[jira] [Commented] (SPARK-26384) CSV schema inferring does not respect spark.sql.legacy.timeParser.enabled

2018-12-18 Thread ASF GitHub Bot (JIRA) 


[ 
https://issues.apache.org/jira/browse/SPARK-26384?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16724201#comment-16724201
 ] 

ASF GitHub Bot commented on SPARK-26384:


asfgit closed pull request #23345: [SPARK-26384][SQL] Propagate SQL configs for 
CSV schema inferring
URL: https://github.com/apache/spark/pull/23345
 
 
   

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diff --git 
a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVDataSource.scala
 
b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVDataSource.scala
index b46dfb94c133e..375cec597166c 100644
--- 
a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVDataSource.scala
+++ 
b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVDataSource.scala
@@ -35,6 +35,7 @@ import org.apache.spark.rdd.{BinaryFileRDD, RDD}
 import org.apache.spark.sql.{Dataset, Encoders, SparkSession}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.csv.{CSVHeaderChecker, CSVInferSchema, 
CSVOptions, UnivocityParser}
+import org.apache.spark.sql.execution.SQLExecution
 import org.apache.spark.sql.execution.datasources._
 import org.apache.spark.sql.execution.datasources.text.TextFileFormat
 import org.apache.spark.sql.types.StructType
@@ -135,7 +136,9 @@ object TextInputCSVDataSource extends CSVDataSource {
   val parser = new CsvParser(parsedOptions.asParserSettings)
   linesWithoutHeader.map(parser.parseLine)
 }
-new CSVInferSchema(parsedOptions).infer(tokenRDD, header)
+SQLExecution.withSQLConfPropagated(csv.sparkSession) {
+  new CSVInferSchema(parsedOptions).infer(tokenRDD, header)
+}
   case _ =>
 // If the first line could not be read, just return the empty schema.
 StructType(Nil)
@@ -208,7 +211,9 @@ object MultiLineCSVDataSource extends CSVDataSource {
 encoding = parsedOptions.charset)
 }
 val sampled = CSVUtils.sample(tokenRDD, parsedOptions)
-new CSVInferSchema(parsedOptions).infer(sampled, header)
+SQLExecution.withSQLConfPropagated(sparkSession) {
+  new CSVInferSchema(parsedOptions).infer(sampled, header)
+}
   case None =>
 // If the first row could not be read, just return the empty schema.
 StructType(Nil)


 


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> CSV schema inferring does not respect spark.sql.legacy.timeParser.enabled
> -
>
> Key: SPARK-26384
> URL: https://issues.apache.org/jira/browse/SPARK-26384
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 3.0.0
>Reporter: Maxim Gekk
>Assignee: Maxim Gekk
>Priority: Major
> Fix For: 3.0.0
>
>
> Starting from the commit 
> [https://github.com/apache/spark/commit/f982ca07e80074bdc1e3b742c5e21cf368e4ede2]
>  , add logging like in the comment 
> https://github.com/apache/spark/pull/23150#discussion_r242021998 and run:
> {code:shell}
> $ ./bin/spark-shell --conf spark.sql.legacy.timeParser.enabled=true
> {code}
> and in the shell:
> {code:scala}
> scala> spark.conf.get("spark.sql.legacy.timeParser.enabled")
> res0: String = true
> scala> 
> Seq("2010|10|10").toDF.repartition(1).write.mode("overwrite").text("/tmp/foo")
> scala> spark.read.option("inferSchema", "true").option("header", 
> "false").option("timestampFormat", "|MM|dd").csv("/tmp/foo").printSchema()
> 18/12/17 12:11:47 ERROR TimestampFormatter: Iso8601TimestampFormatter is 
> being used
> root
>  |-- _c0: timestamp (nullable = true)
> {code}



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[jira] [Commented] (SPARK-26384) CSV schema inferring does not respect spark.sql.legacy.timeParser.enabled

2018-12-18 Thread ASF GitHub Bot (JIRA) 


[ 
https://issues.apache.org/jira/browse/SPARK-26384?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16723886#comment-16723886
 ] 

ASF GitHub Bot commented on SPARK-26384:


MaxGekk opened a new pull request #23345: [SPARK-26384][SQL] Propagate SQL 
configs for CSV schema inferring
URL: https://github.com/apache/spark/pull/23345
 
 
   ## What changes were proposed in this pull request?
   
   Currently, SQL configs are not propagated to executors while schema 
inferring in CSV datasource. For example, changing of 
`spark.sql.legacy.timeParser.enabled` does not impact on inferring timestamp 
types. In the PR, I propose to fix the issue by wrapping schema inferring 
action using `SQLExecution.withSQLConfPropagated`.
   
   ## How was this patch tested?
   
   Added logging to `TimestampFormatter`:
   ```patch
   -object TimestampFormatter {
   +object TimestampFormatter extends Logging {
  def apply(format: String, timeZone: TimeZone, locale: Locale): 
TimestampFormatter = {
if (SQLConf.get.legacyTimeParserEnabled) {
   +  logError("LegacyFallbackTimestampFormatter is being used")
  new LegacyFallbackTimestampFormatter(format, timeZone, locale)
} else {
   +  logError("Iso8601TimestampFormatter is being used")
  new Iso8601TimestampFormatter(format, timeZone, locale)
}
  }
   ```
   and run the command in `spark-shell`:
   ```shell
   $ ./bin/spark-shell --conf spark.sql.legacy.timeParser.enabled=true
   ```
   ```scala
   scala> 
Seq("2010|10|10").toDF.repartition(1).write.mode("overwrite").text("/tmp/foo")
   scala> spark.read.option("inferSchema", "true").option("header", 
"false").option("timestampFormat", "|MM|dd").csv("/tmp/foo").printSchema()
   18/12/18 10:47:27 ERROR TimestampFormatter: LegacyFallbackTimestampFormatter 
is being used
   root
|-- _c0: timestamp (nullable = true)
   ```
   


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> CSV schema inferring does not respect spark.sql.legacy.timeParser.enabled
> -
>
> Key: SPARK-26384
> URL: https://issues.apache.org/jira/browse/SPARK-26384
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 3.0.0
>Reporter: Maxim Gekk
>Priority: Major
>
> Starting from the commit 
> [https://github.com/apache/spark/commit/f982ca07e80074bdc1e3b742c5e21cf368e4ede2]
>  , add logging like in the comment 
> https://github.com/apache/spark/pull/23150#discussion_r242021998 and run:
> {code:shell}
> $ ./bin/spark-shell --conf spark.sql.legacy.timeParser.enabled=true
> {code}
> and in the shell:
> {code:scala}
> scala> spark.conf.get("spark.sql.legacy.timeParser.enabled")
> res0: String = true
> scala> 
> Seq("2010|10|10").toDF.repartition(1).write.mode("overwrite").text("/tmp/foo")
> scala> spark.read.option("inferSchema", "true").option("header", 
> "false").option("timestampFormat", "|MM|dd").csv("/tmp/foo").printSchema()
> 18/12/17 12:11:47 ERROR TimestampFormatter: Iso8601TimestampFormatter is 
> being used
> root
>  |-- _c0: timestamp (nullable = true)
> {code}



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