Yesterday we’ve encountered a curious problem: we needed use Spark to parse JSON data that was produced by AWS Kinesis. Unfortunately, the data was in a concatenated JSON format. Spark does not support that format, so we had to come up with something our selves.
At Wehkamp we use decoration a lot. Decoration is a nice way of separating concerns from the actual code. Most of our repositories need the same set of decorators: exception logging, latency metrics and Jaeger spans. In this article I’ll be joining these 3 types of decorator into a single Swiss Army Knife decorator: one decorator to rule them all.
Last week I was working on our new cockpit application, which is essentially a list of links to parts of our Wehkamp platform. The old application was not being maintained, as the React-stack is not something that’s in the skill-set of most engineers. We kept the new cockpit simple: plain old HTML. Of course we wanted to support a nice dark-theme as well. This article shows how simple it is to implement dark mode.
This week we’ve been working on processing the access logs from Cloudflare with Databricks (Spark). We now have a job that generates a huge CSV file (+1GB) and sends it on towards by FTP for further processing with an external tool. Creating a DataFrame with the right data was easy. Now, let’s explore how to do a CSV export, secrets management and an FTP transfer!
I operate from the Netherlands and that makes my time zone Central European Summer Time (CEST). The data I handle is usually stored in UTC time. Whenever I need to crunch some data with Spark I struggle to do the right date conversion, especially around summer or winter time (do I need to add 1 or 2 hours?). In this blog, I’ll show how to handle these time zones properly in PySpark.