## What changes were proposed in this pull request?
Self-explanatory.
## How was this patch tested?
New python tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#20309 from tdas/SPARK-23143.
## What changes were proposed in this pull request?
This PR completes the docs, specifying the default units assumed in configuration entries of type size.
This is crucial since unit-less values are accepted and the user might assume the base unit is bytes, which in most cases it is not, leading to hard-to-debug problems.
## How was this patch tested?
This patch updates only documentation only.
Author: Fernando Pereira <fernando.pereira@epfl.ch>
Closes#20269 from ferdonline/docs_units.
## What changes were proposed in this pull request?
Stage's task page table will throw an exception when there's no complete tasks. Furthermore, because the `dataSize` doesn't take running tasks into account, so sometimes UI cannot show the running tasks. Besides table will only be displayed when first task is finished according to the default sortColumn("index").
To reproduce this issue, user could try `sc.parallelize(1 to 20, 20).map { i => Thread.sleep(10000); i }.collect()` or `sc.parallelize(1 to 20, 20).map { i => Thread.sleep((20 - i) * 1000); i }.collect` to reproduce the above issue.
Here propose a solution to fix it. Not sure if it is a right fix, please help to review.
## How was this patch tested?
Manual test.
Author: jerryshao <sshao@hortonworks.com>
Closes#20315 from jerryshao/SPARK-23147.
## What changes were proposed in this pull request?
Currently `UDFRegistration.registerJavaFunction` doesn't support data type string as a `returnType` whereas `UDFRegistration.register`, `udf`, or `pandas_udf` does.
We can support it for `UDFRegistration.registerJavaFunction` as well.
## How was this patch tested?
Added a doctest and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20307 from ueshin/issues/SPARK-23141.
## What changes were proposed in this pull request?
When there is an operation between Decimals and the result is a number which is not representable exactly with the result's precision and scale, Spark is returning `NULL`. This was done to reflect Hive's behavior, but it is against SQL ANSI 2011, which states that "If the result cannot be represented exactly in the result type, then whether it is rounded or truncated is implementation-defined". Moreover, Hive now changed its behavior in order to respect the standard, thanks to HIVE-15331.
Therefore, the PR propose to:
- update the rules to determine the result precision and scale according to the new Hive's ones introduces in HIVE-15331;
- round the result of the operations, when it is not representable exactly with the result's precision and scale, instead of returning `NULL`
- introduce a new config `spark.sql.decimalOperations.allowPrecisionLoss` which default to `true` (ie. the new behavior) in order to allow users to switch back to the previous one.
Hive behavior reflects SQLServer's one. The only difference is that the precision and scale are adjusted for all the arithmetic operations in Hive, while SQL Server is said to do so only for multiplications and divisions in the documentation. This PR follows Hive's behavior.
A more detailed explanation is available here: https://mail-archives.apache.org/mod_mbox/spark-dev/201712.mbox/%3CCAEorWNAJ4TxJR9NBcgSFMD_VxTg8qVxusjP%2BAJP-x%2BJV9zH-yA%40mail.gmail.com%3E.
## How was this patch tested?
modified and added UTs. Comparisons with results of Hive and SQLServer.
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20023 from mgaido91/SPARK-22036.
## What changes were proposed in this pull request?
`DataSourceV2Strategy` is missing in `HiveSessionStateBuilder`'s planner, which will throw exception as described in [SPARK-23140](https://issues.apache.org/jira/browse/SPARK-23140).
## How was this patch tested?
Manual test.
Author: jerryshao <sshao@hortonworks.com>
Closes#20305 from jerryshao/SPARK-23140.
## What changes were proposed in this pull request?
Migrate ConsoleSink to data source V2 api.
Note that this includes a missing piece in DataStreamWriter required to specify a data source V2 writer.
Note also that I've removed the "Rerun batch" part of the sink, because as far as I can tell this would never have actually happened. A MicroBatchExecution object will only commit each batch once for its lifetime, and a new MicroBatchExecution object would have a new ConsoleSink object which doesn't know it's retrying a batch. So I think this represents an anti-feature rather than a weakness in the V2 API.
