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import re
import sys
import csv
import json
import time
import itertools
import datetime
import requests
from confluent_kafka import Producer, KafkaException
from fatcat_tools.workers import most_recent_message
from .harvest_common import HarvestState, requests_retry_session
class HarvestCrossrefWorker:
"""
Notes on crossref API:
- from-index-date is the updated time
https://api.crossref.org/works?filter=from-index-date:2018-11-14&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):
self.api_host_url = api_host_url
self.produce_topic = produce_topic
self.state_topic = state_topic
self.contact_email = contact_email
self.kafka_config = {
'bootstrap.servers': kafka_hosts,
'message.max.bytes': 20000000, # ~20 MBytes; broker is ~50 MBytes
}
self.state = HarvestState(start_date, end_date)
self.state.initialize_from_kafka(self.state_topic, self.kafka_config)
self.loop_sleep = 60*60 # how long to wait, in seconds, between date checks
self.api_batch_size = 50
self.name = "Crossref"
self.producer = self._kafka_producer()
def _kafka_producer(self):
def fail_fast(err, msg):
if err is not None:
print("Kafka producer delivery error: {}".format(err))
print("Bailing out...")
# TODO: should it be sys.exit(-1)?
raise KafkaException(err)
self._kafka_fail_fast = fail_fast
producer_conf = self.kafka_config.copy()
producer_conf.update({
'delivery.report.only.error': True,
'default.topic.config': {
'request.required.acks': -1, # all brokers must confirm
},
})
return Producer(producer_conf)
def params(self, date_str):
filter_param = 'from-index-date:{},until-index-date:{}'.format(
date_str, date_str)
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):
date_str = date.isoformat()
params = self.params(date_str)
http_session = requests_retry_session()
http_session.headers.update({
'User-Agent': 'fatcat_tools/0.1.0 (https://fatcat.wiki; mailto:{}) python-requests'.format(
self.contact_email),
})
count = 0
while True:
http_resp = http_session.get(self.api_host_url, params=params)
if http_resp.status_code == 503:
# crude backoff; now redundant with session exponential
# backoff, but allows for longer backoff/downtime on remote end
print("got HTTP {}, pausing for 30 seconds".format(http_resp.status_code))
# keep kafka producer connection alive
self.producer.poll(0)
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:
self.producer.produce(
self.produce_topic,
json.dumps(work).encode('utf-8'),
key=self.extract_key(work),
on_delivery=self._kafka_fail_fast)
self.producer.poll(0)
if len(items) < self.api_batch_size:
break
params = self.update_params(params, resp)
self.producer.flush()
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.state_topic,
kafka_config=self.kafka_config)
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
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