## What changes were proposed in this pull request?
This is to fix the code for the latest R changes in R-devel, when running CRAN check
```
checking for code/documentation mismatches ... WARNING
Codoc mismatches from documentation object 'attach':
attach
Code: function(what, pos = 2L, name = deparse(substitute(what),
backtick = FALSE), warn.conflicts = TRUE)
Docs: function(what, pos = 2L, name = deparse(substitute(what)),
warn.conflicts = TRUE)
Mismatches in argument default values:
Name: 'name' Code: deparse(substitute(what), backtick = FALSE) Docs: deparse(substitute(what))
Codoc mismatches from documentation object 'glm':
glm
Code: function(formula, family = gaussian, data, weights, subset,
na.action, start = NULL, etastart, mustart, offset,
control = list(...), model = TRUE, method = "glm.fit",
x = FALSE, y = TRUE, singular.ok = TRUE, contrasts =
NULL, ...)
Docs: function(formula, family = gaussian, data, weights, subset,
na.action, start = NULL, etastart, mustart, offset,
control = list(...), model = TRUE, method = "glm.fit",
x = FALSE, y = TRUE, contrasts = NULL, ...)
Argument names in code not in docs:
singular.ok
Mismatches in argument names:
Position: 16 Code: singular.ok Docs: contrasts
Position: 17 Code: contrasts Docs: ...
```
With attach, it's pulling in the function definition from base::attach. We need to disable that but we would still need a function signature for roxygen2 to build with.
With glm it's pulling in the function definition (ie. "usage") from the stats::glm function. Since this is "compiled in" when we build the source package into the .Rd file, when it changes at runtime or in CRAN check it won't match the latest signature. The solution is not to pull in from stats::glm since there isn't much value in doing that (none of the param we actually use, the ones we do use we have explicitly documented them)
Also with attach we are changing to call dynamically.
## How was this patch tested?
Manually.
- [x] check documentation output - yes
- [x] check help `?attach` `?glm` - yes
- [x] check on other platforms, r-hub, on r-devel etc..
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#19557 from felixcheung/rattachglmdocerror.
## What changes were proposed in this pull request?
Move ```ClusteringEvaluatorSuite``` test data(iris) to data/mllib, to prevent from re-creating a new folder.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#19648 from yanboliang/spark-14516.
This change modifies the status listener to collect the information
needed to render the envionment page, and populates that page and the
API with information collected by the listener.
Tested with existing and added unit tests.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19677 from vanzin/SPARK-20645.
## What changes were proposed in this pull request?
`<=>` is not supported by Hive metastore partition predicate pushdown. We should not push down it to Hive metastore when they are be using in partition predicates.
## How was this patch tested?
Added a test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19682 from gatorsmile/fixLimitPushDown.
## What changes were proposed in this pull request?
Currently, a pandas.DataFrame that contains a timestamp of type 'datetime64[ns]' when converted to a Spark DataFrame with `createDataFrame` will interpret the values as LongType. This fix will check for a timestamp type and convert it to microseconds which will allow Spark to read as TimestampType.
## How was this patch tested?
Added unit test to verify Spark schema is expected for TimestampType and DateType when created from pandas
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#19646 from BryanCutler/pyspark-non-arrow-createDataFrame-ts-fix-SPARK-22417.
## What changes were proposed in this pull request?
`spark.sql.statistics.autoUpdate.size` should be `spark.sql.statistics.size.autoUpdate.enabled`. The previous name is confusing as users may treat it as a size config.
This config is in master branch only, no backward compatibility issue.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19667 from cloud-fan/minor.
## What changes were proposed in this pull request?
This PR proposes to fix `dev/run-tests.py` script to support Python 3.
Here are some backgrounds. Up to my knowledge,
In Python 2,
- `unicode` is NOT `str` in Python 2 (`type("foo") != type(u"foo")`).
- `str` has an alias, `bytes` in Python 2 (`type("foo") == type(b"foo")`).
In Python 3,
- `unicode` was (roughly) replaced by `str` in Python 3 (`type("foo") == type(u"foo")`).
- `str` is NOT `bytes` in Python 3 (`type("foo") != type(b"foo")`).
