|author||Bryan Newbold <email@example.com>||2018-08-24 13:38:49 -0700|
|committer||Bryan Newbold <firstname.lastname@example.org>||2018-08-24 13:38:49 -0700|
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+**Title:** Journal Archiving Pipeline
+**Author:** Bryan Newbold <email@example.com>
+**Date:** March 2018
+This is an RFC-style technical proposal for a journal crawling, archiving,
+extracting, resolving, and cataloging pipeline.
+Design work funded by a Mellon Foundation grant in 2018.
+Let's start with data stores first:
+- crawled original fulltext (PDF, JATS, HTML) ends up in petabox/global-wayback
+- file-level extracted fulltext and metadata is stored in HBase, with the hash
+ of the original file as the key
+- cleaned metadata is stored in a "catalog" relational (SQL) database (probably
+ PostgreSQL or some hip scalable NewSQL thing compatible with Postgres or
+**Resources:** back-of-the-envelope, around 100 TB petabox storage total (for
+100 million PDF files); 10-20 TB HBase table total. Can start small.
+All "system" (aka, pipeline) state (eg, "what work has been done") is ephemeral
+and is rederived relatively easily (but might be cached for performance).
+The overall "top-down", metadata-driven cycle is:
+1. Partners and public sources provide metadata (for catalog) and seed lists
+ (for crawlers)
+2. Crawlers pull in fulltext and HTTP/HTML metadata from the public web
+3. Extractors parse raw fulltext files (PDFs) and store structured metadata (in
+4. Data Mungers match extracted metadata (from HBase) against the catalog, or
+ create new records if none found.
+In the "bottom up" cycle, batch jobs run as map/reduce jobs against the
+catalog, HBase, global wayback, and partner metadata datasets to identify
+potential new public or already-archived content to process, and pushes tasks
+to the crawlers, extractors, and mungers.
+## Partner Metadata
+Periodic Luigi scripts run on a regular VM to pull in metadata from partners.
+All metadata is saved to either petabox (for public stuff) or HDFS (for
+restricted). Scripts process/munge the data and push directly to the catalog
+(for trusted/authoritative sources like Crossref, ISSN, PubMed, DOAJ); others
+extract seedlists and push to the crawlers (
+**Resources:** 1 VM (could be a devbox), with a large attached disk (spinning
+All fulltext content comes in from the public web via crawling, and all crawled
+content ends up in global wayback.
+One or more VMs serve as perpetual crawlers, with multiple active ("perpetual")
+Heritrix crawls operating with differing configuration. These could be
+orchestrated (like h3), or just have the crawl jobs cut off and restarted every
+year or so.
+In a starter configuration, there would be two crawl queues. One would target
+direct PDF links, landing pages, author homepages, DOI redirects, etc. It would
+process HTML and look for PDF outlinks, but wouldn't crawl recursively.
+HBase is used for de-dupe, with records (pointers) stored in WARCs.
+A second config would take seeds as entire journal websites, and would crawl
+Other components of the system "push" tasks to the crawlers by copying schedule
+files into the crawl action directories.
+WARCs would be uploaded into petabox via draintasker as usual, and CDX
+derivation would be left to the derive process. Other processes are notified of
+"new crawl content" being available when they see new unprocessed CDX files in
+items from specific collections. draintasker could be configured to "cut" new
+items every 24 hours at most to ensure this pipeline moves along regularly, or
+we could come up with other hacks to get lower "latency" at this stage.
+**Resources:** 1-2 crawler VMs, each with a large attached disk (spinning)
+### De-Dupe Efficiency
+We would certainly feed CDX info from all bulk journal crawling into HBase
+before any additional large crawling, to get that level of de-dupe.
+As to whether all GWB PDFs should be de-dupe against is a policy question: is
+there something special about the journal-specific crawls that makes it worth
+having second copies? Eg, if we had previously domain crawled and access is
+restricted, we then wouldn't be allowed to provide researcher access to those
+files... on the other hand, we could extract for researchers given that we
+"refound" the content at a new URL?
+Only fulltext files (PDFs) would be de-duped against (by content), so we'd be
+recrawling lots of HTML. Presumably this is a fraction of crawl data size; what
+Watermarked files would be refreshed repeatedly from the same PDF, and even
+extracted/processed repeatedly (because the hash would be different). This is
+hard to de-dupe/skip, because we would want to catch "content drift" (changes
+Off-the-shelf PDF extraction software runs on high-CPU VM nodes (probably
+GROBID running on 1-2 data nodes, which have 30+ CPU cores and plenty of RAM
+and network throughput).
+A hadoop streaming job (written in python) takes a CDX file as task input. It
+filters for only PDFs, and then checks each line against HBase to see if it has
+already been extracted. If it hasn't, the script downloads directly from
+petabox using the full CDX info (bypassing wayback, which would be a
+bottleneck). It optionally runs any "quick check" scripts to see if the PDF
+should be skipped ("definitely not a scholarly work"), then if it looks Ok
+submits the file over HTTP to the GROBID worker pool for extraction. The
+results are pushed to HBase, and a short status line written to Hadoop. The
+overall Hadoop job has a reduce phase that generates a human-meaningful report
+of job status (eg, number of corrupt files) for monitoring.
+A side job as part of extracting can "score" the extracted metadata to flag
+problems with GROBID, to be used as potential training data for improvement.
+**Resources:** 1-2 datanode VMs; hadoop cluster time. Needed up-front for
+backlog processing; less CPU needed over time.
+The matcher runs as a "scan" HBase map/reduce job over new (unprocessed) HBasej
+rows. It pulls just the basic metadata (title, author, identifiers, abstract)
+and calls the catalog API to identify potential match candidates. If no match
+is found, and the metadata "look good" based on some filters (to remove, eg,
+spam), works are inserted into the catalog (eg, for those works that don't have
+globally available identifiers or other metadata; "long tail" and legacy
+**Resources:** Hadoop cluster time
+The catalog is a versioned relational database. All scripts interact with an
+API server (instead of connecting directly to the database). It should be
+reliable and low-latency for simple reads, so it can be relied on to provide a
+public-facing API and have public web interfaces built on top. This is in
+contrast to Hadoop, which for the most part could go down with no public-facing
+impact (other than fulltext API queries). The catalog does not contain
+copywritable material, but it does contain strong (verified) links to fulltext
+content. Policy gets implemented here if necessary.
+A global "changelog" (append-only log) is used in the catalog to record every
+change, allowing for easier replication (internal or external, to partners). As
+little as possible is implemented in the catalog itself; instead helper and
+cleanup bots use the API to propose and verify edits, similar to the wikidata
+and git data models.
+Public APIs and any front-end services are built on the catalog. Elasticsearch
+(for metadata or fulltext search) could build on top of the catalog.
+**Resources:** Unknown, but estimate 1+ TB of SSD storage each on 2 or more
+## Machine Learning and "Bottom Up"
+Ansible is used to deploy all components. Luigi is used as a task scheduler for
+batch jobs, with cron to initiate periodic tasks. Errors and actionable
+problems are aggregated in Sentry.
+Logging, metrics, and other debugging and monitoring are TBD.