Alternatives to Google Cloud SQL logo

Alternatives to Google Cloud SQL

MySQL, Apache Aurora, Google Cloud Datastore, Google Cloud Spanner, and PostgreSQL are the most popular alternatives and competitors to Google Cloud SQL.
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What is Google Cloud SQL and what are its top alternatives?

MySQL databases deployed in the cloud without a fuss. Google Cloud Platform provides you with powerful databases that run fast, don鈥檛 run out of space and give your application the redundant, reliable storage it needs.
Google Cloud SQL is a tool in the SQL Database as a Service category of a tech stack.

Top Alternatives to Google Cloud 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 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. ...

  • Google Cloud Datastore

    Google Cloud Datastore

    Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries. ...

  • Google Cloud Spanner

    Google Cloud Spanner

    It is a globally distributed database service that gives developers a production-ready storage solution. It provides key features such as global transactions, strongly consistent reads, and automatic multi-site replication and failover. ...

  • 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. ...

  • Firebase

    Firebase

    Firebase is a cloud service designed to power real-time, collaborative applications. Simply add the Firebase library to your application to gain access to a shared data structure; any changes you make to that data are automatically synchronized with the Firebase cloud and with other clients within milliseconds. ...

  • Amazon RDS

    Amazon RDS

    Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call. ...

  • Amazon Aurora

    Amazon Aurora

    Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability. ...

Google Cloud SQL alternatives & related posts

MySQL logo

MySQL

68K
52.3K
3.7K
The world's most popular open source database
68K
52.3K
+ 1
3.7K
PROS OF MYSQL
  • 789
    Sql
  • 674
    Free
  • 557
    Easy
  • 527
    Widely used
  • 485
    Open source
  • 180
    High availability
  • 158
    Cross-platform support
  • 103
    Great community
  • 77
    Secure
  • 75
    Full-text indexing and searching
  • 25
    Fast, open, available
  • 14
    SSL support
  • 13
    Robust
  • 13
    Reliable
  • 8
    Enterprise Version
  • 7
    Easy to set up on all platforms
  • 1
    Relational database
  • 1
    Sequel Pro (best SQL GUI)
  • 1
    Replica Support
  • 1
    NoSQL access to JSON data type
  • 1
    Easy, light, scalable
CONS OF MYSQL
  • 13
    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.

See more
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 20 upvotes 路 913.1K 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鈥檒l 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 Aurora logo

Apache Aurora

58
73
0
An Apcahe Mesos framework for scheduling jobs, originally developed by Twitter
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73
+ 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
      Google Cloud Datastore logo

      Google Cloud Datastore

      186
      264
      12
      A Fully Managed NoSQL Data Storage Service
      186
      264
      + 1
      12
      PROS OF GOOGLE CLOUD DATASTORE
      • 7
        High scalability
      • 2
        Serverless
      • 2
        Ability to query any property
      • 1
        Pay for what you use
      CONS OF GOOGLE CLOUD DATASTORE
        Be the first to leave a con

        related Google Cloud Datastore posts

        Google Cloud Spanner logo

        Google Cloud Spanner

        22
        59
        1
        Fully managed, scalable, relational database service for regional and global application data
        22
        59
        + 1
        1
        PROS OF GOOGLE CLOUD SPANNER
        • 1
          Scalable
        CONS OF GOOGLE CLOUD SPANNER
          Be the first to leave a con

          related Google Cloud Spanner posts

          PostgreSQL logo

          PostgreSQL

          51.3K
          39.5K
          3.5K
          A powerful, open source object-relational database system
          51.3K
          39.5K
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          3.5K
          PROS OF POSTGRESQL
          • 755
            Relational database
          • 505
            High availability
          • 437
            Enterprise class database
          • 379
            Sql
          • 299
            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
          • 25
            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
            Scalable
          • 7
            Intelligent optimizer
          • 6
            Transactional DDL
          • 6
            Modern
          • 6
            Reliable
          • 5
            One stop solution for all things sql no matter the os
          • 5
            Free
          • 4
            Relational database with MVCC
          • 3
            Full-Text Search
          • 3
            Developer friendly
          • 3
            Faster Development
          • 2
            Excellent source code
          • 2
            Great DB for Transactional system or Application
          • 1
            Free version
          • 1
            Text
          • 1
            Open-source
          • 1
            search
          • 1
            Full-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
          Firebase logo

