mirror of
https://github.com/ilri/dspace-statistics-api.git
synced 2024-11-22 14:25:01 +01:00
Alan Orth
2acd08e0ab
It is easier for humans to understand one-based paging output like "page 1 of 3" than "page 0 of 2" in the indexer.
267 lines
9.9 KiB
Python
267 lines
9.9 KiB
Python
#
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# indexer.py
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#
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# Copyright 2018 Alan Orth.
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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#
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# ---
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#
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# Connects to a DSpace Solr statistics core and ingests item views and downloads
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# into a PostgreSQL database for use by other applications (like an API).
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#
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# This script is written for Python 3.5+ and requires several modules that you
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# can install with pip (I recommend using a Python virtual environment):
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#
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# $ pip install SolrClient psycopg2-binary
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#
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# See: https://solrclient.readthedocs.io/en/latest/SolrClient.html
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# See: https://wiki.duraspace.org/display/DSPACE/Solr
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from .config import SOLR_SERVER
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from .database import DatabaseManager
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import json
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import psycopg2.extras
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import re
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import requests
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# Enumerate the cores in Solr to determine if statistics have been sharded into
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# yearly shards by DSpace's stats-util or not (for example: statistics-2018).
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def get_statistics_shards():
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# Initialize an empty list for statistics core years
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statistics_core_years = []
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# URL for Solr status to check active cores
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solr_query_params = {
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'action': 'STATUS',
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'wt': 'json'
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}
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solr_url = SOLR_SERVER + '/admin/cores'
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res = requests.get(solr_url, params=solr_query_params)
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if res.status_code == requests.codes.ok:
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data = res.json()
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# Iterate over active cores from Solr's STATUS response (cores are in
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# the status array of this response).
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for core in data['status']:
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# Pattern to match, for example: statistics-2018
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pattern = re.compile('^statistics-[0-9]{4}$')
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if not pattern.match(core):
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continue
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# Append current core to list
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statistics_core_years.append(core)
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# Initialize a string to hold our shards (may end up being empty if the Solr
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# core has not been processed by stats-util).
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shards = str()
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if len(statistics_core_years) > 0:
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# Begin building a string of shards starting with the default one
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shards = '{}/statistics'.format(SOLR_SERVER)
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for core in statistics_core_years:
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# Create a comma-separated list of shards to pass to our Solr query
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#
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# See: https://wiki.apache.org/solr/DistributedSearch
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shards += ',{}/{}'.format(SOLR_SERVER, core)
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# Return the string of shards, which may actually be empty. Solr doesn't
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# seem to mind if the shards query parameter is empty and I haven't seen
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# any negative performance impact so this should be fine.
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return shards
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def index_views():
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# get total number of distinct facets for items with a minimum of 1 view,
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# otherwise Solr returns all kinds of weird ids that are actually not in
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# the database. Also, stats are expensive, but we need stats.calcdistinct
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# so we can get the countDistinct summary.
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#
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# see: https://lucene.apache.org/solr/guide/6_6/the-stats-component.html
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solr_query_params = {
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'q': 'type:2',
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'fq': 'isBot:false AND statistics_type:view',
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'facet': 'true',
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'facet.field': 'id',
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'facet.mincount': 1,
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'facet.limit': 1,
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'facet.offset': 0,
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'stats': 'true',
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'stats.field': 'id',
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'stats.calcdistinct': 'true',
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'shards': shards,
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'rows': 0,
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'wt': 'json'
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}
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solr_url = SOLR_SERVER + '/statistics/select'
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res = requests.get(solr_url, params=solr_query_params)
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try:
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# get total number of distinct facets (countDistinct)
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results_totalNumFacets = res.json()['stats']['stats_fields']['id']['countDistinct']
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except TypeError:
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print('No item views to index, exiting.')
