Spark算子：RDD创建操作

关键字：Spark RDD 创建、parallelize、makeRDD、textFile、hadoopFile、hadoopRDD、newAPIHadoopFile、newAPIHadoopRDD

从集合创建RDD

parallelize

def parallelize[T](seq: Seq[T], numSlices: Int = defaultParallelism)(implicit arg0: ClassTag[T]): RDD[T]

从一个Seq集合创建RDD。

参数1：Seq集合，必须。

参数2：分区数，默认为该Application分配到的资源的CPU核数

scala> var rdd = sc.parallelize(1 to 10)
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[2] at parallelize at :21

scala> rdd.collect
res3: Array[Int] = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

scala> rdd.partitions.size
res4: Int = 15

//设置RDD为3个分区
scala> var rdd2 = sc.parallelize(1 to 10,3)
rdd2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[3] at parallelize at :21

scala> rdd2.collect
res5: Array[Int] = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

scala> rdd2.partitions.size
res6: Int = 3

makeRDD

def makeRDD[T](seq: Seq[T], numSlices: Int = defaultParallelism)(implicit arg0: ClassTag[T]): RDD[T]

这种用法和parallelize完全相同

def makeRDD[T](seq: Seq[(T, Seq[String])])(implicit arg0: ClassTag[T]): RDD[T]

该用法可以指定每一个分区的preferredLocations。

scala> var collect = Seq((1 to 10,Seq("slave007.lxw1234.com","slave002.lxw1234.com")),
(11 to 15,Seq("slave013.lxw1234.com","slave015.lxw1234.com")))
collect: Seq[(scala.collection.immutable.Range.Inclusive, Seq[String])] = List((Range(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
List(slave007.lxw1234.com, slave002.lxw1234.com)), (Range(11, 12, 13, 14, 15),List(slave013.lxw1234.com, slave015.lxw1234.com)))

scala> var rdd = sc.makeRDD(collect)
rdd: org.apache.spark.rdd.RDD[scala.collection.immutable.Range.Inclusive] = ParallelCollectionRDD[6] at makeRDD at :23

scala> rdd.partitions.size
res33: Int = 2

scala> rdd.preferredLocations(rdd.partitions(0))
res34: Seq[String] = List(slave007.lxw1234.com, slave002.lxw1234.com)

scala> rdd.preferredLocations(rdd.partitions(1))
res35: Seq[String] = List(slave013.lxw1234.com, slave015.lxw1234.com)

指定分区的优先位置，对后续的调度优化有帮助。

从外部存储创建RDD

textFile

//从hdfs文件创建.

//从hdfs文件创建
scala> var rdd = sc.textFile("hdfs:///tmp/lxw1234/1.txt")
rdd: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[26] at textFile at :21

scala> rdd.count
res48: Long = 4

//从本地文件创建
scala> var rdd = sc.textFile("file:///etc/hadoop/conf/core-site.xml")
rdd: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[28] at textFile at :21

scala> rdd.count
res49: Long = 97

注意这里的本地文件路径需要在Driver和Executor端存在。

从其他HDFS文件格式创建

hadoopFile

sequenceFile

objectFile

newAPIHadoopFile

从Hadoop接口API创建

hadoopRDD

newAPIHadoopRDD

比如：从HBase创建RDD

scala> import org.apache.hadoop.hbase.{HBaseConfiguration, HTableDescriptor, TableName}
import org.apache.hadoop.hbase.{HBaseConfiguration, HTableDescriptor, TableName}

scala> import org.apache.hadoop.hbase.mapreduce.TableInputFormat
import org.apache.hadoop.hbase.mapreduce.TableInputFormat

scala> import org.apache.hadoop.hbase.client.HBaseAdmin
import org.apache.hadoop.hbase.client.HBaseAdmin

scala> val conf = HBaseConfiguration.create()
scala> conf.set(TableInputFormat.INPUT_TABLE,"lxw1234")
scala> var hbaseRDD = sc.newAPIHadoopRDD(
conf,classOf[org.apache.hadoop.hbase.mapreduce.TableInputFormat],classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable],classOf[org.apache.hadoop.hbase.client.Result])

scala> hbaseRDD.count
res52: Long = 1

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