## What changes were proposed in this pull request?
Fix code style from ad hoc review of RC4 doc
## How was this patch tested?
manual
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14250 from felixcheung/rdocs2rc4.
## What changes were proposed in this pull request?
In ScriptInputOutputSchema, we read default RecordReader and RecordWriter from conf. Since Spark 2.0 has deleted those config keys from hive conf, we have to set default reader/writer class name by ourselves. Otherwise we will get None for LazySimpleSerde, the data written would not be able to read by script. The test case added worked fine with previous version of Spark, but would fail now.
## How was this patch tested?
added a test case in SQLQuerySuite.
Closes#14169
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#14249 from yhuai/scriptTransformation.
## What changes were proposed in this pull request?
Currently, `JacksonGenerator.apply` is doing type-based dispatch for each row to write appropriate values.
It might not have to be done like this because the schema is already kept.
So, appropriate writers can be created first according to the schema once, and then apply them to each row. This approach is similar with `CatalystWriteSupport`.
This PR corrects `JacksonGenerator` so that it creates all writers for the schema once and then applies them to each row rather than type dispatching for every row.
Benchmark was proceeded with the codes below:
```scala
test("Benchmark for JSON writer") {
val N = 500 << 8
val row =
"""{"struct":{"field1": true, "field2": 92233720368547758070},
"structWithArrayFields":{"field1":[4, 5, 6], "field2":["str1", "str2"]},
"arrayOfString":["str1", "str2"],
"arrayOfInteger":[1, 2147483647, -2147483648],
"arrayOfLong":[21474836470, 9223372036854775807, -9223372036854775808],
"arrayOfBigInteger":[922337203685477580700, -922337203685477580800],
"arrayOfDouble":[1.2, 1.7976931348623157E308, 4.9E-324, 2.2250738585072014E-308],
"arrayOfBoolean":[true, false, true],
"arrayOfNull":[null, null, null, null],
"arrayOfStruct":[{"field1": true, "field2": "str1"}, {"field1": false}, {"field3": null}],
"arrayOfArray1":[[1, 2, 3], ["str1", "str2"]],
"arrayOfArray2":[[1, 2, 3], [1.1, 2.1, 3.1]]
}"""
val df = spark.sqlContext.read.json(spark.sparkContext.parallelize(List.fill(N)(row)))
val benchmark = new Benchmark("JSON writer", N)
benchmark.addCase("writing JSON file", 10) { _ =>
withTempPath { path =>
df.write.format("json").save(path.getCanonicalPath)
}
}
benchmark.run()
}
```
This produced the results below
- **Before**
```
JSON writer: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
writing JSON file 1675 / 1767 0.1 13087.5 1.0X
```
- **After**
```
JSON writer: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
writing JSON file 1597 / 1686 0.1 12477.1 1.0X
```
In addition, I ran this benchmark 10 times for each and calculated the average elapsed time as below:
| **Before** | **After**|
|---------------|------------|
|17478ms |16669ms |
It seems roughly ~5% is improved.
## How was this patch tested?
Existing tests should cover this.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#14028 from HyukjinKwon/SPARK-16351.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-16055
sparkPackages - argument is passed and we detect that we are in the R script mode, we should print some warning like --packages flag should be used with with spark-submit
## How was this patch tested?
In my system locally
Author: krishnakalyan3 <krishnakalyan3@gmail.com>
Closes#14179 from krishnakalyan3/spark-pkg.
## What changes were proposed in this pull request?
fininsh => finish
## How was this patch tested?
N/A
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#14238 from WeichenXu123/fix_fininsh_typo.
## What changes were proposed in this pull request?
Fix R SparkSession init/stop, and warnings of reusing existing Spark Context
## How was this patch tested?
unit tests
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14177 from felixcheung/rsessiontest.
## What changes were proposed in this pull request?
This patch moves regexp related unit tests from StringExpressionsSuite to RegexpExpressionsSuite to match the file name for regexp expressions.
## How was this patch tested?
This is a test only change.
Author: Reynold Xin <rxin@databricks.com>
Closes#14230 from rxin/SPARK-16584.
## 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?
Updates programming guide for spark.gapply/spark.gapplyCollect.
Similar to other examples I used `faithful` dataset to demonstrate gapply's functionality.
Please, let me know if you prefer another example.
## How was this patch tested?
Existing test cases in R
Author: Narine Kokhlikyan <narine@slice.com>
Closes#14090 from NarineK/gapplyProgGuide.
## What changes were proposed in this pull request?
These are yet more changes that resolve problems with unidoc/genjavadoc and Java 8. It does not fully resolve the problem, but gets rid of as many errors as we can from this end.
## How was this patch tested?
Jenkins build of docs
Author: Sean Owen <sowen@cloudera.com>
Closes#14221 from srowen/SPARK-3359.3.
## What changes were proposed in this pull request?
This patch is just a slightly safer way to fix the issue we encountered in https://github.com/apache/spark/pull/14168 should this pattern re-occur at other places in the code.
## How was this patch tested?
Existing tests. Also, I manually tested that it fixes the problem in SPARK-16514 without having the proposed change in https://github.com/apache/spark/pull/14168
Author: Sameer Agarwal <sameerag@cs.berkeley.edu>
Closes#14227 from sameeragarwal/codegen.
## What changes were proposed in this pull request?
With the fix from SPARK-13112, I see that `LaunchTask` is always processed after `RegisteredExecutor` is done and so it gets chance to do all retries to startup an executor. There is still a problem that if `Executor` creation itself fails and there is some exception, it gets unnoticed and the executor is killed when it tries to process the `LaunchTask` as `executor` is null : https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala#L88 So if one looks at the logs, it does not tell that there was problem during `Executor` creation and thats why it was killed.
This PR explicitly catches exception in `Executor` creation, logs a proper message and then exits the JVM. Also, I have changed the `exitExecutor` method to accept `reason` so that backends can use that reason and do stuff like logging to a DB to get an aggregate of such exits at a cluster level
## How was this patch tested?
I am relying on existing tests
Author: Tejas Patil <tejasp@fb.com>
Closes#14202 from tejasapatil/exit_executor_failure.
## What changes were proposed in this pull request?
More tests
I don't think this is critical for Spark 2.0.0 RC, maybe Spark 2.0.1 or 2.1.0.
## How was this patch tested?
unit tests
shivaram dongjoon-hyun
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14206 from felixcheung/rroutetests.
## What changes were proposed in this pull request?
Made DataFrame-based API primary
* Spark doc menu bar and other places now link to ml-guide.html, not mllib-guide.html
* mllib-guide.html keeps RDD-specific list of features, with a link at the top redirecting people to ml-guide.html
* ml-guide.html includes a "maintenance mode" announcement about the RDD-based API
* **Reviewers: please check this carefully**
* (minor) Titles for DF API no longer include "- spark.ml" suffix. Titles for RDD API have "- RDD-based API" suffix
* Moved migration guide to ml-guide from mllib-guide
* Also moved past guides from mllib-migration-guides to ml-migration-guides, with a redirect link on mllib-migration-guides
* **Reviewers**: I did not change any of the content of the migration guides.
Reorganized DataFrame-based guide:
* ml-guide.html mimics the old mllib-guide.html page in terms of content: overview, migration guide, etc.
* Moved Pipeline description into ml-pipeline.html and moved tuning into ml-tuning.html
* **Reviewers**: I did not change the content of these guides, except some intro text.
* Sidebar remains the same, but with pipeline and tuning sections added
Other:
* ml-classification-regression.html: Moved text about linear methods to new section in page
## How was this patch tested?
Generated docs locally
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#14213 from jkbradley/ml-guide-2.0.
## What changes were proposed in this pull request?
Fixed a bug that caused `NaN`s in `IsotonicRegression`. The problem occurs when training rows with the same feature value but different labels end up on different partitions. This patch changes a `sortBy` call to a `partitionBy(RangePartitioner)` followed by a `mapPartitions(sortBy)` in order to ensure that all rows with the same feature value end up on the same partition.
## How was this patch tested?
Added a unit test.
Author: z001qdp <Nicholas.Eggert@target.com>
Closes#14140 from neggert/SPARK-16426-isotonic-nan.
## What changes were proposed in this pull request?
Make `dataframe.drop` API in python support multi-columns parameters,
so that it is the same with scala API.
## How was this patch tested?
The doc test.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#14203 from WeichenXu123/drop_python_api.
## What changes were proposed in this pull request?
Most of the documentation in https://github.com/apache/spark/blob/master/sql/README.md is stale. It would be useful to keep the list of projects to explain what's going on, and everything else should be removed.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#14211 from rxin/SPARK-16557.
If a custom Jekyll template tag throws Ruby's equivalent of a "file not found" exception, then Jekyll will stop the doc building process but will exit with a successful status, causing our doc publishing jobs to silently fail.
This is caused by https://github.com/jekyll/jekyll/issues/5104, a case of bad error-handling logic in Jekyll. This patch works around this by updating our `include_example.rb` plugin to catch the exception and exit rather than allowing it to bubble up and be ignored by Jekyll.
I tested this manually with
```
rm ./examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala
cd docs
SKIP_API=1 jekyll build
echo $?
```
Author: Josh Rosen <joshrosen@databricks.com>
Closes#14209 from JoshRosen/fix-doc-building.
## What changes were proposed in this pull request?
Fixes a typo in the sql programming guide
## How was this patch tested?
Building docs locally
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#14208 from shivaram/spark-sql-doc-fix.
## What changes were proposed in this pull request?
Currently when running spark on yarn, jars specified with --jars, --packages will be added twice, one is Spark's own file server, another is yarn's distributed cache, this can be seen from log:
for example:
```
./bin/spark-shell --master yarn-client --jars examples/target/scala-2.11/jars/scopt_2.11-3.3.0.jar
```
If specified the jar to be added is scopt jar, it will added twice:
```
...
16/07/14 15:06:48 INFO Server: Started 5603ms
16/07/14 15:06:48 INFO Utils: Successfully started service 'SparkUI' on port 4040.
16/07/14 15:06:48 INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://192.168.0.102:4040
16/07/14 15:06:48 INFO SparkContext: Added JAR file:/Users/sshao/projects/apache-spark/examples/target/scala-2.11/jars/scopt_2.11-3.3.0.jar at spark://192.168.0.102:63996/jars/scopt_2.11-3.3.0.jar with timestamp 1468480008637
16/07/14 15:06:49 INFO RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
16/07/14 15:06:49 INFO Client: Requesting a new application from cluster with 1 NodeManagers
16/07/14 15:06:49 INFO Client: Verifying our application has not requested more than the maximum memory capability of the cluster (8192 MB per container)
16/07/14 15:06:49 INFO Client: Will allocate AM container, with 896 MB memory including 384 MB overhead
16/07/14 15:06:49 INFO Client: Setting up container launch context for our AM
16/07/14 15:06:49 INFO Client: Setting up the launch environment for our AM container
16/07/14 15:06:49 INFO Client: Preparing resources for our AM container
16/07/14 15:06:49 WARN Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
16/07/14 15:06:50 INFO Client: Uploading resource file:/private/var/folders/tb/8pw1511s2q78mj7plnq8p9g40000gn/T/spark-a446300b-84bf-43ff-bfb1-3adfb0571a42/__spark_libs__6486179704064718817.zip -> hdfs://localhost:8020/user/sshao/.sparkStaging/application_1468468348998_0009/__spark_libs__6486179704064718817.zip
16/07/14 15:06:51 INFO Client: Uploading resource file:/Users/sshao/projects/apache-spark/examples/target/scala-2.11/jars/scopt_2.11-3.3.0.jar -> hdfs://localhost:8020/user/sshao/.sparkStaging/application_1468468348998_0009/scopt_2.11-3.3.0.jar
16/07/14 15:06:51 INFO Client: Uploading resource file:/private/var/folders/tb/8pw1511s2q78mj7plnq8p9g40000gn/T/spark-a446300b-84bf-43ff-bfb1-3adfb0571a42/__spark_conf__326416236462420861.zip -> hdfs://localhost:8020/user/sshao/.sparkStaging/application_1468468348998_0009/__spark_conf__.zip
...
```
So here try to avoid adding jars to Spark's fileserver unnecessarily.
## How was this patch tested?
Manually verified both in yarn client and cluster mode, also in standalone mode.
Author: jerryshao <sshao@hortonworks.com>
Closes#14196 from jerryshao/SPARK-16540.
## What changes were proposed in this pull request?
There are some calls to methods or fields (getParameters, properties) which are then passed to Java/Scala collection converters. Unfortunately those fields can be null in some cases and then the conversions throws NPE. We fix it by wrapping calls to those fields and methods with option and then do the conversion.
## How was this patch tested?
Manually tested with a custom Hive metastore.
Author: Jacek Lewandowski <lewandowski.jacek@gmail.com>
Closes#14200 from jacek-lewandowski/SPARK-16528.
## What changes were proposed in this pull request?
`SQLTestUtils.withTempDatabase` is a frequently used test harness to setup a temporary table and clean up finally. This issue improves like the following for usability.
```scala
- try f(dbName) finally spark.sql(s"DROP DATABASE $dbName CASCADE")
+ try f(dbName) finally {
+ if (spark.catalog.currentDatabase == dbName) {
+ spark.sql(s"USE ${DEFAULT_DATABASE}")
+ }
+ spark.sql(s"DROP DATABASE $dbName CASCADE")
+ }
```
In case of forgetting to reset the databaes, `withTempDatabase` will not raise Exception.
## How was this patch tested?
This improves test harness.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14184 from dongjoon-hyun/SPARK-16529.
## What changes were proposed in this pull request?
Fix function routing to work with and without namespace operator `SparkR::createDataFrame`
## How was this patch tested?
manual, unit tests
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14195 from felixcheung/rroutedefault.
## What changes were proposed in this pull request?
Rename window.partitionBy and window.orderBy to windowPartitionBy and windowOrderBy to pass CRAN package check.
## How was this patch tested?
SparkR unit tests.
Author: Sun Rui <sunrui2016@gmail.com>
Closes#14192 from sun-rui/SPARK-16509.
## What changes were proposed in this pull request?
This PR changes the name of columns returned by `SHOW PARTITION` and `SHOW COLUMNS` commands. Currently, both commands uses `result` as a column name.
**Comparison: Column Name**
Command|Spark(Before)|Spark(After)|Hive
----------|--------------|------------|-----
SHOW PARTITIONS|result|partition|partition
SHOW COLUMNS|result|col_name|field
Note that Spark/Hive uses `col_name` in `DESC TABLES`. So, this PR chooses `col_name` for consistency among Spark commands.
**Before**
```scala
scala> sql("show partitions p").show()
+------+
|result|
+------+
| b=2|
+------+
scala> sql("show columns in p").show()
+------+
|result|
+------+
| a|
| b|
+------+
```
**After**
```scala
scala> sql("show partitions p").show
+---------+
|partition|
+---------+
| b=2|
+---------+
scala> sql("show columns in p").show
+--------+
|col_name|
+--------+
| a|
| b|
+--------+
```
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14199 from dongjoon-hyun/SPARK-16543.
#### What changes were proposed in this pull request?
Based on the [Hive SQL syntax](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-ChangeColumnName/Type/Position/Comment), the command to change column name/type/position/comment is `ALTER TABLE CHANGE COLUMN`. However, in our .g4 file, it is `ALTER TABLE CHANGE COLUMNS`. Because it is the last optional keyword, it does not take any effect. Thus, I put the issue as a Trivial level.
cc hvanhovell
#### How was this patch tested?
Existing test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14186 from gatorsmile/changeColumns.
This prevents the NM from starting when something is wrong, which would
lead to later errors which are confusing and harder to debug.
Added a unit test to verify startup fails if something is wrong.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#14162 from vanzin/SPARK-16505.
## What changes were proposed in this pull request?
Cleanup of examples, mostly from PySpark-ML to fix minor issues: unused imports, style consistency, pipeline_example is a duplicate, use future print funciton, and a spelling error.
* The "Pipeline Example" is duplicated by "Simple Text Classification Pipeline" in Scala, Python, and Java.
* "Estimator Transformer Param Example" is duplicated by "Simple Params Example" in Scala, Python and Java
* Synced random_forest_classifier_example.py with Scala by adding IndexToString label converted
* Synced train_validation_split.py (in Scala ModelSelectionViaTrainValidationExample) by adjusting data split, adding grid for intercept.
