`DataFrame.collect()` calls `SparkPlan.executeCollect()`, which consists of a single line:
```scala
execute().map(ScalaReflection.convertRowToScala(_, schema)).collect()
```
The problem is that, `QueryPlan.schema` is a function. And since 1.3.0, `convertRowToScala` starts returning a `GenericRowWithSchema`. Thus, every `GenericRowWithSchema` instance holds a separate copy of the schema object. Also, YJP profiling result of the following simple micro benchmark (executed in Spark shell) shows that constructing the schema object takes up to ~35% CPU time.
```scala
sc.parallelize(1 to 10000000).
map(i => (i, s"val_$i")).
toDF("key", "value").
saveAsParquetFile("file:///tmp/src.parquet")
// Profiling started from this line
sqlContext.parquetFile("file:///tmp/src.parquet").collect()
```
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Author: Cheng Lian <lian@databricks.com>
Closes#5398 from liancheng/spark-6748 and squashes the following commits:
3159469 [Cheng Lian] Makes QueryPlan.schema a lazy val
77bcceb9f0
