## What changes were proposed in this pull request?
The PR makes hardcoded configs below to use `ConfigEntry`.
* spark.ui
* spark.ssl
* spark.authenticate
* spark.master.rest
* spark.master.ui
* spark.metrics
* spark.admin
* spark.modify.acl
This patch doesn't change configs which are not relevant to SparkConf (e.g. system properties).
## How was this patch tested?
Existing tests.
Closes#23423 from HeartSaVioR/SPARK-26466.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Currently, `Client#createConfArchive` do not handle IOException, and some detail info is not provided in logs. Sometimes, this may delay the time of locating the root cause of io error.
This PR will add debug logs for confArchive when preparing local resource.
## How was this patch tested?
unittest
Closes#23444 from liupc/Add-logs-for-IOException-when-preparing-local-resource.
Authored-by: Liupengcheng <liupengcheng@xiaomi.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
Currently there is code scattered in a bunch of places to do different
things related to HTTP security, such as access control, setting
security-related headers, and filtering out bad content. This makes it
really easy to miss these things when writing new UI code.
This change creates a new filter that does all of those things, and
makes sure that all servlet handlers that are attached to the UI get
the new filter and any user-defined filters consistently. The extent
of the actual features should be the same as before.
The new filter is added at the end of the filter chain, because authentication
is done by custom filters and thus needs to happen first. This means that
custom filters see unfiltered HTTP requests - which is actually the current
behavior anyway.
As a side-effect of some of the code refactoring, handlers added after
the initial set also get wrapped with a GzipHandler, which didn't happen
before.
Tested with added unit tests and in a history server with SPNEGO auth
configured.
Closes#23302 from vanzin/SPARK-24522.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
This change modifies the behavior of the delegation token code when running
on YARN, so that the driver controls the renewal, in both client and cluster
mode. For that, a few different things were changed:
* The AM code only runs code that needs DTs when DTs are available.
In a way, this restores the AM behavior to what it was pre-SPARK-23361, but
keeping the fix added in that bug. Basically, all the AM code is run in a
"UGI.doAs()" block; but code that needs to talk to HDFS (basically the
distributed cache handling code) was delayed to the point where the driver
is up and running, and thus when valid delegation tokens are available.
* SparkSubmit / ApplicationMaster now handle user login, not the token manager.
The previous AM code was relying on the token manager to keep the user
logged in when keytabs are used. This required some odd APIs in the token
manager and the AM so that the right UGI was exposed and used in the right
places.
After this change, the logged in user is handled separately from the token
manager, so the API was cleaned up, and, as explained above, the whole AM
runs under the logged in user, which also helps with simplifying some more code.
* Distributed cache configs are sent separately to the AM.
Because of the delayed initialization of the cached resources in the AM, it
became easier to write the cache config to a separate properties file instead
of bundling it with the rest of the Spark config. This also avoids having
to modify the SparkConf to hide things from the UI.
* Finally, the AM doesn't manage the token manager anymore.
The above changes allow the token manager to be completely handled by the
driver's scheduler backend code also in YARN mode (whether client or cluster),
making it similar to other RMs. To maintain the fix added in SPARK-23361 also
in client mode, the AM now sends an extra message to the driver on initialization
to fetch delegation tokens; and although it might not really be needed, the
driver also keeps the running AM updated when new tokens are created.
Tested in a kerberized cluster with the same tests used to validate SPARK-23361,
in both client and cluster mode. Also tested with a non-kerberized cluster.
Closes#23338 from vanzin/SPARK-25689.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
This PR upgrades Mockito from 1.10.19 to 2.23.4. The following changes are required.
- Replace `org.mockito.Matchers` with `org.mockito.ArgumentMatchers`
- Replace `anyObject` with `any`
- Replace `getArgumentAt` with `getArgument` and add type annotation.
- Use `isNull` matcher in case of `null` is invoked.
```scala
saslHandler.channelInactive(null);
- verify(handler).channelInactive(any(TransportClient.class));
+ verify(handler).channelInactive(isNull());
```
- Make and use `doReturn` wrapper to avoid [SI-4775](https://issues.scala-lang.org/browse/SI-4775)
```scala
private def doReturn(value: Any) = org.mockito.Mockito.doReturn(value, Seq.empty: _*)
```
## How was this patch tested?
Pass the Jenkins with the existing tests.
Closes#23452 from dongjoon-hyun/SPARK-26536.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
The PR makes hardcoded spark.driver, spark.executor, and spark.cores.max configs to use `ConfigEntry`.
Note that some config keys are from `SparkLauncher` instead of defining in the config package object because the string is already defined in it and it does not depend on core module.
## How was this patch tested?
Existing tests.
Closes#23415 from ueshin/issues/SPARK-26445/hardcoded_driver_executor_configs.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The PR makes hardcoded configs below to use ConfigEntry.
* spark.pyspark
* spark.python
* spark.r
This patch doesn't change configs which are not relevant to SparkConf (e.g. system properties, python source code)
## How was this patch tested?
Existing tests.
Closes#23428 from HeartSaVioR/SPARK-26489.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
Recently, the ability to expose the metrics for YARN Shuffle Service was added as part of [SPARK-18364](https://github.com/apache/spark/pull/22485). We need to add some metrics to be able to determine the number of active connections as well as open connections to the external shuffle service to benchmark network and connection issues on large cluster environments.
Added two more shuffle server metrics for Spark Yarn shuffle service: numRegisteredConnections which indicate the number of registered connections to the shuffle service and numActiveConnections which indicate the number of active connections to the shuffle service at any given point in time.
If these metrics are outputted to a file, we get something like this:
1533674653489 default.shuffleService: Hostname=server1.abc.com, openBlockRequestLatencyMillis_count=729, openBlockRequestLatencyMillis_rate15=0.7110833548897356, openBlockRequestLatencyMillis_rate5=1.657808981793011, openBlockRequestLatencyMillis_rate1=2.2404486061620474, openBlockRequestLatencyMillis_rateMean=0.9242558551196706,
numRegisteredConnections=35,
blockTransferRateBytes_count=2635880512, blockTransferRateBytes_rate15=2578547.6094160094, blockTransferRateBytes_rate5=6048721.726302424, blockTransferRateBytes_rate1=8548922.518223226, blockTransferRateBytes_rateMean=3341878.633637769, registeredExecutorsSize=5, registerExecutorRequestLatencyMillis_count=5, registerExecutorRequestLatencyMillis_rate15=0.0027973949328659836, registerExecutorRequestLatencyMillis_rate5=0.0021278007987206426, registerExecutorRequestLatencyMillis_rate1=2.8270296777387467E-6, registerExecutorRequestLatencyMillis_rateMean=0.006339206380043053, numActiveConnections=35
Closes#22498 from pgandhi999/SPARK-18364.
