import re import sys import csv import json import time import requests import itertools import datetime from pykafka import KafkaClient from fatcat_tools.workers.worker_common import most_recent_message # Skip pylint due to: # AttributeError: 'NoneType' object has no attribute 'scope' # in 'astroid/node_classes.py' # pylint: skip-file DATE_FMT = "%Y-%m-%d" class HarvestCrossrefWorker: """ Notes on crossref API: - from-index-date is the updated time - is-update can be false, to catch only new or only old works https://api.crossref.org/works?filter=from-index-date:2018-11-14,is-update:false&rows=2 I think the design is going to have to be a cronjob or long-running job (with long sleeps) which publishes "success through" to a separate state queue, as simple YYYY-MM-DD strings. Within a day, will need to use a resumption token. Maybe should use a crossref library... meh. will want to have some mechanism in kafka consumer (pushing to fatcat) to group in batches as well. maybe even pass through as batches? or just use timeouts on iteration. logic of this worker: - on start, fetch latest date from state feed - in a function (unit-testable), decide which dates to ingest - for each date needing update: - start a loop for just that date, using resumption token for this query - when done, publish to state feed, with immediate sync TODO: what sort of parallelism? I guess multi-processing on dates, but need to be careful how state is serialized back into kafka. """ def __init__(self, kafka_hosts, produce_topic, state_topic, contact_email, api_host_url="https://api.crossref.org/works", start_date=None, end_date=None, is_update_filter=None): self.api_host_url = api_host_url self.produce_topic = produce_topic self.state_topic = state_topic self.contact_email = contact_email self.kafka = KafkaClient(hosts=kafka_hosts, broker_version="1.0.0") self.is_update_filter = is_update_filter # these are both optional, and should be datetime.date self.start_date = start_date self.end_date = end_date self.loop_sleep = 60*60 # how long to wait, in seconds, between date checks self.api_batch_size = 50 # for crossref, it's "from-index-date" self.name = "Crossref" def get_latest_date(self): state_topic = self.kafka.topics[self.state_topic] latest = most_recent_message(state_topic) if latest: latest = datetime.datetime.strptime(latest.decode('utf-8'), DATE_FMT).date() print("Latest date found: {}".format(latest)) return latest def params(self, date_str): filter_param = 'from-index-date:{},until-index-date:{}'.format( date_str, date_str) if self.is_update_filter is not None: filter_param += ',is_update:{}'.format(bool(self.is_update_filter)) return { 'filter': filter_param, 'rows': self.api_batch_size, 'cursor': '*', } def update_params(self, params, resp): params['cursor'] = resp['message']['next-cursor'] return params def fetch_date(self, date): state_topic = self.kafka.topics[self.state_topic] produce_topic = self.kafka.topics[self.produce_topic] date_str = date.strftime(DATE_FMT) params = self.params(date_str) headers = { 'User-Agent': 'fatcat_tools/0.1.0 (https://fatcat.wiki; mailto:{}) python-requests'.format(self.contact_email), } count = 0 with produce_topic.get_producer() as producer: while True: http_resp = requests.get(self.api_host_url, params, headers=headers) if http_resp.status_code == 503: # crud backoff print("got HTTP {}, pausing for 30 seconds".format(http_resp.status_code)) time.sleep(30.0) continue assert http_resp.status_code == 200 resp = http_resp.json() items = self.extract_items(resp) count += len(items) print("... got {} ({} of {}) in {}".format(len(items), count, self.extract_total(resp), http_resp.elapsed)) #print(json.dumps(resp)) for work in items: producer.produce(json.dumps(work).encode('utf-8')) if len(items) < self.api_batch_size: break params = self.update_params(params, resp) # record our completion state with state_topic.get_sync_producer() as producer: producer.produce(date.strftime(DATE_FMT).encode('utf-8')) def extract_items(self, resp): return resp['message']['items'] def extract_total(self, resp): return resp['message']['total-results'] def run_once(self): today_utc = datetime.datetime.utcnow().date() if self.start_date is None: self.start_date = self.get_latest_date() if self.start_date: # if we are continuing, start day after last success self.start_date = self.start_date + datetime.timedelta(days=1) if self.start_date is None: # bootstrap to yesterday (don't want to start on today until it's over) self.start_date = datetime.datetime.utcnow().date() if self.end_date is None: # bootstrap to yesterday (don't want to start on today until it's over) self.end_date = today_utc - datetime.timedelta(days=1) print("Harvesting from {} through {}".format(self.start_date, self.end_date)) current = self.start_date while current <= self.end_date: print("Fetching DOIs updated on {} (UTC)".format(current)) self.fetch_date(current) current += datetime.timedelta(days=1) print("{} DOI ingest caught up through {}".format(self.name, self.end_date)) return self.end_date def run_loop(self): while True: last = self.run_once() self.start_date = last self.end_date = None print("Sleeping {} seconds...".format(self.loop_sleep)) time.sleep(self.loop_sleep()) class HarvestDataciteWorker(HarvestCrossrefWorker): """ datacite has a REST API as well as OAI-PMH endpoint. have about 8 million bulk export notes: https://github.com/datacite/datacite/issues/188 fundamentally, very similar to crossref. don't have a scrape... maybe could/should use this script for that, and dump to JSON? """ def __init__(self, kafka_hosts, produce_topic, state_topic, contact_email, api_host_url="https://api.datacite.org/works", start_date=None, end_date=None): super().__init__(kafka_hosts=kafka_hosts, produce_topic=produce_topic, state_topic=state_topic, api_host_url=api_host_url, contact_email=contact_email, start_date=start_date, end_date=end_date) # for datecite, it's "from-update-date" self.name = "Datacite" def params(self, date_str): return { 'from-update-date': date_str, 'until-update-date': date_str, 'page[size]': self.api_batch_size, 'page[number]': 1, } def extract_items(self, resp): return resp['data'] def extract_total(self, resp): return resp['meta']['total'] def update_params(self, params, resp): params['page[number]'] = resp['meta']['page'] + 1 return params