Joining by values
I have a two pair RDDs in spark like this rdd1 = (1 - [4,5,6,7]) (2 - [4,5]) (3 - [6,7]) rdd2 = (4 - [1001,1000,1002,1003]) (5 - [1004,1001,1006,1007]) (6 - [1007,1009,1005,1008]) (7 - [1011,1012,1013,1010]) I would like to combine them to look like this. joinedRdd = (1 - [1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013]) (2 - [1000,1001,1002,1003,1004,1006,1007]) (3 - [1005,1007,1008,1009,1010,1011,1012,1013]) Can someone suggest me how to do this. Thanks Dilip -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Joining-by-values-tp20954.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
Re: Joining by values
This is my design. Now let me try and code it in Spark. rdd1.txt =1~4,5,6,72~4,53~6,7 rdd2.txt 4~1001,1000,1002,10035~1004,1001,1006,10076~1007,1009,1005,10087~1011,1012,1013,1010 TRANSFORM 1===map each value to key (like an inverted index)4~15~16~17~15~24~26~37~3 TRANSFORM 2===Join keys in transform 1 and rdd24~1,1001,1000,1002,10034~2,1001,1000,1002,10035~1,1004,1001,1006,10075~2,1004,1001,1006,10076~1,1007,1009,1005,10086~3,1007,1009,1005,10087~1,1011,1012,1013,10107~3,1011,1012,1013,1010 TRANSFORM 3===Split key in transform 2 with ~ and keep key(1) i.e. 1,2,31~1001,1000,1002,10032~1001,1000,1002,10031~1004,1001,1006,10072~1004,1001,1006,10071~1007,1009,1005,10083~1007,1009,1005,10081~1011,1012,1013,10103~1011,1012,1013,1010 TRANSFORM 4===join by key 1~1001,1000,1002,1003,1004,1001,1006,1007,1007,1009,1005,1008,1011,1012,1013,10102~1001,1000,1002,1003,1004,1001,1006,10073~1007,1009,1005,1008,1011,1012,1013,1010 From: dcmovva dilip.mo...@gmail.com To: user@spark.apache.org Sent: Saturday, January 3, 2015 10:10 AM Subject: Joining by values I have a two pair RDDs in spark like this rdd1 = (1 - [4,5,6,7]) (2 - [4,5]) (3 - [6,7]) rdd2 = (4 - [1001,1000,1002,1003]) (5 - [1004,1001,1006,1007]) (6 - [1007,1009,1005,1008]) (7 - [1011,1012,1013,1010]) I would like to combine them to look like this. joinedRdd = (1 - [1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013]) (2 - [1000,1001,1002,1003,1004,1006,1007]) (3 - [1005,1007,1008,1009,1010,1011,1012,1013]) Can someone suggest me how to do this. Thanks Dilip -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Joining-by-values-tp20954.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
Re: Joining by values
hi Take a look at the code here I
wrotehttps://raw.githubusercontent.com/sanjaysubramanian/msfx_scala/master/src/main/scala/org/medicalsidefx/common/utils/PairRddJoin.scala
/*rdd1.txt
1~4,5,6,7
2~4,5
3~6,7
rdd2.txt
4~1001,1000,1002,1003
5~1004,1001,1006,1007
6~1007,1009,1005,1008
7~1011,1012,1013,1010
*/
val sconf = new
SparkConf().setMaster(local).setAppName(MedicalSideFx-PairRddJoin)
val sc = new SparkContext(sconf)
val rdd1 = /path/to/rdd1.txt
val rdd2 = /path/to/rdd2.txt
val rdd1InvIndex = sc.textFile(rdd1).map(x = (x.split('~')(0),
x.split('~')(1))).flatMapValues(str = str.split(',')).map(str = (str._2,
str._1))
val rdd2Pair = sc.textFile(rdd2).map(str = (str.split('~')(0),
str.split('~')(1)))
rdd1InvIndex.join(rdd2Pair).map(str =
str._2).groupByKey().collect().foreach(println)
This outputs the following . I think this may be essentially what u r looking
for(I have to understand how to NOT print as
CompactBuffer)(2,CompactBuffer(1001,1000,1002,1003, 1004,1001,1006,1007))
(3,CompactBuffer(1011,1012,1013,1010, 1007,1009,1005,1008))
(1,CompactBuffer(1001,1000,1002,1003, 1011,1012,1013,1010, 1004,1001,1006,1007,
1007,1009,1005,1008))
From: Sanjay Subramanian sanjaysubraman...@yahoo.com.INVALID
To: dcmovva dilip.mo...@gmail.com; user@spark.apache.org
user@spark.apache.org
Sent: Saturday, January 3, 2015 12:19 PM
Subject: Re: Joining by values
This is my design. Now let me try and code it in Spark.
