## What changes were proposed in this pull request?
In this PR we make a few changes to the list hive partitions code, to make the code more extensible.
The following changes are made:
1. In `HiveClientImpl.getPartitions()`, call `client.getPartitions` instead of `shim.getAllPartitions` when `spec` is empty;
2. In `HiveTableScanExec`, previously we always call `listPartitionsByFilter` if the config `metastorePartitionPruning` is enabled, but actually, we'd better call `listPartitions` if `partitionPruningPred` is empty;
3. We should use sessionCatalog instead of SharedState.externalCatalog in `HiveTableScanExec`.
## How was this patch tested?
Tested by existing test cases since this is code refactor, no regression or behavior change is expected.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#19444 from jiangxb1987/hivePartitions.
## What changes were proposed in this pull request?
All the windows command scripts can not handle quotes in parameter.
Run a windows command shell with parameter which has quotes can reproduce the bug:
```
C:\Users\meng\software\spark-2.2.0-bin-hadoop2.7> bin\spark-shell --driver-java-options " -Dfile.encoding=utf-8 "
'C:\Users\meng\software\spark-2.2.0-bin-hadoop2.7\bin\spark-shell2.cmd" --driver-java-options "' is not recognized as an internal or external command,
operable program or batch file.
```
Windows recognize "--driver-java-options" as part of the command.
All the Windows command script has the following code have the bug.
```
cmd /V /E /C "<other command>" %*
```
We should quote command and parameters like
```
cmd /V /E /C ""<other command>" %*"
```
## How was this patch tested?
Test manually on Windows 10 and Windows 7
We can verify it by the following demo:
```
C:\Users\meng\program\demo>cat a.cmd
echo off
cmd /V /E /C "b.cmd" %*
C:\Users\meng\program\demo>cat b.cmd
echo off
echo %*
C:\Users\meng\program\demo>cat c.cmd
echo off
cmd /V /E /C ""b.cmd" %*"
C:\Users\meng\program\demo>a.cmd "123"
'b.cmd" "123' is not recognized as an internal or external command,
operable program or batch file.
C:\Users\meng\program\demo>c.cmd "123"
"123"
```
With the spark-shell.cmd example, change it to the following code will make the command execute succeed.
```
cmd /V /E /C ""%~dp0spark-shell2.cmd" %*"
```
```
C:\Users\meng\software\spark-2.2.0-bin-hadoop2.7> bin\spark-shell --driver-java-options " -Dfile.encoding=utf-8 "
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
...
```
Author: minixalpha <xkzalpha@gmail.com>
Closes#19090 from minixalpha/master.
## What changes were proposed in this pull request?
Move flume behind a profile, take 2. See https://github.com/apache/spark/pull/19365 for most of the back-story.
This change should fix the problem by removing the examples module dependency and moving Flume examples to the module itself. It also adds deprecation messages, per a discussion on dev about deprecating for 2.3.0.
## How was this patch tested?
Existing tests, which still enable flume integration.
Author: Sean Owen <sowen@cloudera.com>
Closes#19412 from srowen/SPARK-22142.2.
## What changes were proposed in this pull request?
When exceeding `spark.sql.codegen.hugeMethodLimit`, the runtime fallbacks to the Volcano iterator solution. This could cause an infinite loop when `FileSourceScanExec` can use the columnar batch to read the data. This PR is to fix the issue.
## How was this patch tested?
Added a test
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19440 from gatorsmile/testt.
## What changes were proposed in this pull request?
Looks like `FlatMapGroupsInRExec.requiredChildDistribution` didn't consider empty grouping attributes. It should be a problem when running `EnsureRequirements` and `gapply` in R can't work on empty grouping columns.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19436 from viirya/fix-flatmapinr-distribution.
## What changes were proposed in this pull request?
Currently, the group state of user-defined-type is encoded as top-level columns in the UnsafeRows stores in the state store. The timeout timestamp is also saved as (when needed) as the last top-level column. Since the group state is serialized to top-level columns, you cannot save "null" as a value of state (setting null in all the top-level columns is not equivalent). So we don't let the user set the timeout without initializing the state for a key. Based on user experience, this leads to confusion.
This PR is to change the row format such that the state is saved as nested columns. This would allow the state to be set to null, and avoid these confusing corner cases.
## How was this patch tested?
Refactored tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#19416 from tdas/SPARK-22187.
