Concurrent processing

Rasterio affords concurrent processing of raster data. Python’s global interpreter lock (GIL) is released when calling GDAL’s GDALRasterIO() function, which means that Python threads can read and write concurrently.

The Numpy library also often releases the GIL, e.g., in applying universal functions to arrays, and this makes it possible to distribute processing of an array across cores of a processor.

This means that it is possible to parallelize tasks that need to be performed for a set of windows/pixels in the raster. Reading, writing and processing can always be done concurrently. But it depends on the hardware and where the bottlenecks are, how much of a speedup can be obtained. In the case that the processing function releases the GIL, multiple threads processing simultaneously can lead to further speedups.


If you wish to do multiprocessing that is not trivially parallelizable accross very large images that do not fit in memory, or if you wish to do multiprocessing across multiple machines. You might want to have a look at dask and in particular this example.

The Cython function below, included in Rasterio’s _example module, simulates a GIL-releasing CPU-intensive raster processing function. You can also easily create GIL-releasing functions by using numba

# cython: boundscheck=False

import numpy as np

def compute(unsigned char[:, :, :] input):
    """reverses bands inefficiently

    Given input and output uint8 arrays, fakes an CPU-intensive
    cdef int I, J, K
    cdef int i, j, k, l
    cdef double val
    I = input.shape[0]
    J = input.shape[1]
    K = input.shape[2]
    output = np.empty((I, J, K), dtype='uint8')
    cdef unsigned char[:, :, :] output_view = output
    with nogil:
        for i in range(I):
            for j in range(J):
                for k in range(K):
                    val = <double>input[i, j, k]
                    for l in range(2000):
                        val += 1.0
                    val -= 2000.0
                    output_view[~i, j, k] = <unsigned char>val
    return output

Here is the program in examples/ It is set up in such a way that at most 1 thread is reading and at most 1 thread is writing at the same time. Processing is not protected by a lock and can be done by multiple threads simultaneously.


Operate on a raster dataset window-by-window using a ThreadPoolExecutor.

Simulates a CPU-bound thread situation where multiple threads can improve

With -j 4, the program returns in about 1/4 the time as with -j 1.

import concurrent.futures
import multiprocessing
import threading

import rasterio
from rasterio._example import compute

def main(infile, outfile, num_workers=4):
    """Process infile block-by-block and write to a new file

    The output is the same as the input, but with band order

    with as src:

        # Create a destination dataset based on source params. The
        # destination will be tiled, and we'll process the tiles
        # concurrently.
        profile = src.profile
        profile.update(blockxsize=128, blockysize=128, tiled=True)

        with, "w", **src.profile) as dst:
            windows = [window for ij, window in dst.block_windows()]

            # We cannot write to the same file from multiple threads
            # without causing race conditions. To safely read/write
            # from multiple threads, we use a lock to protect the
            # DatasetReader/Writer
            read_lock = threading.Lock()
            write_lock = threading.Lock()

            def process(window):
                with read_lock:
                    src_array =

                # The computation can be performed concurrently
                result = compute(src_array)

                with write_lock:
                    dst.write(result, window=window)

            # We map the process() function over the list of
            # windows.
            with concurrent.futures.ThreadPoolExecutor(
            ) as executor:
      , windows)

The code above simulates a CPU-intensive calculation that runs faster when spread over multiple cores using concurrent.futures.ThreadPoolExecutor compared to the case of one concurrent job (-j 1),

$ time python examples/ tests/data/RGB.byte.tif /tmp/test.tif -j 1

real    0m4.277s
user    0m4.356s
sys     0m0.184s

we get over 3x speed up with four concurrent jobs.

$ time python examples/ tests/data/RGB.byte.tif /tmp/test.tif -j 4

real    0m1.251s
user    0m4.402s
sys     0m0.168s

If the function that you’d like to map over raster windows doesn’t release the GIL, you unfortunately cannot simply replace ThreadPoolExecutor with ProcessPoolExecutor, the DatasetReader/DatasetWriter cannot be shared by multiple processes, which means that each process needs to open the file seperately, or you can do all the reading and writing from the main thread, as shown in this next example. This is much less efficient memory wise, however.

arrays = [ for window in windows]

with concurrent.futures.ProcessPoolExecutor(
) as executor:
    futures =, arrays)
    for window, result in zip(windows, futures):
        dst.write(result, window=window)