Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/12640#discussion_r61397517
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/linalg/UDTSerializationBenchmark.scala
---
@@ -0,0 +1,70 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.linalg
+
+import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
+import org.apache.spark.util.Benchmark
+
+/**
+ * Serialization benchmark for VectorUDT.
+ */
+object UDTSerializationBenchmark {
+
+ def main(args: Array[String]): Unit = {
+ val iters = 1e2.toInt
+ val numRows = 1e3.toInt
+
+ val encoder = ExpressionEncoder[Vector].defaultBinding
+
+ val vectors = (1 to numRows).map { i =>
+ Vectors.dense(Array.fill(1e5.toInt)(1.0 * i))
+ }.toArray
+ val rows = vectors.map(encoder.toRow)
+
+ val benchmark = new Benchmark("VectorUDT de/serialization", numRows,
iters)
+
+ benchmark.addCase("serialize") { _ =>
+ var sum = 0
+ var i = 0
+ while (i < numRows) {
+ sum += encoder.toRow(vectors(i)).numFields
+ i += 1
+ }
+ }
+
+ benchmark.addCase("deserialize") { _ =>
+ var sum = 0
+ var i = 0
+ while (i < numRows) {
+ sum += encoder.fromRow(rows(i)).numActives
+ i += 1
+ }
+ }
+
+ /*
+ Java HotSpot(TM) 64-Bit Server VM 1.8.0_60-b27 on Mac OS X 10.11.4
+ Intel(R) Core(TM) i7-4960HQ CPU @ 2.60GHz
+
+ VectorUDT de/serialization: Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-------------------------------------------------------------------------------------------
+ serialize 380 / 392 0.0
379730.0 1.0X
+ deserialize 138 / 142 0.0
137816.6 2.8X
+ */
+ benchmark.run()
--- End diff --
result on master:
```
VectorUDT de/serialization: Best/Avg Time(ms) Rate(M/s) Per
Row(ns) Relative
-------------------------------------------------------------------------------------------
serialize 1414 / 1462 0.0
1414104.1 1.0X
deserialize 169 / 178 0.0
169323.7 8.4X
```
The deserialize is much faster now, but the deserialize isn't ,
investigating
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