Issues scaling kafka consumers with Dask
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- kafka, python
调研方向
Start by running the supplied Stream.from_kafka_batched pipelines with dask disabled and enabled, varying Kafka topic partitions as described. Inspect the LocalCluster configuration and compare throughput measurements; done means documenting the cause of the scaling behavior and identifying a concrete corrective change, since no file or test is named.
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描述
I have run two simple pipelines(Non-dask and Dask) to read stream of messages from Kafka using Streamz FromKafkaBatched method. I seem to get similar throughput for both the pipelines.
Non-Dask pipeline:
from distributed import Client
from streamz import Stream
import time
import json
def preprocess(messages):
start_time = int(round(time.time()))
no_of_rows = len(messages)
if no_of_rows > 0:
size = no_of_rows*len(messages[0])
kafka_timestamp = json.loads(messages[0].decode('utf-8'))['timestamp']
else:
size = 0
kafka_timestamp = start_time
end_time = int(round(time.time()))
return "{},{},{},{},{}".format(no_of_rows, kafka_timestamp, start_time, end_time, size)
topic = "topic-1"
bootstrap_servers = 'localhost:9092'
consumer_conf = {'bootstrap.servers': bootstrap_servers,
'group.id': 'group_1', 'session.timeout.ms': 60000}
stream = Stream.from_kafka_batched(topic, consumer_conf, poll_interval='10s',
npartitions=5, asynchronous = True, dask= False)
kafka_out = stream.map(preprocess).to_kafka('test-out', consumer_conf)
kafka_out.flush()
The pipeline gives maximum throughput = ~43 MBps (topic partitions = 5).
Throughput computation = (Σ size) / (max(end_time) - min(start_time))
By setting dask=True the above pipeline is executed on a local dask cluster
Starting Dask Cluster:
# dask imports
from distributed import Client, LocalCluster
# dask client
cluster = LocalCluster(ip="0.0.0.0", scheduler_port=8786, diagnostics_port = 8787, processes=False, threads_per_worker=10, n_workers=1)
client = Client(cluster)
client.get_versions(check=True)
The pipeline with Dask gives maximum throughput of ~44.8 MBps when number of partitions in the topic = 1. Increasing the number of partitions in a topic decreases the performance.
My understanding from these experimentations is that confluent kafka performs as expected with single Dask thread (when number of partitions is 1) but performs poorly with multiple dask threads or multiple partitions. This may be due to underlying tornado async library. It would be great if experts help me understand this better as I am fairly new to async programming.
Hardware and Software Specs:
10 CPU cores, 30 GB RAM
Ubuntu 16.04, Streamz(latest-build off latest code base)
Kafka 2.2.0, confluent-kafka-python 1.0.0
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