Authored-by: pgandhi <pgandhi@oath.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
As I mentioned in jira [SPARK-26269](https://issues.apache.org/jira/browse/SPARK-26269), in order to maxmize the use of cluster resource, this pr try to make `YarnAllocator` have the same blacklist behaviour with YARN.
## How was this patch tested?
Added.
Closes#23223 from Ngone51/dev-YarnAllocator-should-have-same-blacklist-behaviour-with-YARN.
Lead-authored-by: wuyi <ngone_5451@163.com>
Co-authored-by: Ngone51 <ngone_5451@163.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
Right now, we cancel pending allocate requests by its sending order. I thing we can take
locality preference into account when do this to perfom least impact on task locality preference.
## How was this patch tested?
N.A.
Closes#23344 from Ngone51/dev-cancel-pending-allocate-requests-by-taking-locality-preference-into-account.
Authored-by: Ngone51 <ngone_5451@163.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
This change hooks up the k8s backed to the updated HadoopDelegationTokenManager,
so that delegation tokens are also available in client mode, and keytab-based token
renewal is enabled.
The change re-works the k8s feature steps related to kerberos so
that the driver does all the credential management and provides all
the needed information to executors - so nothing needs to be added
to executor pods. This also makes cluster mode behave a lot more
similarly to client mode, since no driver-related config steps are run
in the latter case.
The main two things that don't need to happen in executors anymore are:
- adding the Hadoop config to the executor pods: this is not needed
since the Spark driver will serialize the Hadoop config and send
it to executors when running tasks.
- mounting the kerberos config file in the executor pods: this is
not needed once you remove the above. The Hadoop conf sent by
the driver with the tasks is already resolved (i.e. has all the
kerberos names properly defined), so executors do not need access
to the kerberos realm information anymore.
The change also avoids creating delegation tokens unnecessarily.
This means that they'll only be created if a secret with tokens
was not provided, and if a keytab is not provided. In either of
those cases, the driver code will handle delegation tokens: in
cluster mode by creating a secret and stashing them, in client
mode by using existing mechanisms to send DTs to executors.
One last feature: the change also allows defining a keytab with
a "local:" URI. This is supported in client mode (although that's
the same as not saying "local:"), and in k8s cluster mode. This
allows the keytab to be mounted onto the image from a pre-existing
secret, for example.
Finally, the new code always sets SPARK_USER in the driver and
executor pods. This is in line with how other resource managers
behave: the submitting user reflects which user will access
Hadoop services in the app. (With kerberos, that's overridden
by the logged in user.) That user is unrelated to the OS user
the app is running as inside the containers.
Tested:
- client and cluster mode with kinit
- cluster mode with keytab
- cluster mode with local: keytab
- YARN cluster with keytab (to make sure it isn't broken)
Closes#22911 from vanzin/SPARK-25815.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
User specified filters are not applied to SQL tab in yarn mode, as it is overridden by the yarn AmIp filter.
So we need to append user provided filters (spark.ui.filters) with yarn filter.
## How was this patch tested?
【Test step】：
1) Launch spark sql with authentication filter as below:
2) spark-sql --master yarn --conf spark.ui.filters=org.apache.hadoop.security.authentication.server.AuthenticationFilter --conf spark.org.apache.hadoop.security.authentication.server.AuthenticationFilter.params="type=simple"
3) Go to Yarn application list UI link
4) Launch the application master for the Spark-SQL app ID and access all the tabs by appending tab name.
5) It will display an error for all tabs including SQL tab.(before able to access SQL tab,as Authentication filter is not applied for SQL tab)
6) Also can be verified with info logs,that Authentication filter applied to SQL tab.(before it is not applied).
I have attached the behaviour below in following order..
1) Command used
2) Before fix (logs and UI)
3) After fix (logs and UI)
**1) COMMAND USED**:
launching spark-sql with authentication filter.
**2) BEFORE FIX:**
**UI result:**
able to access SQL tab.
**logs**:
authentication filter not applied to SQL tab.
**3) AFTER FIX:**
**UI result**:
Not able to access SQL tab.
**in logs**:
Both yarn filter and Authentication filter applied to SQL tab.
Closes#23312 from chakravarthiT/SPARK-26255_ui.
Authored-by: chakravarthi <tcchakra@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
YARN applicationMaster metrics registration introduced in SPARK-24594 causes further registration of static metrics (Codegenerator and HiveExternalCatalog) and of JVM metrics, which I believe do not belong in this context.
This looks like an unintended side effect of using the start method of [[MetricsSystem]].
A possible solution proposed here, is to introduce startNoRegisterSources to avoid these additional registrations of static sources and of JVM sources in the case of YARN applicationMaster metrics (this could be useful for other metrics that may be added in the future).
## How was this patch tested?
Manually tested on a YARN cluster,
Closes#22279 from LucaCanali/YarnMetricsRemoveExtraSourceRegistration.
Lead-authored-by: Luca Canali <luca.canali@cern.ch>
Co-authored-by: LucaCanali <luca.canali@cern.ch>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
… incorrect.
## What changes were proposed in this pull request?
In the reported heartbeat information, the unit of the memory data is bytes, which is converted by the formatBytes() function in the utils.js file before being displayed in the interface. The cardinality of the unit conversion in the formatBytes function is 1000, which should be 1024.
Change the cardinality of the unit conversion in the formatBytes function to 1024.
## How was this patch tested?
manual tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22683 from httfighter/SPARK-25696.
