Python Memory Tracing

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Memory tracing is essentially keeping track of how much memory your program uses at any given time during its execution. This can be incredibly useful for debugging purposes or optimizing performance by identifying areas where resources are being wasted.

So, how do we go about doing this in Python? Well, my friend, it’s actually quite simple!

First, you need to import the `resource` module which provides us with a handy function called `getrusage()`. This function returns a resource usage structure that contains information on various system resources such as CPU time and memory. For our purposes, we’re interested in the `ru_maxrss` field which tells us the maximum resident set size (i.e., how much memory was used at any given point during execution).

Here’s an example script to get you started:

# Import the resource and time modules
import resource
import time

# Get the current time before starting the loop
start = time.time()

# Loop 100000 times
for i in range(100000):
    # Do some computationally expensive task here...
    x = 2 * (3 ** i) # Corrected syntax error, raise 3 to the power of i

# Get the maximum resident set size before and after our loop
before_memory = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024**2 # Convert to MB for readability
after_memory = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024**2

# Calculate the difference in memory usage and print it out
delta_memory = after_memory - before_memory # Corrected syntax error, subtract before_memory from after_memory
print("Memory usage increased by:", delta_memory, "MB")

# Get the current time after the loop has finished
end = time.time()

# Calculate total execution time
total_time = end - start # Corrected syntax error, subtract start from end
print("Execution took", round(total_time), "seconds.")

In this example script, we’re using a simple loop to calculate the value of `x` for 100,000 iterations. We then use the `getrusage()` function before and after our computationally expensive task to get the maximum resident set size (i.e., memory usage) at those points in time. By subtracting these values, we can calculate how much memory was used during execution.

With just a few lines of code, you’re now able to trace your program’s memory usage and optimize performance accordingly.

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