Alternatives to FoundationDB logo

Alternatives to FoundationDB

CockroachDB, MongoDB, Cassandra, Redis, and Couchbase are the most popular alternatives and competitors to FoundationDB.
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What is FoundationDB and what are its top alternatives?

FoundationDB is a NoSQL database with a shared nothing architecture. Designed around a "core" ordered key-value database, additional features and data models are supplied in layers. The key-value database, as well as all layers, supports full, cross-key and cross-server ACID transactions.
FoundationDB is a tool in the Databases category of a tech stack.

Top Alternatives to FoundationDB

  • CockroachDB

    CockroachDB

    It allows you to deploy a database on-prem, in the cloud or even across clouds, all as a single store. It is a simple and straightforward bridge to your future, cloud-based data architecture. ...

  • 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. ...

  • Cassandra

    Cassandra

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

  • Redis

    Redis

    Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets. ...

  • Couchbase

    Couchbase

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

  • 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. ...

  • HBase

    HBase

    Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop. ...

  • VoltDB

    VoltDB

    VoltDB is a fundamental redesign of the RDBMS that provides unparalleled performance and scalability on bare-metal, virtualized and cloud infrastructures. VoltDB is a modern in-memory architecture that supports both SQL + Java with data durability and fault tolerance. ...

FoundationDB alternatives & related posts

CockroachDB logo

CockroachDB

124
200
0
A cloud-native SQL database for building global, scalable cloud services that survive disasters.
124
200
+ 1
0
PROS OF COCKROACHDB
    Be the first to leave a pro
    CONS OF COCKROACHDB
      Be the first to leave a con

      related CockroachDB posts

      MongoDB logo

      MongoDB

      58.1K
      47.7K
      4.1K
      The database for giant ideas
      58.1K
      47.7K
      + 1
      4.1K
      PROS OF MONGODB
      • 822
        Document-oriented storage
      • 589
        No sql
      • 545
        Ease of use
      • 464
        Fast
      • 405
        High performance
      • 254
        Free
      • 214
        Open source
      • 178
        Flexible
      • 141
        Replication & high availability
      • 108
        Easy to maintain
      • 40
        Querying
      • 36
        Easy scalability
      • 35
        Auto-sharding
      • 34
        High availability
      • 30
        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
        Enterprise
      • 7
        Easy integration with Node.Js
      • 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
        Fast
      • 2
        Awesome
      • 2
        Managed service
      • 2
        Easy to Scale
      • 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
      Cassandra logo

      Cassandra

      3.1K
      3K
      482
      A partitioned row store. Rows are organized into tables with a required primary key.
      3.1K
      3K
      + 1
      482
      PROS OF CASSANDRA
      • 110
        Distributed
      • 92
        High performance
      • 80
        High availability
      • 73
        Easy scalability
      • 52
        Replication
      • 26
        Reliable
      • 26
        Multi datacenter deployments
      • 8
        OLTP
      • 7
        Schema optional
      • 6
        Open source
      • 2
        Workload separation (via MDC)
      CONS OF CASSANDRA
      • 2
        Reliability of replication
      • 1
        Updates

      related Cassandra posts

      Thierry Schellenbach
      Shared insights
      on
      Redis
      Cassandra
      RocksDB
      at

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

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

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

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

      #InMemoryDatabases #DataStores #Databases

      See more
      Umair Iftikhar
      Technical Architect at Vappar · | 3 upvotes · 86.6K views

      Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

      My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

      See more
      Redis logo

      Redis

      39.2K
      28.9K
      3.9K
      An in-memory database that persists on disk
      39.2K
      28.9K
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      3.9K
      PROS OF REDIS
      • 877
        Performance
      • 535
        Super fast
      • 511
        Ease of use
      • 441
        In-memory cache
      • 321
        Advanced key-value cache
      • 190
        Open source
      • 179
        Easy to deploy
      • 163
        Stable
      • 153
        Free
      • 120
        Fast
      • 40
        High-Performance
      • 39
        High Availability
      • 34
        Data Structures
      • 32
        Very Scalable
      • 23
        Replication
      • 20
        Great community
      • 19
        Pub/Sub
      • 17
        "NoSQL" key-value data store
      • 14
        Hashes
      • 12
        Sets
      • 10
        Sorted Sets
      • 9
        Lists
      • 8
        BSD licensed
      • 8
        NoSQL
      • 7
        Async replication
      • 7
        Integrates super easy with Sidekiq for Rails background
      • 7
        Bitmaps
      • 6
        Open Source
      • 6
        Keys with a limited time-to-live
      • 5
        Strings
      • 5
        Lua scripting
      • 4
        Awesomeness for Free!
      • 4
        Hyperloglogs
      • 3
        outstanding performance
      • 3
        Runs server side LUA
      • 3
        Networked
      • 3
        LRU eviction of keys
      • 3
        Written in ANSI C
      • 3
        Feature Rich
      • 3
        Transactions
      • 2
        Data structure server
      • 2
        Performance & ease of use
      • 1
        Existing Laravel Integration
      • 1
        Automatic failover
      • 1
        Easy to use
      • 1
        Object [key/value] size each 500 MB
      • 1
        Simple
      • 1
        Channels concept
      • 1
        Scalable
      • 1
        Temporarily kept on disk
      • 1
        Dont save data if no subscribers are found
      • 0
        Jk
      CONS OF REDIS
      • 12
        Cannot query objects directly
      • 1
        No WAL
      • 1
        No secondary indexes for non-numeric data types

