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@@ -24,3 +24,50 @@ Use gunicorn plus uvicorn, to get multiple worker processes, each running async: gunicorn example:app -w 4 -k uvicorn.workers.UvicornWorker + +## Prototype Pipeline + +Requires staff credentials in environment for `internetarchive` python library. + +TODO: pass these credentials via ansible/dotenv + +Generate complete SIM issue database: + + ia search "collection:periodicals collection:sim_microfilm mediatype:collection" --itemlist | rg "^pub_" > data/sim_collections.tsv + ia search "collection:periodicals collection:sim_microfilm mediatype:texts" --itemlist | rg "^sim_" > data/sim_items.tsv + + cat data/sim_collections.tsv | parallel -j4 ia metadata {} | jq . -c | pv -l > data/sim_collections.json + cat data/sim_items.tsv | parallel -j8 ia metadata {} | jq . -c | pv -l > data/sim_items.json + + cat data/sim_collections.2020-05-15.json | pv -l | python -m fatcat_scholar.issue_db load_pubs + cat data/sim_items.2020-05-15.json | pv -l | python -m fatcat_scholar.issue_db load_issues + python -m fatcat_scholar.issue_db load_counts + +Create QA elasticsearch index (localhost): + + http put ":9200/qa_scholar_fulltext_v01?include_type_name=true" < schema/scholar_fulltext.v01.json + http put ":9200/qa_scholar_fulltext_v01/_alias/qa_scholar_fulltext" + +Fetch "heavy" fulltext documents (JSON) for full SIM database: + + python -m fatcat_scholar.sim_pipeline run_issue_db | pv -l | gzip > data/sim_intermediate.json.gz + +Re-use existing COVID-19 database to index releases: + + cat /srv/fatcat_covid19/metadata/fatcat_hits.2020-04-27.enrich.json \ + | jq -c .fatcat_release \ + | rg -v "^null" \ + | parallel -j8 --linebuffer --round-robin --pipe python -m fatcat_scholar.work_pipeline run_releases --fulltext-cache-dir /srv/fatcat_covid19/fulltext_web \ + | pv -l \ + | gzip > data/work_intermediate.json.gz + + => 48.3k 0:17:58 [44.8 /s] + +Transform and index both into local elasticsearch: + + zcat data/work_intermediate.json.gz data/sim_intermediate.json.gz \ + | parallel -j8 --linebuffer --round-robin --pipe python -m fatcat_scholar.transform run_transform \ + | esbulk -verbose -size 100 -id key -w 4 -index qa_scholar_fulltext_v01 -type _doc + + => 132635 docs in 2m18.787824205s at 955.667 docs/s with 4 workers + |