Alternatives to Percona logo

Alternatives to Percona

MariaDB, MySQL, MongoDB, PostgreSQL, and Apache Aurora are the most popular alternatives and competitors to Percona.
138
97
+ 1
0

What is Percona and what are its top alternatives?

It delivers enterprise-class software, support, consulting and managed services for both MySQL and MongoDB across traditional and cloud-based platforms.
Percona is a tool in the Databases category of a tech stack.

Top Alternatives to Percona

  • 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. ...

  • 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. ...

  • 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. ...

  • 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. ...

  • Apache Aurora
    Apache Aurora

    Apache Aurora is a service scheduler that runs on top of Mesos, enabling you to run long-running services that take advantage of Mesos' scalability, fault-tolerance, and resource isolation. ...

  • 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. ...

  • 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. ...

Percona alternatives & related posts

MariaDB logo

MariaDB

16K
12.4K
468
An enhanced, drop-in replacement for MySQL
16K
12.4K
+ 1
468
PROS OF MARIADB
  • 149
    Drop-in mysql replacement
  • 100
    Great performance
  • 74
    Open source
  • 55
    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
    Consistent and robust
  • 4
    Learning curve easy
  • 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 · 657.2K 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
    MySQL logo

    MySQL

    123.6K
    102.9K
    3.7K
    The world's most popular open source database
    123.6K
    102.9K
    + 1
    3.7K
    PROS OF MYSQL
    • 800
      Sql
    • 679
      Free
    • 562
      Easy
    • 528
      Widely used
    • 489
      Open source
    • 180
      High availability
    • 160
      Cross-platform support
    • 104
      Great community
    • 78
      Secure
    • 75
      Full-text indexing and searching
    • 25
      Fast, open, available
    • 16
      SSL support
    • 15
      Reliable
    • 14
      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
    • 16
      Owned by a company with their own agenda
    • 3
      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 · | 23 upvotes · 2.3M 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
    MongoDB logo

    MongoDB

    93K
    78.7K
    4.1K
    The database for giant ideas
    93K
    78.7K
    + 1
    4.1K
    PROS OF MONGODB
    • 827
      Document-oriented storage
    • 593
      No sql
    • 553
      Ease of use
    • 464
      Fast
    • 410
      High performance
    • 257
      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
      Drivers support is good
    • 3
      Aggregation Framework
    • 3
      Schemaless
    • 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
    • 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
    PostgreSQL logo

    PostgreSQL

    98.3K
    79.7K
    3.5K
    A powerful, open source object-relational database system
    98.3K
    79.7K
    + 1
    3.5K
    PROS OF POSTGRESQL
    • 761
      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
    • 32
      Extensible
    • 28
      Replication
    • 26
      Triggers
    • 23
      Rollback
    • 22
      Multiversion concurrency control
    • 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
    • 8
      Intelligent optimizer
    • 8
      Free
    • 8
      Scalable
    • 8
      Reliable
    • 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
      Developer friendly
    • 4
      Full-Text Search
    • 3
      Free version
    • 3
      Great DB for Transactional system or Application
    • 3
      Relational datanbase
    • 3
      search
    • 3
      Open-source
    • 3
      Excellent source code
    • 2
      Full-text
    • 2
      Text
    • 0
      Native
    CONS OF POSTGRESQL
    • 10
      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
    Apache Aurora logo

    Apache Aurora

    69
    96
    0
    An Apcahe Mesos framework for scheduling jobs, originally developed by Twitter
    69
    96
    + 1
    0
    PROS OF APACHE AURORA
      Be the first to leave a pro
      CONS OF APACHE AURORA
        Be the first to leave a con

        related Apache Aurora posts

        Docker containers on Mesos run their microservices with consistent configurations at scale, along with Aurora for long-running services and cron jobs.

        See more
        Microsoft SQL Server logo

        Microsoft SQL Server

        20.5K
        14.9K
        540
        A relational database management system developed by Microsoft
        20.5K
        14.9K
        + 1
        540
        PROS OF MICROSOFT SQL SERVER
        • 139
          Reliable and easy to use
        • 102
          High performance
        • 95
          Great with .net
        • 65
          Works well with .net
        • 56
          Easy to maintain
        • 21
          Azure support
        • 17
          Full Index Support
        • 17
          Always on
        • 10
          Enterprise manager is fantastic
        • 9
          In-Memory OLTP Engine
        • 2
          Easy to setup and configure
        • 2
          Security is forefront
        • 1
          Faster Than Oracle
        • 1
          Decent management tools
        • 1
          Great documentation
        • 1
          Docker Delivery
        • 1
          Columnstore indexes
        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.
        See more
        SQLite logo

        SQLite

        19K
        14.6K
        530
        A software library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine
        19K
        14.6K
        + 1
        530
        PROS OF SQLITE
        • 162
          Lightweight
        • 135
          Portable
        • 121
          Simple
        • 80
          Sql
        • 28
          Preinstalled on iOS and Android
        • 2
          Tcl integration
        • 1
          Free
        • 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

        Hi all. I want to rewrite my system. I was a complete newbie 4 years ago and have developed a comprehensive business / finance web application that has been running successfully for 3 years (I am a business person and not a developer primarily although it seems I have become a developer). Front-end is written in native PHP (no framework) and jQuery with backend and where many processes run in MySQL. Hosted on Linux and also sends emails with attachments etc. The system logic is great and the business has grown and the system is creaking and needs to be modernised. I feel I would stick with MySql as DB and update / use Django / Spring or Laravel (because its php which I understand). To me, PHP feels old fashioned. I don't mind learning new things and also I want to set the system up that it can be easily migrated to Android/iOS app with SQLite. I would probably employ an experienced developer while also doing some myself. Please provide advice -- from my research it seems Spring/Java is the way to go ... not sure. Thanks

        See more
        Memcached logo

        Memcached

        7.7K
        5.5K
        473
        High-performance, distributed memory object caching system
        7.7K
        5.5K
        + 1
        473
        PROS OF MEMCACHED
        • 139
          Fast object cache
        • 129
          High-performance
        • 91
          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

        Kir Shatrov
        Engineering Lead at Shopify · | 17 upvotes · 1.2M 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
        Julien DeFrance
        Principal Software Engineer at Tophatter · | 16 upvotes · 3.1M 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