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Profiling CPU usage for blocks of code

Project description

Python Code Profiler

For measuring how much time different functions or pieces of code take to execute.



pip install codeprofile

For more details, check the Python Package Index project.

Source Code

Hosted on Github. At this time, practically just one file.


Add the provided decorators to the pieces of code / functions you want to profile.

This document uses the term trace point. It refers to part of code that is profiled as a single block. If you profile execution time of a specific function, that function is the trace point. If you profile a specific block of code inside a function, that block is the trace point.


To trace / profile a function:

from codeprofile import profiler
import time

def my_func():
    print("hello world")
    x = some_other_func()
    return int(x)

In the above, every time the function my_func is executed, its execution time is recorded. It becomes a trace point named my_func.

Code snippets

To trace the performance of a code snippet inside a function / module:

from codeprofile import profiler

for x in range(100):
    with profiler.profile("block read"):
        block = read_block(x)
    with profiler.profile("db insert"):

In the above, there are two trace points name block read and db insert. The code above executes each trace point 100 times, resulting in 100 performance measurements for each trace point. The profiler will then provice access to cumulative, average, min, max, and count statistics for each trace point. If configured to keep the raw measurements, those and the median are also available per trace point.

Imagine the above block read as an example of reading a block of data over the network (e.g., scanning a blockchain). And writing this to a database in db insert. Maybe it seems slow. With the above profiling, you can look at your program and wonder, where does the time go? Now there is a question we all would like to know..


AsyncIO is the Python way of making use of inherent processing delays in IO-intensive operations to execute code in parallel. For example, the system might be waiting for some disk or network IO to proceed. At such points, the CPU is just idle. So AsyncIO is intended as a way to execute other code in parallel while waiting on IO. Since Python has no true multithreading (the multi-processing module is not quite the same), this can be a nice feature.

AsyncIO uses special keywords (such as async) and code-structures to manage all this. Functions used with AsyncIO thus need to be defined with the async keyword.

Because of this, the approach to profile regular functions with @profiler.profile_func does not work with AsyncIO, as the profile_func decorator is not async. A different decorator using the async definition is provided for this purpose:

from codeprofile import profiler
import asyncio

async def a_blocker(self):
    block = read_block() # <- assume this function uses asyncio to access network / disk
    await asyncio.sleep(1)
    insert_into_database(block) # <- again, assume this call uses asyncio access to a database

In the above example, a_blocker becomes a trace point measuring the function execution time for AsyncIO.

For code blocks inside AsyncIO methods / functions, the same approach as for other code blocks should work.

from codeprofile import profiler

async def hundred_blocks():
    for x in range(100):
        with profiler.profile("block read"):
            block = read_block()
        with profiler.profile("db insert"):


  • ignore_sleep: If true, use a performance counter that ignores time spent in sleep mode. Defaults to false.
  • collect_raw: If true, keep the raw measurement data for each trace point. Takes more memory, but gives access to more detailed profiling information. Defaults to true.

Setting them:

from codeprofile import profiler

profiler.collect_raw = False #by default this is true
profiler.ignore_sleep = True #by default this is false

Data Analysis

The performance data collected is stored and available as part of the profiler module.

Access raw results

The following variables are available as part of the profiler module:

  • cumulative_times: sum of all recorded execution times for a trace point.
  • max_times: highest time per trace point
  • min_times: minimum time per trace point
  • counts: number of times a trace point execution has been recorded.
  • raw_times: list of all recorded execution times per trace point.

The following function can be used if collect_raw is enabled:

  • median_times: median time per trace point.

Like so:

from codeprofile import profiler

cumulative_block_time = profiler.cumulative_times["block read"]
max_block_time = profiler.max_times["block read"]
min_block_time = profiler.min_times["block read"]
all_block_times = profiler.raw_times["block read"]

median_block_time = profiler.median("block read")

If you want to reset the statistics while running:

from codeprofile import profiler


Summary Printouts

  • print_run_stats(*names, file=sys.stdout)

The names parameter for print_run_stats is optional. It defaults to all names. A name is simply a reference to name of a trace point.

The file parameter allows writing the results to a file, a string, or elsewhere. By default, it uses the system output, writing the summary to console. You can also use an actual filesystem file as target, or build a string using io.StringIO, or whatever else the Python filesystem can do.

Export to CSV and Pandas

  • print_csv(*names, file=sys.stdout)

The result of print_csv is a CSV file, where each column represents a trace-point. The rows are not synchronized in any way, since only the person who implements the trace-points knows if they are related. So each column is just a list of traced values (performance times) for that trace-point. Each column has equal number of values, which is the largest number of points recorded for any trace-point. The ones with fewer values have the last rows left empty (nan in Pandas).

from codeprofile import profiler
import pandas as pd

with open("output.csv", "wb") as f:

df = pd.read_csv("output.csv")

Hierarchical profiling

You can also nest the trace profiling calls. For example:

from codeprofile import profiler

with profiler.profile("loop"):
    for x in range(100):
        with profiler.profile("block read"):
            block = read_block(x)
        with profiler.profile("db insert"):

With the above, you would have the loop trace point collecting stats for the read and write operations together, and the block read and db insert trace points for the specific operations.



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