## What changes were proposed in this pull request?
In this PR stage blacklisting is propagated to UI by introducing a new Spark listener event (SparkListenerExecutorBlacklistedForStage) which indicates the executor is blacklisted for a stage. Either because of the number of failures are exceeded a limit given for an executor (spark.blacklist.stage.maxFailedTasksPerExecutor) or because of the whole node is blacklisted for a stage (spark.blacklist.stage.maxFailedExecutorsPerNode). In case of the node is blacklisting all executors will listed as blacklisted for the stage.
Blacklisting state for a selected stage can be seen "Aggregated Metrics by Executor" table's blacklisting column, where after this change three possible labels could be found:
- "for application": when the executor is blacklisted for the application (see the configuration spark.blacklist.application.maxFailedTasksPerExecutor for details)
- "for stage": when the executor is **only** blacklisted for the stage
- "false" : when the executor is not blacklisted at all
## How was this patch tested?
It is tested both manually and with unit tests.
#### Unit tests
- HistoryServerSuite
- TaskSetBlacklistSuite
- AppStatusListenerSuite
#### Manual test for executor blacklisting
Running Spark as a local cluster:
```
$ bin/spark-shell --master "local-cluster[2,1,1024]" --conf "spark.blacklist.enabled=true" --conf "spark.blacklist.stage.maxFailedTasksPerExecutor=1" --conf "spark.blacklist.application.maxFailedTasksPerExecutor=10" --conf "spark.eventLog.enabled=true"
```
Executing:
``` scala
import org.apache.spark.SparkEnv
sc.parallelize(1 to 10, 10).map { x =>
if (SparkEnv.get.executorId == "0") throw new RuntimeException("Bad executor")
else (x % 3, x)
}.reduceByKey((a, b) => a + b).collect()
```
To see result check the "Aggregated Metrics by Executor" section at the bottom of picture:
#### Manual test for node blacklisting
Running Spark as on a cluster:
``` bash
./bin/spark-shell --master yarn --deploy-mode client --executor-memory=2G --num-executors=8 --conf "spark.blacklist.enabled=true" --conf "spark.blacklist.stage.maxFailedTasksPerExecutor=1" --conf "spark.blacklist.stage.maxFailedExecutorsPerNode=1" --conf "spark.blacklist.application.maxFailedTasksPerExecutor=10" --conf "spark.eventLog.enabled=true"
```
And the job was:
``` scala
import org.apache.spark.SparkEnv
sc.parallelize(1 to 10000, 10).map { x =>
if (SparkEnv.get.executorId.toInt >= 4) throw new RuntimeException("Bad executor")
else (x % 3, x)
}.reduceByKey((a, b) => a + b).collect()
```
The result is:
Here you can see apiros3.gce.test.com was node blacklisted for the stage because of failures on executor 4 and 5. As expected executor 3 is also blacklisted even it has no failures itself but sharing the node with 4 and 5.
Author: “attilapiros” <piros.attila.zsolt@gmail.com>
Author: Attila Zsolt Piros <2017933+attilapiros@users.noreply.github.com>
Closes#20203 from attilapiros/SPARK-22577.
## 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?
This patch fixes a bug in `KafkaSource` with the (de)serialization of the length of the JSON string that contains the initial partition offsets.
## How was this patch tested?
I ran the test suite for spark-sql-kafka-0-10.
Author: Roberto Agostino Vitillo <ra.vitillo@gmail.com>
Closes#16857 from vitillo/kafka_source_fix.
Builds on top of work in SPARK-8425 to update Application Level Blacklisting in the scheduler.
## What changes were proposed in this pull request?
Adds a UI to these patches by:
- defining new listener events for blacklisting and unblacklisting, nodes and executors;
- sending said events at the relevant points in BlacklistTracker;
- adding JSON (de)serialization code for these events;
- augmenting the Executors UI page to show which, and how many, executors are blacklisted;
- adding a unit test to make sure events are being fired;
- adding HistoryServerSuite coverage to verify that the SHS reads these events correctly.
- updates the Executor UI to show Blacklisted/Active/Dead as a tri-state in Executors Status
Updates .rat-excludes to pass tests.
username squito
## How was this patch tested?
./dev/run-tests
testOnly org.apache.spark.util.JsonProtocolSuite
testOnly org.apache.spark.scheduler.BlacklistTrackerSuite
testOnly org.apache.spark.deploy.history.HistoryServerSuite
https://github.com/jsoltren/jose-utils/blob/master/blacklist/test-blacklist.sh
Author: José Hiram Soltren <jose@cloudera.com>
Closes#16346 from jsoltren/SPARK-16654-submit.
## What changes were proposed in this pull request?
To be able to restart StreamingQueries across Spark version, we have already made the logs (offset log, file source log, file sink log) use json. We should added tests with actual json files in the Spark such that any incompatible changes in reading the logs is immediately caught. This PR add tests for FileStreamSourceLog, FileStreamSinkLog, and OffsetSeqLog.
## How was this patch tested?
new unit tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#16128 from tdas/SPARK-18671.
## What changes were proposed in this pull request?
Excludes the `spark-warehouse` directory from the Apache RAT checks that src/run-tests performs. `spark-warehouse` is created by some of the Spark SQL tests, as well as by `bin/spark-sql`.
## How was this patch tested?
Ran src/run-tests twice. The second time, the script failed because the first iteration
Made the change in this PR.
