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…VM calls ## What changes were proposed in this pull request? Issue: Omitting the full classpath can cause problems when calling JVM methods or classes from pyspark. This PR: Changed all uses of jvm.X in pyspark.ml and pyspark.mllib to use full classpath for X ## How was this patch tested? Existing unit tests. Manual testing in an environment where this was an issue. Author: Joseph K. Bradley <joseph@databricks.com> Closes #14023 from jkbradley/SPARK-16348.
## What changes were proposed in this pull request? This PR implements `stack` table generating function. ## How was this patch tested? Pass the Jenkins tests including new testcases. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14033 from dongjoon-hyun/SPARK-16286.
…iner and SparkHiveDynamicPartitionWriterContainer #### What changes were proposed in this pull request? - Remove useless `MetastoreRelation` from the signature of `SparkHiveWriterContainer` and `SparkHiveDynamicPartitionWriterContainer`. - Avoid unnecessary metadata retrieval using Hive client in `InsertIntoHiveTable`. #### How was this patch tested? Existing test cases already cover it. Author: gatorsmile <gatorsmile@gmail.com> Closes #14062 from gatorsmile/removeMetastoreRelation.
…set operation ## What changes were proposed in this pull request? Currently, `regexp_replace` function supports `Column` arguments in a query. This PR supports that in a `Dataset` operation, too. ## How was this patch tested? Pass the Jenkins tests with a updated testcase. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14060 from dongjoon-hyun/SPARK-16340.
…eam is lost ## What changes were proposed in this pull request? Currently, if due to some failure, the outstream gets destroyed or closed and later `outstream.close()` leads to IOException in such case. Due to this, the `stderrBuffer` does not get logged and there is no way for users to see why the job failed. The change is to first display the stderr buffer and then try closing the outstream. ## How was this patch tested? The correct way to test this fix would be to grep the log to see if the `stderrBuffer` gets logged but I dont think having test cases which do that is a good idea. (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) … Author: Tejas Patil <tejasp@fb.com> Closes #13834 from tejasapatil/script_transform.
…blic for loading ## What changes were proposed in this pull request? jira: https://issues.apache.org/jira/browse/SPARK-16249 Change visibility of Object ml.clustering.LDA to public for loading, thus users can invoke LDA.load("path"). ## How was this patch tested? existing ut and manually test for load ( saved with current code) Author: Yuhao Yang <yuhao.yang@intel.com> Author: Yuhao Yang <hhbyyh@gmail.com> Closes #13941 from hhbyyh/ldapublic.
…ew.canonical config ## What changes were proposed in this pull request? These two configs should always be true after Spark 2.0. This patch removes them from the config list. Note that ideally this should've gone into branch-2.0, but due to the timing of the release we should only merge this in master for Spark 2.1. ## How was this patch tested? Updated test cases. Author: Reynold Xin <rxin@databricks.com> Closes #14061 from rxin/SPARK-16388.
…on toy data ## What changes were proposed in this pull request? The current tests assumes that `impurity.calculate()` returns the variance correctly. It should be better to make the tests independent of this assumption. In other words verify that the variance computed equals the variance computed manually on a small tree. ## How was this patch tested? The patch is a test.... Author: MechCoder <mks542@nyu.edu> Closes #13981 from MechCoder/dt_variance.
#### What changes were proposed in this pull request? In `CREATE TABLE AS SELECT`, if the `SELECT` query failed, the table should not exist. For example, ```SQL CREATE TABLE tab STORED AS TEXTFILE SELECT 1 AS a, (SELECT a FROM (SELECT 1 AS a UNION ALL SELECT 2 AS a) t) AS b ``` The above query failed as expected but an empty table `t` is created. This PR is to drop the created table when hitting any non-fatal exception. #### How was this patch tested? Added a test case to verify the behavior Author: gatorsmile <gatorsmile@gmail.com> Closes #13926 from gatorsmile/dropTableAfterException.
