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import json
import signal
import sys
from collections import Counter
from typing import Any, List
from confluent_kafka import Consumer, KafkaException, Producer
class KafkaWorker:
"""
Base class for Scholar workers which consume from Kafka topics.
Configuration (passed to __init__):
kafka_brokers (List[str]): brokers to connect to
consume_topics (List[str]): topics to consume from
consumer_group (str): kafka consumer group
batch_size (int): number of records to consume and process at a time
batch_timeout_sec (int): max seconds for each batch to process. set to 0 to disable
API:
__init__()
run()
starts consuming, calling process_batch() for each message batch
process_batch(batch: List[dict]) -> None
implemented by sub-class
process_msg(msg: dict) -> None
implemented by sub-class
Example of producing (in a worker):
producer = self.create_kafka_producer(...)
producer.produce(
topic,
some_obj.json(exclude_none=True).encode('UTF-8'),
key=key,
on_delivery=self._fail_fast_produce)
# check for errors etc
producer.poll(0)
"""
def __init__(
self,
kafka_brokers: List[str],
consume_topics: List[str],
consumer_group: str,
**kwargs: Any,
):
self.counts: Counter = Counter()
self.kafka_brokers = kafka_brokers
self.batch_size = kwargs.get("batch_size", 1)
self.batch_timeout_sec = kwargs.get("batch_timeout_sec", 60)
self.poll_interval_sec = kwargs.get("poll_interval_sec", 5.0)
self.consumer = self.create_kafka_consumer(
kafka_brokers, consume_topics, consumer_group
)
@staticmethod
def _fail_fast_produce(err: Any, msg: Any) -> None:
if err is not None:
print(f"Kafka producer delivery error: {err}", file=sys.stderr)
raise KafkaException(err)
@staticmethod
def _timeout_handler(signum: Any, frame: Any) -> None:
raise TimeoutError("timeout processing record")
@staticmethod
def create_kafka_consumer(
kafka_brokers: List[str], consume_topics: List[str], consumer_group: str
) -> Consumer:
"""
NOTE: it is important that consume_topics be str, *not* bytes
"""
def _on_rebalance(consumer: Any, partitions: Any) -> None:
for p in partitions:
if p.error:
raise KafkaException(p.error)
print(
f"Kafka partitions rebalanced: {consumer} / {partitions}",
file=sys.stderr,
)
def _fail_fast_consume(err: Any, partitions: Any) -> None:
if err is not None:
print(f"Kafka consumer commit error: {err}", file=sys.stderr)
raise KafkaException(err)
for p in partitions:
# check for partition-specific commit errors
if p.error:
print(
f"Kafka consumer commit error: {p.error}", file=sys.stderr,
)
raise KafkaException(p.error)
config = {
"bootstrap.servers": ",".join(kafka_brokers),
"group.id": consumer_group,
"on_commit": _fail_fast_consume,
# messages don't have offset marked as stored until processed,
# but we do auto-commit stored offsets to broker
"enable.auto.offset.store": False,
"enable.auto.commit": True,
# user code timeout; if no poll after this long, assume user code
# hung and rebalance (default: 6min)
"max.poll.interval.ms": 360000,
"default.topic.config": {"auto.offset.reset": "latest",},
}
consumer = Consumer(config)
consumer.subscribe(
consume_topics, on_assign=_on_rebalance, on_revoke=_on_rebalance,
)
print(
f"Consuming from kafka topics {consume_topics}, group {consumer_group}",
file=sys.stderr,
)
return consumer
@staticmethod
def create_kafka_producer(kafka_brokers: List[str]) -> Producer:
"""
This configuration is for large compressed messages.
"""
config = {
"bootstrap.servers": ",".join(kafka_brokers),
"message.max.bytes": 30000000, # ~30 MBytes; broker is ~50 MBytes
"api.version.request": True,
"api.version.fallback.ms": 0,
"compression.codec": "gzip",
"retry.backoff.ms": 250,
"linger.ms": 1000,
"batch.num.messages": 50,
"delivery.report.only.error": True,
"default.topic.config": {
"message.timeout.ms": 30000,
"request.required.acks": -1, # all brokers must confirm
},
}
return Producer(config)
def run(self) -> Counter:
if self.batch_timeout_sec:
signal.signal(signal.SIGALRM, self._timeout_handler)
while True:
batch = self.consumer.consume(
num_messages=self.batch_size, timeout=self.poll_interval_sec,
)
print(
f"... got {len(batch)} kafka messages ({self.poll_interval_sec}sec poll interval). stats: {self.counts}",
file=sys.stderr,
)
if not batch:
continue
# first check errors on entire batch...
for msg in batch:
if msg.error():
raise KafkaException(msg.error())
# ... then process, with optional timeout
self.counts["total"] += len(batch)
records = [json.loads(msg.value().decode("utf-8")) for msg in batch]
if self.batch_timeout_sec:
signal.alarm(int(self.batch_timeout_sec))
try:
self.process_batch(records)
except TimeoutError as te:
raise te
finally:
signal.alarm(0)
else:
self.process_batch(records)
self.counts["processed"] += len(batch)
# ... then record progress
for msg in batch:
# will be auto-commited by librdkafka from this "stored" value
self.consumer.store_offsets(message=msg)
# Note: never actually get here, but including as documentation on how to clean up
self.consumer.close()
return self.counts
def process_batch(self, batch: List[dict]) -> None:
"""
Workers can override this method for batch processing. By default it
calls process_msg() for each message in the batch.
"""
for msg in batch:
self.process_msg(msg)
def process_msg(self, msg: dict) -> None:
"""
Workers can override this method for individual record processing.
"""
raise NotImplementedError("implementation required")
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