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From "Zhujie (zhujie, Smartcare)" <first.zhu...@huawei.com>
Subject kafka performance question
Date Wed, 14 May 2014 01:32:20 GMT
our version is kafka_2.10-0.8.1

发件人: Zhujie (zhujie, Smartcare)
发送时间: 2014年5月14日 8:56
收件人: 'users@kafka.apache.org'; 'dev@kafka.apache.org'
主题: kafka performance question

Dear all,

We want to use kafka to collect and dispatch data file, but the performance is maybe lower
than we want.

In our cluster,there is a provider and a broker. We use a one thread read file from local
disk of provider and send it to broker. The average throughput is only 3 MB/S~4MB/S.
But if we just use java NIO API to send file ,the throughput can exceed 200MB/S.
Why the kafka performance is so bad in our test, are we missing something??



Our server:
Cpu: Intel(R) Xeon(R) CPU E5-4650 0 @ 2.70GHz*4
Mem:300G
Disk:600G 15K RPM SAS*8

Configuration of provider:
props.put("serializer.class", "kafka.serializer.NullEncoder");
props.put("metadata.broker.list", "169.10.35.57:9092");
props.put("request.required.acks", "0");
props.put("producer.type", "async");//异步
props.put("queue.buffering.max.ms","500");
props.put("queue.buffering.max.messages","1000000000");
props.put("batch.num.messages", "1200");
props.put("queue.enqueue.timeout.ms", "-1");
props.put("send.buffer.bytes", "102400000");

Configuration of broker:

# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# see kafka.server.KafkaConfig for additional details and defaults

############################# Server Basics #############################

# The id of the broker. This must be set to a unique integer for each broker.
broker.id=0

############################# Socket Server Settings #############################

# The port the socket server listens on
port=9092

# Hostname the broker will bind to. If not set, the server will bind to all interfaces
#host.name=localhost

# Hostname the broker will advertise to producers and consumers. If not set, it uses the
# value for "host.name" if configured.  Otherwise, it will use the value returned from
# java.net.InetAddress.getCanonicalHostName().
#advertised.host.name=<hostname routable by clients>

# The port to publish to ZooKeeper for clients to use. If this is not set,
# it will publish the same port that the broker binds to.
#advertised.port=<port accessible by clients>

# The number of threads handling network requests
#num.network.threads=2

# The number of threads doing disk I/O
#num.io.threads=8

# The send buffer (SO_SNDBUF) used by the socket server
#socket.send.buffer.bytes=1048576

# The receive buffer (SO_RCVBUF) used by the socket server
#socket.receive.buffer.bytes=1048576

# The maximum size of a request that the socket server will accept (protection against OOM)
#socket.request.max.bytes=104857600


############################# Log Basics #############################

# A comma seperated list of directories under which to store log files
log.dirs=/data/kafka-logs

# The default number of log partitions per topic. More partitions allow greater
# parallelism for consumption, but this will also result in more files across
# the brokers.
#num.partitions=2

############################# Log Flush Policy #############################

# Messages are immediately written to the filesystem but by default we only fsync() to sync
# the OS cache lazily. The following configurations control the flush of data to disk.
# There are a few important trade-offs here:
#    1. Durability: Unflushed data may be lost if you are not using replication.
#    2. Latency: Very large flush intervals may lead to latency spikes when the flush does
occur as there will be a lot of data to flush.
#    3. Throughput: The flush is generally the most expensive operation, and a small flush
interval may lead to exceessive seeks.
# The settings below allow one to configure the flush policy to flush data after a period
of time or
# every N messages (or both). This can be done globally and overridden on a per-topic basis.

# The number of messages to accept before forcing a flush of data to disk
#log.flush.interval.messages=10000

# The maximum amount of time a message can sit in a log before we force a flush
#log.flush.interval.ms=1000

############################# Log Retention Policy #############################

# The following configurations control the disposal of log segments. The policy can
# be set to delete segments after a period of time, or after a given size has accumulated.
# A segment will be deleted whenever *either* of these criteria are met. Deletion always happens
# from the end of the log.

# The minimum age of a log file to be eligible for deletion
#log.retention.hours=168

# A size-based retention policy for logs. Segments are pruned from the log as long as the
remaining
# segments don't drop below log.retention.bytes.
#log.retention.bytes=1073741824

# The maximum size of a log segment file. When this size is reached a new log segment will
be created.
#log.segment.bytes=536870912

# The interval at which log segments are checked to see if they can be deleted according
# to the retention policies
log.retention.check.interval.ms=60000

# By default the log cleaner is disabled and the log retention policy will default to just
delete segments after their retention expires.
# If log.cleaner.enable=true is set the cleaner will be enabled and individual logs can then
be marked for log compaction.
log.cleaner.enable=false

############################# Zookeeper #############################

# Zookeeper connection string (see zookeeper docs for details).
# This is a comma separated host:port pairs, each corresponding to a zk
# server. e.g. "127.0.0.1:3000,127.0.0.1:3001,127.0.0.1:3002".
# You can also append an optional chroot string to the urls to specify the
# root directory for all kafka znodes.
zookeeper.connect=localhost:2181

# Timeout in ms for connecting to zookeeper
zookeeper.connection.timeout.ms=1000000

# Replication configurations
num.replica.fetchers=0
replica.fetch.max.bytes=104857600
#replica.fetch.wait.max.ms=500
#replica.high.watermark.checkpoint.interval.ms=5000
#replica.socket.timeout.ms=30000
#replica.socket.receive.buffer.bytes=65536
#replica.lag.time.max.ms=10000
#replica.lag.max.messages=4000

#controller.socket.timeout.ms=30000
#controller.message.queue.size=10

# Log configuration
num.partitions=8
message.max.bytes=104857600
auto.create.topics.enable=true
log.index.interval.bytes=4096
log.index.size.max.bytes=10485760
log.retention.hours=168
log.flush.interval.ms=10000
log.flush.interval.messages=20000
log.flush.scheduler.interval.ms=2000
log.roll.hours=168
log.cleanup.interval.mins=30
log.segment.bytes=1073741824

# ZK configuration
zk.connection.timeout.ms=1000000
zk.sync.time.ms=20000

# Socket server configuration
num.io.threads=8
num.network.threads=20
socket.request.max.bytes=104857600
socket.receive.buffer.bytes=1048576
socket.send.buffer.bytes=1048576
queued.max.requests=5000
fetch.purgatory.purge.interval.requests=10000
producer.purgatory.purge.interval.requests=10000


kafka.metrics.polling.interval.secs=5
kafka.metrics.reporters=kafka.metrics.KafkaCSVMetricsReporter
kafka.csv.metrics.dir=/data/kafka_metrics
kafka.csv.metrics.reporter.enabled=false


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