So, this PR fixes:
1. Use `b''` instead of `''` so that both `str` in Python 2 and `bytes` in Python 3 can be hanlded. `sbt_proc.stdout.readline()` returns `str` (which has an alias, `bytes`) in Python 2 and `bytes` in Python 3
2. Similarily, use `b''` instead of `''` so that both `str` in Python 2 and `bytes` in Python 3 can be hanlded. `re.compile` with `str` pattern does not seem supporting to match `bytes` in Python 3:
Actually, this change is recommended up to my knowledge - https://docs.python.org/3/howto/pyporting.html#text-versus-binary-data:
> Mark all binary literals with a b prefix, textual literals with a u prefix
## How was this patch tested?
I manually tested this via Python 3 with few additional changes to reduce the elapsed time.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19665 from HyukjinKwon/SPARK-22376.
## What changes were proposed in this pull request?
`ExternalShuffleClient.close()` should check `clientFactory` null. otherwise it will throw NPE sometimes:
```
17/11/06 20:08:05 ERROR Utils: Uncaught exception in thread main
java.lang.NullPointerException
at org.apache.spark.network.shuffle.ExternalShuffleClient.close(ExternalShuffleClient.java:152)
at org.apache.spark.storage.BlockManager.stop(BlockManager.scala:1407)
at org.apache.spark.SparkEnv.stop(SparkEnv.scala:89)
at org.apache.spark.SparkContext$$anonfun$stop$11.apply$mcV$sp(SparkContext.scala:1849)
```
## How was this patch tested?
manual tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#19670 from wangyum/SPARK-22454.
…alization.
## What changes were proposed in this pull request?
Use non-linear containsKey operation for serialized maps, lookup into underlying map.
## How was this patch tested?
unit tests
Author: Alexander Istomin <istomin@rutarget.ru>
Closes#19553 from Whoosh/SPARK-22330.
## What changes were proposed in this pull request?
clarify exception behaviors for all data source v2 interfaces.
## How was this patch tested?
document change only
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19623 from cloud-fan/data-source-exception.
## What changes were proposed in this pull request?
This PR adds a check between the R package version used and the version reported by SparkContext running in the JVM. The goal here is to warn users when they have a R package downloaded from CRAN and are using that to connect to an existing Spark cluster.
This is raised as a warning rather than an error as users might want to use patch versions interchangeably (e.g., 2.1.3 with 2.1.2 etc.)
## How was this patch tested?
Manually by changing the `DESCRIPTION` file
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#19624 from shivaram/sparkr-version-check.
There are two somewhat unrelated things going on in this patch, but
both are meant to make integration of individual UI pages later on
much easier.
The first part is some tweaking of the code in the listener so that
it does less updates of the kvstore for data that changes fast; for
example, it avoids writing changes down to the store for every
task-related event, since those can arrive very quickly at times.
Instead, for these kinds of events, it chooses to only flush things
if a certain interval has passed. The interval is based on how often
the current spark-shell code updates the progress bar for jobs, so
that users can get reasonably accurate data.
The code also delays as much as possible hitting the underlying kvstore
when replaying apps in the history server. This is to avoid unnecessary
writes to disk.
The second set of changes prepare the history server and SparkUI for
integrating with the kvstore. A new class, AppStatusStore, is used
for translating between the stored data and the types used in the
UI / API. The SHS now populates a kvstore with data loaded from
event logs when an application UI is requested.
Because this store can hold references to disk-based resources, the
code was modified to retrieve data from the store under a read lock.
This allows the SHS to detect when the store is still being used, and
only update it (e.g. because an updated event log was detected) when
there is no other thread using the store.
This change ended up creating a lot of churn in the ApplicationCache
code, which was cleaned up a lot in the process. I also removed some
metrics which don't make too much sense with the new code.
Tested with existing and added unit tests, and by making sure the SHS
still works on a real cluster.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19582 from vanzin/SPARK-20644.
## What changes were proposed in this pull request?
`CodegenContext.copyResult` is kind of a global status for whole stage codegen. But the tricky part is, it is only used to transfer an information from child to parent when calling the `consume` chain. We have to be super careful in `produce`/`consume`, to set it to true when producing multiple result rows, and set it to false in operators that start new pipeline(like sort).