* RegexTokenizer was doing nothing in tokenizer_example.py and JavaTokenizerExample.java, synced with Scala version
## How was this patch tested?
local tests and run modified examples
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#14081 from BryanCutler/examples-cleanup-SPARK-16403.
## What changes were proposed in this pull request?
Add warning_for the following case when LBFGS training not actually convergence:
1) LogisticRegression
2) AFTSurvivalRegression
3) LBFGS algorithm wrapper in mllib package
## How was this patch tested?
N/A
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#14157 from WeichenXu123/add_lbfgs_convergence_warning_for_all_used_place.
## What changes were proposed in this pull request?
`Alias` with metadata is not a no-op and we should not strip it in `RemoveAliasOnlyProject` rule.
This PR also did some improvement for this rule:
1. extend the semantic of `alias-only`. Now we allow the project list to be partially aliased.
2. add unit test for this rule.
## How was this patch tested?
new `RemoveAliasOnlyProjectSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14106 from cloud-fan/bug.
## What changes were proposed in this pull request?
This patch enables SparkSession to provide spark version.
## How was this patch tested?
Manual test:
```
scala> sc.version
res0: String = 2.1.0-SNAPSHOT
scala> spark.version
res1: String = 2.1.0-SNAPSHOT
```
```
>>> sc.version
u'2.1.0-SNAPSHOT'
>>> spark.version
u'2.1.0-SNAPSHOT'
```
Author: Liwei Lin <lwlin7@gmail.com>
Closes#14165 from lw-lin/add-version.
## What changes were proposed in this pull request?
This PR exposes `sql` in PySpark Shell like Scala/R Shells for consistency.
**Background**
* Scala
```scala
scala> sql("select 1 a")
res0: org.apache.spark.sql.DataFrame = [a: int]
```
* R
```r
> sql("select 1")
SparkDataFrame[1:int]
```
**Before**
* Python
```python
>>> sql("select 1 a")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'sql' is not defined
```
**After**
* Python
```python
>>> sql("select 1 a")
DataFrame[a: int]
```
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14190 from dongjoon-hyun/SPARK-16536.
## What changes were proposed in this pull request?
Fixing issues found during 2.0 API checks:
* GeneralizedLinearRegressionModel: linkObj, familyObj, familyAndLink should not be exposed
* sqlDataTypes: name does not follow conventions. Do we need to expose it?
* Evaluator: inconsistent doc between evaluate and isLargerBetter
* MinMaxScaler: math rendering --> hard to make it great, but I'll change it a little
* GeneralizedLinearRegressionSummary: aic doc is incorrect --> will change to use more common name
## How was this patch tested?
Existing unit tests. Docs generated locally. (MinMaxScaler is improved a tiny bit.)
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#14187 from jkbradley/final-api-check-2.0.
#### What changes were proposed in this pull request?
If we create a table pointing to a parquet/json datasets without specifying the schema, describe table command does not show the schema at all. It only shows `# Schema of this table is inferred at runtime`. In 1.6, describe table does show the schema of such a table.
~~For data source tables, to infer the schema, we need to load the data source tables at runtime. Thus, this PR calls the function `lookupRelation`.~~
For data source tables, we infer the schema before table creation. Thus, this PR set the inferred schema as the table schema when table creation.
#### How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14148 from gatorsmile/describeSchema.
## What changes were proposed in this pull request?
Minor documentation update for code example, code style, and missed reference to "sparkR.init"
## How was this patch tested?
manual
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14178 from felixcheung/rcsvprogrammingguide.
## What changes were proposed in this pull request?
Minor example updates
## How was this patch tested?
manual
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14171 from felixcheung/rexample.
## What changes were proposed in this pull request?
Updated structured streaming programming guide with new windowed example.
## How was this patch tested?
Docs
Author: James Thomas <jamesjoethomas@gmail.com>
Closes#14183 from jjthomas/ss_docs_update.
## What changes were proposed in this pull request?
It's unnecessary. `QueryTest` already sets it.
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#14170 from brkyvz/test-tz.
## What changes were proposed in this pull request?
General decisions to follow, except where noted:
* spark.mllib, pyspark.mllib: Remove all Experimental annotations. Leave DeveloperApi annotations alone.
