Amazon RDS vs Google Cloud SQL

Overview

Amazon RDS

Stacks16.2K

Followers10.8K

Votes761

Google Cloud SQL

Stacks555

Followers580

Votes46

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

Introduction

Amazon RDS and Google Cloud SQL are both managed database services that provide users with a fully-managed solution for SQL databases. They aim to simplify the setup, operation, and scaling of databases, allowing developers to focus on building applications rather than managing infrastructure. While they have similarities in terms of managed database services, there are several key differences between Amazon RDS and Google Cloud SQL.

Pricing Model: The pricing models of Amazon RDS and Google Cloud SQL differ in significant ways. Amazon RDS offers a pay-as-you-go pricing model, where users only pay for the resources they consume. On the other hand, Google Cloud SQL has a tiered pricing model based on the database instance size and usage. This difference in pricing models allows users to choose the option that best fits their budget and usage requirements.
Platform Support: Amazon RDS supports a wide range of database engines, including MySQL, PostgreSQL, Oracle, SQL Server, and Aurora. This provides users with flexibility in choosing the database engine that suits their needs. In contrast, Google Cloud SQL primarily focuses on supporting MySQL and PostgreSQL, with limited support for other database engines. This difference in platform support may influence the choice of database engine for developers.
High Availability: Both Amazon RDS and Google Cloud SQL offer high availability with automatic failover. However, there is a difference in how this is achieved. Amazon RDS uses Multi-AZ deployments, where a standby replica of the database is automatically created in a different Availability Zone. In the event of a failure, Amazon RDS automatically promotes the standby replica to the primary instance. Google Cloud SQL, on the other hand, uses regional availability, where replicas are created in different regions for failover. This difference in high availability configurations may have implications for disaster recovery and performance.
Backup and Restore: Amazon RDS and Google Cloud SQL provide automated backups and restore capabilities. However, there are differences in how these services handle backups and restores. Amazon RDS allows users to schedule automated backups for up to 35 days and provides the ability to restore to any point in time within this retention period. Google Cloud SQL automatically performs daily backups and retains them for up to 7 days. Additionally, Google Cloud SQL offers on-demand backups, which allow users to create a backup at any time. These differences in backup and restore capabilities may influence the recovery options available to users.
Scaling Options: Both Amazon RDS and Google Cloud SQL offer scaling options to handle increased workload demands. Amazon RDS provides the ability to vertically scale database instances by changing the instance type, allowing users to adjust CPU, memory, and storage capacity. It also supports horizontal scaling through Read Replicas, which can offload read traffic from the primary database instance. Google Cloud SQL also supports vertical scaling by allowing users to change the machine type and storage size. However, it does not provide a built-in feature for horizontal scaling. This difference in scaling options may impact the ability to handle increasing workloads effectively.
Integration with Cloud Ecosystem: Amazon RDS is tightly integrated with the wider Amazon Web Services (AWS) ecosystem, providing seamless integration with other AWS services such as Amazon EC2, Amazon S3, and Amazon CloudWatch. This integration allows for easy management and integration of different services within the AWS ecosystem. Google Cloud SQL, on the other hand, integrates with the Google Cloud Platform (GCP) ecosystem, providing seamless integration with other GCP services such as Compute Engine, Cloud Storage, and Stackdriver Monitoring. The choice between Amazon RDS and Google Cloud SQL may depend on the overall cloud ecosystem and services being used.

In summary, the key differences between Amazon RDS and Google Cloud SQL include differences in pricing models, platform support, high availability configurations, backup and restore capabilities, scaling options, and integration with the respective cloud ecosystems. The choice between the two services will depend on specific needs and requirements, considering factors such as budget, database engine preferences, disaster recovery plans, scalability needs, and overall cloud ecosystem.

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Advice on Amazon RDS, Google Cloud SQL

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

Amazon RDS	Google Cloud SQL
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.	Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.
Pre-configured Parameters;Monitoring and Metrics;Automatic Software Patching;Automated Backups;DB Snapshots;DB Event Notifications;Multi-Availability Zone (Multi-AZ) Deployments;Provisioned IOPS;Push-Button Scaling;Automatic Host Replacement;Replication;Isolation and Security	Familiar Infrastructure;Flexible Charging;Security, Availability, Durability;Easier Migration; No Lock-in;Fully managed
Statistics
Stacks 16.2K	Stacks 555
Followers 10.8K	Followers 580
Votes 761	Votes 46
Pros & Cons
Pros 165 Reliable failovers 156 Automated backups 130 Backed by amazon 92 Db snapshots 87 Multi-availability	Pros 13 Fully managed 10 SQL 10 Backed by Google 4 Flexible 3 Automatic Software Patching

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

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.

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 vs Google Cloud SQL: What are the differences?

Introduction

Pricing Model: The pricing models of Amazon RDS and Google Cloud SQL differ in significant ways. Amazon RDS offers a pay-as-you-go pricing model, where users only pay for the resources they consume. On the other hand, Google Cloud SQL has a tiered pricing model based on the database instance size and usage. This difference in pricing models allows users to choose the option that best fits their budget and usage requirements.
Platform Support: Amazon RDS supports a wide range of database engines, including MySQL, PostgreSQL, Oracle, SQL Server, and Aurora. This provides users with flexibility in choosing the database engine that suits their needs. In contrast, Google Cloud SQL primarily focuses on supporting MySQL and PostgreSQL, with limited support for other database engines. This difference in platform support may influence the choice of database engine for developers.
High Availability: Both Amazon RDS and Google Cloud SQL offer high availability with automatic failover. However, there is a difference in how this is achieved. Amazon RDS uses Multi-AZ deployments, where a standby replica of the database is automatically created in a different Availability Zone. In the event of a failure, Amazon RDS automatically promotes the standby replica to the primary instance. Google Cloud SQL, on the other hand, uses regional availability, where replicas are created in different regions for failover. This difference in high availability configurations may have implications for disaster recovery and performance.
Backup and Restore: Amazon RDS and Google Cloud SQL provide automated backups and restore capabilities. However, there are differences in how these services handle backups and restores. Amazon RDS allows users to schedule automated backups for up to 35 days and provides the ability to restore to any point in time within this retention period. Google Cloud SQL automatically performs daily backups and retains them for up to 7 days. Additionally, Google Cloud SQL offers on-demand backups, which allow users to create a backup at any time. These differences in backup and restore capabilities may influence the recovery options available to users.
Scaling Options: Both Amazon RDS and Google Cloud SQL offer scaling options to handle increased workload demands. Amazon RDS provides the ability to vertically scale database instances by changing the instance type, allowing users to adjust CPU, memory, and storage capacity. It also supports horizontal scaling through Read Replicas, which can offload read traffic from the primary database instance. Google Cloud SQL also supports vertical scaling by allowing users to change the machine type and storage size. However, it does not provide a built-in feature for horizontal scaling. This difference in scaling options may impact the ability to handle increasing workloads effectively.
Integration with Cloud Ecosystem: Amazon RDS is tightly integrated with the wider Amazon Web Services (AWS) ecosystem, providing seamless integration with other AWS services such as Amazon EC2, Amazon S3, and Amazon CloudWatch. This integration allows for easy management and integration of different services within the AWS ecosystem. Google Cloud SQL, on the other hand, integrates with the Google Cloud Platform (GCP) ecosystem, providing seamless integration with other GCP services such as Compute Engine, Cloud Storage, and Stackdriver Monitoring. The choice between Amazon RDS and Google Cloud SQL may depend on the overall cloud ecosystem and services being used.

Amazon RDS vs Google Cloud SQL

Overview

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