## How was this patch tested?
new unit test
Author: Jose Torres <jose@databricks.com>
Closes#20243 from jose-torres/console-sink.
## What changes were proposed in this pull request?
This PR proposes to deprecate `register*` for UDFs in `SQLContext` and `Catalog` in Spark 2.3.0.
These are inconsistent with Scala / Java APIs and also these basically do the same things with `spark.udf.register*`.
Also, this PR moves the logcis from `[sqlContext|spark.catalog].register*` to `spark.udf.register*` and reuse the docstring.
This PR also handles minor doc corrections. It also includes https://github.com/apache/spark/pull/20158
## How was this patch tested?
Manually tested, manually checked the API documentation and tests added to check if deprecated APIs call the aliases correctly.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20288 from HyukjinKwon/deprecate-udf.
## What changes were proposed in this pull request?
Structured streaming is now able to read files with space in file name (previously it would skip the file and output a warning)
## How was this patch tested?
Added new unit test.
Author: Xiayun Sun <xiayunsun@gmail.com>
Closes#19247 from xysun/SPARK-21996.
## What changes were proposed in this pull request?
- Added `InterfaceStability.Evolving` annotations
- Improved docs.
## How was this patch tested?
Existing tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#20286 from tdas/SPARK-23119.
## What changes were proposed in this pull request?
Fixed some typos found in ML scaladocs
## How was this patch tested?
NA
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#20300 from BryanCutler/ml-doc-typos-MINOR.
## What changes were proposed in this pull request?
This PR proposes to actually run the doctests in `ml/image.py`.
## How was this patch tested?
doctests in `python/pyspark/ml/image.py`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20294 from HyukjinKwon/trigger-image.
## What changes were proposed in this pull request?
This PR changes usage of `MapVector` in Spark codebase to use `NullableMapVector`.
`MapVector` is an internal Arrow class that is not supposed to be used directly. We should use `NullableMapVector` instead.
## How was this patch tested?
Existing test.
Author: Li Jin <ice.xelloss@gmail.com>
Closes#20239 from icexelloss/arrow-map-vector.
## What changes were proposed in this pull request?
Keep the run ID static, using a different ID for the epoch coordinator to avoid cross-execution message contamination.
## How was this patch tested?
new and existing unit tests
Author: Jose Torres <jose@databricks.com>
Closes#20282 from jose-torres/fix-runid.
## What changes were proposed in this pull request?
Continuous processing tasks will fail on any attempt number greater than 0. ContinuousExecution will catch these failures and restart globally from the last recorded checkpoints.
## How was this patch tested?
unit test
Author: Jose Torres <jose@databricks.com>
Closes#20225 from jose-torres/no-retry.
## What changes were proposed in this pull request?
Temporarily ignoring flaky test `SparkLauncherSuite.testInProcessLauncher` to de-flake the builds. This should be re-enabled when SPARK-23020 is merged.
## How was this patch tested?
N/A (Test Only Change)
Author: Sameer Agarwal <sameerag@apache.org>
Closes#20291 from sameeragarwal/disable-test-2.
## What changes were proposed in this pull request?
Previously, PR #19201 fix the problem of non-converging constraints.
After that PR #19149 improve the loop and constraints is inferred only once.
So the problem of non-converging constraints is gone.
However, the case below will fail.
```
spark.range(5).write.saveAsTable("t")
val t = spark.read.table("t")
val left = t.withColumn("xid", $"id" + lit(1)).as("x")
val right = t.withColumnRenamed("id", "xid").as("y")
val df = left.join(right, "xid").filter("id = 3").toDF()
checkAnswer(df, Row(4, 3))
```
Because `aliasMap` replace all the aliased child. See the test case in PR for details.
This PR is to fix this bug by removing useless code for preventing non-converging constraints.
It can be also fixed with #20270, but this is much simpler and clean up the code.
## How was this patch tested?
Unit test
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#20278 from gengliangwang/FixConstraintSimple.
