[GitHub] [spark] GuoPhilipse commented on a change in pull request #29056: [SPARK-31753][SQL][DOCS] Add missing keywords in the SQL docs
GuoPhilipse commented on a change in pull request #29056: URL: https://github.com/apache/spark/pull/29056#discussion_r455549369 ## File path: docs/sql-ref-syntax-qry.md ## @@ -45,4 +45,7 @@ ability to generate logical and physical plan for a given query using * [TABLESAMPLE](sql-ref-syntax-qry-select-sampling.html) * [Table-valued Function](sql-ref-syntax-qry-select-tvf.html) * [Window Function](sql-ref-syntax-qry-select-window.html) + * [CASE Clause](sql-ref-syntax-qry-select-case.html) Review comment: I saw mysql and hive both define CASE as function singlely, not defined in `SELECT` clause https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF https://dev.mysql.com/doc/refman/8.0/en/control-flow-functions.html This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] [spark] SparkQA commented on pull request #29126: [SPARK-32324][SQL]Fix error messages during using PIVOT and lateral view
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[GitHub] [spark] viirya commented on pull request #29114: [SPARK-32094][PYTHON] Update cloudpickle to v1.5.0
viirya commented on pull request #29114: URL: https://github.com/apache/spark/pull/29114#issuecomment-659186161 Oh I see. So you must import from cloudpickle_fast now. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] [spark] cloud-fan commented on pull request #28833: [SPARK-20680][SQL] Spark-sql do not support for creating table with void column datatype
cloud-fan commented on pull request #28833: URL: https://github.com/apache/spark/pull/28833#issuecomment-659183541 I don't think it's a good idea to diverge the behavior between in-memory and hive catalogs. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] [spark] HyukjinKwon commented on pull request #29114: [SPARK-32094][PYTHON] Update cloudpickle to v1.5.0
HyukjinKwon commented on pull request #29114: URL: https://github.com/apache/spark/pull/29114#issuecomment-659183238 Yeah, it does with Python 3.8. It was clear before because it was conditionally chosen in `__init__.py` but it was changed in the latest version at https://github.com/cloudpipe/cloudpickle/commit/938553fff60bf2b06b2286c3f564a587153dc5e4#diff-17e83ea5b8e0670ba384e6bc36815316L9 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] [spark] cloud-fan commented on pull request #29106: [SPARK-32307][SQL] ScalaUDF's canonicalized expression should exclude inputEncoders
cloud-fan commented on pull request #29106: URL: https://github.com/apache/spark/pull/29106#issuecomment-659183181 > Merged to master/3.0. has it been merged to 3.0 or not? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] [spark] AmplabJenkins commented on pull request #29130: [SPARK-32330][SQL] Preserve shuffled hash join build side partitioning
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[GitHub] [spark] jiangxb1987 commented on a change in pull request #29032: [SPARK-32217] Plumb whether a worker would also be decommissioned along with executor
jiangxb1987 commented on a change in pull request #29032:
URL: https://github.com/apache/spark/pull/29032#discussion_r455525039
##
File path:
core/src/main/scala/org/apache/spark/deploy/client/StandaloneAppClient.scala
##
@@ -181,7 +182,8 @@ private[spark] class StandaloneAppClient(
if (ExecutorState.isFinished(state)) {
listener.executorRemoved(fullId, message.getOrElse(""), exitStatus,
workerLost)
} else if (state == ExecutorState.DECOMMISSIONED) {
- listener.executorDecommissioned(fullId, message.getOrElse(""))
+ listener.executorDecommissioned(fullId,
+ExecutorDecommissionInfo(message.getOrElse(""),
isHostDecommissioned = workerLost))
Review comment:
oh I see https://github.com/apache/spark/pull/29032#discussion_r455401121
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[GitHub] [spark] c21 commented on pull request #29130: [SPARK-32330][SQL] Preserve shuffled hash join build side partitioning
c21 commented on pull request #29130: URL: https://github.com/apache/spark/pull/29130#issuecomment-659174967 cc @maropu, @cloud-fan, @gatorsmile and @sameeragarwal if you guys can help take a look. Thanks! This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] [spark] jiangxb1987 commented on a change in pull request #29032: [SPARK-32217] Plumb whether a worker would also be decommissioned along with executor
jiangxb1987 commented on a change in pull request #29032:
URL: https://github.com/apache/spark/pull/29032#discussion_r455524790
##
File path:
core/src/main/scala/org/apache/spark/deploy/client/StandaloneAppClient.scala
##
@@ -181,7 +182,8 @@ private[spark] class StandaloneAppClient(
if (ExecutorState.isFinished(state)) {
listener.executorRemoved(fullId, message.getOrElse(""), exitStatus,
workerLost)
} else if (state == ExecutorState.DECOMMISSIONED) {
- listener.executorDecommissioned(fullId, message.getOrElse(""))
+ listener.executorDecommissioned(fullId,
+ExecutorDecommissionInfo(message.getOrElse(""),
isHostDecommissioned = workerLost))
Review comment:
how is the flag `isHostDecommissioned` actually used?
##
File path: core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala
##
@@ -101,7 +101,8 @@ private[spark] trait TaskScheduler {
/**
* Process a decommissioning executor.
*/
- def executorDecommission(executorId: String): Unit
+ def executorDecommission(
Review comment:
nit: don't leave an empty implementation here.
##
File path:
core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
##
@@ -191,9 +191,9 @@ class CoarseGrainedSchedulerBackend(scheduler:
TaskSchedulerImpl, val rpcEnv: Rp
executorDataMap.get(executorId).foreach(_.executorEndpoint.send(StopExecutor))
removeExecutor(executorId, reason)
- case DecommissionExecutor(executorId) =>
+ case DecommissionExecutor(executorId, decommissionInfo) =>
logError(s"Received decommission executor message ${executorId}.")
Review comment:
do we want to also include the decommissionInfo in the error msg?
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[GitHub] [spark] c21 opened a new pull request #29130: [SPARK-32330][SQL] Preserve shuffled hash join build side partitioning
c21 opened a new pull request #29130:
URL: https://github.com/apache/spark/pull/29130
### What changes were proposed in this pull request?
Currently `ShuffledHashJoin.outputPartitioning` inherits from
`HashJoin.outputPartitioning`, which only preserves stream side partitioning
(`HashJoin.scala`):
```
override def outputPartitioning: Partitioning =
streamedPlan.outputPartitioning
```
This loses build side partitioning information, and causes extra shuffle if
there's another join / group-by after this join.
Example:
```
withSQLConf(
SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "50",
SQLConf.SHUFFLE_PARTITIONS.key -> "2",
SQLConf.PREFER_SORTMERGEJOIN.key -> "false") {
val df1 = spark.range(10).select($"id".as("k1"))
val df2 = spark.range(30).select($"id".as("k2"))
Seq("inner", "cross").foreach(joinType => {
val plan = df1.join(df2, $"k1" === $"k2",
joinType).groupBy($"k1").count()
.queryExecution.executedPlan
assert(plan.collect { case _: ShuffledHashJoinExec => true }.size === 1)
// No extra shuffle before aggregate
assert(plan.collect { case _: ShuffleExchangeExec => true }.size === 2)
})
}
```
Current physical plan (having an extra shuffle on `k1` before aggregate)
```
*(4) HashAggregate(keys=[k1#220L], functions=[count(1)], output=[k1#220L,
count#235L])
+- Exchange hashpartitioning(k1#220L, 2), true, [id=#117]
+- *(3) HashAggregate(keys=[k1#220L], functions=[partial_count(1)],
output=[k1#220L, count#239L])
+- *(3) Project [k1#220L]
+- ShuffledHashJoin [k1#220L], [k2#224L], Inner, BuildLeft
:- Exchange hashpartitioning(k1#220L, 2), true, [id=#109]
: +- *(1) Project [id#218L AS k1#220L]
: +- *(1) Range (0, 10, step=1, splits=2)
+- Exchange hashpartitioning(k2#224L, 2), true, [id=#111]
+- *(2) Project [id#222L AS k2#224L]
+- *(2) Range (0, 30, step=1, splits=2)
```
Ideal physical plan (no shuffle on `k1` before aggregate)
```
*(3) HashAggregate(keys=[k1#220L], functions=[count(1)], output=[k1#220L,
count#235L])
+- *(3) HashAggregate(keys=[k1#220L], functions=[partial_count(1)],
output=[k1#220L, count#239L])
+- *(3) Project [k1#220L]
+- ShuffledHashJoin [k1#220L], [k2#224L], Inner, BuildLeft
:- Exchange hashpartitioning(k1#220L, 2), true, [id=#107]
: +- *(1) Project [id#218L AS k1#220L]
: +- *(1) Range (0, 10, step=1, splits=2)
+- Exchange hashpartitioning(k2#224L, 2), true, [id=#109]
+- *(2) Project [id#222L AS k2#224L]
+- *(2) Range (0, 30, step=1, splits=2)
```
This can be fixed by overriding `outputPartitioning` method in
`ShuffledHashJoinExec`, similar to `SortMergeJoinExec`.
In addition, also fix one typo in `HashJoin`, as that code path is shared
between broadcast hash join and shuffled hash join.
### Why are the changes needed?
To avoid shuffle (for queries having multiple joins or group-by), for saving
CPU and IO.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added unit test in `JoinSuite`.
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[GitHub] [spark] SparkQA commented on pull request #28961: [SPARK-32143][SQL] Prevent a skewed join from producing too many partition splits
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[GitHub] [spark] zhengruifeng commented on pull request #29095: [SPARK-32298][ML] tree models prediction optimization
zhengruifeng commented on pull request #29095: URL: https://github.com/apache/spark/pull/29095#issuecomment-659171262 @viirya This PR is only on cpu. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] [spark] SparkQA commented on pull request #29032: [SPARK-32217] Plumb whether a worker would also be decommissioned along with executor
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[GitHub] [spark] viirya commented on a change in pull request #29107: [SPARK-32308][SQL] Move by-name resolution logic of unionByName from API code to analysis phase
viirya commented on a change in pull request #29107:
URL: https://github.com/apache/spark/pull/29107#discussion_r455521016
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala
##
@@ -1099,6 +1101,64 @@ object TypeCoercion {
DateSub(l, Literal(days))
}
}
+
+ /**
+ * Coerces different children of Union to a common set of columns. Note that
this must be
+ * run before `WidenSetOperationTypes`, because `WidenSetOperationTypes`
should be run on
+ * correctly resolved column by name.
+ */
+ object UnionCoercion extends TypeCoercionRule {
+private def unionTwoSides(
+left: LogicalPlan, right: LogicalPlan, allowMissingCol: Boolean):
LogicalPlan = {
+ val resolver = SQLConf.get.resolver
+ val leftOutputAttrs = left.output
+ val rightOutputAttrs = right.output
+
+ // Builds a project list for `right` based on `left` output names
+ val rightProjectList = leftOutputAttrs.map { lattr =>
+rightOutputAttrs.find { rattr => resolver(lattr.name, rattr.name)
}.getOrElse {
+ if (allowMissingCol) {
+Alias(Literal(null, lattr.dataType), lattr.name)()
+ } else {
+throw new AnalysisException(
+ s"""Cannot resolve column name "${lattr.name}" among """ +
+s"""(${rightOutputAttrs.map(_.name).mkString(", ")})""")
+ }
+}
+ }
+
+ // Delegates failure checks to `CheckAnalysis`
+ val notFoundAttrs = rightOutputAttrs.diff(rightProjectList)
+ val rightChild = Project(rightProjectList ++ notFoundAttrs, right)
+
+ // Builds a project for `logicalPlan` based on `right` output names, if
allowing
+ // missing columns.
+ val leftChild = if (allowMissingCol) {
+val missingAttrs = notFoundAttrs.map { attr =>
+ Alias(Literal(null, attr.dataType), attr.name)()
+}
+if (missingAttrs.nonEmpty) {
+ Project(leftOutputAttrs ++ missingAttrs, left)
+} else {
+ left
+}
+ } else {
+left
+ }
+ Union(leftChild, rightChild)
+}
+
+override protected def coerceTypes(plan: LogicalPlan): LogicalPlan = plan
resolveOperatorsUp {
+ case e if !e.childrenResolved => e
+
+ case Union(children, byName, allowMissingCol)
+ if byName =>
+val union = children.reduceLeft { (left: LogicalPlan, right:
LogicalPlan) =>
+ unionTwoSides(left, right, allowMissingCol)
Review comment:
If looks not proper after rethinking, we can also move to other rule or
create another rule.
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[GitHub] [spark] viirya commented on pull request #29095: [SPARK-32298][ML] tree models prediction optimization
viirya commented on pull request #29095: URL: https://github.com/apache/spark/pull/29095#issuecomment-659170171 Is this also memory optimization? But looks like cpu time optimization from the description? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] [spark] jiangxb1987 commented on a change in pull request #29015: [SPARK-32215] Expose a (protected) /workers/kill endpoint on the MasterWebUI
jiangxb1987 commented on a change in pull request #29015:
URL: https://github.com/apache/spark/pull/29015#discussion_r455517786
##
File path:
core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala
##
@@ -49,6 +55,26 @@ class MasterWebUI(
"/app/kill", "/", masterPage.handleAppKillRequest, httpMethods =
Set("POST")))
attachHandler(createRedirectHandler(
"/driver/kill", "/", masterPage.handleDriverKillRequest, httpMethods =
Set("POST")))
+attachHandler(createServletHandler("/workers/kill", new HttpServlet {
+ override def doPost(req: HttpServletRequest, resp: HttpServletResponse):
Unit = {
+val hostnames: Seq[String] = Option(req.getParameterValues("host"))
+ .getOrElse(Array[String]()).toSeq
+if (!isDecommissioningRequestAllowed(req)) {
+ resp.sendError(HttpServletResponse.SC_METHOD_NOT_ALLOWED)
+} else {
+ val removedWorkers =
masterEndpointRef.askSync[Integer](DecommissionHosts(hostnames))
+ logInfo(s"Decommissioning of hosts $hostnames decommissioned
${removedWorkers} workers")
Review comment:
nit: `${removedWorkers}` -> `$removedWorkers`
##
File path: core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
##
@@ -726,6 +726,61 @@ class MasterSuite extends SparkFunSuite
}
}
+ def testWorkerDecommissioning(
+ numWorkers: Int,
+ numWorkersExpectedToDecom: Int,
+ hostnames: Seq[String]): Unit = {
+val conf = new SparkConf()
+val master = makeAliveMaster(conf)
+val workerRegs = (1 to numWorkers).map{idx =>
+ val worker = new MockWorker(master.self, conf)
+ worker.rpcEnv.setupEndpoint("worker", worker)
+ val workerReg = RegisterWorker(
+worker.id,
+"localhost",
+worker.self.address.port,
+worker.self,
+10,
+1024,
+"http://localhost:8080";,
+RpcAddress("localhost", 1))
+ master.self.send(workerReg)
+ workerReg
+}
+
+eventually(timeout(10.seconds)) {
+ val masterState =
master.self.askSync[MasterStateResponse](RequestMasterState)
+ assert(masterState.workers.length === numWorkers)
+ assert(masterState.workers.forall(_.state == WorkerState.ALIVE))
+ assert(masterState.workers.map(_.id).toSet == workerRegs.map(_.id).toSet)
+ masterState.workers
Review comment:
nit: this is not needed
##
File path: core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
##
@@ -726,6 +726,61 @@ class MasterSuite extends SparkFunSuite
}
}
+ def testWorkerDecommissioning(
+ numWorkers: Int,
+ numWorkersExpectedToDecom: Int,
+ hostnames: Seq[String]): Unit = {
+val conf = new SparkConf()
+val master = makeAliveMaster(conf)
+val workerRegs = (1 to numWorkers).map{idx =>
+ val worker = new MockWorker(master.self, conf)
+ worker.rpcEnv.setupEndpoint("worker", worker)
+ val workerReg = RegisterWorker(
+worker.id,
+"localhost",
+worker.self.address.port,
+worker.self,
+10,
+1024,
+"http://localhost:8080";,
+RpcAddress("localhost", 1))
+ master.self.send(workerReg)
+ workerReg
+}
+
+eventually(timeout(10.seconds)) {
+ val masterState =
master.self.askSync[MasterStateResponse](RequestMasterState)
+ assert(masterState.workers.length === numWorkers)
+ assert(masterState.workers.forall(_.state == WorkerState.ALIVE))
+ assert(masterState.workers.map(_.id).toSet == workerRegs.map(_.id).toSet)
+ masterState.workers
+}
+
+val decomWorkersCount =
master.self.askSync[Integer](DecommissionHosts(hostnames))
+assert(decomWorkersCount === numWorkersExpectedToDecom)
+
+// Decommissioning is actually async ... wait for the workers to actually
be decommissioned by
+// polling the master's state.
+eventually(timeout(10.seconds)) {
Review comment:
nit: we may want to give a longer timeout to avoid flakyness.
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[GitHub] [spark] viirya commented on a change in pull request #29112: [SPARK-32310][ML][PySpark] ML params default value parity in classification, regression, clustering and fpm
viirya commented on a change in pull request #29112:
URL: https://github.com/apache/spark/pull/29112#discussion_r455519563
##
File path:
mllib/src/main/scala/org/apache/spark/ml/classification/FMClassifier.scala
##
@@ -85,7 +85,6 @@ class FMClassifier @Since("3.0.0") (
*/
@Since("3.0.0")
def setFactorSize(value: Int): this.type = set(factorSize, value)
- setDefault(factorSize -> 8)
Review comment:
Where do the default params of `FMClassifier` move?
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[GitHub] [spark] SparkQA commented on pull request #29126: [SPARK-32324][SQL]Fix error messages during using PIVOT and lateral view
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[GitHub] [spark] SparkQA commented on pull request #29032: [SPARK-32217] Plumb whether a worker would also be decommissioned along with executor
SparkQA commented on pull request #29032: URL: https://github.com/apache/spark/pull/29032#issuecomment-659167771 **[Test build #125926 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/125926/testReport)** for PR 29032 at commit [`cbd92ea`](https://github.com/apache/spark/commit/cbd92ea2529952dabe4cdb2d40d7b6d05bac399b). * This patch passes all tests. * This patch merges cleanly. * This patch adds no public classes. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] [spark] zhengruifeng commented on a change in pull request #29112: [SPARK-32310][ML][PySpark] ML params default value parity in classification, regression, clustering and fpm
zhengruifeng commented on a change in pull request #29112: URL: https://github.com/apache/spark/pull/29112#discussion_r455517498 ## File path: mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala ## @@ -68,6 +68,12 @@ private[clustering] trait BisectingKMeansParams extends Params with HasMaxIter "The minimum number of points (if >= 1.0) or the minimum proportion " + "of points (if < 1.0) of a divisible cluster.", ParamValidators.gt(0.0)) + + setDefault( Review comment: total nit: make these params in single line, like above places This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] [spark] zhengruifeng commented on pull request #29095: [SPARK-32298][ML] tree models prediction optimization
zhengruifeng commented on pull request #29095: URL: https://github.com/apache/spark/pull/29095#issuecomment-659165588 friendly ping @huaxingao @srowen @viirya Different another attempt to save RAM, this should be a clear optimization. I found that those methods can not be marked `@tailrec`, so I use while-loop instead. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] [spark] venkata91 edited a comment on pull request #28287: [SPARK-31418][SCHEDULER] Request more executors in case of dynamic allocation is enabled and a task becomes unschedulable due to spa
venkata91 edited a comment on pull request #28287: URL: https://github.com/apache/spark/pull/28287#issuecomment-658977388 > you can make a common function that has most of the code that gets called from 2 separate tests. one test passes with dynamic allocation on, the other with it off. that will reduce code duplication. nevermind, I made some changes to the test so that it tests the dynamic allocation block of code properly. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] [spark] HyukjinKwon commented on pull request #29117: [WIP] Debug flaky pip installation test failure
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[GitHub] [spark] frankyin-factual opened a new pull request #29129: [SPARK-31831] [SQL] [TESTS] put mocks in hive version subdirectory
frankyin-factual opened a new pull request #29129: URL: https://github.com/apache/spark/pull/29129 ### What changes were proposed in this pull request? put version dependent hive mocks into its own subdirectories. ### Why are the changes needed? Fix broken hive builds ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? This is a fix for tests. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] [spark] frankyin-factual commented on pull request #29069: [SPARK-31831][SQL][TESTS] Use subclasses for mock in HiveSessionImplSuite
frankyin-factual commented on pull request #29069: URL: https://github.com/apache/spark/pull/29069#issuecomment-659157488 @HeartSaVioR @dongjoon-hyun https://github.com/apache/spark/pull/29129 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] [spark] agrawaldevesh commented on pull request #29015: [SPARK-32215] Expose a (protected) /workers/kill endpoint on the MasterWebUI
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[GitHub] [spark] cloud-fan commented on pull request #29014: [SPARK-32199][SPARK-32198] Reduce job failures during decommissioning
cloud-fan commented on pull request #29014: URL: https://github.com/apache/spark/pull/29014#issuecomment-659156705 ok to test This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] [spark] agrawaldevesh commented on a change in pull request #28708: [SPARK-20629][CORE][K8S] Copy shuffle data when nodes are being shutdown
agrawaldevesh commented on a change in pull request #28708:
URL: https://github.com/apache/spark/pull/28708#discussion_r455424422
##
File path:
core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
##
@@ -168,7 +168,10 @@ private[spark] class NettyBlockTransferService(
// Everything else is encoded using our binary protocol.
val metadata =
JavaUtils.bufferToArray(serializer.newInstance().serialize((level, classTag)))
-val asStream = blockData.size() >
conf.get(config.MAX_REMOTE_BLOCK_SIZE_FETCH_TO_MEM)
+// We always transfer shuffle blocks as a stream for simplicity with the
receiving code since
+// they are always written to disk. Otherwise we check the block size.
+val asStream = (blockData.size() >
conf.get(config.MAX_REMOTE_BLOCK_SIZE_FETCH_TO_MEM) ||
Review comment:
nit: A parentheses isn't quite needed, but even if it is, then would it
be easier to read this as:
val asStream = (blockData.size() >
conf.get(config.MAX_REMOTE_BLOCK_SIZE_FETCH_TO_MEM) || blockId.isShuffle
##
File path: core/src/main/scala/org/apache/spark/storage/BlockId.scala
##
@@ -38,7 +38,10 @@ sealed abstract class BlockId {
// convenience methods
def asRDDId: Option[RDDBlockId] = if (isRDD) Some(asInstanceOf[RDDBlockId])
else None
def isRDD: Boolean = isInstanceOf[RDDBlockId]
- def isShuffle: Boolean = isInstanceOf[ShuffleBlockId] ||
isInstanceOf[ShuffleBlockBatchId]
+ def isShuffle: Boolean = {
+(isInstanceOf[ShuffleBlockId] || isInstanceOf[ShuffleBlockBatchId] ||
Review comment:
nit: Are the parentheses needed ?
##
File path:
core/src/main/scala/org/apache/spark/storage/BlockManagerDecommissioner.scala
##
@@ -0,0 +1,330 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.storage
+
+import java.util.concurrent.ExecutorService
+
+import scala.collection.JavaConverters._
+import scala.collection.mutable
+import scala.util.control.NonFatal
+
+import org.apache.spark._
+import org.apache.spark.internal.Logging
+import org.apache.spark.internal.config
+import org.apache.spark.shuffle.{MigratableResolver, ShuffleBlockInfo}
+import org.apache.spark.storage.BlockManagerMessages.ReplicateBlock
+import org.apache.spark.util.ThreadUtils
+
+/**
+ * Class to handle block manager decommissioning retries.
+ * It creates a Thread to retry offloading all RDD cache and Shuffle blocks
+ */
+private[storage] class BlockManagerDecommissioner(
+ conf: SparkConf,
+ bm: BlockManager) extends Logging {
+
+ private val maxReplicationFailuresForDecommission =
+conf.get(config.STORAGE_DECOMMISSION_MAX_REPLICATION_FAILURE_PER_BLOCK)
+
+ /**
+ * This runnable consumes any shuffle blocks in the queue for migration.
This part of a
+ * producer/consumer where the main migration loop updates the queue of
blocks to be migrated
+ * periodically. On migration failure, the current thread will reinsert the
block for another
+ * thread to consume. Each thread migrates blocks to a different particular
executor to avoid
+ * distribute the blocks as quickly as possible without overwhelming any
particular executor.
+ *
+ * There is no preference for which peer a given block is migrated to.
+ * This is notable different than the RDD cache block migration (further
down in this file)
+ * which uses the existing priority mechanism for determining where to
replicate blocks to.
+ * Generally speaking cache blocks are less impactful as they normally
represent narrow
+ * transformations and we normally have less cache present than shuffle data.
+ *
+ * The producer/consumer model is chosen for shuffle block migration to
maximize
+ * the chance of migrating all shuffle blocks before the executor is forced
to exit.
