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. - Source: dev.to / 2 months ago

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? - Source: dev.to / 5 months ago

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. - Source: dev.to / 6 months ago

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! - Source: dev.to / 6 months ago

Want to know more? Check out the QuestDB website and the QuestDB documentation. - Source: dev.to / 8 months ago

If you're serious about scaling up, definitely consider Vespa (https://vespa.ai). At serious scale, Vespa will likely knock all the other options out of the park. - Source: Hacker News / about 1 month ago

Yahoo released their geographic data catalogue under open license and it still lives on as https://whosonfirst.org/ Afaik https://en.wikipedia.org/wiki/Apache_ZooKeeper started at Yahoo https://vespa.ai/ was Yahoo's search engine for news and other content product, now spinned off (https://techcrunch.com/2023/10/04/yahoo-spins-out-vespa-its-search-tech-into-an-independent-company/). - Source: Hacker News / 4 months ago

I think https://vespa.ai/ has the right approach in this space by focusing on being hybrid - vectors alone aren't great for production use cases, it's the combining of vectors+text that lets you use ranking to get meaningful result. (I'm an investor so I'm biased; but it's also the reason why I invested). - Source: Hacker News / 4 months ago

So what’s the catch? Why is this not everywhere? Because IR is not quite NLP — it hasn’t gone fully mainstream, and a lot of the IR frameworks are, quite frankly, a bit of a pain to work with in-production. Some solid efforts to bridge the gap like Vespa [1] are gathering steam, but it’s not quite there. [1] https://vespa.ai. - Source: Hacker News / 5 months ago

When it comes to search I cannot disagree more. https://vespa.ai is a purpose built search engine. If you start bolting search onto your database, your relevance will be terrible, you'll be rewriting a lot of table stakes tools/features from scratch, and your technical debt will skyrocket. - Source: Hacker News / 11 months ago