Lead-authored-by: 韩田田00222924 <han.tiantian@zte.com.cn>
Co-authored-by: han.tiantian@zte.com.cn <han.tiantian@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Current `memLimitExceededLogMessage`:
<img src="https://user-images.githubusercontent.com/5399861/48467789-ec8e1000-e824-11e8-91fc-280d342e1bf3.png" width="360">
It‘s not very clear, because physical memory exceeds but suggestion contains virtual memory config. This pr makes it more clear and replace deprecated config: ```spark.yarn.executor.memoryOverhead```.
## How was this patch tested?
manual tests
Closes#23030 from wangyum/EXECUTOR_MEMORY_OVERHEAD.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The build has a lot of deprecation warnings. Some are new in Scala 2.12 and Java 11. We've fixed some, but I wanted to take a pass at fixing lots of easy miscellaneous ones here.
They're too numerous and small to list here; see the pull request. Some highlights:
- `BeanInfo` is deprecated in 2.12, and BeanInfo classes are pretty ancient in Java. Instead, case classes can explicitly declare getters
- Eta expansion of zero-arg methods; foo() becomes () => foo() in many cases
- Floating-point Range is inexact and deprecated, like 0.0 to 100.0 by 1.0
- finalize() is finally deprecated (just needs to be suppressed)
- StageInfo.attempId was deprecated and easiest to remove here
I'm not now going to touch some chunks of deprecation warnings:
- Parquet deprecations
- Hive deprecations (particularly serde2 classes)
- Deprecations in generated code (mostly Thriftserver CLI)
- ProcessingTime deprecations (we may need to revive this class as internal)
- many MLlib deprecations because they concern methods that may be removed anyway
- a few Kinesis deprecations I couldn't figure out
- Mesos get/setRole, which I don't know well
- Kafka/ZK deprecations (e.g. poll())
- Kinesis
- a few other ones that will probably resolve by deleting a deprecated method
## How was this patch tested?
Existing tests, including manual testing with the 2.11 build and Java 11.
Closes#23065 from srowen/SPARK-26090.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR makes Spark's default Scala version as 2.12, and Scala 2.11 will be the alternative version. This implies that Scala 2.12 will be used by our CI builds including pull request builds.
We'll update the Jenkins to include a new compile-only jobs for Scala 2.11 to ensure the code can be still compiled with Scala 2.11.
## How was this patch tested?
existing tests
Closes#22967 from dbtsai/scala2.12.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Deprecated in Java 11, replace Class.newInstance with Class.getConstructor.getInstance, and primtive wrapper class constructors with valueOf or equivalent
## How was this patch tested?
Existing tests.
Closes#22988 from srowen/SPARK-25984.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
This avoids having two classes to deal with tokens; now the above
class is a one-stop shop for dealing with delegation tokens. The
YARN backend extends that class instead of doing composition like
before, resulting in a bit less code there too.
The renewer functionality is basically the same code that used to
be in YARN's AMCredentialRenewer. That is also the reason why the
public API of HadoopDelegationTokenManager is a little bit odd;
the YARN AM has some odd requirements for how this all should be
initialized, and the weirdness is needed currently to support that.
Tested:
- YARN with stress app for DT renewal
- Mesos and K8S with basic kerberos tests (both tgt and keytab)
Closes#22624 from vanzin/SPARK-23781.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
Py4J 0.10.8.1 is released on October 21st and is the first release of Py4J to support Python 3.7 officially. We had better have this to get the official support. Also, there are some patches related to garbage collections.
https://www.py4j.org/changelog.html#py4j-0-10-8-and-py4j-0-10-8-1
## How was this patch tested?
Pass the Jenkins.
Closes#22901 from dongjoon-hyun/SPARK-25891.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
The test fix is to allocate a `Resource` object only after the resource
types have been initialized. Otherwise the YARN classes get in a weird
state and throw a different exception than expected, because the resource
has a different view of the registered resources.
I also removed a test for a null resource since that seems unnecessary
and made the fix more complicated.
All the other changes are just cleanup; basically simplify the tests by
defining what is being tested and deriving the resource type registration
and the SparkConf from that data, instead of having redundant definitions
in the tests.
Ran tests with Hadoop 3 (and also without it).
Closes#22751 from vanzin/SPARK-20327.fix.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
This PR adds CLI support for YARN custom resources, e.g. GPUs and any other resources YARN defines.
The custom resources are defined with Spark properties, no additional CLI arguments were introduced.
The properties can be defined in the following form:
**AM resources, client mode:**
Format: `spark.yarn.am.resource.<resource-name>`
The property name follows the naming convention of YARN AM cores / memory properties: `spark.yarn.am.memory and spark.yarn.am.cores
`
**Driver resources, cluster mode:**
Format: `spark.yarn.driver.resource.<resource-name>`
The property name follows the naming convention of driver cores / memory properties: `spark.driver.memory and spark.driver.cores.`
**Executor resources:**
Format: `spark.yarn.executor.resource.<resource-name>`
The property name follows the naming convention of executor cores / memory properties: `spark.executor.memory / spark.executor.cores`.
For the driver resources (cluster mode) and executor resources properties, we use the `yarn` prefix here as custom resource types are specific to YARN, currently.
**Validation:**
Please note that a validation logic is added to avoid having requested resources defined in 2 ways, for example defining the following configs:
```
"--conf", "spark.driver.memory=2G",
"--conf", "spark.yarn.driver.resource.memory=1G"
```
will not start execution and will print an error message.
## How was this patch tested?
Unit tests + manual execution with Hadoop2 and Hadoop 3 builds.
Testing have been performed on a real cluster with Spark and YARN configured:
Cluster and client mode
Request Resource Types with lowercase and uppercase units
Start Spark job with only requesting standard resources (mem / cpu)
Error handling cases:
- Request unknown resource type
- Request Resource type (either memory / cpu) with duplicate configs at the same time (e.g. with this config:
```
--conf spark.yarn.am.resource.memory=1G \
--conf spark.yarn.driver.resource.memory=2G \
--conf spark.yarn.executor.resource.memory=3G \
```
), ResourceTypeValidator handles these cases well, so it is not permitted
- Request standard resource (memory / cpu) with the new style configs, e.g. --conf spark.yarn.am.resource.memory=1G, this is not permitted and handled well.
