redis

        58.4K
        45K
        3.9K
        Open source (BSD licensed), in-memory data structure store
        58.4K
        45K
        + 1
        3.9K
        PROS OF REDIS
        • 886
          Performance
        • 542
          Super fast
        • 513
          Ease of use
        • 444
          In-memory cache
        • 324
          Advanced key-value cache
        • 194
          Open source
        • 182
          Easy to deploy
        • 164
          Stable
        • 155
          Free
        • 121
          Fast
        • 42
          High-Performance
        • 40
          High Availability
        • 35
          Data Structures
        • 32
          Very Scalable
        • 24
          Replication
        • 22
          Great community
        • 22
          Pub/Sub
        • 19
          "NoSQL" key-value data store
        • 16
          Hashes
        • 13
          Sets
        • 11
          Sorted Sets
        • 10
          NoSQL
        • 10
          Lists
        • 9
          Async replication
        • 9
          BSD licensed
        • 8
          Bitmaps
        • 8
          Integrates super easy with Sidekiq for Rails background
        • 7
          Keys with a limited time-to-live
        • 7
          Open Source
        • 6
          Lua scripting
        • 6
          Strings
        • 5
          Awesomeness for Free
        • 5
          Hyperloglogs
        • 4
          Transactions
        • 4
          Outstanding performance
        • 4
          Runs server side LUA
        • 4
          LRU eviction of keys
        • 4
          Feature Rich
        • 4
          Written in ANSI C
        • 4
          Networked
        • 3
          Data structure server
        • 3
          Performance & ease of use
        • 2
          Dont save data if no subscribers are found
        • 2
          Automatic failover
        • 2
          Easy to use
        • 2
          Temporarily kept on disk
        • 2
          Scalable
        • 2
          Existing Laravel Integration
        • 2
          Channels concept
        • 2
          Object [key/value] size each 500 MB
        • 2
          Simple
        CONS OF REDIS
        • 15
          Cannot query objects directly
        • 3
          No secondary indexes for non-numeric data types
        • 1
          No WAL

        related Redis posts

        Russel Werner
        Lead Engineer at StackShare · | 32 upvotes · 2.2M views

        StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.

        Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!

        #StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit

        See more
        Simon Reymann
        Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.3M 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
        Cassandra logo

        Cassandra

        3.5K
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        507
        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
        • 74
          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

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        Simon Reymann
        Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.3M 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.
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        Jeyabalaji Subramanian

        Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

        We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

        Based on the above criteria, we selected the following tools to perform the end to end data replication:

        We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

        We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

        In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

        Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

        In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

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