          Firebase

          22.5K
          18.5K
          1.9K
          The Realtime App Platform
          22.5K
          18.5K
          + 1
          1.9K
          PROS OF FIREBASE
          • 357
            Realtime backend made easy
          • 261
            Fast and responsive
          • 233
            Easy setup
          • 206
            Real-time
          • 184
            JSON
          • 126
            Free
          • 120
            Backed by google
          • 80
            Angular adaptor
          • 62
            Reliable
          • 36
            Great customer support
          • 25
            Great documentation
          • 22
            Real-time synchronization
          • 19
            Mobile friendly
          • 17
            Rapid prototyping
          • 12
            Great security
          • 10
            Automatic scaling
          • 9
            Freakingly awesome
          • 8
            Chat
          • 8
            Super fast development
          • 8
            Angularfire is an amazing addition!
          • 6
            Awesome next-gen backend
          • 6
            Ios adaptor
          • 5
            Firebase hosting
          • 5
            Built in user auth/oauth
          • 4
            Very easy to use
          • 3
            Brilliant for startups
          • 3
            It's made development super fast
          • 3
            Great
          • 2
            Low battery consumption
          • 2
            The concurrent updates create a great experience
          • 2
            I can quickly create static web apps with no backend
          • 2
            Great all-round functionality
          • 2
            Speed of light
          • 1
            Easy to use
          • 1
            Good Free Limits
          • 1
            .net
          • 1
            Serverless
          • 1
            Large
          • 1
            JS Offline and Sync suport
          • 1
            Easy Reactjs integration
          • 1
            Faster workflow
          • 1
            Push notification
          CONS OF FIREBASE
          • 25
            Can become expensive
          • 14
            No open source, you depend on external company
          • 14
            Scalability is not infinite
          • 9
            Not Flexible Enough
          • 5
            Cant filter queries
          • 3
            Very unstable server
          • 2
            Too many errors
          • 2
            No Relational Data

          related Firebase posts

          Tassanai Singprom

          This is my stack in Application & Data

          JavaScript PHP HTML5 jQuery Redis Amazon EC2 Ubuntu Sass Vue.js Firebase Laravel Lumen Amazon RDS GraphQL MariaDB

          My Utilities Tools

          Google Analytics Postman Elasticsearch

          My Devops Tools

          Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack

          My Business Tools

          Slack

          See more

          We are starting to work on a web-based platform aiming to connect artists (clients) and professional freelancers (service providers). In-app, timeline-based, real-time communication between users (& storing it), file transfers, and push notifications are essential core features. We are considering using Node.js, ExpressJS, React, MongoDB stack with Socket.IO & Apollo, or maybe using Real-Time Database and functionalities of Firebase.

          See more
          Amazon RDS logo

          Amazon RDS

          10.2K
          6.4K
          752
          Set up, operate, and scale a relational database in the cloud.
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          PROS OF AMAZON RDS
          • 163
            Reliable failovers
          • 154
            Automated backups
          • 129
            Backed by amazon
          • 92
            Db snapshots
          • 86
            Multi-availability
          • 29
            Control iops, fast restore to point of time
          • 27
            Security
          • 23
            Elastic
          • 20
            Automatic software patching
          • 20
            Push-button scaling
          • 4
            Replication
          • 3
            Reliable
          • 2
            Isolation
          CONS OF AMAZON RDS
            Be the first to leave a con

            related Amazon RDS posts

            Ganesa Vijayakumar
            Full Stack Coder | Module Lead | 18 upvotes 路 2M views

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

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

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

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

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

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

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

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

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

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

            Happy Coding! Suggestions are welcome! :)

            Thanks, Ganesa

            See more
            John Kodumal

            As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data鈥攖his is made HA with the use of Patroni and Consul.

            We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

            See more
            Amazon Aurora logo

            Amazon Aurora

            586
            514
            53
            MySQL and PostgreSQL compatible relational database with several times better performance
            586
            514
            + 1
            53
            PROS OF AMAZON AURORA
            • 13
              MySQL compatibility
            • 12
              Better performance
            • 10
              Easy read scalability
            • 8
              Speed
            • 7
              Low latency read replica
            • 2
              High IOPS cost
            • 1
              Good cost performance
            CONS OF AMAZON AURORA
            • 0
              Vendor locking

            related Amazon Aurora posts

            Julien DeFrance
            Principal Software Engineer at Tophatter | 16 upvotes 路 2.2M 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
            Tim Specht
            鈥嶤o-Founder and CTO at Dubsmash | 13 upvotes 路 101K views

            Over the years we have added a wide variety of different storages to our stack including PostgreSQL (some hosted by Heroku, some by Amazon RDS) for storing relational data, Amazon DynamoDB to store non-relational data like recommendations & user connections, or Redis to hold pre-aggregated data to speed up API endpoints.

            Since we started running Postgres ourselves on RDS instead of only using the managed offerings of Heroku, we've gained additional flexibility in scaling our application while reducing costs at the same time.

            We are also heavily testing Amazon RDS for Aurora in its Postgres-compatible version and will also give the new release of Aurora Serverless a try!

            #SqlDatabaseAsAService #NosqlDatabaseAsAService #Databases #PlatformAsAService

            See more