stream reading features #206
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michaelkirk
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frewsxcv
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Sep 3, 2022
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bors r=frewsxcv
I'd be curious to hear if it helps any! With rgis I assume you're ultimately holding the entire feature collection in memory anyway, so it might not be a huge win. Though it does seem like geojson's in-memory geometry representations are particularly inefficient, so it could help a bit! |
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CHANGES.mdif knowledge of this change could be valuable to users.This greatly reduces the peak memory usage needed to parse a GeoJSON FeatureCollection by utilizing the FeatureIterator's trick of seeking to the
"features": [array without actually parsing the FeatureCollection. Note there are some robustness issues with this approach, some of which can be improved, but overall I think it's worth the trade-off.There is a small (0-1%) CPU regression in our deserialization bench due to this change, but the peak memory profile of the
stream_reader_writerexamples decreased 90% - from 2.22MiB to 237Kib.Before:
After:
Notice that overall memory allocations stayed the same (see #88 (comment)), but the peak usage is much lower since we never hold the entire data structure in memory at once.