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Last active Jul 8, 2020
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Rasterio concurrency example
"""Concurrent read-process-write example"""
import concurrent.futures
from itertools import islice
from time import sleep
import rasterio
CHUNK = 100
def compute(path, window):
"""Simulates an expensive computation
Gets source data for a window, sleeps, reverses bands.
Note: Numpy ufuncs release GIL and are parallelizable.
# Uncomment this line to see interleaving of reading and writing.
# print(f"Processing data: window={window}")
with as src:
data =
return window, data[::-1]
def main(infile, outfile, max_workers=1):
with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
with as src:
with, "w", **src.profile) as dst:
windows = (window for ij, window in dst.block_windows())
group = islice(windows, CHUNK)
futures = {executor.submit(compute, infile, window) for window in group}
while futures:
done, futures = concurrent.futures.wait(
futures, return_when=concurrent.futures.FIRST_COMPLETED
for future in done:
window, data = future.result()
# Uncomment this line to see interleaving of reading and writing.
# print(f"Writing data: window={window}")
dst.write(data, window=window)
group = islice(windows, CHUNK)
for window in group:
futures.add(executor.submit(compute, infile, window))
if __name__ == "__main__":
import sys
infile, outfile, num = sys.argv[1:4]
main(infile, outfile, max_workers=int(num))

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@jaredairbusaerial jaredairbusaerial commented Feb 25, 2019

What is the difference between this and rio-mucho? In what case(s) would one choose to use one over the other?

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