## What changes were proposed in this pull request?
This PR proposes to throw an exception with better message rather than `ArrayIndexOutOfBoundsException` when temp directories could not be created.
Running the commands below:
```bash
./bin/spark-shell --conf spark.local.dir=/NONEXISTENT_DIR_ONE,/NONEXISTENT_DIR_TWO
```
produces ...
**Before**
```
Exception in thread "main" java.lang.ExceptionInInitializerError
...
Caused by: java.lang.ArrayIndexOutOfBoundsException: 0
...
```
**After**
```
Exception in thread "main" java.lang.ExceptionInInitializerError
...
Caused by: java.io.IOException: Failed to get a temp directory under [/NONEXISTENT_DIR_ONE,/NONEXISTENT_DIR_TWO].
...
```
## How was this patch tested?
Unit tests in `LocalDirsSuite.scala`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17768 from HyukjinKwon/throws-temp-dir-exception.
## What changes were proposed in this pull request?
When sending accumulator updates back to driver, the network overhead is pretty big as there are a lot of accumulators, e.g. `TaskMetrics` will send about 20 accumulators everytime, there may be a lot of `SQLMetric` if the query plan is complicated.
Therefore, it's critical to reduce the size of serialized accumulator. A simple way is to not send the name of internal accumulators to executor side, as it's unnecessary. When executor sends accumulator updates back to driver, we can look up the accumulator name in `AccumulatorContext` easily. Note that, we still need to send names of normal accumulators, as the user code run at executor side may rely on accumulator names.
In the future, we should reimplement `TaskMetrics` to not rely on accumulators and use custom serialization.
Tried on the example in https://issues.apache.org/jira/browse/SPARK-12837, the size of serialized accumulator has been cut down by about 40%.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17596 from cloud-fan/oom.
## What changes were proposed in this pull request?
This PR changes Executor's threads to `UninterruptibleThread` so that we can use `runUninterruptibly` in `CachedKafkaConsumer`. However, this is just best effort to avoid hanging forever. If the user uses`CachedKafkaConsumer` in another thread (e.g., create a new thread or Future), the potential hang may still happen.
## How was this patch tested?
The new added test.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17761 from zsxwing/int.
This change does a more thorough redaction of sensitive information from logs and UI
Add unit tests that ensure that no regressions happen that leak sensitive information to the logs.
The motivation for this change was appearance of password like so in `SparkListenerEnvironmentUpdate` in event logs under some JVM configurations:
`"sun.java.command":"org.apache.spark.deploy.SparkSubmit ... --conf spark.executorEnv.HADOOP_CREDSTORE_PASSWORD=secret_password ..."
`
Previously redaction logic was only checking if the key matched the secret regex pattern, it'd redact it's value. That worked for most cases. However, in the above case, the key (sun.java.command) doesn't tell much, so the value needs to be searched. This PR expands the check to check for values as well.
## How was this patch tested?
New unit tests added that ensure that no sensitive information is present in the event logs or the yarn logs. Old unit test in UtilsSuite was modified because the test was asserting that a non-sensitive property's value won't be redacted. However, the non-sensitive value had the literal "secret" in it which was causing it to redact. Simply updating the non-sensitive property's value to another arbitrary value (that didn't have "secret" in it) fixed it.
Author: Mark Grover <mark@apache.org>
Closes#17725 from markgrover/spark-20435.
## What changes were proposed in this pull request?
This is a follow-up of #14617 to make the name of memory related fields more meaningful.
Here for the backward compatibility, I didn't change `maxMemory` and `memoryUsed` fields.
## How was this patch tested?
Existing UT and local verification.
CC squito and tgravescs .
Author: jerryshao <sshao@hortonworks.com>
Closes#17700 from jerryshao/SPARK-20391.
## What changes were proposed in this pull request?
Pregel-based iterative algorithms with more than ~50 iterations begin to slow down and eventually fail with a StackOverflowError due to Spark's lack of support for long lineage chains.
This PR causes Pregel to checkpoint the graph periodically if the checkpoint directory is set.
This PR moves PeriodicGraphCheckpointer.scala from mllib to graphx, moves PeriodicRDDCheckpointer.scala, PeriodicCheckpointer.scala from mllib to core
## How was this patch tested?
unit tests, manual tests
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: ding <ding@localhost.localdomain>
Author: dding3 <ding.ding@intel.com>
Author: Michael Allman <michael@videoamp.com>
Closes#15125 from dding3/cp2_pregel.
## What changes were proposed in this pull request?
Current SHS (Spark History Server) two different ACLs:
* ACL of base URL, it is controlled by "spark.acls.enabled" or "spark.ui.acls.enabled", and with this enabled, only user configured with "spark.admin.acls" (or group) or "spark.ui.view.acls" (or group), or the user who started SHS could list all the applications, otherwise none of them can be listed. This will also affect REST APIs which listing the summary of all apps and one app.
* Per application ACL. This is controlled by "spark.history.ui.acls.enabled". With this enabled only history admin user and user/group who ran this app can access the details of this app.
With this two ACLs, we may encounter several unexpected behaviors:
1. if base URL's ACL (`spark.acls.enable`) is enabled but user A has no view permission. User "A" cannot see the app list but could still access details of it's own app.
2. if ACLs of base URL (`spark.acls.enable`) is disabled, then user "A" could download any application's event log, even it is not run by user "A".
3. The changes of Live UI's ACL will affect History UI's ACL which share the same conf file.
The unexpected behaviors is mainly because we have two different ACLs, ideally we should have only one to manage all.
So to improve SHS's ACL mechanism, here in this PR proposed to:
1. Disable "spark.acls.enable" and only use "spark.history.ui.acls.enable" for history server.
2. Check permission for event-log download REST API.
With this PR:
1. Admin user could see/download the list of all applications, as well as application details.
2. Normal user could see the list of all applications, but can only download and check the details of applications accessible to him.
## How was this patch tested?
New UTs are added, also verified in real cluster.
CC tgravescs vanzin please help to review, this PR changes the semantics you did previously. Thanks a lot.
Author: jerryshao <sshao@hortonworks.com>
Closes#17582 from jerryshao/SPARK-20239.
## What changes were proposed in this pull request?
In the current Spark's HistoryServer we expected to get `AccessControlException` during listing all the files, but unfortunately it was not worked because we actually doesn't check the access permission and no other calls will throw such exception. What was worse is that this check will be deferred until reading files, which is not necessary and quite verbose, since it will be printed out the exception in every 10 seconds when checking the files.
So here with this fix, we actually check the read permission during listing the files, which could avoid unnecessary file read later on and suppress the verbose log.
## How was this patch tested?
Add unit test to verify.
Author: jerryshao <sshao@hortonworks.com>
Closes#17495 from jerryshao/SPARK-20172.
## What changes were proposed in this pull request?
This was a regression introduced by my earlier PR here: https://github.com/apache/spark/pull/17531
It turns out NonFatal() does not in fact catch InterruptedException.
## How was this patch tested?
Extended cancellation unit test coverage. The first test fails before this patch.
cc JoshRosen mridulm
Author: Eric Liang <ekl@databricks.com>
Closes#17659 from ericl/spark-20358.
## What changes were proposed in this pull request?
This PR proposes to run Spark unidoc to test Javadoc 8 build as Javadoc 8 is easily re-breakable.
There are several problems with it:
- It introduces little extra bit of time to run the tests. In my case, it took 1.5 mins more (`Elapsed :[94.8746569157]`). How it was tested is described in "How was this patch tested?".
- > One problem that I noticed was that Unidoc appeared to be processing test sources: if we can find a way to exclude those from being processed in the first place then that might significantly speed things up.
(see joshrosen's [comment](https://issues.apache.org/jira/browse/SPARK-18692?focusedCommentId=15947627&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15947627))
To complete this automated build, It also suggests to fix existing Javadoc breaks / ones introduced by test codes as described above.
There fixes are similar instances that previously fixed. Please refer https://github.com/apache/spark/pull/15999 and https://github.com/apache/spark/pull/16013
Note that this only fixes **errors** not **warnings**. Please see my observation https://github.com/apache/spark/pull/17389#issuecomment-288438704 for spurious errors by warnings.
## How was this patch tested?
Manually via `jekyll build` for building tests. Also, tested via running `./dev/run-tests`.
This was tested via manually adding `time.time()` as below:
```diff
profiles_and_goals = build_profiles + sbt_goals
print("[info] Building Spark unidoc (w/Hive 1.2.1) using SBT with these arguments: ",
" ".join(profiles_and_goals))
+ import time
+ st = time.time()
exec_sbt(profiles_and_goals)
+ print("Elapsed :[%s]" % str(time.time() - st))
```
produces
```
...
========================================================================
Building Unidoc API Documentation
========================================================================
...
[info] Main Java API documentation successful.
...
Elapsed :[94.8746569157]
...
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17477 from HyukjinKwon/SPARK-18692.
## What changes were proposed in this pull request?
Add Locale.ROOT to internal calls to String `toLowerCase`, `toUpperCase`, to avoid inadvertent locale-sensitive variation in behavior (aka the "Turkish locale problem").
The change looks large but it is just adding `Locale.ROOT` (the locale with no country or language specified) to every call to these methods.
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#17527 from srowen/SPARK-20156.
## What changes were proposed in this pull request?
Synchronize access to openStreams map.
## How was this patch tested?
Existing tests.
Author: Bogdan Raducanu <bogdan@databricks.com>
Closes#17592 from bogdanrdc/SPARK-20243.
## What changes were proposed in this pull request?
With [SPARK-13992](https://issues.apache.org/jira/browse/SPARK-13992), Spark supports persisting data into off-heap memory, but the usage of on-heap and off-heap memory is not exposed currently, it is not so convenient for user to monitor and profile, so here propose to expose off-heap memory as well as on-heap memory usage in various places:
1. Spark UI's executor page will display both on-heap and off-heap memory usage.
2. REST request returns both on-heap and off-heap memory.
3. Also this can be gotten from MetricsSystem.
4. Last this usage can be obtained programmatically from SparkListener.
Attach the UI changes:
Backward compatibility is also considered for event-log and REST API. Old event log can still be replayed with off-heap usage displayed as 0. For REST API, only adds the new fields, so JSON backward compatibility can still be kept.
## How was this patch tested?
Unit test added and manual verification.
Author: jerryshao <sshao@hortonworks.com>
Closes#14617 from jerryshao/SPARK-17019.
## What changes were proposed in this pull request?
If tasks throw non-interrupted exceptions on kill (e.g. java.nio.channels.ClosedByInterruptException), their death is reported back as TaskFailed instead of TaskKilled. This causes stage failure in some cases.
This is reproducible as follows. Run the following, and then use SparkContext.killTaskAttempt to kill one of the tasks. The entire stage will fail since we threw a RuntimeException instead of InterruptedException.
```
spark.range(100).repartition(100).foreach { i =>
try {
Thread.sleep(10000000)
} catch {
case t: InterruptedException =>
throw new RuntimeException(t)
}
}
```
Based on the code in TaskSetManager, I think this also affects kills of speculative tasks. However, since the number of speculated tasks is few, and usually you need to fail a task a few times before the stage is cancelled, it unlikely this would be noticed in production unless both speculation was enabled and the num allowed task failures was = 1.
We should probably unconditionally return TaskKilled instead of TaskFailed if the task was killed by the driver, regardless of the actual exception thrown.
## How was this patch tested?
Unit test. The test fails before the change in Executor.scala
cc JoshRosen
Author: Eric Liang <ekl@databricks.com>
Closes#17531 from ericl/fix-task-interrupt.
## What changes were proposed in this pull request?
Make sure SESSION_LOCAL_TIMEZONE reflects the change in JVM's default timezone setting. Currently several timezone related tests fail as the change to default timezone is not picked up by SQLConf.
## How was this patch tested?
Added an unit test in ConfigEntrySuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#17537 from dilipbiswal/timezone_debug.
[SPARK-9002][CORE] KryoSerializer initialization does not include 'Array[Int]'
## What changes were proposed in this pull request?
Array[Int] has been registered in KryoSerializer.
The following file has been changed
core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
## How was this patch tested?
First, the issue was reproduced by new unit test.
Then, the issue was fixed to pass the failed test.
Author: Denis Bolshakov <denis.bolshakov@onefactor.com>
Closes#17482 from dbolshak/SPARK-9002.
## What changes were proposed in this pull request?
while submit apps with -v or --verbose， we can print the right queue name, but if we set a queue name with `spark.yarn.queue` by --conf or in the spark-default.conf, we just got `null` for the queue in Parsed arguments.
```
bin/spark-shell -v --conf spark.yarn.queue=thequeue
Using properties file: /home/hadoop/spark-2.1.0-bin-apache-hdp2.7.3/conf/spark-defaults.conf
....
Adding default property: spark.yarn.queue=default
Parsed arguments:
master yarn
deployMode client
...
queue null
....
verbose true
Spark properties used, including those specified through
--conf and those from the properties file /home/hadoop/spark-2.1.0-bin-apache-hdp2.7.3/conf/spark-defaults.conf:
spark.yarn.queue -> thequeue
....
```
## How was this patch tested?
ut and local verify
Author: Kent Yao <yaooqinn@hotmail.com>
Closes#17430 from yaooqinn/SPARK-20096.
## What changes were proposed in this pull request?
Implementations of strategies for resilient block replication for different resource managers that replicate the 3-replica strategy used by HDFS, where the first replica is on an executor, the second replica within the same rack as the executor and a third replica on a different rack.
The implementation involves providing two pluggable classes, one running in the driver that provides topology information for every host at cluster start and the second prioritizing a list of peer BlockManagerIds.
The prioritization itself can be thought of an optimization problem to find a minimal set of peers that satisfy certain objectives and replicating to these peers first. The objectives can be used to express richer constraints over and above HDFS like 3-replica strategy.
## How was this patch tested?
This patch was tested with unit tests for storage, along with new unit tests to verify prioritization behaviour.
Author: Shubham Chopra <schopra31@bloomberg.net>
Closes#13932 from shubhamchopra/PrioritizerStrategy.
## What changes were proposed in this pull request?
Currently we use system classloader to find HBase jars, if it is specified by `--jars`, then it will be failed with ClassNotFound issue. So here changing to use child classloader.
Also putting added jars and main jar into classpath of submitted application in yarn cluster mode, otherwise HBase jars specified with `--jars` will never be honored in cluster mode, and fetching tokens in client side will always be failed.
## How was this patch tested?
Unit test and local verification.
Author: jerryshao <sshao@hortonworks.com>
Closes#17388 from jerryshao/SPARK-20059.
This change modifies the way block data is encrypted to make the more
common cases faster, while penalizing an edge case. As a side effect
of the change, all data that goes through the block manager is now
encrypted only when needed, including the previous path (broadcast
variables) where that did not happen.
The way the change works is by not encrypting data that is stored in
memory; so if a serialized block is in memory, it will only be encrypted
once it is evicted to disk.
The penalty comes when transferring that encrypted data from disk. If the
data ends up in memory again, it is as efficient as before; but if the
evicted block needs to be transferred directly to a remote executor, then
there's now a performance penalty, since the code now uses a custom
FileRegion implementation to decrypt the data before transferring.
This also means that block data transferred between executors now is
not encrypted (and thus relies on the network library encryption support
for secrecy). Shuffle blocks are still transferred in encrypted form,
since they're handled in a slightly different way by the code. This also
keeps compatibility with existing external shuffle services, which transfer
encrypted shuffle blocks, and avoids having to make the external service
aware of encryption at all.
The serialization and deserialization APIs in the SerializerManager now
do not do encryption automatically; callers need to explicitly wrap their
streams with an appropriate crypto stream before using those.
As a result of these changes, some of the workarounds added in SPARK-19520
are removed here.
Testing: a new trait ("EncryptionFunSuite") was added that provides an easy
way to run a test twice, with encryption on and off; broadcast, block manager
and caching tests were modified to use this new trait so that the existing
tests exercise both encrypted and non-encrypted paths. I also ran some
applications with encryption turned on to verify that they still work,
including streaming tests that failed without the fix for SPARK-19520.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#17295 from vanzin/SPARK-19556.
## What changes were proposed in this pull request?
We must set the taskset to zombie before the DAGScheduler handles the taskEnded event. It's possible the taskEnded event will cause the DAGScheduler to launch a new stage attempt (this happens when map output data was lost), and if this happens before the taskSet has been set to zombie, it will appear that we have conflicting task sets.
Author: liujianhui <liujianhui@didichuxing>
Closes#17208 from liujianhuiouc/spark-19868.
## What changes were proposed in this pull request?
Executors cache a list of their peers that is refreshed by default every minute. The cached stale references were randomly being used for replication. Since those executors were removed from the master, they did not occur in the block locations as reported by the master. This was fixed by
1. Refreshing peer cache in the block manager before trying to pro-actively replicate. This way the probability of replicating to a failed executor is eliminated.
2. Explicitly stopping the block manager in the tests. This shuts down the RPC endpoint use by the block manager. This way, even if a block manager tries to replicate using a stale reference, the replication logic should take care of refreshing the list of peers after failure.
## How was this patch tested?
Tested manually
Author: Shubham Chopra <schopra31@bloomberg.net>
Author: Kay Ousterhout <kayousterhout@gmail.com>
Author: Shubham Chopra <shubhamchopra@users.noreply.github.com>
Closes#17325 from shubhamchopra/SPARK-19803.
## What changes were proposed in this pull request?
Commit 91fa80fe8a broke the build for scala 2.10. The commit uses `Regex.regex` field which is not available in Scala 2.10. This PR fixes this.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17420 from hvanhovell/SPARK-20070-2.0.
## What changes were proposed in this pull request?
The explain output of `DataSourceScanExec` can contain sensitive information (like Amazon keys). Such information should not end up in logs, or be exposed to non privileged users.
This PR addresses this by adding a redaction facility for the `DataSourceScanExec.treeString`. A user can enable this by setting a regex in the `spark.redaction.string.regex` configuration.
## How was this patch tested?
Added a unit test to check the output of DataSourceScanExec.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17397 from hvanhovell/SPARK-20070.
This commit adds a killTaskAttempt method to SparkContext, to allow users to
kill tasks so that they can be re-scheduled elsewhere.
This also refactors the task kill path to allow specifying a reason for the task kill. The reason is propagated opaquely through events, and will show up in the UI automatically as `(N killed: $reason)` and `TaskKilled: $reason`. Without this change, there is no way to provide the user feedback through the UI.
Currently used reasons are "stage cancelled", "another attempt succeeded", and "killed via SparkContext.killTask". The user can also specify a custom reason through `SparkContext.killTask`.
cc rxin
In the stage overview UI the reasons are summarized:
Within the stage UI you can see individual task kill reasons:
Existing tests, tried killing some stages in the UI and verified the messages are as expected.
Author: Eric Liang <ekl@databricks.com>
Author: Eric Liang <ekl@google.com>
Closes#17166 from ericl/kill-reason.
## What changes were proposed in this pull request?
1. Use a MedianHeap to record durations of successful tasks. When check speculatable tasks, we can get the median duration with O(1) time complexity.
2. `checkSpeculatableTasks` will synchronize `TaskSchedulerImpl`. If `checkSpeculatableTasks` doesn't finish with 100ms, then the possibility exists for that thread to release and then immediately re-acquire the lock. Change `scheduleAtFixedRate` to be `scheduleWithFixedDelay` when call method of `checkSpeculatableTasks`.
## How was this patch tested?
Added MedianHeapSuite.
Author: jinxing <jinxing6042@126.com>
Closes#16867 from jinxing64/SPARK-16929.
## What changes were proposed in this pull request?
Some `Schedulable` Entities(`Pool` and `TaskSetManager`) variables need refactoring for _immutability_ and _access modifiers_ levels as follows:
- From `var` to `val` (if there is no requirement): This is important to support immutability as much as possible.
- Sample => `Pool`: `weight`, `minShare`, `priority`, `name` and `taskSetSchedulingAlgorithm`.
- Access modifiers: Specially, `var`s access needs to be restricted from other parts of codebase to prevent potential side effects.
