With SQLite's `.expert` mode you can delay that day a little longer: https://www.sqlite.org/cli.html#index_recommendations_sqlite...
sqlite> CREATE TABLE x1(a, b, c); -- Create table in database
sqlite> .expert
sqlite> SELECT * FROM x1 WHERE a=? AND b>?; -- Analyze this SELECT
CREATE INDEX x1_idx_000123a7 ON x1(a, b);
0|0|0|SEARCH TABLE x1 USING INDEX x1_idx_000123a7 (a=? AND b>?)
sqlite> CREATE INDEX x1ab ON x1(a, b); -- Create the recommended index
sqlite> .expert
sqlite> SELECT * FROM x1 WHERE a=? AND b>?; -- Re-analyze the same SELECT
(no new indexes)
0|0|0|SEARCH TABLE x1 USING INDEX x1ab (a=? AND b>?)
Also wrt> My approach so far has been to just do these cleanup operations in small batches so that I don’t need to do database queries that take more than 5 seconds to run. This whole experience has given me more of an appreciation for why someone might want to use a “real” database like Postgres which can have more than one writer at the same time though.
The advice for those " “real” " databases is generally to also do cleanup operations in small batches, they just tend to make it less obvious you're doing something unperformant in the smaller case. You're more right than you thought!
Every DB needs it, eventually. Even NoSQL darlings like Cassandra - I've seen it go into a resource-constrained death-spiral on stuff that should be async / non-blocking and safe. If you need to stay up, it's always worth planning on, and making sure your logic works during long-running gradual migrations.
Once you get to about 1M rows of data, batching is essential.
Is this being positioned as a strength, in your comment?
I got so annoyed with that a few years ago that I ended up building a whole tool just to solve that one problem:
uvx s3-credentials create my-existing-s3-bucket
This spits out read-write credentials that are scoped JUST for that bucket. You can add --read-only or --write-only to have credentials that are further locked down, or even add --prefix foo/bar for credentials that can only read/write keys that start with that prefix within the bucket.> Maybe one day I’ll move away to some other S3-compatible alternative.
I've used Restic with Cloudflare R2 and it worked great.
OUT="${i}.sql.zst"
PART="${OUT}.part"
sqlite3 -readonly "${i}" .dump | zstd --fast --rsyncable -v -o "${PART}" -
mv "${PART}" "${OUT}"
That doesn't block writers (when the writer uses WAL), and gives me a dump that's compressed well while also being easy to sync. My Home Assistant DB is 1.8GB, my dump is 286MB compressed, and I'd guess 90% of that is consistent from one day to the next.Neither does VACUUM INTO or ".backup" (which uses the backup API) or sqlite3_rsync or litestream.
Various statistical views over the value distributions of the indexes, so that the planner can estimate how useful (selective) the index should be.
sqlite_stat1 just gives an average (number of records in the index, and average number of records per value), and if enabled sqlite_stat4 stores histogram data.
-Delete it batches
-Delay between batches
-Preload the rowids before deleteing with SELECT (Select does not block)
Additionally if data was added sequentially primary to the same table the data is likely stored this way in the file and deleting it in this or in reversed order can be faster (depends on storage medium and other factors).
If you’re in a situation where partition pruning or other strategies for getting useless data out of the hot path don’t make sense, this is a killer strategy.
That’s not really accurate any longer.
Mostly depends on how you layout your tables & files. If you shard the databases then multiple machines can act as writers for their shard. You can also split read requests from write requests and have read only machines scale up/down as much as you’d like. You can use multiple files in a query (there is a limit there).
So for example you can split the user table based on the first letter of the username and then depending on the rest of the database either a database file per user or per customer (organization). Of course more of everything is manual but it’s not as hard as you’d expect if you build for it.
https://rivet.dev/blog/2025-02-16-sqlite-on-the-server-is-mi...
If you need sqlite over the network you can look at https://turso.tech/ it’s a almost drop in replacement for sqlite (https://github.com/tursodatabase/turso/blob/main/COMPAT.md)
I assume this is opposed to alerting when the backup job fails, which is an issue if the job never runs, or hangs forever, or crashes in a way that doesn't trigger your monitoring.
However I don't see how any of this solves the issue of not testing your backup. Because you can definitely have a backup task succeed regularly but the thing it is backing up is still unusable.
In this context it means upon a successful backup, update a timestamp somewhere. Some other system monitors the timestamp and if it ever becomes more than for example 1 day ago, it fires an alert.
Query plans aren't that hard to read! [0]