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dspace-statistics-api/dspace_statistics_api/indexer.py
Alan Orth 043d897cef
dspace_statistics_api/indexer.py: Catch case of no views/downloads 
Don't fail with an exception when there are no views or downloads,
for example on a new DSpace installation.
2019-01-22 09:00:22 +02:00

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8.8 KiB
Python
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#
# 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 3.5+ and requires several modules that you
# can install with pip (I recommend using a Python virtual environment):
#
# $ pip install SolrClient psycopg2-binary
#
# See: https://solrclient.readthedocs.io/en/latest/SolrClient.html
# See: https://wiki.duraspace.org/display/DSPACE/Solr
from .database import DatabaseManager
import json
import psycopg2.extras
import re
import requests
from .solr import solr_connection
# Enumerate the cores in Solr to determine if statistics have been sharded into
# yearly shards by DSpace's stats-util or not (for example: statistics-2018).
def get_statistics_shards():
# Initialize an empty list for statistics core years
statistics_core_years = []
# URL for Solr status to check active cores
solr_url = solr.host + '/admin/cores?action=STATUS&wt=json'
res = requests.get(solr_url)
if res.status_code == requests.codes.ok:
data = res.json()
# Iterate over active cores from Solr's STATUS response (cores are in
# the status array of this response).
for core in data['status']:
# Pattern to match, for example: statistics-2018
pattern = re.compile('^statistics-[0-9]{4}$')
if not pattern.match(core):
continue
# Append current core to list
statistics_core_years.append(core)
# Initialize a string to hold our shards (may end up being empty if the Solr
# core has not been processed by stats-util).
shards = str()
if len(statistics_core_years) > 0:
# Begin building a string of shards starting with the default one
shards = '{}/statistics'.format(solr.host)
for core in statistics_core_years:
# Create a comma-separated list of shards to pass to our Solr query
#
# See: https://wiki.apache.org/solr/DistributedSearch
shards += ',{}/{}'.format(solr.host, core)
# Return the string of shards, which may actually be empty. Solr doesn't
# seem to mind if the shards query parameter is empty and I haven't seen
# any negative performance impact so this should be fine.
return 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.
#
# see: https://lucene.apache.org/solr/guide/6_6/the-stats-component.html
res = solr.query('statistics', {
'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)
try:
# get total number of distinct facets (countDistinct)
results_totalNumFacets = json.loads(res.get_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:
print('Indexing item views (page {} of {})'.format(results_current_page, results_num_pages))
res = solr.query('statistics', {
'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)
# SolrClient's get_facets() returns a dict of dicts
views = res.get_facets()
# in this case 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
res = solr.query('statistics', {
'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)
try:
# get total number of distinct facets (countDistinct)
results_totalNumFacets = json.loads(res.get_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:
print('Indexing item downloads (page {} of {})'.format(results_current_page, results_num_pages))
res = solr.query('statistics', {
'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)
# SolrClient's get_facets() returns a dict of dicts
downloads = res.get_facets()
# in this case 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
solr = solr_connection()
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 INT 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:
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