Spark operator: RDD creation operation

Create RDD from the collection

Parallelize 

    

  1.   Scala> var rdd = sc.parallelize (1 to 10)
    2. 

  2.  Rdd: org.apache.spark.rdd.RDD [Int] = ParallelCollectionRDD [2] at parallelize at: 21
    3. 

  3.  
    4. 

  4.  Scala> rdd.collect
    5. 

  5.  Res3: Array [Int] = Array (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
    6. 

  6.  
    7. 

  7.  Scala> rdd.partitions.size
    8. 

  8.  Res4: Int = 15
    9. 

  9.  
    10. 

  10.  // set RDD to 3 partitions scala> var rdd2 = sc.parallelize (1 to 10,3)
    11. 

  11.  Rdd2: org.apache.spark.rdd.RDD [Int] = ParallelCollectionRDD [3] at parallelize at: 21
    12. 

  12.  
    13. 

  13.  Scala> rdd2.collect
    14. 

  14.  Res5: Array [Int] = Array (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
    15. 

  15.  
    16. 

  16.  Scala> rdd2.partitions.size
    17. 

  17.  Res6: Int = 3
    18. 

  18.  
    19.

  MakeRDD

    

  1.   Scala> var collect = Seq ((1 to 10, Seq ("slave007.lxw1234.com", "slave002.lxw1234.com")),
    2. 

  2.  (11 to 15, Seq ("slave013.lxw1234.com", "slave015.lxw1234.com"))))
    3. 

  3.  Collect: Seq [(scala.collection.immutable.Range.Inclusive, Seq [String])] = List ((Range (1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
    4. 

  4.  List (slave (s) (slave00.lxw1234.com), (Range (11, 12, 13, 14, 15), List (slave013.lxw1234.com, slave015.lxw1234.com)))
    5. 

  5.  
    6. 

  6.  Scala> var rdd = sc.makeRDD (collect)
    7. 

  7.  Rdd: org.apache.spark.rdd.RDD [scala.collection.immutable.Range.Inclusive] = ParallelCollectionRDD [6] at makeRDD at: 23
    8. 

  8.  
    9. 

  9.  Scala> rdd.partitions.size
    10. 

  10.  Res33: Int = 2
    11. 

  11.  
    12. 

  12.  Scala> rdd.preferredLocations (rdd.partitions (0))
    13. 

  13.  Res34: Seq [String] = List (slave007.lxw1234.com, slave002.lxw1234.com)
    14. 

  14.  
    15. 

  15.  Scala> rdd.preferredLocations (rdd.partitions (1))
    16. 

  16.  Res35: Seq [String] = List (slave013.lxw1234.com, slave015.lxw1234.com)
    17. 

  17.  
    18. 

  18.   
    19.

Create RDD from external storage

TextFile

    

  1.   // create scala from the hdfs file var rdd = sc.textFile ("hdfs: ///tmp/lxw1234/1.txt")
    2. 

  2.  Rdd: org.apache.spark.rdd.RDD [String] = MapPartitionsRDD [26] at textFile at: 21
    3. 

  3.  
    4. 

  4.  Scala> rdd.count
    5. 

  5.  Res48: Long = 4
    6. 

  6.  
    7. 

  7.  // create scala> var rdd = sc.textFile ("file: ///etc/hadoop/conf/core-site.xml" from local file )
    8. 

  8.  Rdd: org.apache.spark.rdd.RDD [String] = MapPartitionsRDD [28] at textFile at: 21
    9. 

  9.  
    10. 

  10.  Scala> rdd.count
    11. 

  11.  Res49: Long = 97  
    12. 

  12.  
    13.

 Created from other HDFS file formats

HadoopFile
SequenceFile
ObjectFile
NewAPIHadoopFile
  • Created from the Hadoop interface API
HadoopRDD
NewAPIHadoopRDD
For example: Create an RDD from HBase 

    

  1.   Scala> import org.apache.hadoop.hbase. {HBaseConfiguration, HTableDescriptor, TableName}
    2. 

  2.  Import org.apache.hadoop.hbase. {HBaseConfiguration, HTableDescriptor, TableName}
    3. 

  3.  
    4. 

  4.  Scala> import org.apache.hadoop.hbase.mapreduce.TableInputFormat
    5. 

  5.  Import org.apache.hadoop.hbase.mapreduce.TableInputFormat
    6. 

  6.  
    7. 

  7.  Scala> import org.apache.hadoop.hbase.client.HBaseAdmin
    8. 

  8.  Import org.apache.hadoop.hbase.client.HBaseAdmin
    9. 

  9.  
    10. 

  10.  Scala> val conf = HBaseConfiguration.create ()
    11. 

  11.  Scala> conf.set (TableInputFormat.INPUT_TABLE, "lxw1234")
    12. 

  12.  Scala> var hbaseRDD = sc.newAPIHadoopRDD ( 
    13. 

  13.   Conf, classOf [org.apache.hadoop.hbase.mapreduce.TableInputFormat], classOf [org.apache.hadoop.hbase.io.ImmutableBytesWritable], classOf [org.apache.hadoop.hbase.client.Result]) 
    14. 

  14.  
    15. 

  15.  Scala> hbaseRDD.count
    16. 

  16.  Res52: Long = 1
    17. 

  17.  
    18.

 

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