运行kafka ，需要依赖 zookeeper，你可以使用已有的 zookeeper 集群或者利用 kafka自带的zookeeper。

  单机模式，用的是kafka自带的zookeeper，

  分布式模式，用的是外部安装的zookeeper，即公共的zookeeper。

见博客  

 

（这也是单节点安装）

 

 

 

kafka_2.10-0.8.1.1.tgz的1节点集群　

 

我这里是使用的是，kafka自带的zookeeper。

以及关于kafka的日志文件啊，都放在默认里即/tmp下，我没修改。保存默认的

1、 [hadoop@sparksinglenode kafka_2.10-0.8.1.1]$ jps

2625 Jps

2、 [hadoop@sparksinglenode kafka_2.10-0.8.1.1]$ bin/zookeeper-server-start.sh config/zookeeper.properties & 

此刻，这时，会一直停在这，因为是前端运行。

另开一窗口，

3、 [hadoop@sparksinglenode kafka_2.10-0.8.1.1]$ bin/kafka-server-start.sh config/server.properties &

也是前端运行。

推荐做法！！！

但是，我这里，自己在kafka安装目录下，为了自己的方便，写了个startkafka.sh和startzookeeper.sh

nohup bin/kafka-server-start.sh config/server.properties > kafka.log 2>&1 &

nohup bin/zookeeper-server-start.sh config/zookeeper.properties > zookeeper.log 2>&1 &

注意还要，root用户来，附上执行权限。chmod +x ./startkafka.sh chmod +x ./startzookeeper.sh 

这样，就会在kafka安装目录下，对应生出kafka.log和zookeeper.log。

1、[spark@sparksinglenode kafka_2.10-0.8.1.1]$ jps

5098 Jps

2、[spark@sparksinglenode kafka_2.10-0.8.1.1]$ bash startzookeeper.sh

[spark@sparksinglenode kafka_2.10-0.8.1.1]$ jps

5125 Jps

5109 QuorumPeerMain

3、[spark@sparksinglenode kafka_2.10-0.8.1.1]$ bash startkafka.sh

[spark@sparksinglenode kafka_2.10-0.8.1.1]$ jps

5155 Jps

5140 Kafka

5109 QuorumPeerMain

[spark@sparksinglenode kafka_2.10-0.8.1.1]$ 

 

 　　我了个去，启动是多么方便！

　　

 

 

 

kafka_2.10-0.8.1.1.tgz的3节点集群

　　关于下载，和安装，解压，这些，我不多赘述了。见我的单节点博客。

 

 

 

root@SparkMaster:/usr/local/kafka/kafka_2.10-0.8.1.1/config# cat server.properties

# 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=/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=SparkMaster:2181,SparkWorker1:2181,SparkWorker2:2181

# Timeout in ms for connecting to zookeeper

zookeeper.connection.timeout.ms=1000000

root@SparkMaster:/usr/local/kafka/kafka_2.10-0.8.1.1/config#

 

 　　SparkWorker1和SparkWorker2分别只把 broker.id=0改成 broker.id=1 ，broker.id=2。

即SparkMaster：

　　broker.id=0 

　　log.dirs=/kafka-logs

 　　zookeeper.connect=SparkMaster:2181,SparkWorker1:2181,SparkWorker2:2181

即SparkWorker1：

　　broker.id=1 

　　log.dirs=/kafka-logs

 　　zookeeper.connect=SparkMaster:2181,SparkWorker1:2181,SparkWorker2:2181

即SparkWorker2：

　　broker.id=2 

　　log.dirs=/kafka-logs

 　　zookeeper.connect=SparkMaster:2181,SparkWorker1:2181,SparkWorker2:2181

 

 

 

 

 

 

 

 

 

 

 

 

 

　　　kafka的3节点如何启动

　　步骤一：先，分别在SparkMaster、SpakrWorker1、SparkWorker2节点上，启动zookeeper进程。

 

root@SparkMaster:/usr/local/kafka/kafka_2.10-0.8.1.1# bash startkafka.sh

 　　其他，两台机器，一样的，不多赘述。