## What changes were proposed in this pull request? This patch adds pagination support for the Stage Tables in the Stage tab. Pagination is provided for all of the four Job Tables (active, pending, completed, and failed). Besides, the paged stage tables are also used in JobPage (the detail page for one job) and PoolPage. Interactions (jumping, sorting, and setting page size) for paged tables are also included. ## How was this patch tested? Tested manually by using checking the Web UI after completing and failing hundreds of jobs. Same as the testings for [Paginate Job Table in Jobs tab](#13620). This shows the pagination for completed stages:  Author: Tao Lin <nblintao@gmail.com> Closes #13708 from nblintao/stageTable.
## What changes were proposed in this pull request? PR #13696 renamed various Parquet support classes but left `CatalystWriteSupport` behind. This PR is renames it as a follow-up. ## How was this patch tested? N/A. Author: Cheng Lian <lian@databricks.com> Closes #14070 from liancheng/spark-15979-follow-up.
…ocuments. ## What changes were proposed in this pull request? I search the whole documents directory using SQLContext, and update the following places: - docs/configuration.md, sparkR code snippets. - docs/streaming-programming-guide.md, several example code. ## How was this patch tested? N/A Author: WeichenXu <WeichenXu123@outlook.com> Closes #14025 from WeichenXu123/WIP_SQLContext_update.
…entation
## What changes were proposed in this pull request?
This PR fixes wrongly formatted examples in PySpark documentation as below:
- **`SparkSession`**
- **Before**
- **After**
- **`Builder`**
- **Before**
- **After**
This PR also fixes several similar instances across the documentation in `sql` PySpark module.
## How was this patch tested?
N/A
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #14063 from HyukjinKwon/minor-pyspark-builder.
## What changes were proposed in this pull request? This patch updates the failure handling logic so Spark executor does not crash when seeing LinkageError. ## How was this patch tested? Added an end-to-end test in FailureSuite. Author: petermaxlee <petermaxlee@gmail.com> Closes #13982 from petermaxlee/SPARK-16304.
…me and outer name are the same in Parquet
## What changes were proposed in this pull request?
Currently, if there is a schema as below:
```
root
|-- _1: struct (nullable = true)
| |-- _1: integer (nullable = true)
```
and if we execute the codes below:
```scala
df.filter("_1 IS NOT NULL").count()
```
This pushes down a filter although this filter is being applied to `StructType`.(If my understanding is correct, Spark does not pushes down filters for those).
The reason is, `ParquetFilters.getFieldMap` produces results below:
```
(_1,StructType(StructField(_1,IntegerType,true)))
(_1,IntegerType)
```
and then it becomes a `Map`
```
(_1,IntegerType)
```
Now, because of ` ....lift(dataTypeOf(name)).map(_(name, value))`, this pushes down filters for `_1` which Parquet thinks is `IntegerType`. However, it is actually `StructType`.
So, Parquet filter2 produces incorrect results, for example, the codes below:
```
df.filter("_1 IS NOT NULL").count()
```
produces always 0.
This PR prevents this by not finding nested fields.
## How was this patch tested?
Unit test in `ParquetFilterSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #14067 from HyukjinKwon/SPARK-16371.
… in maven jenkins builds ## What changes were proposed in this pull request? "test big model load / save" in Word2VecSuite, lately resulted into OOM. Therefore we decided to make the partitioning adaptive (not based on spark default "spark.kryoserializer.buffer.max" conf) and then testing it using a small buffer size in order to trigger partitioning without allocating too much memory for the test. ## How was this patch tested? It was tested running the following unit test: org.apache.spark.mllib.feature.Word2VecSuite Author: tmnd1991 <antonio.murgia2@studio.unibo.it> Closes #13509 from tmnd1991/SPARK-15740.