## What changes were proposed in this pull request?
ORC filter push-down is disabled by default from the beginning, [SPARK-2883](aa31e431fc (diff-41ef65b9ef5b518f77e2a03559893f4dR149)
).
Now, Apache Spark starts to depend on Apache ORC 1.4.1. For Apache Spark 2.3, this PR turns on ORC filter push-down by default like Parquet ([SPARK-9207](https://issues.apache.org/jira/browse/SPARK-21783)) as a part of [SPARK-20901](https://issues.apache.org/jira/browse/SPARK-20901), "Feature parity for ORC with Parquet".
## How was this patch tested?
Pass the existing tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#20265 from dongjoon-hyun/SPARK-21783.
## What changes were proposed in this pull request?
Make the default behavior of EXCEPT (i.e. EXCEPT DISTINCT) more
explicit in the documentation, and call out the change in behavior
from 1.x.
Author: Henry Robinson <henry@cloudera.com>
Closes#20254 from henryr/spark-23062.
## What changes were proposed in this pull request?
The first commit added a new test, and the second refactored the class the test was in. The automatic merge put the test in the wrong place.
## How was this patch tested?
-
Author: Jose Torres <jose@databricks.com>
Closes#20289 from jose-torres/fix.
## What changes were proposed in this pull request?
The Kafka reader is now interruptible and can close itself.
## How was this patch tested?
I locally ran one of the ContinuousKafkaSourceSuite tests in a tight loop. Before the fix, my machine ran out of open file descriptors a few iterations in; now it works fine.
Author: Jose Torres <jose@databricks.com>
Closes#20253 from jose-torres/fix-data-reader.
## What changes were proposed in this pull request?
There are already quite a few integration tests using window frames, but the unit tests coverage is not ideal.
In this PR the already existing tests are reorganized, extended and where gaps found additional cases added.
## How was this patch tested?
Automated: Pass the Jenkins.
Author: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Closes#20019 from gaborgsomogyi/SPARK-22361.
## What changes were proposed in this pull request?
The following SQL involving scalar correlated query returns a map exception.
``` SQL
SELECT t1a
FROM t1
WHERE t1a = (SELECT count(*)
FROM t2
WHERE t2c = t1c
HAVING count(*) >= 1)
```
``` SQL
key not found: ExprId(278,786682bb-41f9-4bd5-a397-928272cc8e4e)
java.util.NoSuchElementException: key not found: ExprId(278,786682bb-41f9-4bd5-a397-928272cc8e4e)
at scala.collection.MapLike$class.default(MapLike.scala:228)
at scala.collection.AbstractMap.default(Map.scala:59)
at scala.collection.MapLike$class.apply(MapLike.scala:141)
at scala.collection.AbstractMap.apply(Map.scala:59)
at org.apache.spark.sql.catalyst.optimizer.RewriteCorrelatedScalarSubquery$.org$apache$spark$sql$catalyst$optimizer$RewriteCorrelatedScalarSubquery$$evalSubqueryOnZeroTups(subquery.scala:378)
at org.apache.spark.sql.catalyst.optimizer.RewriteCorrelatedScalarSubquery$$anonfun$org$apache$spark$sql$catalyst$optimizer$RewriteCorrelatedScalarSubquery$$constructLeftJoins$1.apply(subquery.scala:430)
at org.apache.spark.sql.catalyst.optimizer.RewriteCorrelatedScalarSubquery$$anonfun$org$apache$spark$sql$catalyst$optimizer$RewriteCorrelatedScalarSubquery$$constructLeftJoins$1.apply(subquery.scala:426)
```
In this case, after evaluating the HAVING clause "count(*) > 1" statically
against the binding of aggregtation result on empty input, we determine
that this query will not have a the count bug. We should simply return
the evalSubqueryOnZeroTups with empty value.
(Please fill in changes proposed in this fix)
## How was this patch tested?
A new test was added in the Subquery bucket.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#20283 from dilipbiswal/scalar-count-defect.