An example about how I ran the testcases:
```
cd ~;export HADOOP_CONF_DIR=/opt/hadoop/etc/hadoop/;
./spark-2.4.0-SNAPSHOT-bin-custom-spark/bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--master yarn \
--deploy-mode cluster \
--driver-memory 1G \
--driver-cores 1 \
--executor-memory 1G \
--executor-cores 1 \
--conf spark.logConf=true \
--conf spark.yarn.executor.resource.gpu=3G \
--verbose \
./spark-2.4.0-SNAPSHOT-bin-custom-spark/examples/jars/spark-examples_2.11-2.4.0-SNAPSHOT.jar \
10;
```
Closes#20761 from szyszy/SPARK-20327.
Authored-by: Szilard Nemeth <snemeth@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Remove Hadoop 2.6 references and make 2.7 the default.
Obviously, this is for master/3.0.0 only.
After this we can also get rid of the separate test jobs for Hadoop 2.6.
## How was this patch tested?
Existing tests
Closes#22615 from srowen/SPARK-25016.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This patch is to bump the master branch version to 3.0.0-SNAPSHOT.
## How was this patch tested?
N/A
Closes#22606 from gatorsmile/bump3.0.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR is follow-up of closed https://github.com/apache/spark/pull/17401 which only ended due to of inactivity, but its still nice feature to have.
Given review by jerryshao taken in consideration and edited:
- VisibleForTesting deleted because of dependency conflicts
- removed unnecessary reflection for `MetricsSystemImpl`
- added more available types for gauge
## How was this patch tested?
Manual deploy of new yarn-shuffle jar into a Node Manager and verifying that the metrics appear in the Node Manager-standard location. This is JMX with an query endpoint running on `hostname:port`
Resulting metrics look like this:
```
curl -sk -XGET hostname:port | grep -v '#' | grep 'shuffleService'
hadoop_nodemanager_openblockrequestlatencymillis_rate15{name="shuffleService",} 0.31428910657834713
hadoop_nodemanager_blocktransferratebytes_rate15{name="shuffleService",} 566144.9983653595
hadoop_nodemanager_blocktransferratebytes_ratemean{name="shuffleService",} 2464409.9678099006
hadoop_nodemanager_openblockrequestlatencymillis_rate1{name="shuffleService",} 1.2893844732240272
hadoop_nodemanager_registeredexecutorssize{name="shuffleService",} 2.0
hadoop_nodemanager_openblockrequestlatencymillis_ratemean{name="shuffleService",} 1.255574678369966
hadoop_nodemanager_openblockrequestlatencymillis_count{name="shuffleService",} 315.0
hadoop_nodemanager_openblockrequestlatencymillis_rate5{name="shuffleService",} 0.7661929192569739
hadoop_nodemanager_registerexecutorrequestlatencymillis_ratemean{name="shuffleService",} 0.0
hadoop_nodemanager_registerexecutorrequestlatencymillis_count{name="shuffleService",} 0.0
hadoop_nodemanager_registerexecutorrequestlatencymillis_rate1{name="shuffleService",} 0.0
hadoop_nodemanager_registerexecutorrequestlatencymillis_rate5{name="shuffleService",} 0.0
hadoop_nodemanager_blocktransferratebytes_count{name="shuffleService",} 6.18271213E8
hadoop_nodemanager_registerexecutorrequestlatencymillis_rate15{name="shuffleService",} 0.0
hadoop_nodemanager_blocktransferratebytes_rate5{name="shuffleService",} 1154114.4881816586
hadoop_nodemanager_blocktransferratebytes_rate1{name="shuffleService",} 574745.0749848988
```
Closes#22485 from mareksimunek/SPARK-18364.
Lead-authored-by: marek.simunek <marek.simunek@firma.seznam.cz>
Co-authored-by: Andrew Ash <andrew@andrewash.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
In the dev list, we can still discuss whether the next version is 2.5.0 or 3.0.0. Let us first bump the master branch version to `2.5.0-SNAPSHOT`.
## How was this patch tested?
N/A
Closes#22426 from gatorsmile/bumpVersionMaster.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Correct some comparisons between unrelated types to what they seem to… have been trying to do
## How was this patch tested?
Existing tests.
Closes#22384 from srowen/SPARK-25398.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The configuration parameter "spark.shuffle.service.enabled" has defined in `package.scala`, and it is also used in many place, so we can replace it with `SHUFFLE_SERVICE_ENABLED`.
and unified this configuration parameter "spark.shuffle.service.port" together.
## How was this patch tested?
N/A
Closes#22306 from 10110346/unifiedserviceenable.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This adds `spark.executor.pyspark.memory` to configure Python's address space limit, [`resource.RLIMIT_AS`](https://docs.python.org/3/library/resource.html#resource.RLIMIT_AS). Limiting Python's address space allows Python to participate in memory management. In practice, we see fewer cases of Python taking too much memory because it doesn't know to run garbage collection. This results in YARN killing fewer containers. This also improves error messages so users know that Python is consuming too much memory:
```
File "build/bdist.linux-x86_64/egg/package/library.py", line 265, in fe_engineer
fe_eval_rec.update(f(src_rec_prep, mat_rec_prep))
File "build/bdist.linux-x86_64/egg/package/library.py", line 163, in fe_comp
comparisons = EvaluationUtils.leven_list_compare(src_rec_prep.get(item, []), mat_rec_prep.get(item, []))
File "build/bdist.linux-x86_64/egg/package/evaluationutils.py", line 25, in leven_list_compare
permutations = sorted(permutations, reverse=True)
MemoryError
```
The new pyspark memory setting is used to increase requested YARN container memory, instead of sharing overhead memory between python and off-heap JVM activity.
## How was this patch tested?
Tested memory limits in our YARN cluster and verified that MemoryError is thrown.
Author: Ryan Blue <blue@apache.org>
Closes#21977 from rdblue/SPARK-25004-add-python-memory-limit.
## What changes were proposed in this pull request?
YARN `AmIpFilter` adds a new parameter "RM_HA_URLS" to support RM HA, but Spark on YARN doesn't provide a such parameter, so it will be failed to redirect when running on RM HA. The detailed exception can be checked from JIRA. So here fixing this issue by adding "RM_HA_URLS" parameter.
