logchain 0.2.1

Python Logging secured by blockchain

Logchain

Python Logging based on blockchain 📜 ⛓️.

Logs get chained

The current log line contains the signature of the previous line with your secret.

• detect lines deleted / lost
• detect logs tampering

Philosophy

The package is intended to be a lightweight util for generating incorruptible logs.

For this pupose we rely as much as possible on standard packages: few dependencies, high quality.

The formatters are easy extensible by simply deriving from Basic.

Usage

Install

pip install logchain

Choose your log type

Many types of logs are supported out-of-the-box:

• Basic raw text, relying on the standard formatter
• Json structured log lines with static & dynamic fields
• CSV work in progress

You can write a custom formatter in 20-ish lines.

Init once in main

from logchain import LogChainer

# Initialize a default chainer.
theLogger = LogChainer()

# Register the formatter to the logger.
theLogger.initLogging()

Have a look at the comprehensive guide of constructor parameters.

Use everywhere with python logging module

import logging

logging.debug("My message")
logging.info("Some information")

Check your logs integrity afterwards

from logchain import LogChainer

aLogChain = [
	"2020-03-30 13:38:00.782|0ec90b9839fdd964|TestChaining.py:20 test_logging_happy_case hello gg",
	"2020-03-30 13:38:00.782|2e3f1b4a7b946fb1|TestChaining.py:21 test_logging_happy_case voila1",
	"2020-03-30 13:38:00.782|10d1ab606618492a|TestChaining.py:22 test_logging_happy_case voila2",
	"2020-03-30 13:38:00.782|805757e144f4e385|TestChaining.py:23 test_logging_happy_case voila5",
	"2020-03-30 13:38:00.782|3bda90b5af77d3fe|TestChaining.py:24 test_logging_happy_case voila4"
]
result = LogChainer.verify(aLogChain)

if not result:
	print("Last good line", result.prevLine)
	print("First bad line", result.line)
else:
	print("All right")

Constructor parameters

They are passed as a dict and/or named arguments.

from logchain import LogChainer

theLogger = LogChainer(verbosity = 3, secret = "mySignatureKey")

params = {"verbosity": 3, "secret": "mySignatureKey"}
theLogger = LogChainer(params, timestampFmt = "iso")

Param	Default value	Description
formatterCls	formatters.Basic	Type of logging to perform, raw text, json, custom
format	see below	Placeholder string used by raw-text loggers
secret	secrets.token_urlsafe(128)	Signature key to compute the line signature
seed	secrets.token_urlsafe()	Random string to sign into the first log line
timestampFmt	"iso"	iso for 8601 or strftime compatible placeholders
timestampPrecision	"milliseconds"	timespec element used by the datetime library
timestampUtc	False	Transform the timestamp to its value in UTC
stream	cout	Where the logs are sent, file/console/custom stream
verbosity	0	Number [0..5] mapped to a logging.level

The default format is %(timestamp)s %(levelLetters)s %(fileLine)-15s %(funcName)-15s %(message)-60s |%(signature)s. It relies on some extra fields like the signature at its end.

Logchain extra logging fields

We enrich the standard logging record with some handy fields:

Name	Description
fileLine	Widespread filename:lineno
levelLetters	4 first letters of logging level names: short and unambiguous
signature	The digital signature of the previous line. Include it in all your lines to benefit from the chaining
timestamp	Improved version of asctime, see below

The timestamp field offers more flexibility than asctime in regards to:

• the precision; can go up to the micro seconds (msecs cannot)
• the decimal separator; you choose, '.' by default
• utc or local timezone
• customize the format only in one place: timestampFmt

Dynamic logging fields

The package is suitable for server/app logging which context changes from one transaction to another. Here is an example of setting contextual information throughout the lifecycle of an app:

App.py

class App:
	def __init__(self, appName, logger):
		self.logger = logger
		self.logger.setField(appName = appName)
		logging.info("Creating the app")

	def handleTransaction(self, userId, callback):
		with self.logger.managedField(uId = userId, trxId = secrets.token_urlsafe(8)):
			callback()

	def close(self):
		logging.info("Closing the app")

Callbacks.py

	# The log chain in transparent for the callbacks
	def callback1():
		logging.warn("Something happened")

	def callback2():
		logging.info("Serving a resource")

main.py

def main():
	theLogger = logchain.LogChainer(formatterCls = logchain.formatters.Json)
	theLogger.initLogging()

	app = App("MyApp", theLogger)
	app.handleTransaction("user1", callback1)
	app.handleTransaction("user1", callback2)
	app.close()

You can either use:

• setField: set a permanant field, remove it by setting it to None.
• managedField: set a temporary field for the scope of the context manager.

---

Contributing

Install

The code is hosted on Gitlab 🦊

Simply clone and submit pull requests.

Testing

The unit tests are located in the test folder which contains the __main__.py entrypoint.

# Run all
python test

# Get additional options
python test --help

Releasing

The process is triggered by a tag added to a commit. The tag must match the pattern release_<VERSION> and VERSION has to comply to semver.

A CI/CD job handles the new tag event and publishes the package to PYPI using using to the awesome Poetry tool:

poetry version $VERSION
poetry build
poetry publish