Github user mateiz commented on the pull request:
https://github.com/apache/spark/pull/136#issuecomment-37749785
For things like n-grams, isn't it okay to do them just per-partition and
not worry about doing stuff across partitions? I agree that both this approach
and the one in https
Github user mridulm commented on the pull request:
https://github.com/apache/spark/pull/136#issuecomment-37748284
@pwendell I was referring not to the actual implementation, but expectation
when using the exposed API.
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Github user pwendell commented on the pull request:
https://github.com/apache/spark/pull/136#issuecomment-37742731
@mridulm I think the RDD definition is actually `private[spark]` and it's
just intended to be used internally for higher level algorithms.
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Github user mridulm commented on the pull request:
https://github.com/apache/spark/pull/136#issuecomment-37736435
To a step back, given how niche this seems to be and how it violates the
"usual" expectations of how our users use spark (lazy execution, etc as
mentioned above) - d
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/136#issuecomment-37735835
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Github user AmplabJenkins commented on the pull request:
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Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/136#issuecomment-37734634
It is hard to say what threshold to use. I couldn't think of a use case
that requires a large window size, but I cannot say there is none.
Another possible approach
Github user AmplabJenkins commented on the pull request:
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Github user pwendell commented on the pull request:
https://github.com/apache/spark/pull/136#issuecomment-37733908
Even if it's private we can end up with cases where users have a e.g.
10,000 partition RDD with only a few items in each partition. Do we know a
priori when calling this
Github user pwendell commented on the pull request:
https://github.com/apache/spark/pull/136#issuecomment-37733845
Ah I see - so this isn't going to be externally a user-visible class (I
didn't notice it was `private[spark]`)? Would it make sense to throw an
assertion error if the sli
Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/136#issuecomment-37732906
@pwendell , the limit case is not a practical example. In that case, we
need re-partition for most operations to be efficient. Also, this is really for
small window sizes l
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/136#discussion_r10635646
--- Diff: core/src/main/scala/org/apache/spark/rdd/SlidedRDD.scala ---
@@ -0,0 +1,102 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under o
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/136#discussion_r10635644
--- Diff: core/src/main/scala/org/apache/spark/rdd/SlidedRDD.scala ---
@@ -0,0 +1,102 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under o
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Github user pwendell commented on the pull request:
https://github.com/apache/spark/pull/136#issuecomment-37732233
I don't think we typically run jobs inside of getPartitions - so this
changes some semantics of calling that function. For instance a lot of the
other RDD constructors im
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/136#discussion_r10635557
--- Diff: core/src/main/scala/org/apache/spark/rdd/SlidedRDD.scala ---
@@ -0,0 +1,102 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under o
Github user pwendell commented on a diff in the pull request:
https://github.com/apache/spark/pull/136#discussion_r10635447
--- Diff: core/src/main/scala/org/apache/spark/rdd/SlidedRDD.scala ---
@@ -0,0 +1,102 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under
Github user pwendell commented on a diff in the pull request:
https://github.com/apache/spark/pull/136#discussion_r10635444
--- Diff: core/src/main/scala/org/apache/spark/rdd/SlidedRDD.scala ---
@@ -0,0 +1,102 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under
Github user pwendell commented on a diff in the pull request:
https://github.com/apache/spark/pull/136#discussion_r10635413
--- Diff: core/src/main/scala/org/apache/spark/rdd/SlidedRDD.scala ---
@@ -0,0 +1,102 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under
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GitHub user mengxr opened a pull request:
https://github.com/apache/spark/pull/136
[SPARK-1241] Add sliding to RDD
Sliding is useful for operations like creating n-grams, calculating total
variation, numerical integration, etc. This is similar to
https://github.com/apache/incubator
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