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[SPARK-24645][SQL] Skip parsing when csvColumnPruning enabled and partitions scanned only #21631

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[SPARK-24645][SQL] Skip parsing when csvColumnPruning enabled and partitions scanned only #21631

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59a7f14
fix
maropu Jun 25, 2018
c7362dc
Fix
maropu Jun 25, 2018
39811a3
Fix
maropu Jun 25, 2018
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10 changes: 9 additions & 1 deletion sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/UnivocityParser.scala
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Expand Up @@ -183,11 +183,19 @@ class UnivocityParser(
}
}

private lazy val doParse = if (schema.nonEmpty) {
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@MaxGekk MaxGekk Jun 25, 2018

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Do you really need lazy here. In most cases, time interval between calling of the constructor and the parse() method is pretty short. I don't think we win something here.

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@maropu maropu Jun 25, 2018

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yea, I missed. Thanks!

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@maropu maropu Jun 25, 2018

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recheck: it seems parse is called in executor sides only, so we can add lazy here to avoid unnecessary instantiation?

(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 def convert(tokens: Array[String]): InternalRow = {
if (tokens.length != schema.length) {
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10 changes: 10 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala
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Expand Up @@ -1602,4 +1602,14 @@ class CSVSuite extends QueryTest with SharedSQLContext with SQLTestUtils with Te
assert(testAppender2.events.asScala
.exists(msg => msg.getRenderedMessage.contains("CSV header does not conform to the schema")))
}

test("SPARK-24645 skip parsing when columnPruning enabled and partitions scanned only") {
withSQLConf(SQLConf.CSV_PARSER_COLUMN_PRUNING.key -> "true") {
withTempPath { path =>
val dir = path.getAbsolutePath
spark.range(10).selectExpr("id % 2 AS p", "id").write.partitionBy("p").csv(dir)
spark.read.csv(dir).selectExpr("sum(p)").collect()
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@maropu maropu Jun 25, 2018

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oh, I forgot to add assert here. I'll update soon.

}
}
}
}