Alternatives to OrbitDB logo

Alternatives to OrbitDB

Gun, MySQL, PostgreSQL, MongoDB, and Microsoft SQL Server are the most popular alternatives and competitors to OrbitDB.
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What is OrbitDB and what are its top alternatives?

It is a serverless, distributed, peer-to-peer database. It uses IPFS as its data storage and IPFS Pubsub to automatically sync databases with peers. It’s an eventually consistent database that uses CRDTs for conflict-free database merges making it an excellent choice for decentralized apps (dApps), blockchain applications and offline-first web applications.
OrbitDB is a tool in the Databases category of a tech stack.
OrbitDB is an open source tool with 6.2K GitHub stars and 424 GitHub forks. Here’s a link to OrbitDB's open source repository on GitHub

Top Alternatives to OrbitDB

  • Gun

    Gun

    GUN is a realtime, decentralized, embedded, graph database engine.

  • 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. ...

  • Microsoft SQL Server

    Microsoft SQL Server

    Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions. ...

  • SQLite

    SQLite

    SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file. ...

  • MariaDB

    MariaDB

    Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance. ...

  • Memcached

    Memcached

    Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering. ...

OrbitDB alternatives & related posts

Gun logo

Gun

17
68
0
Self-hosted Firebase.
17
68
+ 1
0
PROS OF GUN
  • 0
    Open source
  • 0
    Small size
  • 0
    Real time
CONS OF GUN
    Be the first to leave a con

    related Gun posts

    MySQL logo

    MySQL

    84.9K
    68.7K
    3.7K
    The world's most popular open source database
    84.9K
    68.7K
    + 1
    3.7K
    PROS OF MYSQL
    • 793
      Sql
    • 672
      Free
    • 555
      Easy
    • 526
      Widely used
    • 485
      Open source
    • 180
      High availability
    • 160
      Cross-platform support
    • 103
      Great community
    • 78
      Secure
    • 75
      Full-text indexing and searching
    • 25
      Fast, open, available
    • 14
      SSL support
    • 13
      Reliable
    • 13
      Robust
    • 8
      Enterprise Version
    • 7
      Easy to set up on all platforms
    • 2
      NoSQL access to JSON data type
    • 1
      Relational database
    • 1
      Easy, light, scalable
    • 1
      Sequel Pro (best SQL GUI)
    • 1
      Replica Support
    CONS OF MYSQL
    • 14
      Owned by a company with their own agenda
    • 1
      Can't roll back schema changes

    related MySQL posts

    Tim Abbott

    We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

    We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

    And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

    I can't recommend it highly enough.

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    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 21 upvotes · 1.1M views

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

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

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

    See more
    PostgreSQL logo

    PostgreSQL

    64.9K
    52K
    3.5K
    A powerful, open source object-relational database system
    64.9K
    52K
    + 1
    3.5K
    PROS OF POSTGRESQL
    • 755
      Relational database
    • 508
      High availability
    • 436
      Enterprise class database
    • 380
      Sql
    • 302
      Sql + nosql
    • 171
      Great community
    • 145
      Easy to setup
    • 129
      Heroku
    • 128
      Secure by default
    • 111
      Postgis
    • 48
      Supports Key-Value
    • 46
      Great JSON support
    • 32
      Cross platform
    • 30
      Extensible
    • 26
      Replication
    • 24
      Triggers
    • 22
      Rollback
    • 21
      Multiversion concurrency control
    • 20
      Open source
    • 17
      Heroku Add-on
    • 14
      Stable, Simple and Good Performance
    • 13
      Powerful
    • 12
      Lets be serious, what other SQL DB would you go for?
    • 9
      Good documentation
    • 7
      Intelligent optimizer
    • 7
      Scalable
    • 6
      Reliable
    • 6
      Transactional DDL
    • 6
      Modern
    • 5
      Free
    • 5
      One stop solution for all things sql no matter the os
    • 4
      Relational database with MVCC
    • 3
      Faster Development
    • 3
      Full-Text Search
    • 3
      Developer friendly
    • 2
      Excellent source code
    • 2
      Great DB for Transactional system or Application
    • 2
      search
    • 1
      Free version
    • 1
      Open-source
    • 1
      Full-text
    • 1
      Text
    CONS OF POSTGRESQL
    • 9
      Table/index bloatings

    related PostgreSQL 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
    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
    MongoDB logo

