一、准备环境: 创建Kafka Topic和HBase表

1. 在kerberos环境下创建Kafka Topic

1.1 因为kafka默认使用的协议为PLAINTEXT,在kerberos环境下需要变更其通信协议: 在${KAFKA_HOME}/​​config/producer.properties​​和​​config/consumer.properties​​下添加



security.protocol=SASL_PLAINTEXT


1.2 在执行前,需要在环境变量中添加KAFKA_OPT选项,否则kafka无法使用keytab:



export KAFKA_OPTS="$KAFKA_OPTS -Djava.security.auth.login.config=/usr/ndp/current/kafka_broker/conf/kafka_jaas.conf"


其中​​kafka_jaas.conf​​内容如下:



cat /usr/ndp/current/kafka_broker/conf/kafka_jaas.conf

KafkaServer {
com.sun.security.auth.module.Krb5LoginModule required
useKeyTab=true
keyTab="/etc/security/keytabs/kafka.service.keytab"
storeKey=true
useTicketCache=false
serviceName="kafka"
principal="kafka/hzadg-mammut-platform3.server.163.org@BDMS.163.COM";
};
KafkaClient {
com.sun.security.auth.module.Krb5LoginModule required
useTicketCache=true
renewTicket=true
serviceName="kafka";
};
Client {
com.sun.security.auth.module.Krb5LoginModule required
useKeyTab=true
keyTab="/etc/security/keytabs/kafka.service.keytab"
storeKey=true
useTicketCache=false
serviceName="zookeeper"
principal="kafka/hzadg-mammut-platform3.server.163.org@BDMS.163.COM";
};


1.3 创建新的topic:



bin/kafka-topics.sh --create --zookeeper hzadg-mammut-platform2.server.163.org:2181,hzadg-mammut-platform3.server.163.org:2181 --replication-factor 1 --partitions 1 --topic spark-test


1.4 创建生产者:



bin/kafka-console-producer.sh  --broker-list hzadg-mammut-platform2.server.163.org:6667,hzadg-mammut-platform3.server.163.org:6667,hzadg-mammut-platform4.server.163.org:6667 --topic spark-test --producer.config ./config/producer.properties


1.5 测试消费者:



bin/kafka-console-consumer.sh --zookeeper hzadg-mammut-platform2.server.163.org:2181,hzadg-mammut-platform3.server.163.org:2181 --bootstrap-server hzadg-mammut-platform2.server.163.org:6667 --topic spark-test --from-beginning --new-consumer  --consumer.config ./config/consumer.properties


2. 创建HBase表

2.1 kinit到hbase账号,否则无法创建hbase表



kinit -kt /etc/security/keytabs/hbase.service.keytab hbase/hzadg-mammut-platform2.server.163.org@BDMS.163.COM



./bin/hbase shell
> create 'recsys_logs', 'f'


 

 


二、编写Spark代码

编写简单的Spark Java程序,功能为: 从Kafka消费信息,同时将batch内统计的数量写入Hbase中,具体可以参考项目:

​https://github.com/LiShuMing/spark-demos​



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* this work for additional information regarding copyright ownership.
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package com.netease.spark.streaming.hbase;

import com.netease.spark.utils.Consts;
import com.netease.spark.utils.JConfig;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.client.HConnection;
import org.apache.hadoop.hbase.client.HConnectionManager;
import org.apache.hadoop.hbase.client.HTableInterface;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.common.serialization.StringDeserializer;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.VoidFunction;
import org.apache.spark.streaming.Duration;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.kafka010.ConsumerStrategies;
import org.apache.spark.streaming.kafka010.KafkaUtils;
import org.apache.spark.streaming.kafka010.LocationStrategies;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.io.IOException;
import java.text.SimpleDateFormat;
import java.util.Arrays;
import java.util.Date;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Map;
import java.util.Set;

public class JavaKafkaToHBaseKerberos {
private final static Logger LOGGER = LoggerFactory.getLogger(JavaKafkaToHBaseKerberos.class);

private static HConnection connection = null;
private static HTableInterface table = null;

public static void openHBase(String tablename) throws IOException {
Configuration conf = HBaseConfiguration.create();
synchronized (HConnection.class) {
if (connection == null)
connection = HConnectionManager.createConnection(conf);
}

synchronized (HTableInterface.class) {
if (table == null) {
table = connection.getTable("recsys_logs");
}
}
}

public static void closeHBase() {
if (table != null)
try {
table.close();
} catch (IOException e) {
LOGGER.error("关闭 table 出错", e);
}
if (connection != null)
try {
connection.close();
} catch (IOException e) {
LOGGER.error("关闭 connection 出错", e);
}
}

public static void main(String[] args) throws Exception {
String hbaseTable = JConfig.getInstance().getProperty(Consts.HBASE_TABLE);
String kafkaBrokers = JConfig.getInstance().getProperty(Consts.KAFKA_BROKERS);
String kafkaTopics = JConfig.getInstance().getProperty(Consts.KAFKA_TOPICS);
String kafkaGroup = JConfig.getInstance().getProperty(Consts.KAFKA_GROUP);

// open hbase
try {
openHBase(hbaseTable);
} catch (IOException e) {
LOGGER.error("建立HBase 连接失败", e);
System.exit(-1);
}

SparkConf conf = new SparkConf().setAppName("JavaKafakaToHBase");
JavaStreamingContext ssc = new JavaStreamingContext(conf, new Duration(1000));

Set<String> topicsSet = new HashSet<>(Arrays.asList(kafkaTopics.split(",")));
Map<String, Object> kafkaParams = new HashMap<>();
kafkaParams.put("bootstrap.servers", kafkaBrokers);
kafkaParams.put("key.deserializer", StringDeserializer.class);
kafkaParams.put("value.deserializer", StringDeserializer.class);
kafkaParams.put("group.id", kafkaGroup);
kafkaParams.put("auto.offset.reset", "earliest");
kafkaParams.put("enable.auto.commit", false);
// 在kerberos环境下,这个配置需要增加
kafkaParams.put("security.protocol", "SASL_PLAINTEXT");

// Create direct kafka stream with brokers and topics
final JavaInputDStream<ConsumerRecord<String, String>> stream =
KafkaUtils.createDirectStream(
ssc,
LocationStrategies.PreferConsistent(),
ConsumerStrategies.<String, String>Subscribe(Arrays.asList(topicsSet.toArray(new String[0])), kafkaParams)
);

JavaDStream<String> lines = stream.map(new Function<ConsumerRecord<String, String>, String>() {
private static final long serialVersionUID = -1801798365843350169L;

@Override
public String call(ConsumerRecord<String, String> record) {
return record.value();
}
}).filter(new Function<String, Boolean>() {
private static final long serialVersionUID = 7786877762996470593L;

@Override
public Boolean call(String msg) throws Exception {
return msg.length() > 0;
}
});

JavaDStream<Long> nums = lines.count();

nums.foreachRDD(new VoidFunction<JavaRDD<Long>>() {
private SimpleDateFormat sdf = new SimpleDateFormat("yyyyMMdd HH:mm:ss");

@Override
public void call(JavaRDD<Long> rdd) throws Exception {
Long num = rdd.take(1).get(0);
String ts = sdf.format(new Date());
Put put = new Put(Bytes.toBytes(ts));
put.add(Bytes.toBytes("f"), Bytes.toBytes("nums"), Bytes.toBytes(num));
table.put(put);
}
});

ssc.start();
ssc.awaitTermination();
closeHBase();
}
}


 

三、 编译并在Yarn环境下运行

3.1 切到项目路径下,编译项目:

mvn clean package

 

3.2 运行Spark环境

  • 由于executor需要访问kafka,所以需要将Kafka授权过的kerberos用户下发至executor中;
  • 由于集群环境的hdfs也是kerberos加密的,需要通过spark.yarn.keytab/spark.yarn.principal配置可以访问Hdfs/HBase的keytab信息;

在项目目录下执行如下:



/usr/ndp/current/spark2_client/bin/spark-submit \
--files ./kafka_client_jaas.conf,./kafka.service.keytab \
--conf "spark.executor.extraJavaOptions=-Djava.security.auth.login.config=./kafka_client_jaas.conf" \
--driver-java-options "-Djava.security.auth.login.config=./kafka_client_jaas.conf" \
--conf spark.yarn.keytab=/etc/security/keytabs/hbase.service.keytab \
--conf spark.yarn.principal=hbase/hzadg-mammut-platform1.server.163.org@BDMS.163.COM \
--class com.netease.spark.streaming.hbase.JavaKafkaToHBaseKerberos \
--master yarn \
--deploy-mode client \
./target/spark-demo-0.1.0-jar-with-dependencies.jar


其中​​kafka_client_jaas.conf​​文件具体内容如下:



cat kafka_client_jaas.conf

KafkaClient {
com.sun.security.auth.module.Krb5LoginModule required
useKeyTab=true
renewTicket=true
keyTab="./kafka.service.keytab"
storeKey=true
useTicketCache=false
serviceName="kafka"
principal="kafka/hzadg-mammut-platform1.server.163.org@BDMS.163.COM";
};


 

3.2 执行结果




 


kerberos环境下spark消费kafka写入到Hbase_apache




 


kerberos环境下spark消费kafka写入到Hbase_java_02