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Measures back-pressure on the job queue, and exerts control strategies at Block Generation #11
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adds a basic Listener test: In two (close) listener configurations, the speed of processing should remain (roughly) constant.
Equipped CongestionStrategies with default Ignore
…is > 10. Leaving it in this previous state would have provoked spurious failures.
Plugged them into Block Generation
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Closing and re-opening with the Reactive API. Please follow-up there: #13 |
nraychaudhuri
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Because `lazy val` uses `this` lock, if JobGenerator.stop and JobGenerator.doCheckpoint (JobGenerator.shouldCheckpoint has not yet been initialized) run at the same time, it may hang. Here are the stack traces for the deadlock: ```Java "pool-1-thread-1-ScalaTest-running-StreamingListenerSuite" #11 prio=5 os_prio=31 tid=0x00007fd35d094800 nid=0x5703 in Object.wait() [0x000000012ecaf000] java.lang.Thread.State: WAITING (on object monitor) at java.lang.Object.wait(Native Method) at java.lang.Thread.join(Thread.java:1245) - locked <0x00000007b5d8d7f8> (a org.apache.spark.util.EventLoop$$anon$1) at java.lang.Thread.join(Thread.java:1319) at org.apache.spark.util.EventLoop.stop(EventLoop.scala:81) at org.apache.spark.streaming.scheduler.JobGenerator.stop(JobGenerator.scala:155) - locked <0x00000007b5d8cea0> (a org.apache.spark.streaming.scheduler.JobGenerator) at org.apache.spark.streaming.scheduler.JobScheduler.stop(JobScheduler.scala:95) - locked <0x00000007b5d8ced8> (a org.apache.spark.streaming.scheduler.JobScheduler) at org.apache.spark.streaming.StreamingContext.stop(StreamingContext.scala:687) "JobGenerator" apache#67 daemon prio=5 os_prio=31 tid=0x00007fd35c3b9800 nid=0x9f03 waiting for monitor entry [0x0000000139e4a000] java.lang.Thread.State: BLOCKED (on object monitor) at org.apache.spark.streaming.scheduler.JobGenerator.shouldCheckpoint$lzycompute(JobGenerator.scala:63) - waiting to lock <0x00000007b5d8cea0> (a org.apache.spark.streaming.scheduler.JobGenerator) at org.apache.spark.streaming.scheduler.JobGenerator.shouldCheckpoint(JobGenerator.scala:63) at org.apache.spark.streaming.scheduler.JobGenerator.doCheckpoint(JobGenerator.scala:290) at org.apache.spark.streaming.scheduler.JobGenerator.org$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:182) at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:83) at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:82) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) ``` I can use this patch to produce this deadlock: zsxwing@8a88f28 And a timeout build in Jenkins due to this deadlock: https://amplab.cs.berkeley.edu/jenkins/job/NewSparkPullRequestBuilder/1654/ This PR initializes `checkpointWriter` before `eventLoop` uses it to avoid this deadlock. Author: zsxwing <[email protected]> Closes apache#8326 from zsxwing/SPARK-10125.
skyluc
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Mar 16, 2016
## What changes were proposed in this pull request?
This PR support visualization for subquery in SQL web UI, also improve the explain of subquery, especially when it's used together with whole stage codegen.
