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dspace-statistics-api/dspace_statistics_api/indexer.py

224 lines
8.1 KiB
Python

#
# indexer.py
#
# Copyright 2018 Alan Orth.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# ---
#
# Connects to a DSpace Solr statistics core and ingests item views and downloads
# into a PostgreSQL database for use by other applications (like an API).
#
# This script is written for Python 3.6+ and requires several modules that you
# can install with pip (I recommend using a Python virtual environment):
#
# $ pip install psycopg2-binary
#
# See: https://wiki.duraspace.org/display/DSPACE/Solr
import psycopg2.extras
import requests
from .config import SOLR_SERVER
from .database import DatabaseManager
from .util import get_statistics_shards
def index_views():
# get total number of distinct facets for items with a minimum of 1 view,
# otherwise Solr returns all kinds of weird ids that are actually not in
# the database. Also, stats are expensive, but we need stats.calcdistinct
# so we can get the countDistinct summary to calculate how many pages of
# results we have.
#
# see: https://lucene.apache.org/solr/guide/6_6/the-stats-component.html
solr_query_params = {
"q": "type:2",
"fq": "isBot:false AND statistics_type:view",
"facet": "true",
"facet.field": "id",
"facet.mincount": 1,
"facet.limit": 1,
"facet.offset": 0,
"stats": "true",
"stats.field": "id",
"stats.calcdistinct": "true",
"shards": shards,
"rows": 0,
"wt": "json",
}
solr_url = SOLR_SERVER + "/statistics/select"
res = requests.get(solr_url, params=solr_query_params)
try:
# get total number of distinct facets (countDistinct)
results_totalNumFacets = res.json()["stats"]["stats_fields"]["id"][
"countDistinct"
]
except TypeError:
print("No item views to index, exiting.")
exit(0)
# divide results into "pages" (cast to int to effectively round down)
results_per_page = 100
results_num_pages = int(results_totalNumFacets / results_per_page)
results_current_page = 0
with DatabaseManager() as db:
with db.cursor() as cursor:
# create an empty list to store values for batch insertion
data = []
while results_current_page <= results_num_pages:
# "pages" are zero based, but one based is more human readable
print(
f"Indexing item views (page {results_current_page + 1} of {results_num_pages + 1})"
)
solr_query_params = {
"q": "type:2",
"fq": "isBot:false AND statistics_type:view",
"facet": "true",
"facet.field": "id",
"facet.mincount": 1,
"facet.limit": results_per_page,
"facet.offset": results_current_page * results_per_page,
"shards": shards,
"rows": 0,
"wt": "json",
"json.nl": "map", # return facets as a dict instead of a flat list
}
res = requests.get(solr_url, params=solr_query_params)
# Solr returns facets as a dict of dicts (see json.nl parameter)
views = res.json()["facet_counts"]["facet_fields"]
# iterate over the 'id' dict and get the item ids and views
for item_id, item_views in views["id"].items():
data.append((item_id, item_views))
# do a batch insert of values from the current "page" of results
sql = "INSERT INTO items(id, views) VALUES %s ON CONFLICT(id) DO UPDATE SET views=excluded.views"
psycopg2.extras.execute_values(cursor, sql, data, template="(%s, %s)")
db.commit()
# clear all items from the list so we can populate it with the next batch
data.clear()
results_current_page += 1
def index_downloads():
# get the total number of distinct facets for items with at least 1 download
solr_query_params = {
"q": "type:0",
"fq": "isBot:false AND statistics_type:view AND bundleName:ORIGINAL",
"facet": "true",
"facet.field": "owningItem",
"facet.mincount": 1,
"facet.limit": 1,
"facet.offset": 0,
"stats": "true",
"stats.field": "owningItem",
"stats.calcdistinct": "true",
"shards": shards,
"rows": 0,
"wt": "json",
}
solr_url = SOLR_SERVER + "/statistics/select"
res = requests.get(solr_url, params=solr_query_params)
try:
# get total number of distinct facets (countDistinct)
results_totalNumFacets = res.json()["stats"]["stats_fields"]["owningItem"][
"countDistinct"
]
except TypeError:
print("No item downloads to index, exiting.")
exit(0)
# divide results into "pages" (cast to int to effectively round down)
results_per_page = 100
results_num_pages = int(results_totalNumFacets / results_per_page)
results_current_page = 0
with DatabaseManager() as db:
with db.cursor() as cursor:
# create an empty list to store values for batch insertion
data = []
while results_current_page <= results_num_pages:
# "pages" are zero based, but one based is more human readable
print(
f"Indexing item downloads (page {results_current_page + 1} of {results_num_pages + 1})"
)
solr_query_params = {
"q": "type:0",
"fq": "isBot:false AND statistics_type:view AND bundleName:ORIGINAL",
"facet": "true",
"facet.field": "owningItem",
"facet.mincount": 1,
"facet.limit": results_per_page,
"facet.offset": results_current_page * results_per_page,
"shards": shards,
"rows": 0,
"wt": "json",
"json.nl": "map", # return facets as a dict instead of a flat list
}
res = requests.get(solr_url, params=solr_query_params)
# Solr returns facets as a dict of dicts (see json.nl parameter)
downloads = res.json()["facet_counts"]["facet_fields"]
# iterate over the 'owningItem' dict and get the item ids and downloads
for item_id, item_downloads in downloads["owningItem"].items():
data.append((item_id, item_downloads))
# do a batch insert of values from the current "page" of results
sql = "INSERT INTO items(id, downloads) VALUES %s ON CONFLICT(id) DO UPDATE SET downloads=excluded.downloads"
psycopg2.extras.execute_values(cursor, sql, data, template="(%s, %s)")
db.commit()
# clear all items from the list so we can populate it with the next batch
data.clear()
results_current_page += 1
with DatabaseManager() as db:
with db.cursor() as cursor:
# create table to store item views and downloads
cursor.execute(
"""CREATE TABLE IF NOT EXISTS items
(id UUID PRIMARY KEY, views INT DEFAULT 0, downloads INT DEFAULT 0)"""
)
# commit the table creation before closing the database connection
db.commit()
shards = get_statistics_shards()
index_views()
index_downloads()
# vim: set sw=4 ts=4 expandtab: