Choosing a Database

If you're in charge of a startup's tech, one of your big decisions will be picking a database. In this article, we'll talk about why Postgres and MySQL are usually great choices for most database needs, when you might want to think about other options, and what those other options could be.

Starting with PostgreSQL

If you're not sure where to start, just go with Postgres. It's a solid, feature-rich database system that's good for lots of different needs.

On top of the basic functions of a RDBMS, Postgres offers:

• PGVector: This add-on lets Postgres work as a vector database. This means it can be used for things like similarity search and machine learning.
• JSON Support: Postgres gives a lot of support for JSON, which lets it handle schema-less data and gives it some of the flexibility of NoSQL databases.

These features, along with its ACID transactions, multi-version concurrency control, and extensibility, make Postgres a powerful and versatile tool.

Why MySQL is Also a Good Pick

Like Postgres, MySQL is a well-known, open-source database system with a big community and regular updates. While the two have a lot in common, there are a few key differences:

• JSON Support: Even though both support JSON, Postgres has more advanced functions, like JSON indexing.
• Replication: MySQL usually has a simpler replication setup and offers more choices. This makes it potentially a better fit for applications that need a lot of reading.
• ACID Compliance: Both databases are ACID-compliant. But Postgres is fully ACID-compliant right out of the box, while in MySQL, you need to use the InnoDB storage engine.

These differences mean that whether you pick Postgres or MySQL will depend on what your project needs.

When Not to Use NoSQL

Some alternatives to consider are NoSQL databases, especially MongoDB. They're attractive because they offer schema-less data modeling. The idea of "not having to think about the data model" can be really appealing, especially during the prototyping phase.

However, be careful here. While this flexibility can speed up the start of development, it often leads to sloppy data management and relational modeling using NoSQL. The result? You might end up stuck with a tool that's not great for your relational data.

To put it simply: MongoDB isn't bad, and there are good reasons to use NoSQL. But if you're choosing NoSQL mainly for the schema-less benefit, you might be setting yourself up for problems.

Extensions and Cloud Versions

The digital landscape now includes cloud versions of popular databases like AWS Aurora. Aurora works with both MySQL and Postgres, while offering the performance and availability of commercial databases at a fraction of the cost.

TimescaleDB and CockroachDB are other options, built as extensions of Postgres. They offer scalability and improved performance for time-series data and geo-distributed deployments, respectively.

When to Consider Other Database Types

While Postgres and MySQL are excellent starting points, there are times when other options might be a better fit. For example, you might consider a NoSQL database like Google's BigTable for handling petabytes of data, or Amazon's DynamoDB for applications that need low-latency data access at any scale.

For global online transaction processing, Google's Cloud Spanner offers horizontal scalability without giving up ACID compliance. Like any tool, these databases have their strengths and are best used when those strengths match what your project needs.

In Conclusion

Choosing a database is a critical decision for any startup that wants to scale. While Postgres and MySQL are strong, versatile foundations, understanding your project's specific needs will tell you when to think about alternatives. Remember, the best database for your project is the one that aligns closely with your project's requirements and strategic goals, even if it might seem 'boring'.