最近一直在研究presto接口hive和mysql的一些使用和功能，因此，我在想是否能将数据实时的写入到hive呢，刚好公司项目有需求数据实时写入到hive中，对此，我特定实现了一下。
pom文件
spark-streaming-kafka-0-10_2.112.1.0
spark-core_2.11
spark-sql_2.11
scala-library
采用的是scala2.11.8
实现逻辑：
实时的获取kafka中的数据，然后保存偏移量，实时的写入到hive中
实现过程如下:
以下代码是针对topic的分区只有一个的情况下：
object FamilyBandService {
val logger = LoggerFactory.getLogger(this.getClass)
def main(args: Array[String]): Unit = {
val conf =new SparkConf().setAppName(s”${this.getClass.getSimpleName}”).setMaster(“local[*]”)
conf.set(“spark.defalut.parallelism”,”500″)
//每秒钟每个分区kafka拉取消息的速率
.set(“spark.streaming.kafka.maxRatePerPartition”,”500″)
// 序列化
.set(“spark.serilizer”,”org.apache.spark.serializer.KryoSerializer”)
// 建议开启rdd的压缩
.set(“spark.rdd.compress”,”true”)
val sc =new SparkContext(conf)
val ssc =new StreamingContext(sc,Seconds(3))
val brokers = PropertyUtil.getInstance().getProperty(“brokerList”,””) //配置文件读取工具类需自行编写
val groupId = PropertyUtil.getInstance().getProperty(“groupid”,””)
val topic = PropertyUtil.getInstance().getProperty(“topic”,””)
var topics =Array(topic)
Logger.getLogger(“org”).setLevel(Level.ERROR) //临时测试的时候只开启error级别，方便排错。
//封装参数
val kafkaParams =Map[String, Object](
“bootstrap.servers” -> brokers,
“key.deserializer” ->classOf[StringDeserializer],
“value.deserializer” ->classOf[StringDeserializer],
“group.id” -> groupId,
“auto.offset.reset” ->”latest”,
“enable.auto.commit” -> (false: java.lang.Boolean))
//从redis中获取到偏移量
val offsets: Long = RedisUtil.hashGet(“offset”,”offsets”).toLong
val topicPartition: TopicPartition =new TopicPartition(topic,0)
val partitionoffsets:Map[TopicPartition, Long] =Map(topicPartition -> offsets)
//获取到实时流对象
val kafkaStream =if (offsets ==0) {
KafkaUtils.createDirectStream[String,String](
ssc,
PreferConsistent, //这里有3种模式,一般情况下，都是使用PreferConsistent
//LocationStrategies.PreferConsistent：将在可用的执行器之间均匀分配分区。
//PreferBrokers 执行程序与Kafka代理所在的主机相同，将更喜欢在该分区的Kafka leader上安排分区
//PreferFixed 如果您在分区之间的负载有显着偏差，这允许您指定分区到主机的显式映射（任何未指定的分区将使用一致的位置）。
Subscribe[String,String](topics, kafkaParams) //消息订阅
)
}else {
KafkaUtils.createDirectStream[String,String](
ssc,
PreferConsistent,
Subscribe[String,String](topics, kafkaParams, partitionoffsets) //此种方式是针对具体某个分区或者topic只有一个分区的情况
)
}
//业务处理
kafkaStream.foreachRDD(rdd => {
val ranges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges //获取到分区和偏移量信息
val events: RDD[Some[String]] = rdd.map(x => {
val data = x.value()
Some(data)
})
val session = SQLContextSingleton.getSparkSession(events.sparkContext) //构建一个Sparksession的单例
session.sql(“set hive.exec.dynamic.partition=true”) //配置hive支持动态分区
session.sql(“set hive.exec.dynamic.partition.mode=nonstrict”) //配置hive动态分区为非严格模式
//如果将数据转换为Seq(xxxx),然后倒入隐式转换import session.implicalit._ 是否能实现呢，答案是否定的。
val dataRow = events.map(line => { //构建row
val temp = line.get.split(“###”)
Row(temp(0), temp(1), temp(2), temp(3), temp(4), temp(5))
})
//”deviceid”,”code”,”time”,”info”,”sdkversion”,”appversion”
val structType =StructType(Array( //确定字段的类别
StructField(“deviceid”, StringType,true),
StructField(“code”, StringType,true),
StructField(“time”, StringType,true),
StructField(“info”, StringType,true),
StructField(“sdkversion”, StringType,true),
StructField(“appversion”, StringType,true)
))
val df = session.createDataFrame(dataRow, structType) //构建df
df.createOrReplaceTempView(“jk_device_info”)
session.sql(“insert into test.jk_device_info select * from jk_device_info”)
for (rs <- ranges) {
//实时保存偏移量到redis
val value = rs.untilOffset.toString
RedisUtil.hashSet(“offset”,”offsets”, value) //偏移量保存
println(s”the offset:${value}”)
}
})
println(“xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx”)
ssc.start()
ssc.awaitTermination()
}
}