* spark.ml, pyspark.ml
** Annotate Estimator-Model pairs of classes and companion objects the same way.
** For all algorithms marked Experimental with Since tag <= 1.6, remove Experimental annotation.
** For all algorithms marked Experimental with Since tag = 2.0, leave Experimental annotation.
* DeveloperApi annotations are left alone, except where noted.
* No changes to which types are sealed.
Exceptions where I am leaving items Experimental in spark.ml, pyspark.ml, mainly because the items are new:
* Model Summary classes
* MLWriter, MLReader, MLWritable, MLReadable
* Evaluator and subclasses: There is discussion of changes around evaluating multiple metrics at once for efficiency.
* RFormula: Its behavior may need to change slightly to match R in edge cases.
* AFTSurvivalRegression
* MultilayerPerceptronClassifier
DeveloperApi changes:
* ml.tree.Node, ml.tree.Split, and subclasses should no longer be DeveloperApi
## How was this patch tested?
N/A
Note to reviewers:
* spark.ml.clustering.LDA underwent significant changes (additional methods), so let me know if you want me to leave it Experimental.
* Be careful to check for cases where a class should no longer be Experimental but has an Experimental method, val, or other feature. I did not find such cases, but please verify.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#14147 from jkbradley/experimental-audit.
## What changes were proposed in this pull request?
Currently if `spark.dynamicAllocation.initialExecutors` is less than `spark.dynamicAllocation.minExecutors`, Spark will automatically pick the minExecutors without any warning. While in 1.6 Spark will throw exception if configured like this. So here propose to add warning log if these parameters are configured invalidly.
## How was this patch tested?
Unit test added to verify the scenario.
Author: jerryshao <sshao@hortonworks.com>
Closes#14149 from jerryshao/SPARK-16435.
## What changes were proposed in this pull request?
Currently our Optimizer may reorder the predicates to run them more efficient, but in non-deterministic condition, change the order between deterministic parts and non-deterministic parts may change the number of input rows. For example:
```SELECT a FROM t WHERE rand() < 0.1 AND a = 1```
And
```SELECT a FROM t WHERE a = 1 AND rand() < 0.1```
may call rand() for different times and therefore the output rows differ.
This PR improved this condition by checking whether the predicate is placed before any non-deterministic predicates.
## How was this patch tested?
Expanded related testcases in FilterPushdownSuite.
Author: 蒋星博 <jiangxingbo@meituan.com>
Closes#14012 from jiangxb1987/ppd.
## What changes were proposed in this pull request?
We have a use case of multiplying very big sparse matrices. we have about 1000x1000 distributed block matrices multiplication and the simulate multiply goes like O(n^4) (n being 1000). it takes about 1.5 hours. We modified it slightly with classical hashmap and now run in about 30 seconds O(n^2).
## How was this patch tested?
We have added a performance test and verified the reduced time.
Author: oraviv <oraviv@paypal.com>
Closes#14068 from uzadude/master.
## What changes were proposed in this pull request?
Unpersist broadcasted vars in Word2Vec.fit for more timely / reliable resource cleanup
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#14153 from srowen/SPARK-16440.
## What changes were proposed in this pull request?
Fix code error in yarn-cluster unit test.
## How was this patch tested?
Use exist tests
Author: sharkd <sharkd.tu@gmail.com>
Closes#14166 from sharkdtu/master.
## What changes were proposed in this pull request?
Add Asynchronous Actions documentation inside action of programming guide
## How was this patch tested?
check the documentation indentation and formatting with md preview.
Author: sandy <phalodi@gmail.com>
Closes#14104 from phalodi/SPARK-16438.
## What changes were proposed in this pull request?
Fix Java style errors and remove unused imports, which are randomly found
## How was this patch tested?
Tested on my local machine.
Author: Xin Ren <iamshrek@126.com>
Closes#14161 from keypointt/SPARK-16437.
## What changes were proposed in this pull request?
I fixed a misassigned var, numCompletedTasks was assigned to numSkippedTasks in the convertJobData method
## How was this patch tested?
dev/run-tests
Author: Alex Bozarth <ajbozart@us.ibm.com>
Closes#14141 from ajbozarth/spark16375.
