tlaplus VS QuestDB

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https://github.com/tlaplus/tlaplus/blob/master/tlatools/org....

In any case, our whole team thinks TLA is great, and we're happy people like you and Ron find it so useful and insightful. We also think it is a very insightful.

There are no tests! There are various ways to test concurrent data structures. You could use a stress test, where you spawn a lot of threads and let them mutate the map in a random way and then check the consistency of the map and some invariants. You could learn TLA+ and write a formal model of the map and then verify it.

Dafny and Whiley are two examples with explicit verification support. Idris and other dependently typed languages should all be rich enough to express the required predicate but might not necessarily be able to accept a reasonable implementation as proof. Isabelle, Lean, Coq, and other theorem provers definitely can express the capability but aren't going to churn out much in the way of executable programs; they're more useful to guide an implementation in a more practical functional language but then the proof is separated from the implementation, and you could also use tools like TLA+.

https://dafny.org/

https://whiley.org/

https://www.idris-lang.org/

https://isabelle.in.tum.de/

https://leanprover.github.io/

https://coq.inria.fr/

http://lamport.azurewebsites.net/tla/tla.html

I wish something like Lamport's TLA+ (https://lamport.azurewebsites.net/tla/tla.html) was supported in modern language compilers - perhaps with annotations/macros and a mini formal DSL.

Checking the invariant with assert is also useful in my limited experience with concurrency.

https://lamport.azurewebsites.net/tla/tla.html

One advantage of formal methods is in determining "what was expected" (including all the goofy edge cases) without having to burrow into the details of code.

Take a look at Alloy (http://alloytools.org/) and TLA+ (https://lamport.azurewebsites.net/tla/tla.html) for example. (Or even the ancient Z ("Zed") notation (https://www.cs.cmu.edu/~15819/zedbook.pdf)).

The pcal generator does *not* generate a definition for the set of labels. However, some users have suggested to add such a feature: https://github.com/tlaplus/tlaplus/issues/613

If your data lacks uniform time intervals between consecutive entries, QuestDB offers a solution by allowing you to sample your data. After that, MindsDB facilitates creating, training, and deploying your time-series models.

If you're interested in that functionality or have any other feedback, please drop by our open source repository or community Slack and let us know.

QuestDB is a high-performance time series database with SQL analytics that can power through market data ingestion and analysis. It's open source and integrates well with the tools and languages you use. Check us out!

Looks like full keys are always compared if hash codes test equal, which is what I'd expect. For example: https://github.com/questdb/questdb/blob/master/core/src/main...

But of course, I want to run a QuestDB instance on my node, which uses two additional TCP ports for Influx Line Protocol (ILP) and Pgwire communication with the database. So how can I expose these extra ports on my node and route traffic to the QuestDB container running inside of k3s?

In this post, I will detail a way in which I recently used annotations while writing an operator for my company's product, QuestDB. Hopefully this will give you an idea of how you can incorporate annotations into your own operators to harness their full potential.

QuestDB is an open source, high performance time series database. With its massive ingestion throughput speeds and cost effective operation, QuestDB reduces infrastructure costs and helps you overcome tricky ingestion bottlenecks. Thanks for reading!