This PR moves the `copyResult` to `CodegenSupport`, and call it at `WholeStageCodegenExec`. This is much easier to reason about.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19656 from cloud-fan/whole-sage.
## What changes were proposed in this pull request?
Expose the common params from Spark ML as a Developer API.
## How was this patch tested?
Existing tests.
Author: Holden Karau <holden@us.ibm.com>
Author: Holden Karau <holdenkarau@google.com>
Closes#18699 from holdenk/SPARK-7146-ml-shared-params-developer-api.
## What changes were proposed in this pull request?
Add incompatible Hive UDF describe to DOC.
## How was this patch tested?
N/A
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18833 from wangyum/SPARK-21625.
## What changes were proposed in this pull request?
SPARK-14930/SPARK-13693 put in a change to set the fs object to null after a failure, however the retry loop does not include initialization. Moved fs initialization inside the retry while loop to aid recoverability.
## How was this patch tested?
Passes all existing unit tests.
Author: Tristan Stevens <tristan@cloudera.com>
Closes#19645 from tmgstevens/SPARK-22429.
…
## What changes were proposed in this pull request?
override JDBCDialects methods quoteIdentifier, getTableExistsQuery and getSchemaQuery in AggregatedDialect
## How was this patch tested?
Test the new implementation in JDBCSuite test("Aggregated dialects")
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#19658 from huaxingao/spark-22443.
## What changes were proposed in this pull request?
It's not safe in all cases to push down a LIMIT below a FULL OUTER
JOIN. If the limit is pushed to one side of the FOJ, the physical
join operator can not tell if a row in the non-limited side would have a
match in the other side.
*If* the join operator guarantees that unmatched tuples from the limited
side are emitted before any unmatched tuples from the other side,
pushing down the limit is safe. But this is impractical for some join
implementations, e.g. SortMergeJoin.
For now, disable limit pushdown through a FULL OUTER JOIN, and we can
evaluate whether a more complicated solution is necessary in the future.
## How was this patch tested?
Ran org.apache.spark.sql.* tests. Altered full outer join tests in
LimitPushdownSuite.
Author: Henry Robinson <henry@cloudera.com>
Closes#19647 from henryr/spark-22211.
## What changes were proposed in this pull request?
Next fit decreasing bin packing algorithm is used to combine splits in DataSourceScanExec but the comment incorrectly states that first fit decreasing algorithm is used. The current implementation doesn't go back to a previously used bin other than the bin that the last element was put into.
Author: Vinitha Gankidi <vgankidi@netflix.com>
Closes#19634 from vgankidi/SPARK-22412.
## What changes were proposed in this pull request?
When we insert `BatchEvalPython` for Python UDFs into a query plan, if its child has some outputs that are not used by the original parent node, `BatchEvalPython` will still take those outputs and save into the queue. When the data for those outputs are big, it is easily to generate big spill on disk.
For example, the following reproducible code is from the JIRA ticket.
```python
from pyspark.sql.functions import *
from pyspark.sql.types import *
lines_of_file = [ "this is a line" for x in xrange(10000) ]
file_obj = [ "this_is_a_foldername/this_is_a_filename", lines_of_file ]
data = [ file_obj for x in xrange(5) ]
small_df = spark.sparkContext.parallelize(data).map(lambda x : (x[0], x[1])).toDF(["file", "lines"])
exploded = small_df.select("file", explode("lines"))
def split_key(s):
return s.split("/")[1]
split_key_udf = udf(split_key, StringType())
with_filename = exploded.withColumn("filename", split_key_udf("file"))
with_filename.explain(True)
```
The physical plan before/after this change:
Before:
```
*Project [file#0, col#5, pythonUDF0#14 AS filename#9]
+- BatchEvalPython [split_key(file#0)], [file#0, lines#1, col#5, pythonUDF0#14]
+- Generate explode(lines#1), true, false, [col#5]
+- Scan ExistingRDD[file#0,lines#1]
```
After:
```
*Project [file#0, col#5, pythonUDF0#14 AS filename#9]
+- BatchEvalPython [split_key(file#0)], [col#5, file#0, pythonUDF0#14]
+- *Project [col#5, file#0]
+- Generate explode(lines#1), true, false, [col#5]
+- Scan ExistingRDD[file#0,lines#1]
```
Before this change, `lines#1` is a redundant input to `BatchEvalPython`. This patch removes it by adding a Project.
