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Small tool to track time of Python code blocks.

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about-time

Small tool to track time of Python code blocks.

What does it do?

There are several times where we need to instrument and log code execution, to see where complex pipelines are spending the most time in.

A simple start = time.time() and end = time.time() - start does not cut it when we want to track several lines at the same time, and/or whole blocks with line granularity.

This tool measures the execution time of a block of code, and can now even count iterations and the throughput of them, always with a beautiful "human" representation.

How to use it?

There's three modes of operation: context manager, callable handler and iterator metrics.

1. Use it like a context manager:

from about_time import about_time

with about_time() as t_whole:
    with about_time() as t_1:
        func_1()
    with about_time() as t_2:
        func_2('params')

Then, get the timings like this:

print(f'func_1 time: {t_1.duration_human}')
print(f'func_2 time: {t_2.duration_human}')
print(f'total time: {t_whole.duration_human}')

There's also the duration property, which returns the actual float time in seconds.

secs = t_whole.duration

2. You can also use it like a callable handler:

t_1 = about_time(func_1)
t_2 = about_time(lambda: func_2('params'))

If you use the callable handler syntax, there will be a new field called result to get the outcome of the function!

results = t_1.result, t_2.result

Or you can mix and match both:

with about_time() as t_whole:
    t_1 = about_time(func_1)
    t_2 = about_time(lambda: func_2('params'))

3. And you can count and measure throughput:

Wrap your iterable and iterate it! Since it have duration, it can calculate the throughput of the whole block. Specially useful in generators, which do not have length, but you can use with any iterables:

def callback(t_func):
    logger.info('func: size=%d throughput=%s', t_func.count,
                                               t_func.throughput_human)
items = filter(...)
for item in about_time(callback, items):
    # use item any way you want.
    pass

How do I install it?

Just do in python env:

$ pip install about-time

Some nice features

Humans are first class citizens :)

I've considered two key concepts in designing the human friendly functions: 3.44s is more meaningful than 3.43584783784s, and 14.12us is much nicer than .0000141233333s. So saying it another way, I round values to two decimal places at most, and finds the smaller unit to represent it, minimizing values smaller than 1.

Note that it dynamically finds the best unit to represent the value, considering even the rounding been applied. So if a value is for example 0.999999, it would end up like 1000.0ms after rounded, but it is auto-upgraded to the next unit 1.0s.

The duration_human changes seamlessly from nanoseconds to hours. Values smaller than 60 seconds are rendered with two digits precision at most (zeros to the right of the decimal point are not shown), and starting from 1 minute, an "hours:minutes:seconds.M" milliseconds (with only one digit precision). Some examples directly from the unit tests:

duration (float seconds) duration_human
.00000000185 '1.85ns'
.000000999996 '1.0us'
.00001 '10.0us'
.0000156 '15.6us'
.01 '10.0ms'
.0141233333333 '14.12ms'
.1099999 '110.0ms'
.1599999 '160.0ms'
.8015 '801.5ms'
3.434999 '3.43s'
59.999 '0:01:00'
68.5 '0:01:08.5'
125.825 '0:02:05.8'
4488.395 '1:14:48.4'

The throughput_human has similar logic, and to human brain it is much trickier to figure out: If it took 1165263 seconds to handle 123 items, how fast it did? Even dividing to find out the time per item 9473 seconds don't mean much. Dividing by 3600 we get 2.63 hours per item, and the throughput is returned nicely as 0.38/h. The tool has per-second, per-minute and per-hour. Some examples:

duration (float seconds) number of elements throughput_human
1. 10 '10.0/s'
1. 2500 '2500.0/s'
2. 1 '30.0/m'
2. 10 '5.0/s'
1.981981981981982 11 '5.55/s'
100. 10 '6.0/m'
1600. 3 '6.75/h'
.99 1 '1.01/s'
1165263. 123 '0.38/h'

Accuracy

This tool supports all versions of python, but in pythons >= 3.3, the code uses the new time.perf_counter to gain from the higher resolution and smaller propagating of errors. In older versions, it uses time.time.

License

This software is licensed under the MIT License. See the LICENSE file in the top distribution directory for the full license text.

Nice huh?

Thanks for your interest!

I wish you have fun using this tool! :)

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Filename, size & hash SHA256 hash help File type Python version Upload date
about_time-2.0.4-py2-none-any.whl (5.5 kB) Copy SHA256 hash SHA256 Wheel py2 Aug 27, 2018
about-time-2.0.4.tar.gz (5.3 kB) Copy SHA256 hash SHA256 Source None Aug 27, 2018

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