Stats and Logging¶

Log Frequency In Sec¶

log_frequency_in_sec: Integer, default = 0: How often to log the progress. The default of 0 means logging progress is disabled. If you set it to 20, it will log every 20 seconds. This is useful only when running DeepDiff on massive objects that will take a while to run. If you are only dealing with small objects, keep it at 0 to disable progress logging.

For example we have run a diff on 2 nested objects that took 2 seconds to get the results. By passing the log_frequency_in_sec=1, we get the following in the logs:

>>> DeepDiff(t1, t2, log_frequency_in_sec=1)
INFO:deepdiff.diff:DeepDiff 1 seconds in progress. Pass #1634, Diff #8005
INFO:deepdiff.diff:DeepDiff 2 seconds in progress. Pass #3319, Diff #16148
INFO:deepdiff.diff:stats {'PASSES COUNT': 3960, 'DIFF COUNT': 19469, 'DISTANCE CACHE HIT COUNT': 11847, 'MAX PASS LIMIT REACHED': False, 'MAX DIFF LIMIT REACHED': False, 'DURATION SEC': 2}

Note

The default python logger will omit the info logs. You can either set the logging filter to include info logs or pass a different logger via Progress Logger

>>> import logging
>>> logging.basicConfig(level=logging.INFO)

Progress Logger¶

progress_logger: log function, default = logger.info: What logging function to use specifically for progress reporting. This function is only used when progress logging is enabled by setting log_frequency_in_sec to anything above zero. The function that is passed as the progress_logger needs to be thread safe.

For example you can pass progress_logger=logger.warning to the example above and everything is logged as warning level:

>>> DeepDiff(t1, t2, log_frequency_in_sec=1, progress_logger=logger.warning)
WARNING:deepdiff.diff:DeepDiff 1 seconds in progress. Pass #1634, Diff #8005
WARNING:deepdiff.diff:DeepDiff 2 seconds in progress. Pass #3319, Diff #16148
WARNING:deepdiff.diff:stats {'PASSES COUNT': 3960, 'DIFF COUNT': 19469, 'DISTANCE CACHE HIT COUNT': 11847, 'MAX PASS LIMIT REACHED': False, 'MAX DIFF LIMIT REACHED': False, 'DURATION SEC': 2}

Get Stats¶

You can run the get_stats() method on a diff object to get some stats on the object. For example:

>>> from pprint import pprint
>>> from deepdiff import DeepDiff
>>>
>>> t1 = [
...     [1, 2, 3, 9], [9, 8, 5, 9]
... ]
>>>
>>> t2 = [
...     [1, 2, 4, 10], [4, 2, 5]
... ]
>>>
>>> diff = DeepDiff(t1, t2, ignore_order=True, cache_size=5000, cutoff_intersection_for_pairs=1)
>>> pprint(diff.get_stats())
{'DIFF COUNT': 37,
 'DISTANCE CACHE HIT COUNT': 0,
 'MAX DIFF LIMIT REACHED': False,
 'MAX PASS LIMIT REACHED': False,
 'PASSES COUNT': 7}

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