- `TaskSetManager`: `tasksSuccessful`, `totalResultSize`, `calculatedTasks` etc...
This PR is related with #15604 and has been created seperatedly to keep patch content as isolated and to help the reviewers.
## How was this patch tested?
Added new UTs and existing UT coverage.
Author: erenavsarogullari <erenavsarogullari@gmail.com>
Closes#16905 from erenavsarogullari/SPARK-19567.
(Jira: https://issues.apache.org/jira/browse/SPARK-17204)
## What changes were proposed in this pull request?
There are a couple of bugs in the `BlockManager` with respect to support for replicated off-heap storage. First, the locally-stored off-heap byte buffer is disposed of when it is replicated. It should not be. Second, the replica byte buffers are stored as heap byte buffers instead of direct byte buffers even when the storage level memory mode is off-heap. This PR addresses both of these problems.
## How was this patch tested?
`BlockManagerReplicationSuite` was enhanced to fill in the coverage gaps. It now fails if either of the bugs in this PR exist.
Author: Michael Allman <michael@videoamp.com>
Closes#16499 from mallman/spark-17204-replicated_off_heap_storage.
The previously hardcoded max 4 retries per stage is not suitable for all cluster configurations. Since spark retries a stage at the sign of the first fetch failure, you can easily end up with many stage retries to discover all the failures. In particular, two scenarios this value should change are (1) if there are more than 4 executors per node; in that case, it may take 4 retries to discover the problem with each executor on the node and (2) during cluster maintenance on large clusters, where multiple machines are serviced at once, but you also cannot afford total cluster downtime. By making this value configurable, cluster managers can tune this value to something more appropriate to their cluster configuration.
Unit tests
Author: Sital Kedia <skedia@fb.com>
Closes#17307 from sitalkedia/SPARK-13369.
## What changes were proposed in this pull request?
DebugFilesystem.assertNoOpenStreams throws an exception with a cause exception that actually shows the code line which leaked the stream.
## How was this patch tested?
New test in SparkContextSuite to check there is a cause exception.
Author: Bogdan Raducanu <bogdan@databricks.com>
Closes#17292 from bogdanrdc/SPARK-19946.
## What changes were proposed in this pull request?
The following FIFO & FAIR Schedulers Pool usage cases need to have unit test coverage :
- FIFO Scheduler just uses **root pool** so even if `spark.scheduler.pool` property is set, related pool is not created and `TaskSetManagers` are added to **root pool**.
- FAIR Scheduler uses `default pool` when `spark.scheduler.pool` property is not set. This can be happened when
- `Properties` object is **null**,
- `Properties` object is **empty**(`new Properties()`),
- **default pool** is set(`spark.scheduler.pool=default`).
- FAIR Scheduler creates a **new pool** with **default values** when `spark.scheduler.pool` property points a **non-existent** pool. This can be happened when **scheduler allocation file** is not set or it does not contain related pool.
## How was this patch tested?
New Unit tests are added.
Author: erenavsarogullari <erenavsarogullari@gmail.com>
Closes#15604 from erenavsarogullari/SPARK-18066.
## What changes were proposed in this pull request?
This PR introduces the following changes:
1. Move `SparkHadoopWriter` to `core/internal/io/`, so that it's in the same directory with `SparkHadoopMapReduceWriter`;
2. Move `SparkHadoopWriterUtils` to a separated file.
After this PR is merged, we may consolidate `SparkHadoopWriter` and `SparkHadoopMapReduceWriter`, and make the new commit protocol support the old `mapred` package's committer;
## How was this patch tested?
Tested by existing test cases.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#17304 from jiangxb1987/writer.
## What changes were proposed in this pull request?
It is sometimes useful to use multiple threads in a task to parallelize tasks. These threads might register some completion/failure listeners to clean up when the task completes or fails. We currently cannot register such a callback and be sure that it will get called, because the context might be in the process of invoking its callbacks, when the the callback gets registered.
This PR improves this by making sure that you cannot add a completion/failure listener from a different thread when the context is being marked as completed/failed in another thread. This is done by synchronizing these methods on the task context itself.
Failure listeners were called only once. Completion listeners now follow the same pattern; this lifts the idempotency requirement for completion listeners and makes it easier to implement them. In some cases we can (accidentally) add a completion/failure listener after the fact, these listeners will be called immediately in order make sure we can safely clean-up after a task.
As a result of this change we could make the `failure` and `completed` flags non-volatile. The `isCompleted()` method now uses synchronization to ensure that updates are visible across threads.
## How was this patch tested?
Adding tests to `TaskContestSuite` to test adding listeners to a completed/failed context.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17244 from hvanhovell/SPARK-19889.
## What changes were proposed in this pull request?
TaskSetManager is now using `System.getCurrentTimeMillis` when mark task as finished in `handleSuccessfulTask` and `handleFailedTask`. Thus developer cannot set the tasks finishing time in unit test. When `handleSuccessfulTask`, task's duration = `System.getCurrentTimeMillis` - launchTime(which can be set by `clock`), the result is not correct.
## How was this patch tested?
Existing tests.
Author: jinxing <jinxing6042@126.com>
Closes#17133 from jinxing64/SPARK-19793.
## What changes were proposed in this pull request?
200ms may be too short. Give more time for replication to happen and new block be reported to master
## How was this patch tested?
test manully
Author: uncleGen <hustyugm@gmail.com>
Author: dylon <hustyugm@gmail.com>
Closes#17144 from uncleGen/SPARK-19803.
## What changes were proposed in this pull request?
The properties that are serialized with a TaskDescription can have very long values (eg. "spark.job.description" which is set to the full sql statement with the thrift-server). DataOutputStream.writeUTF() does not work well for long strings, so this changes the way those values are serialized to handle longer strings.
## How was this patch tested?
Updated existing unit test to reproduce the issue. All unit tests via jenkins.
Author: Imran Rashid <irashid@cloudera.com>
Closes#17140 from squito/SPARK-19796.
## What changes were proposed in this pull request?
Fault-tolerance in spark requires special handling of shuffle fetch
failures. The Executor would catch FetchFailedException and send a
special msg back to the driver.
However, intervening user code could intercept that exception, and wrap
it with something else. This even happens in SparkSQL. So rather than
checking the thrown exception only, we'll store the fetch failure directly
in the TaskContext, where users can't touch it.
## How was this patch tested?
Added a test case which failed before the fix. Full test suite via jenkins.
Author: Imran Rashid <irashid@cloudera.com>
Closes#16639 from squito/SPARK-19276.
## What changes were proposed in this pull request?
Previously it was possible for there to be a race between a task failure and committing the output of a task. For example, the driver may mark a task attempt as failed due to an executor heartbeat timeout (possibly due to GC), but the task attempt actually ends up coordinating with the OutputCommitCoordinator once the executor recovers and committing its result. This will lead to any retry attempt failing because the task result has already been committed despite the original attempt failing.
This ensures that any previously failed task attempts cannot enter the commit protocol.
## How was this patch tested?
Added a unit test
Author: Patrick Woody <pwoody@palantir.com>
Closes#16959 from pwoody/pw/recordFailuresForCommitter.
## What changes were proposed in this pull request?
When function 'executorLost' is invoked in class 'TaskSetManager', it's significant to judge whether variable 'isZombie' is set to true.
This pull request fixes the following hang:
1.Open speculation switch in the application.
2.Run this app and suppose last task of shuffleMapStage 1 finishes. Let's get the record straight, from the eyes of DAG, this stage really finishes, and from the eyes of TaskSetManager, variable 'isZombie' is set to true, but variable runningTasksSet isn't empty because of speculation.
3.Suddenly, executor 3 is lost. TaskScheduler receiving this signal, invokes all executorLost functions of rootPool's taskSetManagers. DAG receiving this signal, removes all this executor's outputLocs.
4.TaskSetManager adds all this executor's tasks to pendingTasks and tells DAG they will be resubmitted (Attention: possibly not on time).
5.DAG starts to submit a new waitingStage, let's say shuffleMapStage 2, and going to find that shuffleMapStage 1 is its missing parent because some outputLocs are removed due to executor lost. Then DAG submits shuffleMapStage 1 again.
6.DAG still receives Task 'Resubmitted' signal from old taskSetManager, and increases the number of pendingTasks of shuffleMapStage 1 each time. However, old taskSetManager won't resolve new task to submit because its variable 'isZombie' is set to true.
7.Finally shuffleMapStage 1 never finishes in DAG together with all stages depending on it.
## How was this patch tested?
It's quite difficult to construct test cases.
Author: GavinGavinNo1 <gavingavinno1@gmail.com>
Author: 16092929 <16092929@cnsuning.com>
Closes#16855 from GavinGavinNo1/resolve-stage-blocked2.
## What changes were proposed in this pull request?
This PR proposes to fix the lint-breaks as below:
```
[ERROR] src/test/java/org/apache/spark/network/TransportResponseHandlerSuite.java:[29,8] (imports) UnusedImports: Unused import - org.apache.spark.network.buffer.ManagedBuffer.
[ERROR] src/main/java/org/apache/spark/unsafe/types/UTF8String.java:[156,10] (modifier) ModifierOrder: 'Nonnull' annotation modifier does not precede non-annotation modifiers.
[ERROR] src/main/java/org/apache/spark/SparkFirehoseListener.java:[122] (sizes) LineLength: Line is longer than 100 characters (found 105).
[ERROR] src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java:[164,78] (coding) OneStatementPerLine: Only one statement per line allowed.
[ERROR] src/test/java/test/org/apache/spark/JavaAPISuite.java:[1157] (sizes) LineLength: Line is longer than 100 characters (found 121).
[ERROR] src/test/java/org/apache/spark/streaming/JavaMapWithStateSuite.java:[149] (sizes) LineLength: Line is longer than 100 characters (found 113).
[ERROR] src/test/java/test/org/apache/spark/streaming/Java8APISuite.java:[146] (sizes) LineLength: Line is longer than 100 characters (found 122).
[ERROR] src/test/java/test/org/apache/spark/streaming/JavaAPISuite.java:[32,8] (imports) UnusedImports: Unused import - org.apache.spark.streaming.Time.
[ERROR] src/test/java/test/org/apache/spark/streaming/JavaAPISuite.java:[611] (sizes) LineLength: Line is longer than 100 characters (found 101).
[ERROR] src/test/java/test/org/apache/spark/streaming/JavaAPISuite.java:[1317] (sizes) LineLength: Line is longer than 100 characters (found 102).
[ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetAggregatorSuite.java:[91] (sizes) LineLength: Line is longer than 100 characters (found 102).
[ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetSuite.java:[113] (sizes) LineLength: Line is longer than 100 characters (found 101).
[ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetSuite.java:[164] (sizes) LineLength: Line is longer than 100 characters (found 110).
[ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetSuite.java:[212] (sizes) LineLength: Line is longer than 100 characters (found 114).
[ERROR] src/test/java/org/apache/spark/mllib/tree/JavaDecisionTreeSuite.java:[36] (sizes) LineLength: Line is longer than 100 characters (found 101).
[ERROR] src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java:[26,8] (imports) UnusedImports: Unused import - com.amazonaws.regions.RegionUtils.
[ERROR] src/test/java/org/apache/spark/streaming/kinesis/JavaKinesisStreamSuite.java:[20,8] (imports) UnusedImports: Unused import - com.amazonaws.regions.RegionUtils.
[ERROR] src/test/java/org/apache/spark/streaming/kinesis/JavaKinesisStreamSuite.java:[94] (sizes) LineLength: Line is longer than 100 characters (found 103).
[ERROR] src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java:[30,8] (imports) UnusedImports: Unused import - org.apache.spark.sql.api.java.UDF1.
[ERROR] src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java:[72] (sizes) LineLength: Line is longer than 100 characters (found 104).
[ERROR] src/main/java/org/apache/spark/examples/mllib/JavaRankingMetricsExample.java:[121] (sizes) LineLength: Line is longer than 100 characters (found 101).
[ERROR] src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java:[28,8] (imports) UnusedImports: Unused import - org.apache.spark.api.java.JavaRDD.
[ERROR] src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java:[29,8] (imports) UnusedImports: Unused import - org.apache.spark.api.java.JavaSparkContext.
```
## How was this patch tested?
Manually via
```bash
./dev/lint-java
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17072 from HyukjinKwon/java-lint.
## What changes were proposed in this pull request?
We are proposing addition of pro-active block replication in case of executor failures. BlockManagerMasterEndpoint does all the book-keeping to keep a track of all the executors and the blocks they hold. It also keeps a track of which executors are alive through heartbeats. When an executor is removed, all this book-keeping state is updated to reflect the lost executor. This step can be used to identify executors that are still in possession of a copy of the cached data and a message could be sent to them to use the existing "replicate" function to find and place new replicas on other suitable hosts. Blocks replicated this way will let the master know of their existence.
This can happen when an executor is lost, and would that way be pro-active as opposed be being done at query time.
## How was this patch tested?
This patch was tested with existing unit tests along with new unit tests added to test the functionality.
Author: Shubham Chopra <schopra31@bloomberg.net>
Closes#14412 from shubhamchopra/ProactiveBlockReplication.
Adds a test case that ensures that Executors gracefully handle a task that fails to deserialize, by sending back a reasonable failure message. This does not change any behavior (the prior behavior was already correct), it just adds a test case to prevent regression.
Author: Imran Rashid <irashid@cloudera.com>
Closes#16930 from squito/executor_task_deserialization.
This commit improves the tests that check the case when a
ShuffleMapTask completes successfully on an executor that has
failed. This commit improves the commenting around the existing
test for this, and adds some additional checks to make it more
clear what went wrong if the tests fail (the fact that these
tests are hard to understand came up in the context of markhamstra's
proposed fix for #16620).
This commit also removes a test that I realized tested exactly
the same functionality.
markhamstra, I verified that the new version of the test still fails (and
in a more helpful way) for your proposed change for #16620.
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#16892 from kayousterhout/SPARK-19560.
## What changes were proposed in this pull request?
Currently we can only check the estimated stats in logical plans by debugging. We need to provide an easier and more efficient way for developers/users.
In this pr, we add EXPLAIN COST command to show stats in the optimized logical plan.
E.g.
```
spark-sql> EXPLAIN COST select count(1) from store_returns;
...
== Optimized Logical Plan ==
Aggregate [count(1) AS count(1)#24L], Statistics(sizeInBytes=16.0 B, rowCount=1, isBroadcastable=false)
+- Project, Statistics(sizeInBytes=4.3 GB, rowCount=5.76E+8, isBroadcastable=false)
+- Relation[sr_returned_date_sk#3,sr_return_time_sk#4,sr_item_sk#5,sr_customer_sk#6,sr_cdemo_sk#7,sr_hdemo_sk#8,sr_addr_sk#9,sr_store_sk#10,sr_reason_sk#11,sr_ticket_number#12,sr_return_quantity#13,sr_return_amt#14,sr_return_tax#15,sr_return_amt_inc_tax#16,sr_fee#17,sr_return_ship_cost#18,sr_refunded_cash#19,sr_reversed_charge#20,sr_store_credit#21,sr_net_loss#22] parquet, Statistics(sizeInBytes=28.6 GB, rowCount=5.76E+8, isBroadcastable=false)
...
```
## How was this patch tested?
Add test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#16594 from wzhfy/showStats.
## What changes were proposed in this pull request?
Currently in Spark there're two issues when we add jars with invalid path:
* If the jar path is a empty string {--jar ",dummy.jar"}, then Spark will resolve it to the current directory path and add to classpath / file server, which is unwanted. This is happened in our programatic way to submit Spark application. From my understanding Spark should defensively filter out such empty path.
* If the jar path is a invalid path (file doesn't exist), `addJar` doesn't check it and will still add to file server, the exception will be delayed until job running. Actually this local path could be checked beforehand, no need to wait until task running. We have similar check in `addFile`, but lacks similar similar mechanism in `addJar`.
## How was this patch tested?
Add unit test and local manual verification.
Author: jerryshao <sshao@hortonworks.com>
Closes#17038 from jerryshao/SPARK-19707.
The REST API has a security filter that performs auth checks
based on the UI root's security manager. That works fine when
the UI root is the app's UI, but not when it's the history server.
In the SHS case, all users would be allowed to see all applications
through the REST API, even if the UI itself wouldn't be available
to them.
This change adds auth checks for each app access through the API
too, so that only authorized users can see the app's data.
The change also modifies the existing security filter to use
`HttpServletRequest.getRemoteUser()`, which is used in other
places. That is not necessarily the same as the principal's
name; for example, when using Hadoop's SPNEGO auth filter,
the remote user strips the realm information, which then matches
the user name registered as the owner of the application.
I also renamed the UIRootFromServletContext trait to a more generic
name since I'm using it to store more context information now.
Tested manually with an authentication filter enabled.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#16978 from vanzin/SPARK-19652.
## What changes were proposed in this pull request?
This PR proposes to fix new test failures on WIndows as below:
**Before**
```
KafkaRelationSuite:
- test late binding start offsets *** FAILED *** (7 seconds, 679 milliseconds)
Cause: java.nio.file.FileSystemException: C:\projects\spark\target\tmp\spark-4c4b0cd1-4cb7-4908-949d-1b0cc8addb50\topic-4-0\00000000000000000000.log -> C:\projects\spark\target\tmp\spark-4c4b0cd1-4cb7-4908-949d-1b0cc8addb50\topic-4-0\00000000000000000000.log.deleted: The process cannot access the file because it is being used by another process.
KafkaSourceSuite:
- deserialization of initial offset with Spark 2.1.0 *** FAILED *** (3 seconds, 542 milliseconds)
java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-97ef64fc-ae61-4ce3-ac59-287fd38bd824
- deserialization of initial offset written by Spark 2.1.0 *** FAILED *** (60 milliseconds)
java.nio.file.InvalidPathException: Illegal char <:> at index 2: /C:/projects/spark/external/kafka-0-10-sql/target/scala-2.11/test-classes/kafka-source-initial-offset-version-2.1.0.b
HiveDDLSuite:
- partitioned table should always put partition columns at the end of table schema *** FAILED *** (657 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-f1b83d09-850a-4bba-8e43-a2a28dfaa757;
DDLSuite:
- create a data source table without schema *** FAILED *** (94 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-a3f3c161-afae-4d6f-9182-e8642f77062b;
- SET LOCATION for managed table *** FAILED *** (219 milliseconds)
org.apache.spark.sql.catalyst.errors.package$TreeNodeException: execute, tree:
Exchange SinglePartit
+- *HashAggregate(keys=[], functions=[partial_count(1)], output=[count#99367L])
+- *FileScan parquet default.tbl[] Batched: true, Format: Parquet, Location: InMemoryFileIndex[file:/C:projectsspark arget mpspark-15be2f2f-4ea9-4c47-bfee-1b7b49363033], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<>
- insert data to a data source table which has a not existed location should succeed *** FAILED *** (16 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-34987671-e8d1-4624-ba5b-db1012e1246b;
- insert into a data source table with no existed partition location should succeed *** FAILED *** (16 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-4c6ccfbf-4091-4032-9fbc-3d40c58267d5;
- read data from a data source table which has a not existed location should succeed *** FAILED *** (0 milliseconds)
- read data from a data source table with no existed partition location should succeed *** FAILED *** (0 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-6af39e37-abd1-44e8-ac68-e2dfcf67a2f3;
InputOutputMetricsSuite:
- output metrics on records written *** FAILED *** (0 milliseconds)
java.lang.IllegalArgumentException: Wrong FS: file://C:\projects\spark\target\tmp\spark-cd69ee77-88f2-4202-bed6-19c0ee05ef55\InputOutputMetricsSuite, expected: file:///
- output metrics on records written - new Hadoop API *** FAILED *** (16 milliseconds)
java.lang.IllegalArgumentException: Wrong FS: file://C:\projects\spark\target\tmp\spark-b69e8fcb-047b-4de8-9cdf-5f026efb6762\InputOutputMetricsSuite, expected: file:///
```
**After**
```
KafkaRelationSuite:
- test late binding start offsets !!! CANCELED !!! (62 milliseconds)
KafkaSourceSuite:
- deserialization of initial offset with Spark 2.1.0 (5 seconds, 341 milliseconds)
- deserialization of initial offset written by Spark 2.1.0 (910 milliseconds)
HiveDDLSuite:
- partitioned table should always put partition columns at the end of table schema (2 seconds)
DDLSuite:
- create a data source table without schema (828 milliseconds)
- SET LOCATION for managed table (406 milliseconds)
- insert data to a data source table which has a not existed location should succeed (406 milliseconds)
- insert into a data source table with no existed partition location should succeed (453 milliseconds)
- read data from a data source table which has a not existed location should succeed (94 milliseconds)
- read data from a data source table with no existed partition location should succeed (265 milliseconds)
InputOutputMetricsSuite:
- output metrics on records written (172 milliseconds)
- output metrics on records written - new Hadoop API (297 milliseconds)
```
## How was this patch tested?
Fixed tests in `InputOutputMetricsSuite`, `KafkaRelationSuite`, `KafkaSourceSuite`, `DDLSuite.scala` and `HiveDDLSuite`.