      related Redis posts

      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

      I'm working as one of the engineering leads in RunaHR. As our platform is a Saas, we thought It'd be good to have an API (We chose Ruby and Rails for this) and a SPA (built with React and Redux ) connected. We started the SPA with Create React App since It's pretty easy to start.

      We use Jest as the testing framework and react-testing-library to test React components. In Rails we make tests using RSpec.

      Our main database is PostgreSQL, but we also use MongoDB to store some type of data. We started to use Redis  for cache and other time sensitive operations.

      We have a couple of extra projects: One is an Employee app built with React Native and the other is an internal back office dashboard built with Next.js for the client and Python in the backend side.

      Since we have different frontend apps we have found useful to have Bit to document visual components and utils in JavaScript.

      See more
      Couchbase logo

      Couchbase

      358
      466
      102
      Document-Oriented NoSQL Database
      358
      466
      + 1
      102
      PROS OF COUCHBASE
      • 18
        High performance
      • 17
        Flexible data model, easy scalability, extremely fast
      • 8
        Mobile app support
      • 6
        You can query it with Ansi-92 SQL
      • 5
        All nodes can be read/write
      • 4
        Local cache capability
      • 4
        Open source, community and enterprise editions
      • 4
        Both a key-value store and document (JSON) db
      • 4
        Equal nodes in cluster, allowing fast, flexible changes
      • 3
        Automatic configuration of sharding
      • 3
        SDKs in popular programming languages
      • 3
        Elasticsearch connector
      • 3
        Easy setup
      • 3
        Web based management, query and monitoring panel
      • 3
        Linearly scalable, useful to large number of tps
      • 3
        Easy cluster administration
      • 3
        Cross data center replication
      • 2
        NoSQL
      • 2
        DBaaS available
      • 2
        Map reduce views
      • 1
        FTS + SQL together
      • 1
        Buckets, Scopes, Collections & Documents
      CONS OF COUCHBASE
      • 3
        Terrible query language

      related Couchbase posts

      Gabriel Pa

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

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

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

      See more
      Ilias Mentzelos
      Software Engineer at Plum Fintech · | 8 upvotes · 24.4K views
      Shared insights
      on
      MongoDB
      Couchbase

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

      See more
      PostgreSQL logo

      PostgreSQL

      58.4K
      46.2K
      3.5K
      A powerful, open source object-relational database system
      58.4K
      46.2K
      + 1
      3.5K
      PROS OF POSTGRESQL
      • 754
        Relational database
      • 506
        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
      • 29
        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
        Transactional DDL
      • 6
        Modern
      • 6
        Reliable
      • 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
        search
      • 2
        Great DB for Transactional system or Application
      • 1
        Full-text
      • 1
        Free version
      • 1
        Text
      • 1
        Open-source
      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
      HBase logo

      HBase

      338
      386
      15
      The Hadoop database, a distributed, scalable, big data store
      338
      386
      + 1
      15
      PROS OF HBASE
      • 9
        Performance
      • 5
        OLTP
      • 1
        Fast Point Queries
      CONS OF HBASE
        Be the first to leave a con

        related HBase posts

        Hi, I'm building a machine learning pipelines to store image bytes and image vectors in the backend.

        So, when users query for the random access image data (key), we return the image bytes and perform machine learning model operations on it.

        I'm currently considering going with Amazon S3 (in the future, maybe add Redis caching layer) as the backend system to store the information (s3 buckets with sharded prefixes).

        As the latency of S3 is 100-200ms (get/put) and it has a high throughput of 3500 puts/sec and 5500 gets/sec for a given bucker/prefix. In the future I need to reduce the latency, I can add Redis cache.

        Also, s3 costs are way fewer than HBase (on Amazon EC2 instances with 3x replication factor)

        I have not personally used HBase before, so can someone help me if I'm making the right choice here? I'm not aware of Hbase latencies and I have learned that the MOB feature on Hbase has to be turned on if we have store image bytes on of the column families as the avg image bytes are 240Kb.

        See more
        VoltDB logo

        VoltDB

        14
        44
        18
        In-memory relational DBMS capable of supporting millions of database operations per second
        14
        44
        + 1
        18
        PROS OF VOLTDB
        • 5
          SQL + Java
        • 4
          In-memory database
        • 4
          A brainchild of Michael Stonebraker
        • 3
          Very Fast
        • 2
          NewSQL
        CONS OF VOLTDB
          Be the first to leave a con

          related VoltDB posts