import re import sys import csv import json import time import itertools import datetime import requests from pykafka import KafkaClient from fatcat_tools.workers import most_recent_message from .harvest_common import HarvestState 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 self.state = HarvestState(start_date, end_date) self.state.initialize_from_kafka(self.kafka.topics[self.state_topic]) self.loop_sleep = 60*60 # how long to wait, in seconds, between date checks self.api_batch_size = 50 self.name = "Crossref" 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 extract_key(self, obj): return obj['DOI'].encode('utf-8') def fetch_date(self, date): produce_topic = self.kafka.topics[self.produce_topic] date_str = date.isoformat() 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: # crude backoff print("got HTTP {}, pausing for 30 seconds".format(http_resp.status_code)) time.sleep(30.0) continue http_resp.raise_for_status() resp = http_resp.json() items = self.extract_items(resp) count += len(items) print("... got {} ({} of {}), HTTP fetch took {}".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'), partition_key=self.extract_key(work)) if len(items) < self.api_batch_size: break params = self.update_params(params, resp) def extract_items(self, resp): return resp['message']['items'] def extract_total(self, resp): return resp['message']['total-results'] def run(self, continuous=False): while True: current = self.state.next(continuous) if current: print("Fetching DOIs updated on {} (UTC)".format(current)) self.fetch_date(current) self.state.complete(current, kafka_topic=self.kafka.topics[self.state_topic]) continue if continuous: print("Sleeping {} seconds...".format(self.loop_sleep)) time.sleep(self.loop_sleep) else: break print("{} DOI ingest caught up".format(self.name)) 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 extract_key(self, obj): return obj['attributes']['doi'].encode('utf-8') def update_params(self, params, resp): params['page[number]'] = resp['meta']['page'] + 1 return params