mirror of
https://github.com/ilri/dspace-statistics-api.git
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Alan Orth
495386856b
Move the get_statistics_shards() method to a utility module so it can be used by other things.
223 lines
8.0 KiB
Python
223 lines
8.0 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 psycopg2-binary
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#
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# See: https://wiki.duraspace.org/display/DSPACE/Solr
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import psycopg2.extras
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import requests
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from .config import SOLR_SERVER
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from .database import DatabaseManager
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from .util import get_statistics_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"][
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"countDistinct"
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]
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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(
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f"Indexing item views (page {results_current_page + 1} of {results_num_pages + 1})"
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)
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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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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"][
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"countDistinct"
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]
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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(
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f"Indexing item downloads (page {results_current_page + 1} of {results_num_pages + 1})"
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)
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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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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(
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"""CREATE TABLE IF NOT EXISTS items
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(id UUID PRIMARY KEY, views INT DEFAULT 0, downloads INT DEFAULT 0)"""
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)
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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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