[SPARK-26745][SPARK-24959][SQL][BRANCH-2.4] Revert count optimization in JSON datasource by

## What changes were proposed in this pull request?

This PR reverts JSON count optimization part of #21909.

We cannot distinguish the cases below without parsing:

```
[{...}, {...}]
```

```
[]
```

```
{...}
```

```bash
# empty string
```

when we `count()`. One line (input: IN) can be, 0 record, 1 record and multiple records and this is dependent on each input.

See also #23665 (comment).

## How was this patch tested?

Manually tested.

Closes #23708 from HyukjinKwon/SPARK-26745-backport.

Authored-by: Hyukjin Kwon <[email protected]>
Signed-off-by: Hyukjin Kwon <[email protected]>

Author: HyukjinKwon

sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala

@@ -450,8 +450,7 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
         input => rawParser.parse(input, createParser, UTF8String.fromString),
         parsedOptions.parseMode,
         schema,
-        parsedOptions.columnNameOfCorruptRecord,
-        parsedOptions.multiLine)
+        parsedOptions.columnNameOfCorruptRecord)
       iter.flatMap(parser.parse)
     }
     sparkSession.internalCreateDataFrame(parsed, schema, isStreaming = jsonDataset.isStreaming)
@@ -526,8 +525,7 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
         input => Seq(rawParser.parse(input)),
         parsedOptions.parseMode,
         schema,
-        parsedOptions.columnNameOfCorruptRecord,
-        parsedOptions.multiLine)
+        parsedOptions.columnNameOfCorruptRecord)
       iter.flatMap(parser.parse)
     }
     sparkSession.internalCreateDataFrame(parsed, schema, isStreaming = csvDataset.isStreaming)

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FailureSafeParser.scala

@@ -29,8 +29,7 @@ class FailureSafeParser[IN](
     rawParser: IN => Seq[InternalRow],
     mode: ParseMode,
     schema: StructType,
-    columnNameOfCorruptRecord: String,
-    isMultiLine: Boolean) {
+    columnNameOfCorruptRecord: String) {
 
   private val corruptFieldIndex = schema.getFieldIndex(columnNameOfCorruptRecord)
   private val actualSchema = StructType(schema.filterNot(_.name == columnNameOfCorruptRecord))
@@ -58,15 +57,9 @@ class FailureSafeParser[IN](
     }
   }
 
-  private val skipParsing = !isMultiLine && mode == PermissiveMode && schema.isEmpty
-
   def parse(input: IN): Iterator[InternalRow] = {
     try {
-     if (skipParsing) {
-       Iterator.single(InternalRow.empty)
-     } else {
-       rawParser.apply(input).toIterator.map(row => toResultRow(Some(row), () => null))
-     }
+      rawParser.apply(input).toIterator.map(row => toResultRow(Some(row), () => null))
     } catch {
       case e: BadRecordException => mode match {
         case PermissiveMode =>

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/UnivocityParser.scala

@@ -203,11 +203,19 @@ class UnivocityParser(
     }
   }
 
+  private val doParse = if (requiredSchema.nonEmpty) {
+    (input: String) => convert(tokenizer.parseLine(input))
+  } else {
+    // If `columnPruning` enabled and partition attributes scanned only,
+    // `schema` gets empty.
+    (_: String) => InternalRow.empty
+  }
+
   /**
    * Parses a single CSV string and turns it into either one resulting row or no row (if the
    * the record is malformed).
    */
-  def parse(input: String): InternalRow = convert(tokenizer.parseLine(input))
+  def parse(input: String): InternalRow = doParse(input)
 
   private val getToken = if (options.columnPruning) {
     (tokens: Array[String], index: Int) => tokens(index)
@@ -290,8 +298,7 @@ private[csv] object UnivocityParser {
       input => Seq(parser.convert(input)),
       parser.options.parseMode,
       schema,
-      parser.options.columnNameOfCorruptRecord,
-      parser.options.multiLine)
+      parser.options.columnNameOfCorruptRecord)
     convertStream(inputStream, shouldDropHeader, tokenizer, checkHeader) { tokens =>
       safeParser.parse(tokens)
     }.flatten
@@ -339,8 +346,7 @@ private[csv] object UnivocityParser {
       input => Seq(parser.parse(input)),
       parser.options.parseMode,
       schema,
-      parser.options.columnNameOfCorruptRecord,
-      parser.options.multiLine)
+      parser.options.columnNameOfCorruptRecord)
     filteredLines.flatMap(safeParser.parse)
   }
 }

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonDataSource.scala

@@ -139,8 +139,7 @@ object TextInputJsonDataSource extends JsonDataSource {
       input => parser.parse(input, textParser, textToUTF8String),
       parser.options.parseMode,
       schema,
-      parser.options.columnNameOfCorruptRecord,
-      parser.options.multiLine)
+      parser.options.columnNameOfCorruptRecord)
     linesReader.flatMap(safeParser.parse)
   }
 
@@ -224,8 +223,7 @@ object MultiLineJsonDataSource extends JsonDataSource {
       input => parser.parse[InputStream](input, streamParser, partitionedFileString),
       parser.options.parseMode,
       schema,
-      parser.options.columnNameOfCorruptRecord,
-      parser.options.multiLine)
+      parser.options.columnNameOfCorruptRecord)
 
     safeParser.parse(
       CodecStreams.createInputStreamWithCloseResource(conf, new Path(new URI(file.filePath))))

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonBenchmarks.scala

@@ -194,9 +194,6 @@ object JSONBenchmarks {
       benchmark.addCase(s"Select 1 column + count()", 3) { _ =>
         ds.select($"col1").filter((_: Row) => true).count()
       }
-      benchmark.addCase(s"count()", 3) { _ =>
-        ds.count()
-      }
 
       /*
       Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz
@@ -205,7 +202,6 @@ object JSONBenchmarks {
       ---------------------------------------------------------------------------------------------
       Select 10 columns + count()               9961 / 10006          1.0         996.1       1.0X
       Select 1 column + count()                  8355 / 8470          1.2         835.5       1.2X
-      count()                                    2104 / 2156          4.8         210.4       4.7X
       */
       benchmark.run()
     }