rdd1.txt =1~4,5,6,72~4,53~6,7
rdd2.txt
4~1001,1000,1002,10035~1004,1001,1006,10076~1007,1009,1005,10087~1011,1012,1013,1010
TRANSFORM 1===map each value to key (like an inverted
index)4~15~16~17~15~24~26~37~3
TRANSFORM 2===Join keys in transform 1 and
rdd24~1,1001,1000,1002,10034~2,1001,1000,1002,10035~1,1004,1001,1006,10075~2,1004,1001,1006,10076~1,1007,1009,1005,10086~3,1007,1009,1005,10087~1,1011,1012,1013,10107~3,1011,1012,1013,1010
TRANSFORM 3===Split key in transform 2 with ~ and keep key(1) i.e.
1,2,31~1001,1000,1002,10032~1001,1000,1002,10031~1004,1001,1006,10072~1004,1001,1006,10071~1007,1009,1005,10083~1007,1009,1005,10081~1011,1012,1013,10103~1011,1012,1013,1010
TRANSFORM 4===join by key
1~1001,1000,1002,1003,1004,1001,1006,1007,1007,1009,1005,1008,1011,1012,1013,10102~1001,1000,1002,1003,1004,1001,1006,10073~1007,1009,1005,1008,1011,1012,1013,1010
From: dcmovva dilip.mo...@gmail.com
To: user@spark.apache.org
Sent: Saturday, January 3, 2015 10:10 AM
Subject: Joining by values
I have a two pair RDDs in spark like this
rdd1 = (1 - [4,5,6,7])
(2 - [4,5])
(3 - [6,7])
rdd2 = (4 - [1001,1000,1002,1003])
(5 - [1004,1001,1006,1007])
(6 - [1007,1009,1005,1008])
(7 - [1011,1012,1013,1010])
I would like to combine them to look like this.
joinedRdd = (1 -
[1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013])
(2 - [1000,1001,1002,1003,1004,1006,1007])
(3 - [1005,1007,1008,1009,1010,1011,1012,1013])
Can someone suggest me how to do this.
Thanks Dilip
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Re: Joining by values
call `map(_.toList)` to convert `CompactBuffer` to `List`
Best Regards,
Shixiong Zhu
2015-01-04 12:08 GMT+08:00 Sanjay Subramanian
sanjaysubraman...@yahoo.com.invalid:
hi
Take a look at the code here I wrote
https://raw.githubusercontent.com/sanjaysubramanian/msfx_scala/master/src/main/scala/org/medicalsidefx/common/utils/PairRddJoin.scala
/*rdd1.txt
1~4,5,6,7
2~4,5
3~6,7
rdd2.txt
4~1001,1000,1002,1003
5~1004,1001,1006,1007
6~1007,1009,1005,1008
7~1011,1012,1013,1010
*/
val sconf = new
SparkConf().setMaster(local).setAppName(MedicalSideFx-PairRddJoin)
val sc = new SparkContext(sconf)
val rdd1 = /path/to/rdd1.txt
val rdd2 = /path/to/rdd2.txt
val rdd1InvIndex = sc.textFile(rdd1).map(x = (x.split('~')(0),
x.split('~')(1))).flatMapValues(str = str.split(',')).map(str = (str._2,
str._1))
val rdd2Pair = sc.textFile(rdd2).map(str = (str.split('~')(0),
str.split('~')(1)))
rdd1InvIndex.join(rdd2Pair).map(str =
str._2).groupByKey().collect().foreach(println)
This outputs the following . I think this may be essentially what u r looking
for
(I have to understand how to NOT print as CompactBuffer)
(2,CompactBuffer(1001,1000,1002,1003, 1004,1001,1006,1007))
(3,CompactBuffer(1011,1012,1013,1010, 1007,1009,1005,1008))
(1,CompactBuffer(1001,1000,1002,1003, 1011,1012,1013,1010,
1004,1001,1006,1007, 1007,1009,1005,1008))
--
*From:* Sanjay Subramanian sanjaysubraman...@yahoo.com.INVALID
*To:* dcmovva dilip.mo...@gmail.com; user@spark.apache.org
user@spark.apache.org
*Sent:* Saturday, January 3, 2015 12:19 PM
*Subject:* Re: Joining by values
This is my design. Now let me try and code it in Spark.