…tion in Logging ## What changes were proposed in this pull request? The commit 044971e introduced a lazy val to simplify code in Logging. Simple enough, though one side effect is that accessing log now means grabbing the instance's lock. This in turn turned up a form of deadlock in the Mesos code. It was arguably a bit of a problem in how this code is structured, but, in any event the safest thing to do seems to be to revert the commit, and that's 90% of the change here; it's just not worth the risk of similar more subtle issues. What I didn't revert here was the removal of this odd override of log in the Mesos code. In retrospect it might have been put in place at some stage as a defense against this type of problem. After all the Logging code still involved a lock at initialization before the change in question. Even after the revert, it doesn't seem like it does anything, given how Logging works now, so I left it removed. However, I also removed the particular log message that ended up playing a part in this problem anyway, maybe being paranoid, to make sure this type of problem can't happen even with how the current locking works in logging initialization. ## How was this patch tested? Jenkins tests Author: Sean Owen <sowen@cloudera.com> Closes #14069 from srowen/SPARK-16379.
## What changes were proposed in this pull request? docs ## How was this patch tested? viewed the docs in github Author: Michael Gummelt <mgummelt@mesosphere.io> Closes #14059 from mgummelt/coarse-grained.
## What changes were proposed in this pull request? This is a small follow-up for SPARK-16371: 1. Hide removeMetadata from public API. 2. Add JIRA ticket number to test case name. ## How was this patch tested? Updated a test comment. Author: Reynold Xin <rxin@databricks.com> Closes #14074 from rxin/parquet-filter.
… well ## What changes were proposed in this pull request? Bring the kafka-0-8 subproject up to date with some test modifications from development on 0-10. Main changes are - eliminating waits on concurrent queue in favor of an assert on received results, - atomics instead of volatile (although this probably doesn't matter) - increasing uniqueness of topic names ## How was this patch tested? Unit tests Author: cody koeninger <cody@koeninger.org> Closes #14073 from koeninger/kafka-0-8-test-direct-cleanup.
## What changes were proposed in this pull request? This patches `MemoryAllocator` to fill clean and freed memory with known byte values, similar to https://github.com/jemalloc/jemalloc/wiki/Use-Case:-Find-a-memory-corruption-bug . Memory filling is flag-enabled in test only by default. ## How was this patch tested? Unit test that it's on in test. cc sameeragarwal Author: Eric Liang <ekl@databricks.com> Closes #13983 from ericl/spark-16021.
…n SQL syntax ## What changes were proposed in this pull request? Currently, Scala API supports to take options with the types, `String`, `Long`, `Double` and `Boolean` and Python API also supports other types. This PR corrects `tableProperty` rule to support other types (string, boolean, double and integer) so that support the options for data sources in a consistent way. This will affect other rules such as DBPROPERTIES and TBLPROPERTIES (allowing other types as values). Also, `TODO add bucketing and partitioning.` was removed because it was resolved in 24bea00 ## How was this patch tested? Unit test in `MetastoreDataSourcesSuite.scala`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #13517 from HyukjinKwon/SPARK-14839.
…logRelation #### What changes were proposed in this pull request? Different from the other leaf nodes, `MetastoreRelation` and `SimpleCatalogRelation` have a pre-defined `alias`, which is used to change the qualifier of the node. However, based on the existing alias handling, alias should be put in `SubqueryAlias`. This PR is to separate alias handling from `MetastoreRelation` and `SimpleCatalogRelation` to make it consistent with the other nodes. It simplifies the signature and conversion to a `BaseRelation`. For example, below is an example query for `MetastoreRelation`, which is converted to a `LogicalRelation`: ```SQL SELECT tmp.a + 1 FROM test_parquet_ctas tmp WHERE tmp.a > 2 ``` Before changes, the analyzed plan is ``` == Analyzed Logical Plan == (a + 1): int Project [(a#951 + 1) AS (a + 1)#952] +- Filter (a#951 > 2) +- SubqueryAlias tmp +- Relation[a#951] parquet ``` After changes, the analyzed plan becomes ``` == Analyzed Logical Plan == (a + 1): int Project [(a#951 + 1) AS (a + 1)#952] +- Filter (a#951 > 2) +- SubqueryAlias tmp +- SubqueryAlias test_parquet_ctas +- Relation[a#951] parquet ``` **Note: the optimized plans are the same.** For `SimpleCatalogRelation`, the existing code always generates two Subqueries. Thus, no change is needed. #### How was this patch tested? Added test cases. Author: gatorsmile <gatorsmile@gmail.com> Closes #14053 from gatorsmile/removeAliasFromMetastoreRelation.
## What changes were proposed in this pull request? Make SparkContext `cancelJob` and `cancelStage` APIs public. This allows applications to use `SparkListener` to do their own management of jobs via events, but without using the REST API. ## How was this patch tested? Existing tests (dev/run-tests) Author: MasterDDT <miteshp@live.com> Closes #14072 from MasterDDT/SPARK-16398.
…ls page in UI ## What changes were proposed in this pull request? This moves over old PR #13664 to target master rather than branch-1.6. Added links to logs (or an indication that there are no logs) for entries which list an executor in the stage details page of the UI. This helps streamline the workflow where a user views a stage details page and determines that they would like to see the associated executor log for further examination. Previously, a user would have to cross reference the executor id listed on the stage details page with the corresponding entry on the executors tab. Link to the JIRA: https://issues.apache.org/jira/browse/SPARK-15885 ## How was this patch tested? Ran existing unit tests. Ran test queries on a platform which did not record executor logs and again on a platform which did record executor logs and verified that the new table column was empty and links to the logs (which were verified as linking to the appropriate files), respectively. Attached is a screenshot of the UI page with no links, with the new columns highlighted. Additional screenshot of these columns with the populated links. Without links:  With links:  This contribution is my original work and I license the work to the project under the Apache Spark project's open source license. Author: Tom Magrino <tmagrino@fb.com> Closes #13861 from tmagrino/uilogstweak.
…ed Column Num #### What changes were proposed in this pull request? When creating a view, a common user error is the number of columns produced by the `SELECT` clause does not match the number of column names specified by `CREATE VIEW`. For example, given Table `t1` only has 3 columns ```SQL create view v1(col2, col4, col3, col5) as select * from t1 ``` Currently, Spark SQL reports the following error: ``` requirement failed java.lang.IllegalArgumentException: requirement failed at scala.Predef$.require(Predef.scala:212) at org.apache.spark.sql.execution.command.CreateViewCommand.run(views.scala:90) ``` This error message is very confusing. This PR is to detect the error and issue a meaningful error message. #### How was this patch tested? Added test cases Author: gatorsmile <gatorsmile@gmail.com> Closes #14047 from gatorsmile/viewMismatchedColumns.
## What changes were proposed in this pull request? This patch removes InSet filter pushdown from Parquet data source, since row-based pushdown is not beneficial to Spark and brings extra complexity to the code base. ## How was this patch tested? N/A Author: Reynold Xin <rxin@databricks.com> Closes #14076 from rxin/SPARK-16400.
## What changes were proposed in this pull request? The following Java code because of type erasing: ```Java JavaRDD<Vector> rows = jsc.parallelize(...); RowMatrix mat = new RowMatrix(rows.rdd()); QRDecomposition<RowMatrix, Matrix> result = mat.tallSkinnyQR(true); ``` We should use retag to restore the type to prevent the following exception: ```Java java.lang.ClassCastException: [Ljava.lang.Object; cannot be cast to [Lorg.apache.spark.mllib.linalg.Vector; ``` ## How was this patch tested? Java unit test Author: Xusen Yin <yinxusen@gmail.com> Closes #14051 from yinxusen/SPARK-16372.
## What changes were proposed in this pull request? I would like to change ```bash if hash python2.7 2>/dev/null; then # Attempt to use Python 2.7, if installed: DEFAULT_PYTHON="python2.7" else DEFAULT_PYTHON="python" fi ``` to just ```DEFAULT_PYTHON="python"``` I'm not sure if it is a great assumption that python2.7 is used by default, when python points to something else. ## 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) Author: MechCoder <mks542@nyu.edu> Closes #14016 from MechCoder/followup.