Manually tested via AppVeyor as below:
`InputOutputMetricsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/633-20170219-windows-test/job/ex8nvwa6tsh7rmto
`KafkaRelationSuite`: https://ci.appveyor.com/project/spark-test/spark/build/633-20170219-windows-test/job/h8dlcowew52y8ncw
`KafkaSourceSuite`: https://ci.appveyor.com/project/spark-test/spark/build/634-20170219-windows-test/job/9ybgjl7yeubxcre4
`DDLSuite`: https://ci.appveyor.com/project/spark-test/spark/build/635-20170219-windows-test
`HiveDDLSuite`: https://ci.appveyor.com/project/spark-test/spark/build/633-20170219-windows-test/job/up6o9n47er087ltb
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16999 from HyukjinKwon/windows-fix.
## What changes were proposed in this pull request?
Use `BytesWritable.copyBytes`, not `getBytes`, because `getBytes` returns the underlying array, which may be reused when repeated reads don't need a different size, as is the case with binaryRecords APIs
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#16974 from srowen/SPARK-19646.
## What changes were proposed in this pull request?
Convert tests to use Java 8 lambdas, and modest related fixes to surrounding code.
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#16964 from srowen/SPARK-19534.
## What changes were proposed in this pull request?
`askSync` is already added in `RpcEndpointRef` (see SPARK-19347 and https://github.com/apache/spark/pull/16690#issuecomment-276850068) and `askWithRetry` is marked as deprecated.
As mentioned SPARK-18113(https://github.com/apache/spark/pull/16503#event-927953218):
>askWithRetry is basically an unneeded API, and a leftover from the akka days that doesn't make sense anymore. It's prone to cause deadlocks (exactly because it's blocking), it imposes restrictions on the caller (e.g. idempotency) and other things that people generally don't pay that much attention to when using it.
Since `askWithRetry` is just used inside spark and not in user logic. It might make sense to replace all of them with `askSync`.
## How was this patch tested?
This PR doesn't change code logic, existing unit test can cover.
Author: jinxing <jinxing@meituan.com>
Closes#16790 from jinxing64/SPARK-19450.
In current `DAGScheduler handleTaskCompletion` code, when event.reason is `Success`, it will first do `stage.pendingPartitions -= task.partitionId`, which maybe a bug when `FetchFailed` happens.
**Think about below**
1. Stage 0 runs and generates shuffle output data.
2. Stage 1 reads the output from stage 0 and generates more shuffle data. It has two tasks: ShuffleMapTask1 and ShuffleMapTask2, and these tasks are launched on executorA.
3. ShuffleMapTask1 fails to fetch blocks locally and sends a FetchFailed to the driver. The driver marks executorA as lost and updates failedEpoch;
4. The driver resubmits stage 0 so the missing output can be re-generated, and then once it completes, resubmits stage 1 with ShuffleMapTask1x and ShuffleMapTask2x.
5. ShuffleMapTask2 (from the original attempt of stage 1) successfully finishes on executorA and sends Success back to driver. This causes DAGScheduler::handleTaskCompletion to remove partition 2 from stage.pendingPartitions (line 1149), but it does not add the partition to the set of output locations (line 1192), because the task’s epoch is less than the failure epoch for the executor (because of the earlier failure on executor A)
6. ShuffleMapTask1x successfully finishes on executorB, causing the driver to remove partition 1 from stage.pendingPartitions. Combined with the previous step, this means that there are no more pending partitions for the stage, so the DAGScheduler marks the stage as finished (line 1196). However, the shuffle stage is not available (line 1215) because the completion for ShuffleMapTask2 was ignored because of its epoch, so the DAGScheduler resubmits the stage.
7. ShuffleMapTask2x is still running, so when TaskSchedulerImpl::submitTasks is called for the re-submitted stage, it throws an error, because there’s an existing active task set
**In this fix**
If a task completion is from a previous stage attempt and the epoch is too low
(i.e., it was from a failed executor), don't remove the corresponding partition
from pendingPartitions.
Author: jinxing <jinxing@meituan.com>
Author: jinxing <jinxing6042@126.com>
Closes#16620 from jinxing64/SPARK-19263.
## What changes were proposed in this pull request?
`ExternalAppendOnlyMap.forceSpill` now uses an assert to check if an iterator is not null in the map. However, the assertion is only true after the map is asked for iterator. Before it, if another memory consumer asks more memory than currently available, `ExternalAppendOnlyMap.forceSpill` is also be called too. In this case, we will see failure like this:
[info] java.lang.AssertionError: assertion failed
[info] at scala.Predef$.assert(Predef.scala:156)
[info] at org.apache.spark.util.collection.ExternalAppendOnlyMap.forceSpill(ExternalAppendOnlyMap.scala:196)
[info] at org.apache.spark.util.collection.Spillable.spill(Spillable.scala:111)
[info] at org.apache.spark.util.collection.ExternalAppendOnlyMapSuite$$anonfun$13.apply$mcV$sp(ExternalAppendOnlyMapSuite.scala:294)
This fixing is motivated by http://apache-spark-developers-list.1001551.n3.nabble.com/java-lang-AssertionError-assertion-failed-tc20277.html.
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16387 from viirya/fix-externalappendonlymap.
- Move external/java8-tests tests into core, streaming, sql and remove
- Remove MaxPermGen and related options
- Fix some reflection / TODOs around Java 8+ methods
- Update doc references to 1.7/1.8 differences
- Remove Java 7/8 related build profiles
- Update some plugins for better Java 8 compatibility
- Fix a few Java-related warnings
For the future:
- Update Java 8 examples to fully use Java 8
- Update Java tests to use lambdas for simplicity
- Update Java internal implementations to use lambdas
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#16871 from srowen/SPARK-19493.
## What changes were proposed in this pull request?
This change add an optional argument to `SparkContext.cancelStage()` and `SparkContext.cancelJob()` functions, which allows the caller to provide exact reason for the cancellation.
## How was this patch tested?
Adds unit test.
Author: Ala Luszczak <ala@databricks.com>
Closes#16887 from ala/cancel.
## What changes were proposed in this pull request?
All the process of offering resource and generating `TaskDescription` should be guarded by taskScheduler.synchronized in `reviveOffers`, otherwise there is race condition.
## How was this patch tested?
Existing unit tests.
Author: jinxing <jinxing@meituan.com>
Closes#16831 from jinxing64/SPARK-19263-FixRaceInTest.
## What changes were proposed in this pull request?
In SPARK-8425, we introduced a mechanism for blacklisting executors and nodes (hosts). After a certain number of failures, these resources would be "blacklisted" and no further work would be assigned to them for some period of time.
In some scenarios, it is better to fail fast, and to simply kill these unreliable resources. This changes proposes to do so by having the BlacklistTracker kill unreliable resources when they would otherwise be "blacklisted".
In order to be thread safe, this code depends on the CoarseGrainedSchedulerBackend sending a message to the driver backend in order to do the actual killing. This also helps to prevent a race which would permit work to begin on a resource (executor or node), between the time the resource is marked for killing and the time at which it is finally killed.
## How was this patch tested?
./dev/run-tests
Ran https://github.com/jsoltren/jose-utils/blob/master/blacklist/test-blacklist.sh, and checked logs to see executors and nodes being killed.
Testing can likely be improved here; suggestions welcome.
Author: José Hiram Soltren <jose@cloudera.com>
Closes#16650 from jsoltren/SPARK-16554-submit.
Make the SSL port configuration explicit, instead of deriving it
from the non-SSL port, but retain the existing functionality in
case anyone depends on it.
The change starts the HTTPS and HTTP connectors separately, so
that it's possible to use independent ports for each. For that to
work, the initialization of the server needs to be shuffled around
a bit. The change also makes it so the initialization of both
connectors is similar, and end up using the same Scheduler - previously
only the HTTP connector would use the correct one.
Also fixed some outdated documentation about a couple of services
that were removed long ago.
Tested with unit tests and by running spark-shell with SSL configs.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#16625 from vanzin/SPARK-17874.
## What changes were proposed in this pull request?
SPARK-19265 had made table relation cache general; this follow-up aims to make `tableRelationCache`'s maximum size configurable.
In order to do sanity-check, this patch also adds a `checkValue()` method to `TypedConfigBuilder`.
## How was this patch tested?
new test case: `test("conf entry: checkValue()")`
Author: Liwei Lin <lwlin7@gmail.com>
Closes#16736 from lw-lin/conf.
## What changes were proposed in this pull request?
- Remove support for Hadoop 2.5 and earlier
- Remove reflection and code constructs only needed to support multiple versions at once
- Update docs to reflect newer versions
- Remove older versions' builds and profiles.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#16810 from srowen/SPARK-19464.
JIRA Issue: https://issues.apache.org/jira/browse/SPARK-19260
## What changes were proposed in this pull request?
1. “spark.history.fs.logDirectory” supports with space character and “%20” characters.
2. As usually, if the run classpath includes hdfs-site.xml and core-site.xml files, the supplied path eg."/test" which does not contain a scheme should be taken as a HDFS path rather than a local path since the path parameter is a Hadoop dir.
## How was this patch tested?
Update Unit Test and take some manual tests
local:
.sbin/start-history-server.sh "file:/a b"
.sbin/start-history-server.sh "/abc%20c" (without hdfs-site.xml,core-site.xml)
.sbin/start-history-server.sh "/a b" (without hdfs-site.xml,core-site.xml)
.sbin/start-history-server.sh "/a b/a bc%20c" (without hdfs-site.xml,core-site.xml)
hdfs:
.sbin/start-history-server.sh "hdfs:/namenode:9000/a b"
.sbin/start-history-server.sh "/a b" (with hdfs-site.xml,core-site.xml)
.sbin/start-history-server.sh "/a b/a bc%20c" (with hdfs-site.xml,core-site.xml)
Author: zuotingbing <zuo.tingbing9@zte.com.cn>
Closes#16614 from zuotingbing/SPARK-19260.
## What changes were proposed in this pull request?
Before this change, with delay scheduling off, spark would effectively
ignore locality preferences for bulk scheduling. With this change,
locality preferences are used when multiple offers are made
simultaneously.
## How was this patch tested?
Test case added which fails without this change. All unit tests run via jenkins.
Author: Imran Rashid <irashid@cloudera.com>
Closes#16376 from squito/locality_without_delay.
## What changes were proposed in this pull request?
If `spark.scheduler.allocation.file` has invalid `minShare` or/and `weight` values, these cause :
- `NumberFormatException` due to `toInt` function
- `SparkContext` can not be initialized.
- It does not show meaningful error message to user.
In a nutshell, this functionality can be more robust by selecting one of the following flows :
**1-** Currently, if `schedulingMode` has an invalid value, a warning message is logged and default value is set as `FIFO`. Same pattern can be used for `minShare`(default: 0) and `weight`(default: 1) as well
**2-** Meaningful error message can be shown to the user for all invalid cases.
PR offers :
- `schedulingMode` handles just empty values. It also needs to be supported for **whitespace**, **non-uppercase**(fair, FaIr etc...) or `SchedulingMode.NONE` cases by setting default value(`FIFO`)
- `minShare` and `weight` handle just empty values. They also need to be supported for **non-integer** cases by setting default values.
- Some refactoring of `PoolSuite`.
**Code to Reproduce :**
```
val conf = new SparkConf().setAppName("spark-fairscheduler").setMaster("local")
conf.set("spark.scheduler.mode", "FAIR")
conf.set("spark.scheduler.allocation.file", "src/main/resources/fairscheduler-invalid-data.xml")
val sc = new SparkContext(conf)
```
**fairscheduler-invalid-data.xml :**
```
<allocations>
<pool name="production">
<schedulingMode>FIFO</schedulingMode>
<weight>invalid_weight</weight>
<minShare>2</minShare>
</pool>
</allocations>
```
**Stacktrace :**
```
Exception in thread "main" java.lang.NumberFormatException: For input string: "invalid_weight"
at java.lang.NumberFormatException.forInputString(NumberFormatException.java:65)
at java.lang.Integer.parseInt(Integer.java:580)
at java.lang.Integer.parseInt(Integer.java:615)
at scala.collection.immutable.StringLike$class.toInt(StringLike.scala:272)
at scala.collection.immutable.StringOps.toInt(StringOps.scala:29)
at org.apache.spark.scheduler.FairSchedulableBuilder$$anonfun$org$apache$spark$scheduler$FairSchedulableBuilder$$buildFairSchedulerPool$1.apply(SchedulableBuilder.scala:127)
at org.apache.spark.scheduler.FairSchedulableBuilder$$anonfun$org$apache$spark$scheduler$FairSchedulableBuilder$$buildFairSchedulerPool$1.apply(SchedulableBuilder.scala:102)
```
## How was this patch tested?
Added Unit Test Case.
Author: erenavsarogullari <erenavsarogullari@gmail.com>
Closes#15237 from erenavsarogullari/SPARK-17663.
## What changes were proposed in this pull request?
In `TaskMemoryManager `, when we acquire memory by calling `acquireExecutionMemory` and we can't acquire required memory, we will try to spill other memory consumers.
Currently, we simply iterates the memory consumers in a hash set. Normally each time the consumer will be iterated in the same order.
The first issue is that we might spill additional consumers. For example, if consumer 1 uses 10MB, consumer 2 uses 50MB, then consumer 3 acquires 100MB but we can only get 60MB and spilling is needed. We might spill both consumer 1 and consumer 2. But we actually just need to spill consumer 2 and get the required 100MB.
The second issue is that if we spill consumer 1 in first time spilling. After a while, consumer 1 now uses 5MB. Then consumer 4 may acquire some memory and spilling is needed again. Because we iterate the memory consumers in the same order, we will spill consumer 1 again. So for consumer 1, we will produce many small spilling files.
This patch modifies the way iterating the memory consumers. It sorts the memory consumers by their memory usage. So the consumer using more memory will spill first. Once it is spilled, even it acquires few memory again, in next time spilling happens it will not be the consumers to spill again if there are other consumers using more memory than it.
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16603 from viirya/sort-memoryconsumer-when-spill.
## What changes were proposed in this pull request?
The current code in `HeartbeatReceiverSuite`, executorId is set as below:
```
private val executorId1 = "executor-1"
private val executorId2 = "executor-2"
```
The executorId is sent to driver when register as below:
```
test("expire dead hosts should kill executors with replacement (SPARK-8119)") {
...
fakeSchedulerBackend.driverEndpoint.askSync[Boolean](
RegisterExecutor(executorId1, dummyExecutorEndpointRef1, "1.2.3.4", 0, Map.empty))
...
}
```
Receiving `RegisterExecutor` in `CoarseGrainedSchedulerBackend`, the executorId will be compared with `currentExecutorIdCounter` as below:
```
case RegisterExecutor(executorId, executorRef, hostname, cores, logUrls) =>
if (executorDataMap.contains(executorId)) {
executorRef.send(RegisterExecutorFailed("Duplicate executor ID: " + executorId))
context.reply(true)
} else {
...
executorDataMap.put(executorId, data)
if (currentExecutorIdCounter < executorId.toInt) {
currentExecutorIdCounter = executorId.toInt
}
...
```
`executorId.toInt` will cause NumberformatException.
This unit test can pass currently because of `askWithRetry`, when catching exception, RPC will call again, thus it will go `if` branch and return true.
**To fix**
Rectify executorId and replace `askWithRetry` with `askSync`, refer to https://github.com/apache/spark/pull/16690
## How was this patch tested?
This fix is for unit test and no need to add another one.(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: jinxing <jinxing@meituan.com>
Closes#16779 from jinxing64/SPARK-19437.
## What changes were proposed in this pull request?
Right now Netty PRC serializes `RequestMessage` using Java serialization, and the size of a single message (e.g., RequestMessage(..., "hello")`) is almost 1KB.
This PR optimizes it by serializing `RequestMessage` manually (eliminate unnecessary information from most messages, e.g., class names of `RequestMessage`, `NettyRpcEndpointRef`, ...), and reduces the above message size to 100+ bytes.
## How was this patch tested?
Jenkins
I did a simple test to measure the improvement:
Before
```
$ bin/spark-shell --master local-cluster[1,4,1024]
...
scala> for (i <- 1 to 10) {
| val start = System.nanoTime
| val s = sc.parallelize(1 to 1000000, 10 * 1000).count()
| val end = System.nanoTime
| println(s"$i\t" + ((end - start)/1000/1000))
| }
1 6830
2 4353
3 3322
4 3107
5 3235
6 3139
7 3156
8 3166
9 3091
10 3029
```
After:
```
$ bin/spark-shell --master local-cluster[1,4,1024]
...
scala> for (i <- 1 to 10) {
| val start = System.nanoTime
| val s = sc.parallelize(1 to 1000000, 10 * 1000).count()
| val end = System.nanoTime
| println(s"$i\t" + ((end - start)/1000/1000))
| }
1 6431
2 3643
3 2913
4 2679
5 2760
6 2710
7 2747
8 2793
9 2679
10 2651
```
I also captured the TCP packets for this test. Before this patch, the total size of TCP packets is ~1.5GB. After it, it reduces to ~1.2GB.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16706 from zsxwing/rpc-opt.
The redirect handler was installed only for the root of the server;
any other context ended up being served directly through the HTTP
port. Since every sub page (e.g. application UIs in the history
server) is a separate servlet context, this meant that everything
but the root was accessible via HTTP still.
The change adds separate names to each connector, and binds contexts
to specific connectors so that content is only served through the
HTTPS connector when it's enabled. In that case, the only thing that
binds to the HTTP connector is the redirect handler.
Tested with new unit tests and by checking a live history server.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#16582 from vanzin/SPARK-19220.
This change introduces a new auth mechanism to the transport library,
to be used when users enable strong encryption. This auth mechanism
has better security than the currently used DIGEST-MD5.
The new protocol uses symmetric key encryption to mutually authenticate
the endpoints, and is very loosely based on ISO/IEC 9798.
The new protocol falls back to SASL when it thinks the remote end is old.
Because SASL does not support asking the server for multiple auth protocols,
which would mean we could re-use the existing SASL code by just adding a
new SASL provider, the protocol is implemented outside of the SASL API
to avoid the boilerplate of adding a new provider.
Details of the auth protocol are discussed in the included README.md
file.
This change partly undos the changes added in SPARK-13331; AES encryption
is now decoupled from SASL authentication. The encryption code itself,
though, has been re-used as part of this change.
## How was this patch tested?
- Unit tests
- Tested Spark 2.2 against Spark 1.6 shuffle service with SASL enabled
- Tested Spark 2.2 against Spark 2.2 shuffle service with SASL fallback disabled
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#16521 from vanzin/SPARK-19139.
## What changes were proposed in this pull request?
Currently, spark history server REST API provides functionality to query applications by application start time range based on minDate and maxDate query parameters, but it lacks support to query applications by their end time. In this pull request we are proposing optional minEndDate and maxEndDate query parameters and filtering capability based on these parameters to spark history server REST API. This functionality can be used for following queries,
1. Applications finished in last 'x' minutes
2. Applications finished before 'y' time
3. Applications finished between 'x' time to 'y' time
4. Applications started from 'x' time and finished before 'y' time.
For backward compatibility, we can keep existing minDate and maxDate query parameters as they are and they can continue support filtering based on start time range.
