Get HashMap from RDD












-2















I have a requirement to get a global HashMap from RDD[HashMap]. For example, the RDD is RDD[HashMap[Key, value]]. I want to get a global HashMap from this so that I can use this HashMap for enriching messages present in other RDD.



Could anyone please help me how to do this.



Thanks






scala apache-spark







share|improve this question


















  • 2





    Does the RDD have only one element?, or do you want to merge all maps in one (which strategy do you want for merging)?, or is it a RDD of tuples (Key -> Value)?

    – Luis Miguel Mejía Suárez
    Nov 13 '18 at 0:48
















-2















I have a requirement to get a global HashMap from RDD[HashMap]. For example, the RDD is RDD[HashMap[Key, value]]. I want to get a global HashMap from this so that I can use this HashMap for enriching messages present in other RDD.



Could anyone please help me how to do this.



Thanks






scala apache-spark







share|improve this question


















  • 2





    Does the RDD have only one element?, or do you want to merge all maps in one (which strategy do you want for merging)?, or is it a RDD of tuples (Key -> Value)?

    – Luis Miguel Mejía Suárez
    Nov 13 '18 at 0:48














-2












-2








-2








I have a requirement to get a global HashMap from RDD[HashMap]. For example, the RDD is RDD[HashMap[Key, value]]. I want to get a global HashMap from this so that I can use this HashMap for enriching messages present in other RDD.



Could anyone please help me how to do this.



Thanks






scala apache-spark







share|improve this question














I have a requirement to get a global HashMap from RDD[HashMap]. For example, the RDD is RDD[HashMap[Key, value]]. I want to get a global HashMap from this so that I can use this HashMap for enriching messages present in other RDD.



Could anyone please help me how to do this.



Thanks






scala apache-spark




scala apache-spark






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Nov 13 '18 at 0:30









IndiraIndira

215




215








  • 2





    Does the RDD have only one element?, or do you want to merge all maps in one (which strategy do you want for merging)?, or is it a RDD of tuples (Key -> Value)?

    – Luis Miguel Mejía Suárez
    Nov 13 '18 at 0:48














  • 2





    Does the RDD have only one element?, or do you want to merge all maps in one (which strategy do you want for merging)?, or is it a RDD of tuples (Key -> Value)?

    – Luis Miguel Mejía Suárez
    Nov 13 '18 at 0:48








2




2





Does the RDD have only one element?, or do you want to merge all maps in one (which strategy do you want for merging)?, or is it a RDD of tuples (Key -> Value)?

– Luis Miguel Mejía Suárez
Nov 13 '18 at 0:48





Does the RDD have only one element?, or do you want to merge all maps in one (which strategy do you want for merging)?, or is it a RDD of tuples (Key -> Value)?

– Luis Miguel Mejía Suárez
Nov 13 '18 at 0:48












1 Answer
1






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oldest

votes


















0














So like the comment says, you'll need a merge function. Assuming a simple hashmap merge works for you such as if the keys/values are unique, then you can merge it into a local map using something as simple as rdd.reduce(_++_). Then you'll want to broadcast it so that its efficiently sent to each executor once. Once it's in the broadcast variable then this can be used within your RDD operations on other RDDs such as enriching messages as you said.



val brodcast = sparkContext.broadcast( rdd.reduce(_++_) )


And this can be used by using broadcast.value






share|improve this answer
























  • Thanks. It worked

    – Indira
    Nov 13 '18 at 9:12











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1 Answer
1






active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes









0














So like the comment says, you'll need a merge function. Assuming a simple hashmap merge works for you such as if the keys/values are unique, then you can merge it into a local map using something as simple as rdd.reduce(_++_). Then you'll want to broadcast it so that its efficiently sent to each executor once. Once it's in the broadcast variable then this can be used within your RDD operations on other RDDs such as enriching messages as you said.



val brodcast = sparkContext.broadcast( rdd.reduce(_++_) )


And this can be used by using broadcast.value






share|improve this answer
























  • Thanks. It worked

    – Indira
    Nov 13 '18 at 9:12
















0














So like the comment says, you'll need a merge function. Assuming a simple hashmap merge works for you such as if the keys/values are unique, then you can merge it into a local map using something as simple as rdd.reduce(_++_). Then you'll want to broadcast it so that its efficiently sent to each executor once. Once it's in the broadcast variable then this can be used within your RDD operations on other RDDs such as enriching messages as you said.



val brodcast = sparkContext.broadcast( rdd.reduce(_++_) )


And this can be used by using broadcast.value






share|improve this answer
























  • Thanks. It worked

    – Indira
    Nov 13 '18 at 9:12














0












0








0







So like the comment says, you'll need a merge function. Assuming a simple hashmap merge works for you such as if the keys/values are unique, then you can merge it into a local map using something as simple as rdd.reduce(_++_). Then you'll want to broadcast it so that its efficiently sent to each executor once. Once it's in the broadcast variable then this can be used within your RDD operations on other RDDs such as enriching messages as you said.



val brodcast = sparkContext.broadcast( rdd.reduce(_++_) )


And this can be used by using broadcast.value






share|improve this answer













So like the comment says, you'll need a merge function. Assuming a simple hashmap merge works for you such as if the keys/values are unique, then you can merge it into a local map using something as simple as rdd.reduce(_++_). Then you'll want to broadcast it so that its efficiently sent to each executor once. Once it's in the broadcast variable then this can be used within your RDD operations on other RDDs such as enriching messages as you said.



val brodcast = sparkContext.broadcast( rdd.reduce(_++_) )


And this can be used by using broadcast.value







share|improve this answer












share|improve this answer



share|improve this answer










answered Nov 13 '18 at 8:26









user1084563user1084563

1,7401324




1,7401324













  • Thanks. It worked

    – Indira
    Nov 13 '18 at 9:12



















  • Thanks. It worked

    – Indira
    Nov 13 '18 at 9:12

















Thanks. It worked

– Indira
Nov 13 '18 at 9:12





Thanks. It worked

– Indira
Nov 13 '18 at 9:12


















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