Github user AndreSchumacher commented on a diff in the pull request:
https://github.com/apache/spark/pull/195#discussion_r10892494
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala ---
@@ -72,16 +73,43 @@ case class ParquetRelation(val tableName: String, val
path: String) extends Base
/** Output **/
override val output = attributes
+ /** Name (dummy value) */
+ // TODO: rethink whether ParquetRelation should inherit from BaseRelation
+ // (currently required to re-use HiveStrategies but should be removed)
+ override def tableName = "parquet"
+
// Parquet files have no concepts of keys, therefore no Partitioner
// Note: we could allow Block level access; needs to be thought through
override def isPartitioned = false
}
object ParquetRelation {
+ // change this to enable/disable Parquet logging
+ var DEBUG: Boolean = false
+
+ // TODO: consider redirecting Parquet's log output to log4j logger and
+ // using config file for log settings
+ def setParquetLogLevel() {
+ val level: Level = if (DEBUG) Level.FINEST else Level.WARNING
--- End diff --
Now I'm actually reading this here: <em>j.u.l. to SLF4J translation can
seriously increase the cost of disabled logging statements (60 fold or
6000%)</em> Apparently there is a way to void this by using logback (a fork of
log4j?). Parquet does fairly low-level logging and relies statements on these
now being compiled as I understand. Any opinions? I could try if it would work
via logback or see how this would degrade performance.
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