## What changes were proposed in this pull request?
By definition the table name in Spark can be something like `123x`, `25a`, etc., with exceptions for literals like `12L`, `23BD`, etc. However, Spark SQL has a special byte length literal, which stops users to use digits followed by `b`, `k`, `m`, `g` as identifiers.
byte length literal is not a standard sql literal and is only used in the `tableSample` parser rule. This PR move the parsing of byte length literal from lexer to parser, so that users can use it as identifiers.
## How was this patch tested?
regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19392 from cloud-fan/parser-bug.
## What changes were proposed in this pull request?
This pr added code to check actual bytecode size when compiling generated code. In #18810, we added code to give up code compilation and use interpreter execution in `SparkPlan` if the line number of generated functions goes over `maxLinesPerFunction`. But, we already have code to collect metrics for compiled bytecode size in `CodeGenerator` object. So,we could easily reuse the code for this purpose.
## How was this patch tested?
Added tests in `WholeStageCodegenSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#19083 from maropu/SPARK-21871.
## What changes were proposed in this pull request?
This PR abstracts data compressed by `CompressibleColumnAccessor` using `ColumnVector` in batch method. When `ColumnAccessor.decompress` is called, `ColumnVector` will have uncompressed data. This batch decompress does not use `InternalRow` to reduce the number of memory accesses.
As first step of this implementation, this JIRA supports primitive data types. Another PR will support array and other data types.
This implementation decompress data in batch into uncompressed column batch, as rxin suggested at [here](https://github.com/apache/spark/pull/18468#issuecomment-316914076). Another implementation uses adapter approach [as cloud-fan suggested](https://github.com/apache/spark/pull/18468).
## How was this patch tested?
Added test suites
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#18704 from kiszk/SPARK-20783a.
## What changes were proposed in this pull request?
[SPARK-22193][SQL] Minor typo fix in SortMergeJoinExec. Nothing major, but it bothered me going into.Hence fixing
## How was this patch tested?
existing tests
Author: Rekha Joshi <rekhajoshm@gmail.com>
Author: rjoshi2 <rekhajoshm@gmail.com>
Closes#19422 from rekhajoshm/SPARK-22193.
## What changes were proposed in this pull request?
Allow one-sided outer joins between two streams when a watermark is defined.
## How was this patch tested?
new unit tests
Author: Jose Torres <jose@databricks.com>
Closes#19327 from joseph-torres/outerjoin.
## What changes were proposed in this pull request?
Users could hit `java.lang.NullPointerException` when the tables were created by Hive and the table's owner is `null` that are got from Hive metastore. `DESC EXTENDED` failed with the error:
> SQLExecutionException: java.lang.NullPointerException at scala.collection.immutable.StringOps$.length$extension(StringOps.scala:47) at scala.collection.immutable.StringOps.length(StringOps.scala:47) at scala.collection.IndexedSeqOptimized$class.isEmpty(IndexedSeqOptimized.scala:27) at scala.collection.immutable.StringOps.isEmpty(StringOps.scala:29) at scala.collection.TraversableOnce$class.nonEmpty(TraversableOnce.scala:111) at scala.collection.immutable.StringOps.nonEmpty(StringOps.scala:29) at org.apache.spark.sql.catalyst.catalog.CatalogTable.toLinkedHashMap(interface.scala:300) at org.apache.spark.sql.execution.command.DescribeTableCommand.describeFormattedTableInfo(tables.scala:565) at org.apache.spark.sql.execution.command.DescribeTableCommand.run(tables.scala:543) at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:66) at
## How was this patch tested?
Added a unit test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19395 from gatorsmile/desc.
## What changes were proposed in this pull request?
Fix for SPARK-20466, full description of the issue in the JIRA. To summarize, `HadoopRDD` uses a metadata cache to cache `JobConf` objects. The cache uses soft-references, which means the JVM can delete entries from the cache whenever there is GC pressure. `HadoopRDD#getJobConf` had a bug where it would check if the cache contained the `JobConf`, if it did it would get the `JobConf` from the cache and return it. This doesn't work when soft-references are used as the JVM can delete the entry between the existence check and the get call.
## How was this patch tested?
Haven't thought of a good way to test this yet given the issue only occurs sometimes, and happens during high GC pressure. Was thinking of using mocks to verify `#getJobConf` is doing the right thing. I deleted the method `HadoopRDD#containsCachedMetadata` so that we don't hit this issue again.
Author: Sahil Takiar <stakiar@cloudera.com>
Closes#19413 from sahilTakiar/master.
