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v0.4.0
dspace-statistics-api/database.py

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Use PostgreSQL instead of SQLite I was very surprised how easy and fast and robust SQLite was, but in the end I realized that its UPSERT support only came in version 3.24 and both Ubuntu 16.04 and 18.04 have older versions than that! I did manage to install libsqlite3-0 from Ubuntu 18.04 cosmic on my xenial host, but that feels dirty. PostgreSQL has support for UPSERT since 9.5, not to mention the same nice LIMIT and OFFSET clauses.
2018-09-24 23:49:47 +02:00
from config import DATABASE_NAME
from config import DATABASE_USER
from config import DATABASE_PASS
from config import DATABASE_HOST
import psycopg2
database.py: Use psycopg2.extras.DictCursor This allows us to access records using their column name. I didn't notice that this was not working, as I had been testing the wrong server! See: http://initd.org/psycopg/docs/extras.html
2018-09-25 01:03:49 +02:00
import psycopg2.extras
Use PostgreSQL instead of SQLite I was very surprised how easy and fast and robust SQLite was, but in the end I realized that its UPSERT support only came in version 3.24 and both Ubuntu 16.04 and 18.04 have older versions than that! I did manage to install libsqlite3-0 from Ubuntu 18.04 cosmic on my xenial host, but that feels dirty. PostgreSQL has support for UPSERT since 9.5, not to mention the same nice LIMIT and OFFSET clauses.
2018-09-24 23:49:47 +02:00
def database_connection():
database.py: Use psycopg2.extras.DictCursor This allows us to access records using their column name. I didn't notice that this was not working, as I had been testing the wrong server! See: http://initd.org/psycopg/docs/extras.html
2018-09-25 01:03:49 +02:00
connection = psycopg2.connect("dbname={} user={} password={} host='{}'".format(DATABASE_NAME, DATABASE_USER, DATABASE_PASS, DATABASE_HOST), cursor_factory=psycopg2.extras.DictCursor)
Split database access into RW and RO The indexer need to be able to write to the database, but the API only needs to read it.
2018-09-23 23:00:05 +02:00
return connection
app.py: Get item statistics from SQLite database 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).
2018-09-23 15:47:00 +02:00
# vim: set sw=4 ts=4 expandtab:
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