Just if you use JS make sure to google about how to do money calculations accurately.
I worked at a place that used doubles and there was no fallout. Also when dealing with very small quantities, fixed point might not work so well either.
Personally, as a general rule I use integer's and convert for display only (as mentioned in the above SO post). This removes the accuracy issue and works in every programming language that is in common use today (at least that I know of). I even do this in Postgres (and other DB's) which keeps everything consistent and removes the chance a driver between the DB and client jacks up the decimal conversion.
But that being said, anything really - rails/node etc most would work just fine for your use-case
End result was a lot of wasted effort. It worked for one team who didn't have those requirements and even then they spent more time than they should have and then ripped out debezium and went with standard mysql replication as it doesn't require the entire kafka stack.
My experience with it reaffirmed my standard technique of data migration of write to both and move reads over when ready.
- The tech stack that is best is the one you/team are most comfortable with using.
Specific Answer:
- If you were using Elixir, you could have 1 process (GenServer) per stock ticker that calls the API for just that stock. GenServers are lightweight processes that run concurrently in the BEAM. That process could then have a set of subscribing user processes to notify which would then do an alert to a user. This is assuming you can call an API for one stock vs. having to parse a feed of data. If I were doing this I wouldn't store any of the stock data in any way, its ephemeral and by the time a DB commit happens the price changed.
Caveats:
You mention API so I assume that means you are calling an API, parsing data, (dealing with rate limits) then alerting. I'd caution the use of the term "real-time" as you are farther away from trading data than big name brokers. My intent isn't to downplay the idea, just thinking it through based on the info in the one sentence question.
For example, is this software aimed at the end user or will the startup use it themselves? If the end user will use it (like alerts for their portfolio/watch list), then the definition of "real-time" is going to be lax because you need human interaction. If it's used by an institution for trading, then you will need to choose highly performant language, architecture, and hardware so that your algorithm can place it's orders before other institutions. You would also likely need to integrate with Swift (financial).
So, tech doesn’t really matter, pick something simple and mainstream so you can get help as needed.
Ruby/Rails, Python/Django, Nodejs, Java, whatever. Postgres for a database.
You likely won’t need any exotic cool tech, novel databases etc.
What language you use is up to you/your team. If you like python, I'd look into Airflow or Celery as frameworks to build your workflows.
For java/scala I'd take a look at Google Dataflow or Flink or Kafka Streams, depending on how heavy the "monitor share prices" part is.
I think the major issue you're going to encounter is not building a system that works, but building a process that allows your team to upgrade the system, test new features, debug, etc.
Dataflow, for example, lets you run more or less the same code both on a stream of data and on batches of historical data.
You can start thinking about an efficient implementation when you've got a defined feature set and are seriously thinking about delivering an actual product. And when that happens you can just use which tech works best for which type of service anyway.
It was pretty cool for something that took a couple of days and I believe it still works that way.
