Alternatives to Sequelize logo

Alternatives to Sequelize

Marvel, Sequel, TypeORM, Mongoose, and Prisma are the most popular alternatives and competitors to Sequelize.
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1.4K
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What is Sequelize and what are its top alternatives?

Sequelize is a promise-based ORM for Node.js and io.js. It supports the dialects PostgreSQL, MySQL, MariaDB, SQLite and MSSQL and features solid transaction support, relations, read replication and more.
Sequelize is a tool in the Object Relational Mapper (ORM) category of a tech stack.
Sequelize is an open source tool with 29.6K GitHub stars and 4.3K GitHub forks. Here’s a link to Sequelize's open source repository on GitHub

Top Alternatives to Sequelize

  • Marvel
    Marvel

    A super simple tool that turns any image (including PSDs) or sketch into interactive prototypes for any device. Powered by Dropbox. ...

  • Sequel
    Sequel

    It is a simple, flexible, and powerful SQL database access toolkit for Ruby. It includes a comprehensive ORM layer for mapping records to Ruby objects and handling associated records. ...

  • TypeORM
    TypeORM

    It supports both Active Record and Data Mapper patterns, unlike all other JavaScript ORMs currently in existence, which means you can write high quality, loosely coupled, scalable, maintainable applications the most productive way. ...

  • Mongoose
    Mongoose

    Let's face it, writing MongoDB validation, casting and business logic boilerplate is a drag. That's why we wrote Mongoose. Mongoose provides a straight-forward, schema-based solution to modeling your application data and includes built-in type casting, validation, query building, business logic hooks and more, out of the box. ...

  • Prisma
    Prisma

    Prisma is an open-source database toolkit. It replaces traditional ORMs and makes database access easy with an auto-generated query builder for TypeScript & Node.js. ...

  • 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. ...

  • 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. ...

  • 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. ...

Sequelize alternatives & related posts

Marvel logo

Marvel

187
46
Prototyping for everyone
187
46
PROS OF MARVEL
  • 15
    Nice UI
  • 14
    Free
  • 7
    Easy animations
  • 5
    Iphone app
  • 5
    Dropbox integration
CONS OF MARVEL
    Be the first to leave a con

    related Marvel posts

    Sequel logo

    Sequel

    22
    0
    The database toolkit for Ruby
    22
    0
    PROS OF SEQUEL
      Be the first to leave a pro
      CONS OF SEQUEL
        Be the first to leave a con

        related Sequel posts

        TypeORM logo

        TypeORM

        601
        81
        An ORM that can run in NodeJS and others
        601
        81
        PROS OF TYPEORM
        • 30
          Typescript
        • 12
          Supports MySQL, PostgreSQL, MariaDB, SQLite, MS SQL Ser
        • 9
          Easy setup
        • 9
          Cons of TypeORM
        • 7
          Promise Based
        • 7
          Works in NodeJS, Browser, Ionic, Cordova and Electron p
        • 6
          Support MySQL & MariaDB, PostgreSQL, MSSQL, Sqlite
        • 1
          Support MongoDB
        CONS OF TYPEORM
        • 5
          Completely abandoned by its creator
        • 3
          Too complex for what it does
        • 2
          Doesn't really support native javascript
        • 1
          Not proper/real type safety
        • 1
          Cannot use query on any relation

        related TypeORM posts

        Simon Reymann
        Senior Fullstack Developer at QUANTUSflow Software GmbH · | 27 upvotes · 5.1M views

        Our whole Node.js backend stack consists of the following tools:

        • Lerna as a tool for multi package and multi repository management
        • npm as package manager
        • NestJS as Node.js framework
        • TypeScript as programming language
        • ExpressJS as web server
        • Swagger UI for visualizing and interacting with the API’s resources
        • Postman as a tool for API development
        • TypeORM as object relational mapping layer
        • JSON Web Token for access token management

        The main reason we have chosen Node.js over PHP is related to the following artifacts:

        • Made for the web and widely in use: Node.js is a software platform for developing server-side network services. Well-known projects that rely on Node.js include the blogging software Ghost, the project management tool Trello and the operating system WebOS. Node.js requires the JavaScript runtime environment V8, which was specially developed by Google for the popular Chrome browser. This guarantees a very resource-saving architecture, which qualifies Node.js especially for the operation of a web server. Ryan Dahl, the developer of Node.js, released the first stable version on May 27, 2009. He developed Node.js out of dissatisfaction with the possibilities that JavaScript offered at the time. The basic functionality of Node.js has been mapped with JavaScript since the first version, which can be expanded with a large number of different modules. The current package managers (npm or Yarn) for Node.js know more than 1,000,000 of these modules.
        • Fast server-side solutions: Node.js adopts the JavaScript "event-loop" to create non-blocking I/O applications that conveniently serve simultaneous events. With the standard available asynchronous processing within JavaScript/TypeScript, highly scalable, server-side solutions can be realized. The efficient use of the CPU and the RAM is maximized and more simultaneous requests can be processed than with conventional multi-thread servers.
        • A language along the entire stack: Widely used frameworks such as React or AngularJS or Vue.js, which we prefer, are written in JavaScript/TypeScript. If Node.js is now used on the server side, you can use all the advantages of a uniform script language throughout the entire application development. The same language in the back- and frontend simplifies the maintenance of the application and also the coordination within the development team.
        • Flexibility: Node.js sets very few strict dependencies, rules and guidelines and thus grants a high degree of flexibility in application development. There are no strict conventions so that the appropriate architecture, design structures, modules and features can be freely selected for the development.
        See more
        Martin Johannesson
        Senior Software Developer at IT Minds · | 14 upvotes · 2.9M views

        At IT Minds we create customized internal or #B2B web and mobile apps. I have a go to stack that I pitch to our customers consisting of 3 core areas. 1) A data core #backend . 2) A micro #serverless #backend. 3) A user client #frontend.

        For the Data Core I create a backend using TypeScript Node.js and with TypeORM connecting to a PostgreSQL Exposing an action based api with Apollo GraphQL

        For the micro serverless backend, which purpose is verification for authentication, autorization, logins and the likes. It is created with Next.js api pages. Using MongoDB to store essential information, caching etc.

        Finally the frontend is built with React using Next.js , TypeScript and @Apollo. We create the frontend as a PWA and have a AMP landing page by default.

        See more
        Mongoose logo

        Mongoose

        2.1K
        56
        MongoDB object modeling designed to work in an asynchronous environment
        2.1K
        56
        PROS OF MONGOOSE
        • 17
          Several bad ideas mixed together
        • 17
          Well documented
        • 10
          JSON
        • 8
          Actually terrible documentation
        • 2
          Recommended and used by Valve. See steamworks docs
        • 1
          Can be used with passportjs for oauth
        • 1
          Yeah
        CONS OF MONGOOSE
        • 3
          Model middleware/hooks are not user friendly

        related Mongoose posts

        Repost

        Overview: To put it simply, we plan to use the MERN stack to build our web application. MongoDB will be used as our primary database. We will use ExpressJS alongside Node.js to set up our API endpoints. Additionally, we plan to use React to build our SPA on the client side and use Redis on the server side as our primary caching solution. Initially, while working on the project, we plan to deploy our server and client both on Heroku . However, Heroku is very limited and we will need the benefits of an Infrastructure as a Service so we will use Amazon EC2 to later deploy our final version of the application.

        Serverside: nodemon will allow us to automatically restart a running instance of our node app when files changes take place. We decided to use MongoDB because it is a non relational database which uses the Document Object Model. This allows a lot of flexibility as compared to a RDMS like SQL which requires a very structural model of data that does not change too much. Another strength of MongoDB is its ease in scalability. We will use Mongoose along side MongoDB to model our application data. Additionally, we will host our MongoDB cluster remotely on MongoDB Atlas. Bcrypt will be used to encrypt user passwords that will be stored in the DB. This is to avoid the risks of storing plain text passwords. Moreover, we will use Cloudinary to store images uploaded by the user. We will also use the Twilio SendGrid API to enable automated emails sent by our application. To protect private API endpoints, we will use JSON Web Token and Passport. Also, PayPal will be used as a payment gateway to accept payments from users.

