251 lines
12 KiB
Python
251 lines
12 KiB
Python
"""
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This script starts at a seed instance and loads the list of connected
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peers. From there, it scrapes the peers of all instances it finds,
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gradually mapping the fediverse.
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"""
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import json
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import multiprocessing as mp
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import requests
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import time
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import os
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from dateutil.parser import parse as datetime_parser
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from datetime import datetime, timedelta, timezone
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from functional import seq
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from django_bulk_update.helper import bulk_update
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from django.core.management.base import BaseCommand
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from django import db
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from django.conf import settings
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from django.utils import timezone
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from scraper.models import Instance, PeerRelationship
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from scraper.management.commands._util import require_lock, InvalidResponseException, get_key, log, validate_int, PersonalInstanceException
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# TODO: use the /api/v1/server/followers and /api/v1/server/following endpoints in peertube instances
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SEED = 'mastodon.social'
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TIMEOUT = 20 # seconds
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NUM_THREADS = 16 # roughly 40MB each
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PERSONAL_INSTANCE_THRESHOLD = 10 # instances with < this many users won't be crawled
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MAX_STATUSES_PER_PAGE = 100
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STATUS_SCRAPE_LIMIT = 5000
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INSTANCE_SCRAPE_LIMIT = 50 # note: this does not include newly discovered instances! they will always be crawled.
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class Command(BaseCommand):
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help = "Scrapes the entire fediverse"
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def add_arguments(self, parser):
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# Named (optional) arguments
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parser.add_argument(
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'--unlimited',
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action='store_true',
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dest='unlimited',
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help="Crawl all stale instances rather than limiting to {}".format(INSTANCE_SCRAPE_LIMIT),
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)
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parser.add_argument(
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'--all',
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action='store_true',
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dest='all',
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help="Crawl all instances rather than limiting to stale ones"
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)
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.scraped_count = 0
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f = open(os.path.join(settings.BASE_DIR, '../whitelist.txt'), 'r')
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self.whitelist = seq(f.readlines()).map(lambda i: i.lower().strip()).to_list()
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f.close()
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@staticmethod
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def get_instance_info(instance_name: str):
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"""Collect info about instance"""
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url = 'https://' + instance_name + '/api/v1/instance'
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response = requests.get(url, timeout=TIMEOUT)
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json = response.json()
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if response.status_code != 200 or get_key(json, ['error']):
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raise InvalidResponseException("Could not get info for {}".format(instance_name))
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return json
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@staticmethod
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def get_instance_peers(instance_name: str):
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"""Collect connected instances"""
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# The peers endpoint returns a "list of all domain names known to this instance"
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# (https://github.com/tootsuite/mastodon/pull/6125)
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url = 'https://' + instance_name + '/api/v1/instance/peers'
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response = requests.get(url, timeout=TIMEOUT)
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peers = response.json()
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if response.status_code != 200 or not isinstance(peers, list) or get_key(peers, ['error']):
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raise InvalidResponseException("Could not get peers for {}".format(instance_name))
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# Get rid of peers that just say "null" and the instance itself
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# Also make sure to lowercase all instance names; otherwise there'll be some duplicates
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return [peer.lower() for peer in peers if peer and peer != instance_name]
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@staticmethod
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def get_statuses(instance_name: str):
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"""Collect all statuses that mention users on other instances"""
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mentions = []
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datetime_threshold = datetime.now(timezone.utc) - timedelta(days=31)
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statuses_seen = 0
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# We'll ask for lots of statuses, but Mastodon never returns more than 40. Some Pleroma instances will ignore
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# the limit and return 20.
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url = 'https://{}/api/v1/timelines/public?local=true&limit={}/'.format(instance_name, MAX_STATUSES_PER_PAGE)
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while True:
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response = requests.get(url, timeout=TIMEOUT)
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statuses = response.json()
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if response.status_code != 200 or get_key(statuses, ['error']):
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raise InvalidResponseException("Could not get statuses for {}".format(instance_name))
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elif len(statuses) == 0:
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break
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# Get mentions from this instance
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mentions.extend((seq(statuses)
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.filter(lambda s: datetime_parser(s['created_at']) > datetime_threshold)
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.flat_map(lambda s: s['mentions']))) # map to mentions
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# Find out if we should stop here
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earliest_status = statuses[-1]
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earliest_time_seen = datetime_parser(earliest_status['created_at'])
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statuses_seen += len(statuses)
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# Mastodon returns max 40 statuses; if we ever see less than that we know there aren't any more
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if earliest_time_seen < datetime_threshold or statuses_seen >= STATUS_SCRAPE_LIMIT:
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break
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# Continuing, so get url for next page
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min_id = earliest_status['id']
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url = 'https://{}/api/v1/timelines/public?local=true&limit={}&max_id={}'.format(instance_name, MAX_STATUSES_PER_PAGE, min_id)
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time.sleep(2) # Sleep to avoid overloading the instance
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mentions_seq = (seq(mentions)
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.filter(lambda m: not m['acct'].endswith(instance_name) and '@' in m['acct'])
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.map(lambda m: m['acct'].split('@')[-1]) # map to instance name
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.map(lambda m: (m, 1))
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.reduce_by_key(lambda x, y: x+y)) # sequence of tuples (instance, count)
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mentions_by_instance = {t[0]: t[1] for t in mentions_seq} # dict of instance -> number of mentions
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return mentions_by_instance, statuses_seen
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def process_instance(self, instance: Instance):
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"""Given an instance, get all the data we're interested in"""
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data = dict()
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try:
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data['instance_name'] = instance.name
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data['info'] = self.get_instance_info(instance.name)
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# Check if this is a personal instance before continuing
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user_count = get_key(data, ['info', 'stats', 'user_count'])
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if isinstance(user_count, int)\
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and user_count < PERSONAL_INSTANCE_THRESHOLD\
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and instance.name not in self.whitelist:
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raise PersonalInstanceException
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data['peers'] = self.get_instance_peers(instance.name)
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if not data['info'] and not data['peers']:
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# We got a response from the instance, but it didn't have any of the information we were expecting.
