Flume与Kafka整合案例详解

环境配置

名称版本下载地址Centos 7.064x百度Zookeeper3.4.5Flume1.6.0Kafka2.1.0

配置Flume

这里就不介绍了零基础出门右转看Flume的文章

flume笔记

直接贴配置文件

[root@zero239 kafka_2.10-0.10.1.1]# cat /opt/hadoop/apache-flume-1.6.0-bin/conf/kafka-conf.properties # The configuration file needs to define the sources, # the channels and the sinks. # Sources, channels and sinks are defined per agent, # in this case called 'agent' agent.sources = r1 agent.channels = c1 agent.sinks = s1 # For each one of the sources, the type is defined #agent.sources.r1.type = spooldir #agent.sources.r1.command = /opt/test/logs/data #agent.sources.r1.fileHeader = true #agent.sources.r1.channels = c1 agent.sources.r1.type = spooldir agent.sources.r1.spoolDir = /opt/test/logs/data agent.sources.r1.fileHeader = true # Each sink's type must be defined #agent.sinks.s1.type = logger agent.sinks.s1.type = org.apache.flume.sink.kafka.KafkaSink agent.sinks.s1.topic = logstest agent.sinks.s1.brokerList = zero230:9092 agent.sinks.s1.requiredAcks = 1 agent.sinks.s1.batchSize = 2 # Each channel's type is defined. agent.channels.c1.type = memory agent.channels.c1.capacity = 100 agent.sources.r1.channels = c1 agent.sinks.s1.channel = c1

配置Kafka

# 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=2 # Switch to enable topic deletion or not, default value is false #delete.topic.enable=true ############################# Socket Server Settings ############################# # The address the socket server listens on. It will get the value returned from # java.net.InetAddress.getCanonicalHostName() if not configured. # FORMAT: # listeners = security_protocol://host_name:port # EXAMPLE: # listeners = PLAINTEXT://your.host.name:9092 #listeners=PLAINTEXT://:9092 # Hostname and port the broker will advertise to producers and consumers. If not set, # it uses the value for "listeners" if configured. Otherwise, it will use the value # returned from java.net.InetAddress.getCanonicalHostName(). #advertised.listeners=PLAINTEXT://your.host.name:9092 # The number of threads handling network requests num.network.threads=3 # 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=102400 # The receive buffer (SO_RCVBUF) used by the socket server socket.receive.buffer.bytes=102400 # 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=/opt/hadoop/kafka_2.10-0.10.1.1/logs/tmp # 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=1 # The number of threads per data directory to be used for log recovery at startup and flushing at shutdown. # This value is recommended to be increased for installations with data dirs located in RAID array. num.recovery.threads.per.data.dir=1 ############################# 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=1073741824 # 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=300000 ############################# 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=zero230:2181,zero231:2181,zero239:2181 # Timeout in ms for connecting to zookeeper zookeeper.connection.timeout.ms=6000

我已经配置了集群Zookeeper所以在这里我指定是我配置的Zookeeper地址如果你没有配置的话可以直接使用Kafka内置的Zokeeper

Zookeeper集群搭建配置

启动Kafka验证是否成功

启动Zookeeper 如果没有配置集群的这一步跳过

启动Kafka内置Zookeeper

bin/zookeeper-server-start.sh config/zookeeper.properties

3.启动Kafka

server1.properties 为刚刚自己编辑的名称 bin/kafka-server-start.sh config/server1.properties

4.创建一个名为logstest的topic

./bin/kafka-topics.sh --create --zookeeper zero230:2181 --replication-factor 1 --partitions 1 --topic logstest

5.查看Topic是否创建成功

./bin/kafka-topics.sh --list --zookeeper localhost:2181

6.创建一个生产端(相当于是一个已经数据产生的用户吧)这样容易理解

bin/kafka-console-producer.sh --broker-list zero230:9092 --topic logstest

7.创建一个消费端(意思就是可以看到生产者意思就是生产出来的数据可以看到输出)

bin/kafka-console-consumer.sh --zookeeper zero230:2181 --topic logstest --from-beginning

启动验证Flume是否能与Kafka对接

[root@zero239 apache-flume-1.6.0-bin]# ./bin/flume-ng agent --conf conf -f ./conf/kafka-conf.properties -n agent -Dflume.root.logger=INFO,console

对接成功截图

各位同学可以看到在Flumesinks配置中我设置的是Kafka意思就是输出到Kafka中

agent.sinks.s1.type = org.apache.flume.sink.kafka.KafkaSink agent.sinks.s1.topic = logstest 刚刚创建的Topic名称 agent.sinks.s1.brokerList = zero230:9092 创建生产的机

在这里Flume与Kafka已经整合完毕了。

下节剧透

JAVA实现Kafka读取