        Client Side: As mentioned earlier, we will use React to build our SPA. React uses a virtual DOM which is very efficient in rendering a page. Also React will allow us to reuse components. Furthermore, it is very popular and there is a large community that uses React so it can be helpful if we run into issues. We also plan to make a cross platform mobile application later and using React will allow us to reuse a lot of our code with React Native. Redux will be used to manage state. Redux works great with React and will help us manage a global state in the app and avoid the complications of each component having its own state. Additionally, we will use Bootstrap components and custom CSS to style our app.

        Other: Git will be used for version control. During the later stages of our project, we will use Google Analytics to collect useful data regarding user interactions. Moreover, Slack will be our primary communication tool. Also, we will use Visual Studio Code as our primary code editor because it is very light weight and has a wide variety of extensions that will boost productivity. Postman will be used to interact with and debug our API endpoints.

        See more

        REST API for SaaS application

        I'm currently developing an Azure Functions REST API with TypeScript, tsoa, Mongoose, and Typegoose that contains simple CRUD activities. It does the job and has type-safety as well as the ability to generate OpenAPI specs for me.

        However, as the app scales up, there are more duplicated codes (for similar operations - like CRUD in each different model). It's also becoming more complex because I need to implement a multi-tenancy SaaS for both the API and the database.

        So I chose to implement a repository pattern, and I have a "feeling" that .NET and C# will make development easier because, unlike TypeScript, it includes native support for Dependency Injection and great things like LINQ.

        It wouldn't take much effort to migrate because I can easily translate interfaces and basic CRUD operations to C#. So, I'm looking for advice on whether it's worth converting from TypeScript to.NET.

        See more
        Prisma logo

        Prisma

        1.2K
        54
        Modern Database Access for TypeScript & Node.js
        1.2K
        54
        PROS OF PRISMA
        • 12
          Type-safe database access
        • 10
          Open Source
        • 8
          Auto-generated query builder
        • 6
          Supports multible database systems
        • 6
          Increases confidence during development
        • 4
          Built specifically for Postgres and TypeScript
        • 4
          Productive application development
        • 2
          Supports multible RDBMSs
        • 2
          Robust migrations system
        CONS OF PRISMA
        • 2
          Doesn't support downward/back migrations
        • 1
          Doesn't support JSONB
        • 1
          Do not support JSONB
        • 1
          Mutation of JSON is really confusing
        • 1
          Do not support JSONB

        related Prisma posts

        Divine Bawa
        at PayHub Ghana Limited · | 16 upvotes · 486.6K views

        I just finished a web app meant for a business that offers training programs for certain professional courses. I chose this stack to test out my skills in graphql and react. I used Node.js , GraphQL , MySQL for the #Backend utilizing Prisma as a database interface for MySQL to provide CRUD APIs and graphql-yoga as a server. For the #frontend I chose React, styled-components for styling, Next.js for routing and SSR and Apollo for data management. I really liked the outcome and I will definitely use this stack in future projects.

        See more
        Collins Ogbuzuru
        Front-end dev at Evolve credit · | 15 upvotes · 28K views
        Shared insights
        on
        GraphQLGraphQLPrismaPrismaAWS LambdaAWS Lambda

        We are starting to build one shirt data logic, structure and as an online clothing store we believe good ux and ui is a goal to drive a lot of click through. The problem is, how do we fetch data and how do we abstract the gap between the Front-end devs and backend-devs as we are just two in the technical unit. We decided to go for GraphQL as our application-layer tool and Prisma for our database-layer abstracter.