## How was this patch tested?
Manually test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19642 from viirya/SPARK-22410.
## What changes were proposed in this pull request?
Scala test source files like TestHiveSingleton.scala should be in scala source root
## How was this patch tested?
Just move scala file from java directory to scala directory
No new test case in this PR.
```
renamed: mllib/src/test/java/org/apache/spark/ml/util/IdentifiableSuite.scala -> mllib/src/test/scala/org/apache/spark/ml/util/IdentifiableSuite.scala
renamed: streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala -> streaming/src/test/scala/org/apache/spark/streaming/JavaTestUtils.scala
renamed: streaming/src/test/java/org/apache/spark/streaming/api/java/JavaStreamingListenerWrapperSuite.scala -> streaming/src/test/scala/org/apache/spark/streaming/api/java/JavaStreamingListenerWrapperSuite.scala
renamed: sql/hive/src/test/java/org/apache/spark/sql/hive/test/TestHiveSingleton.scala sql/hive/src/test/scala/org/apache/spark/sql/hive/test/TestHiveSingleton.scala
```
Author: xubo245 <601450868@qq.com>
Closes#19639 from xubo245/scalaDirectory.
## What changes were proposed in this pull request?
When writing using jdbc with python currently we are wrongly assigning by default None as writing mode. This is due to wrongly calling mode on the `_jwrite` object instead of `self` and it causes an exception.
## How was this patch tested?
manual tests
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#19654 from mgaido91/SPARK-22437.
## What changes were proposed in this pull request?
This PR replaces the old the maximum array size (`Int.MaxValue`) with the new one (`ByteArrayMethods.MAX_ROUNDED_ARRAY_LENGTH`).
This PR also refactor the code to calculate the new array size to easily understand why we have to use `newSize - 2` for allocating a new array.
## How was this patch tested?
Used the existing test
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19650 from kiszk/SPARK-22254.
## What changes were proposed in this pull request?
Added a test class to check NULL handling behavior.
The expected behavior is defined as the one of the most well-known databases as specified here: https://sqlite.org/nulls.html.
SparkSQL behaves like other DBs:
- Adding anything to null gives null -> YES
- Multiplying null by zero gives null -> YES
- nulls are distinct in SELECT DISTINCT -> NO
- nulls are distinct in a UNION -> NO
- "CASE WHEN null THEN 1 ELSE 0 END" is 0? -> YES
- "null OR true" is true -> YES
- "not (null AND false)" is true -> YES
- null in aggregation are skipped -> YES
## How was this patch tested?
Added test class
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#19653 from mgaido91/SPARK-22418.
forward-port https://github.com/apache/spark/pull/19622 to master branch.
This bug doesn't exist in master because we've added hive bucketing support and the hive bucketing metadata can be recognized by Spark, but we should still port it to master: 1) there may be other unsupported hive metadata removed by Spark. 2) reduce code difference between master and 2.2 to ease the backport in the feature.
***
When we alter table schema, we set the new schema to spark `CatalogTable`, convert it to hive table, and finally call `hive.alterTable`. This causes a problem in Spark 2.2, because hive bucketing metedata is not recognized by Spark, which means a Spark `CatalogTable` representing a hive table is always non-bucketed, and when we convert it to hive table and call `hive.alterTable`, the original hive bucketing metadata will be removed.
To fix this bug, we should read out the raw hive table metadata, update its schema, and call `hive.alterTable`. By doing this we can guarantee only the schema is changed, and nothing else.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19644 from cloud-fan/infer.
## What changes were proposed in this pull request?
the previous [PR](https://github.com/apache/spark/pull/19469) is deleted by mistake.
the solution is straight forward.
adding "spark.yarn.jars" to propertiesToReload so this property will load from config.
## How was this patch tested?
manual tests
Author: ZouChenjun <zouchenjun@youzan.com>
Closes#19637 from ChenjunZou/checkpoint-yarn-jars.
## What changes were proposed in this pull request?
According to the [discussion](https://github.com/apache/spark/pull/19571#issuecomment-339472976) on SPARK-15474, we will add new OrcFileFormat in `sql/core` module and allow users to use both old and new OrcFileFormat.
