Currently the code monitoring the launch of the client AM uses the value of
spark.yarn.report.interval as the interval for polling the RM; if someone
has that value to a really large interval, it would take that long to detect
that the client AM has started, which is not expected.
Instead, have a separate config for the interval to use when the client AM is
starting. The other config is still used in cluster mode, and to detect the
status of the client AM after it is already running.
Tested by running client and cluster mode apps with a modified value of
spark.yarn.report.interval, verifying client AM launch is detected before
that interval elapses.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18380 from vanzin/SPARK-16019.
## What changes were proposed in this pull request?
This PR aims to bump Py4J in order to fix the following float/double bug.
Py4J 0.10.5 fixes this (https://github.com/bartdag/py4j/issues/272) and the latest Py4J is 0.10.6.
**BEFORE**
```
>>> df = spark.range(1)
>>> df.select(df['id'] + 17.133574204226083).show()
+--------------------+
|(id + 17.1335742042)|
+--------------------+
| 17.1335742042|
+--------------------+
```
**AFTER**
```
>>> df = spark.range(1)
>>> df.select(df['id'] + 17.133574204226083).show()
+-------------------------+
|(id + 17.133574204226083)|
+-------------------------+
| 17.133574204226083|
+-------------------------+
```
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#18546 from dongjoon-hyun/SPARK-21278.
## What changes were proposed in this pull request?
Currently the shuffle service registration timeout and retry has been hardcoded. This works well for small workloads but under heavy workload when the shuffle service is busy transferring large amount of data we see significant delay in responding to the registration request, as a result we often see the executors fail to register with the shuffle service, eventually failing the job. We need to make these two parameters configurable.
## How was this patch tested?
* Updated `BlockManagerSuite` to test registration timeout and max attempts configuration actually works.
cc sitalkedia
Author: Li Yichao <lyc@zhihu.com>
Closes#18092 from liyichao/SPARK-20640.
## What changes were proposed in this pull request?
When I set different clusters for "spark.hadoop.fs.defaultFS" and "spark.yarn.stagingDir" as follows：
```
spark.hadoop.fs.defaultFS hdfs://tl-nn-tdw.tencent-distribute.com:54310
spark.yarn.stagingDir hdfs://ss-teg-2-v2/tmp/spark
```
The staging dir can not be deleted, it will prompt following message:
```
java.lang.IllegalArgumentException: Wrong FS: hdfs://ss-teg-2-v2/tmp/spark/.sparkStaging/application_1496819138021_77618, expected: hdfs://tl-nn-tdw.tencent-distribute.com:54310
```
## How was this patch tested?
Existing tests
Author: sharkdtu <sharkdtu@tencent.com>
Closes#18352 from sharkdtu/master.
## What changes were proposed in this pull request?
Move Hadoop delegation token code from `spark-yarn` to `spark-core`, so that other schedulers (such as Mesos), may use it. In order to avoid exposing Hadoop interfaces in spark-core, the new Hadoop delegation token classes are kept private. In order to provider backward compatiblity, and to allow YARN users to continue to load their own delegation token providers via Java service loading, the old YARN interfaces, as well as the client code that uses them, have been retained.
Summary:
- Move registered `yarn.security.ServiceCredentialProvider` classes from `spark-yarn` to `spark-core`. Moved them into a new, private hierarchy under `HadoopDelegationTokenProvider`. Client code in `HadoopDelegationTokenManager` now loads credentials from a whitelist of three providers (`HadoopFSDelegationTokenProvider`, `HiveDelegationTokenProvider`, `HBaseDelegationTokenProvider`), instead of service loading, which means that users are not able to implement their own delegation token providers, as they are in the `spark-yarn` module.
- The `yarn.security.ServiceCredentialProvider` interface has been kept for backwards compatibility, and to continue to allow YARN users to implement their own delegation token provider implementations. Client code in YARN now fetches tokens via the new `YARNHadoopDelegationTokenManager` class, which fetches tokens from the core providers through `HadoopDelegationTokenManager`, as well as service loads them from `yarn.security.ServiceCredentialProvider`.
