Add support for extra in structured mode

README.md

@@ -27,13 +27,13 @@ The logging setup supports two main modes:
 To initialize logging in your application, call the `setup()` function. You can specify whether to enable structured logging or use the default human-readable format.
 
 ```python
-from my_logging_module import setup
+from flogging import flogging
 
 # Initialize logging
-setup(level="INFO", structured=False)  # Human-readable format
+flogging.setup(level="INFO", structured=False)  # Human-readable format
 
 # Enable structured logging for production
-setup(level="INFO", structured=True)
+flogging.setup(level="INFO", structured=True)
 ```
 
 #### Parameters for `setup()`
@@ -99,10 +99,10 @@ Example structured log output:
 In structured logging mode, you can attach additional context to each log message by calling `set_context()`. This context is logged alongside the usual fields, allowing you to track custom metadata.
 
 ```python
-from my_logging_module import set_context
+from flogging import flogging
 
 # Set custom context
-set_context({"user_id": "12345", "transaction_id": "abcde"})
+flogging.set_context({"user_id": "12345", "transaction_id": "abcde"})
 
 # The custom context will now appear in each structured log message
 ```
@@ -112,10 +112,11 @@ set_context({"user_id": "12345", "transaction_id": "abcde"})
 When logging large messages (e.g., serialized data or files), the `log_multipart()` function compresses and splits the message into smaller chunks to prevent issues with log size limits.
 
 ```python
-from my_logging_module import log_multipart
+import logging
+from flogging import flogging
 
 # Log a large message
-log_multipart(logging.getLogger(), b"Large data to be logged")
+flogging.log_multipart(logging.getLogger(), b"Large data to be logged")
 ```
 
 This function will automatically split the message and log each chunk, ensuring the entire message is captured.
@@ -140,9 +141,9 @@ You can further customize the format by modifying the `AwesomeFormatter` class,
 To enforce standards in your logging messages, such as preventing trailing dots in log messages, the module provides the `check_trailing_dot()` decorator. This can be applied to logging functions to raise an error if a message ends with a dot:
 
 ```python
-from my_logging_module import check_trailing_dot
+from flogging import flogging
 
-@check_trailing_dot
+@flogging.check_trailing_dot
 def log_message(record):
     # Your custom logging logic
     pass
@@ -166,7 +167,7 @@ Here's a full example of how to use structured logging with command-line configu
 ```python
 import argparse
 import logging
-from flogging import add_logging_args, set_context, setup
+from flogging.flogging import add_logging_args, set_context, setup
 
 # Initialize logging
 setup(level="INFO", structured=False)  # Human-readable format

flogging/flogging.py

@@ -189,6 +189,8 @@ class StructuredHandler(logging.Handler):
 
     """logging handler for structured logging."""
 
+    default_fields = set(logging.LogRecord("", logging.NOTSET, "", 1, "msg", (), None).__dict__)
+
     def __init__(
         self, level=logging.NOTSET, level_from_msg: Callable[[str], str | None] | None = None
     ):
@@ -219,6 +221,7 @@ def emit(self, record: logging.LogRecord):  # noqa: PLR0912
             "thread": reduce_thread_id(record.thread),
             "name": record.name,
         }
+        obj.update((k, getattr(record, k)) for k in record.__dict__.keys() - self.default_fields)
         try:
             rank = os.environ["RANK"]
         except KeyError:

flogging/tests/test_main.py

@@ -1,7 +1,8 @@
 import pytest
 
 
-def test_main():
+@pytest.mark.parametrize("structured", [True, False])
+def test_main(structured):
     from flogging.flogging import setup as setup_logging
 
-    setup_logging(level="info", structured=False)
+    setup_logging(level="info", structured=structured)

flogging/version.py

@@ -1,2 +1,2 @@
 """Current version of the project. Do not modify manually."""
-__version__ = "0.0.25"
+__version__ = "1.0.0"

setup.py

@@ -21,15 +21,15 @@
     version=version.__version__,
     license="MIT",
     author="fragiletech",
-    author_email="",
+    author_email="gmarkhor@gmail.com",
     url="",
-    keywords=["Machine learning", "artificial intelligence"],
+    keywords=["logging"],
     tests_require=["pytest>=5.3.5", "hypothesis>=5.6.0"],
     extras_require={},
     install_requires=["xxhash"],
     package_data={"": ["README.md"]},
     classifiers=[
-        "Development Status :: 3 - Alpha",
+        "Development Status :: 5 - Production/Stable",
         "Intended Audience :: Developers",
         "License :: OSI Approved :: MIT License",
         "Programming Language :: Python :: 3.10",