You can now POST a JSON request to /items with a list of items and
a date range. This allows the possibility to get view and download
statistics for arbitrary items and arbitrary date ranges.
The JSON request should be in the following format:
{
"limit": 100,
"page": 0,
"dateFrom": "2020-01-01T00:00:00Z",
"dateTo": "2020-09-09T00:00:00Z",
"items": [
"f44cf173-2344-4eb2-8f00-ee55df32c76f",
"2324aa41-e9de-4a2b-bc36-16241464683e",
"8542f9da-9ce1-4614-abf4-f2e3fdb4b305",
"0fe573e7-042a-4240-a4d9-753b61233908"
]
}
The limit, page, and date parameters are all optional. By default
it will use a limit of 100, page 0, and [* TO *] Solr date range.
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 adapt our PostgreSQL queries to use those. Note that we
can no longer sort results in the "all items" endpoint by ID. Also,
we need to use parameterized psycopg2 queries instead of strings to
support queries with UUIDs properly. To use the Python UUID objects
elsewhere in the code we need to make sure that we cast them to str.
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
I had imagined plugging in an interactive Swagger or OpenAPI instance
here, but that's actually much more involved in Falcon than I want to
deal with right now.
