Add support for SIGTTIN and SIGTTOU signals to the dirty arbiter,
allowing dynamic scaling of dirty workers at runtime without restarting
gunicorn.
Changes:
- Add TTIN/TTOU to DirtyArbiter.SIGNALS
- Add num_workers instance variable for dynamic count
- Add _get_minimum_workers() to enforce app worker constraints
- Add signal handlers for TTIN (increase) and TTOU (decrease)
- Update manage_workers() to use dynamic count
- Add documentation for dynamic scaling
- Add unit tests for signal handling
- Add Docker integration tests
The minimum worker constraint ensures TTOU cannot reduce workers below
what apps require (e.g., if an app has workers=3, minimum is 3).
Closes#3489
Update client and streaming tests to work with the binary protocol:
- Update MockStreamWriter/MockStreamReader to use BinaryProtocol
- Replace string request IDs with integers
- Update test assertions to decode binary protocol messages
- Use HEADER_SIZE and decode_header/decode_message instead of old API
- Add get_app_workers_attribute() to read workers class attribute
- Update _parse_app_specs() to check class attribute when no config override
- Add Docker-based e2e tests for per-app worker allocation
- Add test apps: HeavyModelApp (workers=2), LightweightApp
- Add unit tests for get_app_workers_attribute function
- Add integration tests for class attribute detection
Allow dirty apps to specify how many workers should load them, enabling
significant memory savings for heavy applications like ML models.
- Add `workers` class attribute to DirtyApp (None = all workers)
- Add `parse_dirty_app_spec()` to parse "module:Class:N" format
- Add `DirtyNoWorkersAvailableError` for app-specific error handling
- Update DirtyArbiter with per-app worker tracking and routing
- Maintain backward compatibility when no dirty_apps configured
Example: 8 workers x 10GB model = 80GB RAM needed
With workers=2: 2 x 10GB = 20GB RAM (75% savings)
Configuration formats:
- Class attribute: `workers = 2` on DirtyApp subclass
- Config format: `module:class:N` (e.g., `myapp.ml:HugeModel:2`)
Add support for streaming responses when dirty app actions return
generators (sync or async). This enables real-time delivery of
incremental results for use cases like LLM token generation.
Features:
- Streaming protocol with chunk/end/error message types
- Worker support for sync and async generators
- Arbiter forwarding of streaming messages
- Deadline-based timeout handling
- Async client streaming API
Protocol:
- Chunk messages (type: "chunk") contain partial data
- End messages (type: "end") signal stream completion
- Error messages can occur mid-stream
New files:
- benchmarks/dirty_streaming.py: Streaming benchmark suite
- tests/dirty/test_*_streaming*.py: Streaming test coverage
- docs/content/dirty.md: Streaming documentation with examples
Add tests to verify that when multiple dirty apps are configured,
messages are correctly routed to the appropriate app based on app_path.
New files:
- tests/support_dirty_apps.py: CounterApp and EchoApp test apps
- tests/dirty/test_multi_app_routing.py: 13 routing tests covering
app loading, routing, state separation, error handling, and
concurrent requests