## What changes were proposed in this pull request?
* If there is any error while trying to assign the jira, prompt again
* Filter out the "Apache Spark" choice
* allow arbitrary user ids to be entered
## How was this patch tested?
Couldn't really test the error case, just some testing of similar-ish code in python shell. Haven't run a merge yet.
Author: Imran Rashid <irashid@cloudera.com>
Closes#20236 from squito/SPARK-23044.
## What changes were proposed in this pull request?
RFormula should use VectorSizeHint & OneHotEncoderEstimator in its pipeline to avoid using the deprecated OneHotEncoder & to ensure the model produced can be used in streaming.
## How was this patch tested?
Unit tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Bago Amirbekian <bago@databricks.com>
Closes#20229 from MrBago/rFormula.
## What changes were proposed in this pull request?
Lots of our tests don't properly shutdown everything they create, and end up leaking lots of threads. For example, `TaskSetManagerSuite` doesn't stop the extra `TaskScheduler` and `DAGScheduler` it creates. There are a couple more instances, eg. in `DAGSchedulerSuite`.
This PR adds the possibility to print out the not properly stopped thread list after a test suite executed. The format is the following:
```
===== FINISHED o.a.s.scheduler.DAGSchedulerSuite: 'task end event should have updated accumulators (SPARK-20342)' =====
...
===== Global thread whitelist loaded with name /thread_whitelist from classpath: rpc-client.*, rpc-server.*, shuffle-client.*, shuffle-server.*' =====
ScalaTest-run:
===== THREADS NOT STOPPED PROPERLY =====
ScalaTest-run: dag-scheduler-event-loop
ScalaTest-run: globalEventExecutor-2-5
ScalaTest-run:
===== END OF THREAD DUMP =====
ScalaTest-run:
===== EITHER PUT THREAD NAME INTO THE WHITELIST FILE OR SHUT IT DOWN PROPERLY =====
```
With the help of this leaking threads has been identified in TaskSetManagerSuite. My intention is to hunt down and fix such bugs in later PRs.
## How was this patch tested?
Manual: TaskSetManagerSuite test executed and found out where are the leaking threads.
Automated: Pass the Jenkins.
Author: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Closes#19893 from gaborgsomogyi/SPARK-16139.
## What changes were proposed in this pull request?
a new Data Source V2 interface to allow the data source to return `ColumnarBatch` during the scan.
## How was this patch tested?
new tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#20153 from cloud-fan/columnar-reader.
## What changes were proposed in this pull request?
Register Vectorized UDFs for SQL Statement. For example,
```Python
>>> from pyspark.sql.functions import pandas_udf, PandasUDFType
>>> pandas_udf("integer", PandasUDFType.SCALAR)
... def add_one(x):
... return x + 1
...
>>> _ = spark.udf.register("add_one", add_one)
>>> spark.sql("SELECT add_one(id) FROM range(3)").collect()
[Row(add_one(id)=1), Row(add_one(id)=2), Row(add_one(id)=3)]
```
## How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20171 from gatorsmile/supportVectorizedUDF.
The race in the code is because the handle might update
its state to the wrong state if the connection handling
thread is still processing incoming data; so the handle
needs to wait for the connection to finish up before
checking the final state.
The race in the test is because when waiting for a handle
to reach a final state, the waitFor() method needs to wait
until all handle state is updated (which also includes
waiting for the connection thread above to finish).
Otherwise, waitFor() may return too early, which would cause
a bunch of different races (like the listener not being yet
notified of the state change, or being in the middle of
being notified, or the handle not being properly disposed
and causing postChecks() to assert).
On top of that I found, by code inspection, a couple of
potential races that could make a handle end up in the
wrong state when being killed.
Tested by running the existing unit tests a lot (and not
seeing the errors I was seeing before).
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20223 from vanzin/SPARK-23020.
## What changes were proposed in this pull request?
This problem reported by yanlin-Lynn ivoson and LiangchangZ. Thanks!