## How was this patch tested?
Local verification.
Closes#22164 from jerryshao/SPARK-23679.
Authored-by: jerryshao <sshao@hortonworks.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Our HDFS cluster configured 5 nameservices: `nameservices1`, `nameservices2`, `nameservices3`, `nameservices-dev1` and `nameservices4`, but `nameservices-dev1` unstable. So sometimes an error occurred and causing the entire job failed since [SPARK-24149](https://issues.apache.org/jira/browse/SPARK-24149):
I think it's best to add a switch here.
## How was this patch tested?
manual tests
Closes#21734 from wangyum/SPARK-24149.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
When using older versions of spark releases, a use case generated a huge code-gen file which hit the limitation `Constant pool has grown past JVM limit of 0xFFFF`. In this situation, it should fail immediately. But the diagnosis message sent to RM is too large, the ApplicationMaster suspended and RM's ZKStateStore was crashed. For 2.3 or later spark releases the limitation of code-gen has been removed, but maybe there are still some uncaught exceptions that contain oversized error message will cause such a problem.
This PR is aim to cut down the diagnosis message size.
## How was this patch tested?
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22180 from yaooqinn/SPARK-25174.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
**## What changes were proposed in this pull request?**
When the yarn.nodemanager.resource.memory-mb or yarn.scheduler.maximum-allocation-mb
memory assignment is insufficient, Spark always reports an error request to adjust
yarn.scheduler.maximum-allocation-mb even though in message it shows the memory value
of yarn.nodemanager.resource.memory-mb parameter,As the error Message is bit misleading to the user we can modify the same, We can keep the error message same as executor memory validation message.
Defintion of **yarn.nodemanager.resource.memory-mb:**
Amount of physical memory, in MB, that can be allocated for containers. It means the amount of memory YARN can utilize on this node and therefore this property should be lower then the total memory of that machine.
**yarn.scheduler.maximum-allocation-mb:**
It defines the maximum memory allocation available for a container in MB
it means RM can only allocate memory to containers in increments of "yarn.scheduler.minimum-allocation-mb" and not exceed "yarn.scheduler.maximum-allocation-mb" and It should not be more than total allocated memory of the Node.
**## How was this patch tested?**
Manually tested in hdfs-Yarn clustaer
Closes#22199 from sujith71955/maste_am_log.
Authored-by: s71955 <sujithchacko.2010@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Fixing typos is sometimes very hard. It's not so easy to visually review them. Recently, I discovered a very useful tool for it, [misspell](https://github.com/client9/misspell).
This pull request fixes minor typos detected by [misspell](https://github.com/client9/misspell) except for the false positives. If you would like me to work on other files as well, let me know.
## How was this patch tested?
### before
```
$ misspell . | grep -v '.js'
R/pkg/R/SQLContext.R:354:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:424:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:445:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:495:43: "definiton" is a misspelling of "definition"
NOTICE-binary:454:16: "containd" is a misspelling of "contained"
R/pkg/R/context.R:46:43: "definiton" is a misspelling of "definition"
R/pkg/R/context.R:74:43: "definiton" is a misspelling of "definition"
R/pkg/R/DataFrame.R:591:48: "persistance" is a misspelling of "persistence"
R/pkg/R/streaming.R:166:44: "occured" is a misspelling of "occurred"
R/pkg/inst/worker/worker.R:65:22: "ouput" is a misspelling of "output"
R/pkg/tests/fulltests/test_utils.R:106:25: "environemnt" is a misspelling of "environment"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/InMemoryStoreSuite.java:38:39: "existant" is a misspelling of "existent"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/LevelDBSuite.java:83:39: "existant" is a misspelling of "existent"
common/network-common/src/main/java/org/apache/spark/network/crypto/TransportCipher.java:243:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:234:19: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:238:63: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:244:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:276:39: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
common/unsafe/src/test/scala/org/apache/spark/unsafe/types/UTF8StringPropertyCheckSuite.scala:195:15: "orgin" is a misspelling of "origin"
core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala:621:39: "gauranteed" is a misspelling of "guaranteed"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/main/scala/org/apache/spark/storage/DiskStore.scala:282:18: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/util/ListenerBus.scala:64:17: "overriden" is a misspelling of "overridden"
core/src/test/scala/org/apache/spark/ShuffleSuite.scala:211:7: "substracted" is a misspelling of "subtracted"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:2468:84: "truely" is a misspelling of "truly"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:25:18: "persistance" is a misspelling of "persistence"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:26:69: "persistance" is a misspelling of "persistence"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
dev/run-pip-tests:55:28: "enviroments" is a misspelling of "environments"
dev/run-pip-tests:91:37: "virutal" is a misspelling of "virtual"
dev/merge_spark_pr.py:377:72: "accross" is a misspelling of "across"
dev/merge_spark_pr.py:378:66: "accross" is a misspelling of "across"
dev/run-pip-tests:126:25: "enviroments" is a misspelling of "environments"
docs/configuration.md:1830:82: "overriden" is a misspelling of "overridden"
docs/structured-streaming-programming-guide.md:525:45: "processs" is a misspelling of "processes"
docs/structured-streaming-programming-guide.md:1165:61: "BETWEN" is a misspelling of "BETWEEN"
docs/sql-programming-guide.md:1891:810: "behaivor" is a misspelling of "behavior"
examples/src/main/python/sql/arrow.py:98:8: "substract" is a misspelling of "subtract"
examples/src/main/python/sql/arrow.py:103:27: "substract" is a misspelling of "subtract"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala:230:24: "inital" is a misspelling of "initial"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala:237:26: "descripiton" is a misspelling of "descriptions"
python/pyspark/find_spark_home.py:30:13: "enviroment" is a misspelling of "environment"
python/pyspark/context.py:937:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:938:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:939:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:940:12: "supress" is a misspelling of "suppress"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:713:8: "probabilty" is a misspelling of "probability"