For example:
```python
>>> sqlContext.range(100).registerTempTable("range")
>>> sqlContext.sql("select id / (select sum(id) from range) from range where id > (select id from range limit 1)").explain(True)
== Parsed Logical Plan ==
'Project [unresolvedalias(('id / subquery#9), None)]
: +- 'SubqueryAlias subquery#9
: +- 'Project [unresolvedalias('sum('id), None)]
: +- 'UnresolvedRelation `range`, None
+- 'Filter ('id > subquery#8)
: +- 'SubqueryAlias subquery#8
: +- 'GlobalLimit 1
: +- 'LocalLimit 1
: +- 'Project [unresolvedalias('id, None)]
: +- 'UnresolvedRelation `range`, None
+- 'UnresolvedRelation `range`, None
== Analyzed Logical Plan ==
(id / scalarsubquery()): double
Project [(cast(id#0L as double) / cast(subquery#9 as double)) AS (id / scalarsubquery())#11]
: +- SubqueryAlias subquery#9
: +- Aggregate [(sum(id#0L),mode=Complete,isDistinct=false) AS sum(id)#10L]
: +- SubqueryAlias range
: +- Range 0, 100, 1, 4, [id#0L]
+- Filter (id#0L > subquery#8)
: +- SubqueryAlias subquery#8
: +- GlobalLimit 1
: +- LocalLimit 1
: +- Project [id#0L]
: +- SubqueryAlias range
: +- Range 0, 100, 1, 4, [id#0L]
+- SubqueryAlias range
+- Range 0, 100, 1, 4, [id#0L]
== Optimized Logical Plan ==
Project [(cast(id#0L as double) / cast(subquery#9 as double)) AS (id / scalarsubquery())#11]
: +- SubqueryAlias subquery#9
: +- Aggregate [(sum(id#0L),mode=Complete,isDistinct=false) AS sum(id)#10L]
: +- Range 0, 100, 1, 4, [id#0L]
+- Filter (id#0L > subquery#8)
: +- SubqueryAlias subquery#8
: +- GlobalLimit 1
: +- LocalLimit 1
: +- Project [id#0L]
: +- Range 0, 100, 1, 4, [id#0L]
+- Range 0, 100, 1, 4, [id#0L]
== Physical Plan ==
WholeStageCodegen
: +- Project [(cast(id#0L as double) / cast(subquery#9 as double)) AS (id / scalarsubquery())#11]
: : +- Subquery subquery#9
: : +- WholeStageCodegen
: : : +- TungstenAggregate(key=[], functions=[(sum(id#0L),mode=Final,isDistinct=false)], output=[sum(id)#10L])
: : : +- INPUT
: : +- Exchange SinglePartition, None
: : +- WholeStageCodegen
: : : +- TungstenAggregate(key=[], functions=[(sum(id#0L),mode=Partial,isDistinct=false)], output=[sum#14L])
: : : +- Range 0, 1, 4, 100, [id#0L]
: +- Filter (id#0L > subquery#8)
: : +- Subquery subquery#8
: : +- CollectLimit 1
: : +- WholeStageCodegen
: : : +- Project [id#0L]
: : : +- Range 0, 1, 4, 100, [id#0L]
: +- Range 0, 1, 4, 100, [id#0L]
```
The web UI looks like:
This PR also change the tree structure of WholeStageCodegen to make it consistent than others. Before this change, Both WholeStageCodegen and InputAdapter hold a references to the same plans, those could be updated without notify another, causing problems, this is discovered by apache#11403 .
## How was this patch tested?
Existing tests, also manual tests with the example query, check the explain and web UI.
Author: Davies Liu <[email protected]>
Closes apache#11417 from davies/viz_subquery.
typesafe-tools
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May 27, 2016
CheckpointWriter.stop() is prone to a race condition: if one thread calls `stop()` right as a checkpoint write task begins to execute, that write task may become blocked when trying to access `fs`, the shared Hadoop FileSystem, since both the `fs` getter and `stop` method synchronize on the same lock. Here's a thread-dump excerpt which illustrates the problem: ```java "pool-31-thread-1" apache#156 prio=5 os_prio=31 tid=0x00007fea02cd2000 nid=0x5c0b waiting for monitor entry [0x000000013bc4c000] java.lang.Thread.State: BLOCKED (on object monitor) at org.apache.spark.streaming.CheckpointWriter.org$apache$spark$streaming$CheckpointWriter$$fs(Checkpoint.scala:302) - waiting to lock <0x00000007bf53ee78> (a org.apache.spark.streaming.CheckpointWriter) at org.apache.spark.streaming.CheckpointWriter$CheckpointWriteHandler.run(Checkpoint.scala:224) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) "pool-1-thread-1-ScalaTest-running-MapWithStateSuite" #11 prio=5 os_prio=31 tid=0x00007fe9ff879800 nid=0x5703 waiting on condition [0x000000012e54c000] java.lang.Thread.State: TIMED_WAITING (parking) at sun.misc.Unsafe.park(Native Method) - parking to wait for <0x00000007bf564568> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject) at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215) at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078) at java.util.concurrent.ThreadPoolExecutor.awaitTermination(ThreadPoolExecutor.java:1465) at org.apache.spark.streaming.CheckpointWriter.stop(Checkpoint.scala:291) - locked <0x00000007bf53ee78> (a org.apache.spark.streaming.CheckpointWriter) at org.apache.spark.streaming.scheduler.JobGenerator.stop(JobGenerator.scala:159) - locked <0x00000007bf53ea90> (a org.apache.spark.streaming.scheduler.JobGenerator) at org.apache.spark.streaming.scheduler.JobScheduler.stop(JobScheduler.scala:115) - locked <0x00000007bf53d3f0> (a org.apache.spark.streaming.scheduler.JobScheduler) at org.apache.spark.streaming.StreamingContext$$anonfun$stop$1.apply$mcV$sp(StreamingContext.scala:680) at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1219) at org.apache.spark.streaming.StreamingContext.stop(StreamingContext.scala:679) - locked <0x00000007bf516a70> (a org.apache.spark.streaming.StreamingContext) at org.apache.spark.streaming.StreamingContext.stop(StreamingContext.scala:644) - locked <0x00000007bf516a70> (a org.apache.spark.streaming.StreamingContext) [...] ``` We can fix this problem by having `stop` and `fs` be synchronized on different locks: the synchronization on `stop` only needs to guard against multiple threads calling `stop` at the same time, whereas the synchronization on `fs` is only necessary for cross-thread visibility. There's only ever a single active checkpoint writer thread at a time, so we don't need to guard against concurrent access to `fs`. Thus, `fs` can simply become a `volatile` var, similar to `lastCheckpointTime`. This change should fix [SPARK-13693](https://issues.apache.org/jira/browse/SPARK-13693), a flaky `MapWithStateSuite` test suite which has recently been failing several times per day. It also results in a huge test speedup: prior to this patch, `MapWithStateSuite` took about 80 seconds to run, whereas it now runs in less than 10 seconds. For the `streaming` project's tests as a whole, they now run in ~220 seconds vs. ~354 before. /cc zsxwing and tdas for review. Author: Josh Rosen <[email protected]> Closes apache#12712 from JoshRosen/fix-checkpoint-writer-race.
skonto
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Jun 3, 2018
…pressions
## What changes were proposed in this pull request?
This PR changes the direction of expression transformation in the DecimalPrecision rule. Previously, the expressions were transformed down, which led to incorrect result types when decimal expressions had other decimal expressions as their operands. The root cause of this issue was in visiting outer nodes before their children. Consider the example below:
```
val inputSchema = StructType(StructField("col", DecimalType(26, 6)) :: Nil)
val sc = spark.sparkContext
val rdd = sc.parallelize(1 to 2).map(_ => Row(BigDecimal(12)))
val df = spark.createDataFrame(rdd, inputSchema)
// Works correctly since no nested decimal expression is involved
// Expected result type: (26, 6) * (26, 6) = (38, 12)
df.select($"col" * $"col").explain(true)
df.select($"col" * $"col").printSchema()
// Gives a wrong result since there is a nested decimal expression that should be visited first
// Expected result type: ((26, 6) * (26, 6)) * (26, 6) = (38, 12) * (26, 6) = (38, 18)
df.select($"col" * $"col" * $"col").explain(true)
df.select($"col" * $"col" * $"col").printSchema()
```
The example above gives the following output:
```
// Correct result without sub-expressions
== Parsed Logical Plan ==
'Project [('col * 'col) AS (col * col)#4]
+- LogicalRDD [col#1]
== Analyzed Logical Plan ==
(col * col): decimal(38,12)
Project [CheckOverflow((promote_precision(cast(col#1 as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) AS (col * col)#4]
+- LogicalRDD [col#1]
== Optimized Logical Plan ==
Project [CheckOverflow((col#1 * col#1), DecimalType(38,12)) AS (col * col)#4]
+- LogicalRDD [col#1]
== Physical Plan ==
*Project [CheckOverflow((col#1 * col#1), DecimalType(38,12)) AS (col * col)#4]
+- Scan ExistingRDD[col#1]
// Schema
root
|-- (col * col): decimal(38,12) (nullable = true)
// Incorrect result with sub-expressions
== Parsed Logical Plan ==
'Project [(('col * 'col) * 'col) AS ((col * col) * col)#11]
+- LogicalRDD [col#1]
== Analyzed Logical Plan ==
((col * col) * col): decimal(38,12)
Project [CheckOverflow((promote_precision(cast(CheckOverflow((promote_precision(cast(col#1 as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) AS ((col * col) * col)#11]
+- LogicalRDD [col#1]
== Optimized Logical Plan ==
Project [CheckOverflow((cast(CheckOverflow((col#1 * col#1), DecimalType(38,12)) as decimal(26,6)) * col#1), DecimalType(38,12)) AS ((col * col) * col)#11]
+- LogicalRDD [col#1]
== Physical Plan ==
*Project [CheckOverflow((cast(CheckOverflow((col#1 * col#1), DecimalType(38,12)) as decimal(26,6)) * col#1), DecimalType(38,12)) AS ((col * col) * col)#11]
+- Scan ExistingRDD[col#1]
// Schema
root
|-- ((col * col) * col): decimal(38,12) (nullable = true)
