1.MySQL创建存储offset的表格
mysql> use hxh
mysql> create table hxh_offset(
topic varchar(32),
groupid varchar(50),
partitions int,
fromoffset bigint,
untiloffset bigint,
primary key(topic,groupid,partitions)
);
2. Maven依赖包
<scala.version>2.11.8</scala.version>
<spark.version>2.3.1</spark.version>
<scalikejdbc.version>2.5.0</scalikejdbc.version>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka-0-8_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>5.1.27</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.scalikejdbc/scalikejdbc -->
<dependency>
<groupId>org.scalikejdbc</groupId>
<artifactId>scalikejdbc_2.11</artifactId>
<version>2.5.0</version>
</dependency>
<dependency>
<groupId>org.scalikejdbc</groupId>
<artifactId>scalikejdbc-config_2.11</artifactId>
<version>2.5.0</version>
</dependency>
<dependency>
<groupId>com.typesafe</groupId>
<artifactId>config</artifactId>
<version>1.3.0</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-lang3</artifactId>
<version>3.5</version>
</dependency>
3. 处理思路
1）StreamingContext
2）从kafka中获取数据(从外部存储获取offset-->根据offset获取kafka中的数据)
3）根据业务进行逻辑处理
4）将处理结果存到外部存储中--保存offset
5）启动程序，等待程序结束
4. 代码实现
4.1 SparkStreaming主体代码如下：
import kafka.common.TopicAndPartition
import kafka.message.MessageAndMetadata
import kafka.serializer.StringDecoder
import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka.{HasOffsetRanges, KafkaUtils}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import scalikejdbc._
import scalikejdbc.config._
object JDBCOffsetApp {
def main(args: Array[String]): Unit = {
//创建SparkStreaming入口
val conf = new SparkConf().setMaster("local[2]").setAppName("JDBCOffsetApp")
val ssc = new StreamingContext(conf,Seconds(5))
//kafka消费主题
val topics = ValueUtils.getStringValue("kafka.topics").split(",").toSet
//kafka参数
//这里应用了自定义的ValueUtils工具类，来获取application.conf里的参数，方便后期修改
val kafkaParams = Map[String,String](
"metadata.broker.list"->ValueUtils.getStringValue("metadata.broker.list"),
"auto.offset.reset"->ValueUtils.getStringValue("auto.offset.reset"),
"group.id"->ValueUtils.getStringValue("group.id")
)
//先使用scalikejdbc从MySQL数据库中读取offset信息
//+------------+------------------+------------+------------+-------------+
//| topic | groupid | partitions | fromoffset | untiloffset |
//+------------+------------------+------------+------------+-------------+
//MySQL表结构如上，将“topic”，“partitions”，“untiloffset”列读取出来
//组成 fromOffsets: Map[TopicAndPartition, Long]，后面createDirectStream用到
DBs.setup()
val fromOffset = DB.readOnly( implicit session => {
SQL("select * from hlw_offset").map(rs => {
(TopicAndPartition(rs.string("topic"),rs.int("partitions")),rs.long("untiloffset"))
}).list().apply()
}).toMap
//如果MySQL表中没有offset信息，就从0开始消费；如果有，就从已经存在的offset开始消费
val messages = if (fromOffset.isEmpty) {
println("从头开始消费...")
KafkaUtils.createDirectStream[String,String,StringDecoder,StringDecoder](ssc,kafkaParams,topics)
} else {
println("从已存在记录开始消费...")
val messageHandler = (mm:MessageAndMetadata[String,String]) => (mm.key(),mm.message())
KafkaUtils.createDirectStream[String,String,StringDecoder,StringDecoder,(String,String)](ssc,kafkaParams,fromOffset,messageHandler)
}
messages.foreachRDD(rdd=>{
if(!rdd.isEmpty()){
//输出rdd的数据量
println("数据统计记录为："+rdd.count())
//官方案例给出的获得rdd offset信息的方法，offsetRanges是由一系列offsetRange组成的数组
// trait HasOffsetRanges {
// def offsetRanges: Array[OffsetRange]
// }
val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
offsetRanges.foreach(x => {
//输出每次消费的主题，分区，开始偏移量和结束偏移量
println(s"---${x.topic},${x.partition},${x.fromOffset},${x.untilOffset}---")
//将最新的偏移量信息保存到MySQL表中
DB.autoCommit( implicit session => {
SQL("replace into hlw_offset(topic,groupid,partitions,fromoffset,untiloffset) values (?,?,?,?,?)")
