use new unique jobs implementation

This moves the library to use the new unique jobs implementation from
riverqueue/river#590 and migrates the sqlalchemy
driver to use a unified insertion path, allowing bulk inserts to use
unique jobs.

Author: bgentry

.github/workflows/ci.yaml

@@ -63,7 +63,7 @@ jobs:
         run: psql --echo-errors --quiet -c '\timing off' -c "CREATE DATABASE ${TEST_DATABASE_NAME};" ${ADMIN_DATABASE_URL}
 
       - name: river migrate-up
-        run: river migrate-up --database-url "$TEST_DATABASE_URL" --max-steps 5 # temporarily include max steps so tests can pass with unique fixes
+        run: river migrate-up --database-url "$TEST_DATABASE_URL"
 
       - name: Test
         run: rye test
@@ -109,7 +109,7 @@ jobs:
         run: psql --echo-errors --quiet -c '\timing off' -c "CREATE DATABASE ${DATABASE_NAME};" ${ADMIN_DATABASE_URL}
 
       - name: river migrate-up
-        run: river migrate-up --database-url "$DATABASE_URL" --max-steps 5 # temporarily include max steps so tests can pass with unique fixes
+        run: river migrate-up --database-url "$DATABASE_URL"
 
       - name: Run examples
         run: rye run python3 -m examples.all

README.md

@@ -48,8 +48,8 @@ class JobArgs(Protocol):
         pass
 ```
 
-* `kind` is a unique string that identifies them the job in the database, and which a Go worker will recognize.
-* `to_json()` defines how the job will serialize to JSON, which of course will have to be parseable as an object in Go.
+- `kind` is a unique string that identifies them the job in the database, and which a Go worker will recognize.
+- `to_json()` defines how the job will serialize to JSON, which of course will have to be parseable as an object in Go.
 
 They may also respond to `insert_opts()` with an instance of `InsertOpts` to define insertion options that'll be used for all jobs of the kind.
 
@@ -95,16 +95,6 @@ insert_res.job
 insert_res.unique_skipped_as_duplicated
 ```
 
-### Custom advisory lock prefix
-
-Unique job insertion takes a Postgres advisory lock to make sure that its uniqueness check still works even if two conflicting insert operations are occurring in parallel. Postgres advisory locks share a global 64-bit namespace, which is a large enough space that it's unlikely for two advisory locks to ever conflict, but to _guarantee_ that River's advisory locks never interfere with an application's, River can be configured with a 32-bit advisory lock prefix which it will use for all its locks:
-
-```python
-client = riverqueue.Client(riversqlalchemy.Driver(engine), advisory_lock_prefix=123456)
-```
-
-Doing so has the downside of leaving only 32 bits for River's locks (64 bits total - 32-bit prefix), making them somewhat more likely to conflict with each other.
-
 ## Inserting jobs in bulk
 
 Use `#insert_many` to bulk insert jobs as a single operation for improved efficiency:

requirements-dev.lock

@@ -7,6 +7,7 @@
 #   all-features: false
 #   with-sources: false
 #   generate-hashes: false
+#   universal: false
 
 -e file:.
 asyncpg==0.29.0

requirements.lock

@@ -7,6 +7,7 @@
 #   all-features: false
 #   with-sources: false
 #   generate-hashes: false
+#   universal: false
 
 -e file:.
 sqlalchemy==2.0.30