Flume sink Kafka Spout Storm Bolt Hbase or Redis (Kafka)

原创
2017/01/03 11:51
阅读数 125

storm集群搭建完成之后,自娱自乐的玩了一局dota,JUGG竟然23杀,3死,两度超神,人到高处就只剩孤独寂寞冷啦,喝完一壶花茶,还是决定继续奋战kafka.

同样,小编还是喜欢去官网看看,毕竟好多时间不玩kafka.

Kafka官网

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建议大家阅读以下Introduction,当然,你也可以直接进入Documentation

看了以下灌完的quick start,描述略简单,只是说要启动Zookeeper,然后再启动kafka

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Zookeeper的搭建一如既往的略过,下面直接进入正题,搭建kafka,手头的机器资源如下:

192.168.2.141 node2

192.168.2.142 node3

192.168.2.143 node4

下载kafka,解压等就不做赘述了,直接进入server配置环节:server.prpperties

# 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://node2:9092

# 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=/home/hadoop/app/kafka/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=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=node1:2181,node2:2181,node3:2181

# Timeout in ms for connecting to zookeeper
zookeeper.connection.timeout.ms=6000

详细参数说明相信大家看一下就懂,望文生义一下...... 唯一要注意的一点是,三个broker的配置可不能一行,各自的host要修改成对应的服务器节点的ip或者机器名称(前提是你配置了hosts)

配置文件完成之后,接下来就是启动集群:

bin/kafka-server-start.sh -daemon config/server.properties 执行这条命令来启动kafka的broker,三台机器都要启动.

-deamon是指以后台进程的形式启动(要不终端关闭或者退出当前的会话都会停止kafka的服务)

接下来测试一下kafka,那就创建一个topic

bin/kafka-topics.sh --create --zookeeper node1:2181,node2:2181,node3:2181 --replication-factor 2 --partitions 2 --topic test

小编默认你会创建成功,就不截图解释了

接下来还是测试一下javaApi,我这里的测试代码需要创建一个叫dccfront的主题:

kafka的生产者

import java.util.Properties;

import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.testng.annotations.Test;

/**
 * yingyinglicai.com Inc.
 * Copyright (c) 2013-2016 All Rights Reserved.
 *
 * @author ZhangZhong
 * @version v 0.1 2016/12/31 11:43 Exp $$
 */
public class KafkaProducerTest {

    @Test
    public void test() throws InterruptedException {
        Properties props = new Properties();
        props.put("bootstrap.servers", "node1:9092,node2:9092,node3:9092");
        props.put("acks", "all");
        props.put("retries", 0);
        props.put("batch.size", 16384);
        props.put("linger.ms", 1);
        props.put("buffer.memory", 33554432);
        props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

        Producer<String, String> producer = new KafkaProducer<String, String>(props);
        for (int i = 0; i < 200; i++) {
            producer.send(new ProducerRecord<String, String>("dccfront", Integer.toString(i),
                Integer.toString(i)));
        }

        producer.close();
    }

}

kafka的消费者

import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.testng.annotations.Test;

import java.util.Arrays;
import java.util.Properties;

/**
 * yingyinglicai.com Inc.
 * Copyright (c) 2013-2016 All Rights Reserved.
 *
 * @author ZhangZhong
 * @version v 0.1 2016/12/31 17:30 Exp $$
 */
public class KafkaConsumerTest {

    @Test
    public void test() {
        Properties props = new Properties();
        props.put("bootstrap.servers", "node1:9092,node2:9092,node3:9092");
        props.put("group.id", "test");
        props.put("enable.auto.commit", "true");
        props.put("auto.commit.interval.ms", "1000");
        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        KafkaConsumer<String, String> consumer = new KafkaConsumer<String, String>(props);
        consumer.subscribe(Arrays.asList("dccfront"));
        while (true) {
            ConsumerRecords<String, String> records = consumer.poll(100);
            for (ConsumerRecord<String, String> record : records)
                System.out.printf("offset = %d, key = %s, value = %s%n", record.offset(),
                    record.key(), record.value());
        }

    }
}

测试的时候,你可先启动consumer,然后再启动producer,再观察consumer的消费情况

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/猫小鞭/

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