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exit(0)
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# divide results into "pages" (cast to int to effectively round down)
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results_per_page = 100
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results_num_pages = int(results_totalNumFacets / results_per_page)
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results_current_page = 0
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with DatabaseManager() as db:
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with db.cursor() as cursor:
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# create an empty list to store values for batch insertion
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data = []
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while results_current_page <= results_num_pages:
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# "pages" are zero based, but one based is more human readable
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print('Indexing item views (page {} of {})'.format(results_current_page + 1, results_num_pages + 1))
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solr_query_params = {
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'q': 'type:2',
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'fq': 'isBot:false AND statistics_type:view',
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'facet': 'true',
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'facet.field': 'id',
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'facet.mincount': 1,
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'facet.limit': results_per_page,
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'facet.offset': results_current_page * results_per_page,
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'shards': shards,
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'rows': 0,
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'wt': 'json',
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'json.nl': 'map' # return facets as a dict instead of a flat list
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}
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solr_url = SOLR_SERVER + '/statistics/select'
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res = requests.get(solr_url, params=solr_query_params)
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# Solr returns facets as a dict of dicts (see json.nl parameter)
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views = res.json()['facet_counts']['facet_fields']
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# iterate over the 'id' dict and get the item ids and views
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for item_id, item_views in views['id'].items():
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data.append((item_id, item_views))
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# do a batch insert of values from the current "page" of results
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sql = 'INSERT INTO items(id, views) VALUES %s ON CONFLICT(id) DO UPDATE SET views=excluded.views'
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psycopg2.extras.execute_values(cursor, sql, data, template='(%s, %s)')
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db.commit()
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# clear all items from the list so we can populate it with the next batch
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data.clear()
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results_current_page += 1
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def index_downloads():
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# get the total number of distinct facets for items with at least 1 download
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solr_query_params= {
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'q': 'type:0',
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'fq': 'isBot:false AND statistics_type:view AND bundleName:ORIGINAL',
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'facet': 'true',
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'facet.field': 'owningItem',
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'facet.mincount': 1,
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'facet.limit': 1,
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'facet.offset': 0,
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'stats': 'true',
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'stats.field': 'owningItem',
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'stats.calcdistinct': 'true',
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'shards': shards,
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'rows': 0,
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'wt': 'json'
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}
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solr_url = SOLR_SERVER + '/statistics/select'
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res = requests.get(solr_url, params=solr_query_params)
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try:
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# get total number of distinct facets (countDistinct)
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results_totalNumFacets = res.json()['stats']['stats_fields']['owningItem']['countDistinct']
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except TypeError:
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print('No item downloads to index, exiting.')
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exit(0)
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# divide results into "pages" (cast to int to effectively round down)
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results_per_page = 100
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results_num_pages = int(results_totalNumFacets / results_per_page)
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results_current_page = 0
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with DatabaseManager() as db:
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with db.cursor() as cursor:
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# create an empty list to store values for batch insertion
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data = []
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while results_current_page <= results_num_pages:
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# "pages" are zero based, but one based is more human readable
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print('Indexing item downloads (page {} of {})'.format(results_current_page + 1, results_num_pages + 1))
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solr_query_params = {
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'q': 'type:0',
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'fq': 'isBot:false AND statistics_type:view AND bundleName:ORIGINAL',
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'facet': 'true',
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'facet.field': 'owningItem',
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'facet.mincount': 1,
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'facet.limit': results_per_page,
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'facet.offset': results_current_page * results_per_page,
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'shards': shards,
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'rows': 0,
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'wt': 'json',
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'json.nl': 'map' # return facets as a dict instead of a flat list
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}
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solr_url = SOLR_SERVER + '/statistics/select'
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res = requests.get(solr_url, params=solr_query_params)
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# Solr returns facets as a dict of dicts (see json.nl parameter)
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downloads = res.json()['facet_counts']['facet_fields']
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# iterate over the 'owningItem' dict and get the item ids and downloads
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for item_id, item_downloads in downloads['owningItem'].items():
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data.append((item_id, item_downloads))
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# do a batch insert of values from the current "page" of results
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sql = 'INSERT INTO items(id, downloads) VALUES %s ON CONFLICT(id) DO UPDATE SET downloads=excluded.downloads'
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psycopg2.extras.execute_values(cursor, sql, data, template='(%s, %s)')
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db.commit()
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# clear all items from the list so we can populate it with the next batch
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data.clear()
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results_current_page += 1
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with DatabaseManager() as db:
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with db.cursor() as cursor:
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# create table to store item views and downloads
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cursor.execute('''CREATE TABLE IF NOT EXISTS items
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(id INT PRIMARY KEY, views INT DEFAULT 0, downloads INT DEFAULT 0)''')
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# commit the table creation before closing the database connection
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db.commit()
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shards = get_statistics_shards()
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index_views()
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index_downloads()
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# vim: set sw=4 ts=4 expandtab:
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