    MongoDB

    64.3K
    53.5K
    4.1K
    The database for giant ideas
    64.3K
    53.5K
    + 1
    4.1K
    PROS OF MONGODB
    • 824
      Document-oriented storage
    • 591
      No sql
    • 546
      Ease of use
    • 465
      Fast
    • 406
      High performance
    • 256
      Free
    • 215
      Open source
    • 179
      Flexible
    • 142
      Replication & high availability
    • 109
      Easy to maintain
    • 41
      Querying
    • 37
      Easy scalability
    • 36
      Auto-sharding
    • 35
      High availability
    • 31
      Map/reduce
    • 26
      Document database
    • 24
      Easy setup
    • 24
      Full index support
    • 15
      Reliable
    • 14
      Fast in-place updates
    • 13
      Agile programming, flexible, fast
    • 11
      No database migrations
    • 7
      Easy integration with Node.Js
    • 7
      Enterprise
    • 5
      Enterprise Support
    • 4
      Great NoSQL DB
    • 3
      Aggregation Framework
    • 3
      Support for many languages through different drivers
    • 3
      Drivers support is good
    • 2
      Schemaless
    • 2
      Easy to Scale
    • 2
      Fast
    • 2
      Awesome
    • 2
      Managed service
    • 1
      Consistent
    CONS OF MONGODB
    • 5
      Very slowly for connected models that require joins
    • 3
      Not acid compliant
    • 1
      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
    Microsoft SQL Server logo

    Microsoft SQL Server

    13K
    9.5K
    535
    A relational database management system developed by Microsoft
    13K
    9.5K
    + 1
    535
    PROS OF MICROSOFT SQL SERVER
    • 137
      Reliable and easy to use
    • 101
      High performance
    • 94
      Great with .net
    • 65
      Works well with .net
    • 56
      Easy to maintain
    • 21
      Azure support
    • 17
      Always on
    • 17
      Full Index Support
    • 10
      Enterprise manager is fantastic
    • 9
      In-Memory OLTP Engine
    • 2
      Security is forefront
    • 1
      Columnstore indexes
    • 1
      Great documentation
    • 1
      Faster Than Oracle
    • 1
      Decent management tools
    • 1
      Easy to setup and configure
    • 1
      Docker Delivery
    CONS OF MICROSOFT SQL SERVER
    • 4
      Expensive Licensing
    • 2
      Microsoft

    related Microsoft SQL Server posts

    We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.

    We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.

    In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.

    Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache

    See more

    I am a Microsoft SQL Server programmer who is a bit out of practice. I have been asked to assist on a new project. The overall purpose is to organize a large number of recordings so that they can be searched. I have an enormous music library but my songs are several hours long. I need to include things like time, date and location of the recording. I don't have a problem with the general database design. I have two primary questions:

    1. I need to use either MySQL or PostgreSQL on a Linux based OS. Which would be better for this application?
    2. I have not dealt with a sound based data type before. How do I store that and put it in a table? Thank you.
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    SQLite logo

    SQLite

    12.2K
    9.6K
    528
    A software library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine
    12.2K
    9.6K
    + 1
    528
    PROS OF SQLITE
    • 160
      Lightweight
    • 134
      Portable
    • 121
      Simple
    • 80
      Sql
    • 28
      Preinstalled on iOS and Android
    • 2
      Tcl integration
    • 1
      Free
    • 1
      Telefon
    • 1
      Portable A database on my USB 'love it'
    CONS OF SQLITE
    • 2
      Not for multi-process of multithreaded apps
    • 1
      Needs different binaries for each platform

    related SQLite posts

    Dimelo Waterson
    Shared insights
    on
    PostgreSQLPostgreSQLMySQLMySQLSQLiteSQLite

    I need to add a DBMS to my stack, but I don't know which. I'm tempted to learn SQLite since it would be useful to me with its focus on local access without concurrency. However, doing so feels like I would be defeating the purpose of trying to expand my skill set since it seems like most enterprise applications have the opposite requirements.

    To be able to apply what I learn to more projects, what should I try to learn? MySQL? PostgreSQL? Something else? Is there a comfortable middle ground between high applicability and ease of use?

