feat: use msgspec JSON encoder #6
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WalkthroughThe pull request includes modifications to test cases for CSV and TSV record readers and writers, enhancing expected output formats and coverage for edge cases. Changes involve updating attributes and assertions, adding new test cases, and correcting documentation. The Changes
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (8)
tests/test_writer.py (1)
Line range hint
127-133: Consider improving error handling and type safety.The custom encoder could benefit from:
- Explicit error handling for invalid list elements
- Using type hints instead of pyright ignores
Consider this improvement:
@override def _encode(self, item: Any) -> Any: """A callback for overriding the encoding of builtin types and custom types.""" if isinstance(item, list): - return ",".join(map(str, item)) # pyright: ignore[reportUnknownVariableType, reportUnknownArgumentType] + try: + return ",".join(str(x) for x in item) + except Exception as e: + raise ValueError(f"Failed to encode list: {e}") from e return itemtypeline/_writer.py (3)
18-18: Good choice using msgspec for improved performance!Using
msgspec.json.Encoderinstead of the standard library's JSON encoder is a great optimization. msgspec is known for its high-performance serialization capabilities, particularly for dataclass handling.Consider documenting the performance benefits in the class docstring to help future maintainers understand this design choice.
Line range hint
89-89: Consider improving type annotations to avoid suppressing pyright.The pyright ignore comments suggest potential type annotation improvements could be made.
Consider using a more specific type annotation:
- def _encode(self, item: Any) -> Any: + def _encode(self, item: Any) -> Union[list, Any]: """A callback for overriding the encoding of builtin types and custom types.""" if isinstance(item, tuple): - return list(item) # pyright: ignore[reportUnknownVariableType, reportUnknownArgumentType] + return list(item) return item
122-127: Consider enhancing the docstring with return type and examples.While the parameter documentation is clear, the docstring could be even more helpful.
Consider adding:
- Return type description
- Usage example (similar to the ones in CsvRecordWriter and TsvRecordWriter)
@classmethod def from_path( cls, path: Path | str, record_type: type[RecordType] ) -> "DelimitedRecordWriter[RecordType]": """Construct a delimited data writer from a file path. Args: path: the path to the file to write delimited data to. record_type: the type of the object we will be writing. + Returns: + A DelimitedRecordWriter instance configured for the specified record type. + + Example: + ```python + writer = DelimitedRecordWriter.from_path("data.csv", MyRecord) + ``` """typeline/_reader.py (3)
Line range hint
8-8: Consider implementing line number tracking.There's a TODO comment about adding line number support for error messages. This would be valuable for debugging and error reporting.
Would you like me to help implement line number tracking for error messages? This could involve:
- Adding a line counter to
_filter_out_comments- Enhancing error messages in
__iter__and_csv_dict_to_json- Adding tests for line number reporting
Line range hint
134-189: Consider simplifying type decoding logic.The
_decodemethod has complex nested conditionals for type handling. Consider refactoring to use a mapping of types to decoder functions for better maintainability.Here's a suggested refactor:
def _decode(self, field_type: type[Any] | str | Any, item: str) -> str: """A callback for overriding the string formatting of builtin and custom types.""" # Basic type handlers type_handlers = { str: lambda x: f'"{x}"', float: str, int: str, bool: lambda x: x.lower(), } # Handle basic types if field_type in type_handlers: return type_handlers[field_type](item) # Handle Union types if isinstance(field_type, UnionType): type_args = get_args(field_type) # Handle Optional (Union with None) if NoneType in type_args: if item == "": return "null" other_types = set(type_args) - {NoneType} if len(other_types) == 1: return self._decode(next(iter(other_types)), item) return self._decode(build_union(*other_types), item) # Handle other Union types for handler_type, handler in type_handlers.items(): if handler_type in type_args: return handler(item) return str(item)
Line range hint
67-76: Consider enhancing error messages.The validation error message could be more descriptive by including the actual vs expected types of fields that failed validation.
