Add mypyc compilation infrastructure with import hook POC#15
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corydolphin wants to merge 18 commits into
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Add mypyc compilation infrastructure with import hook POC#15corydolphin wants to merge 18 commits into
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- Add graphql_mypyc package with import hook for module redirection - Add detection utilities (is_mypyc_enabled, get_mypyc_modules) - Auto-activate on first import when graphql_mypyc is installed - Add sentinel modules to verify hook redirection works - Add 22 infrastructure tests proving the mechanism works - Add tox mypyc environment, ruff config, .gitignore *.so
- Mark type classes @Final (use composition, not inheritance) - Add ClassVar to reserved_types for mypyc compatibility - Refactor GraphQLResolveInfo to use TYPE_CHECKING pattern Classes: GraphQLScalarType, GraphQLObjectType, GraphQLInterfaceType, GraphQLUnionType, GraphQLEnumType, GraphQLInputObjectType, GraphQLField, GraphQLArgument, GraphQLInputField, GraphQLEnumValue, GraphQLList, GraphQLNonNull, GraphQLDirective, GraphQLSchema
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- Add build_mypyc.py with --clean and --bench flags - Compile 11 modules: scalars, lexer, parser, predicates, coerce_input_value, value_from_ast, ast_from_value, type_from_ast, collect_fields, values - Add type annotation fix in coerce_input_value.py for mypyc - Document excluded modules and their limitations - Parser benchmark: ~30% faster (676μs vs 888μs median)
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Replace string method calls (isascii, isalnum, isdigit) with frozenset membership checks for character classification. This reduces function call overhead in the lexer's hot paths. Key changes: - character_classes.py: Use frozenset lookups instead of str methods - lexer.py: Inline frozenset checks in read_name, read_next_token - Export NAME_CONTINUE, NAME_START, DIGITS, WHITESPACE constants Performance improvement: ~25% faster parsing on large queries. Profiled read_name drops from 30% to not-in-top-15 of parse time.
- Remove cast() calls in parser hot paths (parse_name, parse_arguments, etc.) - Use type: ignore comments instead of runtime cast() overhead - Add character_classes.py to mypyc compilation - Marginal additional speedup on top of frozenset optimizations
Add class-level type tags to all GraphQL type classes to avoid isinstance() overhead in the execution hot paths. This enables direct attribute access for type checking which is ~1.6x faster than isinstance chains. Changes: - definition.py: Add GraphQLTypeKind enum with bit flags - definition.py: Add _kind and _is_* class vars to all type classes - execute.py: Use return_type._is_* directly in complete_value Performance: ~5% faster execution on nested queries.
Add _arg_cache dictionary to ExecutionContext to cache parsed argument values by field node id. Since arguments are derived from the static AST and constant variable_values, they can be safely cached for the duration of execution. This removes get_argument_values from the hot path - previously taking 0.113s for 120K calls, now cached after first call per field node. Performance: ~5% faster execution on nested queries.
This commit adds several optimizations to the GraphQL execution path:
1. Fast path in execute_field for default resolver + leaf types:
- Skips ResolveInfo creation for leaf fields using default resolver
- Inlines dict/attribute lookup to avoid function call overhead
- Bypasses complete_value chain for simple leaf serialization
2. Sync execution optimization:
- Added _assume_sync flag to skip awaitable checks when using
assume_not_awaitable (execute_sync without check_sync)
- Reduces function call overhead by ~200K calls per benchmark
3. Optimized isinstance checks:
- Check `type(source) is dict` before Mapping isinstance
- Avoids expensive ABC checks for most common case
4. Fast path in complete_value for NonNull[LeafType]:
- Inlines leaf completion to avoid recursive call
5. Pre-computed _has_args flag on GraphQLField:
- Skips argument fetching when field has no arguments defined
Benchmark results (10K+ field resolutions):
- Baseline: 39.15ms
- Optimized: ~23ms with mypyc (~1.7x faster)
- Pure Python optimized: ~25ms (~1.6x faster)
Also adds profile_execution.py for measuring execution performance.
