As a follow up to our previous blog post about retrieving related data from that REST API, I wanted to dive deeper into the power of the “related records” feature of DreamFactory’s database-backed APIs. One of the powerful features of SQL databases is the ability to relate separate data records and perform queries that retrieve that related data all at once. One problem is that this requires a fairly in-depth knowledge of your database, it’s schema, and SQL syntax.

One of the Scout’s key features is its ability to quickly highlight N+1 queries in your application that you might not have been aware of, and then show you the exact line of code that you need to look at in order to fix it. In this video, we will use a Ruby on Rails application as an example, but the same concepts apply to other popular web frameworks.

Few things are scarier than a database slowly losing integrity over weeks or years. For a while, nobody notices anything. Then users start reporting bugs, yet you can't find any code that's broken. By the time you realize the problem, it may be happening for so long that your backups are unusable. We can avoid problems like these with skillful use of transactions.

The N+1 Queries Problem is a perennial database performance issue. It affects many ORM’s and custom SQL code, and Django’s ORM is not immune either. In this post, we’ll examine what the N+1 Queries Problem looks like in Django, some tools for fixing it, and most importantly some tools for detecting it. Naturally, Scout is one of those tools, with its built-in N+1 Insights tool.

When it comes to data, let’s start with the obvious. Averages suck. As developers, we all know that percentiles are much more useful. Metrics like P90, P95, P99 give us a much better indication of how our software is performing. The challenge, historically, is how to track the underlying data and calculate the percentiles. Today I will show you how amazingly easy it is to aggregate and create SQL based percentile rollups with Postgresql and t-digest histograms!

Although the differences between PostgreSQL vs. MySQL lessen with each passing year, both database systems continue to offer distinct advantages for different use cases. In this guide, we’ll take a detailed look at the characteristics of PostgreSQL vs. MySQL – and why you might choose one DBMS over the other.

Oracle Database is one of the world’s leading relational database management systems (RDBMS) offering high performance, robust security, and a wide range of capabilities for businesses who want to build and deploy applications in the cloud. Here’s how DreamFactory works best with Oracle.

The Cloudera Operational Database (COD) experience is a managed dbPaaS solution which abstracts the underlying cluster instance as a Database. It can auto-scale based on the workload utilization of the cluster and will be adding the ability to auto-tune (better performance within the existing infrastructure footprint) and auto-heal (resolve operational problems automatically) later this year.

Cloudera’s Operational Database provides unparalleled scale and flexibility for applications, enabling enterprises to bring together and process data of all types and from more sources, while providing developers with the flexibility they need. In this blog, we’ll look into capabilities that make Operational Database the right choice for hyperscale.