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  1. Stackups
  2. Application & Data
  3. Relational Databases
  4. SQL Database As A Service
  5. Google Cloud SQL vs Serverless

Google Cloud SQL vs Serverless

OverviewDecisionsComparisonAlternatives

Overview

Google Cloud SQL
Google Cloud SQL
Stacks555
Followers580
Votes46
Serverless
Serverless
Stacks2.2K
Followers1.2K
Votes28
GitHub Stars46.9K
Forks5.7K

Google Cloud SQL vs Serverless: What are the differences?

Introduction

Google Cloud SQL and Serverless are both services provided by Google that offer database management solutions. While they share some similarities, they also have some key differences that set them apart.

  1. Pricing Models: One major difference between Google Cloud SQL and Serverless is their pricing models. Google Cloud SQL follows a standard pricing model where you are charged based on the size of your database instance and the number of vCPUs and memory allocated. On the other hand, Serverless follows a consumption-based pricing model, where you pay only for the actual usage of your database, without having to provision or pay for any fixed compute resources.

  2. Scalability and Elasticity: When it comes to scalability, Google Cloud SQL allows you to scale up and down by manually adjusting the instance size or by enabling automatic scaling. However, Serverless offers automatic scaling by default, which means it can automatically handle high traffic loads and scale the database capacity based on demand without any manual intervention. This gives Serverless a more elastic and flexible nature compared to Google Cloud SQL.

  3. Deployment and Management: Google Cloud SQL requires you to provision and manage the underlying infrastructure for your database instances, including managing backups, failover, and software updates. Serverless, on the other hand, abstracts away the infrastructure management and automates several operational tasks, such as patching, backups, and monitoring. This makes Serverless a more hands-off and easy-to-manage option compared to Google Cloud SQL.

  4. High Availability and Replication: Google Cloud SQL supports replication by offering options for read replicas and failover replicas, ensuring high availability and data redundancy. Serverless, on the other hand, automatically replicates your data across multiple zones, providing built-in high availability without the need for manual configuration of replicas.

  5. Connection Limits: Google Cloud SQL imposes connection limits per instance, depending on the machine type and size of the instance. Serverless, on the other hand, does not have any connection limits, allowing for unlimited concurrent connections to the database.

  6. Supported Database Engines: Google Cloud SQL supports a wide range of database engines, including MySQL, PostgreSQL, and SQL Server. Serverless, however, currently only supports MySQL as the database engine. This limitation should be taken into consideration when choosing between the two services, depending on the specific database engine requirements of your application.

In summary, Google Cloud SQL offers more control over infrastructure and pricing but requires more manual management, while Serverless provides a more hands-off and scalable solution with automatic management and a consumption-based pricing model. Serverless is a great option for those looking for a fully managed and elastic database solution, while Google Cloud SQL provides more flexibility and support for multiple database engines.

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Advice on Google Cloud SQL, Serverless

Tim
Tim

CTO at Checkly Inc.

Sep 18, 2019

Needs adviceonHerokuHerokuAWS LambdaAWS Lambda

When adding a new feature to Checkly rearchitecting some older piece, I tend to pick Heroku for rolling it out. But not always, because sometimes I pick AWS Lambda . The short story:

  • Developer Experience trumps everything.
  • AWS Lambda is cheap. Up to a limit though. This impact not only your wallet.
  • If you need geographic spread, AWS is lonely at the top.

The setup

Recently, I was doing a brainstorm at a startup here in Berlin on the future of their infrastructure. They were ready to move on from their initial, almost 100% Ec2 + Chef based setup. Everything was on the table. But we crossed out a lot quite quickly:

  • Pure, uncut, self hosted Kubernetes — way too much complexity
  • Managed Kubernetes in various flavors — still too much complexity
  • Zeit — Maybe, but no Docker support
  • Elastic Beanstalk — Maybe, bit old but does the job
  • Heroku
  • Lambda

It became clear a mix of PaaS and FaaS was the way to go. What a surprise! That is exactly what I use for Checkly! But when do you pick which model?

I chopped that question up into the following categories:

  • Developer Experience / DX 🤓
  • Ops Experience / OX 🐂 (?)
  • Cost 💵
  • Lock in 🔐

Read the full post linked below for all details

357k views357k
Comments

Detailed Comparison

Google Cloud SQL
Google Cloud SQL
Serverless
Serverless

Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.

Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.

Familiar Infrastructure;Flexible Charging;Security, Availability, Durability;Easier Migration; No Lock-in;Fully managed
-
Statistics
GitHub Stars
-
GitHub Stars
46.9K
GitHub Forks
-
GitHub Forks
5.7K
Stacks
555
Stacks
2.2K
Followers
580
Followers
1.2K
Votes
46
Votes
28
Pros & Cons
Pros
  • 13
    Fully managed
  • 10
    SQL
  • 10
    Backed by Google
  • 4
    Flexible
  • 3
    Automatic Software Patching
Pros
  • 14
    API integration
  • 7
    Supports cloud functions for Google, Azure, and IBM
  • 3
    Lower cost
  • 1
    3. Simplified Management for developers to focus on cod
  • 1
    Openwhisk
Integrations
No integrations available
Azure Functions
Azure Functions
AWS Lambda
AWS Lambda
Amazon API Gateway
Amazon API Gateway

What are some alternatives to Google Cloud SQL, Serverless?

Amazon RDS

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.

AWS Lambda

AWS Lambda

AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.

Azure Functions

Azure Functions

Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.

Google Cloud Run

Google Cloud Run

A managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It's serverless by abstracting away all infrastructure management.

Amazon Aurora

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

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.

Google Cloud Functions

Google Cloud Functions

Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running

Knative

Knative

Knative provides a set of middleware components that are essential to build modern, source-centric, and container-based applications that can run anywhere: on premises, in the cloud, or even in a third-party data center

OpenFaaS

OpenFaaS

Serverless Functions Made Simple for Docker and Kubernetes

Azure SQL Database

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.

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