## What changes were proposed in this pull request? Fix some mistake in ```LinearRegression``` formula. ## How was this patch tested? Documents change, no tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #14369 from yanboliang/LiR-formula.
## What changes were proposed in this pull request? Fix two places in SQLConf documents regarding size in bytes and statistics. ## How was this patch tested? No. Just change document. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #14341 from viirya/fix-doc-size-in-bytes.
## What changes were proposed in this pull request? Updated ML pipeline Cross Validation Scaladoc & PyDoc. ## How was this patch tested? Documentation update (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Author: krishnakalyan3 <krishnakalyan3@gmail.com> Closes #13894 from krishnakalyan3/kfold-cv.
…D to namespace all metrics ## What changes were proposed in this pull request? Adding a new property to SparkConf called spark.metrics.namespace that allows users to set a custom namespace for executor and driver metrics in the metrics systems. By default, the root namespace used for driver or executor metrics is the value of `spark.app.id`. However, often times, users want to be able to track the metrics across apps for driver and executor metrics, which is hard to do with application ID (i.e. `spark.app.id`) since it changes with every invocation of the app. For such use cases, users can set the `spark.metrics.namespace` property to another spark configuration key like `spark.app.name` which is then used to populate the root namespace of the metrics system (with the app name in our example). `spark.metrics.namespace` property can be set to any arbitrary spark property key, whose value would be used to set the root namespace of the metrics system. Non driver and executor metrics are never prefixed with `spark.app.id`, nor does the `spark.metrics.namespace` property have any such affect on such metrics. ## How was this patch tested? Added new unit tests, modified existing unit tests. Author: Mark Grover <mark@apache.org> Closes #14270 from markgrover/spark-5847.
## What changes were proposed in this pull request? added missing keyword for java example ## How was this patch tested? wasn't Author: Bartek Wiśniewski <wedi@Ava.local> Closes #14381 from wedi-dev/quickfix/missing_keyword.
…ied per submit for cluster mode. ## What changes were proposed in this pull request? This fix allows submit of pyspark jobs to specify python 2 or 3. Change ordering in setup for application master environment so env vars PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON can be overridden by spark.yarn.appMasterEnv.* conf settings. This applies to YARN in cluster mode. This allows them to be set per submission without needing the unset the env vars (which is not always possible - e.g. batch submit with LIVY only exposes the arguments to spark-submit) ## How was this patch tested? Manual and existing unit tests. Author: KevinGrealish <KevinGre@microsoft.com> Closes #13824 from KevinGrealish/SPARK-16110.
## What changes were proposed in this pull request? Spark 1.x supports using the Hive type name as function names for doing casts, e.g. ```sql SELECT int(1.0); SELECT string(2.0); ``` The above query would work in Spark 1.x because Spark 1.x fail back to Hive for unimplemented functions, and break in Spark 2.0 because the fall back was removed. This patch implements function aliases using an analyzer rule for the following cast functions: - boolean - tinyint - smallint - int - bigint - float - double - decimal - date - timestamp - binary - string ## How was this patch tested? Added end-to-end tests in SQLCompatibilityFunctionSuite. Author: petermaxlee <petermaxlee@gmail.com> Closes #14364 from petermaxlee/SPARK-16730-2.
…LSuite ## What changes were proposed in this pull request? We currently test subquery SQL building using the `HiveCompatibilitySuite`. The is not desired since SQL building is actually a part of `sql/core` and because we are slowly reducing our dependency on Hive. This PR adds the same tests from the whitelist of `HiveCompatibilitySuite` into `LogicalPlanToSQLSuite`. ## How was this patch tested? This adds more testcases. Pass the Jenkins tests. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14383 from dongjoon-hyun/SPARK-15232.