```
## How was this patch tested?
This PR was tested with available unit tests. Moreover, there are tests to cover previously failing scenarios.
Author: aokolnychyi <[email protected]>
Closes apache#18583 from aokolnychyi/spark-21332.
(cherry picked from commit 0be5fb4)
Signed-off-by: gatorsmile <[email protected]>
fdp-ci
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Nov 1, 2018
…/`to_avro`
## What changes were proposed in this pull request?
Previously in from_avro/to_avro, we override the method `simpleString` and `sql` for the string output. However, the override only affects the alias naming:
```
Project [from_avro('col,
...
, (mode,PERMISSIVE)) AS from_avro(col, struct<col1:bigint,col2:double>, Map(mode -> PERMISSIVE))#11]
```
It only makes the alias name quite long: `from_avro(col, struct<col1:bigint,col2:double>, Map(mode -> PERMISSIVE))`).
We should follow `from_csv`/`from_json` here, to override the method prettyName only, and we will get a clean alias name
```
... AS from_avro(col)#11
```
## How was this patch tested?
Manual check
Closes apache#22890 from gengliangwang/revise_from_to_avro.
Authored-by: Gengliang Wang <[email protected]>
Signed-off-by: gatorsmile <[email protected]>
yuchaoran2011
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May 7, 2019
…from_avro`/`to_avro` Back port apache#22890 to branch-2.4. It is a bug fix for this issue: https://issues.apache.org/jira/browse/SPARK-26063 ## What changes were proposed in this pull request? Previously in from_avro/to_avro, we override the method `simpleString` and `sql` for the string output. However, the override only affects the alias naming: ``` Project [from_avro('col, ... , (mode,PERMISSIVE)) AS from_avro(col, struct<col1:bigint,col2:double>, Map(mode -> PERMISSIVE))#11] ``` It only makes the alias name quite long: `from_avro(col, struct<col1:bigint,col2:double>, Map(mode -> PERMISSIVE))`). We should follow `from_csv`/`from_json` here, to override the method prettyName only, and we will get a clean alias name ``` ... AS from_avro(col)#11 ``` ## How was this patch tested? Manual check Closes apache#23047 from gengliangwang/backport_avro_pretty_name. Authored-by: Gengliang Wang <[email protected]> Signed-off-by: hyukjinkwon <[email protected]>
fdp-ci
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Dec 27, 2019
### Why are the changes needed? `EnsureRequirements` adds `ShuffleExchangeExec` (RangePartitioning) after Sort if `RoundRobinPartitioning` behinds it. This will cause 2 shuffles, and the number of partitions in the final stage is not the number specified by `RoundRobinPartitioning. **Example SQL** ``` SELECT /*+ REPARTITION(5) */ * FROM test ORDER BY a ``` **BEFORE** ``` == Physical Plan == *(1) Sort [a#0 ASC NULLS FIRST], true, 0 +- Exchange rangepartitioning(a#0 ASC NULLS FIRST, 200), true, [id=#11] +- Exchange RoundRobinPartitioning(5), false, [id=#9] +- Scan hive default.test [a#0, b#1], HiveTableRelation `default`.`test`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [a#0, b#1] ``` **AFTER** ``` == Physical Plan == *(1) Sort [a#0 ASC NULLS FIRST], true, 0 +- Exchange rangepartitioning(a#0 ASC NULLS FIRST, 5), true, [id=#11] +- Scan hive default.test [a#0, b#1], HiveTableRelation `default`.`test`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [a#0, b#1] ``` ### Does this PR introduce any user-facing change? No ### How was this patch tested? Run suite Tests and add new test for this. Closes apache#26946 from stczwd/RoundRobinPartitioning. Lead-authored-by: lijunqing <[email protected]> Co-authored-by: stczwd <[email protected]> Signed-off-by: Wenchen Fan <[email protected]>
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