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author | Reza Zadeh <reza@databricks.com> | 2015-04-06 13:15:01 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2015-04-06 13:15:01 -0700 |
commit | 30363ede8635f2548e444697dbcf60a795b61a84 (patch) | |
tree | b3ee41a5b9dd3dcceec93c89f5db3897cab62d39 | |
parent | 9fe41252198df71f4629843d363db8c83f36440c (diff) | |
download | spark-30363ede8635f2548e444697dbcf60a795b61a84.tar.gz spark-30363ede8635f2548e444697dbcf60a795b61a84.tar.bz2 spark-30363ede8635f2548e444697dbcf60a795b61a84.zip |
[MLlib] [SPARK-6713] Iterators in columnSimilarities for mapPartitionsWithIndex
Use Iterators in columnSimilarities to allow mapPartitionsWithIndex to spill to disk. This could happen in a dense and large column - this way Spark can spill the pairs onto disk instead of building all the pairs before handing them to Spark.
Another PR coming to update documentation.
Author: Reza Zadeh <reza@databricks.com>
Closes #5364 from rezazadeh/optmemsim and squashes the following commits:
47c90ba [Reza Zadeh] Iterators in columnSimilarities for flatMap
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala | 19 |
1 files changed, 9 insertions, 10 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala index 961111507f..9a89a6f3a5 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala @@ -531,7 +531,6 @@ class RowMatrix( val rand = new XORShiftRandom(indx) val scaled = new Array[Double](p.size) iter.flatMap { row => - val buf = new ListBuffer[((Int, Int), Double)]() row match { case SparseVector(size, indices, values) => val nnz = indices.size @@ -540,8 +539,9 @@ class RowMatrix( scaled(k) = values(k) / q(indices(k)) k += 1 } - k = 0 - while (k < nnz) { + + Iterator.tabulate (nnz) { k => + val buf = new ListBuffer[((Int, Int), Double)]() val i = indices(k) val iVal = scaled(k) if (iVal != 0 && rand.nextDouble() < p(i)) { @@ -555,8 +555,8 @@ class RowMatrix( l += 1 } } - k += 1 - } + buf + }.flatten case DenseVector(values) => val n = values.size var i = 0 @@ -564,8 +564,8 @@ class RowMatrix( scaled(i) = values(i) / q(i) i += 1 } - i = 0 - while (i < n) { + Iterator.tabulate (n) { i => + val buf = new ListBuffer[((Int, Int), Double)]() val iVal = scaled(i) if (iVal != 0 && rand.nextDouble() < p(i)) { var j = i + 1 @@ -577,10 +577,9 @@ class RowMatrix( j += 1 } } - i += 1 - } + buf + }.flatten } - buf } }.reduceByKey(_ + _).map { case ((i, j), sim) => MatrixEntry(i.toLong, j.toLong, sim) |