Alternatives to Entity Framework logo

Alternatives to Entity Framework

NHibernate, Entity Framework Core, Hibernate, MySQL, and PostgreSQL are the most popular alternatives and competitors to Entity Framework.
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What is Entity Framework and what are its top alternatives?

Entity Framework is a popular Object-Relational Mapping (ORM) framework for .NET applications. It simplifies the process of interacting with databases by allowing developers to work with database data as if they were working with objects. Some key features of Entity Framework include LINQ support, automatic schema generation, and tracking changes to objects. However, Entity Framework has some limitations such as performance issues with complex queries, lack of support for some advanced database features, and difficulty in working with stored procedures. 1. Dapper: Dapper is a lightweight ORM that focuses on simplicity and performance. Its key features include high performance, support for dynamic and static parameter types, and easy mapping of query results to objects. Pros include speed, simplicity, and ability to execute raw SQL queries easily. Cons include lack of certain advanced features compared to Entity Framework. 2. NHibernate: NHibernate is a mature and feature-rich ORM framework for .NET applications. Its key features include support for mapping objects to database tables, lazy loading, and an extensive set of configuration options. Pros include flexibility, extensive feature set, and good community support. Cons include steep learning curve and complexity compared to Entity Framework. 3. DapperExtensions: DapperExtensions is an extension library for Dapper that adds additional features such as support for CRUD operations, predicates, and mapping objects to database tables. Pros include simplicity, additional features on top of Dapper, and good performance. Cons include lack of advanced features compared to Entity Framework. 4. PetaPoco: PetaPoco is a lightweight ORM framework that focuses on simplicity and performance. Its key features include support for querying databases using POCOs (Plain Old CLR Objects) and easy mapping of query results to objects. Pros include simplicity, performance, and flexibility. Cons include lack of advanced features compared to Entity Framework. 5. Marten: Marten is a library that integrates PostgreSQL with .NET applications, providing a document database and event store functionality. Its key features include support for ACID transactions, event sourcing, and LINQ support. Pros include flexibility, document database functionality, and event sourcing capabilities. Cons include limited support for other databases and complexity compared to Entity Framework. 6. Insight.Database: Insight.Database is a lightweight ORM framework that focuses on performance and flexibility. Its key features include support for executing stored procedures, automatic object mapping, and easy database integration. Pros include simplicity, performance, and support for stored procedures. Cons include lack of advanced features compared to Entity Framework. 7. Beetle: Beetle is a lightweight ORM framework that focuses on simplicity and performance. Its key features include support for query composition, automatic object mapping, and easy database integration. Pros include simplicity, performance, and ease of use. Cons include lack of advanced features compared to Entity Framework. 8. ServiceStack.OrmLite: ServiceStack.OrmLite is a lightweight ORM framework that focuses on speed and flexibility. Its key features include support for multiple database providers, support for POCOs, and ease of use. Pros include speed, flexibility, and ease of use. Cons include lack of advanced features compared to Entity Framework. 9. BLToolkit: BLToolkit is a lightweight ORM framework that focuses on performance and simplicity. Its key features include support for mapping objects to database tables, automatic object tracking, and easy database integration. Pros include performance, simplicity, and ease of use. Cons include lack of advanced features compared to Entity Framework. 10. RepoDb: RepoDb is a lightweight and fast ORM framework that focuses on simplicity and performance. Its key features include support for query operations, automatic object mapping, and easy database integration. Pros include speed, simplicity, and performance. Cons include lack of advanced features compared to Entity Framework.

Top Alternatives to Entity Framework

  • NHibernate
    NHibernate

    It is a mature, open source object-relational mapper for the .NET framework. It's actively developed, fully featured and used in thousands of successful projects. ...

  • Entity Framework Core
    Entity Framework Core

    It is a lightweight, extensible, open source and cross-platform version of the popular Entity Framework data access technology. It can serve as an object-relational mapper (O/RM), enabling .NET developers to work with a database using .NET objects, and eliminating the need for most of the data-access code they usually need to write. ...

  • Hibernate
    Hibernate

    Hibernate is a suite of open source projects around domain models. The flagship project is Hibernate ORM, the Object Relational Mapper. ...

