## What changes were proposed in this pull request?
The PR adds the option to specify a distance measure in BisectingKMeans. Moreover, it introduces the ability to use the cosine distance measure in it.
## How was this patch tested?
added UTs + existing UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20600 from mgaido91/SPARK-23412.
## What changes were proposed in this pull request?
This change initializes BUILD_ARGS to an empty array when $SPARK_HOME/RELEASE exists.
In function build, "local BUILD_ARGS" effectively creates an array of one element where the first and only element is an empty string, so "${BUILD_ARGS[]}" expands to "" and passes an extra argument to docker.
Setting BUILD_ARGS to an empty array makes "${BUILD_ARGS[]}" expand to nothing.
## How was this patch tested?
Manually tested.
$ cat RELEASE
Spark 2.3.0 (git revision a0d7949896) built for Hadoop 2.7.3
Build flags: -Phadoop-2.7 -Phive -Phive-thriftserver -Pkafka-0-8 -Pmesos -Pyarn -Pkubernetes -Pflume -Psparkr -DzincPort=3036
$ ./bin/docker-image-tool.sh -m t testing build
Sending build context to Docker daemon 256.4MB
...
vanzin
Author: Jooseong Kim <jooseong@pinterest.com>
Closes#20791 from jooseong/SPARK-23618.
## What changes were proposed in this pull request?
The error message ```s"""Field "$name" does not exist."""``` is thrown when looking up an unknown field in StructType. In the error message, we should also contain the information about which columns/fields exist in this struct.
## How was this patch tested?
Added new unit tests.
Note: I created a new `StructTypeSuite.scala` as I couldn't find an existing suite that's suitable to place these tests. I may be missing something so feel free to propose new locations.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Xiayun Sun <xiayunsun@gmail.com>
Closes#20649 from xysun/SPARK-23462.
Signed-off-by: DylanGuedes <djmgguedesgmail.com>
## What changes were proposed in this pull request?
Changes variable name conflict: [input is a built-in python function](https://stackoverflow.com/questions/20670732/is-input-a-keyword-in-python).
## How was this patch tested?
I runned the example and it works fine.
Author: DylanGuedes <djmgguedes@gmail.com>
Closes#20775 from DylanGuedes/input_variable.
## What changes were proposed in this pull request?
In the PR https://github.com/apache/spark/pull/20671, I forgot to update the doc about this new support.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20789 from gatorsmile/docUpdate.
## What changes were proposed in this pull request?
Revise doc of method pushFilters in SupportsPushDownFilters/SupportsPushDownCatalystFilters
In `FileSourceStrategy`, except `partitionKeyFilters`(the references of which is subset of partition keys), all filters needs to be evaluated after scanning. Otherwise, Spark will get wrong result from data sources like Orc/Parquet.
This PR is to improve the doc.
Author: Wang Gengliang <gengliang.wang@databricks.com>
Closes#20769 from gengliangwang/revise_pushdown_doc.
## What changes were proposed in this pull request?
The from_json() function accepts an additional parameter, where the user might specify the schema. The issue is that the specified schema might not be compatible with data. In particular, the JSON data might be missing data for fields declared as non-nullable in the schema. The from_json() function does not verify the data against such errors. When data with missing fields is sent to the parquet encoder, there is no verification either. The end results is a corrupt parquet file.
To avoid corruptions, make sure that all fields in the user-specified schema are set to be nullable.
Since this changes the behavior of a public function, we need to include it in release notes.
The behavior can be reverted by setting `spark.sql.fromJsonForceNullableSchema=false`
## How was this patch tested?
Added two new tests.
Author: Michał Świtakowski <michal.switakowski@databricks.com>
Closes#20694 from mswit-databricks/SPARK-23173.
This change restores functionality that was inadvertently removed as part
of the fix for SPARK-22372.
Also modified an existing unit test to make sure the feature works as intended.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20776 from vanzin/SPARK-23630.
## What changes were proposed in this pull request?
Below are the two cases.
