bersprockets commented on a change in pull request #23336: [SPARK-26378][SQL]
Restore performance of queries against wide CSV tables
URL: https://github.com/apache/spark/pull/23336#discussion_r242384016
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File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/FailureSafeParser.scala
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@@ -40,14 +40,20 @@ class FailureSafeParser[IN](
// set the bad record to this field, and set other fields according to the
partial result or null.
private val toResultRow: (Option[InternalRow], () => UTF8String) =>
InternalRow = {
(row, badRecord) => {
- var i = 0
- while (i < actualSchema.length) {
- val from = actualSchema(i)
- resultRow(schema.fieldIndex(from.name)) = row.map(_.get(i,
from.dataType)).orNull
- i += 1
+ // save the value. Some implementations of badRecord do not like to be
called twice
+ val badRec = badRecord()
Review comment:
Actually, with the fix for
[SPARK-26372](https://issues.apache.org/jira/browse/SPARK-26372), which fills
in null for bad or missing fields, I wonder if we need to rewrite the row at
all for bad records if schema == actualSchema.
There's probably an obvious reason we need to rewrite that I've forgotten,
but I will investigate if it's needed (which could save some more time).
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