The logic to get views and downloads is very similar to that used
for items, but we facet by different fields. This uses a generic
function for indexing that takes an "indexType" and a "facetField"
parameter. The indexType parameter controls which database table
to insert into, and the facetField parameter indicates which field
to facet by in Solr.
When indexing item views and downloads the only field we need is the
the id. The `fl` parameter tells Solr which fields to return in the
search results. This should theoretically be more efficient, though
I don't have any time to figure out how to measure it right now.
Minor change to bot filtering. We should use a negated match for
documents that have `isBot:true` rather than looking for documents
that are tagged with `isBot:false` (the distinction is subtle, but
important).
I don't remember why we needed the stats, but it seems that it was
because without them there is no way to know how many results were
returned and therefore no way to know how many pages we'll need to
iterate over. Having the total number allows us to use a limit and
and offset to page through them deterministically.
We had previously been avoiding the f-strings because we needed to
run on Python 3.5 and they were only available in Python 3.6+, but
now the black formatter requires Python 3.6 and all our systems are
running Python 3.6+ anyways.
DSpace 6+ uses a UUID for item identifiers instead of an integer so
we need to update the PostgreSQL schema accordingly. Solr still re-
fers to them as "id" in its schema so we don't need to change anyt-
hing there.
The SolrClient library is unmaintained, which is starting to cause
problems due to the moving Python ecosystem. Switching to requests
does not change my code in any meaningful way and makes maintenance
easier.
DSpace's stats-util script splits the Solr statistics core into yearly
shards. We need to use Solr's `shards` query parameter in order to get
the statistics for previous years. This commit adds a helper function
to enumerate the active Solr cores to find yearly shards matching the
statistics-YYYY pattern and add them to the query.
Flake8 validates code style against PEP 8 in order to encourage the
writing of idiomatic Python. For reference, I am currently ignoring
errors about line length (E501) because I feel it makes code harder
to read.
This is the invocation I am using:
$ flake8 --ignore E501 dspace_statistics_api