## How was this patch tested?
Existing unit tests and 4 new unit tests.
Author: Parag Chaudhari <paragpc@amazon.com>
Closes#11867 from paragpc/master-SHS-query-by-endtime_2.
## What changes were proposed in this pull request?
Drop more elements when `stageData.taskData.size > retainedTasks` to reduce the number of times on call drop function.
## How was this patch tested?
Jenkins
Author: Yuming Wang <wgyumg@gmail.com>
Closes#16527 from wangyum/SPARK-19146.
## What changes were proposed in this pull request?
Although Spark history server UI shows task ‘status’ and ‘duration’ fields, it does not expose these fields in the REST API response. For the Spark history server API users, it is not possible to determine task status and duration. Spark history server has access to task status and duration from event log, but it is not exposing these in API. This patch is proposed to expose task ‘status’ and ‘duration’ fields in Spark history server REST API.
## How was this patch tested?
Modified existing test cases in org.apache.spark.deploy.history.HistoryServerSuite.
Author: Parag Chaudhari <paragpc@amazon.com>
Closes#16473 from paragpc/expose_task_status.
Builds on top of work in SPARK-8425 to update Application Level Blacklisting in the scheduler.
## What changes were proposed in this pull request?
Adds a UI to these patches by:
- defining new listener events for blacklisting and unblacklisting, nodes and executors;
- sending said events at the relevant points in BlacklistTracker;
- adding JSON (de)serialization code for these events;
- augmenting the Executors UI page to show which, and how many, executors are blacklisted;
- adding a unit test to make sure events are being fired;
- adding HistoryServerSuite coverage to verify that the SHS reads these events correctly.
- updates the Executor UI to show Blacklisted/Active/Dead as a tri-state in Executors Status
Updates .rat-excludes to pass tests.
username squito
## How was this patch tested?
./dev/run-tests
testOnly org.apache.spark.util.JsonProtocolSuite
testOnly org.apache.spark.scheduler.BlacklistTrackerSuite
testOnly org.apache.spark.deploy.history.HistoryServerSuite
https://github.com/jsoltren/jose-utils/blob/master/blacklist/test-blacklist.sh
Author: José Hiram Soltren <jose@cloudera.com>
Closes#16346 from jsoltren/SPARK-16654-submit.
## What changes were proposed in this pull request?
Method canCommit sends AskPermissionToCommitOutput using askWithRetry. If timeout, it will send again. Thus AskPermissionToCommitOutput can be received multi times. Method canCommit should return the same value when called by the same attempt multi times.
In implementation before this fix, method handleAskPermissionToCommit just check if there is committer already registered, which is not enough. When worker retries AskPermissionToCommitOutput it will get CommitDeniedException, then the task will fail with reason TaskCommitDenied, which is not regarded as a task failure(SPARK-11178), so TaskScheduler will schedule this task infinitely.
In this fix, use `ask` to replace `askWithRetry` in `canCommit` and make receiver idempotent.
## How was this patch tested?
Added a new unit test to OutputCommitCoordinatorSuite.
Author: jinxing <jinxing@meituan.com>
Closes#16503 from jinxing64/SPARK-18113.
## What changes were proposed in this pull request?
remove ununsed imports and outdated comments, and fix some minor code style issue.
## How was this patch tested?
existing ut
Author: uncleGen <hustyugm@gmail.com>
Closes#16591 from uncleGen/SPARK-19227.
## What changes were proposed in this pull request?
Adding option in spark-submit to allow overriding the default IvySettings used to resolve artifacts as part of the Spark Packages functionality. This will allow all artifact resolution to go through a central managed repository, such as Nexus or Artifactory, where site admins can better approve and control what is used with Spark apps.
This change restructures the creation of the IvySettings object in two distinct ways. First, if the `spark.ivy.settings` option is not defined then `buildIvySettings` will create a default settings instance, as before, with defined repositories (Maven Central) included. Second, if the option is defined, the ivy settings file will be loaded from the given path and only repositories defined within will be used for artifact resolution.
## How was this patch tested?
Existing tests for default behaviour, Manual tests that load a ivysettings.xml file with local and Nexus repositories defined. Added new test to load a simple Ivy settings file with a local filesystem resolver.
Author: Bryan Cutler <cutlerb@gmail.com>
Author: Ian Hummel <ian@themodernlife.net>
Closes#15119 from BryanCutler/spark-custom-IvySettings.
## What changes were proposed in this pull request?
This PR proposes to skip the tests for script transformation failed on Windows due to fixed bash location.
```
SQLQuerySuite:
- script *** FAILED *** (553 milliseconds)
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 56.0 failed 1 times, most recent failure: Lost task 0.0 in stage 56.0 (TID 54, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- Star Expansion - script transform *** FAILED *** (2 seconds, 375 milliseconds)
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 389.0 failed 1 times, most recent failure: Lost task 0.0 in stage 389.0 (TID 725, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- test script transform for stdout *** FAILED *** (2 seconds, 813 milliseconds)
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 391.0 failed 1 times, most recent failure: Lost task 0.0 in stage 391.0 (TID 726, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- test script transform for stderr *** FAILED *** (2 seconds, 407 milliseconds)
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 393.0 failed 1 times, most recent failure: Lost task 0.0 in stage 393.0 (TID 727, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- test script transform data type *** FAILED *** (171 milliseconds)
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 395.0 failed 1 times, most recent failure: Lost task 0.0 in stage 395.0 (TID 728, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
```
```
HiveQuerySuite:
- transform *** FAILED *** (359 milliseconds)
Failed to execute query using catalyst:
Error: Job aborted due to stage failure: Task 0 in stage 1347.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1347.0 (TID 2395, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- schema-less transform *** FAILED *** (344 milliseconds)
Failed to execute query using catalyst:
Error: Job aborted due to stage failure: Task 0 in stage 1348.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1348.0 (TID 2396, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- transform with custom field delimiter *** FAILED *** (296 milliseconds)
Failed to execute query using catalyst:
Error: Job aborted due to stage failure: Task 0 in stage 1349.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1349.0 (TID 2397, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- transform with custom field delimiter2 *** FAILED *** (297 milliseconds)
Failed to execute query using catalyst:
Error: Job aborted due to stage failure: Task 0 in stage 1350.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1350.0 (TID 2398, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- transform with custom field delimiter3 *** FAILED *** (312 milliseconds)
Failed to execute query using catalyst:
Error: Job aborted due to stage failure: Task 0 in stage 1351.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1351.0 (TID 2399, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- transform with SerDe2 *** FAILED *** (437 milliseconds)
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1355.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1355.0 (TID 2403, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
```
```
LogicalPlanToSQLSuite:
- script transformation - schemaless *** FAILED *** (78 milliseconds)
...
Cause: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1968.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1968.0 (TID 3932, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- script transformation - alias list *** FAILED *** (94 milliseconds)
...
Cause: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1969.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1969.0 (TID 3933, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- script transformation - alias list with type *** FAILED *** (93 milliseconds)
...
Cause: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1970.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1970.0 (TID 3934, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- script transformation - row format delimited clause with only one format property *** FAILED *** (78 milliseconds)
...
Cause: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1971.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1971.0 (TID 3935, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- script transformation - row format delimited clause with multiple format properties *** FAILED *** (94 milliseconds)
...
Cause: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1972.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1972.0 (TID 3936, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- script transformation - row format serde clauses with SERDEPROPERTIES *** FAILED *** (78 milliseconds)
...
Cause: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1973.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1973.0 (TID 3937, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- script transformation - row format serde clauses without SERDEPROPERTIES *** FAILED *** (78 milliseconds)
...
Cause: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1974.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1974.0 (TID 3938, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
```
```
ScriptTransformationSuite:
- cat without SerDe *** FAILED *** (156 milliseconds)
...
Caused by: java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- cat with LazySimpleSerDe *** FAILED *** (63 milliseconds)
...
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 2383.0 failed 1 times, most recent failure: Lost task 0.0 in stage 2383.0 (TID 4819, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- script transformation should not swallow errors from upstream operators (no serde) *** FAILED *** (78 milliseconds)
...
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 2384.0 failed 1 times, most recent failure: Lost task 0.0 in stage 2384.0 (TID 4820, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- script transformation should not swallow errors from upstream operators (with serde) *** FAILED *** (47 milliseconds)
...
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 2385.0 failed 1 times, most recent failure: Lost task 0.0 in stage 2385.0 (TID 4821, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
- SPARK-14400 script transformation should fail for bad script command *** FAILED *** (47 milliseconds)
"Job aborted due to stage failure: Task 0 in stage 2386.0 failed 1 times, most recent failure: Lost task 0.0 in stage 2386.0 (TID 4822, localhost, executor driver): java.io.IOException: Cannot run program "/bin/bash": CreateProcess error=2, The system cannot find the file specified
```
## How was this patch tested?
AppVeyor as below:
```
SQLQuerySuite:
- script !!! CANCELED !!! (63 milliseconds)
- Star Expansion - script transform !!! CANCELED !!! (0 milliseconds)
- test script transform for stdout !!! CANCELED !!! (0 milliseconds)
- test script transform for stderr !!! CANCELED !!! (0 milliseconds)
- test script transform data type !!! CANCELED !!! (0 milliseconds)
```
```
HiveQuerySuite:
- transform !!! CANCELED !!! (31 milliseconds)
- schema-less transform !!! CANCELED !!! (0 milliseconds)
- transform with custom field delimiter !!! CANCELED !!! (0 milliseconds)
- transform with custom field delimiter2 !!! CANCELED !!! (0 milliseconds)
- transform with custom field delimiter3 !!! CANCELED !!! (0 milliseconds)
- transform with SerDe2 !!! CANCELED !!! (0 milliseconds)
```
```
LogicalPlanToSQLSuite:
- script transformation - schemaless !!! CANCELED !!! (78 milliseconds)
- script transformation - alias list !!! CANCELED !!! (0 milliseconds)
- script transformation - alias list with type !!! CANCELED !!! (0 milliseconds)
- script transformation - row format delimited clause with only one format property !!! CANCELED !!! (15 milliseconds)
- script transformation - row format delimited clause with multiple format properties !!! CANCELED !!! (0 milliseconds)
- script transformation - row format serde clauses with SERDEPROPERTIES !!! CANCELED !!! (0 milliseconds)
- script transformation - row format serde clauses without SERDEPROPERTIES !!! CANCELED !!! (0 milliseconds)
```
```
ScriptTransformationSuite:
- cat without SerDe !!! CANCELED !!! (62 milliseconds)
- cat with LazySimpleSerDe !!! CANCELED !!! (0 milliseconds)
- script transformation should not swallow errors from upstream operators (no serde) !!! CANCELED !!! (0 milliseconds)
- script transformation should not swallow errors from upstream operators (with serde) !!! CANCELED !!! (0 milliseconds)
- SPARK-14400 script transformation should fail for bad script command !!! CANCELED !!! (0 milliseconds)
```
Jenkins tests
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16501 from HyukjinKwon/windows-bash.
## What changes were proposed in this pull request?
This PR proposes to fix all the test failures identified by testing with AppVeyor.
**Scala - aborted tests**
```
WindowQuerySuite:
Exception encountered when attempting to run a suite with class name: org.apache.spark.sql.hive.execution.WindowQuerySuite *** ABORTED *** (156 milliseconds)
org.apache.spark.sql.AnalysisException: LOAD DATA input path does not exist: C:projectssparksqlhive argetscala-2.11 est-classesdatafilespart_tiny.txt;
OrcSourceSuite:
Exception encountered when attempting to run a suite with class name: org.apache.spark.sql.hive.orc.OrcSourceSuite *** ABORTED *** (62 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
ParquetMetastoreSuite:
Exception encountered when attempting to run a suite with class name: org.apache.spark.sql.hive.ParquetMetastoreSuite *** ABORTED *** (4 seconds, 703 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
ParquetSourceSuite:
Exception encountered when attempting to run a suite with class name: org.apache.spark.sql.hive.ParquetSourceSuite *** ABORTED *** (3 seconds, 907 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-581a6575-454f-4f21-a516-a07f95266143;
KafkaRDDSuite:
Exception encountered when attempting to run a suite with class name: org.apache.spark.streaming.kafka.KafkaRDDSuite *** ABORTED *** (5 seconds, 212 milliseconds)
java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-4722304d-213e-4296-b556-951df1a46807
DirectKafkaStreamSuite:
Exception encountered when attempting to run a suite with class name: org.apache.spark.streaming.kafka.DirectKafkaStreamSuite *** ABORTED *** (7 seconds, 127 milliseconds)
java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-d0d3eba7-4215-4e10-b40e-bb797e89338e
at org.apache.spark.util.Utils$.deleteRecursively(Utils.scala:1010)
ReliableKafkaStreamSuite
Exception encountered when attempting to run a suite with class name: org.apache.spark.streaming.kafka.ReliableKafkaStreamSuite *** ABORTED *** (5 seconds, 498 milliseconds)
java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-d33e45a0-287e-4bed-acae-ca809a89d888
KafkaStreamSuite:
Exception encountered when attempting to run a suite with class name: org.apache.spark.streaming.kafka.KafkaStreamSuite *** ABORTED *** (2 seconds, 892 milliseconds)
java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-59c9d169-5a56-4519-9ef0-cefdbd3f2e6c
KafkaClusterSuite:
Exception encountered when attempting to run a suite with class name: org.apache.spark.streaming.kafka.KafkaClusterSuite *** ABORTED *** (1 second, 690 milliseconds)
java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-3ef402b0-8689-4a60-85ae-e41e274f179d
DirectKafkaStreamSuite:
Exception encountered when attempting to run a suite with class name: org.apache.spark.streaming.kafka010.DirectKafkaStreamSuite *** ABORTED *** (59 seconds, 626 milliseconds)
java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-426107da-68cf-4d94-b0d6-1f428f1c53f6
KafkaRDDSuite:
Exception encountered when attempting to run a suite with class name: org.apache.spark.streaming.kafka010.KafkaRDDSuite *** ABORTED *** (2 minutes, 6 seconds)
java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-b9ce7929-5dae-46ab-a0c4-9ef6f58fbc2
```
**Java - failed tests**
```
Test org.apache.spark.streaming.kafka.JavaKafkaRDDSuite.testKafkaRDD failed: java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-1cee32f4-4390-4321-82c9-e8616b3f0fb0, took 9.61 sec
Test org.apache.spark.streaming.kafka.JavaKafkaStreamSuite.testKafkaStream failed: java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-f42695dd-242e-4b07-847c-f299b8e4676e, took 11.797 sec
Test org.apache.spark.streaming.kafka.JavaDirectKafkaStreamSuite.testKafkaStream failed: java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-85c0d062-78cf-459c-a2dd-7973572101ce, took 1.581 sec
Test org.apache.spark.streaming.kafka010.JavaKafkaRDDSuite.testKafkaRDD failed: java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-49eb6b5c-8366-47a6-83f2-80c443c48280, took 17.895 sec
org.apache.spark.streaming.kafka010.JavaDirectKafkaStreamSuite.testKafkaStream failed: java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-898cf826-d636-4b1c-a61a-c12a364c02e7, took 8.858 sec
```
**Scala - failed tests**
```
PartitionProviderCompatibilitySuite:
- insert overwrite partition of new datasource table overwrites just partition *** FAILED *** (828 milliseconds)
java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-bb6337b9-4f99-45ab-ad2c-a787ab965c09
- SPARK-18635 special chars in partition values - partition management true *** FAILED *** (5 seconds, 360 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- SPARK-18635 special chars in partition values - partition management false *** FAILED *** (141 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
```
```
UtilsSuite:
- reading offset bytes of a file (compressed) *** FAILED *** (0 milliseconds)
java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-ecb2b7d5-db8b-43a7-b268-1bf242b5a491
- reading offset bytes across multiple files (compressed) *** FAILED *** (0 milliseconds)
java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-25cc47a8-1faa-4da5-8862-cf174df63ce0
```
```
StatisticsSuite:
- MetastoreRelations fallback to HDFS for size estimation *** FAILED *** (110 milliseconds)
org.apache.spark.sql.catalyst.analysis.NoSuchTableException: Table or view 'csv_table' not found in database 'default';
```
```
SQLQuerySuite:
- permanent UDTF *** FAILED *** (125 milliseconds)
org.apache.spark.sql.AnalysisException: Undefined function: 'udtf_count_temp'. This function is neither a registered temporary function nor a permanent function registered in the database 'default'.; line 1 pos 24
- describe functions - user defined functions *** FAILED *** (125 milliseconds)
org.apache.spark.sql.AnalysisException: Undefined function: 'udtf_count'. This function is neither a registered temporary function nor a permanent function registered in the database 'default'.; line 1 pos 7
- CTAS without serde with location *** FAILED *** (16 milliseconds)
java.lang.IllegalArgumentException: java.net.URISyntaxException: Relative path in absolute URI: file:C:projectsspark%09arget%09mpspark-ed673d73-edfc-404e-829e-2e2b9725d94e/c1
- derived from Hive query file: drop_database_removes_partition_dirs.q *** FAILED *** (47 milliseconds)
java.lang.IllegalArgumentException: java.net.URISyntaxException: Relative path in absolute URI: file:C:projectsspark%09arget%09mpspark-d2ddf08e-699e-45be-9ebd-3dfe619680fe/drop_database_removes_partition_dirs_table
- derived from Hive query file: drop_table_removes_partition_dirs.q *** FAILED *** (0 milliseconds)
java.lang.IllegalArgumentException: java.net.URISyntaxException: Relative path in absolute URI: file:C:projectsspark%09arget%09mpspark-d2ddf08e-699e-45be-9ebd-3dfe619680fe/drop_table_removes_partition_dirs_table2
- SPARK-17796 Support wildcard character in filename for LOAD DATA LOCAL INPATH *** FAILED *** (109 milliseconds)
java.nio.file.InvalidPathException: Illegal char <:> at index 2: /C:/projects/spark/sql/hive/projectsspark arget mpspark-1a122f8c-dfb3-46c4-bab1-f30764baee0e/*part-r*
```
```
HiveDDLSuite:
- drop external tables in default database *** FAILED *** (16 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- add/drop partitions - external table *** FAILED *** (16 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- create/drop database - location without pre-created directory *** FAILED *** (16 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- create/drop database - location with pre-created directory *** FAILED *** (32 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- drop database containing tables - CASCADE *** FAILED *** (94 milliseconds)
CatalogDatabase(db1,,file:/C:/projects/spark/target/tmp/warehouse-d0665ee0-1e39-4805-b471-0b764f7838be/db1.db,Map()) did not equal CatalogDatabase(db1,,file:C:/projects/spark/target/tmp/warehouse-d0665ee0-1e39-4805-b471-0b764f7838be\db1.db,Map()) (HiveDDLSuite.scala:675)
- drop an empty database - CASCADE *** FAILED *** (63 milliseconds)
CatalogDatabase(db1,,file:/C:/projects/spark/target/tmp/warehouse-d0665ee0-1e39-4805-b471-0b764f7838be/db1.db,Map()) did not equal CatalogDatabase(db1,,file:C:/projects/spark/target/tmp/warehouse-d0665ee0-1e39-4805-b471-0b764f7838be\db1.db,Map()) (HiveDDLSuite.scala:675)
- drop database containing tables - RESTRICT *** FAILED *** (47 milliseconds)
CatalogDatabase(db1,,file:/C:/projects/spark/target/tmp/warehouse-d0665ee0-1e39-4805-b471-0b764f7838be/db1.db,Map()) did not equal CatalogDatabase(db1,,file:C:/projects/spark/target/tmp/warehouse-d0665ee0-1e39-4805-b471-0b764f7838be\db1.db,Map()) (HiveDDLSuite.scala:675)
- drop an empty database - RESTRICT *** FAILED *** (47 milliseconds)
CatalogDatabase(db1,,file:/C:/projects/spark/target/tmp/warehouse-d0665ee0-1e39-4805-b471-0b764f7838be/db1.db,Map()) did not equal CatalogDatabase(db1,,file:C:/projects/spark/target/tmp/warehouse-d0665ee0-1e39-4805-b471-0b764f7838be\db1.db,Map()) (HiveDDLSuite.scala:675)
- CREATE TABLE LIKE an external data source table *** FAILED *** (140 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-c5eba16d-07ae-4186-95bb-21c5811cf888;
- CREATE TABLE LIKE an external Hive serde table *** FAILED *** (16 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- desc table for data source table - no user-defined schema *** FAILED *** (125 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-e8bf5bf5-721a-4cbe-9d6 at scala.collection.immutable.List.foreach(List.scala:381)d-5543a8301c1d;
```
```
MetastoreDataSourcesSuite
- CTAS: persisted bucketed data source table *** FAILED *** (16 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
```
```
ShowCreateTableSuite:
- simple external hive table *** FAILED *** (0 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
```
```
PartitionedTablePerfStatsSuite:
- hive table: partitioned pruned table reports only selected files *** FAILED *** (313 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- datasource table: partitioned pruned table reports only selected files *** FAILED *** (219 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-311f45f8-d064-4023-a4bb-e28235bff64d;
- hive table: lazy partition pruning reads only necessary partition data *** FAILED *** (203 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- datasource table: lazy partition pruning reads only necessary partition data *** FAILED *** (187 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-fde874ca-66bd-4d0b-a40f-a043b65bf957;
- hive table: lazy partition pruning with file status caching enabled *** FAILED *** (188 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- datasource table: lazy partition pruning with file status caching enabled *** FAILED *** (187 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-e6d20183-dd68-4145-acbe-4a509849accd;
- hive table: file status caching respects refresh table and refreshByPath *** FAILED *** (172 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- datasource table: file status caching respects refresh table and refreshByPath *** FAILED *** (203 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-8b2c9651-2adf-4d58-874f-659007e21463;
- hive table: file status cache respects size limit *** FAILED *** (219 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- datasource table: file status cache respects size limit *** FAILED *** (171 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-7835ab57-cb48-4d2c-bb1d-b46d5a4c47e4;
- datasource table: table setup does not scan filesystem *** FAILED *** (266 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-20598d76-c004-42a7-8061-6c56f0eda5e2;
- hive table: table setup does not scan filesystem *** FAILED *** (266 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- hive table: num hive client calls does not scale with partition count *** FAILED *** (2 seconds, 281 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- datasource table: num hive client calls does not scale with partition count *** FAILED *** (2 seconds, 422 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-4cfed321-4d1d-4b48-8d34-5c169afff383;
- hive table: files read and cached when filesource partition management is off *** FAILED *** (234 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- datasource table: all partition data cached in memory when partition management is off *** FAILED *** (203 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-4bcc0398-15c9-4f6a-811e-12d40f3eec12;
- SPARK-18700: table loaded only once even when resolved concurrently *** FAILED *** (1 second, 266 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
```
```
HiveSparkSubmitSuite:
- temporary Hive UDF: define a UDF and use it *** FAILED *** (2 seconds, 94 milliseconds)
java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specified
- permanent Hive UDF: define a UDF and use it *** FAILED *** (281 milliseconds)
java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specified
- permanent Hive UDF: use a already defined permanent function *** FAILED *** (718 milliseconds)
java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specified
- SPARK-8368: includes jars passed in through --jars *** FAILED *** (3 seconds, 521 milliseconds)
java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specified
- SPARK-8020: set sql conf in spark conf *** FAILED *** (0 milliseconds)
java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specified
- SPARK-8489: MissingRequirementError during reflection *** FAILED *** (94 milliseconds)
java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specified
- SPARK-9757 Persist Parquet relation with decimal column *** FAILED *** (16 milliseconds)
java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specified
- SPARK-11009 fix wrong result of Window function in cluster mode *** FAILED *** (16 milliseconds)
java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specified
- SPARK-14244 fix window partition size attribute binding failure *** FAILED *** (78 milliseconds)
java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specified
- set spark.sql.warehouse.dir *** FAILED *** (16 milliseconds)
java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specified
- set hive.metastore.warehouse.dir *** FAILED *** (15 milliseconds)
java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specified
- SPARK-16901: set javax.jdo.option.ConnectionURL *** FAILED *** (16 milliseconds)
java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specified
- SPARK-18360: default table path of tables in default database should depend on the location of default database *** FAILED *** (15 milliseconds)
java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specified
```
```
UtilsSuite:
- resolveURIs with multiple paths *** FAILED *** (0 milliseconds)
".../jar3,file:/C:/pi.py[%23]py.pi,file:/C:/path%..." did not equal ".../jar3,file:/C:/pi.py[#]py.pi,file:/C:/path%..." (UtilsSuite.scala:468)
```
```
CheckpointSuite:
- recovery with file input stream *** FAILED *** (10 seconds, 205 milliseconds)
The code passed to eventually never returned normally. Attempted 660 times over 10.014272499999999 seconds. Last failure message: Unexpected internal error near index 1
\
^. (CheckpointSuite.scala:680)