python/pyspark/ml/clustering.py:1038:8: "Currenlty" is a misspelling of "Currently"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/ml/regression.py:1378:20: "paramter" is a misspelling of "parameter"
python/pyspark/mllib/stat/_statistics.py:262:8: "probabilty" is a misspelling of "probability"
python/pyspark/rdd.py:1363:32: "paramter" is a misspelling of "parameter"
python/pyspark/streaming/tests.py:825:42: "retuns" is a misspelling of "returns"
python/pyspark/sql/tests.py:768:29: "initalization" is a misspelling of "initialization"
python/pyspark/sql/tests.py:3616:31: "initalize" is a misspelling of "initialize"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala:120:39: "arbitary" is a misspelling of "arbitrary"
resource-managers/mesos/src/test/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcherArgumentsSuite.scala:26:45: "sucessfully" is a misspelling of "successfully"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala:358:27: "constaints" is a misspelling of "constraints"
resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala:111:24: "senstive" is a misspelling of "sensitive"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalog.scala:1063:5: "overwirte" is a misspelling of "overwrite"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala:1348:17: "compatability" is a misspelling of "compatibility"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala:77:36: "paramter" is a misspelling of "parameter"
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala:1374:22: "precendence" is a misspelling of "precedence"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala:238:27: "unnecassary" is a misspelling of "unnecessary"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala:212:17: "whn" is a misspelling of "when"
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinHelper.scala:147:60: "timestmap" is a misspelling of "timestamp"
sql/core/src/test/scala/org/apache/spark/sql/TPCDSQuerySuite.scala:150:45: "precentage" is a misspelling of "percentage"
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchemaSuite.scala:135:29: "infered" is a misspelling of "inferred"
sql/hive/src/test/resources/golden/udf_instr-1-2e76f819563dbaba4beb51e3a130b922:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_instr-2-32da357fc754badd6e3898dcc8989182:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-1-6e41693c9c6dceea4d7fab4c02884e4e:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-2-d9b5934457931447874d6bb7c13de478:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:9:79: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:13:110: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/annotate_stats_join.q:46:105: "distint" is a misspelling of "distinct"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/auto_sortmerge_join_11.q:29:3: "Currenly" is a misspelling of "Currently"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/avro_partitioned.q:72:15: "existant" is a misspelling of "existent"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/decimal_udf.q:25:3: "substraction" is a misspelling of "subtraction"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby2_map_multi_distinct.q:16:51: "funtion" is a misspelling of "function"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby_sort_8.q:15:30: "issueing" is a misspelling of "issuing"
sql/hive/src/test/scala/org/apache/spark/sql/sources/HadoopFsRelationTest.scala:669:52: "wiht" is a misspelling of "with"
sql/hive-thriftserver/src/main/java/org/apache/hive/service/cli/session/HiveSessionImpl.java:474:9: "Refering" is a misspelling of "Referring"
```
### after
```
$ misspell . | grep -v '.js'
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
```
Closes#22070 from seratch/fix-typo.
Authored-by: Kazuhiro Sera <seratch@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
In this PR metrics are introduced for YARN. As up to now there was no metrics in the YARN module a new metric system is created with the name "applicationMaster".
To support both client and cluster mode the metric system lifecycle is bound to the AM.
## How was this patch tested?
Both client and cluster mode was tested manually.
Before the test on one of the YARN node spark-core was removed to cause the allocation failure.
Spark was started as (in case of client mode):
```
spark2-submit \
--class org.apache.spark.examples.SparkPi \
--conf "spark.yarn.blacklist.executor.launch.blacklisting.enabled=true" --conf "spark.blacklist.application.maxFailedExecutorsPerNode=2" --conf "spark.dynamicAllocation.enabled=true" --conf "spark.metrics.conf.*.sink.console.class=org.apache.spark.metrics.sink.ConsoleSink" \
--master yarn \
--deploy-mode client \
original-spark-examples_2.11-2.4.0-SNAPSHOT.jar \
1000
```
In both cases the YARN logs contained the new metrics as:
```
$ yarn logs --applicationId application_1529926424933_0015
...
-- Gauges ----------------------------------------------------------------------
application_1531751594108_0046.applicationMaster.numContainersPendingAllocate
value = 0
application_1531751594108_0046.applicationMaster.numExecutorsFailed
value = 3
application_1531751594108_0046.applicationMaster.numExecutorsRunning
value = 9
application_1531751594108_0046.applicationMaster.numLocalityAwareTasks
value = 0
application_1531751594108_0046.applicationMaster.numReleasedContainers
value = 0
...
```
Author: “attilapiros” <piros.attila.zsolt@gmail.com>
Author: Attila Zsolt Piros <2017933+attilapiros@users.noreply.github.com>
Closes#21635 from attilapiros/SPARK-24594.
## What changes were proposed in this pull request?
[SPARK-24182](https://github.com/apache/spark/pull/21243) changed the `logApplicationReport` from `false` to `true`. This pr revert it to `false`. otherwise `spark-shell` will show noisy log output:
```java
...
18/07/16 04:46:25 INFO Client: Application report for application_1530676576026_54551 (state: RUNNING)
18/07/16 04:46:26 INFO Client: Application report for application_1530676576026_54551 (state: RUNNING)
...
```
Closes https://github.com/apache/spark/pull/21827
## How was this patch tested?
manual tests
Author: Yuming Wang <yumwang@ebay.com>
Closes#21784 from wangyum/SPARK-24182.
…rEnv properly
Running in yarn cluster mode and trying to set pythonpath via spark.yarn.appMasterEnv.PYTHONPATH doesn't work.
the yarn Client code looks at the env variables:
val pythonPathStr = (sys.env.get("PYTHONPATH") ++ pythonPath)
But when you set spark.yarn.appMasterEnv it puts it into the local env.
So the python path set in spark.yarn.appMasterEnv isn't properly set.
You can work around if you are running in cluster mode by setting it on the client like:
PYTHONPATH=./addon/python/ spark-submit
## What changes were proposed in this pull request?
In Client.scala, PYTHONPATH was being overridden, so changed code to append values to PYTHONPATH instead of overriding them.
## How was this patch tested?
Added log statements to ApplicationMaster.scala to check for environment variable PYTHONPATH, ran a spark job in cluster mode before the change and verified the issue. Performed the same test after the change and verified the fix.
Author: pgandhi <pgandhi@oath.com>
Closes#21468 from pgandhi999/SPARK-22151.