上面说到了单分区情况下的实时写入hive的情况，此种情况要求数据具有很高的时序性，但是并发会受到影响。那么我们采用多分区的情况下，如何实现呢，代码如下：
//单分区情况
/* //从redis中获取到偏移量
val offsets: Long = RedisUtil.hashGet(“offset”, “offsets”).toLong //redis工具类需要自行编写
val topicPartition: TopicPartition = new TopicPartition(topic, 0)
val partitionoffsets: Map[TopicPartition, Long] = Map(topicPartition -> offsets)*/
//多分区情况
val partitions = 3
var fromdbOffset =Map[TopicPartition, Long]()
for (partition <-0 until partitions) {
val topicPartition =new TopicPartition(topic, partition)
val offsets = RedisUtil.hashGet(“offset”,s”${topic}_${partition}”).toLong
fromdbOffset += (topicPartition -> offsets)
}
//获取到实时流对象
val kafkaStream =if (fromdbOffset.size==0) {
KafkaUtils.createDirectStream[String,String](
ssc,
PreferConsistent,
Subscribe[String,String](topics, kafkaParams)
)
}else {
KafkaUtils.createDirectStream[String,String](
ssc,
PreferConsistent,
// Subscribe[String, String](topics, kafkaParams, partitionoffsets) 订阅具体某个分区
ConsumerStrategies.Assign[String,String](fromdbOffset.keys, kafkaParams, fromdbOffset)
)
}
针对Assign 和 Subscribe ，我们来看下官方源码
Assign：
//2个参数
def Assign[K,V](
topicPartitions:Iterable[TopicPartition],
kafkaParams: collection.Map[String, Object]): ConsumerStrategy[K,V] = {
new Assign[K,V](
new ju.ArrayList(topicPartitions.asJavaCollection),
new ju.HashMap[String, Object](kafkaParams.asJava),
ju.Collections.emptyMap[TopicPartition, jl.Long]())
}
//3个参数
def Assign[K,V](
topicPartitions:Iterable[TopicPartition],
kafkaParams: collection.Map[String, Object],
offsets: collection.Map[TopicPartition, Long]): ConsumerStrategy[K,V] = {
new Assign[K,V](
new ju.ArrayList(topicPartitions.asJavaCollection),
new ju.HashMap[String, Object](kafkaParams.asJava),
new ju.HashMap[TopicPartition, jl.Long](offsets.mapValues(l =>new jl.Long(l)).asJava))
}
Subscribe ：
2个参数
def Subscribe[K,V](
topics:Iterable[jl.String],
kafkaParams: collection.Map[String, Object]): ConsumerStrategy[K,V] = {
new Subscribe[K,V](
new ju.ArrayList(topics.asJavaCollection),
new ju.HashMap[String, Object](kafkaParams.asJava),
ju.Collections.emptyMap[TopicPartition, jl.Long]())
}
3个参数
def Subscribe[K,V](
topics:Iterable[jl.String],
kafkaParams: collection.Map[String, Object],
offsets: collection.Map[TopicPartition, Long]): ConsumerStrategy[K,V] = {
new Subscribe[K,V](
new ju.ArrayList(topics.asJavaCollection),
new ju.HashMap[String, Object](kafkaParams.asJava),
new ju.HashMap[TopicPartition, jl.Long](offsets.mapValues(l =>new jl.Long(l)).asJava))
}
经过对比，我们发现，区别就在第一个参数上，一个为topics，一个为topicPartitions
SQLContextSingleton单例
def getSparkSession(sc: SparkContext): SparkSession = {
if (sparkSession ==null) {
sparkSession = SparkSession
.builder()
.enableHiveSupport()
.master(“local[*]”)
.config(sc.getConf)
.config(“spark.files.openCostInBytes”, PropertyUtil.getInstance().getProperty(“spark.files.openCostInBytes”))
//連接到hive元數據庫
.config(“hive.metastore.uris”,”thrift://192.168.1.61:9083″)
//–files hdfs:///user/processuser/hive-site.xml 集群上運行需要指定hive-site.xml的位置
.config(“spark.sql.warehouse.dir”,”hdfs://192.168.1.61:8020/user/hive/warehouse”)
.getOrCreate()
}
sparkSession
}
如果需要连接到hive必须要注意的几个事项：
1，指定hive的元数据地址
2，指定spark.sql.warehouse.dir的数据存储位置
3，enableHiveSupport()
4，resource下要放hive-site.xml文件
xml文件需要配置的信息，以下信息均可从集群的配置中得到：
hive.exec.scratchdir
hive.metastore.warehouse.dir
hive.querylog.location
hive.metastore.uris
javax.jdo.option.ConnectionURL
javax.jdo.option.ConnectionDriverName
javax.jdo.option.ConnectionUserName
javax.jdo.option.ConnectionPassword
5，本地执行要指定hadoop的目录
System.setProperty(“hadoop.home.dir”, PropertyUtil.getInstance().getProperty(“localMode”))
#hadoop info
localMode=D://hadoop-2.6.5//hadoop-2.6.5
clusterMode=file://usr//hdp//2.6.2.0-205//hadoop
