ComputeCurrentTime Logical Optimization¶
ComputeCurrentTime is a base logical optimization that <
ComputeCurrentTime is part of the Finish Analysis once-executed batch in the standard batches of the Logical Optimizer.
ComputeCurrentTime is simply a <Rule[LogicalPlan].
[source, scala]¶
// Query with two current_date's import org.apache.spark.sql.functions.current_date val q = spark.range(1).select(current_date() as "d1", current_date() as "d2") val analyzedPlan = q.queryExecution.analyzed
scala> println(analyzedPlan.numberedTreeString) 00 Project [current_date(Some(Europe/Warsaw)) AS d1#12, current_date(Some(Europe/Warsaw)) AS d2#13] 01 +- Range (0, 1, step=1, splits=Some(8))
import org.apache.spark.sql.catalyst.optimizer.ComputeCurrentTime
val afterComputeCurrentTime = ComputeCurrentTime(analyzedPlan) scala> println(afterComputeCurrentTime.numberedTreeString) 00 Project [17773 AS d1#12, 17773 AS d2#13] 01 +- Range (0, 1, step=1, splits=Some(8))
// Another query with two current_timestamp's // Here the millis play a bigger role so it is easier to notice the results import org.apache.spark.sql.functions.current_timestamp val q = spark.range(1).select(current_timestamp() as "ts1", current_timestamp() as "ts2") val analyzedPlan = q.queryExecution.analyzed val afterComputeCurrentTime = ComputeCurrentTime(analyzedPlan) scala> println(afterComputeCurrentTime.numberedTreeString) 00 Project [1535629687768000 AS ts1#18, 1535629687768000 AS ts2#19] 01 +- Range (0, 1, step=1, splits=Some(8))
Executing Rule¶
apply(plan: LogicalPlan): LogicalPlan
apply...FIXME
apply is part of the Rule abstraction.