## What changes were proposed in this pull request?
The underlying tables of persistent views are not refreshed when users issue the REFRESH TABLE command against the persistent views.
## How was this patch tested?
Added a test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19405 from gatorsmile/refreshView.
## What changes were proposed in this pull request?
The definition of `maxRows` in `LocalLimit` operator was simply wrong. This patch introduces a new `maxRowsPerPartition` method and uses that in pruning. The patch also adds more documentation on why we need local limit vs global limit.
Note that this previously has never been a bug because the way the code is structured, but future use of the maxRows could lead to bugs.
## How was this patch tested?
Should be covered by existing test cases.
Closes#18851
Author: gatorsmile <gatorsmile@gmail.com>
Author: Reynold Xin <rxin@databricks.com>
Closes#19393 from gatorsmile/pr-18851.
## What changes were proposed in this pull request?
This pr fixed an overflow issue below in `Dataset.show`:
```
scala> Seq((1, 2), (3, 4)).toDF("a", "b").show(Int.MaxValue)
org.apache.spark.sql.AnalysisException: The limit expression must be equal to or greater than 0, but got -2147483648;;
GlobalLimit -2147483648
+- LocalLimit -2147483648
+- Project [_1#27218 AS a#27221, _2#27219 AS b#27222]
+- LocalRelation [_1#27218, _2#27219]
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:41)
at org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:89)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.org$apache$spark$sql$catalyst$analysis$CheckAnalysis$$checkLimitClause(CheckAnalysis.scala:70)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:234)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:80)
at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)
```
## How was this patch tested?
Added tests in `DataFrameSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#19401 from maropu/MaxValueInShowString.
## What changes were proposed in this pull request?
Improve the Spark-Mesos coarse-grained scheduler to consider the preferred locations when dynamic allocation is enabled.
## How was this patch tested?
Added a unittest, and performed manual testing on AWS.
Author: Gene Pang <gene.pang@gmail.com>
Closes#18098 from gpang/mesos_data_locality.
## What changes were proposed in this pull request?
When zinc is running the pwd might be in the root of the project. A quick solution to this is to not go a level up incase we are in the root rather than root/core/. If we are in the root everything works fine, if we are in core add a script which goes and runs the level up
## How was this patch tested?
set -x in the SparkR install scripts.
Author: Holden Karau <holden@us.ibm.com>
Closes#19402 from holdenk/SPARK-22167-sparkr-packaging-issue-allow-zinc.
## What changes were proposed in this pull request?
SPARK-21690 makes one-pass `Imputer` by parallelizing the computation of all input columns. When we transform dataset with `ImputerModel`, we do `withColumn` on all input columns sequentially. We can also do this on all input columns at once by adding a `withColumns` API to `Dataset`.
The new `withColumns` API is for internal use only now.
## How was this patch tested?
Existing tests for `ImputerModel`'s change. Added tests for `withColumns` API.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19229 from viirya/SPARK-22001.
## What changes were proposed in this pull request?
Currently, we set lintr to jimhester/lintra769c0b (see [this](7d1175011c) and [SPARK-14074](https://issues.apache.org/jira/browse/SPARK-14074)).
I first tested and checked lintr-1.0.1 but it looks many important fixes are missing (for example, checking 100 length). So, I instead tried the latest commit, 5431140ffe, in my local and fixed the check failures.
It looks it has fixed many bugs and now finds many instances that I have observed and thought should be caught time to time, here I filed [the results](https://gist.github.com/HyukjinKwon/4f59ddcc7b6487a02da81800baca533c).
The downside looks it now takes about 7ish mins, (it was 2ish mins before) in my local.
## How was this patch tested?
Manually, `./dev/lint-r` after manually updating the lintr package.
Author: hyukjinkwon <gurwls223@gmail.com>
Author: zuotingbing <zuo.tingbing9@zte.com.cn>
Closes#19290 from HyukjinKwon/upgrade-r-lint.
## What changes were proposed in this pull request?
Since the current code ignores WITH clauses to check input relations in TPCDS queries, this leads to inaccurate per-row processing time for benchmark results. For example, in `q2`, this fix could catch all the input relations: `web_sales`, `date_dim`, and `catalog_sales` (the current code catches `date_dim` only). The one-third of the TPCDS queries uses WITH clauses, so I think it is worth fixing this.
## How was this patch tested?
Manually checked.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#19344 from maropu/RespectWithInTPCDSBench.