To do that, `OrcOptions` should be visible in `sql/core` module, too. Previously, it was `private[orc]` in `sql/hive`. This PR removes `private[orc]` because we don't use `private[sql]` in `sql/execution` package after [SPARK-16964](https://github.com/apache/spark/pull/14554).
## How was this patch tested?
Pass the Jenkins with the existing tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19636 from dongjoon-hyun/SPARK-22416.
## What changes were proposed in this pull request?
Adding a global limit on top of the distinct values before sorting and collecting will reduce the overall work in the case where we have more distinct values. We will also eagerly perform a collect rather than a take because we know we only have at most (maxValues + 1) rows.
## How was this patch tested?
Existing tests cover sorted order
Author: Patrick Woody <pwoody@palantir.com>
Closes#19629 from pwoody/SPARK-22408.
## What changes were proposed in this pull request?
Spark REPL changes for Scala 2.12.4: use command(), not processLine() in ILoop; remove direct dependence on older jline. Not sure whether this became needed in 2.12.4 or just missed this before. This makes spark-shell work in 2.12.
## How was this patch tested?
Existing tests; manual run of spark-shell in 2.11, 2.12 builds
Author: Sean Owen <sowen@cloudera.com>
Closes#19612 from srowen/SPARK-14650.2.
## What changes were proposed in this pull request?
Easy fix in the documentation, which is reporting that only numeric types and string are supported in type inference for partition columns, while Date and Timestamp are supported too since 2.1.0, thanks to SPARK-17388.
## How was this patch tested?
n/a
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#19628 from mgaido91/SPARK-22398.
## What changes were proposed in this pull request?
This patch includes some doc updates for data source API v2. I was reading the code and noticed some minor issues.
## How was this patch tested?
This is a doc only change.
Author: Reynold Xin <rxin@databricks.com>
Closes#19626 from rxin/dsv2-update.
## What changes were proposed in this pull request?
This proposed patch is about making Spark executor task metrics available as Dropwizard metrics. This is intended to be of aid in monitoring Spark jobs and when drilling down on performance troubleshooting issues.
## How was this patch tested?
Manually tested on a Spark cluster (see JIRA for an example screenshot).
Author: LucaCanali <luca.canali@cern.ch>
Closes#19426 from LucaCanali/SparkTaskMetricsDropWizard.
## What changes were proposed in this pull request?
Using zstd compression for Spark jobs spilling 100s of TBs of data, we could reduce the amount of data written to disk by as much as 50%. This translates to significant latency gain because of reduced disk io operations. There is a degradation CPU time by 2 - 5% because of zstd compression overhead, but for jobs which are bottlenecked by disk IO, this hit can be taken.
## Benchmark
Please note that this benchmark is using real world compute heavy production workload spilling TBs of data to disk
| | zstd performance as compred to LZ4 |
| ------------- | -----:|
| spill/shuffle bytes | -48% |
| cpu time | + 3% |
| cpu reservation time | -40%|
| latency | -40% |
## How was this patch tested?
Tested by running few jobs spilling large amount of data on the cluster and amount of intermediate data written to disk reduced by as much as 50%.
Author: Sital Kedia <skedia@fb.com>
Closes#18805 from sitalkedia/skedia/upstream_zstd.
## What changes were proposed in this pull request?
Under the current execution mode of Python UDFs, we don't well support Python UDFs as branch values or else value in CaseWhen expression.
Since to fix it might need the change not small (e.g., #19592) and this issue has simpler workaround. We should just notice users in the document about this.
## How was this patch tested?
Only document change.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19617 from viirya/SPARK-22347-3.
## What changes were proposed in this pull request?
Handling the NonFatal exceptions while starting the external shuffle service, if there are any NonFatal exceptions it logs and continues without the external shuffle service.
## How was this patch tested?
I verified it manually, it logs the exception and continues to serve without external shuffle service when BindException occurs.
Author: Devaraj K <devaraj@apache.org>
Closes#19396 from devaraj-kavali/SPARK-22172.
## What changes were proposed in this pull request?
PeriodicRDDCheckpointer was already moved out of mllib in Spark-5484
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#19618 from zhengruifeng/checkpointer_doc.