…ables when Creating Tables
#### What changes were proposed in this pull request?
Currently, in Spark SQL, the initial creation of schema can be classified into two groups. It is applicable to both Hive tables and Data Source tables:
**Group A. Users specify the schema.**
_Case 1 CREATE TABLE AS SELECT_: the schema is determined by the result schema of the SELECT clause. For example,
```SQL
CREATE TABLE tab STORED AS TEXTFILE
AS SELECT * from input
```
_Case 2 CREATE TABLE_: users explicitly specify the schema. For example,
```SQL
CREATE TABLE jsonTable (_1 string, _2 string)
USING org.apache.spark.sql.json
```
**Group B. Spark SQL infers the schema at runtime.**
_Case 3 CREATE TABLE_. Users do not specify the schema but the path to the file location. For example,
```SQL
CREATE TABLE jsonTable
USING org.apache.spark.sql.json
OPTIONS (path '${tempDir.getCanonicalPath}')
```
Before this PR, Spark SQL does not store the inferred schema in the external catalog for the cases in Group B. When users refreshing the metadata cache, accessing the table at the first time after (re-)starting Spark, Spark SQL will infer the schema and store the info in the metadata cache for improving the performance of subsequent metadata requests. However, the runtime schema inference could cause undesirable schema changes after each reboot of Spark.
This PR is to store the inferred schema in the external catalog when creating the table. When users intend to refresh the schema after possible changes on external files (table location), they issue `REFRESH TABLE`. Spark SQL will infer the schema again based on the previously specified table location and update/refresh the schema in the external catalog and metadata cache.
In this PR, we do not use the inferred schema to replace the user specified schema for avoiding external behavior changes . Based on the design, user-specified schemas (as described in Group A) can be changed by ALTER TABLE commands, although we do not support them now.
#### How was this patch tested?
TODO: add more cases to cover the changes.
Author: gatorsmile <gatorsmile@gmail.com>
Closes #14207 from gatorsmile/userSpecifiedSchema.
…ping by column should work
## What changes were proposed in this pull request?
The query with having condition that contains grouping by column will be failed during analysis. E.g.,
create table tbl(a int, b string);
select count(b) from tbl group by a + 1 having a + 1 = 2;
Having condition should be able to use grouping by column.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes #14296 from viirya/having-contains-grouping-column.
## What changes were proposed in this pull request? Avoid overflow of Long type causing a NegativeArraySizeException a few lines later. ## How was this patch tested? Unit tests for HashedRelationSuite still pass. I can confirm the python script I included in https://issues.apache.org/jira/browse/SPARK-16740 works fine with this patch. Unfortunately I don't have the knowledge/time to write a Scala test case for HashedRelationSuite right now. As the patch is pretty obvious I hope it can be included without this. Thanks! Author: Sylvain Zimmer <sylvain@sylvainzimmer.com> Closes #14373 from sylvinus/master.
…utOfMemoryError ## What changes were proposed in this pull request? We currently don't bound or manage the data array size used by column vectors in the vectorized reader (they're just bound by INT.MAX) which may lead to OOMs while reading data. As a short term fix, this patch intercepts the OutOfMemoryError exception and suggest the user to disable the vectorized parquet reader. ## How was this patch tested? Existing Tests Author: Sameer Agarwal <sameerag@cs.berkeley.edu> Closes #14387 from sameeragarwal/oom.
…ing fixes ## What's Been Changed The PR corrects several broken or missing class references in the Python API docs. It also correct formatting problems. For example, you can see [here](http://spark.apache.org/docs/2.0.0/api/python/pyspark.sql.html#pyspark.sql.SQLContext.registerFunction) how Sphinx is not picking up the reference to `DataType`. That's because the reference is relative to the current module, whereas `DataType` is in a different module. You can also see [here](http://spark.apache.org/docs/2.0.0/api/python/pyspark.sql.html#pyspark.sql.SQLContext.createDataFrame) how the formatting for byte, tinyint, and so on is italic instead of monospace. That's because in ReST single backticks just make things italic, unlike in Markdown. ## Testing I tested this PR by [building the Python docs](https://github.com/apache/spark/tree/master/docs#generating-the-documentation-html) and reviewing the results locally in my browser. I confirmed that the broken or missing class references were resolved, and that the formatting was corrected. Author: Nicholas Chammas <nicholas.chammas@gmail.com> Closes #14393 from nchammas/python-docstring-fixes.