Because the way YARN executes commands via bash -c, everything needs
to be quoted so that the whole command is fully contained inside a
bash string and is interpreted correctly when the string is read by
bash. This is a bit different than the quoting done when executing
things as if typing in a bash shell.
Tweaked unit tests to exercise the bad behavior, which would cause
existing tests to time out without the fix. Also tested on a real
cluster, verifying the shell script created by YARN to run the
container.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#21476 from vanzin/SPARK-24446.
## What changes were proposed in this pull request?
This change extends YARN resource allocation handling with blacklisting functionality.
This handles cases when node is messed up or misconfigured such that a container won't launch on it. Before this change backlisting only focused on task execution but this change introduces YarnAllocatorBlacklistTracker which tracks allocation failures per host (when enabled via "spark.yarn.blacklist.executor.launch.blacklisting.enabled").
## How was this patch tested?
### With unit tests
Including a new suite: YarnAllocatorBlacklistTrackerSuite.
#### Manually
It was tested on a cluster by deleting the Spark jars on one of the node.
#### Behaviour before these changes
Starting Spark as:
```
spark2-shell --master yarn --deploy-mode client --num-executors 4 --conf spark.executor.memory=4g --conf "spark.yarn.max.executor.failures=6"
```
Log is:
```
18/04/12 06:49:36 INFO yarn.ApplicationMaster: Final app status: FAILED, exitCode: 11, (reason: Max number of executor failures (6) reached)
18/04/12 06:49:39 INFO yarn.ApplicationMaster: Unregistering ApplicationMaster with FAILED (diag message: Max number of executor failures (6) reached)
18/04/12 06:49:39 INFO impl.AMRMClientImpl: Waiting for application to be successfully unregistered.
18/04/12 06:49:39 INFO yarn.ApplicationMaster: Deleting staging directory hdfs://apiros-1.gce.test.com:8020/user/systest/.sparkStaging/application_1523459048274_0016
18/04/12 06:49:39 INFO util.ShutdownHookManager: Shutdown hook called
```
#### Behaviour after these changes
Starting Spark as:
```
spark2-shell --master yarn --deploy-mode client --num-executors 4 --conf spark.executor.memory=4g --conf "spark.yarn.max.executor.failures=6" --conf "spark.yarn.blacklist.executor.launch.blacklisting.enabled=true"
```
And the log is:
```
18/04/13 05:37:43 INFO yarn.YarnAllocator: Will request 1 executor container(s), each with 1 core(s) and 4505 MB memory (including 409 MB of overhead)
18/04/13 05:37:43 INFO yarn.YarnAllocator: Submitted 1 unlocalized container requests.
18/04/13 05:37:43 INFO yarn.YarnAllocator: Launching container container_1523459048274_0025_01_000008 on host apiros-4.gce.test.com for executor with ID 6
18/04/13 05:37:43 INFO yarn.YarnAllocator: Received 1 containers from YARN, launching executors on 1 of them.
18/04/13 05:37:43 INFO yarn.YarnAllocator: Completed container container_1523459048274_0025_01_000007 on host: apiros-4.gce.test.com (state: COMPLETE, exit status: 1)
18/04/13 05:37:43 INFO yarn.YarnAllocatorBlacklistTracker: blacklisting host as YARN allocation failed: apiros-4.gce.test.com
18/04/13 05:37:43 INFO yarn.YarnAllocatorBlacklistTracker: adding nodes to YARN application master's blacklist: List(apiros-4.gce.test.com)
18/04/13 05:37:43 WARN yarn.YarnAllocator: Container marked as failed: container_1523459048274_0025_01_000007 on host: apiros-4.gce.test.com. Exit status: 1. Diagnostics: Exception from container-launch.
Container id: container_1523459048274_0025_01_000007
Exit code: 1
Stack trace: ExitCodeException exitCode=1:
at org.apache.hadoop.util.Shell.runCommand(Shell.java:604)
at org.apache.hadoop.util.Shell.run(Shell.java:507)
at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:789)
at org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:213)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:302)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:82)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
```
Where the most important part is:
```
18/04/13 05:37:43 INFO yarn.YarnAllocatorBlacklistTracker: blacklisting host as YARN allocation failed: apiros-4.gce.test.com
18/04/13 05:37:43 INFO yarn.YarnAllocatorBlacklistTracker: adding nodes to YARN application master's blacklist: List(apiros-4.gce.test.com)
```
And execution was continued (no shutdown called).
### Testing the backlisting of the whole cluster
Starting Spark with YARN blacklisting enabled then removing a the Spark core jar one by one from all the cluster nodes. Then executing a simple spark job which fails checking the yarn log the expected exit status is contained:
```
18/06/15 01:07:10 INFO yarn.ApplicationMaster: Final app status: FAILED, exitCode: 11, (reason: Due to executor failures all available nodes are blacklisted)
18/06/15 01:07:13 INFO util.ShutdownHookManager: Shutdown hook called
```
Author: “attilapiros” <piros.attila.zsolt@gmail.com>
Closes#21068 from attilapiros/SPARK-16630.
This PR adds a debugging log for YARN-specific credential providers which is loaded by service loader mechanism.
It took me a while to debug if it's actually loaded or not. I had to explicitly set the deprecated configuration and check if that's actually being loaded.
The change scope is manually tested. Logs are like:
```
Using the following builtin delegation token providers: hadoopfs, hive, hbase.
Using the following YARN-specific credential providers: yarn-test.
```
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21466 from HyukjinKwon/minor-log.
Change-Id: I18e2fb8eeb3289b148f24c47bb3130a560a881cf
## What changes were proposed in this pull request?
In client side before context initialization specifically, .py file doesn't work in client side before context initialization when the application is a Python file. See below:
```
$ cat /home/spark/tmp.py
def testtest():
return 1
```
This works:
```
$ cat app.py
import pyspark
pyspark.sql.SparkSession.builder.getOrCreate()
import tmp
print("************************%s" % tmp.testtest())
$ ./bin/spark-submit --master yarn --deploy-mode client --py-files /home/spark/tmp.py app.py
...
