Skip to main content

Using actors as a throttle

I've been working on a web shop integration project from time to time for last six months. The use case is that there are several different instances of a webshop (slave) and one master shop. All product information should be integrated from the master shop to the slaves on regular intervals. There are also some business rules applied to the products upon integration.

The integration is set to happen on a certain time of day. It is a relatively long process because it checks through all the products in all shops. That's fine though, there is no requirements on how quick the integration should be. It should rather be a resource constrained process so it would not affect the users using the web shops.

Originally I thought this would be a perfect use case for serverless application running in AWS Lambda for example. I also tried out OpenWhisk from IBM which can be run as a self-hosted serverless platform. While it would have been interesting to try out those technologies I ended up using my DigitalOcean virtual server purely to save costs.

I wrote the app with Scala. I wanted to know the language better and also wanted to see how Akka works in the language it is built in (if all you have is a hammer...). The application is really simple, one just triggers the integration task, it will connect to the webshops to fetch all their product information, cache that to RocksDB, run the transformation rules and the updates the slave webshops.

The application has a configurable amount of workers which do the operations (fetch product data, store it...). The workers are essentially actors which do a lot of blocking operations. This is usually a problem especially if the thread pool for Akka is the default one. The operations using REST APIs of the shops are not exactly fast which can result in quick drainage of Akka threads.

The Akka default pool can be configured to have a fixed number of threads on startup. The amount of workers is derived from the maximum threads available. Each shop interface has a set of workers which is roughly calculated from the amount of threads Akka has divided by the number of shops minus a constant to give some processing time to the aggregate actors. This ends up working pretty well especially when I want to limit the load to the webshops and also keeps the integration application itself from hogging all my 1 core virtual machine resources.

Comments

Popular posts from this blog

I'm not a passionate developer

A family friend of mine is an airlane pilot. A dream job for most, right? As a child, I certainly thought so. Now that I can have grown-up talks with him, I have discovered a more accurate description of his profession. He says that the truth about the job is that it is boring. To me, that is not that surprising. Airplanes are cool and all, but when you are in the middle of the Atlantic sitting next to the colleague you have been talking to past five years, how stimulating can that be? When he says the job is boring, it is not a bad kind of boring. It is a very specific boring. The "boring" you would want as a passenger. Uneventful.  Yet, he loves his job. According to him, an experienced pilot is most pleased when each and every tiny thing in the flight plan - goes according to plan. Passengers in the cabin of an expert pilot sit in the comfort of not even noticing who is flying. As someone employed in a field where being boring is not exactly in high demand, this sounds pro...

Bird is causing high CPU on my macOS

There is no lack of people complaining about MacOS Tahoe, mostly about rounded corners and inconsistent design decisions. I have not paid that much attentention to that, but there is one mac bug i have paid attention to. It hasn't been a visual or ux but rather few system processes pegging the CPU. trustd , alongside with ecosystemd and ecosystemanalyticsd all reported high CPU usage. I can't exactly recall when this started, it might have predated my Tahoe upgrade but anyway, the trio of processes all had high CPU usage. Sure, it may have been the virtual efficiency cores and whether it affeced battery life or slowed down other processes i don't know to be hon...

Ousterhout's law

Back in the day, everyone was using Winamp. It is a music player with the user interface of a mixing console. The bloody thing had an equalizer on the front page! As an amateur music producer, I know that EQ is a powerful tool but making changes that sound good is difficult, to say the least. Mixing engineers spend considerable effort with the artist to balance the frequency ranges to arrive at the desired musical outcome. I bet the 15-year-old me butchered a lot of songs with the thing. Now we are using Spotify, a player with basically a search bar and a play button. I ran into something called Ousterhout's Law on the  Operating Systems: Three Easy Pieces book . Here is a quote from the book TIP: AVOID VOO-DOO CONSTANTS (OUSTERHOUT’S LAW) Avoiding voo-doo constants is a good idea whenever possible. Unfortunately, as in the example above, it is often difficult. One could try to make the system learn a good value, but that too is not straightforward. The frequent result: a configura...