Performance & Scalability
Solutions to the finding the odd one out problem are of interest when one must find missing matches in some dataset, for example when performing data quality analysis for missing event pairs.
This article describes and benchmarks three approaches for a simple variation of this problem and then a hybrid approach for optimal performance.
Variations of the binary gap problem are a classic at coding challenge sites nowadays.
This easy-to-solve, not-so-obvious-to-master problem can highlight how a programmer is aware of binary manipulation and how it affects performance.
This article describes two solutions for a variation of this problem and how their performance stacks against each other.
When you are building a real-time Actor System in Akka.NET, message throughput numbers will be one of your top engineering priorities. One wouldn’t be using a performance scalpel such as Akka.NET if speed wasn’t a main business concern. This article details four approaches you can use right now to improve the message processing and delivery throughput of your actor system.
You’ve been there too. A month ago you wrapped up your first release. You were happy and went for a beer with your workmates. The next month things didn’t go so well. As you start integration testing again, you realize something has gone wrong. You dig down, and discover your app had serialized some useful data to disk not so long ago. Yet, as the new version does its thing, it crashes with a vengeance, when it tries to get that data back from the same place. As you sigh, and realize there won’t be any beer that night, you notice that those serialized bits of data have changed schema between deployments. You think - I do want the app to re-use that data without breaking… So how on Earth do I get around this?
The Partition Problem is a very old conundrum in computer science. The usual question goes: given some buckets of some capacity, and some items of some size, how can I distribute the items by the buckets in an optimal manner?
The answer to this is more complex than it seems at first glance, as it depends on what you mean by some buckets and by optimal.
There are lots of ways of answering these questions and even more ways of implementing solutions for them, from simple greedy algorithms to more complex heuristics based ones.
Today I’ll get you started the basics for a simple and generic distribution algorithm based on a descending greedy approach, with a couple of variations:
