[SPARK-2443][SQL] Fix slow read from partitioned tables

This fix obtains a comparable performance boost as [PR #1390](#1390) by moving an array update and deserializer initialization out of a potentially very long loop. Suggested by yhuai. The below results are updated for this fix.

## Benchmarks
Generated a local text file with 10M rows of simple key-value pairs. The data is loaded as a table through Hive. Results are obtained on my local machine using hive/console.

Without the fix:

Type | Non-partitioned | Partitioned (1 part)
------------ | ------------ | -------------
First run | 9.52s end-to-end (1.64s Spark job) | 36.6s (28.3s)
Stablized runs | 1.21s (1.18s) | 27.6s (27.5s)

With this fix:

Type | Non-partitioned | Partitioned (1 part)
------------ | ------------ | -------------
First run | 9.57s (1.46s) | 11.0s (1.69s)
Stablized runs | 1.13s (1.10s) | 1.23s (1.19s)

Author: Zongheng Yang <[email protected]>

Closes #1408 from concretevitamin/slow-read-2 and squashes the following commits:

d86e437 [Zongheng Yang] Move update & initialization out of potentially long loop.

(cherry picked from commit d60b09b)
Signed-off-by: Michael Armbrust <[email protected]>

Author: concretevitamin

sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala

@@ -164,13 +164,17 @@ class HadoopTableReader(@transient _tableDesc: TableDesc, @transient sc: HiveCon
       hivePartitionRDD.mapPartitions { iter =>
         val hconf = broadcastedHiveConf.value.value
         val rowWithPartArr = new Array[Object](2)
+
+        // The update and deserializer initialization are intentionally
+        // kept out of the below iter.map loop to save performance.
+        rowWithPartArr.update(1, partValues)
+        val deserializer = localDeserializer.newInstance()
+        deserializer.initialize(hconf, partProps)
+
         // Map each tuple to a row object
         iter.map { value =>
-          val deserializer = localDeserializer.newInstance()
-          deserializer.initialize(hconf, partProps)
           val deserializedRow = deserializer.deserialize(value)
           rowWithPartArr.update(0, deserializedRow)
-          rowWithPartArr.update(1, partValues)
           rowWithPartArr.asInstanceOf[Object]
         }
       }