Google Cloud SQL vs MySQL

Overview

Google Cloud SQL

Stacks555

Followers580

Votes46

MySQL

Stacks129.6K

Followers108.6K

Votes3.8K

GitHub Stars11.8K

Forks4.1K

Google Cloud SQL vs MySQL: What are the differences?

Google Cloud SQL is a fully managed relational database service provided by Google Cloud Platform, offering MySQL, PostgreSQL, and SQL Server as database options, while MySQL is an open-source relational database management system widely used for building scalable and reliable applications across various platforms. Let's explore the key differences between them.

Deployment and Management: Google Cloud SQL provides a fully managed service, which means that Google takes care of deploying, managing, and scaling the database for you. On the other hand, MySQL requires manual installation, configuration, and maintenance, making it less convenient for developers who prefer a hands-off approach.
Scalability and Performance: Google Cloud SQL offers horizontal scalability by automatically distributing data across multiple instances, allowing for high availability and improved performance. Whereas, MySQL requires manual sharding or clustering to achieve scalability, making it more complicated and time-consuming.
High Availability and Failover: Google Cloud SQL automatically replicates data across multiple zones for failover and provides automatic backups and point-in-time recovery. In contrast, MySQL requires manual configuration and implementation of replication and backup strategies for high availability and failover scenarios.
Security Features: Google Cloud SQL provides built-in security features such as encryption at rest and in transit, automatic security patching, and integrated IAM access controls. MySQL, on the other hand, requires manual configuration and implementation of security measures, making it less secure by default.
Integration with Google Cloud Services: Google Cloud SQL seamlessly integrates with other Google Cloud services like BigQuery, Cloud Storage, and Dataflow, allowing for easy data analysis, storage, and processing. MySQL requires additional configuration and setup to integrate with these services, adding complexity and potentially reducing efficiency.
Pricing and Cost: Google Cloud SQL offers a pay-as-you-go pricing model where you only pay for the resources you use. In contrast, MySQL requires upfront hardware and software investments, along with ongoing maintenance costs, making it less cost-effective, especially for small businesses and startups.

In summary, Google Cloud SQL offers a fully managed service with easy deployment, scalability, high availability, security features, seamless integration, and cost-effectiveness, while MySQL requires manual configuration, lacks native integrations, and involves higher upfront and ongoing costs.

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Advice on Google Cloud SQL, MySQL

Kyle

Web Application Developer at Redacted DevWorks

Dec 3, 2019

Decidedon

PostGIS

While there's been some very clever techniques that has allowed non-natively supported geo querying to be performed, it is incredibly slow in the long game and error prone at best.

MySQL finally introduced it's own GEO functions and special indexing operations for GIS type data. I prototyped with this, as MySQL is the most familiar database to me. But no matter what I did with it, how much tuning i'd give it, how much I played with it, the results would come back inconsistent.

It was very disappointing.

I figured, at this point, that SQL Server, being an enterprise solution authored by one of the biggest worldwide software developers in the world, Microsoft, might contain some decent GIS in it.

I was very disappointed.

Postgres is a Database solution i'm still getting familiar with, but I noticed it had no built in support for GIS. So I hilariously didn't pay it too much attention. That was until I stumbled upon PostGIS and my world changed forever.

449k views449k

Comments

Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k

Comments

Navraj

CEO at SuPragma

Apr 16, 2020

Needs adviceon

MySQL

PostgreSQL

I asked my last question incorrectly. Rephrasing it here.

I am looking for the most secure open source database for my project I'm starting: https://github.com/SuPragma/SuPragma/wiki

Which database is more secure? MySQL or PostgreSQL? Are there others I should be considering? Is it possible to change the encryption keys dynamically?

Thanks,

Raj

401k views401k

Comments

Detailed Comparison

Google Cloud SQL	MySQL
Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.	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.
Familiar Infrastructure;Flexible Charging;Security, Availability, Durability;Easier Migration; No Lock-in;Fully managed	-
Statistics
GitHub Stars -	GitHub Stars 11.8K
GitHub Forks -	GitHub Forks 4.1K
Stacks 555	Stacks 129.6K
Followers 580	Followers 108.6K
Votes 46	Votes 3.8K
Pros & Cons
Pros 13 Fully managed 10 Backed by Google 10 SQL 4 Flexible 3 Encryption at rest and transit	Pros 800 Sql 679 Free 562 Easy 528 Widely used 490 Open source Cons 16 Owned by a company with their own agenda 3 Can't roll back schema changes

What are some alternatives to Google Cloud SQL, MySQL?

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 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.

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.

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 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.

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.

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.

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.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

Google Cloud SQL vs MySQL: What are the differences?

Deployment and Management: Google Cloud SQL provides a fully managed service, which means that Google takes care of deploying, managing, and scaling the database for you. On the other hand, MySQL requires manual installation, configuration, and maintenance, making it less convenient for developers who prefer a hands-off approach.
Scalability and Performance: Google Cloud SQL offers horizontal scalability by automatically distributing data across multiple instances, allowing for high availability and improved performance. Whereas, MySQL requires manual sharding or clustering to achieve scalability, making it more complicated and time-consuming.
High Availability and Failover: Google Cloud SQL automatically replicates data across multiple zones for failover and provides automatic backups and point-in-time recovery. In contrast, MySQL requires manual configuration and implementation of replication and backup strategies for high availability and failover scenarios.
Security Features: Google Cloud SQL provides built-in security features such as encryption at rest and in transit, automatic security patching, and integrated IAM access controls. MySQL, on the other hand, requires manual configuration and implementation of security measures, making it less secure by default.
Integration with Google Cloud Services: Google Cloud SQL seamlessly integrates with other Google Cloud services like BigQuery, Cloud Storage, and Dataflow, allowing for easy data analysis, storage, and processing. MySQL requires additional configuration and setup to integrate with these services, adding complexity and potentially reducing efficiency.
Pricing and Cost: Google Cloud SQL offers a pay-as-you-go pricing model where you only pay for the resources you use. In contrast, MySQL requires upfront hardware and software investments, along with ongoing maintenance costs, making it less cost-effective, especially for small businesses and startups.

Google Cloud SQL vs MySQL

Overview