Alternatives to MemSQL logo

Alternatives to MemSQL

VoltDB, Redis, MongoDB, Cassandra, and MySQL are the most popular alternatives and competitors to MemSQL.
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What is MemSQL and what are its top alternatives?

MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.
MemSQL is a tool in the In-Memory Databases category of a tech stack.

Top Alternatives to MemSQL

  • 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. ...

  • 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. ...

  • 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. ...

  • 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. ...

  • Apache Ignite
    Apache Ignite

    It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale ...

  • CockroachDB
    CockroachDB

    CockroachDB is distributed SQL database that can be deployed in serverless, dedicated, or on-prem. Elastic scale, multi-active availability for resilience, and low latency performance. ...

  • Elasticsearch
    Elasticsearch

    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack). ...

MemSQL alternatives & related posts

VoltDB logo

VoltDB

16
59
18
In-memory relational DBMS capable of supporting millions of database operations per second
16
59
+ 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

    Redis logo

    Redis

    50K
    38K
    3.9K
    Open source (BSD licensed), in-memory data structure store
    50K
    38K
    + 1
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    PROS OF REDIS
    • 880
      Performance
    • 537
      Super fast
    • 510
      Ease of use
    • 441
      In-memory cache
    • 321
      Advanced key-value cache
    • 190
      Open source
    • 179
      Easy to deploy
    • 163
      Stable
    • 152
      Free
    • 120
      Fast
    • 40
      High-Performance
    • 39
      High Availability
    • 34
      Data Structures
    • 32
      Very Scalable
    • 23
      Replication
    • 20
      Pub/Sub
    • 20
      Great community
    • 17
      "NoSQL" key-value data store
    • 14
      Hashes
    • 12
      Sets
    • 10
      Sorted Sets
    • 9
      Lists
    • 8
      BSD licensed
    • 8
      NoSQL
    • 7
      Integrates super easy with Sidekiq for Rails background
    • 7
      Async replication
    • 7
      Bitmaps
    • 6
      Keys with a limited time-to-live
    • 6
      Open Source
    • 5
      Strings
    • 5
      Lua scripting
    • 4
      Hyperloglogs
    • 4
      Awesomeness for Free!
    • 3
      Transactions
    • 3
      Runs server side LUA
    • 3
      outstanding performance
    • 3
      Networked
    • 3
      LRU eviction of keys
    • 3
      Written in ANSI C
    • 3
      Feature Rich
    • 2
      Performance & ease of use
    • 2
      Data structure server
    • 1
      Simple
    • 1
      Channels concept
    • 1
      Scalable
    • 1
      Temporarily kept on disk
    • 1
      Dont save data if no subscribers are found
    • 1
      Automatic failover
    • 1
      Easy to use
    • 1
      Existing Laravel Integration
    • 1
      Object [key/value] size each 500 MB
    CONS OF REDIS
    • 14
      Cannot query objects directly
    • 2
      No secondary indexes for non-numeric data types
    • 1
      No WAL

    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
    MongoDB logo

    MongoDB

    76.3K
    64.8K
    4.1K
    The database for giant ideas
    76.3K
    64.8K
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    4.1K
    PROS OF MONGODB
    • 828
      Document-oriented storage
    • 593
      No sql
    • 549
      Ease of use
    • 465
      Fast
    • 408
      High performance
    • 256
      Free
    • 216
      Open source
    • 180
      Flexible
    • 143
      Replication & high availability
    • 110
      Easy to maintain
    • 42
      Querying
    • 38
      Easy scalability
    • 37
      Auto-sharding
    • 36
      High availability
    • 31
      Map/reduce
    • 27
      Document database
    • 25
      Full index support
    • 25
      Easy setup
    • 16
      Reliable
    • 15
      Fast in-place updates
    • 14
      Agile programming, flexible, fast
    • 12
      No database migrations
    • 8
      Enterprise
    • 8
      Easy integration with Node.Js
    • 6
      Enterprise Support
    • 5
      Great NoSQL DB
    • 4
      Support for many languages through different drivers
    • 3
      Drivers support is good
    • 3
      Aggregation Framework
    • 2
      Fast
    • 2
      Easy to Scale
    • 2
      Awesome
    • 2
      Schemaless
    • 2
      Managed service
    • 1
      Consistent
    • 1
      Acid Compliant
    CONS OF MONGODB
    • 6
      Very slowly for connected models that require joins
    • 3
      Not acid compliant
    • 1
      Proprietary query language