``` SQL
case 1
scala> List.empty[String].toDF().rdd.partitions.length
res18: Int = 1
```
When we write the above data frame as parquet, we create a parquet file containing
just the schema of the data frame.
Case 2
``` SQL
scala> val anySchema = StructType(StructField("anyName", StringType, nullable = false) :: Nil)
anySchema: org.apache.spark.sql.types.StructType = StructType(StructField(anyName,StringType,false))
scala> spark.read.schema(anySchema).csv("/tmp/empty_folder").rdd.partitions.length
res22: Int = 0
```
For the 2nd case, since number of partitions = 0, we don't call the write task (the task has logic to create the empty metadata only parquet file)
The fix is to create a dummy single partition RDD and set up the write task based on it to ensure
the metadata-only file.
## How was this patch tested?
A new test is added to DataframeReaderWriterSuite.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#20525 from dilipbiswal/spark-23271.
## What changes were proposed in this pull request?
`PrintToStderr` was doing what is it supposed to only when code generation is enabled.
The PR adds the same behavior in interpreted mode too.
## How was this patch tested?
added UT
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20773 from mgaido91/SPARK-23602.
## What changes were proposed in this pull request?
There was a bug in `calculateParamLength` which caused it to return always 1 + the number of expressions. This could lead to Exceptions especially with expressions of type long.
## How was this patch tested?
added UT + fixed previous UT
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20772 from mgaido91/SPARK-23628.
## What changes were proposed in this pull request?
As I mentioned in [SPARK-22751](https://issues.apache.org/jira/browse/SPARK-22751?jql=project%20%3D%20SPARK%20AND%20component%20%3D%20ML%20AND%20text%20~%20randomforest), there is a shuffle performance problem in ML Randomforest when train a RF in high dimensional data.
The reason is that, in _org.apache.spark.tree.impl.RandomForest_, the function _findSplitsBySorting_ will actually flatmap a sparse vector into a dense vector, then in groupByKey there will be a huge shuffle write size.
To avoid this, we can add a filter in flatmap, to filter out zero value. And in function _findSplitsForContinuousFeature_, we can infer the number of zero value by _metadata_.
In addition, if a feature only contains zero value, _continuousSplits_ will not has the key of feature id. So I add a check when using _continuousSplits_.
## How was this patch tested?
Ran model locally using spark-submit.
Author: lucio <576632108@qq.com>
Closes#20472 from lucio-yz/master.
## What changes were proposed in this pull request?
The PR adds interpreted execution to DecodeUsingSerializer.
## How was this patch tested?
added UT
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20760 from mgaido91/SPARK-23592.
The exit() builtin is only for interactive use. applications should use sys.exit().
## What changes were proposed in this pull request?
All usage of the builtin `exit()` function is replaced by `sys.exit()`.
## How was this patch tested?
I ran `python/run-tests`.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Benjamin Peterson <benjamin@python.org>
Closes#20682 from benjaminp/sys-exit.
## What changes were proposed in this pull request?
This PR proposes to support an alternative function from with group aggregate pandas UDF.
The current form:
```
def foo(pdf):
return ...
```
Takes a single arg that is a pandas DataFrame.
With this PR, an alternative form is supported:
```
def foo(key, pdf):
return ...
```
The alternative form takes two argument - a tuple that presents the grouping key, and a pandas DataFrame represents the data.
## How was this patch tested?
GroupbyApplyTests
Author: Li Jin <ice.xelloss@gmail.com>
Closes#20295 from icexelloss/SPARK-23011-groupby-apply-key.
## What changes were proposed in this pull request?
This PR adds a configuration to control the fallback of Arrow optimization for `toPandas` and `createDataFrame` with Pandas DataFrame.
## How was this patch tested?
Manually tested and unit tests added.
