#
# 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 psycopg2-binary
#
# See: https://wiki.duraspace.org/display/DSPACE/Solr

import re

import psycopg2.extras
import requests

from .config import SOLR_SERVER
from .database import DatabaseManager


# 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_query_params = {"action": "STATUS", "wt": "json"}
    solr_url = SOLR_SERVER + "/admin/cores"
    res = requests.get(solr_url, params=solr_query_params)

    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 = f"{SOLR_SERVER}/statistics"

        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 += f",{SOLR_SERVER}/{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
    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
                }

                solr_url = SOLR_SERVER + "/statistics/select"

                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
                }

                solr_url = SOLR_SERVER + "/statistics/select"

                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: