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Prevent triggering spark job when executing the initialization sql #6789

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Prevent triggering spark job when executing the initialization sql #6789

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a3fcbbc
do not trigger job
turboFei Nov 1, 2024
7b67d5d
fix ut
turboFei Nov 1, 2024
da4df40
[SPARK-47270][SQL] Dataset.isEmpty projects CommandResults locally
turboFei Nov 1, 2024
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3 changes: 2 additions & 1 deletion ...uubi-spark-sql-engine/src/main/scala/org/apache/kyuubi/engine/spark/KyuubiSparkUtil.scala
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Expand Up @@ -24,6 +24,7 @@ import scala.annotation.meta.getter

import org.apache.spark.{SPARK_VERSION, SparkContext}
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.kyuubi.SparkDatasetHelper
import org.apache.spark.util.kvstore.KVIndex

import org.apache.kyuubi.Logging
Expand All @@ -48,7 +49,7 @@ object KyuubiSparkUtil extends Logging {
interruptOnCancel = true)
debug(s"Execute initialization sql: $sql")
try {
spark.sql(sql).isEmpty
SparkDatasetHelper.commandResultOptimized(spark.sql(sql)).isEmpty
} finally {
spark.sparkContext.clearJobGroup()
}
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11 changes: 10 additions & 1 deletion ...uubi-spark-sql-engine/src/main/scala/org/apache/spark/sql/kyuubi/SparkDatasetHelper.scala
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Expand Up @@ -24,7 +24,7 @@ import org.apache.spark.internal.Logging
import org.apache.spark.network.util.{ByteUnit, JavaUtils}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Dataset, Row}
import org.apache.spark.sql.catalyst.plans.logical.GlobalLimit
import org.apache.spark.sql.catalyst.plans.logical.{CommandResult, GlobalLimit, LocalRelation, LogicalPlan}
import org.apache.spark.sql.catalyst.plans.logical.statsEstimation.EstimationUtils
import org.apache.spark.sql.execution.{CollectLimitExec, CommandResultExec, HiveResult, LocalTableScanExec, QueryExecution, SparkPlan, SQLExecution}
import org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec
Expand Down Expand Up @@ -290,4 +290,13 @@ object SparkDatasetHelper extends Logging {
nodeName == "org.apache.spark.sql.execution.command.ExecutedCommandExec" ||
nodeName == "org.apache.spark.sql.execution.CommandResultExec"
}

/** SPARK-47270: Returns a optimized plan for CommandResult, convert to `LocalRelation`. */
def commandResultOptimized[T](dataset: Dataset[T]): Dataset[T] = {
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@pan3793 pan3793 Nov 1, 2024

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I'm -0 on this change, I would consider it a Spark side issue. Spark’s master branch is undergoing a major refactoring, I'm worried about accessing Spark's non-public API in the engine's "core code path".

For users who want to avoid triggering executor launch, they can either patch their spark or set init SQL as something like: SET spark.app.id

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@turboFei turboFei Nov 1, 2024

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Ok. I will cherry-pick the pr into our managed spark 3.5.

dataset.logicalPlan match {
case c: CommandResult =>
Dataset(dataset.sparkSession, LocalRelation(c.output, c.rows))(dataset.encoder)
case _ => dataset
}
}
}
5 changes: 3 additions & 2 deletions ...i-spark-sql-engine/src/test/scala/org/apache/kyuubi/engine/spark/WithSparkSQLEngine.scala
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Expand Up @@ -21,7 +21,6 @@ import org.apache.spark.sql.SparkSession

import org.apache.kyuubi.{KyuubiFunSuite, Utils}
import org.apache.kyuubi.config.KyuubiConf
import org.apache.kyuubi.engine.spark.KyuubiSparkUtil.SPARK_ENGINE_RUNTIME_VERSION

trait WithSparkSQLEngine extends KyuubiFunSuite {
protected var spark: SparkSession = _
Expand All @@ -35,7 +34,9 @@ trait WithSparkSQLEngine extends KyuubiFunSuite {
// Behavior is affected by the initialization SQL: 'SHOW DATABASES'
// SPARK-35378 (3.2.0) makes it triggers job
// SPARK-43124 (4.0.0) makes it avoid triggering job
protected val initJobId: Int = if (SPARK_ENGINE_RUNTIME_VERSION >= "4.0") 0 else 1
// KYUUBI #6789 makes it avoid triggering job
// protected val initJobId: Int = if (SPARK_ENGINE_RUNTIME_VERSION >= "4.0") 0 else 1
protected val initJobId: Int = 0
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@turboFei turboFei Nov 1, 2024

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how do you think about? @pan3793

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@turboFei turboFei Nov 1, 2024

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Fixed by @wForget apache/spark#45397

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@turboFei turboFei Nov 1, 2024

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In our use case, I set initial/mini executors to 0 for notebook connections.

If job triggered by initialization SQL, it will need at least 1 executor before the connection ready.

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@turboFei turboFei Nov 1, 2024 •
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And for notebook connections, we will init the spark driver in a temporary queue, and then move it to the user queue eventually, so it should be fast to make the connection ready even the user queue has no available resources.

But if job triggered during initialization, it might make it slow if the user queue has no available resources.


override def beforeAll(): Unit = {
startSparkEngine()
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