实时数仓链路分享：kafka =SparkStreaming=kudu集成kerberos

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本文档主要介绍在cdh集成kerberos情况下，sparkstreaming怎么消费kafka数据，并存储在kudu里面

假设kafka集成kerberos
假设kudu集成kerberos
假设用非root用户操作
spark基于yarn-cluster模式

代码编写，这里只介绍关键代码

主类，以下代码仅供参考

package deng.yb.sparkStreaming;import java.net.URLDecoder;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collection;
import java.util.HashMap;
import java.util.Iterator;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;
import java.util.UUID;import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kudu.spark.kudu.KuduContext;
import org.apache.log4j.Logger;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.StreamingContext;
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.springframework.context.support.ClassPathXmlApplicationContext;import com.alibaba.fastjson.JSONArray;
import com.alibaba.fastjson.JSONObject;import deng.yb.sparkStreaming.kafka.KafkaTools;
import deng.yb.sparkStreaming.utils.NginxInfo;
import deng.yb.sparkStreaming.utils.SpringContextUtil;/*** Hello world!**/
@SuppressWarnings("unchecked")
public class EApp {private static final Logger logger = Logger.getLogger(App.class);private static final String BEAN_CONF = "classpath:spring/spring-bean.xml";private static Map<String, String> conf = new HashMap<String, String>();/*** epp接口-request*/private static final String EPP_REQUEST = "POST /api/sky_server_data_app/track/user_time HTTP/1.1";/*** app接口-request*/private static final String APP_REQUEST = "POST /api/sky_server_data_app/code/app_code HTTP/1.1";/*** 在spring 配置的参数id*/private static final String CONFIG = "commonConfig";/*** 以下配置参数皆为配置key spark模式*/private static final String MASTER = "master";/*** spark-appName*/private static final String APP_NAME = "appName";/*** 自定义字段*/private static final String COLUMNS = "columns";/*** topic*/private static final String TOPIC = "topic";/*** 表名*/private static final String TABLE = "tables";static {String[] confs = new String[] { BEAN_CONF };// 把actx设置进去，后续可以共用SpringContextUtil.setApplicationContext(new ClassPathXmlApplicationContext(confs));conf = (Map<String, String>) SpringContextUtil.getBean(CONFIG);}public static void main(String args[]) {try {SparkSession spark = SparkSession.builder().appName(conf.get(APP_NAME)).master(conf.get(MASTER)).getOrCreate();Map<String, Object> confMap = KafkaTools.kafkaConf(conf);String[] topicArr = conf.get(TOPIC).split(",");Collection<String> topics = Arrays.asList(topicArr);StreamingContext sc = new StreamingContext(spark.sparkContext(),Durations.milliseconds(5000));JavaStreamingContext jssc = new JavaStreamingContext(sc);JavaInputDStream<ConsumerRecord<String, String>> stream = KafkaUtils.createDirectStream(jssc, LocationStrategies.PreferConsistent(), ConsumerStrategies.<String, String> Subscribe(topics, confMap));jssc.sparkContext().setLogLevel("ERROR");stream.context().sparkContext().setLogLevel("ERROR");// nginx日志对应字段String[] columns = conf.get(COLUMNS).split(",");Map<String, String> colimnsMap = new LinkedHashMap<String, String>();// 把字段和类型映射String[] temp;for (String column : columns) {temp = column.split(":");colimnsMap.put(temp[0], temp[1]);}// 表名String[] tables = conf.get(TABLE).split(",");// epp表额外的字段String[] eppExtColumns = { "app_name", "end", "portal_user_id","resource", "start", "username", "app_id" };KuduContext kudu = new KuduContext(conf.get("kudu.instances"),sc.sparkContext());// dstream transform// 第一层封装// 第二层切分// 第三层转换JavaDStream<LinkedHashMap<String,String>> linkMap = stream.map(record -> {logger.info("消息进来：" + record.value());LinkedHashMap<String,String> json = new LinkedHashMap<String, String>();String[] messages = record.value().split(",");int length = colimnsMap.size();int i = 0;for (Map.Entry<String, String> entry : colimnsMap.entrySet()) {if (i < length) {json.put(entry.getKey(), messages[i]);}i += 1;}// 处理http_version字段String httpVersion;if (json.containsKey("http_version")&& (httpVersion = json.get("http_version")) != null) {String[] httpVersionArry = httpVersion.split("_");if (httpVersionArry != null&& httpVersionArry.length > 1) {json.put("portal_name", httpVersionArry[0]);json.put("channel", httpVersionArry[1]);json.put("version", httpVersionArry[2]);}}logger.info("封装完数据格式："+json.toString());return json;}).cache();//EPP表linkMap.flatMap(new FlatMapFunction<LinkedHashMap<String,String>, JSONObject>(){@Overridepublic Iterator<JSONObject> call(LinkedHashMap<String,String> json) throws Exception {// TODO Auto-generated method stubArrayList<JSONObject> jsonArray = new ArrayList<JSONObject>();String request = json.get("request");if (request.indexOf(EPP_REQUEST) > -1) {logger.info("消息拆分：" + json.toString());// 这个进epp表String requestBody = URLDecoder.decode(json.get("app_id"), "utf-8");String[] strArr;JSONArray array = JSONObject.parseArray((strArr = requestBody.split("=")).length > 1 ? strArr[1]: strArr[0]);// 根据appid拆分for (int j = 0; j < array.size(); j++) {JSONObject obj = array.getJSONObject(j);JSONObject newJson = new JSONObject(new LinkedHashMap<String,Object>());// 把原来的属性加上for (String oldColumn : json.keySet()) {newJson.put(oldColumn,json.get(oldColumn));}for (String extColumn : eppExtColumns) {newJson.put(extColumn,obj.get(extColumn));}// kudu表一定要有主键newJson.put("id", UUID.randomUUID().toString().replace("-", ""));logger.info("生成EPP主键："+newJson.getString("id"));jsonArray.add(newJson);}return jsonArray.iterator();}return new ArrayList().iterator();}}).map(eppRowMap -> {logger.info("消息转换为epprow：" + eppRowMap.toString());List<Object> objArry = new ArrayList<Object>();eppRowMap.forEach((key, value) -> {objArry.add(NginxInfo.valueTranForm(key, value));});return RowFactory.create(objArry.toArray());}).foreachRDD(eppRdd -> {Dataset<Row> rows = spark.createDataFrame(eppRdd,DataTypes.createStructType(NginxInfo.getStructFieldList("EPP")));kudu.insertRows(rows,tables[0]);});jssc.start();jssc.awaitTermination();logger.info("完成!");} catch (Exception e) {logger.error("处理消息错误2！", e);}}private StructType contructStructType() {List<StructField> structFields = new ArrayList<StructField>();return null;}
}

