Skill — Python Performance

When this skill activates

Any task involving Python runtime performance: identifying bottlenecks, reducing execution time, fixing memory leaks, optimizing async code, parallelizing workloads, or replacing slow Python loops with vectorized operations.

Mandatory actions when this skill is active

Before writing any code

1. Establish a baseline measurement. Never optimize without numbers. Identify:

• What is slow? (wall time, CPU time, memory growth, I/O wait)
• How slow? (current measurement with units)
• What is the target? (acceptable latency/throughput/memory)

1. Select the correct profiling tool for the problem:

1. Do NOT guess. Profile first, then optimize the hottest path.

During implementation

#### Profiling Workflow

# Quick cProfile workflow
import cProfile
import pstats

profiler = cProfile.Profile()
profiler.enable()
# ... code under test ...
profiler.disable()
stats = pstats.Stats(profiler).sort_stats("cumulative")
stats.print_stats(20)  # Top 20 by cumulative time

#### Asyncio Optimization

• Use asyncio.TaskGroup (Python 3.11+) over asyncio.gather for structured concurrency:

  async with asyncio.TaskGroup() as tg:
      task1 = tg.create_task(fetch_user(user_id))
      task2 = tg.create_task(fetch_orders(user_id))
  # Both complete or both cancel on error

• Never block the event loop with synchronous I/O. Offload with loop.run_in_executor:

  result = await loop.run_in_executor(None, blocking_io_function, arg)

• Avoid await in tight loops. Batch operations:

  # Bad: sequential awaits
  for item in items:
      await process(item)
  # Good: concurrent with bounded concurrency
  sem = asyncio.Semaphore(10)
  async def bounded(item):
      async with sem:
          return await process(item)
  results = await asyncio.gather(*(bounded(i) for i in items))

• Use asyncio.Queue for producer-consumer patterns instead of polling.
• Set appropriate timeouts on all network calls: async with asyncio.timeout(5.0).

#### Multiprocessing

• Use ProcessPoolExecutor for CPU-bound work (bypasses GIL):

  from concurrent.futures import ProcessPoolExecutor
  with ProcessPoolExecutor(max_workers=os.cpu_count()) as pool:
      results = list(pool.map(cpu_heavy_function, data_chunks))

• Use multiprocessing.shared_memory for large data to avoid serialization overhead:

  from multiprocessing import shared_memory
  shm = shared_memory.SharedMemory(create=True, size=array.nbytes)
  shared_array = np.ndarray(array.shape, dtype=array.dtype, buffer=shm.buf)

• Prefer Pool.map / Pool.imap_unordered over manual Process management.
• Watch for pickling overhead: large objects passed to workers are serialized. Keep payloads small.
• Use threading (not multiprocessing) for I/O-bound parallelism (GIL is released during I/O).

#### NumPy Vectorization

• Replace Python loops over numeric data with vectorized operations:

  # Bad: 100x slower
  result = [x * 2 + 1 for x in data]
  # Good: vectorized
  result = data * 2 + 1

• Use boolean indexing instead of conditional loops:

  # Bad
  filtered = [x for x in data if x > threshold]
  # Good
  filtered = data[data > threshold]

• Use np.where for conditional assignment, np.einsum for tensor operations.
• Avoid creating intermediate arrays in chains. Use out= parameter or in-place operations.
• For operations NumPy cannot vectorize: try numba.jit(nopython=True).

#### Generator Pipelines (Memory Efficiency)

• Use generators for processing large datasets that do not fit in memory:

  def read_chunks(path, chunk_size=8192):
      with open(path, "rb") as f:
          while chunk := f.read(chunk_size):
              yield chunk

  def process_pipeline(path):
      chunks = read_chunks(path)
      decoded = (chunk.decode("utf-8") for chunk in chunks)
      lines = (line for chunk in decoded for line in chunk.splitlines())
      return (parse(line) for line in lines if line.strip())

• Use itertools (chain, islice, groupby) for composing lazy pipelines.
• For pandas: use chunksize parameter in read_csv for row-by-row processing.

#### Memory Optimization

• Use __slots__ on data classes with many instances:

  class Point:
      __slots__ = ("x", "y", "z")
      def __init__(self, x: float, y: float, z: float):
          self.x, self.y, self.z = x, y, z
  # Saves ~40% memory per instance vs regular class

• Use sys.getsizeof and pympler.asizeof to measure object memory.
• Prefer array.array over lists for homogeneous numeric data.
• Use weakref for caches that should not prevent garbage collection.
• Intern frequently repeated strings: sys.intern(string).

#### C Extension Paths (When Python Is Not Fast Enough)

• Cython: annotate hot functions with cdef types for 10-100x speedup.
• pybind11: wrap existing C++ code with minimal boilerplate.
• ctypes/cffi: call existing shared libraries without compilation.
• Decision rule: Only reach for C extensions after profiling proves Python is the

bottleneck AND vectorization/multiprocessing cannot solve it.

After implementation

1. Re-run the same profiling tool used for the baseline.
2. Compare before/after measurements with the same input data.
3. Document the optimization in a comment or docstring explaining WHY and the measured improvement:

   # Optimized from 4.2s to 0.3s by vectorizing the distance calculation.
   # See: profiling results in docs/perf/distance-calc-2024-03.md

1. Verify correctness: optimized code must produce identical results to the original.
2. Run the test suite to ensure no regressions.

Performance anti-patterns to flag

• Premature @lru_cache on functions with large/unhashable arguments (memory leak).
• global interpreter lock assumptions (using threads for CPU-bound work).
• String concatenation in loops (+= creates new string each time; use "".join(parts)).
• Repeated dictionary/attribute lookups in tight loops (hoist outside).
• Using pandas for row-by-row iteration (iterrows is 100x slower than vectorized).
• Creating DataFrames inside loops (pre-allocate or use list-of-dicts then single concat).

Self-check before task completion

Before marking a task done when this skill was active:

• [ ] Baseline measurement exists (before optimization).
• [ ] After measurement shows quantified improvement.
• [ ] Profiling tool output confirms the hotspot was addressed.
• [ ] Correctness verified (same output as before optimization).
• [ ] No new memory leaks introduced (check with tracemalloc for long-running processes).
• [ ] Test suite passes without regressions.
• [ ] Optimization is documented with measured numbers in code or docs.
• [ ] No premature optimization: only the measured bottleneck was addressed.