In the previous exercise I felt my absence of a formal CompSci background with the introduction of Binary Sorted Trees, and now I am concious of it again with learning about mutex. I’d heard of them before, mostly when Oracle performance folk were talking about wait types - TIL it stands for mutual exclusion!
I’ve been playing around with the new SerDes (serialisers/deserialisers) that shipped with Confluent Platform 5.5 - Protobuf, and JSON Schema (these were added to the existing support for Avro). The serialisers (and associated Kafka Connect converters) take a payload and serialise it into bytes for sending to Kafka, and I was interested in what those bytes look like. For that I used my favourite Kafka swiss-army knife: kafkacat.
Like Interfaces, the Tour didn’t really do it for me on Errors either. Too absract, and not enough explanation of the code examples for my liking. It also doesn’t cover the errors package which other tutorial do. I’m not clear if that’s because the errors package isn’t used much, or the Tour focusses only on teaching the raw basics.
This is probably bread-and-butter for any seasoned programmer, but I enjoyed the simple process and satisfaction of breaking the problem down into steps to solve using what the tutorial had just covered. Sketching out the logic in pseudo-code first, I figured that I wanted to do this:
My background is not a traditional CompSci one. I studied Music at university, and managed to wangle my way into IT through various means, ending up doing what I do now with no formal training in coding, and a grab-bag of hacky programming attempts on my CV. My weapons of choice have been BBC Basic, VBA, ASP, and more recently some very unpythonic-Python. It’s got me by, but I figured recently I’d like to learn something new, and several people pointed to Go as a good option.
For whatever reason, CSV still exists as a ubiquitous data interchange format. It doesn’t get much simpler: chuck some plaintext with fields separated by commas into a file and stick .csv on the end. If you’re feeling helpful you can include a header row with field names in.
Alfred is one of my favourite productivity apps for the Mac. It’s a file indexer, a clipboard manager, a snippet expander - and that’s just scratching the surface really. I recently got a new machine without it installed and realised just how much I rely on Alfred, particularly its clipboard manager.
Imagine you’ve got a stream of data; it’s not “big data,” but it’s certainly a lot. Within the data, you’ve got some bits you’re interested in, and of those bits, you’d like to be able to query information about them at any point. Sounds fun, right?
What if you didn’t need any datastore other than Apache Kafka itself to be able to do this? What if you could ingest, filter, enrich, aggregate, and query data with just Kafka?
Screenflow has a useful Markers feature for adding notes to the timeline.
You can use these to helpfully add a table of contents to your Youtube video, but unfortunately Screenflow doesn’t have the option to export them directly. Instead, use the free Subler program as an intermediary (download it from here).
Export from Screenflow with a chapters track
Open the file in Subler and export to text file
From there, tidy up the text file from the source
☁️Confluent Cloud is a great solution for a hosted and managed Apache Kafka service, with the additional benefits of Confluent Platform such as ksqlDB and managed Kafka Connect connectors. But as a developer, you won’t always have a reliable internet connection. Train, planes, and automobiles—not to mention crappy hotel or conference Wi-Fi. Wouldn’t it be useful if you could have a replica of your Cloud data on your local machine?