  • 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. ...

  • 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. ...

  • Amazon S3
    Amazon S3

    Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web ...

Entity Framework alternatives & related posts

NHibernate logo

NHibernate

74
92
5
The object-relational mapper for .NET
74
92
+ 1
5
PROS OF NHIBERNATE
  • 5
    DDD friendly
CONS OF NHIBERNATE
    Be the first to leave a con

    related NHibernate posts

    Entity Framework Core logo

    Entity Framework Core

    469
    221
    16
    Lightweight and cross-platform version of the popular Entity Framework
    469
    221
    + 1
    16
    PROS OF ENTITY FRAMEWORK CORE
    • 7
      Fits very well with Microsoft technologies
    • 4
      Fast
    • 2
      Linq
    • 1
      OpenSource
    • 1
      Multiple Database provider
    • 1
      Easy to use
    CONS OF ENTITY FRAMEWORK CORE
    • 1
      Dbcontext

    related Entity Framework Core posts

    Hibernate logo

    Hibernate

    1.5K
    1.2K
    34
    Idiomatic persistence for Java and relational databases.
    1.5K
    1.2K
    + 1
    34
    PROS OF HIBERNATE
    • 22
      Easy ORM
    • 8
      Easy transaction definition
    • 3
      Is integrated with spring jpa
    • 1
      Open Source
    CONS OF HIBERNATE
    • 3
      Can't control proxy associations when entity graph used

    related Hibernate posts

    Ganesa Vijayakumar
    Full Stack Coder | Technical Architect · | 19 upvotes · 5.5M views

    I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.

    I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).

    As per my work experience and knowledge, I have chosen the followings stacks to this mission.

    UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.

    Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.

    Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.

    Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.

    Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.

    Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.

    Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.

    Happy Coding! Suggestions are welcome! :)

    Thanks, Ganesa

    See more
    NIDHISH PUTHIYADATH
    Lead Software Engineer at EDIFECS · | 1 upvote · 311K views

    Material Design for Angular Angular 2 Node.js TypeScript Spring-Boot RxJS Microsoft SQL Server Hibernate Spring MVC

    We built our customer facing portal application using Angular frontend backed by Spring boot.

    See more
    MySQL logo

    MySQL

    125.3K
    106K
    3.8K
    The world's most popular open source database
    125.3K
    106K
    + 1
    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

    98.2K
    82.2K
    3.5K
    A powerful, open source object-relational database system
    98.2K
    82.2K
    + 1
    3.5K
    PROS OF POSTGRESQL
    • 763
      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 · 11.2M 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

    93.5K
    80.7K
    4.1K
    The database for giant ideas
    93.5K
    80.7K
    + 1
    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
    Redis logo

    Redis

    59.4K
    45.7K
    3.9K
    Open source (BSD licensed), in-memory data structure store
    59.4K
    45.7K
    + 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 · 2.8M 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 · 11.2M 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
    Amazon S3 logo

    Amazon S3

    53.2K
    39.8K
    2K
    Store and retrieve any amount of data, at any time, from anywhere on the web
    53.2K
    39.8K
    + 1
    2K
    PROS OF AMAZON S3
    • 590
      Reliable
    • 492
      Scalable
    • 456
      Cheap
    • 329
      Simple & easy
    • 83
      Many sdks
    • 30
      Logical
    • 13
      Easy Setup
    • 11
      REST API
    • 11
      1000+ POPs
    • 6
      Secure
    • 4
      Easy
    • 4
      Plug and play
    • 3
      Web UI for uploading files
    • 2
      Faster on response
    • 2
      Flexible
    • 2
      GDPR ready
    • 1
      Easy to use
    • 1
      Plug-gable
    • 1
      Easy integration with CloudFront
    CONS OF AMAZON S3
    • 7
      Permissions take some time to get right
    • 6
      Requires a credit card
    • 6
      Takes time/work to organize buckets & folders properly
    • 3
      Complex to set up

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    Ashish Singh
    Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3.3M views

    To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

    Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

    We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

    Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

    Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

    #BigData #AWS #DataScience #DataEngineering

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    Russel Werner
    Lead Engineer at StackShare · | 32 upvotes · 2.8M 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

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