    related MongoDB posts

    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

    3.3K
    3.3K
    495
    A partitioned row store. Rows are organized into tables with a required primary key.
    3.3K
    3.3K
    + 1
    495
    PROS OF CASSANDRA
    • 115
      Distributed
    • 96
      High performance
    • 80
      High availability
    • 74
      Easy scalability
    • 52
      Replication
    • 26
      Reliable
    • 26
      Multi datacenter deployments
    • 9
      OLTP
    • 7
      Open source
    • 7
      Schema optional
    • 2
      Workload separation (via MDC)
    • 1
      Fast
    CONS OF CASSANDRA
    • 3
      Reliability of replication
    • 1
      Size
    • 1
      Updates

    related Cassandra posts

    Thierry Schellenbach
    Shared insights
    on
    RedisRedisCassandraCassandraRocksDBRocksDB
    at

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

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

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

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

    #InMemoryDatabases #DataStores #Databases

    See more
    Umair Iftikhar
    Technical Architect at ERP Studio · | 3 upvotes · 251.8K 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
    MySQL logo

    MySQL

    100.6K
    83.5K
    3.7K
    The world's most popular open source database
    100.6K
    83.5K
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    3.7K
    PROS OF MYSQL
    • 796
      Sql
    • 674
      Free
    • 557
      Easy
    • 527
      Widely used
    • 487
      Open source
    • 180
      High availability
    • 160
      Cross-platform support
    • 104
      Great community
    • 78
      Secure
    • 75
      Full-text indexing and searching
    • 25
      Fast, open, available
    • 15
      SSL support
    • 14
      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
    • 15
      Owned by a company with their own agenda
    • 2
      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.

    See more
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 22 upvotes · 1.3M 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
    Apache Ignite logo

    Apache Ignite

    81
    137
    29
    An open-source distributed database, caching and processing platform
    81
    137
    + 1
    29
    PROS OF APACHE IGNITE
    • 4
      Written in java. runs on jvm
    • 4
      Free
    • 3
      Load balancing
    • 3
      Multiple client language support
    • 3
      Sql query support in cluster wide
    • 3
      Rest interface
    • 3
      High Avaliability
    • 2
      Better Documentation
    • 2
      Easy to use
    • 1
      Distributed compute
    • 1
      Distributed Locking
    CONS OF APACHE IGNITE
      Be the first to leave a con

      related Apache Ignite posts

      CockroachDB logo

      CockroachDB

      167
      276
      0
      A distributed SQL database that scales fast, survives disaster, and thrives everywhere
      167
      276
      + 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

          Elasticsearch logo

          Elasticsearch

          29.8K
          22.9K
          1.6K
          Open Source, Distributed, RESTful Search Engine
          29.8K
          22.9K
          + 1
          1.6K
          PROS OF ELASTICSEARCH
          • 322
            Powerful api
          • 314
            Great search engine
          • 230
            Open source
          • 214
            Restful
          • 199
            Near real-time search
          • 96
            Free
          • 83
            Search everything
          • 54
            Easy to get started
          • 45
            Analytics
          • 26
            Distributed
          • 6
            Fast search
          • 5
            More than a search engine
          • 3
            Easy to scale
          • 3
            Awesome, great tool
          • 3
            Great docs
          • 2
            Potato
          • 2
            Document Store
          • 2
            Great customer support
          • 2
            Intuitive API
          • 2
            Reliable
          • 2
            Nosql DB
          • 2
            Fast
          • 2
            Easy setup
          • 2
            Highly Available
          • 2
            Great piece of software
          • 1
            Ecosystem
          • 1
            Scalability
          • 1
            Not stable
          • 1
            Github
          • 1
            Elaticsearch
          • 1
            Actively developing
          • 1
            Responsive maintainers on GitHub
          • 1
            Easy to get hot data
          • 1
            Open
          • 0
            Community
          CONS OF ELASTICSEARCH
          • 7
            Resource hungry
          • 6
            Diffecult to get started
          • 5
            Expensive
          • 4
            Hard to keep stable at large scale

          related Elasticsearch 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.

          See more
          Tymoteusz Paul
          Devops guy at X20X Development LTD · | 23 upvotes · 5.3M views

          Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

          It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

          I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

          We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

          If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

          The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

          Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

          See more