You can test this by:
**`createDataFrame`**
```python
spark.conf.set("spark.sql.execution.arrow.enabled", False)
pdf = spark.createDataFrame([[{'a': 1}]]).toPandas()
spark.conf.set("spark.sql.execution.arrow.enabled", True)
spark.conf.set("spark.sql.execution.arrow.fallback.enabled", True)
spark.createDataFrame(pdf, "a: map<string, int>")
```
```python
spark.conf.set("spark.sql.execution.arrow.enabled", False)
pdf = spark.createDataFrame([[{'a': 1}]]).toPandas()
spark.conf.set("spark.sql.execution.arrow.enabled", True)
spark.conf.set("spark.sql.execution.arrow.fallback.enabled", False)
spark.createDataFrame(pdf, "a: map<string, int>")
```
**`toPandas`**
```python
spark.conf.set("spark.sql.execution.arrow.enabled", True)
spark.conf.set("spark.sql.execution.arrow.fallback.enabled", True)
spark.createDataFrame([[{'a': 1}]]).toPandas()
```
```python
spark.conf.set("spark.sql.execution.arrow.enabled", True)
spark.conf.set("spark.sql.execution.arrow.fallback.enabled", False)
spark.createDataFrame([[{'a': 1}]]).toPandas()
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20678 from HyukjinKwon/SPARK-23380-conf.
## What changes were proposed in this pull request?
I propose to replace `'\n'` by the `<br>` tag in generated html of thread dump page. The `<br>` tag will split thread lines in more reliable way. For now it could look like on
<img width="1265" alt="the screen shot" src="https://user-images.githubusercontent.com/1580697/37118202-bcd98fc0-2253-11e8-9e61-c2f946869ee0.png">
if the html is proxied and `'\n'` is replaced by another whitespace. The changes allow to more easily read and copy stack traces.
## How was this patch tested?
I tested it manually by checking the thread dump page and its source.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#20762 from MaxGekk/br-thread-dump.
## What changes were proposed in this pull request?
The following query doesn't work as expected:
```
CREATE EXTERNAL TABLE ext_table(a STRING, b INT, c STRING) PARTITIONED BY (d STRING)
LOCATION 'sql/core/spark-warehouse/ext_table';
ALTER TABLE ext_table CHANGE a a STRING COMMENT "new comment";
DESC ext_table;
```
The comment of column `a` is not updated, that's because `HiveExternalCatalog.doAlterTable` ignores table schema changes. To fix the issue, we should call `doAlterTableDataSchema` instead of `doAlterTable`.
## How was this patch tested?
Updated `DDLSuite.testChangeColumn`.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#20696 from jiangxb1987/alterColumnComment.
A few different things going on:
- Remove unused methods.
- Move JSON methods to the only class that uses them.
- Move test-only methods to TestUtils.
- Make getMaxResultSize() a config constant.
- Reuse functionality from existing libraries (JRE or JavaUtils) where possible.
The change also includes changes to a few tests to call `Utils.createTempFile` correctly,
so that temp dirs are created under the designated top-level temp dir instead of
potentially polluting git index.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20706 from vanzin/SPARK-23550.
## What changes were proposed in this pull request?
Seems R's substr API treats Scala substr API as zero based and so subtracts the given starting position by 1.
Because Scala's substr API also accepts zero-based starting position (treated as the first element), so the current R's substr test results are correct as they all use 1 as starting positions.
## How was this patch tested?
Modified tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#20464 from viirya/SPARK-23291.
## What changes were proposed in this pull request?
The PR adds interpreted execution to EncodeUsingSerializer.
## How was this patch tested?
added UT
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20751 from mgaido91/SPARK-23591.
## What changes were proposed in this pull request?
This pr added a helper function in `ExpressionEvalHelper` to check exceptions in all the path of expression evaluation.
## How was this patch tested?
Modified the existing tests.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#20748 from maropu/SPARK-23611.
## What changes were proposed in this pull request?
The PR adds interpreted execution to CreateExternalRow
## How was this patch tested?
added UT
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20749 from mgaido91/SPARK-23590.
## What changes were proposed in this pull request?
Remove .md5 files from release artifacts
## How was this patch tested?
N/A
Author: Sean Owen <sowen@cloudera.com>
Closes#20737 from srowen/SPARK-23601.