```
## How was this patch tested?
Manually via AppVeyor as below:
**Scala - aborted tests**
```
WindowQuerySuite - all passed
OrcSourceSuite:
- SPARK-18220: read Hive orc table with varchar column *** FAILED *** (4 seconds, 417 milliseconds)
org.apache.spark.sql.execution.QueryExecutionException: FAILED: Execution Error, return code -101 from org.apache.hadoop.hive.ql.exec.mr.MapRedTask. org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Ljava/lang/String;I)Z
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$runHive$1.apply(HiveClientImpl.scala:625)
ParquetMetastoreSuite - all passed
ParquetSourceSuite - all passed
KafkaRDDSuite - all passed
DirectKafkaStreamSuite - all passed
ReliableKafkaStreamSuite - all passed
KafkaStreamSuite - all passed
KafkaClusterSuite - all passed
DirectKafkaStreamSuite - all passed
KafkaRDDSuite - all passed
```
**Java - failed tests**
```
org.apache.spark.streaming.kafka.JavaKafkaRDDSuite - all passed
org.apache.spark.streaming.kafka.JavaDirectKafkaStreamSuite - all passed
org.apache.spark.streaming.kafka.JavaKafkaStreamSuite - all passed
org.apache.spark.streaming.kafka010.JavaDirectKafkaStreamSuite - all passed
org.apache.spark.streaming.kafka010.JavaKafkaRDDSuite - all passed
```
**Scala - failed tests**
```
PartitionProviderCompatibilitySuite:
- insert overwrite partition of new datasource table overwrites just partition (1 second, 953 milliseconds)
- SPARK-18635 special chars in partition values - partition management true (6 seconds, 31 milliseconds)
- SPARK-18635 special chars in partition values - partition management false (4 seconds, 578 milliseconds)
```
```
UtilsSuite:
- reading offset bytes of a file (compressed) (203 milliseconds)
- reading offset bytes across multiple files (compressed) (0 milliseconds)
```
```
StatisticsSuite:
- MetastoreRelations fallback to HDFS for size estimation (94 milliseconds)
```
```
SQLQuerySuite:
- permanent UDTF (407 milliseconds)
- describe functions - user defined functions (441 milliseconds)
- CTAS without serde with location (2 seconds, 831 milliseconds)
- derived from Hive query file: drop_database_removes_partition_dirs.q (734 milliseconds)
- derived from Hive query file: drop_table_removes_partition_dirs.q (563 milliseconds)
- SPARK-17796 Support wildcard character in filename for LOAD DATA LOCAL INPATH (453 milliseconds)
```
```
HiveDDLSuite:
- drop external tables in default database (3 seconds, 5 milliseconds)
- add/drop partitions - external table (2 seconds, 750 milliseconds)
- create/drop database - location without pre-created directory (500 milliseconds)
- create/drop database - location with pre-created directory (407 milliseconds)
- drop database containing tables - CASCADE (453 milliseconds)
- drop an empty database - CASCADE (375 milliseconds)
- drop database containing tables - RESTRICT (328 milliseconds)
- drop an empty database - RESTRICT (391 milliseconds)
- CREATE TABLE LIKE an external data source table (953 milliseconds)
- CREATE TABLE LIKE an external Hive serde table (3 seconds, 782 milliseconds)
- desc table for data source table - no user-defined schema (1 second, 150 milliseconds)
```
```
MetastoreDataSourcesSuite
- CTAS: persisted bucketed data source table (875 milliseconds)
```
```
ShowCreateTableSuite:
- simple external hive table (78 milliseconds)
```
```
PartitionedTablePerfStatsSuite:
- hive table: partitioned pruned table reports only selected files (1 second, 109 milliseconds)
- datasource table: partitioned pruned table reports only selected files (860 milliseconds)
- hive table: lazy partition pruning reads only necessary partition data (859 milliseconds)
- datasource table: lazy partition pruning reads only necessary partition data (1 second, 219 milliseconds)
- hive table: lazy partition pruning with file status caching enabled (875 milliseconds)
- datasource table: lazy partition pruning with file status caching enabled (890 milliseconds)
- hive table: file status caching respects refresh table and refreshByPath (922 milliseconds)
- datasource table: file status caching respects refresh table and refreshByPath (640 milliseconds)
- hive table: file status cache respects size limit (469 milliseconds)
- datasource table: file status cache respects size limit (453 milliseconds)
- datasource table: table setup does not scan filesystem (328 milliseconds)
- hive table: table setup does not scan filesystem (313 milliseconds)
- hive table: num hive client calls does not scale with partition count (5 seconds, 431 milliseconds)
- datasource table: num hive client calls does not scale with partition count (4 seconds, 79 milliseconds)
- hive table: files read and cached when filesource partition management is off (656 milliseconds)
- datasource table: all partition data cached in memory when partition management is off (484 milliseconds)
- SPARK-18700: table loaded only once even when resolved concurrently (2 seconds, 578 milliseconds)
```
```
HiveSparkSubmitSuite:
- temporary Hive UDF: define a UDF and use it (1 second, 745 milliseconds)
- permanent Hive UDF: define a UDF and use it (406 milliseconds)
- permanent Hive UDF: use a already defined permanent function (375 milliseconds)
- SPARK-8368: includes jars passed in through --jars (391 milliseconds)
- SPARK-8020: set sql conf in spark conf (156 milliseconds)
- SPARK-8489: MissingRequirementError during reflection (187 milliseconds)
- SPARK-9757 Persist Parquet relation with decimal column (157 milliseconds)
- SPARK-11009 fix wrong result of Window function in cluster mode (156 milliseconds)
- SPARK-14244 fix window partition size attribute binding failure (156 milliseconds)
- set spark.sql.warehouse.dir (172 milliseconds)
- set hive.metastore.warehouse.dir (156 milliseconds)
- SPARK-16901: set javax.jdo.option.ConnectionURL (157 milliseconds)
- SPARK-18360: default table path of tables in default database should depend on the location of default database (172 milliseconds)
```
```
UtilsSuite:
- resolveURIs with multiple paths (0 milliseconds)
```
```
CheckpointSuite:
- recovery with file input stream (4 seconds, 452 milliseconds)
```
Note: after resolving the aborted tests, there is a test failure identified as below:
```
OrcSourceSuite:
- SPARK-18220: read Hive orc table with varchar column *** FAILED *** (4 seconds, 417 milliseconds)
org.apache.spark.sql.execution.QueryExecutionException: FAILED: Execution Error, return code -101 from org.apache.hadoop.hive.ql.exec.mr.MapRedTask. org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Ljava/lang/String;I)Z
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$runHive$1.apply(HiveClientImpl.scala:625)
```
This does not look due to this problem so this PR does not fix it here.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16451 from HyukjinKwon/all-path-resource-fixes.
In the existing code, there are three layers of serialization
involved in sending a task from the scheduler to an executor:
- A Task object is serialized
- The Task object is copied to a byte buffer that also
contains serialized information about any additional JARs,
files, and Properties needed for the task to execute. This
byte buffer is stored as the member variable serializedTask
in the TaskDescription class.
- The TaskDescription is serialized (in addition to the serialized
task + JARs, the TaskDescription class contains the task ID and
other metadata) and sent in a LaunchTask message.
While it *is* necessary to have two layers of serialization, so that
the JAR, file, and Property info can be deserialized prior to
deserializing the Task object, the third layer of deserialization is
unnecessary. This commit eliminates a layer of serialization by moving
the JARs, files, and Properties into the TaskDescription class.
This commit also serializes the Properties manually (by traversing the map),
as is done with the JARs and files, which reduces the final serialized size.
Unit tests
This is a simpler alternative to the approach proposed in #15505.
shivaram and I did some benchmarking of this and #15505 on a 20-machine m2.4xlarge EC2 machines (160 cores). We ran ~30 trials of code [1] (a very simple job with 10K tasks per stage) and measured the average time per stage:
Before this change: 2490ms
With this change: 2345 ms (so ~6% improvement over the baseline)
With witgo's approach in #15505: 2046 ms (~18% improvement over baseline)
The reason that #15505 has a more significant improvement is that it also moves the serialization from the TaskSchedulerImpl thread to the CoarseGrainedSchedulerBackend thread. I added that functionality on top of this change, and got almost the same improvement [1] as #15505 (average of 2103ms). I think we should decouple these two changes, both so we have some record of the improvement form each individual improvement, and because this change is more about simplifying the code base (the improvement is negligible) while the other is about performance improvement. The plan, currently, is to merge this PR and then merge the remaining part of #15505 that moves serialization.
[1] The reason the improvement wasn't quite as good as with #15505 when we ran the benchmarks is almost certainly because, at the point when we ran the benchmarks, I hadn't updated the code to manually serialize the Properties (instead the code was using Java's default serialization for the Properties object, whereas #15505 manually serialized the Properties). This PR has since been updated to manually serialize the Properties, just like the other maps.
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#16053 from kayousterhout/SPARK-17931.
## What changes were proposed in this pull request?
Current HistoryServer's ACLs is derived from application event-log, which means the newly changed ACLs cannot be applied to the old data, this will become a problem where newly added admin cannot access the old application history UI, only the new application can be affected.
So here propose to add admin ACLs for history server, any configured user/group could have the view access to all the applications, while the view ACLs derived from application run-time still take effect.
## How was this patch tested?
Unit test added.
Author: jerryshao <sshao@hortonworks.com>
Closes#16470 from jerryshao/SPARK-19033.
## What changes were proposed in this pull request?
TaskResultGetter tries to deserialize the TaskEndReason before handling the failed task. If an error is thrown during deserialization, the failed task won't be handled, which leaves the job hanging.
The PR proposes to handle the failed task in a finally block.
## How was this patch tested?
In my case I hit a NoClassDefFoundError and the job hangs. Manually verified the patch can fix it.
Author: Rui Li <rui.li@intel.com>
Author: Rui Li <lirui@apache.org>
Author: Rui Li <shlr@cn.ibm.com>
Closes#12775 from lirui-intel/SPARK-14958.
This commit changes Utils.writeByteBuffer so that it does not change
the position of the ByteBuffer that it writes out, and adds a unit test for
this functionality.
cc mridulm
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#16462 from kayousterhout/SPARK-19062.
## What changes were proposed in this pull request?
There are many locations in the Spark repo where the same word occurs consecutively. Sometimes they are appropriately placed, but many times they are not. This PR removes the inappropriately duplicated words.
## How was this patch tested?
N/A since only docs or comments were updated.
Author: Niranjan Padmanabhan <niranjan.padmanabhan@gmail.com>
Closes#16455 from neurons/np.structure_streaming_doc.
## What changes were proposed in this pull request?
Add `finally` clause for `sc.stop()` in the `test("register and deregister Spark listener from SparkContext")`.
## How was this patch tested?
Pass the build and unit tests.
Author: Weiqing Yang <yangweiqing001@gmail.com>
Closes#16426 from weiqingy/testIssue.
## What changes were proposed in this pull request?
This is to workaround an implicit result of #4947 which suppressed the
original Kryo exception if the overflow happened during serialization.
## How was this patch tested?
`KryoSerializerSuite` was augmented to reflect this change.
Author: Sergei Lebedev <superbobry@gmail.com>
Closes#16416 from superbobry/patch-1.
## What changes were proposed in this pull request?
The root cause of this issue is that RegisterWorkerResponse and LaunchExecutor are sent via two different channels (TCP connections) and their order is not guaranteed.
This PR changes the master and worker codes to use `workerRef` to send RegisterWorkerResponse, so that RegisterWorkerResponse and LaunchExecutor are sent via the same connection. Hence `LaunchExecutor` will always be after `RegisterWorkerResponse` and never be ignored.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16345 from zsxwing/SPARK-17755.
## What changes were proposed in this pull request?
In current Spark we could add customized SparkListener through `SparkContext#addListener` API, but there's no equivalent API to remove the registered one. In our scenario SparkListener will be added repeatedly accordingly to the changed environment. If lacks the ability to remove listeners, there might be many registered listeners finally, this is unnecessary and potentially affects the performance. So here propose to add an API to remove registered listener.
## How was this patch tested?
Add an unit test to verify it.
Author: jerryshao <sshao@hortonworks.com>
Closes#16382 from jerryshao/SPARK-18975.
## What changes were proposed in this pull request?
There are several tests failing due to resource-closing-related and path-related problems on Windows as below.
- `SQLQuerySuite`:
```
- specifying database name for a temporary table is not allowed *** FAILED *** (125 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-1f4471ab-aac0-4239-ae35-833d54b37e52;
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:382)
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:370)
```
- `JsonSuite`:
```
- Loading a JSON dataset from a text file with SQL *** FAILED *** (94 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-c918a8b7-fc09-433c-b9d0-36c0f78ae918;
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:382)
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:370)
```
- `StateStoreSuite`:
```
- SPARK-18342: commit fails when rename fails *** FAILED *** (16 milliseconds)
java.lang.IllegalArgumentException: java.net.URISyntaxException: Relative path in absolute URI: StateStoreSuite29777261fs://C:%5Cprojects%5Cspark%5Ctarget%5Ctmp%5Cspark-ef349862-7281-4963-aaf3-add0d670a4ad%5C?????-2218c2f8-2cf6-4f80-9cdf-96354e8246a77685899733421033312/0
at org.apache.hadoop.fs.Path.initialize(Path.java:206)
at org.apache.hadoop.fs.Path.<init>(Path.java:116)
at org.apache.hadoop.fs.Path.<init>(Path.java:89)
...
Cause: java.net.URISyntaxException: Relative path in absolute URI: StateStoreSuite29777261fs://C:%5Cprojects%5Cspark%5Ctarget%5Ctmp%5Cspark-ef349862-7281-4963-aaf3-add0d670a4ad%5C?????-2218c2f8-2cf6-4f80-9cdf-96354e8246a77685899733421033312/0
at java.net.URI.checkPath(URI.java:1823)
at java.net.URI.<init>(URI.java:745)
at org.apache.hadoop.fs.Path.initialize(Path.java:203)
```
- `HDFSMetadataLogSuite`:
```
- FileManager: FileContextManager *** FAILED *** (94 milliseconds)
java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-415bb0bd-396b-444d-be82-04599e025f21
at org.apache.spark.util.Utils$.deleteRecursively(Utils.scala:1010)
at org.apache.spark.sql.test.SQLTestUtils$class.withTempDir(SQLTestUtils.scala:127)
at org.apache.spark.sql.execution.streaming.HDFSMetadataLogSuite.withTempDir(HDFSMetadataLogSuite.scala:38)
- FileManager: FileSystemManager *** FAILED *** (78 milliseconds)
java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-ef8222cd-85aa-47c0-a396-bc7979e15088
at org.apache.spark.util.Utils$.deleteRecursively(Utils.scala:1010)
at org.apache.spark.sql.test.SQLTestUtils$class.withTempDir(SQLTestUtils.scala:127)
at org.apache.spark.sql.execution.streaming.HDFSMetadataLogSuite.withTempDir(HDFSMetadataLogSuite.scala:38)
```
And, there are some tests being failed due to the length limitation on cmd in Windows as below:
- `LauncherBackendSuite`:
```
- local: launcher handle *** FAILED *** (30 seconds, 120 milliseconds)
The code passed to eventually never returned normally. Attempted 283 times over 30.0960053 seconds. Last failure message: The reference was null. (LauncherBackendSuite.scala:56)
org.scalatest.exceptions.TestFailedDueToTimeoutException:
at org.scalatest.concurrent.Eventually$class.tryTryAgain$1(Eventually.scala:420)
at org.scalatest.concurrent.Eventually$class.eventually(Eventually.scala:438)
- standalone/client: launcher handle *** FAILED *** (30 seconds, 47 milliseconds)
The code passed to eventually never returned normally. Attempted 282 times over 30.037987100000002 seconds. Last failure message: The reference was null. (LauncherBackendSuite.scala:56)
org.scalatest.exceptions.TestFailedDueToTimeoutException:
at org.scalatest.concurrent.Eventually$class.tryTryAgain$1(Eventually.scala:420)
at org.scalatest.concurrent.Eventually$class.eventually(Eventually.scala:438)
```
The executed command is, https://gist.github.com/HyukjinKwon/d3fdd2e694e5c022992838a618a516bd, which is 16K length; however, the length limitation is 8K on Windows. So, it is being failed to launch.