Ran src/run-tests a third time; RAT checks succeeded.
Author: frreiss <frreiss@us.ibm.com>
Closes#14870 from frreiss/fred-17303.
## What changes were proposed in this pull request?
Add a configurable token manager for Spark on running on yarn.
### Current Problems ###
1. Supported token provider is hard-coded, currently only hdfs, hbase and hive are supported and it is impossible for user to add new token provider without code changes.
2. Also this problem exits in timely token renewer and updater.
### Changes In This Proposal ###
In this proposal, to address the problems mentioned above and make the current code more cleaner and easier to understand, mainly has 3 changes:
1. Abstract a `ServiceTokenProvider` as well as `ServiceTokenRenewable` interface for token provider. Each service wants to communicate with Spark through token way needs to implement this interface.
2. Provide a `ConfigurableTokenManager` to manage all the register token providers, also token renewer and updater. Also this class offers the API for other modules to obtain tokens, get renewal interval and so on.
3. Implement 3 built-in token providers `HDFSTokenProvider`, `HiveTokenProvider` and `HBaseTokenProvider` to keep the same semantics as supported today. Whether to load in these built-in token providers is controlled by configuration "spark.yarn.security.tokens.${service}.enabled", by default for all the built-in token providers are loaded.
### Behavior Changes ###
For the end user there's no behavior change, we still use the same configuration `spark.yarn.security.tokens.${service}.enabled` to decide which token provider is enabled (hbase or hive).
For user implemented token provider (assume the name of token provider is "test") needs to add into this class should have two configurations:
1. `spark.yarn.security.tokens.test.enabled` to true
2. `spark.yarn.security.tokens.test.class` to the full qualified class name.
So we still keep the same semantics as current code while add one new configuration.
### Current Status ###
- [x] token provider interface and management framework.
- [x] implement built-in token providers (hdfs, hbase, hive).
- [x] Coverage of unit test.
- [x] Integrated test with security cluster.
## How was this patch tested?
Unit test and integrated test.
Please suggest and review, any comment is greatly appreciated.
Author: jerryshao <sshao@hortonworks.com>
Closes#14065 from jerryshao/SPARK-16342.
## What changes were proposed in this pull request?
Add a check-cran.sh script that runs `R CMD check` as CRAN. Also fixes a number of issues pointed out by the check. These include
- Updating `DESCRIPTION` to be appropriate
- Adding a .Rbuildignore to ignore lintr, src-native, html that are non-standard files / dirs
- Adding aliases to all S4 methods in DataFrame, Column, GroupedData etc. This is required as stated in https://cran.r-project.org/doc/manuals/r-release/R-exts.html#Documenting-S4-classes-and-methods
- Other minor fixes
## How was this patch tested?
SparkR unit tests, running the above mentioned script
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#14173 from shivaram/sparkr-cran-changes.
## What changes were proposed in this pull request?
Now that SparkSQL supports all TPC-DS queries, this patch adds all 99 benchmark queries inside SparkSQL.
## How was this patch tested?
Benchmark only
Author: Sameer Agarwal <sameer@databricks.com>
Closes#13188 from sameeragarwal/tpcds-all.
## What changes were proposed in this pull request?
Since `LZ4BlockInputStream.java` is not licensed to Apache Software Foundation (ASF), the Apache License header of that file is not monitored until now.
This PR aims to enable RAT checking on `LZ4BlockInputStream.java` by excluding from `dev/.rat-excludes`.
This will prevent accidental removal of Apache License header from that file.
## How was this patch tested?
Pass the Jenkins tests (Specifically, RAT check stage).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12677 from dongjoon-hyun/minor_rat_exclusion_file.
## What changes were proposed in this pull request?
This commit adds support for pluggable cluster manager. And also allows a cluster manager to clean up tasks without taking the parent process down.
To plug a new external cluster manager, ExternalClusterManager trait should be implemented. It returns task scheduler and backend scheduler that will be used by SparkContext to schedule tasks. An external cluster manager is registered using the java.util.ServiceLoader mechanism (This mechanism is also being used to register data sources like parquet, json, jdbc etc.). This allows auto-loading implementations of ExternalClusterManager interface.
Currently, when a driver fails, executors exit using system.exit. This does not bode well for cluster managers that would like to reuse the parent process of an executor. Hence,
1. Moving system.exit to a function that can be overriden in subclasses of CoarseGrainedExecutorBackend.
2. Added functionality of killing all the running tasks in an executor.
## How was this patch tested?
ExternalClusterManagerSuite.scala was added to test this patch.
Author: Hemant Bhanawat <hemant@snappydata.io>
Closes#11723 from hbhanawat/pluggableScheduler.
## What changes were proposed in this pull request?
Move many top-level files in dev/ or other appropriate directory. In particular, put `make-distribution.sh` in `dev` and update docs accordingly. Remove deprecated `sbt/sbt`.
I was (so far) unable to figure out how to move `tox.ini`. `scalastyle-config.xml` should be movable but edits to the project `.sbt` files didn't work; config file location is updatable for compile but not test scope.
## How was this patch tested?
`./dev/run-tests` to verify RAT and checkstyle work. Jenkins tests for the rest.
Author: Sean Owen <sowen@cloudera.com>
Closes#11522 from srowen/SPARK-13596.
