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: 'us...@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
