Spark RDD Windowing using pyspark
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There is a Spark RDD, called rdd1. It has(key, value) pair and I have a list, whose elements are a tuple(key1,key2).
I want to get a rdd2, with rows `((key1,key2), (value of key1 in rdd1, value of key2 in rdd1)).
Can somebody help me?
rdd1:
key1, value1,
key2, value2,
key3, value3
array: [(key1,key2),(key2,key3)]
Result:
(key1,key2),value1,value2
(key2,key3),value2,value3
I have tried
spark.parallize(array).map(lambda x:)
apache-spark join pyspark rdd
edited Nov 23 '18 at 11:32
thebluephantom
3,37141033
asked Nov 22 '18 at 13:35
user9465775user9465775
6
add a comment |
There is a Spark RDD, called rdd1. It has(key, value) pair and I have a list, whose elements are a tuple(key1,key2).
I want to get a rdd2, with rows `((key1,key2), (value of key1 in rdd1, value of key2 in rdd1)).
Can somebody help me?
rdd1:
key1, value1,
key2, value2,
key3, value3
array: [(key1,key2),(key2,key3)]
Result:
(key1,key2),value1,value2
(key2,key3),value2,value3
I have tried
spark.parallize(array).map(lambda x:)
apache-spark join pyspark rdd
edited Nov 23 '18 at 11:32
thebluephantom
3,37141033
asked Nov 22 '18 at 13:35
user9465775user9465775
6
add a comment |
There is a Spark RDD, called rdd1. It has(key, value) pair and I have a list, whose elements are a tuple(key1,key2).
I want to get a rdd2, with rows `((key1,key2), (value of key1 in rdd1, value of key2 in rdd1)).
Can somebody help me?
rdd1:
key1, value1,
key2, value2,
key3, value3
array: [(key1,key2),(key2,key3)]
Result:
(key1,key2),value1,value2
(key2,key3),value2,value3
I have tried
spark.parallize(array).map(lambda x:)
apache-spark join pyspark rdd
edited Nov 23 '18 at 11:32
thebluephantom
3,37141033
asked Nov 22 '18 at 13:35
user9465775user9465775
6
There is a Spark RDD, called rdd1. It has(key, value) pair and I have a list, whose elements are a tuple(key1,key2).
I want to get a rdd2, with rows `((key1,key2), (value of key1 in rdd1, value of key2 in rdd1)).
Can somebody help me?
rdd1:
key1, value1,
key2, value2,
key3, value3
array: [(key1,key2),(key2,key3)]
Result:
(key1,key2),value1,value2
(key2,key3),value2,value3
I have tried
spark.parallize(array).map(lambda x:)
apache-spark join pyspark rdd
apache-spark join pyspark rdd
edited Nov 23 '18 at 11:32
thebluephantom
3,37141033
asked Nov 22 '18 at 13:35
user9465775user9465775
6
edited Nov 23 '18 at 11:32
thebluephantom
3,37141033
asked Nov 22 '18 at 13:35
user9465775user9465775
6
edited Nov 23 '18 at 11:32
thebluephantom
3,37141033
edited Nov 23 '18 at 11:32
thebluephantom
3,37141033
edited Nov 23 '18 at 11:32
thebluephantom
3,37141033
3,37141033
asked Nov 22 '18 at 13:35
user9465775user9465775
6
asked Nov 22 '18 at 13:35
user9465775user9465775
6
asked Nov 22 '18 at 13:35
user9465775user9465775
6
6
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
sliding with SCALA vs mllib sliding - two implementations, a bit fiddly but here it is:
import org.apache.spark.mllib.rdd.RDDFunctions._
val rdd1 = sc.parallelize(Seq(
( "key1", "value1"),
( "key2", "value2"),
( "key3", "value3"),
( "key4", "value4"),
( "key5", "value5")
))
val rdd2 = rdd1.sliding(2)
val rdd3 = rdd2.map(x => (x(0), x(1)))
val rdd4 = rdd3.map(x => ((x._1._1, x._2._1),x._1._2, x._2._2))
rdd4.collect
also, the following and this is actually better of course... :
val rdd5 = rdd2.map{case Array(x,y) => ((x._1, y._1), x._2, y._2)}
rdd5.collect
returns in both cases:
res70: Array[((String, String), String, String)] = Array(((key1,key2),value1,value2), ((key2,key3),value2,value3), ((key3,key4),value3,value4), ((key4,key5),value4,value5))
which I believe meets your needs, but not in pyspark.
On Stack Overflow you can find statements that pyspark does not have an equivalent for RDDs unless you "roll your own". You could look at this How to transform data with sliding window over time series data in Pyspark. However, I would advise Data Frames with the use of pyspark.sql.functions.lead() and pyspark.sql.functions.lag(). Somewhat easier.
answered Nov 22 '18 at 21:55
thebluephantomthebluephantom
3,37141033
You will need to convert to pyspark.
– thebluephantom
Nov 22 '18 at 22:52
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
sliding with SCALA vs mllib sliding - two implementations, a bit fiddly but here it is:
import org.apache.spark.mllib.rdd.RDDFunctions._
val rdd1 = sc.parallelize(Seq(
( "key1", "value1"),
( "key2", "value2"),
( "key3", "value3"),
( "key4", "value4"),
( "key5", "value5")
))
val rdd2 = rdd1.sliding(2)
val rdd3 = rdd2.map(x => (x(0), x(1)))
val rdd4 = rdd3.map(x => ((x._1._1, x._2._1),x._1._2, x._2._2))
rdd4.collect
also, the following and this is actually better of course... :
val rdd5 = rdd2.map{case Array(x,y) => ((x._1, y._1), x._2, y._2)}
rdd5.collect
returns in both cases:
res70: Array[((String, String), String, String)] = Array(((key1,key2),value1,value2), ((key2,key3),value2,value3), ((key3,key4),value3,value4), ((key4,key5),value4,value5))
which I believe meets your needs, but not in pyspark.
