Move serialize to separate module #437
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WalkthroughThe changes in this pull request involve significant modifications to the Changes
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Actionable comments posted: 3
🧹 Outside diff range and nitpick comments (4)
executorlib/shared/serialize.py (1)
1-6: Consider grouping imports by type.For better readability, consider organizing imports into standard library, third-party, and local imports with a blank line between groups.
import hashlib import re from typing import Any, Tuple + import cloudpickletests/test_cache_shared.py (2)
10-14: Consider reorganizing imports for better readability.The imports could be organized better by grouping related imports together:
from executorlib.shared.cache import ( FutureItem, execute_task_in_file, _check_task_output, ) + from executorlib.shared.hdf import dump from executorlib.shared.serialize import serialize_funct_h5 - from executorlib.shared.hdf import dump
Error handling test cases needed for cache and serialization operations
The codebase has error handling tests for executors and task processing, but lacks specific tests for cache operations and serialization failures. Add test cases to cover:
- Invalid argument types in
serialize_funct_h5- File system errors in cache operations
- Malformed data handling in
dumpand_check_task_outputExample test case to add:
def test_serialize_invalid_args(self): with self.assertRaises(TypeError): serialize_funct_h5(my_funct, "invalid", b=2) def test_cache_file_errors(self): with self.assertRaises(IOError): _check_task_output(task_key="invalid", future_obj=Future(), cache_directory="/nonexistent")🔗 Analysis chain
Line range hint
27-95: Consider adding error handling test cases.While the current tests cover successful scenarios well, consider adding test cases for error conditions:
- Invalid argument types
- Missing required arguments
- Serialization failures
Example test case to add:
def test_execute_function_invalid_args(self): cache_directory = os.path.abspath("cache") os.makedirs(cache_directory, exist_ok=True) with self.assertRaises(TypeError): serialize_funct_h5(my_funct, "invalid", b=2)🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Check if there are any error handling tests in the codebase rg -l "def test.*error" tests/ rg -l "def test.*invalid" tests/ rg -l "def test.*fail" tests/Length of output: 264
Script:
#!/bin/bash # Let's check the content of existing error handling tests to understand the coverage rg "def test.*error|def test.*fail" -A 5 tests/Length of output: 3025
Script:
#!/bin/bash # Let's check if there are any existing error handling tests specifically for serialization or cache operations rg "serialize_funct|dump|_check_task_output" -A 5 tests/ | rg "assert.*Raises|with.*assert.*Raises" -B 5Length of output: 106
executorlib/shared/cache.py (1)
Line range hint
1-284: Great architectural improvement moving serialization to a dedicated module.This change follows SOLID principles by:
- Improving separation of concerns
- Making the codebase more maintainable
- Allowing independent testing of serialization logic
Consider adding docstring to the new
serialize_funct_h5function in the serialize module to document the contract it maintains with the cache module.
📜 Review details
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📒 Files selected for processing (3)
- executorlib/shared/cache.py (2 hunks)
- executorlib/shared/serialize.py (1 hunks)
- tests/test_cache_shared.py (4 hunks)
🔇 Additional comments (4)
executorlib/shared/serialize.py (1)
1-40: Verify complete migration of serialization logic.Let's verify that all serialization logic has been properly migrated and there are no remaining references to the old implementation.
✅ Verification successful
Let me gather more information about potential old serialization logic to ensure complete migration.
Migration of serialization logic is complete and properly referenced
The verification shows that:
- The new
serialize.pymodule is the only file containing serialization logic- All uses of
cloudpickle.dumps()are appropriate and don't duplicate the core serialization logic- The new
serialize_funct_h5function is correctly imported by dependent modules- No references to old serialization functions were found
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Check for any remaining references to old serialization functions echo "Checking for remaining references to _serialize_funct_h5..." rg "_serialize_funct_h5" echo -e "\nChecking for any duplicate serialization logic..." ast-grep --pattern 'cloudpickle.dumps($$$)'Length of output: 1575
Script:
#!/bin/bash # Search for any files that might contain old serialization logic echo "Checking for files with 'serialize' in their name or content..." fd serialize echo -e "\nChecking for any other serialization-related functions..." ast-grep --pattern 'def $_serialize$_($$$)' echo -e "\nChecking for imports of serialization modules..." rg "import.*serial"Length of output: 680
tests/test_cache_shared.py (1)
32-35: LGTM! Good test coverage for mixed argument passing.The test properly validates the new serialization function with both positional and keyword arguments.
executorlib/shared/cache.py (2)
11-11: LGTM! Clean modularization of serialization logic.The import change properly reflects the movement of serialization logic to a dedicated module.
