[hail] Better scaling on RVD.union #6943
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Do a tree reduce instead of a linear reduce. This means that the java stack depth is log2(N) instead of N, and prevents stack overflow errors when unioning hundreds of tables together.
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I'm confused by the stack depth problem. def reduceLeft[B >: A](op: (B, A) => B): B = {
if (isEmpty)
throw new UnsupportedOperationException("empty.reduceLeft")
var first = true
var acc: B = 0.asInstanceOf[B]
for (x <- self) {
if (first) {
acc = x
first = false
}
else acc = op(acc, x)
}
acc
} |
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The problem is that in the ordered merge usage, the spark DAG builds up a stack of 200 RDDs / iterators. |
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Ah, right, that stack. |
patrick-schultz
approved these changes
Aug 26, 2019
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Do a tree reduce instead of a linear reduce. This means that the java
stack depth is log2(N) instead of N, and prevents stack overflow errors
when unioning hundreds of tables together.