When we union 2 streams from kafka or other sources, while one of them have no continues data coming and in the same time task restart, this will cause an `IllegalStateException`. This mainly cause because the code in [MicroBatchExecution](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MicroBatchExecution.scala#L190) , while one stream has no continues data, its comittedOffset same with availableOffset during `populateStartOffsets`, and `currentPartitionOffsets` not properly handled in KafkaSource. Also, maybe we should also consider this scenario in other Source.
## How was this patch tested?
Add a UT in KafkaSourceSuite.scala
Author: Yuanjian Li <xyliyuanjian@gmail.com>
Closes#20150 from xuanyuanking/SPARK-22956.
## What changes were proposed in this pull request?
In another attempt to fix DataSourceWithHiveMetastoreCatalogSuite, this patch uses qualified table names (`default.t`) in the individual tests.
## How was this patch tested?
N/A (Test Only Change)
Author: Sameer Agarwal <sameerag@apache.org>
Closes#20273 from sameeragarwal/flaky-test.
## What changes were proposed in this pull request?
When a user puts the wrong number of parameters in a function, an AnalysisException is thrown. If the function is a UDF, he user is told how many parameters the function expected and how many he/she put. If the function, instead, is a built-in one, no information about the number of parameters expected and the actual one is provided. This can help in some cases, to debug the errors (eg. bad quotes escaping may lead to a different number of parameters than expected, etc. etc.)
The PR adds the information about the number of parameters passed and the expected one, analogously to what happens for UDF.
## How was this patch tested?
modified existing UT + manual test
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20271 from mgaido91/SPARK-23080.
## What changes were proposed in this pull request?
Problem: it throw TempTableAlreadyExistsException and output "Temporary table '$table' already exists" when we create temp view by using org.apache.spark.sql.catalyst.catalog.GlobalTempViewManager#create, it's improper.
So fix improper information about TempTableAlreadyExistsException when create temp view:
change "Temporary table" to "Temporary view"
## How was this patch tested?
test("rename temporary view - destination table already exists, with: CREATE TEMPORARY view")
test("rename temporary view - destination table with database name,with:CREATE TEMPORARY view")
Author: xubo245 <601450868@qq.com>
Closes#20227 from xubo245/fixDeprecated.
## What changes were proposed in this pull request?
Bump previousSparkVersion in MimaBuild.scala to be 2.2.0 and add the missing exclusions to `v23excludes` in `MimaExcludes`. No item can be un-excluded in `v23excludes`.
## How was this patch tested?
The existing tests.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20264 from gatorsmile/bump22.
## What changes were proposed in this pull request?
Remove `MaxPermSize` for `sql` module
## How was this patch tested?
Manually tested.
Author: Yuming Wang <yumwang@ebay.com>
Closes#20268 from wangyum/SPARK-19550-MaxPermSize.
## What changes were proposed in this pull request?
The current `Datset.showString` prints rows thru `RowEncoder` deserializers like;
```
scala> Seq(Seq(Seq(1, 2), Seq(3), Seq(4, 5, 6))).toDF("a").show(false)
+------------------------------------------------------------+
|a |
+------------------------------------------------------------+
|[WrappedArray(1, 2), WrappedArray(3), WrappedArray(4, 5, 6)]|
+------------------------------------------------------------+
```
This result is incorrect because the correct one is;
```
scala> Seq(Seq(Seq(1, 2), Seq(3), Seq(4, 5, 6))).toDF("a").show(false)
+------------------------+
|a |
+------------------------+
|[[1, 2], [3], [4, 5, 6]]|
+------------------------+
```
So, this pr fixed code in `showString` to cast field data to strings before printing.
## How was this patch tested?
Added tests in `DataFrameSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#20214 from maropu/SPARK-23023.
## What changes were proposed in this pull request?
When `spark.sql.files.ignoreCorruptFiles=true`, we should ignore corrupted ORC files.
## How was this patch tested?
Pass the Jenkins with a newly added test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#20240 from dongjoon-hyun/SPARK-23049.
