Say I have two RDDs with the following values
x = [(1, 3), (2, 4)]
and
y = [(3, 5), (4, 7)]
and I want to have
z = [(1, 3), (2, 4), (3, 5), (4, 7)]
How can I achieve this. I know you can use outerJoin followed by map to
achieve this, but is there a more direct way for this.
As it is difficult to explain this, I would show what I want. Lets us say,
I have an RDD A with the following value
A = [word1, word2, word3]
I want to have an RDD with the following value
B = [(1, word1), (2, word2), (3, word3)]
That is, it gives a unique number to each entry as a key value.
So let us say I have RDDs A and B with the following values.
A = [ (1, 2), (2, 4), (3, 6) ]
B = [ (1, 3), (2, 5), (3, 6), (4, 5), (5, 6) ]
I want to apply an inner join, such that I get the following as a result.
C = [ (1, (2, 3)), (2, (4, 5)), (3, (6,6)) ]
That is, those keys which are not
Let us say I have the following two RDDs, with the following key-pair
values.
rdd1 = [ (key1, [value1, value2]), (key2, [value3, value4]) ]
and
rdd2 = [ (key1, [value5, value6]), (key2, [value7]) ]
Now, I want to join them by key values, so for example I want to return the
following
I am a newbie to spark, and I program in Python. I use textFile function to
make an RDD from a file. I notice that the default limiter is newline.
However I want to change this default limiter to something else. After
searching the web, I came to know about textinputformat.record.delimiter
I am a Spark newbie and I use python (pyspark). I am trying to run a
program on a 64 core system, but no matter what I do, it always uses 1
core. It doesn't matter if I run it using spark-submit --master local[64]
run.sh or I call x.repartition(64) in my code with an RDD, the spark
program always