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raise InvalidResponseException
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data['mentions'], data['statuses_seen'] = self.get_statuses(instance.name)
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data['status'] = 'success'
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return data
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except (InvalidResponseException,
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PersonalInstanceException,
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requests.exceptions.RequestException,
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json.decoder.JSONDecodeError) as e:
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data['instance_name'] = instance.name
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data['status'] = type(e).__name__
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return data
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@db.transaction.atomic
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@require_lock(Instance, 'ACCESS EXCLUSIVE')
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def save_data(self, instance, data, queue, existing_instance_ids):
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"""Save data"""
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# Validate the ints. Some servers that appear to be fake instances have e.g. negative numbers here.
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instance.domain_count = validate_int(get_key(data, ['info', 'stats', 'domain_count']))
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instance.status_count = validate_int(get_key(data, ['info', 'stats', 'status_count']))
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instance.user_count = validate_int(get_key(data, ['info', 'stats', 'user_count']))
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instance.description = get_key(data, ['info', 'description'])
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instance.version = get_key(data, ['info', 'version'])
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instance.status = get_key(data, ['status'])
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instance.last_updated = timezone.now()
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instance.save()
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if data['status'] == 'success' and data['peers']:
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# TODO: handle a peer disappeer-ing
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# Create instances for the peers we haven't seen before and add them to the queue
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new_instance_ids = [peer_id for peer_id in data['peers'] if peer_id not in existing_instance_ids]
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# bulk_create doesn't call save(), so the auto_now_add field won't get set automatically
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new_instances = [Instance(name=id, first_seen=datetime.now(), last_updated=datetime.utcfromtimestamp(0))
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for id in new_instance_ids]
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existing_instance_ids.extend(new_instance_ids)
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Instance.objects.bulk_create(new_instances)
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for new_instance in new_instances:
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queue.put(new_instance)
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# Create relationships we haven't seen before
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existing_peer_ids = PeerRelationship.objects.filter(source=instance).values_list('target', flat=True)
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new_peer_ids = [peer_id for peer_id in data['peers'] if peer_id not in existing_peer_ids]
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if new_peer_ids:
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# new_peers = Instance.objects.filter(name__in=new_peer_ids)
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new_relationships = [PeerRelationship(source=instance, target_id=new_peer, first_seen=datetime.now())
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for new_peer in new_peer_ids]
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PeerRelationship.objects.bulk_create(new_relationships)
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if data['status'] == 'success' and data['mentions']:
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# At this point, we can assume that a relationship exists for every peer that's mentioned in statuses
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mentions = data['mentions']
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relationships = PeerRelationship.objects.filter(source=instance,
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target_id__in=list(mentions.keys()))
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for relationship in relationships:
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relationship.mention_count = mentions[relationship.target_id]
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relationship.statuses_seen = data['statuses_seen']
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relationship.last_updated = datetime.now()
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bulk_update(relationships, update_fields=['mention_count', 'statuses_seen', 'last_updated'])
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self.stdout.write(log("Processed {}: {}".format(data['instance_name'], data['status'])))
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def worker(self, queue: mp.JoinableQueue, existing_instance_ids, scraped_ids):
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"""The main worker that processes instances"""
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db.connections.close_all() # https://stackoverflow.com/a/38356519/3697202
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while True:
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instance = queue.get()
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if instance.name in scraped_ids:
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# If we hit this branch, it's indicative of a bug
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self.stderr.write(log("Skipping {}, already done. This should not have been added to the queue!"
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.format(instance)))
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queue.task_done()
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else:
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# Fetch data on instance
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self.stdout.write(log("Processing {}".format(instance.name)))
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data = self.process_instance(instance)
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self.save_data(instance, data, queue, existing_instance_ids)
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scraped_ids[instance.name] = 1
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queue.task_done()
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def handle(self, *args, **options):
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start_time = time.time()
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if options['all']:
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stale_instances = Instance.objects.all()
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else:
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stale_instances = Instance.objects.filter(last_updated__lte=datetime.now()-timedelta(days=1))
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if not options['unlimited']:
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stale_instances = stale_instances[:INSTANCE_SCRAPE_LIMIT]
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with mp.Manager() as manager:
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# Share the list of existing instances amongst all threads (to avoid each thread having to query
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# for it on every instance it scrapes)
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existing_instance_ids = manager.list(list(Instance.objects.values_list('name', flat=True)))
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scraped_ids = manager.dict()
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queue = mp.JoinableQueue()
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if stale_instances:
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for instance in stale_instances:
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queue.put(instance)
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elif not Instance.objects.exists():
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instance, _ = Instance.objects.get_or_create(name=SEED)
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existing_instance_ids.append(instance.name)
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queue.put(instance)
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pool = mp.Pool(NUM_THREADS, initializer=self.worker, initargs=(queue, existing_instance_ids, scraped_ids))
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queue.join()
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self.scraped_count = len(scraped_ids.keys())
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end_time = time.time()
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self.stdout.write(self.style.SUCCESS(log("Scraped {} instances in {:.0f}s"
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.format(self.scraped_count, end_time - start_time))))
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