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* [SPARK-16963][STREAMING][SQL] Changes to Source trait and related ↵frreiss2016-10-266-36/+154
| | | | | | | | | | | | | | | | | | | | | | implementation classes ## What changes were proposed in this pull request? This PR contains changes to the Source trait such that the scheduler can notify data sources when it is safe to discard buffered data. Summary of changes: * Added a method `commit(end: Offset)` that tells the Source that is OK to discard all offsets up `end`, inclusive. * Changed the semantics of a `None` value for the `getBatch` method to mean "from the very beginning of the stream"; as opposed to "all data present in the Source's buffer". * Added notes that the upper layers of the system will never call `getBatch` with a start value less than the last value passed to `commit`. * Added a `lastCommittedOffset` method to allow the scheduler to query the status of each Source on restart. This addition is not strictly necessary, but it seemed like a good idea -- Sources will be maintaining their own persistent state, and there may be bugs in the checkpointing code. * The scheduler in `StreamExecution.scala` now calls `commit` on its stream sources after marking each batch as complete in its checkpoint. * `MemoryStream` now cleans committed batches out of its internal buffer. * `TextSocketSource` now cleans committed batches from its internal buffer. ## How was this patch tested? Existing regression tests already exercise the new code. Author: frreiss <frreiss@us.ibm.com> Closes #14553 from frreiss/fred-16963.
* [SPARK-18126][SPARK-CORE] getIteratorZipWithIndex accepts negative value as ↵Miao Wang2016-10-272-0/+4
| | | | | | | | | | | | | | | | | | | | | | index ## What changes were proposed in this pull request? (Please fill in changes proposed in this fix) `Utils.getIteratorZipWithIndex` was added to deal with number of records > 2147483647 in one partition. method `getIteratorZipWithIndex` accepts `startIndex` < 0, which leads to negative index. This PR just adds a defensive check on `startIndex` to make sure it is >= 0. ## How was this patch tested? Add a new unit test. Author: Miao Wang <miaowang@Miaos-MacBook-Pro.local> Closes #15639 from wangmiao1981/zip.
* [SPARK-17157][SPARKR] Add multiclass logistic regression SparkR Wrapperwm624@hotmail.com2016-10-266-3/+410
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? As we discussed in #14818, I added a separate R wrapper spark.logit for logistic regression. This single interface supports both binary and multinomial logistic regression. It also has "predict" and "summary" for binary logistic regression. ## How was this patch tested? New unit tests are added. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #15365 from wangmiao1981/glm.
* [SPARK-18094][SQL][TESTS] Move group analytics test cases from ↵jiangxingbo2016-10-263-192/+290
| | | | | | | | | | | | | | | | | | | | | | | | | | | | `SQLQuerySuite` into a query file test. ## What changes were proposed in this pull request? Currently we have several test cases for group analytics(ROLLUP/CUBE/GROUPING SETS) in `SQLQuerySuite`, should better move them into a query file test. The following test cases are moved to `group-analytics.sql`: ``` test("rollup") test("grouping sets when aggregate functions containing groupBy columns") test("cube") test("grouping sets") test("grouping and grouping_id") test("grouping and grouping_id in having") test("grouping and grouping_id in sort") ``` This is followup work of #15582 ## How was this patch tested? Modified query file `group-analytics.sql`, which will be tested by `SQLQueryTestSuite`. Author: jiangxingbo <jiangxb1987@gmail.com> Closes #15624 from jiangxb1987/group-analytics-test.
* [SPARK-14300][DOCS][MLLIB] Scala MLlib examples code merge and clean upXin Ren2016-10-263-148/+19
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-14300 Duplicated code found in scala/examples/mllib, below all deleted in this PR: - DenseGaussianMixture.scala - StreamingLinearRegression.scala ## delete reasons: #### delete: mllib/DenseGaussianMixture.scala - duplicate of mllib/GaussianMixtureExample #### delete: mllib/StreamingLinearRegression.scala - duplicate of mllib/StreamingLinearRegressionExample When merging and cleaning those code, be sure not disturb the previous example on and off blocks. ## How was this patch tested? Test with `SKIP_API=1 jekyll` manually to make sure that works well. Author: Xin Ren <iamshrek@126.com> Closes #12195 from keypointt/SPARK-14300.
* [SPARK-17961][SPARKR][SQL] Add storageLevel to DataFrame for SparkRWeichenXu2016-10-266-4/+79
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add storageLevel to DataFrame for SparkR. This is similar to this RP: https://github.com/apache/spark/pull/13780 but in R I do not make a class for `StorageLevel` but add a method `storageToString` ## How was this patch tested? test added. Author: WeichenXu <WeichenXu123@outlook.com> Closes #15516 from WeichenXu123/storageLevel_df_r.
* [MINOR][ML] Refactor clustering summary.Yanbo Liang2016-10-264-83/+80
| | | | | | | | | | | | ## What changes were proposed in this pull request? Abstract ```ClusteringSummary``` from ```KMeansSummary```, ```GaussianMixtureSummary``` and ```BisectingSummary```, and eliminate duplicated pieces of code. ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15555 from yanboliang/clustering-summary.
* [SPARK-18104][DOC] Don't build KafkaSource docShixiong Zhu2016-10-261-2/+2
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Don't need to build doc for KafkaSource because the user should use the data source APIs to use KafkaSource. All KafkaSource APIs are internal. ## How was this patch tested? Verified manually. Author: Shixiong Zhu <shixiong@databricks.com> Closes #15630 from zsxwing/kafka-unidoc.
* [SPARK-18063][SQL] Failed to infer constraints over multiple aliasesjiangxingbo2016-10-262-6/+18
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The `UnaryNode.getAliasedConstraints` function fails to replace all expressions by their alias where constraints contains more than one expression to be replaced. For example: ``` val tr = LocalRelation('a.int, 'b.string, 'c.int) val multiAlias = tr.where('a === 'c + 10).select('a.as('x), 'c.as('y)) multiAlias.analyze.constraints ``` currently outputs: ``` ExpressionSet(Seq( IsNotNull(resolveColumn(multiAlias.analyze, "x")), IsNotNull(resolveColumn(multiAlias.analyze, "y")) ) ``` The constraint `resolveColumn(multiAlias.analyze, "x") === resolveColumn(multiAlias.analyze, "y") + 10)` is missing. ## How was this patch tested? Add new test cases in `ConstraintPropagationSuite`. Author: jiangxingbo <jiangxb1987@gmail.com> Closes #15597 from jiangxb1987/alias-constraints.
* [SPARK-13747][SQL] Fix concurrent executions in ForkJoinPool for SQLShixiong Zhu2016-10-264-2/+25
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Calling `Await.result` will allow other tasks to be run on the same thread when using ForkJoinPool. However, SQL uses a `ThreadLocal` execution id to trace Spark jobs launched by a query, which doesn't work perfectly in ForkJoinPool. This PR just uses `Awaitable.result` instead to prevent ForkJoinPool from running other tasks in the current waiting thread. ## How was this patch tested? Jenkins Author: Shixiong Zhu <shixiong@databricks.com> Closes #15520 from zsxwing/SPARK-13747.
* [SPARK-17748][FOLLOW-UP][ML] Reorg variables of WeightedLeastSquares.Yanbo Liang2016-10-262-68/+86
| | | | | | | | | | | | | ## What changes were proposed in this pull request? This is follow-up work of #15394. Reorg some variables of ```WeightedLeastSquares``` and fix one minor issue of ```WeightedLeastSquaresSuite```. ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15621 from yanboliang/spark-17748.
* [SPARK-18093][SQL] Fix default value test in SQLConfSuite to work rega…Mark Grover2016-10-261-2/+5
| | | | | | | | | | | | | | | | | …rdless of warehouse dir's existence ## What changes were proposed in this pull request? Appending a trailing slash, if there already isn't one for the sake comparison of the two paths. It doesn't take away from the essence of the check, but removes any potential mismatch due to lack of trailing slash. ## How was this patch tested? Ran unit tests and they passed. Author: Mark Grover <mark@apache.org> Closes #15623 from markgrover/spark-18093.
* [SPARK-17733][SQL] InferFiltersFromConstraints rule never terminates for queryjiangxingbo2016-10-264-14/+191
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The function `QueryPlan.inferAdditionalConstraints` and `UnaryNode.getAliasedConstraints` can produce a non-converging set of constraints for recursive functions. For instance, if we have two constraints of the form(where a is an alias): `a = b, a = f(b, c)` Applying both these rules in the next iteration would infer: `f(b, c) = f(f(b, c), c)` This process repeated, the iteration won't converge and the set of constraints will grow larger and larger until OOM. ~~To fix this problem, we collect alias from expressions and skip infer constraints if we are to transform an `Expression` to another which contains it.~~ To fix this problem, we apply additional check in `inferAdditionalConstraints`, when it's possible to generate recursive constraints, we skip generate that. ## How was this patch tested? Add new testcase in `SQLQuerySuite`/`InferFiltersFromConstraintsSuite`. Author: jiangxingbo <jiangxb1987@gmail.com> Closes #15319 from jiangxb1987/constraints.
* [SPARK-17802] Improved caller context logging.Shuai Lin2016-10-262-19/+36
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? [SPARK-16757](https://issues.apache.org/jira/browse/SPARK-16757) sets the hadoop `CallerContext` when calling hadoop/hdfs apis to make spark applications more diagnosable in hadoop/hdfs logs. However, the `org.apache.hadoop.ipc.CallerContext` class is only added since [hadoop 2.8](https://issues.apache.org/jira/browse/HDFS-9184), which is not officially releaed yet. So each time `utils.CallerContext.setCurrentContext()` is called (e.g [when a task is created](https://github.com/apache/spark/blob/b678e46/core/src/main/scala/org/apache/spark/scheduler/Task.scala#L95-L96)), a "java.lang.ClassNotFoundException: org.apache.hadoop.ipc.CallerContext" error is logged, which pollutes the spark logs when there are lots of tasks. This patch improves this behaviour by only logging the `ClassNotFoundException` once. ## How was this patch tested? Existing tests. Author: Shuai Lin <linshuai2012@gmail.com> Closes #15377 from lins05/spark-17802-improve-callercontext-logging.
* [SPARK-4411][WEB UI] Add "kill" link for jobs in the UIAlex Bozarth2016-10-267-29/+104
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently users can kill stages via the web ui but not jobs directly (jobs are killed if one of their stages is). I've added the ability to kill jobs via the web ui. This code change is based on #4823 by lianhuiwang and updated to work with the latest code matching how stages are currently killed. In general I've copied the kill stage code warning and note comments and all. I also updated applicable tests and documentation. ## How was this patch tested? Manually tested and dev/run-tests ![screen shot 2016-10-11 at 4 49 43 pm](https://cloud.githubusercontent.com/assets/13952758/19292857/12f1b7c0-8fd4-11e6-8982-210249f7b697.png) Author: Alex Bozarth <ajbozart@us.ibm.com> Author: Lianhui Wang <lianhuiwang09@gmail.com> Closes #15441 from ajbozarth/spark4411.
* [SPARK-18027][YARN] .sparkStaging not clean on RM ApplicationNotFoundExceptionSean Owen2016-10-261-0/+2
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Cleanup YARN staging dir on all `KILLED`/`FAILED` paths in `monitorApplication` ## How was this patch tested? Existing tests Author: Sean Owen <sowen@cloudera.com> Closes #15598 from srowen/SPARK-18027.
* [SPARK-18022][SQL] java.lang.NullPointerException instead of real exception ↵Sean Owen2016-10-261-1/+1
| | | | | | | | | | | | | | | | when saving DF to MySQL ## What changes were proposed in this pull request? On null next exception in JDBC, don't init it as cause or suppressed ## How was this patch tested? Existing tests Author: Sean Owen <sowen@cloudera.com> Closes #15599 from srowen/SPARK-18022.
* [SPARK-17693][SQL] Fixed Insert Failure To Data Source Tables when the ↵gatorsmile2016-10-262-2/+50
| | | | | | | | | | | | | | | | | | | | | | | | | Schema has the Comment Field ### What changes were proposed in this pull request? ```SQL CREATE TABLE tab1(col1 int COMMENT 'a', col2 int) USING parquet INSERT INTO TABLE tab1 SELECT 1, 2 ``` The insert attempt will fail if the target table has a column with comments. The error is strange to the external users: ``` assertion failed: No plan for InsertIntoTable Relation[col1#15,col2#16] parquet, false, false +- Project [1 AS col1#19, 2 AS col2#20] +- OneRowRelation$ ``` This PR is to fix the above bug by checking the metadata when comparing the schema between the table and the query. If not matched, we also copy the metadata. This is an alternative to https://github.com/apache/spark/pull/15266 ### How was this patch tested? Added a test case Author: gatorsmile <gatorsmile@gmail.com> Closes #15615 from gatorsmile/insertDataSourceTableWithCommentSolution2.
* [SPARK-18007][SPARKR][ML] update SparkR MLP - add initalWeights parameterWeichenXu2016-10-253-5/+33
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? update SparkR MLP, add initalWeights parameter. ## How was this patch tested? test added. Author: WeichenXu <WeichenXu123@outlook.com> Closes #15552 from WeichenXu123/mlp_r_add_initialWeight_param.
* [SPARK-16988][SPARK SHELL] spark history server log needs to be fixed to ↵hayashidac2016-10-262-6/+9
| | | | | | | | | | show https url when ssl is enabled spark history server log needs to be fixed to show https url when ssl is enabled Author: chie8842 <chie@chie-no-Mac-mini.local> Closes #15611 from hayashidac/SPARK-16988.
* [SPARK-18019][ML] Add instrumentation to GBTssethah2016-10-252-2/+17
| | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add instrumentation for logging in ML GBT, part of umbrella ticket [SPARK-14567](https://issues.apache.org/jira/browse/SPARK-14567) ## How was this patch tested? Tested locally: ```` 16/10/20 10:24:51 INFO Instrumentation: GBTRegressor-gbtr_2b460d3e2e93-1207021668-45: training: numPartitions=1 storageLevel=StorageLevel(1 replicas) 16/10/20 10:24:51 INFO Instrumentation: GBTRegressor-gbtr_2b460d3e2e93-1207021668-45: {"maxIter":1} 16/10/20 10:24:51 INFO Instrumentation: GBTRegressor-gbtr_2b460d3e2e93-1207021668-45: {"numFeatures":2} 16/10/20 10:24:51 INFO Instrumentation: GBTRegressor-gbtr_2b460d3e2e93-1207021668-45: {"numClasses":0} ... 16/10/20 15:54:21 INFO Instrumentation: GBTRegressor-gbtr_065fad465377-1922077832-22: training finished ```` Author: sethah <seth.hendrickson16@gmail.com> Closes #15574 from sethah/gbt_instr.
* [SPARK-18070][SQL] binary operator should not consider nullability when ↵Wenchen Fan2016-10-252-1/+10
| | | | | | | | | | | | | | | | comparing input types ## What changes were proposed in this pull request? Binary operator requires its inputs to be of same type, but it should not consider nullability, e.g. `EqualTo` should be able to compare an element-nullable array and an element-non-nullable array. ## How was this patch tested? a regression test in `DataFrameSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #15606 from cloud-fan/type-bug.
* [SPARK-18010][CORE] Reduce work performed for building up the application ↵Vinayak2016-10-252-58/+101
| | | | | | | | | | | | | | | | | list for the History Server app list UI page ## What changes were proposed in this pull request? allow ReplayListenerBus to skip deserialising and replaying certain events using an inexpensive check of the event log entry. Use this to ensure that when event log replay is triggered for building the application list, we get the ReplayListenerBus to skip over all but the few events needed for our immediate purpose. Refer [SPARK-18010] for the motivation behind this change. ## How was this patch tested? Tested with existing HistoryServer and ReplayListener unit test suites. All tests pass. Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request. Author: Vinayak <vijoshi5@in.ibm.com> Closes #15556 from vijoshi/SAAS-467_master.
* [SPARK-17748][FOLLOW-UP][ML] Fix build error for Scala 2.10.Yanbo Liang2016-10-251-6/+7
| | | | | | | | | | | | ## What changes were proposed in this pull request? #15394 introduced build error for Scala 2.10, this PR fix it. ## How was this patch tested? Existing test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15625 from yanboliang/spark-17748-scala.
* [SPARK-14634][ML][FOLLOWUP] Delete superfluous line in BisectingKMeansZheng RuiFeng2016-10-252-6/+5
| | | | | | | | | | | | ## What changes were proposed in this pull request? As commented by jkbradley in https://github.com/apache/spark/pull/12394, `model.setSummary(summary)` is superfluous ## How was this patch tested? existing tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #15619 from zhengruifeng/del_superfluous.
* [SPARK-18026][SQL] should not always lowercase partition columns of ↵Wenchen Fan2016-10-259-60/+136
| | | | | | | | | | | | | | | | | | | | partition spec in parser ## What changes were proposed in this pull request? Currently we always lowercase the partition columns of partition spec in parser, with the assumption that table partition columns are always lowercased. However, this is not true for data source tables, which are case preserving. It's safe for now because data source tables don't store partition spec in metastore and don't support `ADD PARTITION`, `DROP PARTITION`, `RENAME PARTITION`, but we should make our code future-proof. This PR makes partition spec case preserving at parser, and improve the `PreprocessTableInsertion` analyzer rule to normalize the partition columns in partition spec, w.r.t. the table partition columns. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #15566 from cloud-fan/partition-spec.
* [SPARK-17748][ML] One pass solver for Weighted Least Squares with ElasticNetsethah2016-10-2411-308/+1057
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? 1. Make a pluggable solver interface for `WeightedLeastSquares` 2. Add a `QuasiNewton` solver to handle elastic net regularization for `WeightedLeastSquares` 3. Add method `BLAS.dspmv` used by QN solver 4. Add mechanism for WLS to handle singular covariance matrices by falling back to QN solver when Cholesky fails. ## How was this patch tested? Unit tests - see below. ## Design choices **Pluggable Normal Solver** Before, the `WeightedLeastSquares` package always used the Cholesky decomposition solver to compute the solution to the normal equations. Now, we specify the solver as a constructor argument to the `WeightedLeastSquares`. We introduce a new trait: ````scala private[ml] sealed trait NormalEquationSolver { def solve( bBar: Double, bbBar: Double, abBar: DenseVector, aaBar: DenseVector, aBar: DenseVector): NormalEquationSolution } ```` We extend this trait for different variants of normal equation solvers. In the future, we can easily add others (like QR) using this interface. **Always train in the standardized space** The normal solver did not previously standardize the data, but this patch introduces a change such that we always solve the normal equations in the standardized space. We convert back to the original space in the same way that is done for distributed L-BFGS/OWL-QN. We add test cases for zero variance features/labels. **Use L-BFGS locally to solve normal equations for singular matrix** When linear regression with the normal solver is called for a singular matrix, we initially try to solve with Cholesky. We use the output of `lapack.dppsv` to determine if the matrix is singular. If it is, we fall back to using L-BFGS locally to solve the normal equations. We add test cases for this as well. ## Test cases I found it helpful to enumerate some of the test cases and hopefully it makes review easier. **WeightedLeastSquares** 1. Constant columns - Cholesky solver fails with no regularization, Auto solver falls back to QN, and QN trains successfully. 2. Collinear features - Cholesky solver fails with no regularization, Auto solver falls back to QN, and QN trains successfully. 3. Label is constant zero - no training is performed regardless of intercept. Coefficients are zero and intercept is zero. 4. Label is constant - if fitIntercept, then no training is performed and intercept equals label mean. If not fitIntercept, then we train and return an answer that matches R's lm package. 5. Test with L1 - go through various combinations of L1/L2, standardization, fitIntercept and verify that output matches glmnet. 6. Initial intercept - verify that setting the initial intercept to label mean is correct by training model with strong L1 regularization so that all coefficients are zero and intercept converges to label mean. 7. Test diagInvAtWA - since we are standardizing features now during training, we should test that the inverse is computed to match R. **LinearRegression** 1. For all existing L1 test cases, test the "normal" solver too. 2. Check that using the normal solver now handles singular matrices. 3. Check that using the normal solver with L1 produces an objective history in the model summary, but does not produce the inverse of AtA. **BLAS** 1. Test new method `dspmv`. ## Performance Testing This patch will speed up linear regression with L1/elasticnet penalties when the feature size is < 4096. I have not conducted performance tests at scale, only observed by testing locally that there is a speed improvement. We should decide if this PR needs to be blocked before performance testing is conducted. Author: sethah <seth.hendrickson16@gmail.com> Closes #15394 from sethah/SPARK-17748.
* [SPARK-17894][HOTFIX] Fix broken build fromKay Ousterhout2016-10-241-1/+1
| | | | | | | | | | The named parameter in an overridden class isn't supported in Scala 2.10 so was breaking the build. cc zsxwing Author: Kay Ousterhout <kayousterhout@gmail.com> Closes #15617 from kayousterhout/hotfix.
* [SPARK-17409][SQL][FOLLOW-UP] Do Not Optimize Query in CTAS More Than Oncegatorsmile2016-10-253-3/+37
| | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? This follow-up PR is for addressing the [comment](https://github.com/apache/spark/pull/15048). We added two test cases based on the suggestion from yhuai . One is a new test case using the `saveAsTable` API to create a data source table. Another is for CTAS on Hive serde table. Note: No need to backport this PR to 2.0. Will submit a new PR to backport the whole fix with new test cases to Spark 2.0 ### How was this patch tested? N/A Author: gatorsmile <gatorsmile@gmail.com> Closes #15459 from gatorsmile/ctasOptimizedTestCases.
* [SPARK-18028][SQL] simplify TableFileCatalogWenchen Fan2016-10-255-36/+84
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Simplify/cleanup TableFileCatalog: 1. pass a `CatalogTable` instead of `databaseName` and `tableName` into `TableFileCatalog`, so that we don't need to fetch table metadata from metastore again 2. In `TableFileCatalog.filterPartitions0`, DO NOT set `PartitioningAwareFileCatalog.BASE_PATH_PARAM`. According to the [classdoc](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningAwareFileCatalog.scala#L189-L209), the default value of `basePath` already satisfies our need. What's more, if we set this parameter, we may break the case 2 which is metioned in the classdoc. 3. add `equals` and `hashCode` to `TableFileCatalog` 4. add `SessionCatalog.listPartitionsByFilter` which handles case sensitivity. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #15568 from cloud-fan/table-file-catalog.
* [SPARK-17624][SQL][STREAMING][TEST] Fixed flaky StateStoreSuite.maintenanceTathagata Das2016-10-243-28/+57
| | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The reason for the flakiness was follows. The test starts the maintenance background thread, and then writes 20 versions of the state store. The maintenance thread is expected to create snapshots in the middle, and clean up old files that are not needed any more. The earliest delta file (1.delta) is expected to be deleted as snapshots will ensure that the earliest delta would not be needed. However, the default configuration for the maintenance thread is to retain files such that last 2 versions can be recovered, and delete the rest. Now while generating the versions, the maintenance thread can kick in and create snapshots anywhere between version 10 and 20 (at least 10 deltas needed for snapshot). Then later it will choose to retain only version 20 and 19 (last 2). There are two cases. - Common case: One of the version between 10 and 19 gets snapshotted. Then recovering versions 19 and 20 just needs 19.snapshot and 20.delta, so 1.delta gets deleted. - Uncommon case (reason for flakiness): Only version 20 gets snapshotted. Then recovering versoin 20 requires 20.snapshot, and recovering version 19 all the previous 19...1.delta. So 1.delta does not get deleted. This PR rearranges the checks such that it create 20 versions, and then waits that there is at least one snapshot, then creates another 20. This will ensure that the latest 2 versions cannot require anything older than the first snapshot generated, and therefore will 1.delta will be deleted. In addition, I have added more logs, and comments that I felt would help future debugging and understanding what is going on. ## How was this patch tested? Ran the StateStoreSuite > 6K times in a heavily loaded machine (10 instances of tests running in parallel). No failures. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #15592 from tdas/SPARK-17624.
* [SPARK-17894][CORE] Ensure uniqueness of TaskSetManager name.Eren Avsarogullari2016-10-243-6/+29
| | | | | | | | | | | | | | | | | | | | | `TaskSetManager` should have unique name to avoid adding duplicate ones to parent `Pool` via `SchedulableBuilder`. This problem has been surfaced with following discussion: [[PR: Avoid adding duplicate schedulables]](https://github.com/apache/spark/pull/15326) **Proposal** : There is 1x1 relationship between `stageAttemptId` and `TaskSetManager` so `taskSet.Id` covering both `stageId` and `stageAttemptId` looks to be used for uniqueness of `TaskSetManager` name instead of just `stageId`. **Current TaskSetManager Name** : `var name = "TaskSet_" + taskSet.stageId.toString` **Sample**: TaskSet_0 **Proposed TaskSetManager Name** : `val name = "TaskSet_" + taskSet.Id ` `// taskSet.Id = (stageId + "." + stageAttemptId)` **Sample** : TaskSet_0.0 Added new Unit Test. Author: erenavsarogullari <erenavsarogullari@gmail.com> Closes #15463 from erenavsarogullari/SPARK-17894.
* [SPARK-17810][SQL] Default spark.sql.warehouse.dir is relative to local FS ↵Sean Owen2016-10-249-51/+29
| | | | | | | | | | | | | | | | but can resolve as HDFS path ## What changes were proposed in this pull request? Always resolve spark.sql.warehouse.dir as a local path, and as relative to working dir not home dir ## How was this patch tested? Existing tests. Author: Sean Owen <sowen@cloudera.com> Closes #15382 from srowen/SPARK-17810.
* [SPARK-18049][MLLIB][TEST] Add missing tests for truePositiveRate and ↵Zheng RuiFeng2016-10-246-6/+14
| | | | | | | | | | | | | | weightedTruePositiveRate ## What changes were proposed in this pull request? Add missing tests for `truePositiveRate` and `weightedTruePositiveRate` in `MulticlassMetricsSuite` ## How was this patch tested? added testing Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #15585 from zhengruifeng/mc_missing_test.
* [SPARKR][BRANCH-2.0] R merge API doc and example fixFelix Cheung2016-10-232-2/+2
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fixes for R doc ## How was this patch tested? N/A Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #15589 from felixcheung/rdocmergefix. (cherry picked from commit 0e0d83a597885ab1773cb69d6dcc10346d6976a3) Signed-off-by: Felix Cheung <felixcheung@apache.org>
* [SPARK-18058][SQL] Comparing column types ignoring Nullability in Union and ↵CodingCat2016-10-233-21/+31
| | | | | | | | | | | | | | | | | | SetOperation ## What changes were proposed in this pull request? The PR tries to fix [SPARK-18058](https://issues.apache.org/jira/browse/SPARK-18058) which refers to a bug that the column types are compared with the extra care about Nullability in Union and SetOperation. This PR converts the columns types by setting all fields as nullable before comparison ## How was this patch tested? regular unit test cases Author: CodingCat <zhunansjtu@gmail.com> Closes #15595 from CodingCat/SPARK-18058.
* [SPARK-18045][SQL][TESTS] Move `HiveDataFrameAnalyticsSuite` to package `sql`jiangxingbo2016-10-233-72/+100
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The testsuite `HiveDataFrameAnalyticsSuite` has nothing to do with HIVE, we should move it to package `sql`. The original test cases in that suite are splited into two existing testsuites: `DataFrameAggregateSuite` tests for the functions and ~~`SQLQuerySuite`~~`SQLQueryTestSuite` tests for the SQL statements. ## How was this patch tested? ~~Modified `SQLQuerySuite` in package `sql`.~~ Add query file for `SQLQueryTestSuite`. Author: jiangxingbo <jiangxb1987@gmail.com> Closes #15582 from jiangxb1987/group-analytics-test.
* [SPARK-18038][SQL] Move output partitioning definition from UnaryNodeExec to ↵Tejas Patil2016-10-2316-11/+76
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | its children ## What changes were proposed in this pull request? Jira : https://issues.apache.org/jira/browse/SPARK-18038 This was a suggestion by rxin over one of the dev list discussion : http://apache-spark-developers-list.1001551.n3.nabble.com/Project-not-preserving-child-partitioning-td19417.html His words: >> It would be better (safer) to move the output partitioning definition into each of the operator and remove it from UnaryExecNode. With this PR, following is the output partitioning and ordering for all the impls of `UnaryExecNode`. UnaryExecNode's impl | outputPartitioning | outputOrdering | comment ------------ | ------------- | ------------ | ------------ AppendColumnsExec | child's | Nil | child's ordering can be used AppendColumnsWithObjectExec | child's | Nil | child's ordering can be used BroadcastExchangeExec | BroadcastPartitioning | Nil | - CoalesceExec | UnknownPartitioning | Nil | - CollectLimitExec | SinglePartition | Nil | - DebugExec | child's | Nil | child's ordering can be used DeserializeToObjectExec | child's | Nil | child's ordering can be used ExpandExec | UnknownPartitioning | Nil | - FilterExec | child's | child's | - FlatMapGroupsInRExec | child's | Nil | child's ordering can be used GenerateExec | child's | Nil | need to dig more GlobalLimitExec | child's | child's | - HashAggregateExec | child's | Nil | - InputAdapter | child's | child's | - InsertIntoHiveTable | child's | Nil | terminal node, doesn't need partitioning LocalLimitExec | child's | child's | - MapElementsExec | child's | child's | - MapGroupsExec | child's | Nil | child's ordering can be used MapPartitionsExec | child's | Nil | child's ordering can be used ProjectExec | child's | child's | - SampleExec | child's | Nil | child's ordering can be used ScriptTransformation | child's | Nil | child's ordering can be used SerializeFromObjectExec | child's | Nil | child's ordering can be used ShuffleExchange | custom | Nil | - SortAggregateExec | child's | sort over grouped exprs | - SortExec | child's | custom | - StateStoreRestoreExec | child's | Nil | child's ordering can be used StateStoreSaveExec | child's | Nil | child's ordering can be used SubqueryExec | child's | child's | - TakeOrderedAndProjectExec | SinglePartition | custom | - WholeStageCodegenExec | child's | child's | - WindowExec | child's | child's | - ## How was this patch tested? This does NOT change any existing functionality so relying on existing tests Author: Tejas Patil <tejasp@fb.com> Closes #15575 from tejasapatil/SPARK-18038_UnaryNodeExec_output_partitioning.
* [SPARK-18035][SQL] Introduce performant and memory efficient APIs to create ↵Tejas Patil2016-10-225-68/+119
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ArrayBasedMapData ## What changes were proposed in this pull request? Jira: https://issues.apache.org/jira/browse/SPARK-18035 In HiveInspectors, I saw that converting Java map to Spark's `ArrayBasedMapData` spent quite sometime in buffer copying : https://github.com/apache/spark/blob/master/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala#L658 The reason being `map.toSeq` allocates a new buffer and copies the map entries to it: https://github.com/scala/scala/blob/2.11.x/src/library/scala/collection/MapLike.scala#L323 This copy is not needed as we get rid of it once we extract the key and value arrays. Here is the call trace: ``` org.apache.spark.sql.hive.HiveInspectors$$anonfun$unwrapperFor$41.apply(HiveInspectors.scala:664) scala.collection.AbstractMap.toSeq(Map.scala:59) scala.collection.MapLike$class.toSeq(MapLike.scala:323) scala.collection.AbstractMap.toBuffer(Map.scala:59) scala.collection.MapLike$class.toBuffer(MapLike.scala:326) scala.collection.AbstractTraversable.copyToBuffer(Traversable.scala:104) scala.collection.TraversableOnce$class.copyToBuffer(TraversableOnce.scala:275) scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48) scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104) scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59) scala.collection.AbstractIterable.foreach(Iterable.scala:54) scala.collection.IterableLike$class.foreach(IterableLike.scala:72) scala.collection.AbstractIterator.foreach(Iterator.scala:1336) scala.collection.Iterator$class.foreach(Iterator.scala:893) scala.collection.generic.Growable$$anonfun$$plus$plus$eq$1.apply(Growable.scala:59) scala.collection.generic.Growable$$anonfun$$plus$plus$eq$1.apply(Growable.scala:59) ``` Also, earlier code was populating keys and values arrays separately by iterating twice. The PR avoids double iteration of the map and does it in one iteration. EDIT: During code review, there were several more places in the code which were found to do similar thing. The PR dedupes those instances and introduces convenient APIs which are performant and memory efficient ## Performance gains The number is subjective and depends on how many map columns are accessed in the query and average entries per map. For one the queries that I tried out, I saw 3% CPU savings (end-to-end) for the query. ## How was this patch tested? This does not change the end result produced so relying on existing tests. Author: Tejas Patil <tejasp@fb.com> Closes #15573 from tejasapatil/SPARK-18035_avoid_toSeq.
* [SPARK-928][CORE] Add support for Unsafe-based serializer in KryoSandeep Singh2016-10-225-11/+206
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Now since we have migrated to Kryo-3.0.0 in https://issues.apache.org/jira/browse/SPARK-11416, we can gives users option to use unsafe SerDer. It can turned by setting `spark.kryo.useUnsafe` to `true` ## How was this patch tested? Ran existing tests ``` Benchmark Kryo Unsafe vs safe Serialization: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ basicTypes: Int unsafe:true 160 / 178 98.5 10.1 1.0X basicTypes: Long unsafe:true 210 / 218 74.9 13.4 0.8X basicTypes: Float unsafe:true 203 / 213 77.5 12.9 0.8X basicTypes: Double unsafe:true 226 / 235 69.5 14.4 0.7X Array: Int unsafe:true 1087 / 1101 14.5 69.1 0.1X Array: Long unsafe:true 2758 / 2844 5.7 175.4 0.1X Array: Float unsafe:true 1511 / 1552 10.4 96.1 0.1X Array: Double unsafe:true 2942 / 2972 5.3 187.0 0.1X Map of string->Double unsafe:true 2645 / 2739 5.9 168.2 0.1X basicTypes: Int unsafe:false 211 / 218 74.7 13.4 0.8X basicTypes: Long unsafe:false 247 / 253 63.6 15.7 0.6X basicTypes: Float unsafe:false 211 / 216 74.5 13.4 0.8X basicTypes: Double unsafe:false 227 / 233 69.2 14.4 0.7X Array: Int unsafe:false 3012 / 3032 5.2 191.5 0.1X Array: Long unsafe:false 4463 / 4515 3.5 283.8 0.0X Array: Float unsafe:false 2788 / 2868 5.6 177.2 0.1X Array: Double unsafe:false 3558 / 3752 4.4 226.2 0.0X Map of string->Double unsafe:false 2806 / 2933 5.6 178.4 0.1X ``` Author: Sandeep Singh <sandeep@techaddict.me> Author: Sandeep Singh <sandeep@origamilogic.com> Closes #12913 from techaddict/SPARK-928.