************************1
```
but this doesn't:
```
$ cat app.py
import pyspark
import tmp
pyspark.sql.SparkSession.builder.getOrCreate()
print("************************%s" % tmp.testtest())
$ ./bin/spark-submit --master yarn --deploy-mode client --py-files /home/spark/tmp.py app.py
Traceback (most recent call last):
File "/home/spark/spark/app.py", line 2, in <module>
import tmp
ImportError: No module named tmp
```
### How did it happen?
In client mode specifically, the paths are being added into PythonRunner as are:
628c7b5179/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala (L430)628c7b5179/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala (L49-L88)
The problem here is, .py file shouldn't be added as are since `PYTHONPATH` expects a directory or an archive like zip or egg.
### How does this PR fix?
We shouldn't simply just add its parent directory because other files in the parent directory could also be added into the `PYTHONPATH` in client mode before context initialization.
Therefore, we copy .py files into a temp directory for .py files and add it to `PYTHONPATH`.
## How was this patch tested?
Unit tests are added and manually tested in both standalond and yarn client modes with submit.
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21426 from HyukjinKwon/SPARK-24384.
## What changes were proposed in this pull request?
Hadoop 3 introduces HDFS federation. This means that multiple namespaces are allowed on the same HDFS cluster. In Spark, we need to ask the delegation token for all the namenodes (for each namespace), otherwise accessing any other namespace different from the default one (for which we already fetch the delegation token) fails.
The PR adds the automatic discovery of all the namenodes related to all the namespaces available according to the configs in hdfs-site.xml.
## How was this patch tested?
manual tests in dockerized env
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21216 from mgaido91/SPARK-24149.
Instead of always throwing a generic exception when the AM fails,
print a generic error and throw the exception with the YARN
diagnostics containing the reason for the failure.
There was an issue with YARN sometimes providing a generic diagnostic
message, even though the AM provides a failure reason when
unregistering. That was happening because the AM was registering
too late, and if errors happened before the registration, YARN would
just create a generic "ExitCodeException" which wasn't very helpful.
Since most errors in this path are a result of not being able to
connect to the driver, this change modifies the AM registration
a bit so that the AM is registered before the connection to the
driver is established. That way, errors are properly propagated
through YARN back to the driver.
As part of that, I also removed the code that retried connections
to the driver from the client AM. At that point, the driver should
already be up and waiting for connections, so it's unlikely that
retrying would help - and in case it does, that means a flaky
network, which would mean problems would probably show up again.
The effect of that is that connection-related errors are reported
back to the driver much faster now (through the YARN report).
One thing to note is that there seems to be a race on the YARN
side that causes a report to be sent to the client without the
corresponding diagnostics string from the AM; the diagnostics are
available later from the RM web page. For that reason, the generic
error messages are kept in the Spark scheduler code, to help
guide users to a way of debugging their failure.
Also of note is that if YARN's max attempts configuration is lower
than Spark's, Spark will not unregister the AM with a proper
diagnostics message. Unfortunately there seems to be no way to
unregister the AM and still allow further re-attempts to happen.
Testing:
- existing unit tests
- some of our integration tests
- hardcoded an invalid driver address in the code and verified
the error in the shell. e.g.
```
scala> 18/05/04 15:09:34 ERROR cluster.YarnClientSchedulerBackend: YARN application has exited unexpectedly with state FAILED! Check the YARN application logs for more details.
18/05/04 15:09:34 ERROR cluster.YarnClientSchedulerBackend: Diagnostics message: Uncaught exception: org.apache.spark.SparkException: Exception thrown in awaitResult:
<AM stack trace>
Caused by: java.io.IOException: Failed to connect to localhost/127.0.0.1:1234
<More stack trace>
```
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#21243 from vanzin/SPARK-24182.
## What changes were proposed in this pull request?
Added support to specify the 'spark.executor.extraJavaOptions' value in terms of the `{{APP_ID}}` and/or `{{EXECUTOR_ID}}`, `{{APP_ID}}` will be replaced by Application Id and `{{EXECUTOR_ID}}` will be replaced by Executor Id while starting the executor.
## How was this patch tested?
I have verified this by checking the executor process command and gc logs. I verified the same in different deployment modes(Standalone, YARN, Mesos) client and cluster modes.
Author: Devaraj K <devaraj@apache.org>
Closes#21088 from devaraj-kavali/SPARK-24003.
## What changes were proposed in this pull request?
When a user specifies the wrong class -- or, in fact, a class instead of an object -- Spark throws an NPE which is not useful for debugging. This was reported in [SPARK-23830](https://issues.apache.org/jira/browse/SPARK-23830). This PR adds a check to ensure the main method was found and logs a useful error in the event that it's null.
## How was this patch tested?
* Unit tests + Manual testing
* The scope of the changes is very limited
Author: eric-maynard <emaynard@cloudera.com>
Author: Eric Maynard <emaynard@cloudera.com>
Closes#21168 from eric-maynard/feature/SPARK-23830.
## What changes were proposed in this pull request?
Log stacktrace for uncaught exception
## How was this patch tested?
UT and manually test
Author: zhoukang <zhoukang199191@gmail.com>
Closes#21151 from caneGuy/zhoukang/log-stacktrace.
## What changes were proposed in this pull request?
We propose not to hard-code the RPC port in the AM registration.
## How was this patch tested?
Tested application reports from a pseudo-distributed cluster
```
18/04/10 14:56:21 INFO Client:
client token: N/A
diagnostics: N/A
ApplicationMaster host: localhost
ApplicationMaster RPC port: 58338
queue: default
start time: 1523397373659
final status: UNDEFINED
tracking URL: http://localhost:8088/proxy/application_1523370127531_0016/
```
Author: Gera Shegalov <gera@apache.org>
Closes#21047 from gerashegalov/gera/am-to-rm-nmhost.
## What changes were proposed in this pull request?
`YarnAllocator` uses `numExecutorsRunning` to track the number of running executor. `numExecutorsRunning` is used to check if there're executors missing and need to allocate more.
In current code, `numExecutorsRunning` can be negative when driver asks to kill a same idle executor multiple times.
## How was this patch tested?
UT added
Author: jinxing <jinxing6042@126.com>
Closes#20781 from jinxing64/SPARK-23637.