Old Hierarchy:
```
yarn.security.ServiceCredentialProvider (service loaded)
HadoopFSCredentialProvider
HiveCredentialProvider
HBaseCredentialProvider
yarn.security.ConfigurableCredentialManager
```
New Hierarchy:
```
HadoopDelegationTokenManager
HadoopDelegationTokenProvider (not service loaded)
HadoopFSDelegationTokenProvider
HiveDelegationTokenProvider
HBaseDelegationTokenProvider
yarn.security.ServiceCredentialProvider (service loaded)
yarn.security.YARNHadoopDelegationTokenManager
```
## How was this patch tested?
unit tests
Author: Michael Gummelt <mgummelt@mesosphere.io>
Author: Dr. Stefan Schimanski <sttts@mesosphere.io>
Closes#17723 from mgummelt/SPARK-20434-refactor-kerberos.
## What changes were proposed in this pull request?
Add Mesos labels support to the Spark Dispatcher
## How was this patch tested?
unit tests
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#18220 from mgummelt/SPARK-21000-dispatcher-labels.
In Spark on YARN, when configuring "spark.yarn.jars" with local jars (jars started with "local" scheme), we will get inaccurate classpath for AM and containers. This is because we don't remove "local" scheme when concatenating classpath. It is OK to run because classpath is separated with ":" and java treat "local" as a separate jar. But we could improve it to remove the scheme.
Updated `ClientSuite` to check "local" is not in the classpath.
cc jerryshao
Author: Li Yichao <lyc@zhihu.com>
Author: Li Yichao <liyichao.good@gmail.com>
Closes#18129 from liyichao/SPARK-20365.
## What changes were proposed in this pull request?
Deleted generated JARs archive after distribution to HDFS
## How was this patch tested?
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Lior Regev <lioregev@gmail.com>
Closes#17986 from liorregev/master.
Restore code that was removed as part of SPARK-17979, but instead of
using the deprecated env variable name to propagate the class path, use
a new one.
Verified by running "./bin/spark-class o.a.s.executor.CoarseGrainedExecutorBackend"
manually.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18037 from vanzin/SPARK-20814.
## What changes were proposed in this pull request?
Add stripXSS and stripXSSMap to Spark Core's UIUtils. Calling these functions at any point that getParameter is called against a HttpServletRequest.
## How was this patch tested?
Unit tests, IBM Security AppScan Standard no longer showing vulnerabilities, manual verification of WebUI pages.
Author: NICHOLAS T. MARION <nmarion@us.ibm.com>
Closes#17686 from n-marion/xss-fix.
## What changes were proposed in this pull request?
After SPARK-10997, client mode Netty RpcEnv doesn't require to start server, so port configurations are not used any more, here propose to remove these two configurations: "spark.executor.port" and "spark.am.port".
## How was this patch tested?
Existing UTs.
Author: jerryshao <sshao@hortonworks.com>
Closes#17866 from jerryshao/SPARK-20605.
## What changes were proposed in this pull request?
Currently, `spark.executor.instances` is deprecated in `spark-env.sh`, because we suggest config it in `spark-defaults.conf` or other config file. And also this parameter is useless even if you set it in `spark-env.sh`, so remove it in this patch.
## How was this patch tested?
Existing tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Xianyang Liu <xianyang.liu@intel.com>
Closes#17881 from ConeyLiu/deprecatedParam.
Signed-off-by: liuxian <liu.xian3zte.com.cn>
## What changes were proposed in this pull request?
When the input parameter is null, may be a runtime exception occurs
## How was this patch tested?
Existing unit tests
Author: liuxian <liu.xian3@zte.com.cn>
Closes#17796 from 10110346/wip_lx_0428.
## What changes were proposed in this pull request?
Fix build warnings primarily related to Breeze 0.13 operator changes, Java style problems
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#17803 from srowen/SPARK-20523.