* [SPARK-18051][SPARK CORE] fix bug of custom PartitionCoalescer causing ↵WeichenXu2016-10-222-1/+6
| | | | | | | | | | | | | | | | | serialization exception ## What changes were proposed in this pull request? add a require check in `CoalescedRDD` to make sure the passed in `partitionCoalescer` to be `serializable`. and update the document for api `RDD.coalesce` ## How was this patch tested? Manual.(test code in jira [SPARK-18051]) Author: WeichenXu <WeichenXu123@outlook.com> Closes #15587 from WeichenXu123/fix_coalescer_bug.
* [SPARK-17123][SQL] Use type-widened encoder for DataFrame rather than ↵hyukjinkwon2016-10-222-4/+30
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | existing encoder to allow type-widening from set operations # What changes were proposed in this pull request? This PR fixes set operations in `DataFrame` to be performed fine without exceptions when the types are non-scala native types. (e.g, `TimestampType`, `DateType` and `DecimalType`). The problem is, it seems set operations such as `union`, `intersect` and `except` uses the encoder belonging to the `Dataset` in caller. So, `Dataset` of the caller holds `ExpressionEncoder[Row]` as it is when the set operations are performed. However, the return types can be actually widen. So, we should use `ExpressionEncoder[Row]` constructed from executed plan rather than using existing one. Otherwise, this will generate some codes wrongly via `StaticInvoke`. Running the codes below: ```scala val dates = Seq( (new Date(0), BigDecimal.valueOf(1), new Timestamp(2)), (new Date(3), BigDecimal.valueOf(4), new Timestamp(5)) ).toDF("date", "timestamp", "decimal") val widenTypedRows = Seq( (new Timestamp(2), 10.5D, "string") ).toDF("date", "timestamp", "decimal") val results = dates.union(widenTypedRows).collect() results.foreach(println) ``` prints below: **Before** ```java 23:08:54.490 ERROR org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator: failed to compile: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 28, Column 107: No applicable constructor/method found for actual parameters "long"; candidates are: "public static java.sql.Date org.apache.spark.sql.catalyst.util.DateTimeUtils.toJavaDate(int)" /* 001 */ public java.lang.Object generate(Object[] references) { /* 002 */ return new SpecificSafeProjection(references); /* 003 */ } /* 004 */ /* 005 */ class SpecificSafeProjection extends org.apache.spark.sql.catalyst.expressions.codegen.BaseProjection { /* 006 */ /* 007 */ private Object[] references; /* 008 */ private MutableRow mutableRow; /* 009 */ private Object[] values; /* 010 */ private org.apache.spark.sql.types.StructType schema; /* 011 */ /* 012 */ /* 013 */ public SpecificSafeProjection(Object[] references) { /* 014 */ this.references = references; /* 015 */ mutableRow = (MutableRow) references[references.length - 1]; /* 016 */ /* 017 */ this.schema = (org.apache.spark.sql.types.StructType) references[0]; /* 018 */ } /* 019 */ /* 020 */ public java.lang.Object apply(java.lang.Object _i) { /* 021 */ InternalRow i = (InternalRow) _i; /* 022 */ /* 023 */ values = new Object[3]; /* 024 */ /* 025 */ boolean isNull2 = i.isNullAt(0); /* 026 */ long value2 = isNull2 ? -1L : (i.getLong(0)); /* 027 */ boolean isNull1 = isNull2; /* 028 */ final java.sql.Date value1 = isNull1 ? null : org.apache.spark.sql.catalyst.util.DateTimeUtils.toJavaDate(value2); /* 029 */ isNull1 = value1 == null; /* 030 */ if (isNull1) { /* 031 */ values[0] = null; /* 032 */ } else { /* 033 */ values[0] = value1; /* 034 */ } /* 035 */ /* 036 */ boolean isNull4 = i.isNullAt(1); /* 037 */ double value4 = isNull4 ? -1.0 : (i.getDouble(1)); /* 038 */ /* 039 */ boolean isNull3 = isNull4; /* 040 */ java.math.BigDecimal value3 = null; /* 041 */ if (!isNull3) { /* 042 */ /* 043 */ Object funcResult = null; /* 044 */ funcResult = value4.toJavaBigDecimal(); /* 045 */ if (funcResult == null) { /* 046 */ isNull3 = true; /* 047 */ } else { /* 048 */ value3 = (java.math.BigDecimal) funcResult; /* 049 */ } /* 050 */ /* 051 */ } /* 052 */ isNull3 = value3 == null; /* 053 */ if (isNull3) { /* 054 */ values[1] = null; /* 055 */ } else { /* 056 */ values[1] = value3; /* 057 */ } /* 058 */ /* 059 */ boolean isNull6 = i.isNullAt(2); /* 060 */ UTF8String value6 = isNull6 ? null : (i.getUTF8String(2)); /* 061 */ boolean isNull5 = isNull6; /* 062 */ final java.sql.Timestamp value5 = isNull5 ? null : org.apache.spark.sql.catalyst.util.DateTimeUtils.toJavaTimestamp(value6); /* 063 */ isNull5 = value5 == null; /* 064 */ if (isNull5) { /* 065 */ values[2] = null; /* 066 */ } else { /* 067 */ values[2] = value5; /* 068 */ } /* 069 */ /* 070 */ final org.apache.spark.sql.Row value = new org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema(values, schema); /* 071 */ if (false) { /* 072 */ mutableRow.setNullAt(0); /* 073 */ } else { /* 074 */ /* 075 */ mutableRow.update(0, value); /* 076 */ } /* 077 */ /* 078 */ return mutableRow; /* 079 */ } /* 080 */ } ``` **After** ```bash [1969-12-31 00:00:00.0,1.0,1969-12-31 16:00:00.002] [1969-12-31 00:00:00.0,4.0,1969-12-31 16:00:00.005] [1969-12-31 16:00:00.002,10.5,string] ``` ## How was this patch tested? Unit tests in `DataFrameSuite` Author: hyukjinkwon <gurwls223@gmail.com> Closes #15072 from HyukjinKwon/SPARK-17123.
