Amazon RDS vs Google Cloud SQL: What are the differences?
Developers describe Amazon RDS as "Set up, operate, and scale a relational database in the cloud". 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. On the other hand, Google Cloud SQL is detailed as "Store and manage data using a fully-managed, relational MySQL database". MySQL databases deployed in the cloud without a fuss. Google Cloud Platform provides you with powerful databases that run fast, don’t run out of space and give your application the redundant, reliable storage it needs.
Amazon RDS and Google Cloud SQL can be primarily classified as "SQL Database as a Service" tools.
Some of the features offered by Amazon RDS are:
- Pre-configured Parameters
- Monitoring and Metrics
- Automatic Software Patching
On the other hand, Google Cloud SQL provides the following key features:
- Familiar Infrastructure
- Flexible Charging
- Security, Availability, Durability
"Reliable failovers" is the primary reason why developers consider Amazon RDS over the competitors, whereas "Fully managed" was stated as the key factor in picking Google Cloud SQL.
According to the StackShare community, Amazon RDS has a broader approval, being mentioned in 1408 company stacks & 509 developers stacks; compared to Google Cloud SQL, which is listed in 71 company stacks and 28 developer stacks.
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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While we initially started off running our own Postgres cluster, we evaluated RDS and found it to be an excellent fit for us.
The failovers, manual scaling, replication, Postgres upgrades, and pretty much everything else has been super smooth and reliable.
We'll probably need something a little more complex in the future, but RDS performs admirably for now.
We are using RDS for managing PostgreSQL and legacy MSSQL databases.
Unfortunately while RDS works great for managing the PostgreSQL systems, MSSQL is very much a second class citizen and they don't offer very much capability. Infact, in order to upgrade instance storage for MSSQL we actually have to spin up a new cluster and migrate the data over.
Our PostgreSQL servers, where we keep the bulk of Wirkn data, are hosted on the fantastically easy and reliable AWS RDS platform.
We use Aurora for our OLTP database, it provides significant speed increases on top of MySQL without the need to manage it
RDS allows us to replicate the development databases locally as well as making it available to CircleCI.