perf-tool 0.9.1

perf_tool

Another time performance tool for python. It is the right support tool if you need to profile an entire program, some piece of code, a module, a class, a function or a single row. The idea here is to profile only the code you want to profile.

Installation

perf_tool is tested on python 2.7, 3.6, 3.7, 3.8

> pip install perf_tool

After installation you can try to run a little example:

$ curl https://raw.githubusercontent.com/glaucouri/perf_tool/master/example.py > perf_tool_sample.py
$ python perf_tool_sample.py 
================== PerfTool ==================
task                     |aver(s) |sum(s)  |count   |std     
main                     |   0.065|   0.065|       1|   0.000
  +-call                 |   0.050|   0.050|       1|   0.000
    +-body               |   0.050|   0.050|       1|   0.000
  +-row                  |   0.001|   0.014|      10|   0.000

overall                  |    0.04|    0.06|      13|-       

Description

I have often been faced with the problem of performance on very challanging scope.

In many cases difficult if given by:

• intrinsic complexity of algorithms, in this cases, piece of codes can't be explained with a right asymptotic representations aka Big O notation

• domain not fully known, yes! if you don't know exacty how and why the developer has done some choices each row of code can be interpreted as an error candidate.

• coupling with data, sometimes algorithms is too much intricated with data, the result is an unreadable spaghetti code.

• use of external framework like Pandas, Scipy, Numpy. In these cases is not simple get a correct idea of where is spent computational time.

In these cases computational analysis can be misleading, on the other hands python profiler is too dispersive so juggling with performance tools give to us very counterintuitive reports.

How it works

When you profile a program your need is to construct a metric that can tell you where the time is spent. So at the beginning you must put sentinels in the entry point in order to create main metrics. Gradually you put other sentinels in the most relevant section of codes, increasing the capillarity until individual lines of code are identified.

perf_tool has 2 sentinel:

> from perf_tool import PerfTool, perf_tool

• PerfTool Is a context manager
• perf_tool Is a function / method decorator

each Sentinel will cover a piece of code, and for that, records how many time is spent on it.

With a function we can print a report containing 4 columns:

• task: task label or name, it is set when you put the sentinel, i suggest to use mnemonic label
• aver(s): mean of time spent ove the piece of code.
• sum(s): sum of time spent ove the piece of code.
• count: how many times that sentinel was called
• std: standard deviation

Sentinels can be nested in order to represent the hierarchy of code

Examples

First step is to put root sentinels:

@perf_tool('main')
def main():
  <some code>

Second step is to place sentinel in subset of code.

@perf_tool('main')
def main():
  with PerfTool('preparation'):
    <some code part 1>

  with PerfTool('calculus'):
    <some code part 2>

  with PerfTool('output'):
    <some code part 3>

And so on

@perf_tool('main')
def main():
  with PerfTool('preparation'):
    <some code part 1>

  with PerfTool('calculus'):
    <some code part 2>

  with PerfTool('output'):
    <some code part 3a>
    for each row:
    with PerfTool('row write'):
      <some code part 3b>

In the last example output is (times are mock)

task                     |aver(s) |sum(s)  |count   |std     
main                     |   0.209|   0.209|       1|   0.000
  +-preparation          |   0.013|   0.013|       1|   0.000
  +-calculus             |   0.030|   0.030|       1|   0.000
  +-output               |   0.166|   0.166|       1|   0.000
    +-row write          |   0.001|   0.143|     100|   0.000

overall                  |    0.08|    0.21|     104|-       

Here a production use.

task                     |aver(s) |sum(s)  |count   |std     
Definition set value     |   0.006| 111.786|   17199|   0.013
  +-Comp3 call           |   0.003|   0.043|      13|   0.000
  +-Comp2 call           |   0.009|  37.078|    4056|   0.009
    +-calculate cache    |   0.018|  36.941|    2016|   0.002
      +-aggregate        |   0.012|  23.772|    2016|   0.002
  +-Comp3   call         |   0.083|  30.259|     364|   0.031
    +-aggregate          |   0.091|  16.431|     180|   0.011
    +-agg+pivot          |   0.092|  13.200|     144|   0.011
  +-Comp1 call           |   0.001|   1.607|    2626|   0.002
  +-Comp4  call          |   0.005|   0.061|      13|   0.000
Comp2 set value          |   1.054|  41.113|      39|   1.497
  +-Comp2 compute meas   |   3.163|  41.113|      13|   0.244
    +-Comp2 tmx          |   0.301|  15.675|      52|   0.014
      +-Broker call      |   0.005|   9.377|    1872|   0.001
        +-Comp3 call     |   0.003|   6.372|    1872|   0.001
    +-Comp2 Comp3        |   0.097|   4.650|      48|   0.124
      +-Broker call      |   0.029|   4.220|     144|   0.041
        +-Comp3   call   |   0.028|   3.985|     144|   0.041
          +-aggregate    |   0.086|   3.851|      45|   0.010
    +-Comp2 Comp1        |   0.056|   2.670|      48|   0.008
      +-Broker call      |   0.003|   1.215|     432|   0.002
        +-Comp1 call     |   0.001|   0.522|     432|   0.002
Comp4  set value         |   0.665|  17.300|      26|   0.719
  +-Comp4  compute all   |   1.441|  17.296|      12|   0.037
    +-call transform     |   0.415|  14.935|      36|   0.019
      +-Broker call      |   0.010|  11.894|    1152|   0.009
        +-Comp2 call     |   0.009|  10.519|    1152|   0.009
          +-calculate cac|   0.018|  10.470|     576|   0.003
            +-agg all    |   0.012|   7.000|     576|   0.002
    +-retrieve data      |   0.003|   0.448|     144|   0.000