AppSignal was built because we were tired of slow and clumsy monitoring setups. Instead, we built monitoring that’s easy yet powerful - an intuitive interface enables you to figure out what’s happening in no time. Today, our team made finding slow events effortless. We’ve fully overhauled our slow events feature, helping developers to quickly find and fix slow queries and API requests.
In this blog post, let us take a look at how you can plan your infrastructure planning that you may have to do when deploying an operational database cluster on a CDP Private Cloud Base deployment. Note that you may have to do some planning assumptions when designing your initial infrastructure, and it must be flexible enough to scale up or down based on your future needs.
In this blogpost, we are going to take a look at some of the OpDB related security features of a CDP Private Cloud Base deployment. We are going to talk about auditing, different security levels, security features of Data Catalog, and Client Considerations.
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!
