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[SPARK-26379][SS] Fix issue on adding current_timestamp/current_date to streaming query #23609

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[SPARK-26379][SS] Fix issue on adding current_timestamp/current_date to streaming query #23609

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afb0dc4
SPARK-26379 Fix issue on adding current_timestamp/current_date to str…
HeartSaVioR Jan 22, 2019
8f30a75
Fix build issue which local maven wasn't caught as error strangely
HeartSaVioR Jan 22, 2019
d09845b
Address review comments from @gaborgsomogyi
HeartSaVioR Jan 22, 2019
687c3e4
Reduce test time
HeartSaVioR Jan 22, 2019
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10 changes: 9 additions & 1 deletion sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MicroBatchExecution.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -508,12 +508,20 @@ class MicroBatchExecution(
cd.dataType, cd.timeZoneId)
}
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@dongjoon-hyun dongjoon-hyun Jan 26, 2019

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The root cause is CurrentBatchTimestamp is TimeZoneAwareExpression which is unresolved without TimeZoneId.


// Pre-resolve new attributes to ensure all attributes are resolved before
// accessing schema of logical plan. Note that it only leverages the information
// of attributes, so we don't need to concern about the value of literals.

val newAttrPlanPreResolvedForSchema = newAttributePlan transformAllExpressions {
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@HeartSaVioR HeartSaVioR Jan 22, 2019

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We can even leverage newBatchesPlan and remove this: this effort is to ensure going through the same path with further transformation when extracting schema.

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@gaborgsomogyi gaborgsomogyi Jan 22, 2019

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I'm fine with this.

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@dongjoon-hyun dongjoon-hyun Jan 23, 2019

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@HeartSaVioR . I prefer to leverage the existing lines (501 ~ 509) as you said like the following. I assume that the following is what you mean. Eventually, it's two line changes (excluding comments). WDYT?

    // Rewire the plan to use the new attributes that were returned by the source.
    val newAttributePlan = newBatchesPlan transformAllExpressions {
      case ct: CurrentTimestamp =>
        CurrentBatchTimestamp(offsetSeqMetadata.batchTimestampMs,
-          ct.dataType)
+          ct.dataType).toLiteral
      case cd: CurrentDate =>
        CurrentBatchTimestamp(offsetSeqMetadata.batchTimestampMs,
-          cd.dataType, cd.timeZoneId)
+          cd.dataType, cd.timeZoneId).toLiteral
    }

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@HeartSaVioR HeartSaVioR Jan 23, 2019

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@dongjoon-hyun
Actually I meant we pass newBatchesPlan instead of newAttributePlan only for extracting schema since we actually guess the name and data type will not be changed from IncrementalExecution. But IMHO I feel safer pre-transforming it.

Transforming CurrentBatchTimestamp to Literal not in IncrementalExecution breaks the intention what CurrentBatchTimestamp javadoc describes. Please note that I used transformed plan only for extracting schema and the actual plan is not changed.

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@dongjoon-hyun dongjoon-hyun Jan 25, 2019

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+1

case cbt: CurrentBatchTimestamp => cbt.toLiteral
}

val triggerLogicalPlan = sink match {
case _: Sink => newAttributePlan
case s: StreamingWriteSupportProvider =>
val writer = s.createStreamingWriteSupport(
s"$runId",
newAttributePlan.schema,
newAttrPlanPreResolvedForSchema.schema,
outputMode,
new DataSourceOptions(extraOptions.asJava))
WriteToDataSourceV2(new MicroBatchWrite(currentBatchId, writer), newAttributePlan)
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46 changes: 46 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamSuite.scala
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Expand Up @@ -34,6 +34,7 @@ import org.apache.spark.scheduler.{SparkListener, SparkListenerJobStart}
import org.apache.spark.sql._
import org.apache.spark.sql.catalyst.plans.logical.Range
import org.apache.spark.sql.catalyst.streaming.InternalOutputModes
import org.apache.spark.sql.catalyst.util.DateTimeUtils
import org.apache.spark.sql.execution.command.ExplainCommand
import org.apache.spark.sql.execution.streaming._
import org.apache.spark.sql.execution.streaming.sources.ContinuousMemoryStream
Expand Down Expand Up @@ -1079,6 +1080,51 @@ class StreamSuite extends StreamTest {
assert(query.exception.isEmpty)
}
}

test("SPARK-26379 Structured Streaming - Exception on adding current_timestamp / current_date" +
" to Dataset - use v2 sink") {
testCurrentTimestampOnStreamingQuery(useV2Sink = true)
}

test("SPARK-26379 Structured Streaming - Exception on adding current_timestamp / current_date" +
" to Dataset - use v1 sink") {
testCurrentTimestampOnStreamingQuery(useV2Sink = false)
}

private def testCurrentTimestampOnStreamingQuery(useV2Sink: Boolean): Unit = {
val input = MemoryStream[Int]
val df = input.toDS()
.withColumn("cur_timestamp", lit(current_timestamp()))
.withColumn("cur_date", lit(current_date()))
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@dongjoon-hyun dongjoon-hyun Jan 26, 2019

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Currently, this hides current_timestamp and makes this test case succeeds always.


def assertBatchOutputAndUpdateLastTimestamp(
rows: Seq[Row],
curTimestamp: Long,
curDate: Int,
expectedValue: Int): Long = {
assert(rows.size === 1)
val row = rows.head
assert(row.getInt(0) === expectedValue)
assert(row.getTimestamp(1).getTime >= curTimestamp)
val days = DateTimeUtils.millisToDays(row.getDate(2).getTime)
assert(days == curDate || days == curDate + 1)
row.getTimestamp(1).getTime
}

var lastTimestamp = System.currentTimeMillis()
val currentDate = DateTimeUtils.millisToDays(lastTimestamp)
testStream(df, useV2Sink = useV2Sink) (
AddData(input, 1),
CheckLastBatch { rows: Seq[Row] =>
lastTimestamp = assertBatchOutputAndUpdateLastTimestamp(rows, lastTimestamp, currentDate, 1)
},
Execute { _ => Thread.sleep(1000) },
AddData(input, 2),
CheckLastBatch { rows: Seq[Row] =>
lastTimestamp = assertBatchOutputAndUpdateLastTimestamp(rows, lastTimestamp, currentDate, 2)
}
)
}
}

abstract class FakeSource extends StreamSourceProvider {
Expand Down