…column type mistake ## What changes were proposed in this pull request? ML ```GaussianMixture``` training failed due to feature column type mistake. The feature column type should be ```ml.linalg.VectorUDT``` but got ```mllib.linalg.VectorUDT``` by mistake. See [SPARK-16750](https://issues.apache.org/jira/browse/SPARK-16750) for how to reproduce this bug. Why the unit tests did not complain this errors? Because some estimators/transformers missed calling ```transformSchema(dataset.schema)``` firstly during ```fit``` or ```transform```. I will also add this function to all estimators/transformers who missed in this PR. ## How was this patch tested? No new tests, should pass existing ones. Author: Yanbo Liang <ybliang8@gmail.com> Closes #14378 from yanboliang/spark-16750.
## What changes were proposed in this pull request? Version of derby upgraded based on important security info at VersionEye. Test scope added so we don't include it in our final package anyway. NB: I think this should be backported to all previous releases as it is a security problem https://www.versioneye.com/java/org.apache.derby:derby/10.11.1.1 The CVE number is 2015-1832. I also suggest we add a SECURITY tag for JIRAs ## How was this patch tested? Existing tests with the change making sure that we see no new failures. I checked derby 10.12.x and not derby 10.11.x is downloaded to our ~/.m2 folder. I then used dev/make-distribution.sh and checked the dist/jars folder for Spark 2.0: no derby jar is present. I don't know if this would also remove it from the assembly jar in our 1.x branches. Author: Adam Roberts <aroberts@uk.ibm.com> Closes #14379 from a-roberts/patch-4.
## What changes were proposed in this pull request?
New config var: spark.mesos.docker.containerizer={"mesos","docker" (default)}
This adds support for running docker containers via the Mesos unified containerizer: http://mesos.apache.org/documentation/latest/container-image/
The benefit is losing the dependency on `dockerd`, and all the costs which it incurs.
I've also updated the supported Mesos version to 0.28.2 for support of the required protobufs.
This is blocked on: #14167
## How was this patch tested?
- manually testing jobs submitted with both "mesos" and "docker" settings for the new config var.
- spark/mesos integration test suite
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes #14275 from mgummelt/unified-containerizer.
## What changes were proposed in this pull request? Fix the link at http://spark.apache.org/docs/latest/ml-guide.html. ## How was this patch tested? None Author: Sun Dapeng <sdp@apache.org> Closes #14386 from sundapeng/doclink.
…ocstrings ## What changes were proposed in this pull request? This PR corrects [an error made in an earlier PR](https://github.com/apache/spark/pull/14393/files#r72843069). ## How was this patch tested? ```sh $ ./dev/lint-python PEP8 checks passed. rm -rf _build/* pydoc checks passed. ``` I also built the docs and confirmed that they looked good in my browser. Author: Nicholas Chammas <nicholas.chammas@gmail.com> Closes #14408 from nchammas/SPARK-16772.
… in TreeNodeException ## What changes were proposed in this pull request? We do not want SparkExceptions from job failures in the planning phase to create TreeNodeException. Hence do not wrap SparkException in TreeNodeException. ## How was this patch tested? New unit test Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #14395 from tdas/SPARK-16748.
…ons whose side effects are required ## What changes were proposed in this pull request? Use foreach/for instead of map where operation requires execution of body, not actually defining a transformation ## How was this patch tested? Jenkins Author: Sean Owen <sowen@cloudera.com> Closes #14332 from srowen/SPARK-16694.
…ed and update code where should release unused broadcast/RDD in proper time ## What changes were proposed in this pull request? update unused broadcast in KMeans/Word2Vec, use destroy(false) to release memory in time. and several place destroy() update to destroy(false) so that it will be async-called, it will better than blocking called. and update bcNewCenters in KMeans to make it destroy in correct time. I use a list to store all historical `bcNewCenters` generated in each loop iteration and delay them to release at the end of loop. fix TODO in `BisectingKMeans.run` "unpersist old indices", Implements the pattern "persist current step RDD, and unpersist previous one" in the loop iteration. ## How was this patch tested? Existing tests. Author: WeichenXu <WeichenXu123@outlook.com> Closes #14333 from WeichenXu123/broadvar_unpersist_to_destroy.