### What changes were proposed in this pull request?
`tempTables` is not right. To be consistent, we need to rename the internal variable names/comments to tempViews in SessionCatalog too.
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19117 from gatorsmile/renameTempTablesToTempViews.
## What changes were proposed in this pull request?
Added IMPALA-modified TPCDS queries to TPC-DS query suites.
- Ref: https://github.com/cloudera/impala-tpcds-kit/tree/master/queries
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19386 from gatorsmile/addImpalaQueries.
## What changes were proposed in this pull request?
Use the GPG_KEY param, fix lsof to non-hardcoded path, remove version swap since it wasn't really needed. Use EXPORT on JAVA_HOME for downstream scripts as well.
## How was this patch tested?
Rolled 2.1.2 RC2
Author: Holden Karau <holden@us.ibm.com>
Closes#19359 from holdenk/SPARK-22129-fix-signing.
## What changes were proposed in this pull request?
Add 'flume' profile to enable Flume-related integration modules
## How was this patch tested?
Existing tests; no functional change
Author: Sean Owen <sowen@cloudera.com>
Closes#19365 from srowen/SPARK-22142.
## What changes were proposed in this pull request?
Add comments for specifying the position of batch "Check Cartesian Products", as rxin suggested in https://github.com/apache/spark/pull/19362 .
## How was this patch tested?
Unit test
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#19379 from gengliangwang/SPARK-22141-followup.
## What changes were proposed in this pull request?
Reading ORC files containing special characters like '%' fails with a FileNotFoundException.
This PR aims to fix the problem.
## How was this patch tested?
Added UT.
Author: Marco Gaido <marcogaido91@gmail.com>
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#19368 from mgaido91/SPARK-22146.
## What changes were proposed in this pull request?
Spark's RangePartitioner hard codes the number of sampling points per partition to be 20. This is sometimes too low. This ticket makes it configurable, via spark.sql.execution.rangeExchange.sampleSizePerPartition, and raises the default in Spark SQL to be 100.
## How was this patch tested?
Added a pretty sophisticated test based on chi square test ...
Author: Reynold Xin <rxin@databricks.com>
Closes#19387 from rxin/SPARK-22160.
## What changes were proposed in this pull request?
spark.sql.execution.arrow.enable and spark.sql.codegen.aggregate.map.twolevel.enable -> enabled
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#19384 from rxin/SPARK-22159.
## What changes were proposed in this pull request?
For some reason when we added the Exec suffix to all physical operators, we missed this one. I was looking for this physical operator today and couldn't find it, because I was looking for ExchangeExec.
## How was this patch tested?
This is a simple rename and should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#19376 from rxin/SPARK-22153.
## What changes were proposed in this pull request?
Un-manage jackson-module-paranamer version to let it use the version desired by jackson-module-scala; manage paranamer up from 2.8 for jackson-module-scala 2.7.9, to override avro 1.7.7's desired paranamer 2.3
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#19352 from srowen/SPARK-22128.
## What changes were proposed in this pull request?
Fix a trivial bug with how metrics are registered in the mesos dispatcher. Bug resulted in creating a new registry each time the metricRegistry() method was called.
## How was this patch tested?
Verified manually on local mesos setup
Author: Paul Mackles <pmackles@adobe.com>
Closes#19358 from pmackles/SPARK-22135.
## What changes were proposed in this pull request?
This patch add latest failure reason for task set blacklist.Which can be showed on spark ui and let user know failure reason directly.
Till now , every job which aborted by completed blacklist just show log like below which has no more information:
`Aborting $taskSet because task $indexInTaskSet (partition $partition) cannot run anywhere due to node and executor blacklist. Blacklisting behavior cannot run anywhere due to node and executor blacklist.Blacklisting behavior can be configured via spark.blacklist.*."`
**After modify:**
```
Aborting TaskSet 0.0 because task 0 (partition 0)
cannot run anywhere due to node and executor blacklist.
Most recent failure:
Some(Lost task 0.1 in stage 0.0 (TID 3,xxx, executor 1): java.lang.Exception: Fake error!
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:73)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:305)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
).
Blacklisting behavior can be configured via spark.blacklist.*.
```
## How was this patch tested?
Unit test and manually test.
Author: zhoukang <zhoukang199191@gmail.com>
Closes#19338 from caneGuy/zhoukang/improve-blacklist.
## What changes were proposed in this pull request?
Fixed some minor issues with pandas_udf related docs and formatting.
## How was this patch tested?
NA
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#19375 from BryanCutler/arrow-pandas_udf-cleanup-minor.