    See more
    Christian Stefanescu
    Head of IT at lawpilots · | 3 upvotes · 9.2K views
    Shared insights
    on
    DjangoDjangoSQLiteSQLitePostgreSQLPostgreSQL

    While I love and use PostgreSQL , I would definitely recommend having a look at SQLite as well. It can be a solid database for lots of applications and it brings some advantages in terms of handling: you don't need a server running, which makes things like testing, deploying or backing up databases much easier. Through the ORM in Django you are one abstraction level away from your database anyway and switching later on is definitely an option, but I believe SQLite is very good for pretty much all the small applications you can think of.

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    MariaDB logo

    MariaDB

    11.1K
    8.4K
    467
    An enhanced, drop-in replacement for MySQL
    11.1K
    8.4K
    + 1
    467
    PROS OF MARIADB
    • 149
      Drop-in mysql replacement
    • 100
      Great performance
    • 74
      Open source
    • 54
      Free
    • 44
      Easy setup
    • 15
      Easy and fast
    • 14
      Lead developer is "monty" widenius the founder of mysql
    • 6
      Also an aws rds service
    • 4
      Learning curve easy
    • 4
      Consistent and robust
    • 2
      Native JSON Support / Dynamic Columns
    • 1
      Real Multi Threaded queries on a table/db
    CONS OF MARIADB
      Be the first to leave a con

      related MariaDB posts

      Joshua Dean Küpper
      CEO at Scrayos UG (haftungsbeschränkt) · | 11 upvotes · 277K views

      We primarily use MariaDB but use PostgreSQL as a part of GitLab , Sentry and Nextcloud , which (initially) forced us to use it anyways. While this isn't much of a decision – because we didn't have one (ha ha) – we learned to love the perks and advantages of PostgreSQL anyways. PostgreSQL's extension system makes it even more flexible than a lot of the other SQL-based DBs (that only offer stored procedures) and the additional JOIN options, the enhanced role management and the different authentication options came in really handy, when doing manual maintenance on the databases.

      See more

      I'm researching what Technology Stack I should use to build my product (something like food delivery App) for Web, iOS, and Android Apps. Please advise which technologies you would recommend from a Scalability, Reliability, Cost, and Efficiency standpoint for a start-up. Here are the technologies I came up with, feel free to suggest any new technology even it's not in the list below.

      For Mobile Apps -

      1. native languages like Swift for IOS and Java/Kotlin for Android
      2. or cross-platform languages like React Native for both IOS and Android Apps

      For UI -

      1. React

      For Back-End or APIs -

      1. Node.js
      2. PHP

      For Database -

      1. PostgreSQL
      2. MySQL
      3. Cloud Firestore
      4. MariaDB

      Thanks!

      See more
      Memcached logo

      Memcached

      5.7K
      4K
      469
      High-performance, distributed memory object caching system
      5.7K
      4K
      + 1
      469
      PROS OF MEMCACHED
      • 137
        Fast object cache
      • 128
        High-performance
      • 90
        Stable
      • 65
        Mature
      • 33
        Distributed caching system
      • 11
        Improved response time and throughput
      • 3
        Great for caching HTML
      • 2
        Putta
      CONS OF MEMCACHED
      • 2
        Only caches simple types

      related Memcached posts

      Julien DeFrance
      Principal Software Engineer at Tophatter · | 16 upvotes · 2.4M views

      Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

      I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

      For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

      Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

      Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

      Future improvements / technology decisions included:

      Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

      As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

      One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

      See more
      Kir Shatrov
      Engineering Lead at Shopify · | 15 upvotes · 576.9K views

      At Shopify, over the years, we moved from shards to the concept of "pods". A pod is a fully isolated instance of Shopify with its own datastores like MySQL, Redis, Memcached. A pod can be spawned in any region. This approach has helped us eliminate global outages. As of today, we have more than a hundred pods, and since moving to this architecture we haven't had any major outages that affected all of Shopify. An outage today only affects a single pod or region.

      As we grew into hundreds of shards and pods, it became clear that we needed a solution to orchestrate those deployments. Today, we use Docker, Kubernetes, and Google Kubernetes Engine to make it easy to bootstrap resources for new Shopify Pods.

      See more