This PR proposes to fix the test failures on Windows and skip the tests failed due to the length limitation
## How was this patch tested?
Manually tested via AppVeyor
**Before**
`SQLQuerySuite `: https://ci.appveyor.com/project/spark-test/spark/build/306-pr-references
`JsonSuite`: https://ci.appveyor.com/project/spark-test/spark/build/307-pr-references
`StateStoreSuite` : https://ci.appveyor.com/project/spark-test/spark/build/305-pr-references
`HDFSMetadataLogSuite`: https://ci.appveyor.com/project/spark-test/spark/build/304-pr-references
`LauncherBackendSuite`: https://ci.appveyor.com/project/spark-test/spark/build/303-pr-references
**After**
`SQLQuerySuite`: https://ci.appveyor.com/project/spark-test/spark/build/293-SQLQuerySuite
`JsonSuite`: https://ci.appveyor.com/project/spark-test/spark/build/294-JsonSuite
`StateStoreSuite`: https://ci.appveyor.com/project/spark-test/spark/build/297-StateStoreSuite
`HDFSMetadataLogSuite`: https://ci.appveyor.com/project/spark-test/spark/build/319-pr-references
`LauncherBackendSuite`: failed test skipped.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16335 from HyukjinKwon/more-fixes-on-windows.
## What changes were proposed in this pull request?
Spark's current task cancellation / task killing mechanism is "best effort" because some tasks may not be interruptible or may not respond to their "killed" flags being set. If a significant fraction of a cluster's task slots are occupied by tasks that have been marked as killed but remain running then this can lead to a situation where new jobs and tasks are starved of resources that are being used by these zombie tasks.
This patch aims to address this problem by adding a "task reaper" mechanism to executors. At a high-level, task killing now launches a new thread which attempts to kill the task and then watches the task and periodically checks whether it has been killed. The TaskReaper will periodically re-attempt to call `TaskRunner.kill()` and will log warnings if the task keeps running. I modified TaskRunner to rename its thread at the start of the task, allowing TaskReaper to take a thread dump and filter it in order to log stacktraces from the exact task thread that we are waiting to finish. If the task has not stopped after a configurable timeout then the TaskReaper will throw an exception to trigger executor JVM death, thereby forcibly freeing any resources consumed by the zombie tasks.
This feature is flagged off by default and is controlled by four new configurations under the `spark.task.reaper.*` namespace. See the updated `configuration.md` doc for details.
## How was this patch tested?
Tested via a new test case in `JobCancellationSuite`, plus manual testing.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#16189 from JoshRosen/cancellation.
## What changes were proposed in this pull request?
Right now we serialize the empty task metrics once per task – Since this is shared across all tasks we could use the same serialized task metrics across all tasks of a stage.
## How was this patch tested?
- [x] Run tests on EC2 to measure performance improvement
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#16261 from shivaram/task-metrics-one-copy.
## What changes were proposed in this pull request?
`NoSuchElementException` will throw since https://github.com/apache/spark/pull/15056 if a broadcast cannot cache in memory. The reason is that that change cannot cover `!unrolled.hasNext` in `next()` function.
This change is to cover the `!unrolled.hasNext` and check `hasNext` before calling `next` in `blockManager.getLocalValues` to make it more robust.
We can cache and read broadcast even it cannot fit in memory from this pull request.
Exception log:
```
16/12/10 10:10:04 INFO UnifiedMemoryManager: Will not store broadcast_131 as the required space (1048576 bytes) exceeds our memory limit (122764 bytes)
16/12/10 10:10:04 WARN MemoryStore: Failed to reserve initial memory threshold of 1024.0 KB for computing block broadcast_131 in memory.
16/12/10 10:10:04 WARN MemoryStore: Not enough space to cache broadcast_131 in memory! (computed 384.0 B so far)
16/12/10 10:10:04 INFO MemoryStore: Memory use = 95.6 KB (blocks) + 0.0 B (scratch space shared across 0 tasks(s)) = 95.6 KB. Storage limit = 119.9 KB.
16/12/10 10:10:04 ERROR Utils: Exception encountered
java.util.NoSuchElementException
at org.apache.spark.util.collection.PrimitiveVector$$anon$1.next(PrimitiveVector.scala:58)
at org.apache.spark.storage.memory.PartiallyUnrolledIterator.next(MemoryStore.scala:700)
at org.apache.spark.util.CompletionIterator.next(CompletionIterator.scala:30)
at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$readBroadcastBlock$1$$anonfun$2.apply(TorrentBroadcast.scala:210)
at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$readBroadcastBlock$1$$anonfun$2.apply(TorrentBroadcast.scala:210)
at scala.Option.map(Option.scala:146)
at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$readBroadcastBlock$1.apply(TorrentBroadcast.scala:210)
at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1269)
at org.apache.spark.broadcast.TorrentBroadcast.readBroadcastBlock(TorrentBroadcast.scala:206)
at org.apache.spark.broadcast.TorrentBroadcast._value$lzycompute(TorrentBroadcast.scala:66)
at org.apache.spark.broadcast.TorrentBroadcast._value(TorrentBroadcast.scala:66)
at org.apache.spark.broadcast.TorrentBroadcast.getValue(TorrentBroadcast.scala:96)
at org.apache.spark.broadcast.Broadcast.value(Broadcast.scala:70)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:86)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
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)
16/12/10 10:10:04 ERROR Executor: Exception in task 1.0 in stage 86.0 (TID 134423)
java.io.IOException: java.util.NoSuchElementException
at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1276)
at org.apache.spark.broadcast.TorrentBroadcast.readBroadcastBlock(TorrentBroadcast.scala:206)
at org.apache.spark.broadcast.TorrentBroadcast._value$lzycompute(TorrentBroadcast.scala:66)
at org.apache.spark.broadcast.TorrentBroadcast._value(TorrentBroadcast.scala:66)
at org.apache.spark.broadcast.TorrentBroadcast.getValue(TorrentBroadcast.scala:96)
at org.apache.spark.broadcast.Broadcast.value(Broadcast.scala:70)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:86)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
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)
Caused by: java.util.NoSuchElementException
at org.apache.spark.util.collection.PrimitiveVector$$anon$1.next(PrimitiveVector.scala:58)
at org.apache.spark.storage.memory.PartiallyUnrolledIterator.next(MemoryStore.scala:700)
at org.apache.spark.util.CompletionIterator.next(CompletionIterator.scala:30)
at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$readBroadcastBlock$1$$anonfun$2.apply(TorrentBroadcast.scala:210)
at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$readBroadcastBlock$1$$anonfun$2.apply(TorrentBroadcast.scala:210)
at scala.Option.map(Option.scala:146)
at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$readBroadcastBlock$1.apply(TorrentBroadcast.scala:210)
at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1269)
... 12 more
```
## How was this patch tested?
Add unit test
Author: Yuming Wang <wgyumg@gmail.com>
Closes#16252 from wangyum/SPARK-18827.
## What changes were proposed in this pull request?
There are several tests failing due to resource-closing-related and path-related problems on Windows as below.
- `RPackageUtilsSuite`:
```
- build an R package from a jar end to end *** FAILED *** (1 second, 625 milliseconds)
java.io.IOException: Unable to delete file: C:\projects\spark\target\tmp\1481729427517-0\a\dep2\d\dep2-d.jar
at org.apache.commons.io.FileUtils.forceDelete(FileUtils.java:2279)
at org.apache.commons.io.FileUtils.cleanDirectory(FileUtils.java:1653)
at org.apache.commons.io.FileUtils.deleteDirectory(FileUtils.java:1535)
- faulty R package shows documentation *** FAILED *** (359 milliseconds)
java.io.IOException: Unable to delete file: C:\projects\spark\target\tmp\1481729428970-0\dep1-c.jar
at org.apache.commons.io.FileUtils.forceDelete(FileUtils.java:2279)
at org.apache.commons.io.FileUtils.cleanDirectory(FileUtils.java:1653)
at org.apache.commons.io.FileUtils.deleteDirectory(FileUtils.java:1535)
- SparkR zipping works properly *** FAILED *** (47 milliseconds)
java.util.regex.PatternSyntaxException: Unknown character property name {r} near index 4
C:\projects\spark\target\tmp\1481729429282-0
^
at java.util.regex.Pattern.error(Pattern.java:1955)
at java.util.regex.Pattern.charPropertyNodeFor(Pattern.java:2781)
```
- `InputOutputMetricsSuite`:
```
- input metrics for old hadoop with coalesce *** FAILED *** (240 milliseconds)
java.io.IOException: Not a file: file:/C:/projects/spark/core/ignored
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:277)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:202)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
- input metrics with cache and coalesce *** FAILED *** (109 milliseconds)
java.io.IOException: Not a file: file:/C:/projects/spark/core/ignored
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:277)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:202)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
- input metrics for new Hadoop API with coalesce *** FAILED *** (0 milliseconds)
java.lang.IllegalArgumentException: Wrong FS: file://C:\projects\spark\target\tmp\spark-9366ec94-dac7-4a5c-a74b-3e7594a692ab\test\InputOutputMetricsSuite.txt, expected: file:///
at org.apache.hadoop.fs.FileSystem.checkPath(FileSystem.java:642)
at org.apache.hadoop.fs.FileSystem.makeQualified(FileSystem.java:462)
at org.apache.hadoop.fs.FilterFileSystem.makeQualified(FilterFileSystem.java:114)
- input metrics when reading text file *** FAILED *** (110 milliseconds)
java.io.IOException: Not a file: file:/C:/projects/spark/core/ignored
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:277)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:202)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
- input metrics on records read - simple *** FAILED *** (125 milliseconds)
java.io.IOException: Not a file: file:/C:/projects/spark/core/ignored
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:277)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:202)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
- input metrics on records read - more stages *** FAILED *** (110 milliseconds)
java.io.IOException: Not a file: file:/C:/projects/spark/core/ignored
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:277)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:202)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
- input metrics on records - New Hadoop API *** FAILED *** (16 milliseconds)
java.lang.IllegalArgumentException: Wrong FS: file://C:\projects\spark\target\tmp\spark-3f10a1a4-7820-4772-b821-25fd7523bf6f\test\InputOutputMetricsSuite.txt, expected: file:///
at org.apache.hadoop.fs.FileSystem.checkPath(FileSystem.java:642)
at org.apache.hadoop.fs.FileSystem.makeQualified(FileSystem.java:462)
at org.apache.hadoop.fs.FilterFileSystem.makeQualified(FilterFileSystem.java:114)
- input metrics on records read with cache *** FAILED *** (93 milliseconds)
java.io.IOException: Not a file: file:/C:/projects/spark/core/ignored
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:277)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:202)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
- input read/write and shuffle read/write metrics all line up *** FAILED *** (93 milliseconds)
java.io.IOException: Not a file: file:/C:/projects/spark/core/ignored
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:277)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:202)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
- input metrics with interleaved reads *** FAILED *** (0 milliseconds)
java.lang.IllegalArgumentException: Wrong FS: file://C:\projects\spark\target\tmp\spark-2638d893-e89b-47ce-acd0-bbaeee78dd9b\InputOutputMetricsSuite_cart.txt, expected: file:///
at org.apache.hadoop.fs.FileSystem.checkPath(FileSystem.java:642)
at org.apache.hadoop.fs.FileSystem.makeQualified(FileSystem.java:462)
at org.apache.hadoop.fs.FilterFileSystem.makeQualified(FilterFileSystem.java:114)
- input metrics with old CombineFileInputFormat *** FAILED *** (157 milliseconds)
17947 was not greater than or equal to 300000 (InputOutputMetricsSuite.scala:324)
org.scalatest.exceptions.TestFailedException:
at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:500)
at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555)
at org.scalatest.Assertions$AssertionsHelper.macroAssert(Assertions.scala:466)
- input metrics with new CombineFileInputFormat *** FAILED *** (16 milliseconds)
java.lang.IllegalArgumentException: Wrong FS: file://C:\projects\spark\target\tmp\spark-11920c08-19d8-4c7c-9fba-28ed72b79f80\test\InputOutputMetricsSuite.txt, expected: file:///
at org.apache.hadoop.fs.FileSystem.checkPath(FileSystem.java:642)
at org.apache.hadoop.fs.FileSystem.makeQualified(FileSystem.java:462)
at org.apache.hadoop.fs.FilterFileSystem.makeQualified(FilterFileSystem.java:114)
```
- `ReplayListenerSuite`:
```
- End-to-end replay *** FAILED *** (121 milliseconds)
java.io.IOException: No FileSystem for scheme: C
at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2421)
at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2428)
- End-to-end replay with compression *** FAILED *** (516 milliseconds)
java.io.IOException: No FileSystem for scheme: C
at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2421)
at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2428)
at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:88)
```
- `EventLoggingListenerSuite`:
```
- End-to-end event logging *** FAILED *** (7 seconds, 435 milliseconds)
java.io.IOException: No FileSystem for scheme: C
at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2421)
at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2428)
at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:88)
- End-to-end event logging with compression *** FAILED *** (1 second)
java.io.IOException: No FileSystem for scheme: C
at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2421)
at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2428)
at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:88)
- Event log name *** FAILED *** (16 milliseconds)
"file:/[]base-dir/app1" did not equal "file:/[C:/]base-dir/app1" (EventLoggingListenerSuite.scala:123)
org.scalatest.exceptions.TestFailedException:
at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:500)
at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555)
at org.scalatest.Assertions$AssertionsHelper.macroAssert(Assertions.scala:466)
```
This PR proposes to fix the test failures on Windows
## How was this patch tested?
Manually tested via AppVeyor
**Before**
`RPackageUtilsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/273-RPackageUtilsSuite-before
`InputOutputMetricsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/272-InputOutputMetricsSuite-before
`ReplayListenerSuite`: https://ci.appveyor.com/project/spark-test/spark/build/274-ReplayListenerSuite-before
`EventLoggingListenerSuite`: https://ci.appveyor.com/project/spark-test/spark/build/275-EventLoggingListenerSuite-before
**After**
`RPackageUtilsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/270-RPackageUtilsSuite
`InputOutputMetricsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/271-InputOutputMetricsSuite
`ReplayListenerSuite`: https://ci.appveyor.com/project/spark-test/spark/build/277-ReplayListenerSuite-after
`EventLoggingListenerSuite`: https://ci.appveyor.com/project/spark-test/spark/build/278-EventLoggingListenerSuite-after
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16305 from HyukjinKwon/RPackageUtilsSuite-InputOutputMetricsSuite.
## What changes were proposed in this pull request?
This builds upon the blacklisting introduced in SPARK-17675 to add blacklisting of executors and nodes for an entire Spark application. Resources are blacklisted based on tasks that fail, in tasksets that eventually complete successfully; they are automatically returned to the pool of active resources based on a timeout. Full details are available in a design doc attached to the jira.
## How was this patch tested?
Added unit tests, ran them via Jenkins, also ran a handful of them in a loop to check for flakiness.
The added tests include:
- verifying BlacklistTracker works correctly
- verifying TaskSchedulerImpl interacts with BlacklistTracker correctly (via a mock BlacklistTracker)
- an integration test for the entire scheduler with blacklisting in a few different scenarios
Author: Imran Rashid <irashid@cloudera.com>
Author: mwws <wei.mao@intel.com>
Closes#14079 from squito/blacklist-SPARK-8425.
## What changes were proposed in this pull request?
This PR proposes to fix the tests failed on Windows as below:
```
[info] - pipe with empty partition *** FAILED *** (672 milliseconds)
[info] Set(0, 4, 5) did not equal Set(0, 5, 6) (PipedRDDSuite.scala:145)
[info] org.scalatest.exceptions.TestFailedException:
...
```
In this case, `wc -c` counts the characters on both Windows and Linux but the newlines characters on Windows are `\r\n` which are two. So, the counts ends up one more for each.
```
[info] - test pipe exports map_input_file *** FAILED *** (62 milliseconds)
[info] java.lang.IllegalStateException: Subprocess exited with status 1. Command ran: printenv map_input_file
[info] at org.apache.spark.rdd.PipedRDD$$anon$1.hasNext(PipedRDD.scala:178)
...
```
```
[info] - test pipe exports mapreduce_map_input_file *** FAILED *** (172 milliseconds)
[info] java.lang.IllegalStateException: Subprocess exited with status 1. Command ran: printenv mapreduce_map_input_file
[info] at org.apache.spark.rdd.PipedRDD$$anon$1.hasNext(PipedRDD.scala:178)
...
```
`printenv` command prints the environment variables; however, when environment variables are set to `ProcessBuilder` as lower-cased keys, `printenv` in Windows ignores and does not print this although it is actually set and accessible. (this was tested in [here](https://ci.appveyor.com/project/spark-test/spark/build/208-PipedRDDSuite) for upper-cases with this [diff](https://github.com/apache/spark/compare/master...spark-test:74d39da) and [here](https://ci.appveyor.com/project/spark-test/spark/build/203-PipedRDDSuite) for lower-cases with this [diff](https://github.com/apache/spark/compare/master...spark-test:fde5e37f28032c15a8d8693ba033a8a779a26317). It seems a bug in `printenv`.
(BTW, note that environment variables on Windows are case-insensitive).
This is (I believe) a thirdparty tool on Windows that resembles `printenv` on Linux (installed in AppVeyor environment or Windows Server 2012 R2). This command does not exist, at least, for Windows 7 and 10 (manually tested).
On Windows, we can use `cmd.exe /C set [varname]` officially for this purpose. We could fix the tests with this in order to test if the environment variable is set.
## How was this patch tested?
Manually tested via AppVeyor.
**Before**
https://ci.appveyor.com/project/spark-test/spark/build/194-PipedRDDSuite
**After**
https://ci.appveyor.com/project/spark-test/spark/build/226-PipedRDDSuite
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16254 from HyukjinKwon/pipe-errors.
## What changes were proposed in this pull request?
Currently, some tests are being failed and hanging on Windows due to this problem. For the reason in SPARK-18718, some tests using `local-cluster` mode were disabled on Windows due to the length limitation by paths given to classpaths.
The limitation seems roughly 32K (see the [blog in MS](https://blogs.msdn.microsoft.com/oldnewthing/20031210-00/?p=41553/) and [another reference](https://support.thoughtworks.com/hc/en-us/articles/213248526-Getting-around-maximum-command-line-length-is-32767-characters-on-Windows)) but in `local-cluster` mode, executors were being launched as processes with the command such as [here](https://gist.github.com/HyukjinKwon/5bc81061c250d4af5a180869b59d42ea) in (only) tests.
This length is roughly 40K due to the classpaths given to `java` command. However, it seems duplicates are almost half of them. So, if we deduplicate the paths, it seems reduced to roughly 20K with the command, [here](https://gist.github.com/HyukjinKwon/dad0c8db897e5e094684a2dc6a417790).
Maybe, we should consider as some more paths are added in the future but it seems better than disabling all the tests for now with minimised changes.
Therefore, this PR proposes to deduplicate the paths in classpaths in case of launching executors as processes in `local-cluster` mode.