## What changes were proposed in this pull request?
This pr added interpreted execution for `GetExternalRowField`.
## How was this patch tested?
Added tests in `ObjectExpressionsSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#20746 from maropu/SPARK-23594.
…lValue
## What changes were proposed in this pull request?
Parquet 1.9 will change the semantics of Statistics.isEmpty slightly
to reflect if the null value count has been set. That breaks a
timestamp interoperability test that cares only about whether there
are column values present in the statistics of a written file for an
INT96 column. Fix by using Statistics.hasNonNullValue instead.
## How was this patch tested?
Unit tests continue to pass against Parquet 1.8, and also pass against
a Parquet build including PARQUET-1217.
Author: Henry Robinson <henry@cloudera.com>
Closes#20740 from henryr/spark-23604.
## What changes were proposed in this pull request?
The PR adds interpreted execution to WrapOption.
## How was this patch tested?
added UT
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20741 from mgaido91/SPARK-23586_2.
The `__del__` method that explicitly detaches the object was moved from `JavaParams` to `JavaWrapper` class, this way model summaries could also be garbage collected in Java. A test case was added to make sure that relevant error messages are thrown after the objects are deleted.
I ran pyspark tests agains `pyspark-ml` module
`./python/run-tests --python-executables=$(which python) --modules=pyspark-ml`
Author: Yogesh Garg <yogesh(dot)garg()databricks(dot)com>
Closes#20724 from yogeshg/java_wrapper_memory.
These options were used to configure the built-in JRE SSL libraries
when downloading files from HTTPS servers. But because they were also
used to set up the now (long) removed internal HTTPS file server,
their default configuration chose convenience over security by having
overly lenient settings.
This change removes the configuration options that affect the JRE SSL
libraries. The JRE trust store can still be configured via system
properties (or globally in the JRE security config). The only lost
functionality is not being able to disable the default hostname
verifier when using spark-submit, which should be fine since Spark
itself is not using https for any internal functionality anymore.
I also removed the HTTP-related code from the REPL class loader, since
we haven't had a HTTP server for REPL-generated classes for a while.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20723 from vanzin/SPARK-23538.
## What changes were proposed in this pull request?
Before this commit, a non-interruptible iterator is returned if aggregator or ordering is specified.
This commit also ensures that sorter is closed even when task is cancelled(killed) in the middle of sorting.
## How was this patch tested?
Add a unit test in JobCancellationSuite
Author: Xianjin YE <advancedxy@gmail.com>
Closes#20449 from advancedxy/SPARK-23040.
## What changes were proposed in this pull request?
Add an epoch ID argument to DataWriterFactory for use in streaming. As a side effect of passing in this value, DataWriter will now have a consistent lifecycle; commit() or abort() ends the lifecycle of a DataWriter instance in any execution mode.
I considered making a separate streaming interface and adding the epoch ID only to that one, but I think it requires a lot of extra work for no real gain. I think it makes sense to define epoch 0 as the one and only epoch of a non-streaming query.
## How was this patch tested?
existing unit tests
Author: Jose Torres <jose@databricks.com>
Closes#20710 from jose-torres/api2.
## What changes were proposed in this pull request?
The PR adds interpreted execution to UnwrapOption.
## How was this patch tested?
added UT
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20736 from mgaido91/SPARK-23586.
## What changes were proposed in this pull request?
adding Structured Streaming tests for all Models/Transformers in spark.ml.classification
## How was this patch tested?
N/A
Author: WeichenXu <weichen.xu@databricks.com>
Closes#20121 from WeichenXu123/ml_stream_test_classification.
## What changes were proposed in this pull request?
Removed export tag to get rid of unknown tag warnings
## How was this patch tested?
Existing tests
Author: Rekha Joshi <rekhajoshm@gmail.com>
Author: rjoshi2 <rekhajoshm@gmail.com>
Closes#20501 from rekhajoshm/SPARK-22430.
## What changes were proposed in this pull request?
Provide more details in trigonometric function documentations. Referenced `java.lang.Math` for further details in the descriptions.
