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Python Benchmark

Using Python's timeit module to measure the performance of operations in Python quickly.

Measuring Lock Performance in Python on Linux​

Here’s a quick look at the cost of acquiring and releasing an uncontended lock using Python’s threading.Lock on a Linux system:

$ ./python -m timeit \
  -s "from threading import Lock; l=Lock(); a=l.acquire; r=l.release" \
  "a(); r()"
10000000 loops, best of 3: 0.127 usec per loop

Now, let’s compare that with the cost of calling a dummy Python function:

$ ./python -m timeit -s "def a(): pass" "a(); a()"
1000000 loops, best of 3: 0.221 usec per loop

And a trivial C function call (returning the False singleton via bool()):

$ ./python -m timeit -s "a=bool" "a(); a()"
10000000 loops, best of 3: 0.164 usec per loop

Interestingly, using a Lock as a context manager is actually slower, not faster, despite what you might expect:

$ ./python -m timeit -s "from threading import Lock; l=Lock()" \
  "with l: pass"
1000000 loops, best of 3: 0.242 usec per loop

So at least on Linux, there doesn't seem to be much low-hanging fruit left when it comes to optimizing lock performance.

Bonus: As of recent Python versions, RLock is now just as fast as Lock in uncontended cases:

$ ./python -m timeit \
  -s "from threading import RLock; l=RLock(); a=l.acquire; r=l.release" \
  "a(); r()"
10000000 loops, best of 3: 0.114 usec per loop