+ */
+ private class ShuffleMigrationRunnable(peer: BlockManagerId) extends
Runnable {
+@volatile var running = true
+override def run(): Unit = {
+ var migrating: Option[(ShuffleBlockInfo, Int)] = None
+ logInfo(s"Starting migration thread for ${peer}")
+ // Once a block fails to transfer to an executor stop trying to transfer
more blocks
+ try {
+w
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[GitHub] [spark] agrawaldevesh commented on pull request #29015: [SPARK-32215] Expose a (protected) /workers/kill endpoint on the MasterWebUI
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[GitHub] [spark] williamhyun opened a new pull request #29128: [SPARK-XXX][TESTS] Rename HADOOP2_MODULE_PROFILES to HADOOP_MODULE_PROFILES
williamhyun opened a new pull request #29128: URL: https://github.com/apache/spark/pull/29128 … ### What changes were proposed in this pull request? ### Why are the changes needed? ### Does this PR introduce _any_ user-facing change? ### How was this patch tested? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] [spark] frankyin-factual commented on pull request #29069: [SPARK-31831][SQL][TESTS] Use subclasses for mock in HiveSessionImplSuite
frankyin-factual commented on pull request #29069: URL: https://github.com/apache/spark/pull/29069#issuecomment-659154829 I am working on a combination of 1) and 2). Will push shortly. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] [spark] imback82 commented on a change in pull request #28676: [SPARK-31869][SQL] BroadcastHashJoinExec can utilize the build side for its output partitioning
imback82 commented on a change in pull request #28676:
URL: https://github.com/apache/spark/pull/28676#discussion_r455505839
##
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoinExec.scala
##
@@ -60,6 +62,67 @@ case class BroadcastHashJoinExec(
}
}
+ override lazy val outputPartitioning: Partitioning = {
+joinType match {
+ case _: InnerLike =>
+streamedPlan.outputPartitioning match {
+ case h: HashPartitioning => expandOutputPartitioning(h)
+ case c: PartitioningCollection => expandOutputPartitioning(c)
+ case other => other
+}
+ case _ => streamedPlan.outputPartitioning
+}
+ }
+
+ // An one-to-many mapping from a streamed key to build keys.
+ private lazy val streamedKeyToBuildKeyMapping = {
+val mapping = mutable.Map.empty[Expression, Seq[Expression]]
+streamedKeys.zip(buildKeys).foreach {
+ case (streamedKey, buildKey) =>
+val key = streamedKey.canonicalized
+mapping.get(key) match {
+ case Some(v) => mapping.put(key, v :+ buildKey)
+ case None => mapping.put(key, Seq(buildKey))
+}
+}
+mapping.toMap
+ }
+
+ // Expands the given partitioning collection recursively.
+ private def expandOutputPartitioning(
+ partitioning: PartitioningCollection): PartitioningCollection = {
+PartitioningCollection(partitioning.partitionings.flatMap {
+ case h: HashPartitioning => expandOutputPartitioning(h).partitionings
+ case c: PartitioningCollection => Seq(expandOutputPartitioning(c))
+ case other => Seq(other)
+})
+ }
+
+ // Expands the given hash partitioning by substituting streamed keys with
build keys.
+ // For example, if the expressions for the given partitioning are Seq("a",
"b", "c")
+ // where the streamed keys are Seq("b", "c") and the build keys are Seq("x",
"y"),
+ // the expanded partitioning will have the following expressions:
+ // Seq("a", "b", "c"), Seq("a", "b", "y"), Seq("a", "x", "c"), Seq("a", "x",
"y").
+ // The expanded expressions are returned as PartitioningCollection.
+ private def expandOutputPartitioning(partitioning: HashPartitioning):
PartitioningCollection = {
+def generateExprCombinations(
+current: Seq[Expression],
+accumulated: Seq[Expression]): Seq[Seq[Expression]] = {
+ if (current.isEmpty) {
+Seq(accumulated)
+ } else {
+val buildKeys =
streamedKeyToBuildKeyMapping.get(current.head.canonicalized)
+generateExprCombinations(current.tail, accumulated :+ current.head) ++
+ buildKeys.map { _.flatMap(b =>
generateExprCombinations(current.tail, accumulated :+ b))
Review comment:
I added a config to limit the expansion. (Please let me know if
introducing a new config knob is too much. Then I can just have a constant in
this class - not configurable).
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dongjoon-hyun commented on pull request #29069: URL: https://github.com/apache/spark/pull/29069#issuecomment-659151759 Thank you. I'm fine for all combination (including Hive 2.3 only testing). Please feel free to choose an option. From my side, this also looks not urgent since this is not blocking both GitHub Action and PRBuilder. It has been broken over 3 days already. I hope `Hive 1.2` is going to be removed in the near future eventually after we build a consensus. Sooner is better. In short, please proceed toward what you think is right, @HeartSaVioR . This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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