rdd1.txt
=
1~4,5,6,7
2~4,5
3~6,7
rdd2.txt
4~1001,1000,1002,1003
5~1004,1001,1006,1007
6~1007,1009,1005,1008
7~1011,1012,1013,1010
TRANSFORM 1
===
map each value to key (like an inverted index)
4~1
5~1
6~1
7~1
5~2
4~2
6~3
7~3
TRANSFORM 2
===
Join keys in transform 1 and rdd2
4~1,1001,1000,1002,1003
4~2,1001,1000,1002,1003
5~1,1004,1001,1006,1007
5~2,1004,1001,1006,1007
6~1,1007,1009,1005,1008
6~3,1007,1009,1005,1008
7~1,1011,1012,1013,1010
7~3,1011,1012,1013,1010
TRANSFORM 3
===
Split key in transform 2 with ~ and keep key(1) i.e. 1,2,3
1~1001,1000,1002,1003
2~1001,1000,1002,1003
1~1004,1001,1006,1007
2~1004,1001,1006,1007
1~1007,1009,1005,1008
3~1007,1009,1005,1008
1~1011,1012,1013,1010
3~1011,1012,1013,1010
TRANSFORM 4
===
join by key
1~1001,1000,1002,1003,1004,1001,1006,1007,1007,1009,1005,1008,1011,1012,1013,1010
2~1001,1000,1002,1003,1004,1001,1006,1007
3~1007,1009,1005,1008,1011,1012,1013,1010
--
*From:* dcmovva dilip.mo...@gmail.com
*To:* user@spark.apache.org
*Sent:* Saturday, January 3, 2015 10:10 AM
*Subject:* Joining by values
I have a two pair RDDs in spark like this
rdd1 = (1 - [4,5,6,7])
(2 - [4,5])
(3 - [6,7])
rdd2 = (4 - [1001,1000,1002,1003])
(5 - [1004,1001,1006,1007])
(6 - [1007,1009,1005,1008])
(7 - [1011,1012,1013,1010])
I would like to combine them to look like this.
joinedRdd = (1 -
[1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013])
(2 - [1000,1001,1002,1003,1004,1006,1007])
(3 - [1005,1007,1008,1009,1010,1011,1012,1013])
Can someone suggest me how to do this.
Thanks Dilip
--
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-
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Re: Joining by values
so I changed the code tordd1InvIndex.join(rdd2Pair).map(str =
str._2).groupByKey().map(str =
(str._1,str._2.toList)).collect().foreach(println)
Now it prints. Don't worry I will work on this to not output as List(...) But I
am hoping that the JOIN question that @Dilip asked is hopefully answered :-)
(2,List(1001,1000,1002,1003, 1004,1001,1006,1007))(3,List(1011,1012,1013,1010,
1007,1009,1005,1008))(1,List(1001,1000,1002,1003, 1011,1012,1013,1010,
1004,1001,1006,1007, 1007,1009,1005,1008))
From: Shixiong Zhu zsxw...@gmail.com
To: Sanjay Subramanian sanjaysubraman...@yahoo.com
Cc: dcmovva dilip.mo...@gmail.com; user@spark.apache.org
user@spark.apache.org
Sent: Saturday, January 3, 2015 8:15 PM
Subject: Re: Joining by values
call `map(_.toList)` to convert `CompactBuffer` to `List`
Best Regards,Shixiong Zhu
2015-01-04 12:08 GMT+08:00 Sanjay Subramanian
sanjaysubraman...@yahoo.com.invalid:
hi Take a look at the code here I
wrotehttps://raw.githubusercontent.com/sanjaysubramanian/msfx_scala/master/src/main/scala/org/medicalsidefx/common/utils/PairRddJoin.scala
/*rdd1.txt
1~4,5,6,7
2~4,5
3~6,7
rdd2.txt
4~1001,1000,1002,1003
5~1004,1001,1006,1007
6~1007,1009,1005,1008
7~1011,1012,1013,1010
*/
val sconf = new
SparkConf().setMaster(local).setAppName(MedicalSideFx-PairRddJoin)
val sc = new SparkContext(sconf)
val rdd1 = /path/to/rdd1.txt
val rdd2 = /path/to/rdd2.txt
val rdd1InvIndex = sc.textFile(rdd1).map(x = (x.split('~')(0),
x.split('~')(1))).flatMapValues(str = str.split(',')).map(str = (str._2,
str._1))
val rdd2Pair = sc.textFile(rdd2).map(str = (str.split('~')(0),
str.split('~')(1)))
rdd1InvIndex.join(rdd2Pair).map(str =
str._2).groupByKey().collect().foreach(println)
This outputs the following . I think this may be essentially what u r looking
for(I have to understand how to NOT print as
CompactBuffer)(2,CompactBuffer(1001,1000,1002,1003, 1004,1001,1006,1007))
(3,CompactBuffer(1011,1012,1013,1010, 1007,1009,1005,1008))
(1,CompactBuffer(1001,1000,1002,1003, 1011,1012,1013,1010, 1004,1001,1006,1007,
1007,1009,1005,1008))
From: Sanjay Subramanian sanjaysubraman...@yahoo.com.INVALID
To: dcmovva dilip.mo...@gmail.com; user@spark.apache.org
user@spark.apache.org
Sent: Saturday, January 3, 2015 12:19 PM
Subject: Re: Joining by values
This is my design. Now let me try and code it in Spark.
