Amazon RDS for Aurora vs Google Cloud SQL

Overview

Google Cloud SQL

Stacks555

Followers580

Votes46

Amazon Aurora

Stacks807

Followers745

Votes55

Amazon RDS for Aurora vs Google Cloud SQL: What are the differences?

Amazon RDS for Aurora and Google Cloud SQL are managed database services that provide scalable and highly available relational databases. Let's explore the key differences between them.

Pricing Models: Amazon RDS for Aurora follows a pay-as-you-go pricing model, where you pay for the resources you consume. On the other hand, Google Cloud SQL offers pricing options based on the number of vCPUs and the amount of memory allocated to your instances.
Replication: Aurora provides an optimized and scalable database replication mechanism, called Aurora Multi-Master, which allows for multiple active database writes across multiple Availability Zones. Google Cloud SQL, on the other hand, offers basic asynchronous replication across zones or regions for disaster recovery purposes.
High Availability: Amazon Aurora automatically replicates your data across multiple Availability Zones for high availability and data durability. In the event of a database failure, Aurora automatically fails over to a replica without any data loss. Google Cloud SQL also offers high availability through automatic failover to a replica instance, but the availability zone selection is more manual.
Scaling: Aurora supports both vertical and horizontal scaling. Vertical scaling involves increasing the instance size to handle higher workloads, while horizontal scaling involves adding read replicas to offload read traffic. Google Cloud SQL also supports vertical scaling by allowing you to change the machine type or increase the storage capacity. However, it does not provide built-in support for horizontal scaling.
Database Compatibility: Amazon Aurora is compatible with MySQL and PostgreSQL, providing the benefits of these databases along with additional performance and scalability enhancements. Google Cloud SQL supports MySQL, PostgreSQL, and SQL Server, offering native support for the respective database engines.
Monitoring and Management Tools: Amazon RDS for Aurora offers a comprehensive set of monitoring and management tools, including Amazon CloudWatch for monitoring, AWS Database Migration Service for easy migration, and AWS Management Console for database administration. Google Cloud SQL also provides monitoring and management tools, such as Cloud Monitoring and Stackdriver Logging, along with the Google Cloud Console for administration.

In summary, Aurora is known for its performance optimization and replication features, tightly integrated with the AWS ecosystem. Google Cloud SQL, on the other hand, emphasizes ease of use and seamless integration with Google Cloud services.

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Advice on Google Cloud SQL, Amazon Aurora

Phillip

Developer at Coach Align

Mar 18, 2021

Decided

Using on-demand read/write capacity while we scale our userbase - means that we're well within the free-tier on AWS while we scale the business and evaluate traffic patterns.

Using single-table design, which is dead simple using Jeremy Daly's dynamodb-toolbox library

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Detailed Comparison

Google Cloud SQL	Amazon Aurora
Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.	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.
Familiar Infrastructure;Flexible Charging;Security, Availability, Durability;Easier Migration; No Lock-in;Fully managed	High Throughput with Low Jitter;Push-button Compute Scaling;Storage Auto-scaling;Amazon Aurora Replicas;Instance Monitoring and Repair;Fault-tolerant and Self-healing Storage;Automatic, Continuous, Incremental Backups and Point-in-time Restore;Database Snapshots;Resource-level Permissions;Easy Migration;Monitoring and Metrics
Statistics
Stacks 555	Stacks 807
Followers 580	Followers 745
Votes 46	Votes 55
Pros & Cons
Pros 13 Fully managed 10 Backed by Google 10 SQL 4 Flexible 3 Automatic Software Patching	Pros 14 MySQL compatibility 12 Better performance 10 Easy read scalability 9 Speed 7 Low latency read replica Cons 2 Vendor locking 1 Rigid schema
Integrations
No integrations available	PostgreSQL MySQL

What are some alternatives to Google Cloud SQL, Amazon Aurora?

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.

ClearDB

ClearDB uses a combination of advanced replication techniques, advanced cluster technology, and layered web services to provide you with a MySQL database that is "smarter" than usual.

Azure SQL Database

It is the intelligent, scalable, cloud database service that provides the broadest SQL Server engine compatibility and up to a 212% return on investment. It is a database service that can quickly and efficiently scale to meet demand, is automatically highly available, and supports a variety of third party software.

Cloud DB for Mysql

It is a fully managed cloud cache service that enables you to easily configure a MySQL database with a few settings and clicks and operate it reliably with NAVER's optimization settings, and that automatically recovers from failures.

PlanetScaleDB

It is a fully managed cloud native database-as-a-service built on Vitess and Kubernetes. A MySQL compatible highly scalable database. Effortlessly deploy, manage, and monitor your databases in multiple regions and across cloud providers.

DigitalOcean Managed Databases

Build apps and store data in minutes with easy access to one or more databases and sleep better knowing your data is backed up and optimized.

Azure Database for MySQL

Azure Database for MySQL provides a managed database service for app development and deployment that allows you to stand up a MySQL database in minutes and scale on the fly – on the cloud you trust most.

Books

It is an immutable double-entry accounting database service. It supports many clients and businesses at global scale, leaning on Google Cloud Spanner and Google Kubernetes Engine to make that possible.

Aiven

A fully-managed and hosted database as a service (DBaaS) that provides enterprises of every size access to secure and scalable open-source database and messaging services on all major clouds across the globe.

Amazon Aurora Serverless

It is an on-demand, autoscaling configuration for Amazon Aurora. It automatically starts up, shuts down, and scales capacity up or down based on your application's needs. You can run your database on AWS without managing database capacity.

Related Comparisons

Amazon RDS for Aurora vs Google Cloud SQL: What are the differences?

Amazon RDS for Aurora and Google Cloud SQL are managed database services that provide scalable and highly available relational databases. Let's explore the key differences between them.

Pricing Models: Amazon RDS for Aurora follows a pay-as-you-go pricing model, where you pay for the resources you consume. On the other hand, Google Cloud SQL offers pricing options based on the number of vCPUs and the amount of memory allocated to your instances.
Replication: Aurora provides an optimized and scalable database replication mechanism, called Aurora Multi-Master, which allows for multiple active database writes across multiple Availability Zones. Google Cloud SQL, on the other hand, offers basic asynchronous replication across zones or regions for disaster recovery purposes.
High Availability: Amazon Aurora automatically replicates your data across multiple Availability Zones for high availability and data durability. In the event of a database failure, Aurora automatically fails over to a replica without any data loss. Google Cloud SQL also offers high availability through automatic failover to a replica instance, but the availability zone selection is more manual.
Scaling: Aurora supports both vertical and horizontal scaling. Vertical scaling involves increasing the instance size to handle higher workloads, while horizontal scaling involves adding read replicas to offload read traffic. Google Cloud SQL also supports vertical scaling by allowing you to change the machine type or increase the storage capacity. However, it does not provide built-in support for horizontal scaling.
Database Compatibility: Amazon Aurora is compatible with MySQL and PostgreSQL, providing the benefits of these databases along with additional performance and scalability enhancements. Google Cloud SQL supports MySQL, PostgreSQL, and SQL Server, offering native support for the respective database engines.
Monitoring and Management Tools: Amazon RDS for Aurora offers a comprehensive set of monitoring and management tools, including Amazon CloudWatch for monitoring, AWS Database Migration Service for easy migration, and AWS Management Console for database administration. Google Cloud SQL also provides monitoring and management tools, such as Cloud Monitoring and Stackdriver Logging, along with the Google Cloud Console for administration.

Amazon RDS for Aurora vs Google Cloud SQL

Overview