## What changes were proposed in this pull request?
Add test case for scenarios where executor.cores is set as a
(non)divisor of spark.cores.max
This tests the change in
#17786
## How was this patch tested?
Ran the existing test suite with the new tests
dbtsai
Author: Davis Shepherd <dshepherd@netflix.com>
Closes#17788 from dgshep/add_mesos_test.
## What changes were proposed in this pull request?
Set maxCores to be a multiple of the smallest executor that can be launched. This ensures that we correctly detect the condition where no more executors will be launched when spark.cores.max is not a multiple of spark.executor.cores
## How was this patch tested?
This was manually tested with other sample frameworks measuring their incoming offers to determine if starvation would occur.
dbtsai mgummelt
Author: Davis Shepherd <dshepherd@netflix.com>
Closes#17786 from dgshep/fix_mesos_max_cores.
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?
Submitted Time' field, the date format **needs to be formatted**, in running Drivers table or Completed Drivers table in master web ui.
Before fix this problem e.g.
Completed Drivers
Submission ID **Submitted Time** Worker State Cores Memory Main Class
driver-20170419145755-0005 **Wed Apr 19 14:57:55 CST 2017** worker-20170419145250-zdh120-40412 FAILED 1 1024.0 MB cn.zte.HdfsTest
please see the attachment:https://issues.apache.org/jira/secure/attachment/12863977/before_fix.png
After fix this problem e.g.
Completed Drivers
Submission ID **Submitted Time** Worker State Cores Memory Main Class
driver-20170419145755-0006 **2017/04/19 16:01:25** worker-20170419145250-zdh120-40412 FAILED 1 1024.0 MB cn.zte.HdfsTest
please see the attachment:https://issues.apache.org/jira/secure/attachment/12863976/after_fix.png
'Submitted Time' field, the date format **has been formatted**, in running Applications table or Completed Applicationstable in master web ui, **it is correct.**
e.g.
Running Applications
Application ID Name Cores Memory per Executor **Submitted Time** User State Duration
app-20170419160910-0000 (kill) SparkSQL::10.43.183.120 1 5.0 GB **2017/04/19 16:09:10** root RUNNING 53 s
**Format after the time easier to observe, and consistent with the applications table,so I think it's worth fixing.**
## How was this patch tested?
(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)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: 郭小龙 10207633 <guo.xiaolong1@zte.com.cn>
Author: guoxiaolong <guo.xiaolong1@zte.com.cn>
Author: guoxiaolongzte <guo.xiaolong1@zte.com.cn>
Closes#17682 from guoxiaolongzte/SPARK-20385.
## What changes were proposed in this pull request?
Typo fix: distitrbuted -> distributed
## How was this patch tested?
Existing tests
Author: Andrew Ash <andrew@andrewash.com>
Closes#17664 from ash211/patch-1.
## What changes were proposed in this pull request?
Allow passing in arbitrary parameters into docker when launching spark executors on mesos with docker containerizer tnachen
## How was this patch tested?
Manually built and tested with passed in parameter
Author: Ji Yan <jiyan@Jis-MacBook-Air.local>
Closes#17109 from yanji84/ji/allow_set_docker_user.
## 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?
Add spark.mesos.task.labels configuration option to add mesos key:value labels to the executor.
"k1:v1,k2:v2" as the format, colons separating key-value and commas to list out more than one.
Discussion of labels with mgummelt at #17404
## How was this patch tested?
Added unit tests to verify labels were added correctly, with incorrect labels being ignored and added a test to test the name of the executor.
Tested with: `./build/sbt -Pmesos mesos/test`
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Kalvin Chau <kalvin.chau@viasat.com>
Closes#17413 from kalvinnchau/mesos-labels.
Current test code tries to override the RackResolver used by setting
configuration params, but because YARN libs statically initialize the
resolver the first time it's used, that means that those configs don't
really take effect during Spark tests.
This change adds a wrapper class that easily allows tests to override the
behavior of the resolver for the Spark code that uses it.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#17508 from vanzin/SPARK-20191.