KafkaTools类，主要获取kafka配置，代码仅供参考

public static Map<String, Object> kafkaConf(Map<String, String> conf) {if (conf == null) {return null;}// kafka配置Map<String, Object> kafkaParams = new HashMap<>();kafkaParams.put(ConsumerConfig.GROUP_ID_CONFIG, "DemoConsumer");kafkaParams.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringDeserializer");kafkaParams.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringDeserializer");kafkaParams.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "true");kafkaParams.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG, "5000");//kafka集成kerberos后的security.inter.broker.protocolkafkaParams.put("security.protocol", "SASL_PLAINTEXT");kafkaParams.put("sasl.kerberos.service.name", "kafka");kafkaParams.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG,conf.get("bootStrapServers"));return kafkaParams;}

注意到，集成kerberos后，身份验证的代码并没有在项目写太多，只有kafka客户端配置加上kafkaParams.put("security.protocol", "SASL_PLAINTEXT")而已

身份验证的操作分别交给spark-submit处理和调度器linux crontab 处理
假设我用的是wms这个账号去跑任务
新建kafka_client_jaas.conf文件

cd /usr/wms/sparkstreaming/#该文件给kafka身份验证用
[wms@node1 sparkstreaming]$ vi kafka_client_jaas.conf
KafkaClient {com.sun.security.auth.module.Krb5LoginModule requireduseKeyTab=truestoreKey=trueuseTicketCache=falseserviceName="kafka"keyTab="./wms.keytab"principal="wms@W.COM";
};#把wms.keytab也放在相应目录下，此时目录机构应该是如此
-rwxr-xr-x 1 root root 352 Jul 16 09:48 wms.keytab
[wms@node1 sparkstreaming]$ ll
总用量 114172
#conf.properties文件是spark应用的配置文件
-rwxr-xr-x 1 wms wms       897 7月  16 09:45 conf.properties
-rwxr-xr-x 1 wms wms       221 7月  16 09:45 kafka_client_jaas.conf
-rwxr-xr-x 1 wms wms       352 7月  16 09:45 wms.keytab#scp到其他目录
scp /usr/wms/sparkstreaming/* root@bi-slave1:/usr/wms/sparkstreaming/
scp /usr/wms/sparkstreaming/* root@bi-slave2:/usr/wms/sparkstreaming/
scp /usr/wms/sparkstreaming/* root@bi-slave3:/usr/wms/sparkstreaming/

spark启动前，先初始化driver和executor是节点票据

#该操作主要是为了保证executor节点执行kudu操作前有权限
#这里我们写了一个批处理脚本，能在所有节点执行某个命令
#我们用linux调度工具，到点初始化wms用户票据，防止票据失效
#在root权限下操作
exit
[root@node1 sparkstreaming]# crontab -e
#每五分钟，在每台机器初始化wms用户票据，防止失效
*/5 * * * * ./doCommand.sh "su wms -c 'kinit -kt /usr/wms/sparkstreaming/wms.keytab wms@W.COM'" > /usr/wms/sparkstreaming/lastupdate

spark-submit

# 注意需要在配置文件目录下执行spark2-submit命令
# driver节点需要配置kafka的security.auth.login.config信息
# executor节点需要配置kafka的security.auth.login.config信息
# driver根据绝对路径读取配置
# executor根据相对路径读取配置
# 通过files配置把kafka_client_jaas.conf,wms.keytab发到executor节点spark2-submit  --driver-java-options=-Djava.security.auth.login.config=/etc/wonhighconf/bi/bi-sparkstreaming/kafka_client_jaas.conf  --conf "spark.executor.extraJavaOptions=-Djava.security.auth.login.config=./kafka_client_jaas.conf" --files kafka_client_jaas.conf,wms.keytab --master yarn --deploy-mode cluster  --class deng.yb.sparkStreaming.App /usr/wms/sparkstreaming/sparkStreaming-0.0.1-SNAPSHOT.jar