rdd1.txt =1~4,5,6,72~4,53~6,7
rdd2.txt
4~1001,1000,1002,10035~1004,1001,1006,10076~1007,1009,1005,10087~1011,1012,1013,1010
TRANSFORM 1===map each value to key (like an inverted
index)4~15~16~17~15~24~26~37~3
TRANSFORM 2===Join keys in transform 1 and
rdd24~1,1001,1000,1002,10034~2,1001,1000,1002,10035~1,1004,1001,1006,10075~2,1004,1001,1006,10076~1,1007,1009,1005,10086~3,1007,1009,1005,10087~1,1011,1012,1013,10107~3,1011,1012,1013,1010
TRANSFORM 3===Split key in transform 2 with ~ and keep key(1) i.e.
1,2,31~1001,1000,1002,10032~1001,1000,1002,10031~1004,1001,1006,10072~1004,1001,1006,10071~1007,1009,1005,10083~1007,1009,1005,10081~1011,1012,1013,10103~1011,1012,1013,1010
TRANSFORM 4===join by key
1~1001,1000,1002,1003,1004,1001,1006,1007,1007,1009,1005,1008,1011,1012,1013,10102~1001,1000,1002,1003,1004,1001,1006,10073~1007,1009,1005,1008,1011,1012,1013,1010
From: dcmovva dilip.mo...@gmail.com
To: user@spark.apache.org
Sent: Saturday, January 3, 2015 10:10 AM
Subject: Joining by values
I have a two pair RDDs in spark like this
rdd1 = (1 - [4,5,6,7])
(2 - [4,5])
(3 - [6,7])
rdd2 = (4 - [1001,1000,1002,1003])
(5 - [1004,1001,1006,1007])
(6 - [1007,1009,1005,1008])
(7 - [1011,1012,1013,1010])
I would like to combine them to look like this.
joinedRdd = (1 -
[1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013])
(2 - [1000,1001,1002,1003,1004,1006,1007])
(3 - [1005,1007,1008,1009,1010,1011,1012,1013])
Can someone suggest me how to do this.
Thanks Dilip
--
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-
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Re: Joining by values
Thanks Sanjay. I will give it a try.
Thanks
Dilip
On Sat, Jan 3, 2015 at 11:25 PM, Sanjay Subramanian
sanjaysubraman...@yahoo.com wrote:
so I changed the code to
rdd1InvIndex.join(rdd2Pair).map(str = str._2).groupByKey().map(str =
(str._1,str._2.toList)).collect().foreach(println)
Now it prints. Don't worry I will work on this to not output as List(...)