## What changes were proposed in this pull request?
Write HDFSBackedStateStoreProvider.loadMap non-recursively. This prevents stack overflow if too many deltas stack up in a low memory environment.
## How was this patch tested?
existing unit tests for functional equivalence, new unit test to check for stack overflow
Author: Jose Torres <jose@databricks.com>
Closes#19611 from joseph-torres/SPARK-22305.
## What changes were proposed in this pull request?
We often see the issue of Spark jobs stuck because the Executor Allocation Manager does not ask for any executor even if there are pending tasks in case dynamic allocation is turned on. Looking at the logic in Executor Allocation Manager, which calculates the running tasks, it can happen that the calculation will be wrong and the number of running tasks can become negative.
## How was this patch tested?
Added unit test
Author: Sital Kedia <skedia@fb.com>
Closes#19580 from sitalkedia/skedia/fix_stuck_job.
## What changes were proposed in this pull request?
We made a mistake in https://github.com/apache/spark/pull/16944 . In `HiveMetastoreCatalog#inferIfNeeded` we infer the data schema, merge with full schema, and return the new full schema. At caller side we treat the full schema as data schema and set it to `HadoopFsRelation`.
This doesn't cause any problem because both parquet and orc can work with a wrong data schema that has extra columns, but it's better to fix this mistake.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19615 from cloud-fan/infer.
## What changes were proposed in this pull request?
The current join estimation logic is only based on basic column statistics (such as ndv, etc). If we want to add estimation for other kinds of statistics (such as histograms), it's not easy to incorporate into the current algorithm:
1. When we have multiple pairs of join keys, the current algorithm computes cardinality in a single formula. But if different join keys have different kinds of stats, the computation logic for each pair of join keys become different, so the previous formula does not apply.
2. Currently it computes cardinality and updates join keys' column stats separately. It's better to do these two steps together, since both computation and update logic are different for different kinds of stats.
## How was this patch tested?
Only refactor, covered by existing tests.
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Closes#19531 from wzhfy/join_est_refactor.
## What changes were proposed in this pull request?
Update the url of reference paper.
## How was this patch tested?
It is comments, so nothing tested.
Author: bomeng <bmeng@us.ibm.com>
Closes#19614 from bomeng/22399.
## What changes were proposed in this pull request?
These are just some straightforward upgrades to use the latest versions of some sbt plugins that also support sbt 1.0.
The remaining sbt plugins that need upgrading will require bigger changes.
## How was this patch tested?
Tested sbt use manually.
Author: pj.fanning <pj.fanning@workday.com>
Closes#19609 from pjfanning/SPARK-21708.
## What changes were proposed in this pull request?
Will need to port to this to branch-1.6, -2.0, -2.1, -2.2
## How was this patch tested?
manually
Jenkins, AppVeyor
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#19549 from felixcheung/rcranversioncheck.
## What changes were proposed in this pull request?
Both `ReadSupport` and `ReadTask` have a method called `createReader`, but they create different things. This could cause some confusion for data source developers. The same issue exists between `WriteSupport` and `DataWriterFactory`, both of which have a method called `createWriter`. This PR renames the method of `ReadTask`/`DataWriterFactory` to `createDataReader`/`createDataWriter`.
Besides, the name of `RowToInternalRowDataWriterFactory` is not correct, because it actually converts `InternalRow`s to `Row`s. It should be renamed `InternalRowDataWriterFactory`.
## How was this patch tested?
Only renaming, should be covered by existing tests.
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#19610 from wzhfy/rename.
## What changes were proposed in this pull request?
When Hive support is not on, users can hit unresolved plan node when trying to call `INSERT OVERWRITE DIRECTORY` using Hive format.
```
"unresolved operator 'InsertIntoDir true, Storage(Location: /private/var/folders/vx/j0ydl5rn0gd9mgrh1pljnw900000gn/T/spark-b4227606-9311-46a8-8c02-56355bf0e2bc, Serde Library: org.apache.hadoop.hive.ql.io.orc.OrcSerde, InputFormat: org.apache.hadoop.hive.ql.io.orc.OrcInputFormat, OutputFormat: org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat), hive, true;;
```
This PR is to issue a better error message.
## How was this patch tested?
Added a test case.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19608 from gatorsmile/hivesupportInsertOverwrite.