152-154: Verify serialization consistency across module boundary.The migration to the external serialization function looks good. However, since this is used in a critical path for task execution, we should verify:
- The new
serialize_funct_h5maintains the same contract for task key generation- Identical inputs produce the same task keys to prevent cache misses
Let's verify the serialization contract:
✅ Verification successful
Serialization contract is maintained and consistent
The implementation in
executorlib/shared/serialize.pyshows that:
- The function maintains the same signature and return type (Tuple[str, dict])
- Task key generation follows the same pattern: function name + hash of pickled data
- The serialized data structure remains unchanged
The migration from internal to external implementation appears safe as the contract is preserved, ensuring cache consistency.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check if the new serialize module maintains consistent behavior # Test 1: Verify the function signature and return types ast-grep --pattern 'def serialize_funct_h5(fn: callable, *args: Any, **kwargs: Any)' # Test 2: Check for any existing tests that verify serialization consistency rg -l "test.*serialize.*funct.*h5" tests/Length of output: 130
Script:
#!/bin/bash # Let's try a broader search to locate the serialization function and related tests # Find the serialization function definition rg -A 5 "def serialize_funct_h5" # Find all files containing serialization related code rg -l "serialize.*funct" # Look for test files containing serialization tests with a broader pattern rg -l "test.*serialize" tests/ # Check imports of serialize_funct_h5 rg "from.*import.*serialize_funct_h5"Length of output: 859
Script:
#!/bin/bash # Let's examine the test file and implementation to verify contract consistency # Check the test file content for serialization consistency tests rg -A 10 "test.*serialize" tests/test_cache_shared.py # Look at the actual implementation rg -A 15 "def serialize_funct_h5" executorlib/shared/serialize.py # Check if there was a previous internal implementation rg "_serialize_funct_h5" executorlib/shared/cache.pyLength of output: 830
| def _get_hash(binary: bytes) -> str: | ||
| """ | ||
| Get the hash of a binary. | ||
| Args: | ||
| binary (bytes): The binary to be hashed. | ||
| Returns: | ||
| str: The hash of the binary. | ||
| """ | ||
| # Remove specification of jupyter kernel from hash to be deterministic | ||
| binary_no_ipykernel = re.sub(b"(?<=/ipykernel_)(.*)(?=/)", b"", binary) | ||
| return str(hashlib.md5(binary_no_ipykernel).hexdigest()) |
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🛠️ Refactor suggestion
Consider adding input validation and improving hash reliability.
The hash function could be more robust with input validation and a more reliable kernel path handling.
Consider these improvements:
def _get_hash(binary: bytes) -> str:
+ if not isinstance(binary, bytes):
+ raise TypeError("Input must be bytes")
+
# Remove specification of jupyter kernel from hash to be deterministic
- binary_no_ipykernel = re.sub(b"(?<=/ipykernel_)(.*)(?=/)", b"", binary)
+ # Handle both Windows and Unix-style paths
+ binary_no_ipykernel = re.sub(
+ b"(?<=/ipykernel_|\\\\ipykernel_)(.*)(?=/|\\\\)",
+ b"",
+ binary
+ )
return str(hashlib.md5(binary_no_ipykernel).hexdigest())Also, consider adding a comment explaining why MD5 is sufficient for this use case:
# MD5 is used here for generating cache keys, not for security purposes| def serialize_funct_h5(fn: callable, *args: Any, **kwargs: Any) -> Tuple[str, dict]: | ||
| """ | ||
| Serialize a function and its arguments and keyword arguments into an HDF5 file. | ||
| Args: | ||
| fn (callable): The function to be serialized. | ||
| *args (Any): The arguments of the function. | ||
| **kwargs (Any): The keyword arguments of the function. | ||
| Returns: | ||
| Tuple[str, dict]: A tuple containing the task key and the serialized data. | ||
| """ | ||
| binary_all = cloudpickle.dumps({"fn": fn, "args": args, "kwargs": kwargs}) | ||
| task_key = fn.__name__ + _get_hash(binary=binary_all) | ||
| data = {"fn": fn, "args": args, "kwargs": kwargs} | ||
| return task_key, data |
There was a problem hiding this comment.
