medusa-store VS Redis

GitHub Repo

Quadtree seems perfect for our use case, we can update the Quadtree every time we receive a new location update from the driver. To reduce the load on the quadtree servers we can use an in-memory datastore such as Redis to cache the latest updates. And with the application of mapping algorithms such as the Hilbert curve, we can perform efficient range queries to find nearby drivers for the customer.

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Especially the client libraries are documentated horribly. Currently checking out redis/py and can't get the alignTimestamp to work on ts.createrule. Does anybody know what I need to pass there? I thought it needs to be epoch time plus whatever milliseconds to get to the timestamp that I want it to start at. But I can't figure out how to use the parameter. Also documentation ts.createrule on redis.io is also too vague. I have yet to find a SINGLE example of how to use alignTimestamp, so I figure it is either useless (as in: doesnt do what it is supposed to do) or so badly documentated nobody knows what to do with it. Either case is a bit sloppy and a shame really.

Web server and Celery communicate via some backend. It could be Redis or RabbitMQ. But we use AWS as a cloud provider. So, we can use the SQS backend. Celery docs says:

Apparently I reinvented the same code several years after Pike:

https://github.com/redis/redis/blob/99ebbee2b260b3466672b4fa...

> …but, make doesn’t scale;

What about redis?

https://github.com/redis/redis/blob/unstable/src/Makefile

Meanwhile, read more about Redis from their official website(Redis)

Simple key-value stores like redis or memcached come to mind. You could then store every sql result there, using the query as key (or a md5 hash of it). If you want to make sure the first latency per day doesn't hit the end user at all you might need to implement a cache-warmup script though.

Checked with the Redis Labs Team. redis.io is opensoruce doc and redis.com is enterprise doc. In both the cases, one edge case is there:

Typically, when a background job is enqueued via ActiveJob, the parameters for the job are passed in as arguments to perform_later. This isn't quite what we want in order to batch jobs together. Instead, we create a new method perform_batch_later that puts the arguments into a data store such as Redis from which the job can later retrieve them.