        Reasons :

        GraphQL :

        1. GraphQL makes fetching of data less painful and organised.

        2. GraphQL gives you 100% assurance on data you getting back as opposed to the Rest design .

        3. GraphQL comes with a bunch of real-time functionality in form of. subscriptions and finally because we are using React (GraphQL is not React demanding, it's doesn't require a specific framework, language or tool, but it definitely makes react apps fly )

        Prisma :

        1. Writing revolvers can be fun, but imagine writing revolvers nested deep down, curry braces flying around. This is sure a welcome note to bugs and as a small team we need to focus more on what that matters more. Prisma generates this necessary CRUD resolves, mutations and subscription out of the box.

        2. We don't really have much budget at the moment so we are going to run our logic in a scalable cheap and cost effective cloud environment. Oh! It's AWS Lambda and deploying our schema to Lambda is our best bet to minimize cost and same time scale.

        We are still at development stage and I believe, working on this start up will increase my dev knowledge. Off for Lunch :)

        See more
        MySQL logo

        MySQL

        125.4K
        3.8K
        The world's most popular open source database
        125.4K
        3.8K
        PROS OF MYSQL
        • 800
          Sql
        • 679
          Free
        • 562
          Easy
        • 528
          Widely used
        • 490
          Open source
        • 180
          High availability
        • 160
          Cross-platform support
        • 104
          Great community
        • 79
          Secure
        • 75
          Full-text indexing and searching
        • 26
          Fast, open, available
        • 16
          Reliable
        • 16
          SSL support
        • 15
          Robust
        • 9
          Enterprise Version
        • 7
          Easy to set up on all platforms
        • 3
          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
        • 16
          Owned by a company with their own agenda
        • 3
          Can't roll back schema changes

        related MySQL posts

        Nick Rockwell
        SVP, Engineering at Fastly · | 46 upvotes · 4.1M views

        When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

        So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

        React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

        Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

        See more
        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
        PostgreSQL logo

        PostgreSQL

        98.3K
        3.5K
        A powerful, open source object-relational database system
        98.3K
        3.5K
        PROS OF POSTGRESQL
        • 764
          Relational database
        • 510
          High availability
        • 439
          Enterprise class database
        • 383
          Sql
        • 304
          Sql + nosql
        • 173
          Great community
        • 147
          Easy to setup
        • 131
          Heroku
        • 130
          Secure by default
        • 113
          Postgis
        • 50
          Supports Key-Value
        • 48
          Great JSON support
        • 34
          Cross platform
        • 33
          Extensible
        • 28
          Replication
        • 26
          Triggers
        • 23
          Multiversion concurrency control
        • 23
          Rollback
        • 21
          Open source
        • 18
          Heroku Add-on
        • 17
          Stable, Simple and Good Performance
        • 15
          Powerful
        • 13
          Lets be serious, what other SQL DB would you go for?
        • 11
          Good documentation
        • 9
          Scalable
        • 8
          Free
        • 8
          Reliable
        • 8
          Intelligent optimizer
        • 7
          Transactional DDL
        • 7
          Modern
        • 6
          One stop solution for all things sql no matter the os
        • 5
          Relational database with MVCC
        • 5
          Faster Development
        • 4
          Full-Text Search
        • 4
          Developer friendly
        • 3
          Excellent source code
        • 3
          Free version
        • 3
          Great DB for Transactional system or Application
        • 3
          Relational datanbase
        • 3
          search
        • 3
          Open-source
        • 2
          Text
        • 2
          Full-text
        • 1
          Can handle up to petabytes worth of size
        • 1
          Composability
        • 1
          Multiple procedural languages supported
        • 0
          Native
        CONS OF POSTGRESQL
        • 10
          Table/index bloatings

        related PostgreSQL posts

        Simon Reymann
        Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 11.6M 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
        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!

        See more
        MongoDB logo

        MongoDB

        93.6K
        4.1K
        The database for giant ideas
        93.6K
        4.1K
        PROS OF MONGODB
        • 828
          Document-oriented storage
        • 593
          No sql
        • 553
          Ease of use
        • 464
          Fast
        • 410
          High performance
        • 255
          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
        • 2
          Proprietary query language

        related MongoDB posts

        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!

        See more
        Robert Zuber

        We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

        As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

        When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

        See more