Add input validation and error handling.
The function should validate inputs and handle serialization errors gracefully.
Consider applying these improvements:
def serialize_funct_h5(fn: callable, *args: Any, **kwargs: Any) -> Tuple[str, dict]:
+ if not callable(fn):
+ raise TypeError("fn must be callable")
+
+ try:
binary_all = cloudpickle.dumps({"fn": fn, "args": args, "kwargs": kwargs})
+ except Exception as e:
+ raise ValueError(f"Failed to serialize function and arguments: {str(e)}")
+
task_key = fn.__name__ + _get_hash(binary=binary_all)
data = {"fn": fn, "args": args, "kwargs": kwargs}
return task_key, data📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def serialize_funct_h5(fn: callable, *args: Any, **kwargs: Any) -> Tuple[str, dict]: | |
| """ | |
| Serialize a function and its arguments and keyword arguments into an HDF5 file. | |
| Args: | |
| fn (callable): The function to be serialized. | |
| *args (Any): The arguments of the function. | |
| **kwargs (Any): The keyword arguments of the function. | |
| Returns: | |
| Tuple[str, dict]: A tuple containing the task key and the serialized data. | |
| """ | |
| binary_all = cloudpickle.dumps({"fn": fn, "args": args, "kwargs": kwargs}) | |
| task_key = fn.__name__ + _get_hash(binary=binary_all) | |
| data = {"fn": fn, "args": args, "kwargs": kwargs} | |
| return task_key, data | |
| def serialize_funct_h5(fn: callable, *args: Any, **kwargs: Any) -> Tuple[str, dict]: | |
| """ | |
| Serialize a function and its arguments and keyword arguments into an HDF5 file. | |
| Args: | |
| fn (callable): The function to be serialized. | |
| *args (Any): The arguments of the function. | |
| **kwargs (Any): The keyword arguments of the function. | |
| Returns: | |
| Tuple[str, dict]: A tuple containing the task key and the serialized data. | |
| """ | |
| if not callable(fn): | |
| raise TypeError("fn must be callable") | |
| try: | |
| binary_all = cloudpickle.dumps({"fn": fn, "args": args, "kwargs": kwargs}) | |
| except Exception as e: | |
| raise ValueError(f"Failed to serialize function and arguments: {str(e)}") | |
| task_key = fn.__name__ + _get_hash(binary=binary_all) | |
| data = {"fn": fn, "args": args, "kwargs": kwargs} | |
| return task_key, data |
| task_key, data_dict = serialize_funct_h5( | ||
| my_funct, | ||
| 1, | ||
| 2, |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider refactoring duplicate test code.
The test methods test_execute_function_args and test_execute_function_kwargs share significant code with test_execute_function_mixed. Consider extracting common test logic into a helper method to reduce duplication.
def _run_serialization_test(self, *args, **kwargs):
cache_directory = os.path.abspath("cache")
os.makedirs(cache_directory, exist_ok=True)
task_key, data_dict = serialize_funct_h5(my_funct, *args, **kwargs)
file_name = os.path.join(cache_directory, task_key + ".h5in")
dump(file_name=file_name, data_dict=data_dict)
execute_task_in_file(file_name=file_name)
future_obj = Future()
_check_task_output(
task_key=task_key,
future_obj=future_obj,
cache_directory=cache_directory
)
self.assertTrue(future_obj.done())
self.assertEqual(future_obj.result(), 3)
future_file_obj = FutureItem(
file_name=os.path.join(cache_directory, task_key + ".h5out")
)
self.assertTrue(future_file_obj.done())
self.assertEqual(future_file_obj.result(), 3)Then use it in your test methods:
def test_execute_function_mixed(self):
self._run_serialization_test(1, b=2)
def test_execute_function_args(self):
self._run_serialization_test(1, 2)
def test_execute_function_kwargs(self):
self._run_serialization_test(a=1, b=2)Also applies to: 78-81
Summary by CodeRabbit
New Features
Bug Fixes
Tests