## What changes were proposed in this pull request?
Now, we are not running TPC-DS queries as regular test cases. Thus, we need to add a test suite using empty tables for ensuring the new code changes will not break them. For example, optimizer/analyzer batches should not exceed the max iteration.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19361 from gatorsmile/tpcdsQuerySuite.
## What changes were proposed in this pull request?
`WriteableColumnVector` does not close its child column vectors. This can create memory leaks for `OffHeapColumnVector` where we do not clean up the memory allocated by a vectors children. This can be especially bad for string columns (which uses a child byte column vector).
## How was this patch tested?
I have updated the existing tests to always use both on-heap and off-heap vectors. Testing and diagnoses was done locally.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#19367 from hvanhovell/SPARK-22143.
## What changes were proposed in this pull request?
Fix finalizer checkstyle violation by just turning it off; re-disable checkstyle as it won't be run by SBT PR builder. See https://github.com/apache/spark/pull/18887#issuecomment-332580700
## How was this patch tested?
`./dev/lint-java` runs successfully
Author: Sean Owen <sowen@cloudera.com>
Closes#19371 from srowen/HotfixFinalizerCheckstlye.
## What changes were proposed in this pull request?
Currently we use Arrow File format to communicate with Python worker when invoking vectorized UDF but we can use Arrow Stream format.
This pr replaces the Arrow File format with the Arrow Stream format.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#19349 from ueshin/issues/SPARK-22125.
## What changes were proposed in this pull request?
This PR allows us to scan a string including only white space (e.g. `" "`) once while the current implementation scans twice (right to left, and then left to right).
## How was this patch tested?
Existing test suites
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19355 from kiszk/SPARK-22130.
The application listing is still generated from event logs, but is now stored
in a KVStore instance. By default an in-memory store is used, but a new config
allows setting a local disk path to store the data, in which case a LevelDB
store will be created.
The provider stores things internally using the public REST API types; I believe
this is better going forward since it will make it easier to get rid of the
internal history server API which is mostly redundant at this point.
I also added a finalizer to LevelDBIterator, to make sure that resources are
eventually released. This helps when code iterates but does not exhaust the
iterator, thus not triggering the auto-close code.
HistoryServerSuite was modified to not re-start the history server unnecessarily;
this makes the json validation tests run more quickly.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18887 from vanzin/SPARK-20642.
## What changes were proposed in this pull request?
When inferring constraints from children, Join's condition can be simplified as None.
For example,
```
val testRelation = LocalRelation('a.int)
val x = testRelation.as("x")
val y = testRelation.where($"a" === 2 && !($"a" === 2)).as("y")
x.join.where($"x.a" === $"y.a")
```
The plan will become
```
Join Inner
:- LocalRelation <empty>, [a#23]
+- LocalRelation <empty>, [a#224]
```
And the Cartesian products check will throw exception for above plan.
Propagate empty relation before checking Cartesian products, and the issue is resolved.
## How was this patch tested?
Unit test
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#19362 from gengliangwang/MoveCheckCartesianProducts.
## What changes were proposed in this pull request?
We added a method to the scala API for creating a `DataFrame` from `DataSet[String]` storing CSV in [SPARK-15463](https://issues.apache.org/jira/browse/SPARK-15463) but PySpark doesn't have `Dataset` to support this feature. Therfore, I add an API to create a `DataFrame` from `RDD[String]` storing csv and it's also consistent with PySpark's `spark.read.json`.
For example as below
```
>>> rdd = sc.textFile('python/test_support/sql/ages.csv')
>>> df2 = spark.read.csv(rdd)
>>> df2.dtypes
[('_c0', 'string'), ('_c1', 'string')]
```
## How was this patch tested?
add unit test cases.
Author: goldmedal <liugs963@gmail.com>
Closes#19339 from goldmedal/SPARK-22112.
## What changes were proposed in this pull request?
Address PR comments that appeared post-merge, to rename `addExtraCode` to `addInnerClass`,
and not count the size of the inner class to the size of the outer class.
## How was this patch tested?
YOLO.
Author: Juliusz Sompolski <julek@databricks.com>
Closes#19353 from juliuszsompolski/SPARK-22103followup.
## What changes were proposed in this pull request?
We can override `usedInputs` to claim that an operator defers input evaluation. `Sample` and `Limit` are two operators which should claim it but don't. We should do it.
## How was this patch tested?
Existing tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19345 from viirya/SPARK-22124.