## 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.
## What changes were proposed in this pull request?
In the current Spark on YARN code, we will obtain tokens from provided services, but we're not going to add these tokens to the current user's credentials. This will make all the following operations to these services still require TGT rather than delegation tokens. This is unnecessary since we already got the tokens, also this will lead to failure in user impersonation scenario, because the TGT is granted by real user, not proxy user.
So here changing to put all the tokens to the current UGI, so that following operations to these services will honor tokens rather than TGT, and this will further handle the proxy user issue mentioned above.
## How was this patch tested?
Local verified in secure cluster.
vanzin tgravescs mridulm dongjoon-hyun please help to review, thanks a lot.
Author: jerryshao <sshao@hortonworks.com>
Closes#17335 from jerryshao/SPARK-19995.
## What changes were proposed in this pull request?
Adding additional information to existing logging messages:
- YarnAllocator: log the executor ID together with the container id when a container for an executor is launched.
- NettyRpcEnv: log the receiver address when there is a timeout waiting for an answer to a remote call.
- ExecutorAllocationManager: fix a typo in the logging message for the list of executors to be removed.
## How was this patch tested?
Build spark and submit the word count example to a YARN cluster using cluster mode
Author: Juan Rodriguez Hortala <hortala@amazon.com>
Closes#17411 from juanrh/logging-improvements.
## What changes were proposed in this pull request?
Adding configurable mesos executor names and labels using `spark.mesos.task.name` and `spark.mesos.task.labels`.
Labels were defined as `k1:v1,k2:v2`.
mgummelt
## How was this patch tested?
Added unit tests to verify labels were added correctly, with incorrect labels being ignored and added a test to test the name of the executor.
Tested with: `./build/sbt -Pmesos mesos/test`
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Kalvin Chau <kalvin.chau@viasat.com>
Closes#17404 from kalvinnchau/mesos-config.
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?
Fixup typo in comment.
## How was this patch tested?
Don't need.
Author: Ye Yin <eyniy@qq.com>
Closes#17396 from hustcat/fix.
Add parentheses so that both lines form a single statement; also add
a log message so that the issue becomes more explicit if it shows up
again.
Tested manually with integration test that exercises the feature.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#17198 from vanzin/SPARK-19857.
## What changes were proposed in this pull request?
Increase default refuse_seconds timeout, and make it configurable. See JIRA for details on how this reduces the risk of starvation.
## How was this patch tested?
Unit tests, Manual testing, and Mesos/Spark integration test suite
cc susanxhuynh skonto jmlvanre
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#17031 from mgummelt/SPARK-19702-suppress-revive.
## What changes were proposed in this pull request?
See JIRA
## How was this patch tested?
Unit tests, Mesos/Spark integration tests
cc skonto susanxhuynh
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#17045 from mgummelt/SPARK-19373-registered-resources.
## What changes were proposed in this pull request?
Adding the default UncaughtExceptionHandler to the MesosClusterDispatcher.
## How was this patch tested?
I verified it manually, when any of the dispatcher thread gets uncaught exceptions then the default UncaughtExceptionHandler will handle those exceptions.
Author: Devaraj K <devaraj@apache.org>
Closes#13072 from devaraj-kavali/SPARK-15288.
## What changes were proposed in this pull request?
self.environment will be propagated to executor. Should set PYTHONHASHSEED as long as the python version is greater than 3.3
## How was this patch tested?
Manually tested it.
Author: Jeff Zhang <zjffdu@apache.org>
Closes#11211 from zjffdu/SPARK-13330.
## What changes were proposed in this pull request?
Because yarn#client will reset the `spark.yarn.keytab` configuration to point to the location in distributed file, so if user still uses the old `SparkConf` to create `SparkSession` with Hive enabled, it will read keytab from the path in distributed cached. This is OK for yarn cluster mode, but in yarn client mode where driver is running out of container, it will be failed to fetch the keytab.
