index.community/backend/scraper/management/commands/scrape.py

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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.
"""
import json
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import multiprocessing as mp
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import requests
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import time
import os
from dateutil.parser import parse as datetime_parser
from datetime import datetime, timedelta, timezone
from functional import seq
from django_bulk_update.helper import bulk_update
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from django.core.management.base import BaseCommand
from django import db
from django.conf import settings
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from django.utils import timezone
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
PERSONAL_INSTANCE_THRESHOLD = 5 # instances with < this many users won't be scraped
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STATUS_SCRAPE_LIMIT = 5000
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class Command(BaseCommand):
help = "Scrapes the entire fediverse"
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def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
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self.scraped_count = 0
f = open(os.path.join(settings.BASE_DIR, '../whitelist.txt'), 'r')
self.whitelist = seq(f.readlines()).map(lambda i: i.lower().strip()).to_list()
f.close()
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@staticmethod
def get_instance_info(instance_name: str):
"""Collect info about instance"""
url = 'https://' + instance_name + '/api/v1/instance'
response = requests.get(url, timeout=TIMEOUT)
json = response.json()
if response.status_code != 200 or get_key(json, ['error']):
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raise InvalidResponseException("Could not get info for {}".format(instance_name))
return json
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@staticmethod
def get_instance_peers(instance_name: str):
"""Collect connected instances"""
# The peers endpoint returns a "list of all domain names known to this instance"
# (https://github.com/tootsuite/mastodon/pull/6125)
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url = 'https://' + instance_name + '/api/v1/instance/peers'
response = requests.get(url, timeout=TIMEOUT)
peers = response.json()
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))
# Get rid of peers that just say "null" and the instance itself
return [peer for peer in peers if peer and peer != instance_name]
@staticmethod
def get_statuses(instance_name: str):
"""Collect all statuses that mention users on other instances"""
mentions = []
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datetime_threshold = datetime.now(timezone.utc) - timedelta(days=31)
statuses_seen = 0
# We'll ask for 1000 statuses, but Mastodon never returns more than 40. Some Pleroma instances will ignore
# the limit and return 20.
url = 'https://' + instance_name + '/api/v1/timelines/public?local=true&limit=1000'
while True:
response = requests.get(url, timeout=TIMEOUT)
statuses = response.json()
if response.status_code != 200 or get_key(statuses, ['error']):
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raise InvalidResponseException("Could not get statuses for {}".format(instance_name))
elif len(statuses) == 0:
break
# Get mentions from this instance
mentions.extend((seq(statuses)
.filter(lambda s: datetime_parser(s['created_at']) > datetime_threshold)
.flat_map(lambda s: s['mentions']))) # map to mentions
# Find out if we should stop here
earliest_status = statuses[-1]
earliest_time_seen = datetime_parser(earliest_status['created_at'])
statuses_seen += len(statuses)
# 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:
break
# Continuing, so get url for next page
min_id = earliest_status['id']
url = 'https://' + instance_name + '/api/v1/timelines/public?local=true&limit=1000&max_id=' + min_id
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time.sleep(2) # Sleep to avoid overloading the instance
mentions_seq = (seq(mentions)
.filter(lambda m: not m['acct'].endswith(instance_name) and '@' in m['acct'])
.map(lambda m: m['acct'].split('@')[-1]) # map to instance name
.map(lambda m: (m, 1))
.reduce_by_key(lambda x, y: x+y)) # sequence of tuples (instance, count)
mentions_by_instance = {t[0]: t[1] for t in mentions_seq} # dict of instance -> number of mentions
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"""
data = dict()
try:
data['instance_name'] = instance.name
data['info'] = self.get_instance_info(instance.name)
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# Check if this is a personal instance before continuing
user_count = get_key(data, ['info', 'stats', 'user_count'])
if isinstance(user_count, int)\
and user_count < PERSONAL_INSTANCE_THRESHOLD\
and instance.name not in self.whitelist:
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raise PersonalInstanceException
data['peers'] = self.get_instance_peers(instance.name)
if not data['info'] and not data['peers']:
# 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
data['mentions'], data['statuses_seen'] = self.get_statuses(instance.name)
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data['status'] = 'success'
return data
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except (InvalidResponseException,
PersonalInstanceException,
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requests.exceptions.RequestException,
json.decoder.JSONDecodeError) as e:
data['instance_name'] = instance.name
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data['status'] = type(e).__name__
return data
@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"""
# Validate the ints. Some servers that appear to be fake instances have e.g. negative numbers here.
instance.domain_count = validate_int(get_key(data, ['info', 'stats', 'domain_count']))
instance.status_count = validate_int(get_key(data, ['info', 'stats', 'status_count']))
instance.user_count = validate_int(get_key(data, ['info', 'stats', 'user_count']))
instance.description = get_key(data, ['info', 'description'])
instance.version = get_key(data, ['info', 'version'])
instance.status = get_key(data, ['status'])
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instance.last_updated = timezone.now()
instance.save()
if data['status'] == 'success' and data['peers']:
# TODO: handle a peer disappeer-ing
# Create instances for the peers we haven't seen before and add them to the queue
new_instance_ids = [peer_id for peer_id in data['peers'] if peer_id not in existing_instance_ids]
# 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))
for id in new_instance_ids]
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existing_instance_ids.extend(new_instance_ids)
Instance.objects.bulk_create(new_instances)
for new_instance in new_instances:
queue.put(new_instance)
# Create relationships we haven't seen before
existing_peer_ids = PeerRelationship.objects.filter(source=instance).values_list('target', flat=True)
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)
new_relationships = [PeerRelationship(source=instance, target_id=new_peer, first_seen=datetime.now())
for new_peer in new_peer_ids]
PeerRelationship.objects.bulk_create(new_relationships)
if data['status'] == 'success' and data['mentions']:
# At this point, we can assume that a relationship exists for every peer that's mentioned in statuses
mentions = data['mentions']
relationships = PeerRelationship.objects.filter(source=instance,
target_id__in=list(mentions.keys()))
for relationship in relationships:
relationship.mention_count = mentions[relationship.target_id]
relationship.statuses_seen = data['statuses_seen']
relationship.last_updated = datetime.now()
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"""
db.connections.close_all() # https://stackoverflow.com/a/38356519/3697202
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while True:
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
self.stderr.write(log("Skipping {}, already done. This should not have been added to the queue!"
.format(instance)))
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queue.task_done()
else:
# Fetch data on instance
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()
stale_instances = Instance.objects.filter(last_updated__lte=datetime.now()-timedelta(days=1))
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with mp.Manager() as manager:
# Share the list of existing instances amongst all threads (to avoid each thread having to query
# for it on every instance it scrapes)
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()
if stale_instances:
for instance in stale_instances:
queue.put(instance)
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elif not Instance.objects.exists():
instance, _ = Instance.objects.get_or_create(name=SEED)
existing_instance_ids.append(instance.name)
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()
self.stdout.write(self.style.SUCCESS(log("Scraped {} instances in {:.0f}s"
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.format(self.scraped_count, end_time - start_time))))