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import json
import time
import requests
from confluent_kafka import Consumer, KafkaException
from fatcat_openapi_client import ReleaseEntity, ContainerEntity, ApiClient
from fatcat_tools import *
from .worker_common import FatcatWorker
class ElasticsearchReleaseWorker(FatcatWorker):
"""
Consumes from release-updates topic and pushes into (presumably local)
elasticsearch.
Uses a consumer group to manage offset.
"""
def __init__(self, kafka_hosts, consume_topic, poll_interval=10.0, offset=None,
elasticsearch_backend="http://localhost:9200", elasticsearch_index="fatcat",
batch_size=200):
super().__init__(kafka_hosts=kafka_hosts,
consume_topic=consume_topic)
self.consumer_group = "elasticsearch-updates3"
self.batch_size = batch_size
self.poll_interval = poll_interval
self.elasticsearch_backend = elasticsearch_backend
self.elasticsearch_index = elasticsearch_index
self.entity_type = ReleaseEntity
self.elasticsearch_document_name = "release"
self.transform_func = release_to_elasticsearch
def run(self):
ac = ApiClient()
def fail_fast(err, partitions):
if err is not None:
print("Kafka consumer commit error: {}".format(err))
print("Bailing out...")
# TODO: should it be sys.exit(-1)?
raise KafkaException(err)
for p in partitions:
# check for partition-specific commit errors
if p.error:
print("Kafka consumer commit error: {}".format(p.error))
print("Bailing out...")
# TODO: should it be sys.exit(-1)?
raise KafkaException(p.error)
#print("Kafka consumer commit successful")
pass
def on_rebalance(consumer, partitions):
for p in partitions:
if p.error:
raise KafkaException(p.error)
print("Kafka partitions rebalanced: {} / {}".format(
consumer, partitions))
consumer_conf = self.kafka_config.copy()
consumer_conf.update({
'group.id': self.consumer_group,
'on_commit': fail_fast,
# messages don't have offset marked as stored until pushed to
# elastic, but we do auto-commit stored offsets to broker
'enable.auto.commit': True,
'enable.auto.offset.store': False,
# user code timeout; if no poll after this long, assume user code
# hung and rebalance (default: 5min)
'max.poll.interval.ms': 60000,
'default.topic.config': {
'auto.offset.reset': 'latest',
},
})
consumer = Consumer(consumer_conf)
consumer.subscribe([self.consume_topic],
on_assign=on_rebalance,
on_revoke=on_rebalance,
)
while True:
batch = consumer.consume(
num_messages=self.batch_size,
timeout=self.poll_interval)
if not batch:
if not consumer.assignment():
print("... no Kafka consumer partitions assigned yet")
print("... nothing new from kafka, try again (interval: {}".format(self.poll_interval))
continue
print("... got {} kafka messages".format(len(batch)))
# first check errors on entire batch...
for msg in batch:
if msg.error():
raise KafkaException(msg.error())
# ... then process
bulk_actions = []
for msg in batch:
json_str = msg.value().decode('utf-8')
# HACK: work around a bug where container entities got published to
# release_v03 topic
if self.elasticsearch_document_name == "release":
entity_dict = json.loads(json_str)
if entity_dict.get('name') and not entity_dict.get('title'):
continue
entity = entity_from_json(json_str, self.entity_type, api_client=ac)
# TODO: handle deletions from index
bulk_actions.append(json.dumps({
"index": { "_id": entity.ident, },
}))
bulk_actions.append(json.dumps(
self.transform_func(entity)))
print("Upserting, eg, {} (of {} releases in elasticsearch)".format(entity.ident, len(batch)))
elasticsearch_endpoint = "{}/{}/{}/_bulk".format(
self.elasticsearch_backend,
self.elasticsearch_index,
self.elasticsearch_document_name)
resp = requests.post(elasticsearch_endpoint,
headers={"Content-Type": "application/x-ndjson"},
data="\n".join(bulk_actions) + "\n")
resp.raise_for_status()
if resp.json()['errors']:
desc = "Elasticsearch errors from post to {}:".format(elasticsearch_endpoint)
print(desc)
print(resp.content)
raise Exception(desc)
for msg in batch:
# offsets are *committed* (to brokers) automatically, but need
# to be marked as processed here
consumer.store_offsets(message=msg)
class ElasticsearchContainerWorker(ElasticsearchReleaseWorker):
def __init__(self, kafka_hosts, consume_topic, poll_interval=10.0, offset=None,
elasticsearch_backend="http://localhost:9200", elasticsearch_index="fatcat",
batch_size=200):
super().__init__(kafka_hosts=kafka_hosts,
consume_topic=consume_topic,
poll_interval=poll_interval,
offset=offset,
elasticsearch_backend=elasticsearch_backend,
elasticsearch_index=elasticsearch_index,
batch_size=batch_size)
# previous group got corrupted (by pykafka library?)
self.consumer_group = "elasticsearch-updates3"
self.entity_type = ContainerEntity
self.elasticsearch_document_name = "container"
self.transform_func = container_to_elasticsearch
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