So here we should avoid reseting this configuration in the `yarn#client` and only overwriting it for AM, so using `spark.yarn.keytab` could get correct keytab path no matter running in client (keytab in local fs) or cluster (keytab in distributed cache) mode.
## How was this patch tested?
Verified in security cluster.
Author: jerryshao <sshao@hortonworks.com>
Closes#16923 from jerryshao/SPARK-19038.
Allow an application to use the History Server URL as the tracking
URL in the YARN RM, so there's still a link to the web UI somewhere
in YARN even if the driver's UI is disabled. This is useful, for
example, if an admin wants to disable the driver UI by default for
applications, since it's harder to secure it (since it involves non
trivial ssl certificate and auth management that admins may not want
to expose to user apps).
This needs to be opt-in, because of the way the YARN proxy works, so
a new configuration was added to enable the option.
The YARN RM will proxy requests to live AMs instead of redirecting
the client, so pages in the SHS UI will not render correctly since
they'll reference invalid paths in the RM UI. The proxy base support
in the SHS cannot be used since that would prevent direct access to
the SHS.
So, to solve this problem, for the feature to work end-to-end, a new
YARN-specific filter was added that detects whether the requests come
from the proxy and redirects the client appropriatly. The SHS admin has
to add this filter manually if they want the feature to work.
Tested with new unit test, and by running with the documented configuration
set in a test cluster. Also verified the driver UI is used when it's
enabled.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#16946 from vanzin/SPARK-19554.
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/14065, we introduced a configurable credential manager for Spark running on YARN. Also two configs `spark.yarn.credentials.renewalTime` and `spark.yarn.credentials.updateTime` were added, one is for the credential renewer and the other updater. But now we just query `spark.yarn.credentials.renewalTime` by mistake during CREDENTIALS UPDATING, where should be actually `spark.yarn.credentials.updateTime` .
This PR fixes this mistake.
## How was this patch tested?
existing test
cc jerryshao vanzin
Author: Kent Yao <yaooqinn@hotmail.com>
Closes#16955 from yaooqinn/cred_update.
## 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.
- 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?
Now handling the spark exception which gets thrown for invalid job configuration, marking that job as failed and continuing to launch the other drivers instead of throwing the exception.
## How was this patch tested?
I verified manually, now the misconfigured jobs move to Finished Drivers section in UI and continue to launch the other jobs.
Author: Devaraj K <devaraj@apache.org>
Closes#13077 from devaraj-kavali/SPARK-10748.
## What changes were proposed in this pull request?
Due to the newly added API in Hadoop 2.6.4+, Spark builds against Hadoop 2.6.0~2.6.3 will meet compile error. So here still reverting back to use reflection to handle this issue.
## How was this patch tested?
Manual verification.
Author: jerryshao <sshao@hortonworks.com>
Closes#16884 from jerryshao/SPARK-19545.
Add back mockito test dep in YARN module, as it ends up being required in a Maven build
## How was this patch tested?
PR builder again, but also a local `mvn` run using the command that the broken Jenkins job uses
Author: Sean Owen <sowen@cloudera.com>
Closes#16853 from srowen/SPARK-19464.2.
## What changes were proposed in this pull request?
After using Apache Parquet 1.8.2, `ParquetAvroCompatibilitySuite` fails on **Maven** test. It is because `org.apache.parquet.avro.AvroParquetWriter` in the test code used new `avro 1.8.0` specific class, `LogicalType`. This PR aims to fix the test dependency of `sql/core` module to use avro 1.8.0.
https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-2.7/2530/consoleFull
```
ParquetAvroCompatibilitySuite:
*** RUN ABORTED ***
java.lang.NoClassDefFoundError: org/apache/avro/LogicalType
at org.apache.parquet.avro.AvroParquetWriter.writeSupport(AvroParquetWriter.java:144)
```
## How was this patch tested?
Pass the existing test with **Maven**.
```
$ build/mvn -Pyarn -Phadoop-2.7 -Pkinesis-asl -Phive -Phive-thriftserver test
...
