Releasing the GIL during coordinate transforms

This page answers one question: how do you release the Global Interpreter Lock around a long-running PROJ coordinate transform so a multi-second reprojection does not freeze every other thread in the interpreter — without corrupting memory or touching a PyObject while the lock is dropped? It sits inside the Memory Management in Geospatial Extensions cluster of the Geospatial C-Extension Fundamentals & ABI Architecture reference, and gives you the exact Py_BEGIN_ALLOW_THREADS placement, the buffer-copy discipline it demands, and a benchmark that proves the parallel speedup.

The GIL-release window around a native coordinate transform A timeline shows a C extension function that first copies coordinate arrays out of Python objects while holding the GIL, then executes Py_BEGIN_ALLOW_THREADS to drop the lock, runs proj_trans_array on the raw C buffers with no Python access, executes Py_END_ALLOW_THREADS to reacquire the lock, and finally builds the result PyObject. A second thread runs concurrently only during the released middle window. C extension thread GIL held copy arrays → C buffer GIL released proj_trans_array() — no PyObject access Py_BEGIN_ALLOW_THREADS … Py_END_ALLOW_THREADS GIL held build result PyObject other Python thread runs concurrently — only while the lock is released blocked before and after this window

Context & Root Cause

CPython’s Global Interpreter Lock serializes execution of Python bytecode: only one thread runs interpreter code at a time. That is fine until a C-extension enters a long native call — a PROJ pipeline transforming a million coordinate pairs takes hundreds of milliseconds to seconds, and for that whole span the thread holds the GIL while doing zero Python work. Every other thread, including asyncio’s event loop and any parallel workers, is frozen behind it. The reprojection is embarrassingly parallel at the C level, yet the process behaves as if single-threaded.

The fix is to bracket the pure-native work with Py_BEGIN_ALLOW_THREADS / Py_END_ALLOW_THREADS, which drop and reacquire the lock. The constraint that makes this a memory-management problem, not just a performance tweak, is absolute: while the GIL is released you may not touch any PyObject — no reference counting, no PyList_GetItem, no buffer object access — because another thread may be mutating interpreter state concurrently. You must therefore copy the coordinate data out of Python objects into a raw C buffer before releasing, and build the result after reacquiring. Get the ordering wrong and you get a race that manifests as a Segmentation fault under load, exactly the class of crash catalogued in Memory Management in Geospatial Extensions.

Solution / Fix

This targets CPython 3.9–3.12 and PROJ 9.2+ (proj_trans_array from the proj.h C API). The pattern holds whether you write raw C or Cython.

1. Copy into a native buffer under the lock 

// Still holding the GIL: validate and extract into a C-owned buffer.
Py_buffer view;
if (PyObject_GetBuffer(coords_obj, &view, PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) < 0)
    return NULL;                       // borrows the Python buffer

size_t n = view.len / (2 * sizeof(double));
double *xy = PyMem_RawMalloc(view.len); // RawMalloc: safe without the GIL
if (!xy) { PyBuffer_Release(&view); return PyErr_NoMemory(); }
memcpy(xy, view.buf, view.len);
PyBuffer_Release(&view);               // done touching Python memory

PyMem_RawMalloc is deliberate: the standard PyMem_Malloc requires the GIL, so a buffer you intend to use after releasing the lock must come from the raw allocator, as Memory Management in Geospatial Extensions details.

2. Release, transform, reacquire 

PJ *P = /* thread-safe PJ* cloned per call via proj_clone on a shared context */;
int ok;

Py_BEGIN_ALLOW_THREADS               // ← GIL dropped here
    ok = proj_trans_array(P, PJ_FWD, n, (PJ_COORD *)xy);
Py_END_ALLOW_THREADS                 // ← GIL reacquired here

// Only now is it legal to touch Python objects again.
if (ok != 0) {
    PyMem_RawFree(xy);
    PyErr_SetString(PyExc_RuntimeError, "proj_trans_array failed");
    return NULL;
}

The PJ object must be safe to use off-thread. PROJ contexts (PJ_CONTEXT) are not thread-safe to share, so clone the transform onto a per-thread context with proj_clone before entering the released window, or create one context per worker thread.

3. Build the result after reacquiring 

PyObject *out = PyByteArray_FromStringAndSize((char *)xy, n * 2 * sizeof(double));
PyMem_RawFree(xy);
return out;                            // ownership handed to the interpreter

Verification 

# 1. Prove the parallel speedup with a threaded benchmark
python - <<'PY'
import threading, time
from _geospatial_ext import transform_bulk   # the extension above
data = [b"\x00" * (2*8*1_000_000)]           # 1M coord pairs
def work(): transform_bulk(data[0])
t0 = time.perf_counter()
ts = [threading.Thread(target=work) for _ in range(4)]
[t.start() for t in ts]; [t.join() for t in ts]
print(f"4 threads: {time.perf_counter()-t0:.2f}s")
PY
# expected: close to 1x the single-call time, not 4x (threads overlap)

# 2. Prove no memory corruption under contention (ASan build)
PYTHONMALLOC=malloc python -X dev -c "import _geospatial_ext; print('ok')"
# expected: ok, with no ASan heap-use-after-free or data-race report

If the four-thread time is ~4× a single call, the GIL was never released — confirm the Py_BEGIN_ALLOW_THREADS macro actually wraps the native call and is not short-circuited by an early return.

Pitfalls & Alternatives

Touching a PyObject inside the released window. Even a stray Py_DECREF or PyList_SET_ITEM between the macros is a data race. Keep the block purely native; if you need Python data, copy it out first. This is the number-one cause of intermittent Segmentation fault (core dumped) in threaded geospatial code.

Sharing one PJ_CONTEXT across threads. PROJ contexts carry mutable error state and a grid cache; concurrent use corrupts both. Clone per thread with proj_clone, or gate context creation per worker.

Releasing the GIL for trivial work. The macros have a real cost (lock handoff). For a transform of a few points the overhead dominates; only release around work measured in milliseconds or more. For the broader allocator and teardown rules these buffers rely on, see fixing memory leaks in GDAL Python bindings.