I've recently been wondering if Lemmy should switch out NGINX for Caddy, while I hadn't had experience with Caddy it looks like a great & fast alternative, What do you all think?
EDIT: I meant beehaw not Lemmy as a whole
this post was submitted on 16 Jun 2023
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The problems I see with Lemmy performance all point to SQL being poorly optimized. In particular, federation is doing database inserts of new content from other servers - and many servers can be incoming at the same time with their new postings, comments, votes. Priority is not given to interactive webapp/API users.
Using a SQL database for a backend of a website with unique data all over the place is very tricky. You have to really program the app to avoid touching the database and create cached output and incoming queues and such when you can. Reddit (at lest 9 years ago when they open sourced it) is also based on PostgreSQL - and you will see they do not do live SQL inserts into comments like Lemmy does - they queue them using something other than the main database then insert them in batch.
email MTA apps I've seen do the same thing, they queue files to disk before putting into the main database.
I don't think nginx is the problem, the bottleneck is the backend of the backend, PostgreSQL doing all that I/O and record locking.
nginx 100% isn't the problem, and you're right on all counts. I'll also add that I've seen reports that Lemmy has some pretty poorly optimized SQL queries.
They need to add support for a message broker system like RabbitMQ. That way their poor postgres instance stops being the bottleneck.
PostgreSQL is tricky to get right and I can't fault anyone for wanting different solution like RabbitMQ to workaround it. One of the thing I did back in the day was that when dealing with high-write traffic and the data itself is not mission critical, I would set up a tmpfs on Linux for specified amount of RAM to serves as a cache to create a duplicate of the same data table used for storing on SSD/HDD and then I create a view that combines them both where it would check the cache first before querying the HDD/SSD.
During an insert/update statement, it would trigger a condition that increment a variable (semaphore) and if reached a certain value, it would run a partitioned check on the cache table and scan for any old data that aren't in active use based on timestamp and then have those written to HDD/SSD as well as writing to HDD/SSD if the data have been on cache long enough. Doing it this way, i was able to increase the throughput more than a 100 folds and still have data that can be retained on database.
Obviously, there are going to be some additional risks incurred by doing this like putting your data on a volatile memory although it's less of a risk on ECC Memory on Servers. If the power goes out, whatever stored on the RAM would be gone, so I assumed in cloud they would have backup power and other solutions in place to ensure it doesn't happen. They might have a network outage, but it's rare for servers to do a hard fail.