Re: Column operation on Spark RDDs.
Two simple suggestions:
1. No need to call zipWithIndex twice. Use the earlier RDD dt.
2. Replace zipWithIndex with zipWithUniqueId which does not trigger a spark
job
Below your code with the above changes:
var dataRDD = sc.textFile(/test.csv).map(_.split(,))
val dt = dataRDD.*zipWithUniqueId*.map(_.swap)
val newCol1 = *dt*.map {case (i, x) = (i, x(1)+x(18)) }
val newCol2 = newCol1.join(dt).map(x= function(.))
Hope this helps.
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Re: Column operation on Spark RDDs.
Two simple suggestions:
1. No need to call zipWithIndex twice. Use the earlier RDD dt.
2. Replace zipWithIndex with zipWithUniqueId which does not trigger a spark
job
Below your code with the above changes:
var dataRDD = sc.textFile(/test.csv).map(_.split(,))
val dt = dataRDD.*zipWithUniqueId*.map(_.swap)
val newCol1 = *dt*.map {case (i, x) = (i, x(1)+x(18)) }
val newCol2 = newCol1.join(dt).map(x= function(.))
Hope this helps.
Kiran
On Fri, Jun 5, 2015 at 8:15 AM, Carter gyz...@hotmail.com wrote:
Hi, I have a RDD with MANY columns (e.g., hundreds), and most of my
operation
is on columns, e.g., I need to create many intermediate variables from
different columns, what is the most efficient way to do this?
For example, if my dataRDD[Array[String]] is like below:
123, 523, 534, ..., 893
536, 98, 1623, ..., 98472
537, 89, 83640, ..., 9265
7297, 98364, 9, ..., 735
..
29, 94, 956, ..., 758
I will need to create a new column or a variable as newCol1 =
2ndCol+19thCol, and another new column based on newCol1 and the existing
columns: newCol2 = function(newCol1, 34thCol), what is the best way of
doing
this?
I have been thinking using index for the intermediate variables and the
dataRDD, and then join them together on the index to do my calculation:
var dataRDD = sc.textFile(/test.csv).map(_.split(,))
val dt = dataRDD.zipWithIndex.map(_.swap)
val newCol1 = dataRDD.map(x = x(1)+x(18)).zipWithIndex.map(_.swap)
val newCol2 = newCol1.join(dt).map(x= function(.))
Is there a better way of doing this?
Thank you very much!
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Column operation on Spark RDDs.
Hi, I have a RDD with MANY columns (e.g., hundreds), and most of my operation is on columns, e.g., I need to create many intermediate variables from different columns, what is the most efficient way to do this? For example, if my dataRDD[Array[String]] is like below: 123, 523, 534, ..., 893 536, 98, 1623, ..., 98472 537, 89, 83640, ..., 9265 7297, 98364, 9, ..., 735 .. 29, 94, 956, ..., 758 I will need to create a new column or a variable as newCol1 = 2ndCol+19thCol, and another new column based on newCol1 and the existing columns: newCol2 = function(newCol1, 34thCol), what is the best way of doing this? I have been thinking using index for the intermediate variables and the dataRDD, and then join them together on the index to do my calculation: var dataRDD = sc.textFile(/test.csv).map(_.split(,)) val dt = dataRDD.zipWithIndex.map(_.swap) val newCol1 = dataRDD.map(x = x(1)+x(18)).zipWithIndex.map(_.swap) val newCol2 = newCol1.join(dt).map(x= function(.)) Is there a better way of doing this? Thank you very much! -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Column-operation-on-Spark-RDDs-tp23165.html Sent from the Apache Spark User List mailing list archive at Nabble.com. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