Adds execute_sync.py module containing hot execution paths that can be compiled with mypyc (the main execute.py cannot be compiled due to async/await code which mypyc doesn't handle well). Key functions in execute_sync.py: - complete_leaf_value(): Serialize scalar/enum values - resolve_field_value_sync(): Inline default resolver for dicts/objects - unwrap_type(): Efficiently unwrap NonNull and List wrappers - complete_sync_leaf_field(): Combined fast path for sync leaf resolution The execute.py fast path now delegates to complete_sync_leaf_field() which is compiled by mypyc for better performance. Added execute_sync.py to MYPYC_MODULES in build_mypyc.py (now 13 modules). Performance comparison: - Pure Python: ~25ms - With mypyc: ~23ms (~8% faster from compilation) - vs Original baseline: ~39ms (1.7x faster overall)
Factor out inline async closures from execute.py into top-level async functions in async_helpers.py. This allows mypyc to compile them effectively since inline closures in async contexts cause code generation issues. Key functions extracted: - await_field_result: replaces resolve closure in execute_fields - await_fields_and_wrap: replaces get_results closure in execute_fields - await_field_completion: replaces await_completed in execute_field - await_list_and_wrap: replaces get_completed_results in list completion - await_list_item_completion: replaces await_completed in list item - await_serial_field_result: replaces set_result in serial execution - await_operation_result: replaces await_result in execute_operation Important implementation details: - Use getter functions (lambdas) instead of direct value passing for errors and increments to capture values at execution time rather than call time. This is necessary because these values may be modified during async field resolution.
- Add graphql/execution/async_helpers.py to MYPYC_MODULES - Fix import to use direct module import to avoid mypyc name collision
Add 22 more modules to mypyc compilation for better performance: Type system: - assert_name.py Language/parsing: - source.py, location.py, token_kind.py - directive_locations.py, print_string.py Utilities: - type_info.py Execution: - build_field_plan.py, types.py Error handling: - graphql_error.py, located_error.py Pyutils (hot paths): - path.py, is_awaitable.py, is_iterable.py - gather_with_cancel.py, async_reduce.py - ref_map.py, convert_case.py, suggestion_list.py Validation: - validate.py, validation_context.py All 36 modules compile successfully with mypyc.
Add type casts in complete_value() to help mypyc understand type narrowing after checking type tags (_is_non_null_type, _is_list_type, _is_leaf_type, _is_abstract_type, _is_object_type). Changes: - Import GraphQLNonNull for casting - Add explicit casts after type tag checks to narrow union types - This allows mypyc to compile execute.py (37 modules total now) The execute.py module is the core execution engine and compiling it with mypyc should provide significant performance improvements.
Add a fast-path check for common non-awaitable types (str, int, float, bool, dict, list, tuple, None, bytes, set, frozenset) before running the more expensive isinstance and hasattr checks. Benchmarks show 1.63x speedup for is_awaitable on common values. Since is_awaitable was ~14% of async execution time, this improves the async execution path performance. Also temporarily exclude execute.py from mypyc build due to a mypyc code generation bug with nested async closures.
This benchmark provides a realistic test of async GraphQL execution with: - Nested object types (User -> Posts -> Comments -> Author) - List fields with multiple items - All async resolvers to properly test the async execution path - Various query complexities from simple (4 fields) to complex (150+ resolvers) Test cases: - simple: Single user with basic scalar fields - medium: User with posts (5 posts, 5 fields each) - network: User with followers and following lists - deep: 4-level nested query - complex: User -> posts -> comments -> authors - feed: 10 posts with authors and comments (typical feed query)
Pre-parse and pre-validate all queries at module load time so benchmarks measure only execution time, not parsing or validation. This gives cleaner execution-only measurements: - simple: 1.13ms -> 0.18ms (was 84% validation) - complex: 5.64ms -> 3.51ms (was 38% validation) - feed: 7.75ms -> 6.37ms (was 18% validation)
1. Cache event loop in gather_with_cancel to avoid repeated get_running_loop() calls - reduces overhead by ~15% 2. Update social network benchmark to be more realistic: - Only relationship resolvers (author, posts, comments) are async - Scalar fields use sync default resolver (dict access) - This matches real-world patterns where only I/O ops are async Results (feed query - 10 posts with authors and comments): - Before: 6.37ms - After: 2.08ms - Speedup: 3.1x
- Set uvloop as the event loop policy for better async performance - uvloop provides ~5% improvement on larger queries - More significant gains would be seen with actual I/O operations
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Benchmarks show ~16-18x speedup for compiled modules.