Consider enhancing the error message in
__init__:if not is_dataclass(record_type): raise ValueError( f"record_type must be a dataclass, got {type(record_type).__name__}" )tests/test_reader.py (1)
130-144: LGTM: Improved test output readabilityBreaking down the expected output into concatenated segments improves readability and maintainability. Each segment clearly represents a field, making it easier to debug test failures.
Consider using a multiline f-string for even better readability:
- expected: str = ( - "1" - + "\t'my\tname'" - + "\t0.2" - + "\t[1,2,3]" - + "\t[3,4,5]" - + "\t[5,6,7]" - + '\t{"field1":1,"field2":2}' - + '\t{"field1":10,"field2":"hi-mom","field3":null}' - + '\t{"first":{"field1":2,"field2":"hi-dad","field3":0.2}' - + ',"second":{"field1":3,"field2":"hi-all","field3":0.3}}' - + "\ttrue" - + "\tnull" - + "\t0.2\n" - ) + expected: str = ( + f"1\t" + f"'my\tname'\t" + f"0.2\t" + f"[1,2,3]\t" + f"[3,4,5]\t" + f"[5,6,7]\t" + f'{{"field1":1,"field2":2}}\t' + f'{{"field1":10,"field2":"hi-mom","field3":null}}\t' + f'{{"first":{{"field1":2,"field2":"hi-dad","field3":0.2}},' + f'"second":{{"field1":3,"field2":"hi-all","field3":0.3}}}}\t' + f"true\t" + f"null\t" + f"0.2\n" + )
📜 Review details
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Review profile: CHILL
📒 Files selected for processing (4)
tests/test_reader.py(1 hunks)tests/test_writer.py(1 hunks)typeline/_reader.py(1 hunks)typeline/_writer.py(4 hunks)
🔇 Additional comments (8)
tests/test_writer.py (2)
Line range hint 114-126: LGTM! Well-structured test case for custom encoding.
The test effectively demonstrates custom callback functionality with clear documentation.
99-113: LGTM! Verify JSON format consistency with msgspec.
The reformatted expected output aligns well with msgspec's compact JSON format. The string concatenation approach improves readability while maintaining the same test functionality.
Let's verify the JSON format consistency across the codebase:
✅ Verification successful
JSON format is consistent with msgspec encoder usage
Based on the codebase analysis:
- The reader and writer modules correctly use msgspec's JSON encoder/decoder (
typeline/_writer.pyandtypeline/_reader.py) - The compact JSON format in the test's expected output matches msgspec's default output format (no spaces after commas)
- The test case is the only one containing JSON assertions, and its format is consistent with the new encoder
- The changes maintain compatibility with both reading and writing operations
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check for other JSON-related tests that might need similar updates
# Look for test files with JSON assertions
rg -l 'assert.*json|json.*assert' --type py
# Look for specific JSON formatting patterns
rg '".*:.*".*assert' --type py
Length of output: 78
Script:
#!/bin/bash
# Let's try a different approach to find JSON-related tests and formatting
# Search for JSON string patterns in Python files
rg -A 2 '{".*":.*}' --type py
# Look for test files containing 'json' keyword
fd -e py | xargs grep -l "json"
# Search for specific JSON test cases
rg "test.*json" --type py -i
Length of output: 219
Script:
#!/bin/bash
# Let's examine the reader and writer files found for JSON handling
# Check JSON-related code in reader and writer
rg -A 5 'json' typeline/_reader.py typeline/_writer.py
# Look for JSON string patterns with proper escaping
rg -A 2 '\{.*:.*\}' tests/test_writer.py
# Check for other test files that might contain JSON patterns
fd -e py -p 'test_.*' | xargs cat
Length of output: 18979
typeline/_writer.py (2)
42-51: Well-structured initialization with clear separation of concerns!
The initialization is logically organized into three distinct sections:
- Internal attributes
- Record type inspection
- JSON encoder initialization
The comments make the code more maintainable by clearly documenting each section's purpose.
99-102: Robust encoding pipeline with proper type handling!
The three-step encoding process ensures reliable serialization:
- Convert to builtin types with msgspec
- JSON encode non-string values
- Decode to UTF-8 strings
Let's verify the encoding handles all edge cases:
✅ Verification successful
Let me gather more context about how these test cases are verified.
