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import json
import time
from itertools import islice
from fatcat_tools.workers.worker_common import FatcatWorker
from pykafka.common import OffsetType
class FatcatChangelogWorker(FatcatWorker):
"""
Periodically polls the fatcat API looking for new changelogs. When they are
found, fetch them and push (as JSON) into a Kafka topic.
"""
def __init__(self, api_host_url, kafka_hosts, produce_topic, poll_interval=10.0, offset=None):
# TODO: should be offset=0
super().__init__(kafka_hosts=kafka_hosts,
produce_topic=produce_topic,
api_host_url=api_host_url)
self.poll_interval = poll_interval
self.offset = offset # the fatcat changelog offset, not the kafka offset
def most_recent_message(self, topic):
"""
Tries to fetch the most recent message from a given topic.
This only makes sense for single partition topics, though could be
extended with "last N" behavior.
Following "Consuming the last N messages from a topic"
from https://pykafka.readthedocs.io/en/latest/usage.html#consumer-patterns
"""
consumer = topic.get_simple_consumer(
auto_offset_reset=OffsetType.LATEST,
reset_offset_on_start=True)
offsets = [(p, op.last_offset_consumed - 1)
for p, op in consumer._partitions.items()]
offsets = [(p, (o if o > -1 else -2)) for p, o in offsets]
if -2 in [o for p, o in offsets]:
return None
else:
consumer.reset_offsets(offsets)
msg = islice(consumer, 1)
if msg:
return list(msg)[0].value
else:
return None
def run(self):
topic = self.kafka.topics[self.produce_topic]
# On start, try to consume the most recent from the topic, and using
# that as the starting offset. Note that this is a single-partition
# topic
if self.offset is None:
print("Checking for most recent changelog offset...")
msg = self.most_recent_message(topic)
if msg:
self.offset = json.loads(msg.decode('utf-8'))['index']
else:
self.offset = 1
with topic.get_sync_producer() as producer:
while True:
latest = int(self.api.get_changelog(limit=1)[0].index)
if latest > self.offset:
print("Fetching changelogs from {} through {}".format(
self.offset+1, latest))
for i in range(self.offset+1, latest+1):
cle = self.api.get_changelog_entry(i)
obj = self.api.api_client.sanitize_for_serialization(cle)
producer.produce(
message=json.dumps(obj).encode('utf-8'),
partition_key=None,
timestamp=None,
#XXX: timestamp=cle.timestamp,
)
self.offset = i
print("Sleeping {} seconds...".format(self.poll_interval))
time.sleep(self.poll_interval)
class FatcatEntityUpdatesWorker(FatcatWorker):
"""
Consumes from the changelog topic and publishes expanded entities (fetched
from API) to update topics.
For now, only release updates are published.
"""
def __init__(self, api_host_url, kafka_hosts, consume_topic, release_topic):
super().__init__(kafka_hosts=kafka_hosts,
consume_topic=consume_topic,
api_host_url=api_host_url)
self.release_topic = release_topic
self.consumer_group = "entity-updates"
def run(self):
changelog_topic = self.kafka.topics[self.consume_topic]
release_topic = self.kafka.topics[self.release_topic]
consumer = changelog_topic.get_balanced_consumer(
consumer_group=self.consumer_group,
managed=True,
auto_offset_reset=OffsetType.LATEST,
reset_offset_on_start=False,
fetch_message_max_bytes=4000000, # up to ~4MBytes
auto_commit_enable=True,
auto_commit_interval_ms=30000, # 30 seconds
compacted_topic=True,
)
with release_topic.get_sync_producer() as producer:
for msg in consumer:
cle = json.loads(msg.value.decode('utf-8'))
#print(cle)
print("processing changelog index {}".format(cle['index']))
release_edits = cle['editgroup']['edits']['releases']
for re in release_edits:
ident = re['ident']
release = self.api.get_release(ident, expand="files,container")
release_dict = self.api.api_client.sanitize_for_serialization(release)
producer.produce(
message=json.dumps(release_dict).encode('utf-8'),
partition_key=ident.encode('utf-8'),
timestamp=None,
)
#consumer.commit_offsets()
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