将 Spark 数据帧插入 hbase

Question

我有一个数据帧，我想把它插入到hbase中。我遵循这个文档.

这是我的数据帧的样子：

 --------------------
|id | name | address |
|--------------------|
|23 |marry |france   |
|--------------------|
|87 |zied  |italie   |
 --------------------

我使用以下代码创建一个 hbase 表：

val tableName = "two"
val conf = HBaseConfiguration.create()
if(!admin.isTableAvailable(tableName)) {
          print("-----------------------------------------------------------------------------------------------------------")
          val tableDesc = new HTableDescriptor(tableName)
          tableDesc.addFamily(new HColumnDescriptor("z1".getBytes()))
          admin.createTable(tableDesc)
        }else{
          print("Table already exists!!--------------------------------------------------------------------------------------")
        }

现在如何将此数据帧插入 hbase ？

在另一个示例中，我使用以下代码成功插入到hbase中：

val myTable = new HTable(conf, tableName)
    for (i <- 0 to 1000) {
      var p = new Put(Bytes.toBytes(""+i))
      p.add("z1".getBytes(), "name".getBytes(), Bytes.toBytes(""+(i*5)))
      p.add("z1".getBytes(), "age".getBytes(), Bytes.toBytes("2017-04-20"))
      p.add("z2".getBytes(), "job".getBytes(), Bytes.toBytes(""+i))
      p.add("z2".getBytes(), "salary".getBytes(), Bytes.toBytes(""+i))
      myTable.put(p)
    }
    myTable.flushCommits()

但是现在我卡住了，如何将我的数据帧的每条记录插入我的 hbase 表中。

感谢您的时间和关注

Answer 1

另一种方法是查看rdd.saveAsNewAPIHadoopDataset，将数据插入到hbase表中。

def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().appName("sparkToHive").enableHiveSupport().getOrCreate()
    import spark.implicits._
    val config = HBaseConfiguration.create()
    config.set("hbase.zookeeper.quorum", "ip's")
    config.set("hbase.zookeeper.property.clientPort","2181")
    config.set(TableInputFormat.INPUT_TABLE, "tableName")
    val newAPIJobConfiguration1 = Job.getInstance(config)
    newAPIJobConfiguration1.getConfiguration().set(TableOutputFormat.OUTPUT_TABLE, "tableName")
    newAPIJobConfiguration1.setOutputFormatClass(classOf[TableOutputFormat[ImmutableBytesWritable]])
    val df: DataFrame  = Seq(("foo", "1", "foo1"), ("bar", "2", "bar1")).toDF("key", "value1", "value2")
    val hbasePuts= df.rdd.map((row: Row) => {
      val  put = new Put(Bytes.toBytes(row.getString(0)))
      put.addColumn(Bytes.toBytes("cf1"), Bytes.toBytes("value1"), Bytes.toBytes(row.getString(1)))
      put.addColumn(Bytes.toBytes("cf2"), Bytes.toBytes("value2"), Bytes.toBytes(row.getString(2)))
      (new ImmutableBytesWritable(), put)
    })
    hbasePuts.saveAsNewAPIHadoopDataset(newAPIJobConfiguration1.getConfiguration())
    }

编号 ： https://sparkkb.wordpress.com/2015/05/04/save-javardd-to-hbase-using-saveasnewapihadoopdataset-spark-api-java-coding/

