The indexer.py script was updated to use a single table because I
learned about UPSERT. This simplifies the database schema and the
Python logic, and makes it easier to page all views and downloads
at once without complicated JOIN queries.
It is much more efficient to cache view and download statistics in
a database than to query Solr on demand (not to mention that it is
not possible to page easily with facets in Solr). I decided to use
SQLite because it is fast, native in Python 3, and doesn't require
any extra steps during provisioning (assuming permissions are ok).
Falcon's get_param_as_int() is really nice in that it gets a query
parameter and does validation for you, but I really wanted to have
cleaner URIs for API routes so I am now using a route URI template
with a field converter. This is cleaner, but means that parameters
not matching the template will return HTTP 404.
See: https://falcon.readthedocs.io/en/stable/api/routing.html#field-converters
According to dspace-api's Constants.java, items are type 2 and they
use a unique ID field of `id` instead of `owningItem`. There is no
need to check the bundleName for item types.
Also, I decided to use the main Solr query for item IDs because the
filter query parameter (fq) stores results in the filterCache and
can be quite expensive with cores storing tens of millions of docu-
ments (we currently have 149 million docs!). It makes sense to use
the filter query parameter to reduce the result set returned by the
main Solr query.
This whole business with negative query ranges is confusing as hell
and I'll definitely forget it in the future. In DSpace's Solr term-
inology a "download" is a view to some bitstream that lives in the
ORIGINAL bundle. This is where bitstreams that are uploaded during
the item submission process go, versus generated thumbnails, etc.