21 docs tagged with "performance"

asyncio internals, event loop tuning, connection pooling, backpressure, and high-throughput async patterns for production Python services.

Take a deliberately slow codebase and systematically optimize it using profiling.

Master ctypes, cffi, Cython, and pybind11 for calling C/C++ from Python - loading shared libraries, writing CPython extensions, and accelerating hot paths with compiled code.

Master functools.lru_cache, functools.cache, TTL caches, memoization patterns, cache invalidation, cachetools, Redis caching, and cache stampede prevention.

Master deterministic profiling with cProfile and pstats - reading profile output, sorting and filtering results, snakeviz visualization, profiling overhead, and real-world endpoint profiling.

Free-threaded Python, the specialising adaptive interpreter, immortal objects, sub-interpreters, and what changed in the 3.10–3.13 internals.

Static typing in Python with Cython - turning Python bottlenecks into C-speed code without leaving the Python ecosystem.

Profile and optimize a data pipeline from 10x slower to baseline.

Understand database indexes from the ground up - B-tree internals, query planning, EXPLAIN ANALYZE, composite indexes, and when indexes hurt performance.

Line-by-line time and memory profiling with line_profiler, memory_profiler, tracemalloc, and pympler - finding the exact lines that are slow or leak memory.

Reduce Python memory usage with __slots__, weakref, array module, struct.pack, memory-mapped files, object pooling, and the flyweight pattern for processing millions of records.

Profiling, Cython, Numba, memory optimisation, async performance, and Python at scale - turning Python code from slow to production-fast.

Master Python performance from measurement to optimization - profiling strategy, caching, memory optimization, vectorization, and C extensions for building high-throughput systems.

LLVM-based JIT compilation for Python numerical code - GPU acceleration, parallel loops, and ufunc creation with @jit and @cuda.jit.

cProfile, line_profiler, py-spy, memory_profiler, and Austin - finding real bottlenecks before optimising anything.

Amdahl's law, the profiling workflow, identifying hotspots, benchmarking methodology with timeit, performance budgets, and the discipline of measuring before optimizing.

Solve 11 Python c extensions and ffi problems. Covers ctypes practice, cffi exercises, C extensions. Hints and solutions.

Solve 11 Python memory optimization problems. Covers __slots__ python, generators memory, array module. Hints and solutions.

Solve 11 Python vectorization with numpy problems. Covers numpy vectorization, numpy broadcasting, numpy ufunc. Hints and solutions.

Understand why Python loops are slow, how NumPy's C-level loops bypass interpreter overhead, broadcasting rules, views vs copies, memory layout, ufuncs, and real-world data pipeline optimization.

The Python C API, writing and building a C extension module, PyArg_ParseTuple, error handling, reference counting in C, and CFFI/ctypes alternatives.