Make Document Processing Pipeline More Fault Tolerant #79
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…rary disk storage to avoid clogging Redis with huge files and to combat timeout conditions encountered with very large (multi-thousand page) files. Need to add code to make this work properly with local deployments as well as S3. Also need to add tests.
…n all style checks and linting.
…sing page-wise queueing, Tuning celerty workers is an ongoing process. For now, trying concurrency=1 and then scaling the celeryworker instances via docker-compose. May be able to increase concurrency. Should document this tuning processing for others with different envs (more cores, less cores, whatever).
Codecov Report
@@ Coverage Diff @@
## main #79 +/- ##
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+ Coverage 66.77% 68.91% +2.13%
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Files 47 47
Lines 1794 1853 +59
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+ Hits 1198 1277 +79
+ Misses 596 576 -20
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Experimenting with some real-world document collections, it's become apparent that the document processing could not be done on a doc-by-doc basis. The variation is too great. Most documents are OK, but some can go to the thousands or pages or have poor image compression. This creates situations where our celery workers crash due to over-consumption of memory or timeout due to insanely long processing times. New processing pipeline splits pdfs by page and then assigns each page to a worker in the celery queue. The bytes contents of the pages are no longer stored in the worker args as this consumes memory in Redis. Instead, they're stored to local FS or S3, depending on if
USE_AWSis set.