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Google Cloud SQL vs Microsoft SQL Server: What are the differences?
Google Cloud SQL and Microsoft SQL Server are two popular relational database management systems (RDBMS) widely used by enterprises for data management. Let's explore the key differences between them.
Hosting and Infrastructure: Google Cloud SQL is a fully managed service that allows users to easily deploy, maintain, and scale MySQL or PostgreSQL databases in the Google Cloud Platform. It handles tasks such as hardware provisioning, backups, and software maintenance, allowing users to focus on their applications. Microsoft SQL Server, on the other hand, can be hosted on both on-premises infrastructure and in the cloud. It provides greater flexibility in terms of deployment options but requires users to manage the infrastructure themselves.
Platform Compatibility: Google Cloud SQL is natively integrated with the Google Cloud Platform, offering seamless integration and compatibility with other Google services such as Google App Engine and Google Kubernetes Engine. This enables developers to build and deploy applications that can leverage various Google Cloud services efficiently. Microsoft SQL Server is designed to work well with Microsoft's ecosystem, including Azure cloud services. It offers tight integration with tools like Visual Studio and Microsoft Azure Active Directory, making it a preferred choice for organizations heavily invested in Microsoft technologies.
Scalability: Google Cloud SQL provides automatic scaling capabilities, allowing the database to handle growing workloads. It can instantly allocate more resources, such as CPU and memory, based on demand, ensuring optimal performance. Microsoft SQL Server also offers scalability options but requires manual configuration and management of resources. Scaling can be more complex and time-consuming compared to Google Cloud SQL's automated approach.
Pricing Model: Google Cloud SQL follows a pay-as-you-go model, where users pay based on their actual usage of resources, such as CPU, memory, and storage. This flexibility allows users to optimize costs by adjusting resources as needed. Microsoft SQL Server typically requires users to purchase licenses or subscriptions upfront. This model may involve additional costs for software licenses, support, and maintenance.
Replication and High Availability: Google Cloud SQL provides automated backups and replication, ensuring data durability and high availability. It automatically replicates data across multiple zones within a region, reducing the risk of data loss and providing failover capabilities. Microsoft SQL Server offers various options for replication and high availability, such as SQL Server Always On Availability Groups. However, setting up and managing replication and high availability configurations can be more complex and require additional resources.
Ecosystem and Community: Google Cloud SQL has a growing ecosystem, with support from various third-party tools and libraries. However, it may have a smaller community compared to Microsoft SQL Server, which has a large user base, extensive documentation, and a vast community of developers and resources available.
In summary, Google Cloud SQL is a fully managed service specifically designed for the Google Cloud Platform, offering automated scaling, seamless integration, and pay-as-you-go pricing. On the other hand, Microsoft SQL Server provides greater deployment flexibility, deeper integration with Microsoft's ecosystem, and a larger community.
I am a Microsoft SQL Server programmer who is a bit out of practice. I have been asked to assist on a new project. The overall purpose is to organize a large number of recordings so that they can be searched. I have an enormous music library but my songs are several hours long. I need to include things like time, date and location of the recording. I don't have a problem with the general database design. I have two primary questions:
- I need to use either MySQL or PostgreSQL on a Linux based OS. Which would be better for this application?
- I have not dealt with a sound based data type before. How do I store that and put it in a table? Thank you.
Hi Erin,
Honestly both databases will do the job just fine. I personally prefer Postgres.
Much more important is how you store the audio. While you could technically use a blob type column, it's really not ideal to be storing audio files which are "several hours long" in a database row. Instead consider storing the audio files in an object store (hosted options include backblaze b2 or aws s3) and persisting the key (which references that object) in your database column.
Hi Erin, Chances are you would want to store the files in a blob type. Both MySQL and Postgres support this. Can you explain a little more about your need to store the files in the database? I may be more effective to store the files on a file system or something like S3. To answer your qustion based on what you are descibing I would slighly lean towards PostgreSQL since it tends to be a little better on the data warehousing side.
Hi Erin! First of all, you'd probably want to go with a managed service. Don't spin up your own MySQL installation on your own Linux box. If you are on AWS, thet have different offerings for database services. Standard RDS vs. Aurora. Aurora would be my preferred choice given the benefits it offers, storage optimizations it comes with... etc. Such managed services easily allow you to apply new security patches and upgrades, set up backups, replication... etc. Doing this on your own would either be risky, inefficient, or you might just give up. As far as which database to chose, you'll have the choice between Postgresql, MySQL, Maria DB, SQL Server... etc. I personally would recommend MySQL (latest version available), as the official tooling for it (MySQL Workbench) is great, stable, and moreover free. Other database services exist, I'd recommend you also explore Dynamo DB.
Regardless, you'd certainly only keep high-level records, meta data in Database, and the actual files, most-likely in S3, so that you can keep all options open in terms of what you'll do with them.
Hey Erin! I would recommend checking out Directus before you start work on building your own app for them. I just stumbled upon it, and so far extremely happy with the functionalities. If your client is just looking for a simple web app for their own data, then Directus may be a great option. It offers "database mirroring", so that you can connect it to any database and set up functionality around it!
Hi Erin,
- Coming from "Big" DB engines, such as Oracle or MSSQL, go for PostgreSQL. You'll get all the features you need with PostgreSQL.
- Your case seems to point to a "NoSQL" or Document Database use case. Since you get covered on this with PostgreSQL which achieves excellent performances on JSON based objects, this is a second reason to choose PostgreSQL. MongoDB might be an excellent option as well if you need "sharding" and excellent map-reduce mechanisms for very massive data sets. You really should investigate the NoSQL option for your use case.
- Starting with AWS Aurora is an excellent advise. since "vendor lock-in" is limited, but I did not check for JSON based object / NoSQL features.
- If you stick to Linux server, the PostgreSQL or MySQL provided with your distribution are straightforward to install (i.e. apt install postgresql). For PostgreSQL, make sure you're comfortable with the pg_hba.conf, especially for IP restrictions & accesses.
Regards,
I recommend Postgres as well. Superior performance overall and a more robust architecture.
Pros of Google Cloud SQL
- Fully managed13
- Backed by Google10
- SQL10
- Flexible4
- Encryption at rest and transit3
- Automatic Software Patching3
- Replication across multiple zone by default3
Pros of Microsoft SQL Server
- Reliable and easy to use139
- High performance101
- Great with .net95
- Works well with .net65
- Easy to maintain56
- Azure support21
- Always on17
- Full Index Support17
- Enterprise manager is fantastic10
- In-Memory OLTP Engine9
- Easy to setup and configure2
- Security is forefront2
- Great documentation1
- Faster Than Oracle1
- Columnstore indexes1
- Decent management tools1
- Docker Delivery1
- Max numar of connection is 140001
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Cons of Google Cloud SQL
Cons of Microsoft SQL Server
- Expensive Licensing4
- Microsoft2
- Data pages is only 8k1
- Allwayon can loose data in asycronious mode1
- Replication can loose the data1
- The maximum number of connections is only 14000 connect1
