Learning a few things about running SQLite
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Hello! I’ve been working on a Django site recently, and I decided to use SQLite<br>as the database.<br>When I was getting started with using SQLite as database for a website I read a bunch<br>of blog posts about how it is totally fine to use SQLite in production for a<br>small site and I think it is totally fine, but what I did not fully appreciate<br>is that SQLite is still a database, databases are complicated, and I do not know<br>a lot about operating databases.
So here are a couple of small things I’ve been learning about running SQLite. This<br>is the 4th website I’ve used SQLite for, and I think this one is harder because<br>with the power of the Django ORM I’ve been making the database do more work than<br>I was previously without Django.
I started by turning on WAL mode like all the blog posts said to do and hoping for the best.
ANALYZE is apparently important
Today I was running a query (using SQLite’s FTS5 for<br>full-text search) on a table with 4000 rows and it took 5 seconds. That seemed<br>wrong to me: computers are fast!
It turned out that what I needed to do was to run ANALYZE!<br>Immediately the problem query went from taking 5 seconds to like 0.05 seconds<br>(or some other number small enough that I didn’t care to investigate further).<br>I still don’t know exactly what went wrong in the query plan,<br>but my best guess is that it was some sort of accidentally quadratic thing.
ANALYZE generates “statistics” (I guess about the number of rows in each table? and presumably other things?)<br>so that the query planner can make better choices.
Maybe one day I’ll learn to read a query plan.
cleaning up the database is tricky
Occasionally I’ve run into situations where I accidentally put a bunch of rows<br>in my database that I don’t want to be there (for example completed tasks from<br>django-tasks-db), and I want to clean them up.
What’s happened to me a few times in this case is:
I run some kind of command to clean up the rows
The command takes more than 5 seconds, since there are a lot of rows (though I still have some questions about why these DELETE statements are so slow honestly, maybe there’s a bunch of Python code running inside a transaction, I’m not sure)
One of the other workers tries to write the database while this is happening, and times out after 5 seconds (I have a timeout of 5 seconds set)
The worker crashes because it couldn’t write to the database and the VM shuts down
My approach so far has been to just do these cleanup operations in small batches<br>so that I don’t need to do database queries that take more than 5 seconds to<br>run. This whole experience has given me more of an appreciation for why someone<br>might want to use a “real” database like Postgres which can have more than one<br>writer at the same time though.
Maybe in the future I’ll just take the site down for scheduled maintenance<br>instead when I need to do this kind of thing, but I haven’t figured out a<br>workflow for that yet.
no notes on performance of ORM queries yet
So far I’ve been using Django’s ORM to make any query I want without paying any<br>attention at all to query performance and it’s mostly been going okay other<br>than the ANALYZE thing. The database is pretty small (maybe 10000 rows?) and<br>I expect it to stay pretty small forever, so I’m hoping that that plan will<br>keep working.
backing up sqlite
I’ve done SQLite backups a couple of ways. I don’t think I’ve actually tested<br>restoring from my backups but I do usually try to monitor them with a dead man’s<br>switch.
way 1: restic
sqlite3 /data/calendar.db "VACUUM INTO '/tmp/calendar.sqlite'"<br>gzip /tmp/calendar.sqlite
# Upload backup to S3<br># Sometimes the backup gets OOM killed and so it stays locked, do an unlock<br>restic -r s3://s3.amazonaws.com/some_bucket/ unlock<br># Do the backup & prune old backups<br>restic -r s3://s3.amazonaws.com/some_bucket/ backup /tmp/calendar.sqlite.gz<br>restic -r s3://s3.amazonaws.com/some_bucket/ snapshots<br>restic -r s3://s3.amazonaws.com/some_bucket/ forget -l 1 -H 6 -d 2 -w 2 -m 2 -y 2<br>restic -r s3://s3.amazonaws.com/some_bucket/ prune
way 2: litestream
I started trying out Litestream recently because I felt like doing incremental backups might<br>be more efficient: my restic backups were sometimes getting OOM killed, and I<br>was a bit tired of it. Basically I just write a config file and run:
litestream replicate -config litestream.yml
I set retention: 400h in my config file in an attempt to<br>retain some amount of history of the database but I have no idea if it works.
I’ve been backing up to AWS, which is always a pain because it’s annoying to<br>navigate the AWS console to generate credentials. Maybe one day I’ll move away<br>to some other S3-compatible alternative.
you can use multiple databases
My current project only has...