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This post originally appeared on the Decodable blog. |
In the previous blog post I looked at the role of catalogs in Flink SQL, the different types, and some of the quirks around their configuration and use. If you are new to Flink SQL and catalogs, I would recommend reading that post just to make sure you’re not making some of the same assumptions that I mistakenly did when looking at this for the first time.
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This post originally appeared on the Decodable blog. |
When you’re using Flink SQL you’ll run queries that interact with objects.
An INSERT against a TABLE, a SELECT against a VIEW, for example.
These objects are defined using DDL—but where do these definitions live?
At Decodable we migrated our docs platform onto Antora. I wrote previously about my escapades in getting cross-repository authentication working using Private Access Tokens (PAT). These are fine for just a single user, but they’re tied to that user, which isn’t a good practice for deployment in this case.
In this article I’ll show how to use GitHub Apps and Installation Access Tokens (IAT) instead, and go into some detail on how we’ve deployed Antora. Our GitHub repositories are private which makes it extra-gnarly.
A friend messaged me late last night with the scary news that Google had emailed him about a ton of spammy subdomains on his own domain.
Any idea how this could have happened, he asked?
Why should the Java folk have all the fun?!
My friend and colleague Gunnar Morling launched a fun challenge this week: how fast can you aggregate and summarise a billion rows of data? Cunningly named The One Billion Row Challenge (1BRC for short), it’s aimed at Java coders to look at new features in the language and optimisation techniques.
Not being a Java coder myself, and seeing how the challenge has already unofficially spread to other communities including Rust and Python I thought I’d join in the fun using what I know best: SQL.
Antora is a modern documentation site generator with many nice features including sourcing documentation content from one or more separate git repositories. This means that your docs can be kept under source control (yay 🎉) and in sync with the code of the product that they are documenting (double yay 🎉🎉).
As you would expect for a documentation tool, the Antora documentation is thorough but there was one sharp edge involving GitHub that caught me out which I’ll detail here.
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This post originally appeared on the Decodable blog. |
Welcome to the Checkpoint Chronicle, a monthly roundup of interesting stuff in the data and streaming space. Your hosts and esteemed curators of said content are Gunnar Morling and Robin Moffatt —feel free to send our way any choice nuggets that you think we should feature in future editions.
AI, what a load of hyped-up bollocks, right? Yet here I am, legit writing a blog about it and not for the clickbait but…gasp…because it’s actually useful.
Used correctly, it’s just like any other tool on your desktop. It helps you get stuff done quicker, better—or both.
As a newcomer to Apache Flink one of the first things I did was join the Slack community (which is vendor-neutral and controlled by the Flink PMC). At the moment I’m pretty much in full-time lurker mode, soaking up the kind of questions that people have and how they’re using Flink.
One question that caught my eye was from Marco Villalobos, in which he asked about the Flink JDBC driver and a SQLDataException he was getting with a particular datatype. Now, unfortunately, I have no idea about the answer to this question—but the idea of a JDBC driver through which Flink SQL could be run sounded like a fascinating path to follow after previously looking at the SQL Client.