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 02:07 h
[INFO] Finished at: 2017-02-04T05:41:43+00:00
[INFO] Final Memory: 77M/987M
[INFO] ------------------------------------------------------------------------
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#16795 from dongjoon-hyun/SPARK-19409-2.
## 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.
That method is prone to stack overflows when the input map is really
large; instead, use plain "map". Also includes a unit test that was
tested and caused stack overflows without the fix.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#16667 from vanzin/SPARK-18750.
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?
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?
`spark.yarn.access.namenodes` configuration cannot actually reflects the usage of it, inside the code it is the Hadoop filesystems we get tokens, not NNs. So here propose to update the name of this configuration, also change the related code and doc.
## How was this patch tested?
Local verification.
Author: jerryshao <sshao@hortonworks.com>
Closes#16560 from jerryshao/SPARK-19179.
## What changes were proposed in this pull request?
#16092 moves YARN resource manager related code to resource-managers/yarn directory. The test case ```YarnSchedulerBackendSuite``` was added after that but with the wrong place. I move it to correct directory in this PR.
## How was this patch tested?
Existing test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#16595 from yanboliang/yarn.
Currently Spark can only get token renewal interval from security HDFS (hdfs://), if Spark runs with other security file systems like webHDFS (webhdfs://), wasb (wasb://), ADLS, it will ignore these tokens and not get token renewal intervals from these tokens. These will make Spark unable to work with these security clusters. So instead of only checking HDFS token, we should generalize to support different DelegationTokenIdentifier.
## How was this patch tested?
Manually verified in security cluster.
Author: jerryshao <sshao@hortonworks.com>
Closes#16432 from jerryshao/SPARK-19021.
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?
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?
LauncherState should be only set to SUBMITTED after the application is submitted.
Currently the state is set before the application is actually submitted.
## How was this patch tested?
no test is added in this patch
Author: mingfei <mingfei.smf@alipay.com>
Closes#16459 from shimingfei/fixLauncher.
## What changes were proposed in this pull request?
Not adding the Killed applications for retry.
## How was this patch tested?
I have verified manually in the Mesos cluster, with the changes the killed applications move to Finished Drivers section and will not retry.
Author: Devaraj K <devaraj@apache.org>
Closes#13323 from devaraj-kavali/SPARK-15555.
## What changes were proposed in this pull request?
The configuration `spark.yarn.security.tokens.{service}.enabled` is deprecated. Now we should use `spark.yarn.security.credentials.{service}.enabled`. Some places in the doc is not updated yet.
## How was this patch tested?
N/A. Just doc change.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16444 from viirya/minor-credential-provider-doc.
Remove spark-tag's compile-scope dependency (and, indirectly, spark-core's compile-scope transitive-dependency) on scalatest by splitting test-oriented tags into spark-tags' test JAR.
Alternative to #16303.
Author: Ryan Williams <ryan.blake.williams@gmail.com>
Closes#16311 from ryan-williams/tt.
## What changes were proposed in this pull request?
93cdb8a7d0 Introduced a compile error under scala 2.10, this fixes that error.
## How was this patch tested?
locally ran
```
dev/change-version-to-2.10.sh
build/sbt -Pyarn -Phadoop-2.4 -Dhadoop.version=2.6.0 -Dscala-2.10 "project yarn" "test-only *YarnAllocatorSuite"
```
(which failed at test compilation before this change)
Author: Imran Rashid <irashid@cloudera.com>
Closes#16298 from squito/blacklist-2.10.
## 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?
Fix `java.util.NoSuchElementException` when running Spark in non-hdfs security environment.
In the current code, we assume `HDFS_DELEGATION_KIND` token will be found in Credentials. But in some cloud environments, HDFS is not required, so we should avoid this exception.
## How was this patch tested?
Manually verified in local environment.
Author: jerryshao <sshao@hortonworks.com>
Closes#16265 from jerryshao/SPARK-18840.
