Spark 结构化流与 Hbase 集成

本文介绍了Spark 结构化流与 Hbase 集成的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

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我们正在对从 MySQL 收集的 kafka 数据进行流式处理.现在，一旦完成所有分析，我想将我的数据直接保存到 Hbase.我已经通过了 spark 结构化流文档，但找不到任何带有 Hbase 的接收器.我用来从 Kafka 读取数据的代码如下.

We are doing streaming on kafka data which being collected from MySQL. Now once all the analytics has been done i want to save my data directly to Hbase. I have through the spark structured streaming document but couldn't find any sink with Hbase. Code which I used to read the data from Kafka is below.

 val records = spark.readStream.format("kafka").option("subscribe", "kaapociot").option("kafka.bootstrap.servers", "XX.XX.XX.XX:6667").option("startingOffsets", "earliest").load
 val jsonschema = StructType(Seq(StructField("header", StringType, true),StructField("event", StringType, true)))
 val uschema = StructType(Seq(
             StructField("MeterNumber", StringType, true),
             StructField("Utility", StringType, true),
             StructField("VendorServiceNumber", StringType, true),
             StructField("VendorName", StringType, true),
             StructField("SiteNumber",  StringType, true),
             StructField("SiteName", StringType, true),
             StructField("Location", StringType, true),
             StructField("timestamp", LongType, true),
             StructField("power", DoubleType, true)
             ))
 val DF_Hbase = records.selectExpr("cast (value as string) as Json").select(from_json($"json",schema=jsonschema).as("data")).select("data.event").select(from_json($"event", uschema).as("mykafkadata")).select("mykafkadata.*")

现在终于，我想在 hbase 中保存 DF_Hbase 数据帧.

Now finally, I want to save DF_Hbase dataframe in hbase.

推荐答案

1- 将这些库添加到您的项目中:

1- add these libraries to your project :

      "org.apache.hbase" % "hbase-client" % "2.0.1"
      "org.apache.hbase" % "hbase-common" % "2.0.1"

2- 将此特征添加到您的代码中:

2- add this trait to your code :

   import java.util.concurrent.ExecutorService
   import org.apache.hadoop.hbase.client.{Connection, ConnectionFactory, Put, Table}
   import org.apache.hadoop.hbase.security.User
   import org.apache.hadoop.hbase.{HBaseConfiguration, TableName}
   import org.apache.spark.sql.ForeachWriter

   trait HBaseForeachWriter[RECORD] extends ForeachWriter[RECORD] {

     val tableName: String
     val hbaseConfResources: Seq[String]

     def pool: Option[ExecutorService] = None

     def user: Option[User] = None

     private var hTable: Table = _
     private var connection: Connection = _


     override def open(partitionId: Long, version: Long): Boolean = {
       connection = createConnection()
       hTable = getHTable(connection)
       true
     }

     def createConnection(): Connection = {
       val hbaseConfig = HBaseConfiguration.create()
       hbaseConfResources.foreach(hbaseConfig.addResource)
       ConnectionFactory.createConnection(hbaseConfig, pool.orNull,                      user.orNull)

     }

     def getHTable(connection: Connection): Table = {
       connection.getTable(TableName.valueOf(tableName))
     }

     override def process(record: RECORD): Unit = {
       val put = toPut(record)
       hTable.put(put)
     }

     override def close(errorOrNull: Throwable): Unit = {
       hTable.close()
       connection.close()
     }

     def toPut(record: RECORD): Put

   }

3- 将其用于您的逻辑:

3- use it for your logic :

    val ds = .... //anyDataset[WhatEverYourDataType]

    val query = ds.writeStream
           .foreach(new HBaseForeachWriter[WhatEverYourDataType] {
                            override val tableName: String = "hbase-table-name"
                            //your cluster files, i assume here it is in resources  
                            override val hbaseConfResources: Seq[String] = Seq("core-site.xml", "hbase-site.xml") 

                            override def toPut(record: WhatEverYourDataType): Put = {
                              val key = .....
                              val columnFamaliyName : String = ....
                              val columnName : String = ....
                              val columnValue = ....

                              val p = new Put(Bytes.toBytes(key))
                              //Add columns ... 
                   p.addColumn(Bytes.toBytes(columnFamaliyName),
                               Bytes.toBytes(columnName), 
                               Bytes.toBytes(columnValue))

                              p
                            }

                          }
           ).start()

         query.awaitTermination()

这篇关于Spark 结构化流与 Hbase 集成的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！