## How was this patch tested?
Ran full build, checked generated documentation manually
Author: Mihaly Toth <misutoth@gmail.com>
Closes#20618 from misutoth/trigonometric-doc.
## What changes were proposed in this pull request?
The algorithm in `DefaultPartitionCoalescer.setupGroups` is responsible for picking preferred locations for coalesced partitions. It analyzes the preferred locations of input partitions. It starts by trying to create one partition for each unique location in the input. However, if the the requested number of coalesced partitions is higher that the number of unique locations, it has to pick duplicate locations.
Previously, the duplicate locations would be picked by iterating over the input partitions in order, and copying their preferred locations to coalesced partitions. If the input partitions were clustered by location, this could result in severe skew.
With the fix, instead of iterating over the list of input partitions in order, we pick them at random. It's not perfectly balanced, but it's much better.
## How was this patch tested?
Unit test reproducing the behavior was added.
Author: Ala Luszczak <ala@databricks.com>
Closes#20664 from ala/SPARK-23496.
## What changes were proposed in this pull request?
A current `CodegenContext` class has immutable value or method without mutable state, too.
This refactoring moves them to `CodeGenerator` object class which can be accessed from anywhere without an instantiated `CodegenContext` in the program.
## How was this patch tested?
Existing tests
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#20700 from kiszk/SPARK-23546.
## What changes were proposed in this pull request?
Check python version to determine whether to use `inspect.getargspec` or `inspect.getfullargspec` before applying `pandas_udf` core logic to a function. The former is python2.7 (deprecated in python3) and the latter is python3.x. The latter correctly accounts for type annotations, which are syntax errors in python2.x.
## How was this patch tested?
Locally, on python 2.7 and 3.6.
Author: Michael (Stu) Stewart <mstewart141@gmail.com>
Closes#20728 from mstewart141/pandas_udf_fix.
## What changes were proposed in this pull request?
It looks like this was incorrectly copied from `XPathFloat` in the class above.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Eric Liang <ekhliang@gmail.com>
Closes#20730 from ericl/fix-typo-xpath.
## What changes were proposed in this pull request?
Currently, when the Kafka source reads from Kafka, it generates as many tasks as the number of partitions in the topic(s) to be read. In some case, it may be beneficial to read the data with greater parallelism, that is, with more number partitions/tasks. That means, offset ranges must be divided up into smaller ranges such the number of records in partition ~= total records in batch / desired partitions. This would also balance out any data skews between topic-partitions.
In this patch, I have added a new option called `minPartitions`, which allows the user to specify the desired level of parallelism.
## How was this patch tested?
New tests in KafkaMicroBatchV2SourceSuite.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#20698 from tdas/SPARK-23541.
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/20679 I missed a few places in SQL tests.
For hygiene, they should also use the sessionState interface where possible.
## How was this patch tested?
Modified existing tests.
Author: Juliusz Sompolski <julek@databricks.com>
Closes#20718 from juliuszsompolski/SPARK-23514-followup.
## What changes were proposed in this pull request?
Added subtree pruning in the translation from LearningNode to Node: a learning node having a single prediction value for all the leaves in the subtree rooted at it is translated into a LeafNode, instead of a (redundant) InternalNode
## How was this patch tested?
Added two unit tests under "mllib/src/test/scala/org/apache/spark/ml/tree/impl/RandomForestSuite.scala":
- test("SPARK-3159 tree model redundancy - classification")
- test("SPARK-3159 tree model redundancy - regression")
4 existing unit tests relying on the tree structure (existence of a specific redundant subtree) had to be adapted as the tested components in the output tree are now pruned (fixed by adding an extra _prune_ parameter which can be used to disable pruning for testing)
Author: Alessandro Solimando <18898964+asolimando@users.noreply.github.com>
Closes#20632 from asolimando/master.
## What changes were proposed in this pull request?
Add Spark 2.3.0 in HiveExternalCatalogVersionsSuite since Spark 2.3.0 is released for ensuring backward compatibility.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20720 from gatorsmile/add2.3.