Let me check how the test verifies the written content.
Let me see the complete expected output to verify all fields.
Encoding pipeline successfully handles all complex data types!
The test suite comprehensively verifies that the encoding pipeline correctly handles:
- Primitive types (int, float, string, bool, None)
- Collection types (list, set, tuple, dict)
- Nested objects with complex hierarchies
- Special characters (tabs, quotes)
- JSON-specific values (null, true)
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for test cases to ensure coverage of complex types
rg -l "test.*writer" | xargs rg "def test.*complicated.*record" -A 10
Length of output: 1426
Script:
#!/bin/bash
# Check the test implementation to see how the complex types are verified
rg -A 20 "test_writer_will_write_a_complicated_record" tests/test_writer.py
Length of output: 909
Script:
#!/bin/bash
# Look for assertions after writing the complex record
rg -B 5 -A 10 "with TsvRecordWriter.from_path.*ComplexMetric.*writer:" tests/test_writer.py
Length of output: 526
Script:
#!/bin/bash
# Get the complete expected output string
rg -B 2 -A 15 'expected: str = \(' tests/test_writer.py
Length of output: 641
typeline/_reader.py (2)
214-214: LGTM! Fixed typo in docstring.
The parameter description has been corrected from "pat" to "path".
Line range hint 1-300: Verify alignment with PR objectives.
The PR title suggests using msgspec JSON encoder, but the changes only show a docstring fix. The code currently uses msgspec's Decoder but not its Encoder. Consider implementing the encoder changes as suggested by the PR title.
tests/test_reader.py (2)
124-124: LGTM: Test data value update
The modification of field12 from 1 to 0.2 aligns with the test requirements for verifying complex record serialization with the msgspec JSON encoder.
Line range hint 1-144: Verify test coverage for msgspec JSON encoder
The test suite comprehensively covers various scenarios including complex types, error cases, and validation. However, let's verify if we have adequate coverage for msgspec-specific functionality.
✅ Verification successful
Test coverage for msgspec JSON encoder is comprehensive
The test suite provides thorough coverage of msgspec functionality:
- Tests basic data type handling with
SimpleMetric - Verifies complex nested structures with
ComplexMetric - Includes specific msgspec error cases:
DecodeErrorfor malformed JSON-like formatValidationErrorfor type validation failures- Custom type coercion and validation
- Covers edge cases like empty files, comments, and escaped delimiters
The test coverage adequately exercises the msgspec integration, including both successful cases and error scenarios.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check for msgspec-specific test coverage
# Look for msgspec-specific test cases and error handling
rg -l "msgspec" .
# Check for any TODO comments related to msgspec testing
rg -i "todo.*msgspec" .
# Look for other test files that might need similar updates
fd -e py -x grep -l "CsvRecordReader\|TsvRecordReader" {}
Length of output: 271
Script:
#!/bin/bash
# Let's examine the actual test coverage for msgspec functionality
# Check test_reader.py for msgspec-related test cases
rg "msgspec" tests/test_reader.py -B2 -A2
# Look for validation error handling tests
rg "ValidationError|DecodeError" tests/test_reader.py -B2 -A2
# Check the implementation to ensure all msgspec features are tested
rg "msgspec" typeline/_reader.py -B2 -A2
Length of output: 1282
Script:
#!/bin/bash
# Let's check the actual test implementations for msgspec validation
# Get the full implementation of msgspec validation tests
ast-grep --pattern 'def test_reader_msgspec_$_($$$) {
$$$
}' tests/test_reader.py
# Check for any other validation test cases
ast-grep --pattern 'with pytest.raises($_) {
$$$
}' tests/test_reader.py
Length of output: 162
Script:
#!/bin/bash
# Let's examine the full test file content to analyze coverage
# Get the full content of test_reader.py
cat tests/test_reader.py
# Check for any test functions containing validation or error handling
rg "def test.*" tests/test_reader.py -A 3
Length of output: 13775
Summary by CodeRabbit
New Features
Bug Fixes
Documentation
Refactor