## How was this patch tested?
Existing tests in `ShuffleSuite` and `BroadcastJoinSuite` manually via AppVeyor
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16266 from HyukjinKwon/disable-local-cluster-tests.
There is a small race in SchedulerIntegrationSuite.
The test assumes that the taskscheduler thread
processing that last task will finish before the DAGScheduler processes
the task event and notifies the job waiter, but that is not 100%
guaranteed.
ran the test locally a bunch of times, never failed, though admittedly
it never failed locally for me before either. However I am nearly 100%
certain this is what caused the failure of one jenkins build
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/68694/consoleFull
(which is long gone now, sorry -- I fixed it as part of
https://github.com/apache/spark/pull/14079 initially)
Author: Imran Rashid <irashid@cloudera.com>
Closes#16270 from squito/sched_integ_flakiness.
## What changes were proposed in this pull request?
Some places in SQL may call `RpcEndpointRef.askWithRetry` (e.g., ParquetFileFormat.buildReader -> SparkContext.broadcast -> ... -> BlockManagerMaster.updateBlockInfo -> RpcEndpointRef.askWithRetry), which will finally call `Await.result`. It may cause `java.lang.IllegalArgumentException: spark.sql.execution.id is already set` when running in Scala ForkJoinPool.
This PR includes the following changes to fix this issue:
- Remove `ThreadUtils.awaitResult`
- Rename `ThreadUtils. awaitResultInForkJoinSafely` to `ThreadUtils.awaitResult`
- Replace `Await.result` in RpcTimeout with `ThreadUtils.awaitResult`.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16230 from zsxwing/fix-SPARK-13747.
This change moves the logic that translates Spark configuration to
commons-crypto configuration to the network-common module. It also
extends TransportConf and ConfigProvider to provide the necessary
interfaces for the translation to work.
As part of the change, I removed SystemPropertyConfigProvider, which
was mostly used as an "empty config" in unit tests, and adjusted the
very few tests that required a specific config.
I also changed the config keys for AES encryption to live under the
"spark.network." namespace, which is more correct than their previous
names under "spark.authenticate.".
Tested via existing unit test.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#16200 from vanzin/SPARK-18773.
## What changes were proposed in this pull request?
During history server startup, the spark configuration is examined. If security.authentication is
set, log at debug and set the value to false, so that {{SecurityManager}} can be created.
## How was this patch tested?
A new test in `HistoryServerSuite` sets the `spark.authenticate` property to true, tries to create a security manager via a new package-private method `HistoryServer.createSecurityManager(SparkConf)`. This is the method used in `HistoryServer.main`. All other instantiations of a security manager in `HistoryServerSuite` have been switched to the new method, for consistency with the production code.
Author: Steve Loughran <stevel@apache.org>
Closes#13579 from steveloughran/history/SPARK-15844-security.
## What changes were proposed in this pull request?
This PR proposes to fix some tests being failed on Windows as below for several problems.
### Incorrect path handling
- FileSuite
```
[info] - binary file input as byte array *** FAILED *** (500 milliseconds)
[info] "file:/C:/projects/spark/target/tmp/spark-e7c3a3b8-0a4b-4a7f-9ebe-7c4883e48624/record-bytestream-00000.bin" did not contain "C:\projects\spark\target\tmp\spark-e7c3a3b8-0a4b-4a7f-9ebe-7c4883e48624\record-bytestream-00000.bin" (FileSuite.scala:258)
[info] org.scalatest.exceptions.TestFailedException:
[info] at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:500)
...
```
```
[info] - Get input files via old Hadoop API *** FAILED *** (1 second, 94 milliseconds)
[info] Set("/C:/projects/spark/target/tmp/spark-cf5b1f8b-c5ed-43e0-8d17-546ebbfa8200/output/part-00000", "/C:/projects/spark/target/tmp/spark-cf5b1f8b-c5ed-43e0-8d17-546ebbfa8200/output/part-00001") did not equal Set("C:\projects\spark\target\tmp\spark-cf5b1f8b-c5ed-43e0-8d17-546ebbfa8200\output/part-00000", "C:\projects\spark\target\tmp\spark-cf5b1f8b-c5ed-43e0-8d17-546ebbfa8200\output/part-00001") (FileSuite.scala:535)
[info] org.scalatest.exceptions.TestFailedException:
[info] at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:500)
...
```
```
[info] - Get input files via new Hadoop API *** FAILED *** (313 milliseconds)
[info] Set("/C:/projects/spark/target/tmp/spark-12bc1540-1111-4df6-9c4d-79e0e614407c/output/part-00000", "/C:/projects/spark/target/tmp/spark-12bc1540-1111-4df6-9c4d-79e0e614407c/output/part-00001") did not equal Set("C:\projects\spark\target\tmp\spark-12bc1540-1111-4df6-9c4d-79e0e614407c\output/part-00000", "C:\projects\spark\target\tmp\spark-12bc1540-1111-4df6-9c4d-79e0e614407c\output/part-00001") (FileSuite.scala:549)
[info] org.scalatest.exceptions.TestFailedException:
...
```
- TaskResultGetterSuite
```
[info] - handling results larger than max RPC message size *** FAILED *** (1 second, 579 milliseconds)
[info] 1 did not equal 0 Expect result to be removed from the block manager. (TaskResultGetterSuite.scala:129)
[info] org.scalatest.exceptions.TestFailedException:
[info] ...
[info] Cause: java.net.URISyntaxException: Illegal character in path at index 12: string:///C:\projects\spark\target\tmp\spark-93c485af-68da-440f-a907-aac7acd5fc25\repro\MyException.java
[info] at java.net.URI$Parser.fail(URI.java:2848)
[info] at java.net.URI$Parser.checkChars(URI.java:3021)
...
```
```
[info] - failed task deserialized with the correct classloader (SPARK-11195) *** FAILED *** (0 milliseconds)
[info] java.lang.IllegalArgumentException: Illegal character in path at index 12: string:///C:\projects\spark\target\tmp\spark-93c485af-68da-440f-a907-aac7acd5fc25\repro\MyException.java
[info] at java.net.URI.create(URI.java:852)
...
```
- SparkSubmitSuite
```
[info] java.lang.IllegalArgumentException: Illegal character in path at index 12: string:///C:\projects\spark\target\tmp\1481210831381-0\870903339\MyLib.java
[info] at java.net.URI.create(URI.java:852)
[info] at org.apache.spark.TestUtils$.org$apache$spark$TestUtils$$createURI(TestUtils.scala:112)
...
```
### Incorrect separate for JarEntry
After the path fix from above, then `TaskResultGetterSuite` throws another exception as below:
```
[info] - failed task deserialized with the correct classloader (SPARK-11195) *** FAILED *** (907 milliseconds)
[info] java.lang.ClassNotFoundException: repro.MyException
[info] at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
...
```
This is because `Paths.get` concatenates the given paths to an OS-specific path (Windows `\` and Linux `/`). However, for `JarEntry` we should comply ZIP specification meaning it should be always `/` according to ZIP specification.
See `4.4.17 file name: (Variable)` in https://pkware.cachefly.net/webdocs/casestudies/APPNOTE.TXT
### Long path problem on Windows
Some tests in `ShuffleSuite` via `ShuffleNettySuite` were skipped due to the same reason with SPARK-18718
## How was this patch tested?
Manually via AppVeyor.
**Before**
- `FileSuite`, `TaskResultGetterSuite`,`SparkSubmitSuite`
https://ci.appveyor.com/project/spark-test/spark/build/164-tmp-windows-base (please grep each to check each)
- `ShuffleSuite`
https://ci.appveyor.com/project/spark-test/spark/build/157-tmp-windows-base
**After**
- `FileSuite`
https://ci.appveyor.com/project/spark-test/spark/build/166-FileSuite
- `TaskResultGetterSuite`
https://ci.appveyor.com/project/spark-test/spark/build/173-TaskResultGetterSuite
- `SparkSubmitSuite`
https://ci.appveyor.com/project/spark-test/spark/build/167-SparkSubmitSuite
- `ShuffleSuite`
https://ci.appveyor.com/project/spark-test/spark/build/176-ShuffleSuite
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16234 from HyukjinKwon/test-errors-windows.
## What changes were proposed in this pull request?
There is an outstanding issue that existed for a long time: Sometimes the shuffle blocks are corrupt and can't be decompressed. We recently hit this in three different workloads, sometimes we can reproduce it by every try, sometimes can't. I also found that when the corruption happened, the beginning and end of the blocks are correct, the corruption happen in the middle. There was one case that the string of block id is corrupt by one character. It seems that it's very likely the corruption is introduced by some weird machine/hardware, also the checksum (16 bits) in TCP is not strong enough to identify all the corruption.
Unfortunately, Spark does not have checksum for shuffle blocks or broadcast, the job will fail if any corruption happen in the shuffle block from disk, or broadcast blocks during network. This PR try to detect the corruption after fetching shuffle blocks by decompressing them, because most of the compression already have checksum in them. It will retry the block, or failed with FetchFailure, so the previous stage could be retried on different (still random) machines.
Checksum for broadcast will be added by another PR.
## How was this patch tested?
Added unit tests
Author: Davies Liu <davies@databricks.com>
Closes#15923 from davies/detect_corrupt.
## What changes were proposed in this pull request?
* This PR changes `JVMObjectTracker` from `object` to `class` and let its instance associated with each RBackend. So we can manage the lifecycle of JVM objects when there are multiple `RBackend` sessions. `RBackend.close` will clear the object tracker explicitly.
* I assume that `SQLUtils` and `RRunner` do not need to track JVM instances, which could be wrong.
* Small refactor of `SerDe.sqlSerDe` to increase readability.
## How was this patch tested?
* Added unit tests for `JVMObjectTracker`.
* Wait for Jenkins to run full tests.
Author: Xiangrui Meng <meng@databricks.com>
Closes#16154 from mengxr/SPARK-17822.
## What changes were proposed in this pull request?
When `SparkContext.stop` is called in `Utils.tryOrStopSparkContext` (the following three places), it will cause deadlock because the `stop` method needs to wait for the thread running `stop` to exit.
- ContextCleaner.keepCleaning
- LiveListenerBus.listenerThread.run
- TaskSchedulerImpl.start
This PR adds `SparkContext.stopInNewThread` and uses it to eliminate the potential deadlock. I also removed my changes in #15775 since they are not necessary now.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16178 from zsxwing/fix-stop-deadlock.
## What changes were proposed in this pull request?
- Removed the`attempt.completed ` filter so cleaner would include the orphan inprogress files.
- Use loading time for inprogress files as lastUpdated. Keep using the modTime for completed files. First one will prevent deletion of inprogress job files. Second one will ensure that lastUpdated time won't change for completed jobs in an event of HistoryServer reboot.
## How was this patch tested?
Added new unittests and via existing tests.
Author: Ergin Seyfe <eseyfe@fb.com>
Closes#16165 from seyfe/clear_old_inprogress_files.
## What changes were proposed in this pull request?
There are some tests failed on Windows due to the wrong format of path and the limitation of path length as below:
This PR proposes both to fix the failed tests by fixing the path for the tests below:
- `InsertSuite`
```
Exception encountered when attempting to run a suite with class name: org.apache.spark.sql.sources.InsertSuite *** ABORTED *** (12 seconds, 547 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-177945ef-9128-42b4-8c07-de31f78bbbd6;
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:382)
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:370)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
```
- `PathOptionSuite`
```
- path option also exist for write path *** FAILED *** (1 second, 93 milliseconds)
"C:[projectsspark arget mp]spark-5ab34a58-df8d-..." did not equal "C:[\projects\spark\target\tmp\]spark-5ab34a58-df8d-..." (PathOptionSuite.scala:93)
org.scalatest.exceptions.TestFailedException:
at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:500)
at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555)
...
```
- `UDFSuite`
```
- SPARK-8005 input_file_name *** FAILED *** (2 seconds, 234 milliseconds)
"file:///C:/projects/spark/target/tmp/spark-e4e5720a-2006-48f9-8b11-797bf59794bf/part-00001-26fb05e4-603d-471d-ae9d-b9549e0c7765.snappy.parquet" did not contain "C:\projects\spark\target\tmp\spark-e4e5720a-2006-48f9-8b11-797bf59794bf" (UDFSuite.scala:67)
org.scalatest.exceptions.TestFailedException:
at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:500)
at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555)
...
```
and to skip the tests belows which are being failed on Windows due to path length limitation.
- `SparkLauncherSuite`
```
Test org.apache.spark.launcher.SparkLauncherSuite.testChildProcLauncher failed: java.lang.AssertionError: expected:<0> but was:<1>, took 0.062 sec
at org.apache.spark.launcher.SparkLauncherSuite.testChildProcLauncher(SparkLauncherSuite.java:177)
...
```
The stderr from the process is `The filename or extension is too long` which is equivalent to the one below.
- `BroadcastJoinSuite`
```
04:09:40.882 ERROR org.apache.spark.deploy.worker.ExecutorRunner: Error running executor
java.io.IOException: Cannot run program "C:\Progra~1\Java\jdk1.8.0\bin\java" (in directory "C:\projects\spark\work\app-20161205040542-0000\51658"): CreateProcess error=206, The filename or extension is too long
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1048)
at org.apache.spark.deploy.worker.ExecutorRunner.org$apache$spark$deploy$worker$ExecutorRunner$$fetchAndRunExecutor(ExecutorRunner.scala:167)
at org.apache.spark.deploy.worker.ExecutorRunner$$anon$1.run(ExecutorRunner.scala:73)
Caused by: java.io.IOException: CreateProcess error=206, The filename or extension is too long
at java.lang.ProcessImpl.create(Native Method)
at java.lang.ProcessImpl.<init>(ProcessImpl.java:386)
at java.lang.ProcessImpl.start(ProcessImpl.java:137)
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1029)
... 2 more
04:09:40.929 ERROR org.apache.spark.deploy.worker.ExecutorRunner: Error running executor
(appearently infinite same error messages)
...
```
## How was this patch tested?
Manually tested via AppVeyor.
**Before**
`InsertSuite`: https://ci.appveyor.com/project/spark-test/spark/build/148-InsertSuite-pr
`PathOptionSuite`: https://ci.appveyor.com/project/spark-test/spark/build/139-PathOptionSuite-pr
`UDFSuite`: https://ci.appveyor.com/project/spark-test/spark/build/143-UDFSuite-pr
`SparkLauncherSuite`: https://ci.appveyor.com/project/spark-test/spark/build/141-SparkLauncherSuite-pr
`BroadcastJoinSuite`: https://ci.appveyor.com/project/spark-test/spark/build/145-BroadcastJoinSuite-pr
**After**
`PathOptionSuite`: https://ci.appveyor.com/project/spark-test/spark/build/140-PathOptionSuite-pr
`SparkLauncherSuite`: https://ci.appveyor.com/project/spark-test/spark/build/142-SparkLauncherSuite-pr
`UDFSuite`: https://ci.appveyor.com/project/spark-test/spark/build/144-UDFSuite-pr
`InsertSuite`: https://ci.appveyor.com/project/spark-test/spark/build/147-InsertSuite-pr
`BroadcastJoinSuite`: https://ci.appveyor.com/project/spark-test/spark/build/149-BroadcastJoinSuite-pr
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16147 from HyukjinKwon/fix-tests.
## What changes were proposed in this pull request?
Fix reservoir sampling bias for small k. An off-by-one error meant that the probability of replacement was slightly too high -- k/(l-1) after l element instead of k/l, which matters for small k.
## How was this patch tested?
Existing test plus new test case.
Author: Sean Owen <sowen@cloudera.com>
Closes#16129 from srowen/SPARK-18678.
## What changes were proposed in this pull request?
`spark.sql.unsafe.enabled` is deprecated since 1.6. There still are codes in UI to check it. We should remove it and clean the codes.
## How was this patch tested?
Changes to related existing unit test.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16095 from viirya/remove-deprecated-config-code.
The problem exists because it's not possible to just concatenate encrypted
partition data from different spill files; currently each partition would
have its own initial vector to set up encryption, and the final merged file
should contain a single initial vector for each merged partiton, otherwise
iterating over each record becomes really hard.
To fix that, UnsafeShuffleWriter now decrypts the partitions when merging,
so that the merged file contains a single initial vector at the start of
the partition data.
Because it's not possible to do that using the fast transferTo path, when
encryption is enabled UnsafeShuffleWriter will revert back to using file
streams when merging. It may be possible to use a hybrid approach when
using encryption, using an intermediate direct buffer when reading from
files and encrypting the data, but that's better left for a separate patch.
As part of the change I made DiskBlockObjectWriter take a SerializerManager
instead of a "wrap stream" closure, since that makes it easier to test the
code without having to mock SerializerManager functionality.
Tested with newly added unit tests (UnsafeShuffleWriterSuite for the write
side and ExternalAppendOnlyMapSuite for integration), and by running some
apps that failed without the fix.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#15982 from vanzin/SPARK-18546.
## What changes were proposed in this pull request?
The method `TaskSchedulerImpl.runningTasksByExecutors()` accesses the mutable `executorIdToRunningTaskIds` map without proper synchronization. In addition, as markhamstra pointed out in #15986, the signature's use of parentheses is a little odd given that this is a pure getter method.
This patch fixes both issues.
## How was this patch tested?
Covered by existing tests.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#16073 from JoshRosen/runningTasksByExecutors-thread-safety.
## What changes were proposed in this pull request?
#15992 provided a solution to fix the bug, i.e. **receiver data can not be deserialized properly**. As zsxwing said, it is a critical bug, but we should not break APIs between maintenance releases. It may be a rational choice to close auto pick kryo serializer for Spark Streaming in the first step. I will continue #15992 to optimize the solution.
## How was this patch tested?
existing ut
Author: uncleGen <hustyugm@gmail.com>
Closes#16052 from uncleGen/SPARK-18617.
## What changes were proposed in this pull request?
_This is the master branch version of #15986; the original description follows:_
This patch fixes a critical resource leak in the TaskScheduler which could cause RDDs and ShuffleDependencies to be kept alive indefinitely if an executor with running tasks is permanently lost and the associated stage fails.
This problem was originally identified by analyzing the heap dump of a driver belonging to a cluster that had run out of shuffle space. This dump contained several `ShuffleDependency` instances that were retained by `TaskSetManager`s inside the scheduler but were not otherwise referenced. Each of these `TaskSetManager`s was considered a "zombie" but had no running tasks and therefore should have been cleaned up. However, these zombie task sets were still referenced by the `TaskSchedulerImpl.taskIdToTaskSetManager` map.
Entries are added to the `taskIdToTaskSetManager` map when tasks are launched and are removed inside of `TaskScheduler.statusUpdate()`, which is invoked by the scheduler backend while processing `StatusUpdate` messages from executors. The problem with this design is that a completely dead executor will never send a `StatusUpdate`. There is [some code](072f4c518c/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala (L338)) in `statusUpdate` which handles tasks that exit with the `TaskState.LOST` state (which is supposed to correspond to a task failure triggered by total executor loss), but this state only seems to be used in Mesos fine-grained mode. There doesn't seem to be any code which performs per-task state cleanup for tasks that were running on an executor that completely disappears without sending any sort of final death message. The `executorLost` and [`removeExecutor`](072f4c518c/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala (L527)) methods don't appear to perform any cleanup of the `taskId -> *` mappings, causing the leaks observed here.
This patch's fix is to maintain a `executorId -> running task id` mapping so that these `taskId -> *` maps can be properly cleaned up following an executor loss.
There are some potential corner-case interactions that I'm concerned about here, especially some details in [the comment](072f4c518c/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala (L523)) in `removeExecutor`, so I'd appreciate a very careful review of these changes.
## How was this patch tested?
I added a new unit test to `TaskSchedulerImplSuite`.
/cc kayousterhout and markhamstra, who reviewed #15986.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#16045 from JoshRosen/fix-leak-following-total-executor-loss-master.
This change modifies the method used to propagate encryption keys used during
shuffle. Instead of relying on YARN's UserGroupInformation credential propagation,
this change explicitly distributes the key using the messages exchanged between
driver and executor during registration. When RPC encryption is enabled, this means
key propagation is also secure.
