[GitHub] maropu commented on a change in pull request #23665: [SPARK-26745][SQL] Skip empty lines in JSON-derived DataFrames when skipParsing optimization in effect
maropu commented on a change in pull request #23665: [SPARK-26745][SQL] Skip
empty lines in JSON-derived DataFrames when skipParsing optimization in effect
URL: https://github.com/apache/spark/pull/23665#discussion_r251281725
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/FailureSafeParser.scala
##
@@ -55,11 +56,15 @@ class FailureSafeParser[IN](
def parse(input: IN): Iterator[InternalRow] = {
try {
- if (skipParsing) {
- Iterator.single(InternalRow.empty)
- } else {
- rawParser.apply(input).toIterator.map(row => toResultRow(Some(row), ()
=> null))
- }
+ if (skipParsing) {
+if (unparsedRecordIsNonEmpty(input)) {
+ Iterator.single(InternalRow.empty)
+} else {
+ Iterator.empty
+}
+ } else {
+rawParser.apply(input).toIterator.map(row => toResultRow(Some(row), ()
=> null))
+ }
Review comment:
I thought this:
https://github.com/maropu/spark/commit/f4df9074619e6cafff679c5f22fa153727be1079
But, you should follow other guys who are familiar this part, @HyukjinKwon
and @MaxGekk
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[GitHub] maropu commented on a change in pull request #23665: [SPARK-26745][SQL] Skip empty lines in JSON-derived DataFrames when skipParsing optimization in effect
maropu commented on a change in pull request #23665: [SPARK-26745][SQL] Skip
empty lines in JSON-derived DataFrames when skipParsing optimization in effect
URL: https://github.com/apache/spark/pull/23665#discussion_r251271104
##
File path:
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
##
@@ -2426,6 +2426,23 @@ class JsonSuite extends QueryTest with SharedSQLContext
with TestJsonData {
countForMalformedJSON(0, Seq(""))
}
+ test("count() for non-multiline input with empty lines") {
+val withEmptyLineData = Array(Map("a" -> 1, "b" -> 2, "c" -> 3),
+ Map("a" -> 4, "b" -> 5, "c" -> 6),
+ Map("a" -> 7, "b" -> 8, "c" -> 9))
+val df =
spark.read.json("src/test/resources/test-data/with-empty-line.json")
+// important to do this .count() first, prior to
caching/persisting/computing/collecting, to
+// test the non-parsed-count pathway
+assert(df.count() === withEmptyLineData.length,
+ "JSON DataFrame unparsed-count should exclude whitespace-only
lines")
+// cache and collect to check that count stays stable under those
operations
+df.cache()
+assert(df.count() === withEmptyLineData.length,
+ "JSON DataFrame parsed-count should exclude whitespace-only lines")
+val collected = df.collect().map(_.getValuesMap(Seq("a", "b", "c")))
+assert(collected === withEmptyLineData)
Review comment:
plz check `checkAnswer`.
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[GitHub] maropu commented on a change in pull request #23665: [SPARK-26745][SQL] Skip empty lines in JSON-derived DataFrames when skipParsing optimization in effect
maropu commented on a change in pull request #23665: [SPARK-26745][SQL] Skip
empty lines in JSON-derived DataFrames when skipParsing optimization in effect
URL: https://github.com/apache/spark/pull/23665#discussion_r251271148
##
File path:
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
##
@@ -2426,6 +2426,23 @@ class JsonSuite extends QueryTest with SharedSQLContext
with TestJsonData {
countForMalformedJSON(0, Seq(""))
}
+ test("count() for non-multiline input with empty lines") {
+val withEmptyLineData = Array(Map("a" -> 1, "b" -> 2, "c" -> 3),
+ Map("a" -> 4, "b" -> 5, "c" -> 6),
+ Map("a" -> 7, "b" -> 8, "c" -> 9))
+val df =
spark.read.json("src/test/resources/test-data/with-empty-line.json")
+// important to do this .count() first, prior to
caching/persisting/computing/collecting, to
+// test the non-parsed-count pathway
+assert(df.count() === withEmptyLineData.length,
+ "JSON DataFrame unparsed-count should exclude whitespace-only
lines")
+// cache and collect to check that count stays stable under those
operations
+df.cache()
Review comment:
we dont need this cache.
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[GitHub] maropu commented on a change in pull request #23665: [SPARK-26745][SQL] Skip empty lines in JSON-derived DataFrames when skipParsing optimization in effect
maropu commented on a change in pull request #23665: [SPARK-26745][SQL] Skip
empty lines in JSON-derived DataFrames when skipParsing optimization in effect
URL: https://github.com/apache/spark/pull/23665#discussion_r251271052
##
File path:
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
##
@@ -2426,6 +2426,23 @@ class JsonSuite extends QueryTest with SharedSQLContext
with TestJsonData {
countForMalformedJSON(0, Seq(""))
}
+ test("count() for non-multiline input with empty lines") {
+val withEmptyLineData = Array(Map("a" -> 1, "b" -> 2, "c" -> 3),
+ Map("a" -> 4, "b" -> 5, "c" -> 6),
+ Map("a" -> 7, "b" -> 8, "c" -> 9))
+val df =
spark.read.json("src/test/resources/test-data/with-empty-line.json")
Review comment:
plz use `testFile`.
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[GitHub] maropu commented on a change in pull request #23665: [SPARK-26745][SQL] Skip empty lines in JSON-derived DataFrames when skipParsing optimization in effect
maropu commented on a change in pull request #23665: [SPARK-26745][SQL] Skip
empty lines in JSON-derived DataFrames when skipParsing optimization in effect
URL: https://github.com/apache/spark/pull/23665#discussion_r251270647
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/FailureSafeParser.scala
##
@@ -55,11 +56,15 @@ class FailureSafeParser[IN](
def parse(input: IN): Iterator[InternalRow] = {
try {
- if (skipParsing) {
- Iterator.single(InternalRow.empty)
- } else {
- rawParser.apply(input).toIterator.map(row => toResultRow(Some(row), ()
=> null))
- }
+ if (skipParsing) {
+if (unparsedRecordIsNonEmpty(input)) {
+ Iterator.single(InternalRow.empty)
+} else {
+ Iterator.empty
+}
+ } else {
+rawParser.apply(input).toIterator.map(row => toResultRow(Some(row), ()
=> null))
+ }
Review comment:
We need to modify this file? Since this is a issue in json stuffs, I think
its better to handle this case in the json parser side. Can't we do handle this
in the same way with CSV one?, e.g.,
https://github.com/apache/spark/blob/3a17c6a06bc4e8a6cae506a61d3d83696d378607/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/csv/UnivocityParser.scala#L333
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