Our db clearing takes like 5ms (large schema from mature company, not a toy). We start by restoring a production schema dump which ensures our test db / devdb schema is basically identical to what we run in production. Any migrations you are working on in your branch get added to the restore of the prod schema after it runs. Building a schema from ORM definitions is what you do if you don't care about your life or time. Clearing the test db takes something similar. This is fine, using template db's is a good way to make a scratch copy of a local database to test migrations (rsync is better if you are technical enough to use it to restore after destructive changes).
Timings:
- create testdb: 9ms
- restore prod schema: 500ms (done once per test process)
- clear test data in 96 tables between tests that write to db (5ms)
The fastest way to clear a test db is to run a query to get every schema/table name, then run ";".join("`DELETE FROM {schema}.{tablename};" for schema, table in my_tables) after putting the db in replica mode. This takes single digit ms a lot of the time even with a decent amount of test data. I've done it every which way and this is by far the fastest way to clear data between tests. -- This will work on basically any postgresql database with basically any schema so just use it.
test_db_2235191=# CREATE OR REPLACE PROCEDURE public.delete_all_table_data()
LANGUAGE plpgsql
AS $procedure$
DECLARE
target record;
previous_replication_role text;
BEGIN
previous_replication_role :=
current_setting('session_replication_role');
PERFORM set_config('session_replication_role', 'replica', true);
BEGIN
FOR target IN
SELECT namespace.nspname AS schema_name,
relation.relname AS table_name
FROM pg_catalog.pg_class AS relation
JOIN pg_catalog.pg_namespace AS namespace
ON namespace.oid = relation.relnamespace
WHERE relation.relkind = 'r'
AND namespace.nspname NOT LIKE 'pg\_%' ESCAPE '\'
AND namespace.nspname <> 'information_schema'
ORDER BY namespace.nspname, relation.relname
LOOP
RAISE NOTICE 'Deleting %.%',
target.schema_name,
target.table_name;
EXECUTE format(
'DELETE FROM %I.%I',
target.schema_name,
target.table_name
);
END LOOP;
EXCEPTION
WHEN OTHERS THEN
PERFORM set_config(
'session_replication_role',
previous_replication_role,
true
);
RAISE;
END;
PERFORM set_config(
'session_replication_role',
previous_replication_role,
true
);
END;
$procedure$;
CREATE PROCEDURE
Time: 0.840 ms
test_db_2235191=# CALL public.delete_all_table_data();
NOTICE: ... (notices removed for 96 tables)
CALL
Time: 5.855 ms
We do similar, although lean into our strict "every table as a sequence" and "all FKs are deferred" conventions and only issue DELETEs for tables that actually were inserted by the test
https://github.com/joist-orm/joist-orm/blob/16cc73f148b6f962...
I forgot the speedup this got us on a 400-500 table schema, but it was noticeable -- curious if you could do the same / what the perf impact would be.
I concur with this approach. TRANSACTION-y tests (the default in Django) often don't quite line up with reality and make it hard to eg. drop in a breakpoint and run a server against the test's db state.
I've experimented (see below) with TEMPLATE dbs and such in Python (with inspiration from this library). IMHO the "around 100ms" mark is pretty slow for a big test suite. Interestingly, pg_restore is only twice as slow as TEMPLATEs.
https://github.com/leontrolski/postgresql-testing
I'd be interested about how all this compares to snapshotting the postrgres dir with ZFS and restoring to that, but don't have a Linux box to hand.
Interesting. How many database copies do you bring up when the test suite starts running, and how is parallelism handled?
We use pytest with xdist and we run as many as the system it is running on can handle. Each xdist process creates and sets up it's own test_db with a unique name (and drops it at the end of its run if possible). Setup and teardown are done via hooks in pytest. The advantage of this is the test run just needs a db running it can create a testdb on and connect to, so I can have tests running on 5 different branches or workdirs and they don't interact at all. On my threadripper machine with 256GB I could run about 60 concurrent tests, on my 9955hx machine I use day to day I can run 13. On a MBP I think it's about 8-16. There is diminishing returns with more processes.
If I was designing it from scratch I would use a single testdb and point all the python processes at it, and never clean up between tests or even between test runs. This is both faster and a better test, as I feel that clearing the db makes it very hard to detect overly broad queries unless you go out of your way to pack in a lot of extra harness data which people almost never do and is a chore.
Being to lazy to think or test it - does the above reset SEQUENCEs?
actually no, and we have something weird for that too:
We have code that creates one master sequence then replaces every sequence in the testdb with that master sequence, so all tables pull from the same sequence. That way you can never accidentally swap two id's in an api response and have the tests pass because you coincidentally both had id 5 or whatever. We can run the tests either way (with sequences swapped out or not). We almost always leave the master sequence in place because the id-swap bug is very common and the alternative (bug caused by two id's being the same on different tables) basically never comes up.