## What changes were proposed in this pull request?
This pr fixed the issue when casting `UserDefinedType`s into strings;
```
>>> from pyspark.ml.classification import MultilayerPerceptronClassifier
>>> from pyspark.ml.linalg import Vectors
>>> df = spark.createDataFrame([(0.0, Vectors.dense([0.0, 0.0])), (1.0, Vectors.dense([0.0, 1.0]))], ["label", "features"])
>>> df.selectExpr("CAST(features AS STRING)").show(truncate = False)
+-------------------------------------------+
|features |
+-------------------------------------------+
|[6,1,0,0,2800000020,2,0,0,0] |
|[6,1,0,0,2800000020,2,0,0,3ff0000000000000]|
+-------------------------------------------+
```
The root cause is that `Cast` handles input data as `UserDefinedType.sqlType`(this is underlying storage type), so we should pass data into `UserDefinedType.deserialize` then `toString`.
This pr modified the result into;
```
+---------+
|features |
+---------+
|[0.0,0.0]|
|[0.0,1.0]|
+---------+
```
## How was this patch tested?
Added tests in `UserDefinedTypeSuite `.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#20246 from maropu/SPARK-23054.
## What changes were proposed in this pull request?
SHOW DATABASES (LIKE pattern = STRING)? Can be like the back increase?
When using this command, LIKE keyword can be removed.
You can refer to the SHOW TABLES command, SHOW TABLES 'test *' and SHOW TABELS like 'test *' can be used.
Similarly SHOW DATABASES 'test *' and SHOW DATABASES like 'test *' can be used.
## How was this patch tested?
unit tests manual tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: guoxiaolong <guo.xiaolong1@zte.com.cn>
Closes#20194 from guoxiaolongzte/SPARK-22999.
## What changes were proposed in this pull request?
In 2.2, Spark UI displayed the stage description if the job description was not set. This functionality was broken, the GUI has shown no description in this case. In addition, the code uses jobName and
jobDescription instead of stageName and stageDescription when JobTableRowData is created.
In this PR the logic producing values for the job rows was modified to find the latest stage attempt for the job and use that as a fallback if job description was missing.
StageName and stageDescription are also set using values from stage and jobName/description is used only as a fallback.
## How was this patch tested?
Manual testing of the UI, using the code in the bug report.
Author: Sandor Murakozi <smurakozi@gmail.com>
Closes#20251 from smurakozi/SPARK-23051.
## What changes were proposed in this pull request?
`AnalysisBarrier` in the current master cuts off explain results for parsed logical plans;
```
scala> Seq((1, 1)).toDF("a", "b").groupBy("a").count().sample(0.1).explain(true)
== Parsed Logical Plan ==
Sample 0.0, 0.1, false, -7661439431999668039
+- AnalysisBarrier Aggregate [a#5], [a#5, count(1) AS count#14L]
```
To fix this, `AnalysisBarrier` needs to override `innerChildren` and this pr changed the output to;
```
== Parsed Logical Plan ==
Sample 0.0, 0.1, false, -5086223488015741426
+- AnalysisBarrier
+- Aggregate [a#5], [a#5, count(1) AS count#14L]
+- Project [_1#2 AS a#5, _2#3 AS b#6]
+- LocalRelation [_1#2, _2#3]
```
## How was this patch tested?
Added tests in `DataFrameSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#20247 from maropu/SPARK-23021-2.
## What changes were proposed in this pull request?
fix doc truncated
## How was this patch tested?
manually
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#20263 from felixcheung/r23docfix.
## What changes were proposed in this pull request?
This PR aims to update the followings in `docker/spark-test`.
- JDK7 -> JDK8
Spark 2.2+ supports JDK8 only.
- Ubuntu 12.04.5 LTS(precise) -> Ubuntu 16.04.3 LTS(xeniel)
The end of life of `precise` was April 28, 2017.
## How was this patch tested?
Manual.
* Master
```
$ cd external/docker
$ ./build
$ export SPARK_HOME=...
$ docker run -v $SPARK_HOME:/opt/spark spark-test-master
CONTAINER_IP=172.17.0.3
...