On Stack Overflow you can find statements that pyspark does not have an equivalent for RDDs unless you "roll your own". You could look at this How to transform data with sliding window over time series data in Pyspark. However, I would advise Data Frames with the use of pyspark.sql.functions.lead() and pyspark.sql.functions.lag(). Somewhat easier.
answered Nov 22 '18 at 21:55
thebluephantomthebluephantom
3,37141033
You will need to convert to pyspark.
– thebluephantom
Nov 22 '18 at 22:52
add a comment |
sliding with SCALA vs mllib sliding - two implementations, a bit fiddly but here it is:
import org.apache.spark.mllib.rdd.RDDFunctions._
val rdd1 = sc.parallelize(Seq(
( "key1", "value1"),
( "key2", "value2"),
( "key3", "value3"),
( "key4", "value4"),
( "key5", "value5")
))
val rdd2 = rdd1.sliding(2)
val rdd3 = rdd2.map(x => (x(0), x(1)))
val rdd4 = rdd3.map(x => ((x._1._1, x._2._1),x._1._2, x._2._2))
rdd4.collect
also, the following and this is actually better of course... :
val rdd5 = rdd2.map{case Array(x,y) => ((x._1, y._1), x._2, y._2)}
rdd5.collect
returns in both cases:
res70: Array[((String, String), String, String)] = Array(((key1,key2),value1,value2), ((key2,key3),value2,value3), ((key3,key4),value3,value4), ((key4,key5),value4,value5))
which I believe meets your needs, but not in pyspark.
On Stack Overflow you can find statements that pyspark does not have an equivalent for RDDs unless you "roll your own". You could look at this How to transform data with sliding window over time series data in Pyspark. However, I would advise Data Frames with the use of pyspark.sql.functions.lead() and pyspark.sql.functions.lag(). Somewhat easier.
answered Nov 22 '18 at 21:55
thebluephantomthebluephantom
3,37141033
You will need to convert to pyspark.
– thebluephantom
Nov 22 '18 at 22:52
add a comment |
sliding with SCALA vs mllib sliding - two implementations, a bit fiddly but here it is:
import org.apache.spark.mllib.rdd.RDDFunctions._
val rdd1 = sc.parallelize(Seq(
( "key1", "value1"),
( "key2", "value2"),
( "key3", "value3"),
( "key4", "value4"),
( "key5", "value5")
))
val rdd2 = rdd1.sliding(2)
val rdd3 = rdd2.map(x => (x(0), x(1)))
val rdd4 = rdd3.map(x => ((x._1._1, x._2._1),x._1._2, x._2._2))
rdd4.collect
also, the following and this is actually better of course... :
val rdd5 = rdd2.map{case Array(x,y) => ((x._1, y._1), x._2, y._2)}
rdd5.collect
returns in both cases:
res70: Array[((String, String), String, String)] = Array(((key1,key2),value1,value2), ((key2,key3),value2,value3), ((key3,key4),value3,value4), ((key4,key5),value4,value5))
which I believe meets your needs, but not in pyspark.
On Stack Overflow you can find statements that pyspark does not have an equivalent for RDDs unless you "roll your own". You could look at this How to transform data with sliding window over time series data in Pyspark. However, I would advise Data Frames with the use of pyspark.sql.functions.lead() and pyspark.sql.functions.lag(). Somewhat easier.
answered Nov 22 '18 at 21:55
thebluephantomthebluephantom
3,37141033
sliding with SCALA vs mllib sliding - two implementations, a bit fiddly but here it is:
import org.apache.spark.mllib.rdd.RDDFunctions._
val rdd1 = sc.parallelize(Seq(
( "key1", "value1"),
( "key2", "value2"),
( "key3", "value3"),
( "key4", "value4"),
( "key5", "value5")
))
val rdd2 = rdd1.sliding(2)
val rdd3 = rdd2.map(x => (x(0), x(1)))
val rdd4 = rdd3.map(x => ((x._1._1, x._2._1),x._1._2, x._2._2))
rdd4.collect
also, the following and this is actually better of course... :
val rdd5 = rdd2.map{case Array(x,y) => ((x._1, y._1), x._2, y._2)}
rdd5.collect
returns in both cases:
res70: Array[((String, String), String, String)] = Array(((key1,key2),value1,value2), ((key2,key3),value2,value3), ((key3,key4),value3,value4), ((key4,key5),value4,value5))
which I believe meets your needs, but not in pyspark.
On Stack Overflow you can find statements that pyspark does not have an equivalent for RDDs unless you "roll your own". You could look at this How to transform data with sliding window over time series data in Pyspark. However, I would advise Data Frames with the use of pyspark.sql.functions.lead() and pyspark.sql.functions.lag(). Somewhat easier.
answered Nov 22 '18 at 21:55
thebluephantomthebluephantom
3,37141033
edited Nov 23 '18 at 19:32
answered Nov 22 '18 at 21:55
thebluephantomthebluephantom
3,37141033
answered Nov 22 '18 at 21:55
thebluephantomthebluephantom
3,37141033
answered Nov 22 '18 at 21:55
thebluephantomthebluephantom
3,37141033
3,37141033
You will need to convert to pyspark.
– thebluephantom
Nov 22 '18 at 22:52
add a comment |
You will need to convert to pyspark.
– thebluephantom
Nov 22 '18 at 22:52
You will need to convert to pyspark.
– thebluephantom
Nov 22 '18 at 22:52
You will need to convert to pyspark.
– thebluephantom
Nov 22 '18 at 22:52
add a comment |
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