This allows shuffle encryption to work in non-YARN mode, which means that it's
easier to write unit tests for areas of the code that are affected by the feature.
The key is stored in the SecurityManager; because there are many instances of
that class used in the code, the key is only guaranteed to exist in the instance
managed by the SparkEnv. This path was chosen to avoid storing the key in the
SparkConf, which would risk having the key being written to disk as part of the
configuration (as, for example, is done when starting YARN applications).
Tested by new and existing unit tests (which were moved from the YARN module to
core), and by running apps with shuffle encryption enabled.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#15981 from vanzin/SPARK-18547.
## What changes were proposed in this pull request?
This adds tests to verify the interaction between TaskSetBlacklist and
TaskSchedulerImpl. TaskSetBlacklist was introduced by SPARK-17675 but
it neglected to add these tests.
This change does not fix any bugs -- it is just for increasing test
coverage.
## How was this patch tested?
Jenkins
Author: Imran Rashid <irashid@cloudera.com>
Closes#15644 from squito/taskset_blacklist_test_update.
## What changes were proposed in this pull request?
This patch adds a new property called `spark.secret.redactionPattern` that
allows users to specify a scala regex to decide which Spark configuration
properties and environment variables in driver and executor environments
contain sensitive information. When this regex matches the property or
environment variable name, its value is redacted from the environment UI and
various logs like YARN and event logs.
This change uses this property to redact information from event logs and YARN
logs. It also, updates the UI code to adhere to this property instead of
hardcoding the logic to decipher which properties are sensitive.
Here's an image of the UI post-redaction:
Here's the text in the YARN logs, post-redaction:
``HADOOP_CREDSTORE_PASSWORD -> *********(redacted)``
Here's the text in the event logs, post-redaction:
``...,"spark.executorEnv.HADOOP_CREDSTORE_PASSWORD":"*********(redacted)","spark.yarn.appMasterEnv.HADOOP_CREDSTORE_PASSWORD":"*********(redacted)",...``
## How was this patch tested?
1. Unit tests are added to ensure that redaction works.
2. A YARN job reading data off of S3 with confidential information
(hadoop credential provider password) being provided in the environment
variables of driver and executor. And, afterwards, logs were grepped to make
sure that no mention of secret password was present. It was also ensure that
the job was able to read the data off of S3 correctly, thereby ensuring that
the sensitive information was being trickled down to the right places to read
the data.
3. The event logs were checked to make sure no mention of secret password was
present.
4. UI environment tab was checked to make sure there was no secret information
being displayed.
Author: Mark Grover <mark@apache.org>
Closes#15971 from markgrover/master_redaction.
## What changes were proposed in this pull request?
This PR avoids that a result of an expression is negative due to signed integer overflow (e.g. 0x10?????? * 8 < 0). This PR casts each operand to `long` before executing a calculation. Since the result is interpreted as long, the result of the expression is positive.
## How was this patch tested?
Manually executed query82 of TPC-DS with 100TB
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#15907 from kiszk/SPARK-18458.
Adds --conf option to set spark configuration properties in mesos dispacther.
Properties provided with --conf take precedence over properties within the properties file.
The reason for this PR is that for simple configuration or testing purposes we need to provide a property file (ideally a shared one for a cluster) even if we just provide a single property.
Manually tested.
Author: Stavros Kontopoulos <st.kontopoulos@gmail.com>
Author: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Closes#14650 from skonto/dipatcher_conf.
## What changes were proposed in this pull request?
This is a follow-up work of #15618.
Close file source;
For any newly created streaming context outside the withContext, explicitly close the context.
## How was this patch tested?
Existing unit tests.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#15818 from wangmiao1981/rtest.
## What changes were proposed in this pull request?
We should call `setConf` if `OutputFormat` is `Configurable`, this should be done before we create `OutputCommitter` and `RecordWriter`.
This is follow up of #15769, see discussion [here](https://github.com/apache/spark/pull/15769/files#r87064229)
## How was this patch tested?
Add test of this case in `PairRDDFunctionsSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15823 from jiangxb1987/config-format.
## What changes were proposed in this pull request?
Application links generated on the history server UI no longer (regression from 1.6) contain the configured spark.ui.proxyBase in the links. To address this, made the uiRoot available globally to all javascripts for Web UI. Updated the mustache template (historypage-template.html) to include the uiroot for rendering links to the applications.
The existing test was not sufficient to verify the scenario where ajax call is used to populate the application listing template, so added a new selenium test case to cover this scenario.
## How was this patch tested?
Existing tests and a new unit test.
No visual changes to the UI.
Author: Vinayak <vijoshi5@in.ibm.com>
Closes#15742 from vijoshi/SPARK-16808_master.
## What changes were proposed in this pull request?
"StandaloneSchedulerBackend.dead" is called in a RPC thread, so it should not call "SparkContext.stop" in the same thread. "SparkContext.stop" will block until all RPC threads exit, if it's called inside a RPC thread, it will be dead-lock.
This PR add a thread local flag inside RPC threads. `SparkContext.stop` uses it to decide if launching a new thread to stop the SparkContext.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15775 from zsxwing/SPARK-18280.
## What changes were proposed in this pull request?
This PR port RDD API to use commit protocol, the changes made here:
1. Add new internal helper class that saves an RDD using a Hadoop OutputFormat named `SparkNewHadoopWriter`, it's similar with `SparkHadoopWriter` but uses commit protocol. This class supports the newer `mapreduce` API, instead of the old `mapred` API which is supported by `SparkHadoopWriter`;
2. Rewrite `PairRDDFunctions.saveAsNewAPIHadoopDataset` function, so it uses commit protocol now.
## How was this patch tested?
Exsiting test cases.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15769 from jiangxb1987/rdd-commit.
## What changes were proposed in this pull request?
When profiling heap dumps from the HistoryServer and live Spark web UIs, I found a large amount of memory being wasted on duplicated objects and strings. This patch's changes remove most of this duplication, resulting in over 40% memory savings for some benchmarks.
- **Task metrics** (6441f0624dfcda9c7193a64bfb416a145b5aabdf): previously, every `TaskUIData` object would have its own instances of `InputMetricsUIData`, `OutputMetricsUIData`, `ShuffleReadMetrics`, and `ShuffleWriteMetrics`, but for many tasks these metrics are irrelevant because they're all zero. This patch changes how we construct these metrics in order to re-use a single immutable "empty" value for the cases where these metrics are empty.
- **TaskInfo.accumulables** (ade86db901127bf13c0e0bdc3f09c933a093bb76): Previously, every `TaskInfo` object had its own empty `ListBuffer` for holding updates from named accumulators. Tasks which didn't use named accumulators still paid for the cost of allocating and storing this empty buffer. To avoid this overhead, I changed the `val` with a mutable buffer into a `var` which holds an immutable Scala list, allowing tasks which do not have named accumulator updates to share the same singleton `Nil` object.
- **String.intern() in JSONProtocol** (7e05630e9a78c455db8c8c499f0590c864624e05): in the HistoryServer, executor hostnames and ids are deserialized from JSON, leading to massive duplication of these string objects. By calling `String.intern()` on the deserialized values we can remove all of this duplication. Since Spark now requires Java 7+ we don't have to worry about string interning exhausting the permgen (see http://java-performance.info/string-intern-in-java-6-7-8/).
## How was this patch tested?
I ran
```
sc.parallelize(1 to 100000, 100000).count()
```
in `spark-shell` with event logging enabled, then loaded that event log in the HistoryServer, performed a full GC, and took a heap dump. According to YourKit, the changes in this patch reduced memory consumption by roughly 28 megabytes (or 770k Java objects):
Here's a table illustrating the drop in objects due to deduplication (the drop is <100k for some objects because some events were dropped from the listener bus; this is a separate, existing bug that I'll address separately after CPU-profiling):
Author: Josh Rosen <joshrosen@databricks.com>
Closes#15743 from JoshRosen/spark-ui-memory-usage.
## What changes were proposed in this pull request?
Close `FileStreams`, `ZipFiles` etc to release the resources after using. Not closing the resources will cause IO Exception to be raised while deleting temp files.
## How was this patch tested?
Existing tests
Author: U-FAREAST\tl <tl@microsoft.com>
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Tao LI <tl@microsoft.com>
Closes#15618 from HyukjinKwon/SPARK-14914-1.
## What changes were proposed in this pull request?
[SPARK-18200](https://issues.apache.org/jira/browse/SPARK-18200) reports Apache Spark 2.x raises `java.lang.IllegalArgumentException: requirement failed: Invalid initial capacity` while running `triangleCount`. The root cause is that `VertexSet`, a type alias of `OpenHashSet`, does not allow zero as a initial size. This PR loosens the restriction to allow zero.
## How was this patch tested?
Pass the Jenkins test with a new test case in `OpenHashSetSuite`.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#15741 from dongjoon-hyun/SPARK-18200.
## What changes were proposed in this pull request?
Fix `Locale.US` for all usages of `DateFormat`, `NumberFormat`
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#15610 from srowen/SPARK-18076.
## What changes were proposed in this pull request?
Removing `appUIAddress` attribute since it is no longer in use.
## How was this patch tested?
Local build
Author: Jacek Laskowski <jacek@japila.pl>
Closes#15603 from jaceklaskowski/sparkui-fixes.
## What changes were proposed in this pull request?
While learning core scheduler code, I found two lines of wrong comments. This PR simply corrects the comments.
## How was this patch tested?
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#15631 from wangmiao1981/Rbug.
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
`Utils.getIteratorZipWithIndex` was added to deal with number of records > 2147483647 in one partition.
method `getIteratorZipWithIndex` accepts `startIndex` < 0, which leads to negative index.
This PR just adds a defensive check on `startIndex` to make sure it is >= 0.
## How was this patch tested?
Add a new unit test.
Author: Miao Wang <miaowang@Miaos-MacBook-Pro.local>
Closes#15639 from wangmiao1981/zip.
## What changes were proposed in this pull request?
[SPARK-16757](https://issues.apache.org/jira/browse/SPARK-16757) sets the hadoop `CallerContext` when calling hadoop/hdfs apis to make spark applications more diagnosable in hadoop/hdfs logs. However, the `org.apache.hadoop.ipc.CallerContext` class is only added since [hadoop 2.8](https://issues.apache.org/jira/browse/HDFS-9184), which is not officially releaed yet. So each time `utils.CallerContext.setCurrentContext()` is called (e.g [when a task is created](https://github.com/apache/spark/blob/b678e46/core/src/main/scala/org/apache/spark/scheduler/Task.scala#L95-L96)), a "java.lang.ClassNotFoundException: org.apache.hadoop.ipc.CallerContext"
error is logged, which pollutes the spark logs when there are lots of tasks.
This patch improves this behaviour by only logging the `ClassNotFoundException` once.
## How was this patch tested?
Existing tests.
Author: Shuai Lin <linshuai2012@gmail.com>
Closes#15377 from lins05/spark-17802-improve-callercontext-logging.
## What changes were proposed in this pull request?
Currently users can kill stages via the web ui but not jobs directly (jobs are killed if one of their stages is). I've added the ability to kill jobs via the web ui. This code change is based on #4823 by lianhuiwang and updated to work with the latest code matching how stages are currently killed. In general I've copied the kill stage code warning and note comments and all. I also updated applicable tests and documentation.
## How was this patch tested?
Manually tested and dev/run-tests
Author: Alex Bozarth <ajbozart@us.ibm.com>
Author: Lianhui Wang <lianhuiwang09@gmail.com>
Closes#15441 from ajbozarth/spark4411.
spark history server log needs to be fixed to show https url when ssl is enabled
Author: chie8842 <chie@chie-no-Mac-mini.local>
Closes#15611 from hayashidac/SPARK-16988.
The named parameter in an overridden class isn't supported in Scala 2.10 so was breaking the build.
cc zsxwing
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#15617 from kayousterhout/hotfix.
`TaskSetManager` should have unique name to avoid adding duplicate ones to parent `Pool` via `SchedulableBuilder`. This problem has been surfaced with following discussion: [[PR: Avoid adding duplicate schedulables]](https://github.com/apache/spark/pull/15326)
**Proposal** :
There is 1x1 relationship between `stageAttemptId` and `TaskSetManager` so `taskSet.Id` covering both `stageId` and `stageAttemptId` looks to be used for uniqueness of `TaskSetManager` name instead of just `stageId`.
**Current TaskSetManager Name** :
`var name = "TaskSet_" + taskSet.stageId.toString`
**Sample**: TaskSet_0
**Proposed TaskSetManager Name** :
`val name = "TaskSet_" + taskSet.Id ` `// taskSet.Id = (stageId + "." + stageAttemptId)`
**Sample** : TaskSet_0.0
Added new Unit Test.
Author: erenavsarogullari <erenavsarogullari@gmail.com>
Closes#15463 from erenavsarogullari/SPARK-17894.
## What changes were proposed in this pull request?
`Array[T]()` -> `Array.empty[T]` to avoid allocating 0-length arrays.
Use regex `find . -name '*.scala' | xargs -i bash -c 'egrep "Array\[[A-Za-z]+\]\(\)" -n {} && echo {}'` to find modification candidates.
cc srowen
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#15564 from zhengruifeng/avoid_0_length_array.
## What changes were proposed in this pull request?
- Fix bug of RDD `zipWithIndex` generating wrong result when one partition contains more than 2147483647 records.
- Fix bug of RDD `zipWithUniqueId` generating wrong result when one partition contains more than 2147483647 records.
## How was this patch tested?
test added.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#15550 from WeichenXu123/fix_rdd_zipWithIndex_overflow.
## What changes were proposed in this pull request?
This PR adds support for executor log compression.
## How was this patch tested?
Unit tests
cc: yhuai tdas mengxr
Author: Yu Peng <loneknightpy@gmail.com>
Closes#15285 from loneknightpy/compress-executor-log.
## What changes were proposed in this pull request?
Currently we use BufferedInputStream to read the shuffle file which copies the file content from os buffer cache to the user buffer. This adds additional latency in reading the spill files. We made a change to use java nio's direct buffer to read the spill files and for certain pipelines spilling significant amount of data, we see up to 7% speedup for the entire pipeline.
## How was this patch tested?
Tested by running the job in the cluster and observed up to 7% speedup.
Author: Sital Kedia <skedia@fb.com>
Closes#15408 from sitalkedia/skedia/nio_spill_read.
This reverts commit ed14633414.
The patch merged had obvious quality and documentation issue. The idea is useful, and we should work towards improving its quality and merging it in again.
## What changes were proposed in this pull request?
Restructure the code and implement two new task assigner.
PackedAssigner: try to allocate tasks to the executors with least available cores, so that spark can release reserved executors when dynamic allocation is enabled.
BalancedAssigner: try to allocate tasks to the executors with more available cores in order to balance the workload across all executors.
By default, the original round robin assigner is used.
We test a pipeline, and new PackedAssigner save around 45% regarding the reserved cpu and memory with dynamic allocation enabled.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Both unit test in TaskSchedulerImplSuite and manual tests in production pipeline.
Author: Zhan Zhang <zhanzhang@fb.com>
Closes#15218 from zhzhan/packed-scheduler.
## What changes were proposed in this pull request?
This is a step along the way to SPARK-8425.
To enable incremental review, the first step proposed here is to expand the blacklisting within tasksets. In particular, this will enable blacklisting for
* (task, executor) pairs (this already exists via an undocumented config)
* (task, node)
* (taskset, executor)
* (taskset, node)
Adding (task, node) is critical to making spark fault-tolerant of one-bad disk in a cluster, without requiring careful tuning of "spark.task.maxFailures". The other additions are also important to avoid many misleading task failures and long scheduling delays when there is one bad node on a large cluster.
Note that some of the code changes here aren't really required for just this -- they put pieces in place for SPARK-8425 even though they are not used yet (eg. the `BlacklistTracker` helper is a little out of place, `TaskSetBlacklist` holds onto a little more info than it needs to for just this change, and `ExecutorFailuresInTaskSet` is more complex than it needs to be).
## How was this patch tested?
Added unit tests, run tests via jenkins.
Author: Imran Rashid <irashid@cloudera.com>
Author: mwws <wei.mao@intel.com>
Closes#15249 from squito/taskset_blacklist_only.
## What changes were proposed in this pull request?
Add a flag to ignore corrupt files. For Spark core, the configuration is `spark.files.ignoreCorruptFiles`. For Spark SQL, it's `spark.sql.files.ignoreCorruptFiles`.
## How was this patch tested?
The added unit tests
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15422 from zsxwing/SPARK-17850.
## What changes were proposed in this pull request?
This PR modified the test case `test("script")` to use resource path for `test_script.sh`. Make the test case portable (even in IntelliJ).
## How was this patch tested?
Passed the test case.
Before:
Run `test("script")` in IntelliJ:
```
Caused by: org.apache.spark.SparkException: Subprocess exited with status 127. Error: bash: src/test/resources/test_script.sh: No such file or directory
```
After:
Test passed.
Author: Weiqing Yang <yangweiqing001@gmail.com>
Closes#15246 from weiqingy/hivetest.
## What changes were proposed in this pull request?
Fix:
- GroupedMeanEvaluator and GroupedSumEvaluator are unused, as is the StudentTCacher support class
- CountEvaluator can return a lower bound < 0, when counts can't be negative
- MeanEvaluator will actually fail on exactly 1 datum (yields t-test with 0 DOF)
- CountEvaluator uses a normal distribution, which may be an inappropriate approximation (leading to above)
- Test for SumEvaluator asserts incorrect expected sums – e.g. after observing 10% of data has sum of 2, expectation should be 20, not 38
- CountEvaluator, MeanEvaluator have no unit tests to catch these
- Duplication of distribution code across CountEvaluator, GroupedCountEvaluator
- The stats in each could use a bit of documentation as I had to guess at them
- (Code could use a few cleanups and optimizations too)
## How was this patch tested?
Existing and new tests
Author: Sean Owen <sowen@cloudera.com>
Closes#15341 from srowen/SPARK-17768.
## What changes were proposed in this pull request?
Mock SparkContext to reduce memory usage of BlockManagerSuite
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15350 from zsxwing/SPARK-17778.
## What changes were proposed in this pull request?
Return Iterator of applications internally in history server, for consistency and performance. See https://github.com/apache/spark/pull/15248 for some back-story.
The code called by and calling HistoryServer.getApplicationList wants an Iterator, but this method materializes an Iterable, which potentially causes a performance problem. It's simpler too to make this internal method also pass through an Iterator.
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#15321 from srowen/SPARK-17671.
## What changes were proposed in this pull request?
This PR proposes to fix/skip some tests failed on Windows. This PR takes over https://github.com/apache/spark/pull/12696.
**Before**
- **SparkSubmitSuite**
```
[info] - launch simple application with spark-submit *** FAILED *** (202 milliseconds)
[info] java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specifie
[info] - includes jars passed in through --jars *** FAILED *** (1 second, 625 milliseconds)
[info] java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specified
```
- **DiskStoreSuite**
```
[info] - reads of memory-mapped and non memory-mapped files are equivalent *** FAILED *** (1 second, 78 milliseconds)
[info] diskStoreMapped.remove(blockId) was false (DiskStoreSuite.scala:41)
```
**After**
- **SparkSubmitSuite**
```
[info] - launch simple application with spark-submit (578 milliseconds)
[info] - includes jars passed in through --jars (1 second, 875 milliseconds)
```
- **DiskStoreSuite**
```
[info] DiskStoreSuite:
[info] - reads of memory-mapped and non memory-mapped files are equivalent !!! CANCELED !!! (766 milliseconds
```
For `CreateTableAsSelectSuite` and `FsHistoryProviderSuite`, I could not reproduce as the Java version seems higher than the one that has the bugs about `setReadable(..)` and `setWritable(...)` but as they are bugs reported clearly, it'd be sensible to skip those. We should revert the changes for both back as soon as we drop the support of Java 7.
## How was this patch tested?
Manually tested via AppVeyor.
Closes#12696
Author: Tao LI <tl@microsoft.com>
Author: U-FAREAST\tl <tl@microsoft.com>
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15320 from HyukjinKwon/SPARK-14914.
