some real-world failure cases and how to fix them
arguably this shouldn’t be required — building containers without /bin/sh, strace, gdb etc. is just silly
This is the best article on chan culture and how it’s taken over
‘Containerized Data Analytics’:
There are two bold new ideas in Pachyderm: Containers as the core processing primitive Version Control for data These ideas lead directly to a system that’s much more powerful, flexible and easy to use. To process data, you simply create a containerized program which reads and writes to the local filesystem. You can use any tools you want because it’s all just going in a container! Pachyderm will take your container and inject data into it. We’ll then automatically replicate your container, showing each copy a different chunk of data. With this technique, Pachyderm can scale any code you write to process up to petabytes of data (Example: distributed grep). Pachyderm also version controls all data using a commit-based distributed filesystem (PFS), similar to what git does with code. Version control for data has far reaching consequences in a distributed filesystem. You get the full history of your data, can track changes and diffs, collaborate with teammates, and if anything goes wrong you can revert the entire cluster with one click! Version control is also very synergistic with our containerized processing engine. Pachyderm understands how your data changes and thus, as new data is ingested, can run your workload on the diff of the data rather than the whole thing. This means that there’s no difference between a batched job and a streaming job, the same code will work for both!
oh, god — I’m not keen on this take: how’s about designing systems that recognise the risks?
“Everything was going fine, but then suddenly, there were an additional 4,000 votes cast. Because it was a local election, which are normally very small, people were surprised and asked, ‘how did this happen?'” The culprit was not voter fraud or hacked machines. It was a single event upset (SEU), a term describing the fallout of an ionizing particle bouncing off a vulnerable node in the machine’s register, causing it to flip a bit, and log the additional votes. The Sun may not have been the direct source of the particle—cosmic rays from outside the solar system are also in the mix—but solar-influenced space weather certainly contributes to these SEUs.