.bind(x.topic,ValueUtils.getStringValue("group.id"),x.partition,x.fromOffset,x.untilOffset)
.update().apply()
})
})
}
})
ssc.start()
ssc.awaitTermination()
}
}
4.2 自定义的ValueUtils工具类如下：
import com.typesafe.config.ConfigFactory
import org.apache.commons.lang3.StringUtils
object ValueUtils {
val load = ConfigFactory.load()
def getStringValue(key:String, defaultValue:String="") = {
val value = load.getString(key)
if(StringUtils.isNotEmpty(value)) {
value
} else {
defaultValue
}
}
}
4.3 application.conf内容如下
metadata.broker.list = "192.168.200.100:9092"
auto.offset.reset = "smallest"
group.id = "hxh_offset_group"
kafka.topics = "hxh_offset"
serializer.class = "kafka.serializer.StringEncoder"
request.required.acks = "1"
# JDBC settings
db.default.driver = "com.mysql.jdbc.Driver"
db.default.url="jdbc:mysql://master:3306/hxh"
db.default.user="root"
db.default.password="123456"
4.4 自定义kafka producer
import java.util.{Date, Properties}
import kafka.producer.{KeyedMessage, Producer, ProducerConfig}
object KafkaProducer {
def main(args: Array[String]): Unit = {
val properties = new Properties()
properties.put("serializer.class",ValueUtils.getStringValue("serializer.class"))
properties.put("metadata.broker.list",ValueUtils.getStringValue("metadata.broker.list"))
properties.put("request.required.acks",ValueUtils.getStringValue("request.required.acks"))
val producerConfig = new ProducerConfig(properties)
val producer = new Producer[String,String](producerConfig)
val topic = ValueUtils.getStringValue("kafka.topics")
//每次产生100条数据
var i = 0
for (i <- 1 to 100) {
val runtimes = new Date().toString
val messages = new KeyedMessage[String, String](topic,i+"","hxh: "+runtimes)
producer.send(messages)
}
println("数据发送完毕...")
}
}
4.启动kafka服务，并创建主题
[hadoop@master bin]$ ./kafka-server-start.sh -daemon /home/hadoop/app/kafka_2.11-0.10.0.1/config/server.properties
[hadoop@maste bin]$ ./kafka-topics.sh --list --zookeeper localhost:2181/kafka
[hadoop@maste bin]$ ./kafka-topics.sh --create --zookeeper localhost:2181/kafka --replication-factor 1 --partitions 1 --topic hxh_offset
5.过程及输出结果展示
5.1查看MySQL中offset表，刚开始是个空表
mysql> select * from hxh_offset;
Empty set (0.00 sec)
5.2通过kafka producer产生500条数据
5.3启动SparkStreaming程序
//控制台输出结果：
从头开始消费...
数据统计记录为：500
---hxh_offset,0,0,500---
查看MySQL表，offset记录成功
mysql> select * from hxh_offset;
+------------+------------------+------------+------------+-------------+
| topic | groupid | partitions | fromoffset | untiloffset |
+------------+------------------+------------+------------+-------------+
| hxh_offset | hxh_offset_group | 0 | 0 | 500 |
+------------+------------------+------------+------------+-------------+
5.4关闭SparkStreaming程序，再使用kafka producer生产300条数据,再次启动spark程序（如果spark从500开始消费，说明成功读取了offset，做到了只读取一次语义）
//控制台结果输出：
从已存在记录开始消费...
数据统计记录为：300
---hxh_offset,0,500,800---
查看MySQL
mysql> select * from hxh_offset;
+------------+------------------+------------+------------+-------------+
| topic | groupid | partitions | fromoffset | untiloffset |
+------------+------------------+------------+------------+-------------+
| hxh_offset | hxh_offset_group | 0 | 500 | 800 |
+------------+------------------+------------+------------+-------------+
原文地址：https://blog.csdn.net/hexinghua0126/article/details/89316580
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