Answer 2

下面是使用Maven中可用的Hortonworks的spark hbase连接器的完整示例。

此示例显示

• 如何检查 HBase 表是否存在
• 创建 HBase 表(如果不存在(
• 将数据帧插入 HBase 表

import org.apache.hadoop.hbase.client.{ColumnFamilyDescriptorBuilder, ConnectionFactory, TableDescriptorBuilder}
import org.apache.hadoop.hbase.{HBaseConfiguration, TableName}
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.execution.datasources.hbase.HBaseTableCatalog
object Main extends App {
  case class Employee(key: String, fName: String, lName: String, mName: String,
                      addressLine: String, city: String, state: String, zipCode: String)
  // as pre-requisites the table 'employee' with column families 'person' and 'address' should exist
  val tableNameString = "default:employee"
  val colFamilyPString = "person"
  val colFamilyAString = "address"
  val tableName = TableName.valueOf(tableNameString)
  val colFamilyP = colFamilyPString.getBytes
  val colFamilyA = colFamilyAString.getBytes
  val hBaseConf = HBaseConfiguration.create()
  val connection = ConnectionFactory.createConnection(hBaseConf);
  val admin = connection.getAdmin();
  println("Check if table 'employee' exists:")
  val tableExistsCheck: Boolean = admin.tableExists(tableName)
  println(s"Table " + tableName.toString + " exists? " + tableExistsCheck)
  if(tableExistsCheck == false) {
    println("Create Table employee with column families 'person' and 'address'")
    val colFamilyBuild1 = ColumnFamilyDescriptorBuilder.newBuilder(colFamilyP).build()
    val colFamilyBuild2 = ColumnFamilyDescriptorBuilder.newBuilder(colFamilyA).build()
    val tableDescriptorBuild = TableDescriptorBuilder.newBuilder(tableName)
      .setColumnFamily(colFamilyBuild1)
      .setColumnFamily(colFamilyBuild2)
      .build()
    admin.createTable(tableDescriptorBuild)
  }
  // define schema for the dataframe that should be loaded into HBase
  def catalog =
    s"""{
       |"table":{"namespace":"default","name":"employee"},
       |"rowkey":"key",
       |"columns":{
       |"key":{"cf":"rowkey","col":"key","type":"string"},
       |"fName":{"cf":"person","col":"firstName","type":"string"},
       |"lName":{"cf":"person","col":"lastName","type":"string"},
       |"mName":{"cf":"person","col":"middleName","type":"string"},
       |"addressLine":{"cf":"address","col":"addressLine","type":"string"},
       |"city":{"cf":"address","col":"city","type":"string"},
       |"state":{"cf":"address","col":"state","type":"string"},
       |"zipCode":{"cf":"address","col":"zipCode","type":"string"}
       |}
       |}""".stripMargin
  // define some test data
  val data = Seq(
    Employee("1","Horst","Hans","A","12main","NYC","NY","123"),
    Employee("2","Joe","Bill","B","1337ave","LA","CA","456"),
    Employee("3","Mohammed","Mohammed","C","1Apple","SanFran","CA","678")
  )
  // create SparkSession
  val spark: SparkSession = SparkSession.builder()
    .master("local[*]")
    .appName("HBaseConnector")
    .getOrCreate()
  // serialize data
  import spark.implicits._
  val df = spark.sparkContext.parallelize(data).toDF
  // write dataframe into HBase
  df.write.options(
    Map(HBaseTableCatalog.tableCatalog -> catalog, HBaseTableCatalog.newTable -> "3")) // create 3 regions
    .format("org.apache.spark.sql.execution.datasources.hbase")
    .save()
}

当我

的资源中有相关的site-xmls("core-site.xml"，"hbase-site.xml"，"hdfs-site.xml"(时，这对我有用。

Answer 3

使用答案进行代码格式化医生说：

sc.parallelize(data).toDF.write.options(
 Map(HBaseTableCatalog.tableCatalog -> catalog, HBaseTableCatalog.newTable -> "5"))
 .format("org.apache.hadoop.hbase.spark ")
 .save()

其中sc.parallelize(data(.toDF是你的DataFrame。文档示例使用 sc.parallelize(data(.toDF 将 scala 集合转换为数据帧

您已经拥有数据帧，只需尝试调用

yourDataFrame.write.options(
     Map(HBaseTableCatalog.tableCatalog -> catalog, HBaseTableCatalog.newTable -> "5"))
     .format("org.apache.hadoop.hbase.spark ")
     .save()

它应该有效。文档很清楚...

UPD

给定具有指定架构的数据帧，上面将创建一个 HBase 包含 5 个区域的表，并将数据帧保存在其中。请注意，如果 未指定 HBaseTableCatalog.newTable，该表必须 预先创建。

这是关于数据分区的。每个 HBase 表可以有 1...X 个区域。应仔细选择区域数。低区域数量是不好的。高地区数量也很糟糕。