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Amazon RDS for Aurora vs Microsoft SQL Server: What are the differences?
Amazon RDS for Aurora and Microsoft SQL Server are two popular database management systems with several key differences.
Performance: Amazon RDS for Aurora is designed to provide high performance and scalability, with the ability to handle millions of transactions per minute. It uses a distributed relational database architecture to achieve this, while Microsoft SQL Server is optimized for transactional workloads but may struggle with large-scale, high transaction environments.
Data Replication: Aurora uses a unique storage engine that replicates six copies of data across three Availability Zones in a single region, ensuring high availability and durability. On the other hand, Microsoft SQL Server offers various replication options, such as transactional replication, merge replication, and peer-to-peer replication, but the configuration and management can be more complex.
Database Engine: Aurora uses a custom-built MySQL-compatible database engine, which means it is fully compatible with MySQL applications and tools. In contrast, Microsoft SQL Server uses a different database engine, which may require some modifications to MySQL applications for migration.
Pricing: Aurora's pricing model is based on a combination of database instance types and storage usage, with separate rates for read and write operations. Microsoft SQL Server, on the other hand, has a different pricing structure that includes licensing fees based on the number of cores and additional costs for features like high availability and data compression.
Backup and Recovery: Aurora offers automated backup and recovery features, including continuous incremental backups and the ability to restore to any point in time within a five-minute window. Microsoft SQL Server also provides backup and recovery capabilities, but the configuration and management may require more manual intervention.
Community Support: Aurora benefits from the large and active MySQL community, which provides a wealth of resources, tutorials, and documentation. Microsoft SQL Server also has a strong community support, but it may not be as extensive as the MySQL community.
In summary, Amazon RDS for Aurora offers high performance, data replication across Availability Zones, compatibility with MySQL applications, flexible pricing, automated backup and recovery, and a strong community support. On the other hand, Microsoft SQL Server provides optimization for transactional workloads, various replication options, a different pricing structure, backup and recovery capabilities, and a community support network.
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.
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! 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.
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 Amazon Aurora
- MySQL compatibility14
- Better performance12
- Easy read scalability10
- Speed9
- Low latency read replica7
- High IOPS cost2
- Good cost performance1
Pros of Microsoft SQL Server
- Reliable and easy to use139
- High performance101
- Great with .net95
- Works well with .net65
- Easy to maintain56
- Azure support21
- Full Index Support17
- Always on17
- Enterprise manager is fantastic10
- In-Memory OLTP Engine9
- Security is forefront2
- Easy to setup and configure2
- Docker Delivery1
- Columnstore indexes1
- Great documentation1
- Faster Than Oracle1
- Decent management tools1
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Cons of Amazon Aurora
- Vendor locking2
- Rigid schema1
Cons of Microsoft SQL Server
- Expensive Licensing4
- Microsoft2