But I am hoping that the JOIN question that @Dilip asked is hopefully
answered :-)
(2,List(1001,1000,1002,1003, 1004,1001,1006,1007))
(3,List(1011,1012,1013,1010, 1007,1009,1005,1008))
(1,List(1001,1000,1002,1003, 1011,1012,1013,1010, 1004,1001,1006,1007,
1007,1009,1005,1008))
--
*From:* Shixiong Zhu zsxw...@gmail.com
*To:* Sanjay Subramanian sanjaysubraman...@yahoo.com
*Cc:* dcmovva dilip.mo...@gmail.com; user@spark.apache.org
user@spark.apache.org
*Sent:* Saturday, January 3, 2015 8:15 PM
*Subject:* Re: Joining by values
call `map(_.toList)` to convert `CompactBuffer` to `List`
Best Regards,
Shixiong Zhu
2015-01-04 12:08 GMT+08:00 Sanjay Subramanian
sanjaysubraman...@yahoo.com.invalid:
hi
Take a look at the code here I wrote
https://raw.githubusercontent.com/sanjaysubramanian/msfx_scala/master/src/main/scala/org/medicalsidefx/common/utils/PairRddJoin.scala
/*rdd1.txt
1~4,5,6,7
2~4,5
3~6,7
rdd2.txt
4~1001,1000,1002,1003
5~1004,1001,1006,1007
6~1007,1009,1005,1008
7~1011,1012,1013,1010
*/
val sconf = new
SparkConf().setMaster(local).setAppName(MedicalSideFx-PairRddJoin)
val sc = new SparkContext(sconf)
val rdd1 = /path/to/rdd1.txt
val rdd2 = /path/to/rdd2.txt
val rdd1InvIndex = sc.textFile(rdd1).map(x = (x.split('~')(0),
x.split('~')(1))).flatMapValues(str = str.split(',')).map(str = (str._2,
str._1))
val rdd2Pair = sc.textFile(rdd2).map(str = (str.split('~')(0),
str.split('~')(1)))
rdd1InvIndex.join(rdd2Pair).map(str =
str._2).groupByKey().collect().foreach(println)
This outputs the following . I think this may be essentially what u r looking
for
(I have to understand how to NOT print as CompactBuffer)
(2,CompactBuffer(1001,1000,1002,1003, 1004,1001,1006,1007))
(3,CompactBuffer(1011,1012,1013,1010, 1007,1009,1005,1008))
(1,CompactBuffer(1001,1000,1002,1003, 1011,1012,1013,1010,
1004,1001,1006,1007, 1007,1009,1005,1008))
--
*From:* Sanjay Subramanian sanjaysubraman...@yahoo.com.INVALID
*To:* dcmovva dilip.mo...@gmail.com; user@spark.apache.org
user@spark.apache.org
*Sent:* Saturday, January 3, 2015 12:19 PM
*Subject:* Re: Joining by values
This is my design. Now let me try and code it in Spark.
rdd1.txt
=
1~4,5,6,7
2~4,5
3~6,7
rdd2.txt
4~1001,1000,1002,1003
5~1004,1001,1006,1007
6~1007,1009,1005,1008
7~1011,1012,1013,1010
TRANSFORM 1
===
map each value to key (like an inverted index)
4~1
5~1
6~1
7~1
5~2
4~2
6~3
7~3
TRANSFORM 2
===
Join keys in transform 1 and rdd2
4~1,1001,1000,1002,1003
4~2,1001,1000,1002,1003
5~1,1004,1001,1006,1007
5~2,1004,1001,1006,1007
6~1,1007,1009,1005,1008
6~3,1007,1009,1005,1008
7~1,1011,1012,1013,1010
7~3,1011,1012,1013,1010
TRANSFORM 3
===
Split key in transform 2 with ~ and keep key(1) i.e. 1,2,3
1~1001,1000,1002,1003
2~1001,1000,1002,1003
1~1004,1001,1006,1007
2~1004,1001,1006,1007
1~1007,1009,1005,1008
3~1007,1009,1005,1008
1~1011,1012,1013,1010
3~1011,1012,1013,1010
TRANSFORM 4
===
join by key
1~1001,1000,1002,1003,1004,1001,1006,1007,1007,1009,1005,1008,1011,1012,1013,1010
2~1001,1000,1002,1003,1004,1001,1006,1007
3~1007,1009,1005,1008,1011,1012,1013,1010
--
*From:* dcmovva dilip.mo...@gmail.com
*To:* user@spark.apache.org
*Sent:* Saturday, January 3, 2015 10:10 AM
*Subject:* Joining by values
I have a two pair RDDs in spark like this
rdd1 = (1 - [4,5,6,7])
(2 - [4,5])
(3 - [6,7])
rdd2 = (4 - [1001,1000,1002,1003])
(5 - [1004,1001,1006,1007])
(6 - [1007,1009,1005,1008])
(7 - [1011,1012,1013,1010])
I would like to combine them to look like this.
joinedRdd = (1 -
[1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013])
(2 - [1000,1001,1002,1003,1004,1006,1007])
(3 - [1005,1007,1008,1009,1010,1011,1012,1013])
Can someone suggest me how to do this.
Thanks Dilip
--
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-
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