refactoring harvesters

1 files changed, 209 insertions, 0 deletions

diff --git a/python/fatcat_tools/harvest/doi_registrars.py b/python/fatcat_tools/harvest/doi_registrars.py
new file mode 100644
index 00000000..1a6807d2
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+++ b/python/fatcat_tools/harvest/doi_registrars.py
@@ -0,0 +1,209 @@
+
+import re
+import sys
+import csv
+import json
+import requests
+import itertools
+import datetime
+from pykafka import KafkaClient
+
+from fatcat_tools.workers.worker_common import most_recent_message
+
+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))
+        params = {
+            '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 is 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 is 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
+