… exception ## What changes were proposed in this pull request? Some Java examples are using mllib.linalg.Vectors instead of ml.linalg.Vectors and causes an exception when run. Also there are some Java examples that incorrectly specify data types in the schema, also causing an exception. ## How was this patch tested? Ran corrected examples locally Author: Bryan Cutler <cutlerb@gmail.com> Closes #14405 from BryanCutler/java-examples-ml.Vectors-fix-SPARK-16800.
…sets of partitions ## What changes were proposed in this pull request? This fixes a bug wherethe file scan operator does not take into account partition pruning in its implementation of `sameResult()`. As a result, executions may be incorrect on self-joins over the same base file relation. The patch here is minimal, but we should reconsider relying on `metadata` for implementing sameResult() in the future, as string representations may not be uniquely identifying. cc rxin ## How was this patch tested? Unit tests. Author: Eric Liang <ekl@databricks.com> Closes #14425 from ericl/spark-16818.
## What changes were proposed in this pull request? This patch makes SparkILoop.getAddedJars a public developer API. It is a useful function to get the list of jars added. ## How was this patch tested? N/A - this is a simple visibility change. Author: Reynold Xin <rxin@databricks.com> Closes #14417 from rxin/SPARK-16812.
…st package ## What changes were proposed in this pull request? The catalyst package is meant to be internal, and as a result it does not make sense to mark things as private[sql] or private[spark]. It simply makes debugging harder when Spark developers need to inspect the plans at runtime. This patch removes all private[sql] and private[spark] visibility modifiers in org.apache.spark.sql.catalyst. ## How was this patch tested? N/A - just visibility changes. Author: Reynold Xin <rxin@databricks.com> Closes #14418 from rxin/SPARK-16813.
…olumn ## What changes were proposed in this pull request? `StructField` has very similar semantic with `CatalogColumn`, except that `CatalogColumn` use string to express data type. I think it's reasonable to use `StructType` as the `CatalogTable.schema` and remove `CatalogColumn`. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #14363 from cloud-fan/column.
## What changes were proposed in this pull request? It is useful to log the timezone when query result does not match, especially on build machines that have different timezone from AMPLab Jenkins. ## How was this patch tested? This is a test-only change. Author: Reynold Xin <rxin@databricks.com> Closes #14413 from rxin/SPARK-16805.
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## What changes were proposed in this pull request? This patch introduces SQLQueryTestSuite, a basic framework for end-to-end SQL test cases defined in spark/sql/core/src/test/resources/sql-tests. This is a more standard way to test SQL queries end-to-end in different open source database systems, because it is more manageable to work with files. This is inspired by HiveCompatibilitySuite, but simplified for general Spark SQL tests. Once this is merged, I can work towards porting SQLQuerySuite over, and eventually also move the existing HiveCompatibilitySuite to use this framework. Unlike HiveCompatibilitySuite, SQLQueryTestSuite compares both the output schema and the output data (in string form). When there is a mismatch, the error message looks like the following: ``` [info] - blacklist.sql !!! IGNORED !!! [info] - number-format.sql *** FAILED *** (2 seconds, 405 milliseconds) [info] Expected "...147483648 -214748364[8]", but got "...147483648 -214748364[9]" Result should match for query #1 (SQLQueryTestSuite.scala:171) [info] org.scalatest.exceptions.TestFailedException: [info] at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:495) [info] at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555) [info] at org.scalatest.Assertions$class.assertResult(Assertions.scala:1171) ``` ## How was this patch tested? This is a test infrastructure change. Author: petermaxlee <petermaxlee@gmail.com> Closes apache#14472 from petermaxlee/SPARK-16866. (cherry picked from commit b9f8a11) Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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