Google Cloud SQL vs PostgreSQL

Overview

Google Cloud SQL

Stacks555

Followers580

Votes46

PostgreSQL

Stacks103.0K

Followers83.9K

Votes3.6K

GitHub Stars19.0K

Forks5.2K

Google Cloud SQL vs PostgreSQL: What are the differences?

Google Cloud SQL and PostgreSQL are both popular database management systems. Let's explore the key differences between them.

Managed Service vs. Community-Driven: Google Cloud SQL is a managed service provided by Google that takes care of database administration tasks, such as backups, patches, and updates. On the other hand, PostgreSQL is an open-source database system that relies on its active community for support and maintenance.
Scalability and Availability: Google Cloud SQL provides automatic scaling and high availability features, allowing users to quickly adjust resources based on demand, and ensuring that the database is always accessible. PostgreSQL, while capable of scaling and achieving high availability, requires manual configuration and setup to achieve similar capabilities.
Integration with Cloud Services: Google Cloud SQL is tightly integrated with other Google Cloud Platform services, such as Google Compute Engine, Cloud Storage, and Cloud Monitoring. This allows for seamless integration and easy access to additional resources. PostgreSQL can also be integrated with various cloud services, but requires additional configuration and setup.
Pricing Model: Google Cloud SQL follows a pay-as-you-go pricing model, where users are billed based on their actual resource usage. PostgreSQL, being an open-source system, does not have a specific pricing model and can be deployed on various hosting providers or on-premises at no additional cost. However, hosting providers may charge for their services.
Performance and Optimization: Google Cloud SQL offers managed performance optimization, automatic backups, and built-in monitoring tools for fine-tuning the database. PostgreSQL requires manual optimization, backups, and monitoring, although there are various third-party tools available to assist with these tasks.
Data Replication and Scaling: Google Cloud SQL provides built-in replication and scaling features, allowing users to easily replicate data across multiple regions and scale resources as needed. PostgreSQL, while capable of replication and scaling, requires manual configuration and setup to achieve similar capabilities.

In summary, Google Cloud SQL is a managed service with seamless integration and automatic scaling, while PostgreSQL is an open-source solution with more manual configuration requirements but flexible deployment options.

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Advice on Google Cloud SQL, PostgreSQL

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

George

Student

Mar 18, 2020

Needs adviceon

PostgreSQL

Python

Django

Hello everyone,

Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

*The project is going to use React for the front-end and GraphQL is going to be used for the API.

Thank you all. Any answer or advice would be really helpful!

620k views620k

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	PostgreSQL
Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.	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.
Familiar Infrastructure;Flexible Charging;Security, Availability, Durability;Easier Migration; No Lock-in;Fully managed	-
Statistics
GitHub Stars -	GitHub Stars 19.0K
GitHub Forks -	GitHub Forks 5.2K
Stacks 555	Stacks 103.0K
Followers 580	Followers 83.9K
Votes 46	Votes 3.6K
Pros & Cons
Pros 13 Fully managed 10 Backed by Google 10 SQL 4 Flexible 3 Automatic Software Patching	Pros 765 Relational database 511 High availability 439 Enterprise class database 383 Sql 304 Sql + nosql Cons 10 Table/index bloatings

What are some alternatives to Google Cloud SQL, PostgreSQL?

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.

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.

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 PostgreSQL: What are the differences?

Google Cloud SQL and PostgreSQL are both popular database management systems. Let's explore the key differences between them.

Managed Service vs. Community-Driven: Google Cloud SQL is a managed service provided by Google that takes care of database administration tasks, such as backups, patches, and updates. On the other hand, PostgreSQL is an open-source database system that relies on its active community for support and maintenance.
Scalability and Availability: Google Cloud SQL provides automatic scaling and high availability features, allowing users to quickly adjust resources based on demand, and ensuring that the database is always accessible. PostgreSQL, while capable of scaling and achieving high availability, requires manual configuration and setup to achieve similar capabilities.
Integration with Cloud Services: Google Cloud SQL is tightly integrated with other Google Cloud Platform services, such as Google Compute Engine, Cloud Storage, and Cloud Monitoring. This allows for seamless integration and easy access to additional resources. PostgreSQL can also be integrated with various cloud services, but requires additional configuration and setup.
Pricing Model: Google Cloud SQL follows a pay-as-you-go pricing model, where users are billed based on their actual resource usage. PostgreSQL, being an open-source system, does not have a specific pricing model and can be deployed on various hosting providers or on-premises at no additional cost. However, hosting providers may charge for their services.
Performance and Optimization: Google Cloud SQL offers managed performance optimization, automatic backups, and built-in monitoring tools for fine-tuning the database. PostgreSQL requires manual optimization, backups, and monitoring, although there are various third-party tools available to assist with these tasks.
Data Replication and Scaling: Google Cloud SQL provides built-in replication and scaling features, allowing users to easily replicate data across multiple regions and scale resources as needed. PostgreSQL, while capable of replication and scaling, requires manual configuration and setup to achieve similar capabilities.

Google Cloud SQL vs PostgreSQL

Overview