Details
-
Bug
-
Status: Resolved
-
Major
-
Resolution: Fixed
-
2.4.0
-
None
Description
When we use data api to write a structured streaming query job we usually specify a watermark on event time column. If we define a window on the event time column, the delayKey metadata of the event time column is supposed to be propagated to the new column generated by time window expression. But if we add additional alias on the time window column, the delayKey metadata is lost.
Currently I only find the bug will affect stream-stream join with equal window join keys. In terms of aggregation, the gourping expression can be trimed(in CleanupAliases rule) so additional alias are removed and the metadata is kept.
Here is an example:
val sparkSession = SparkSession .builder() .master("local") .getOrCreate() val rateStream = sparkSession.readStream .format("rate") .option("rowsPerSecond", 10) .load() val fooStream = rateStream .select( col("value").as("fooId"), col("timestamp").as("fooTime") ) .withWatermark("fooTime", "2 seconds") .select($"fooId", $"fooTime", window($"fooTime", "2 seconds").alias("fooWindow")) val barStream = rateStream .where(col("value") % 2 === 0) .select( col("value").as("barId"), col("timestamp").as("barTime") ) .withWatermark("barTime", "2 seconds") .select($"barId", $"barTime", window($"barTime", "2 seconds").alias("barWindow")) val joinedDf = fooStream .join( barStream, $"fooId" === $"barId" && fooStream.col("fooWindow") === barStream.col("barWindow"), joinType = "LeftOuter" ) val query = joinedDf .writeStream .format("console") .option("truncate", 100) .trigger(Trigger.ProcessingTime("5 seconds")) .start() query.awaitTermination()
this program will end with an exception, and from the analyzed plan we can see there is no delayKey metadata on 'fooWindow'
org.apache.spark.sql.AnalysisException: Stream-stream outer join between two streaming DataFrame/Datasets is not supported without a watermark in the join keys, or a watermark on the nullable side and an appropriate range condition;; Join LeftOuter, ((fooId#4L = barId#14L) && (fooWindow#9 = barWindow#19)) :- Project [fooId#4L, fooTime#5-T2000ms, window#10-T2000ms AS fooWindow#9] : +- Filter isnotnull(fooTime#5-T2000ms) : +- Project [named_struct(start, precisetimestampconversion(((((CASE WHEN (cast(CEIL((cast((precisetimestampconversion(fooTime#5-T2000ms, TimestampType, LongType) - 0) as double) / cast(2000000 as double))) as double) = (cast((precisetimestampconversion(fooTime#5-T2000ms, TimestampType, LongType) - 0) as double) / cast(2000000 as double))) THEN (CEIL((cast((precisetimestampconversion(fooTime#5-T2000ms, TimestampType, LongType) - 0) as double) / cast(2000000 as double))) + cast(1 as bigint)) ELSE CEIL((cast((precisetimestampconversion(fooTime#5-T2000ms, TimestampType, LongType) - 0) as double) / cast(2000000 as double))) END + cast(0 as bigint)) - cast(1 as bigint)) * 2000000) + 0), LongType, TimestampType), end, precisetimestampconversion((((((CASE WHEN (cast(CEIL((cast((precisetimestampconversion(fooTime#5-T2000ms, TimestampType, LongType) - 0) as double) / cast(2000000 as double))) as double) = (cast((precisetimestampconversion(fooTime#5-T2000ms, TimestampType, LongType) - 0) as double) / cast(2000000 as double))) THEN (CEIL((cast((precisetimestampconversion(fooTime#5-T2000ms, TimestampType, LongType) - 0) as double) / cast(2000000 as double))) + cast(1 as bigint)) ELSE CEIL((cast((precisetimestampconversion(fooTime#5-T2000ms, TimestampType, LongType) - 0) as double) / cast(2000000 as double))) END + cast(0 as bigint)) - cast(1 as bigint)) * 2000000) + 0) + 2000000), LongType, TimestampType)) AS window#10-T2000ms, fooId#4L, fooTime#5-T2000ms] : +- EventTimeWatermark fooTime#5: timestamp, interval 2 seconds : +- Project [value#1L AS fooId#4L, timestamp#0 AS fooTime#5] : +- StreamingRelationV2 org.apache.spark.sql.execution.streaming.sources.RateStreamProvider@2cae5fa7, rate, Map(numPartitions -> 1, rowsPerSecond -> 1), [timestamp#0, value#1L] +- Project [barId#14L, barTime#15-T2000ms, window#20-T2000ms AS barWindow#19] +- Filter isnotnull(barTime#15-T2000ms) +- Project [named_struct(start, precisetimestampconversion(((((CASE WHEN (cast(CEIL((cast((precisetimestampconversion(barTime#15-T2000ms, TimestampType, LongType) - 0) as double) / cast(2000000 as double))) as double) = (cast((precisetimestampconversion(barTime#15-T2000ms, TimestampType, LongType) - 0) as double) / cast(2000000 as double))) THEN (CEIL((cast((precisetimestampconversion(barTime#15-T2000ms, TimestampType, LongType) - 0) as double) / cast(2000000 as double))) + cast(1 as bigint)) ELSE CEIL((cast((precisetimestampconversion(barTime#15-T2000ms, TimestampType, LongType) - 0) as double) / cast(2000000 as double))) END + cast(0 as bigint)) - cast(1 as bigint)) * 2000000) + 0), LongType, TimestampType), end, precisetimestampconversion((((((CASE WHEN (cast(CEIL((cast((precisetimestampconversion(barTime#15-T2000ms, TimestampType, LongType) - 0) as double) / cast(2000000 as double))) as double) = (cast((precisetimestampconversion(barTime#15-T2000ms, TimestampType, LongType) - 0) as double) / cast(2000000 as double))) THEN (CEIL((cast((precisetimestampconversion(barTime#15-T2000ms, TimestampType, LongType) - 0) as double) / cast(2000000 as double))) + cast(1 as bigint)) ELSE CEIL((cast((precisetimestampconversion(barTime#15-T2000ms, TimestampType, LongType) - 0) as double) / cast(2000000 as double))) END + cast(0 as bigint)) - cast(1 as bigint)) * 2000000) + 0) + 2000000), LongType, TimestampType)) AS window#20-T2000ms, barId#14L, barTime#15-T2000ms] +- EventTimeWatermark barTime#15: timestamp, interval 2 seconds +- Project [value#1L AS barId#14L, timestamp#0 AS barTime#15] +- Filter ((value#1L % cast(2 as bigint)) = cast(0 as bigint)) +- StreamingRelationV2 org.apache.spark.sql.execution.streaming.sources.RateStreamProvider@2cae5fa7, rate, Map(numPartitions -> 1, rowsPerSecond -> 1), [timestamp#0, value#1L]