* [SPARK-17994][SQL] Add back a file status cache for catalog tablesEric Liang2016-10-229-96/+385
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In SPARK-16980, we removed the full in-memory cache of table partitions in favor of loading only needed partitions from the metastore. This greatly improves the initial latency of queries that only read a small fraction of table partitions. However, since the metastore does not store file statistics, we need to discover those from remote storage. With the loss of the in-memory file status cache this has to happen on each query, increasing the latency of repeated queries over the same partitions. The proposal is to add back a per-table cache of partition contents, i.e. Map[Path, Array[FileStatus]]. This cache would be retained per-table, and can be invalidated through refreshTable() and refreshByPath(). Unlike the prior cache, it can be incrementally updated as new partitions are read. ## How was this patch tested? Existing tests and new tests in `HiveTablePerfStatsSuite`. cc mallman Author: Eric Liang <ekl@databricks.com> Author: Michael Allman <michael@videoamp.com> Author: Eric Liang <ekhliang@gmail.com> Closes #15539 from ericl/meta-cache.
* [SPARK-17986][ML] SQLTransformer should remove temporary tablesDrew Robb2016-10-222-1/+4
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? A call to the method `SQLTransformer.transform` previously would create a temporary table and never delete it. This change adds a call to `dropTempView()` that deletes this temporary table before returning the result so that the table will not remain in spark's table catalog. Because `tableName` is randomized and not exposed, there should be no expected use of this table outside of the `transform` method. ## How was this patch tested? A single new assertion was added to the existing test of the `SQLTransformer.transform` method that all temporary tables are removed. Without the corresponding code change, this new assertion fails. I am not aware of any circumstances in which removing this temporary view would be bad for performance or correctness in other ways, but some expertise here would be helpful. Author: Drew Robb <drewrobb@gmail.com> Closes #15526 from drewrobb/SPARK-17986.
* [SPARK-17898][DOCS] repositories needs username and passwordSean Owen2016-10-222-4/+6
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Document `user:password` syntax as possible means of specifying credentials for password-protected `--repositories` ## How was this patch tested? Doc build Author: Sean Owen <sowen@cloudera.com> Closes #15584 from srowen/SPARK-17898.
* [SPARK-17944][DEPLOY] sbin/start-* scripts use of `hostname -f` fail with ↵Erik O'Shaughnessy2016-10-223-3/+24
| | | | | | | | | | | | | | | | Solaris ## What changes were proposed in this pull request? Modify sbin/start-master.sh, sbin/start-mesos-dispatcher.sh and sbin/start-slaves.sh to use the output of 'uname' to select which OS-specific command-line is used to determine the host's fully qualified host name. ## How was this patch tested? Tested by hand; starting on Solaris, Linux and macOS. Author: Erik O'Shaughnessy <erik.oshaughnessy@gmail.com> Closes #15557 from JnyJny/SPARK-17944.
* [SPARK-16606][MINOR] Tiny follow-up to , to correct more instances of the ↵Sean Owen2016-10-211-2/+2
| | | | | | | | | | | | | | | | same log message typo ## What changes were proposed in this pull request? Tiny follow-up to SPARK-16606 / https://github.com/apache/spark/pull/14533 , to correct more instances of the same log message typo ## How was this patch tested? Existing tests (no functional change anyway) Author: Sean Owen <sowen@cloudera.com> Closes #15586 from srowen/SPARK-16606.2.
* [SPARK-18042][SQL] OutputWriter should expose file path writtenReynold Xin2016-10-2111-39/+90
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch adds a new "path" method on OutputWriter that returns the path of the file written by the OutputWriter. This is part of the necessary work to consolidate structured streaming and batch write paths. The batch write path has a nice feature that each data source can define the extension of the files, and allow Spark to specify the staging directory and the prefix for the files. However, in the streaming path we need to collect the list of files written, and there is no interface right now to do that. ## How was this patch tested? N/A - there is no behavior change and this should be covered by existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #15580 from rxin/SPARK-18042.
* [STREAMING][KAFKA][DOC] clarify kafka settings needed for larger batchescody koeninger2016-10-211-0/+1
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Minor doc change to mention kafka configuration for larger spark batches. ## How was this patch tested? Doc change only, confirmed via jekyll. The configuration issue was discussed / confirmed with users on the mailing list. Author: cody koeninger <cody@koeninger.org> Closes #15570 from koeninger/kafka-doc-heartbeat.
* [SPARK-17812][SQL][KAFKA] Assign and specific startingOffsets for structured ↵cody koeninger2016-10-218-57/+395
| | | | | | | | | | | | | | | | | | stream ## What changes were proposed in this pull request? startingOffsets takes specific per-topicpartition offsets as a json argument, usable with any consumer strategy assign with specific topicpartitions as a consumer strategy ## How was this patch tested? Unit tests Author: cody koeninger <cody@koeninger.org> Closes #15504 from koeninger/SPARK-17812.
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