18/01/11 06:50:25 INFO MasterWebUI: Bound MasterWebUI to 172.17.0.3, and started at http://172.17.0.3:8080
18/01/11 06:50:25 INFO Utils: Successfully started service on port 6066.
18/01/11 06:50:25 INFO StandaloneRestServer: Started REST server for submitting applications on port 6066
18/01/11 06:50:25 INFO Master: I have been elected leader! New state: ALIVE
```
* Slave
```
$ docker run -v $SPARK_HOME:/opt/spark spark-test-worker spark://172.17.0.3:7077
CONTAINER_IP=172.17.0.4
...
18/01/11 06:51:54 INFO Worker: Successfully registered with master spark://172.17.0.3:7077
```
After slave starts, master will show
```
18/01/11 06:51:54 INFO Master: Registering worker 172.17.0.4:8888 with 4 cores, 1024.0 MB RAM
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#20230 from dongjoon-hyun/SPARK-23038.
## What changes were proposed in this pull request?
Including the `-Pkubernetes` flag in a few places it was missed.
## How was this patch tested?
checkstyle, mima through manual tests.
Author: foxish <ramanathana@google.com>
Closes#20256 from foxish/SPARK-23063.
## What changes were proposed in this pull request?
We are now forced to use `pyspark/daemon.py` and `pyspark/worker.py` in PySpark.
This doesn't allow a custom modification for it (well, maybe we can still do this in a super hacky way though, for example, setting Python executable that has the custom modification). Because of this, for example, it's sometimes hard to debug what happens inside Python worker processes.
This is actually related with [SPARK-7721](https://issues.apache.org/jira/browse/SPARK-7721) too as somehow Coverage is unable to detect the coverage from `os.fork`. If we have some custom fixes to force the coverage, it works fine.
This is also related with [SPARK-20368](https://issues.apache.org/jira/browse/SPARK-20368). This JIRA describes Sentry support which (roughly) needs some changes within worker side.
With this configuration advanced users will be able to do a lot of pluggable workarounds and we can meet such potential needs in the future.
As an example, let's say if I configure the module `coverage_daemon` and had `coverage_daemon.py` in the python path:
```python
import os
from pyspark import daemon
if "COVERAGE_PROCESS_START" in os.environ:
from pyspark.worker import main
def _cov_wrapped(*args, **kwargs):
import coverage
cov = coverage.coverage(
config_file=os.environ["COVERAGE_PROCESS_START"])
cov.start()
try:
main(*args, **kwargs)
finally:
cov.stop()
cov.save()
daemon.worker_main = _cov_wrapped
if __name__ == '__main__':
daemon.manager()
```
I can track the coverages in worker side too.
More importantly, we can leave the main code intact but allow some workarounds.
## How was this patch tested?
Manually tested.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20151 from HyukjinKwon/configuration-daemon-worker.
## What changes were proposed in this pull request?
This pr fixed code to compare values in `compareAndGetNewStats`.
The test below fails in the current master;
```
val oldStats2 = CatalogStatistics(sizeInBytes = BigInt(Long.MaxValue) * 2)
val newStats5 = CommandUtils.compareAndGetNewStats(
Some(oldStats2), newTotalSize = BigInt(Long.MaxValue) * 2, None)
assert(newStats5.isEmpty)
```
## How was this patch tested?
Added some tests in `CommandUtilsSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#20245 from maropu/SPARK-21213-FOLLOWUP.
## What changes were proposed in this pull request?
as per discussion in https://github.com/apache/spark/pull/19864#discussion_r156847927
the current HadoopFsRelation is purely based on the underlying file size which is not accurate and makes the execution vulnerable to errors like OOM
Users can enable CBO with the functionalities in https://github.com/apache/spark/pull/19864 to avoid this issue
This JIRA proposes to add a configurable factor to sizeInBytes method in HadoopFsRelation class so that users can mitigate this problem without CBO
## How was this patch tested?
Existing tests
Author: CodingCat <zhunansjtu@gmail.com>
Author: Nan Zhu <nanzhu@uber.com>
Closes#20072 from CodingCat/SPARK-22790.
