jira: https://issues.apache.org/jira/browse/SPARK-11507
"In certain situations when adding two block matrices, I get an error regarding colPtr and the operation fails. External issue URL includes full error and code for reproducing the problem."
root cause: colPtr.last does NOT always equal to values.length in breeze SCSMatrix, which fails the require in SparseMatrix.
easy step to repro:
```
val m1: BM[Double] = new CSCMatrix[Double] (Array (1.0, 1, 1), 3, 3, Array (0, 1, 2, 3), Array (0, 1, 2) )
val m2: BM[Double] = new CSCMatrix[Double] (Array (1.0, 2, 2, 4), 3, 3, Array (0, 0, 2, 4), Array (1, 2, 1, 2) )
val sum = m1 + m2
Matrices.fromBreeze(sum)
```
Solution: By checking the code in [CSCMatrix](28000a7b90/math/src/main/scala/breeze/linalg/CSCMatrix.scala), CSCMatrix in breeze can have extra zeros in the end of data array. Invoking compact will make sure it aligns with the require of SparseMatrix. This should add limited overhead as the actual compact operation is only performed when necessary.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#9520 from hhbyyh/matricesFromBreeze.
## What changes were proposed in this pull request?
```MultilayerPerceptronClassifier``` supports save/load for Python API.
## How was this patch tested?
doctest.
cc mengxr jkbradley yinxusen
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#11952 from yanboliang/spark-14152.
## What changes were proposed in this pull request?
Fix the wrong param name of LDA ```topicDistributionCol```.
## How was this patch tested?
No tests.
cc jkbradley
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#12065 from yanboliang/lda-topicDistributionCol.
https://issues.apache.org/jira/browse/SPARK-14181
TrainValidationSplit should have HasSeed for the random split of RDD. I also changed the random split from the RDD function to the DataFrame function.
Author: Xusen Yin <yinxusen@gmail.com>
Closes#11985 from yinxusen/SPARK-14181.
Move the logic to find Spark jars to CommandBuilderUtils and make it
available for YARN code, so that it's possible to easily launch Spark
on YARN from a build directory.
Tested by running SparkPi from the build directory on YARN.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#11970 from vanzin/SPARK-13955.
## What changes were proposed in this pull request?
In `ExpressionEncoder`, we use `constructorFor` to build `fromRowExpression` as the `deserializer` in `ObjectOperator`. It's kind of confusing, we should make the name consistent.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12058 from cloud-fan/rename.
## What changes were proposed in this pull request?
This PR implements buildReader for text data source and enable it in the new data source code path.
## How was this patch tested?
Existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11934 from cloud-fan/text.
## What changes were proposed in this pull request?
It would be very helpful for network performance investigation if we log the time spent on connecting and resolving host.
## How was this patch tested?
Jenkins unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#12046 from zsxwing/connection-time.
#### What changes were proposed in this pull request?
This PR is to implement the following four Database-related DDL commands:
- `CREATE DATABASE|SCHEMA [IF NOT EXISTS] database_name`
- `DROP DATABASE [IF EXISTS] database_name [RESTRICT|CASCADE]`
- `DESCRIBE DATABASE [EXTENDED] db_name`
- `ALTER (DATABASE|SCHEMA) database_name SET DBPROPERTIES (property_name=property_value, ...)`
Another PR will be submitted to handle the unsupported commands. In the Database-related DDL commands, we will issue an error exception for `ALTER (DATABASE|SCHEMA) database_name SET OWNER [USER|ROLE] user_or_role`.
cc yhuai andrewor14 rxin Could you review the changes? Is it in the right direction? Thanks!
#### How was this patch tested?
Added a few test cases in `command/DDLSuite.scala` for testing DDL command execution in `SQLContext`. Since `HiveContext` also shares the same implementation, the existing test cases in `\hive` also verifies the correctness of these commands.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#12009 from gatorsmile/dbDDL.
## What changes were proposed in this pull request?
For MemoryMode.OFF_HEAP, Unsafe.getInt etc. are used with no restriction.
However, the Oracle implementation uses these methods only if the class variable unaligned (commented as "Cached unaligned-access capability") is true, which seems to be calculated whether the architecture is i386, x86, amd64, or x86_64.
I think we should perform similar check for the use of Unsafe.
Reference: https://github.com/netty/netty/blob/4.1/common/src/main/java/io/netty/util/internal/PlatformDependent0.java#L112
## How was this patch tested?
Unit test suite
Author: tedyu <yuzhihong@gmail.com>
Closes#11943 from tedyu/master.
## What changes were proposed in this pull request?
Builds on https://github.com/apache/spark/pull/12022 and (a) appends "..." to truncated comment strings and (b) fixes indentation in lines after the commented strings if they happen to have a `(`, `{`, `)` or `}`
## How was this patch tested?
Manually examined the generated code.
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12044 from sameeragarwal/comment.
## What changes were proposed in this pull request?
This PR brings the support for chained Python UDFs, for example
```sql
select udf1(udf2(a))
select udf1(udf2(a) + 3)
select udf1(udf2(a) + udf3(b))
```
Also directly chained unary Python UDFs are put in single batch of Python UDFs, others may require multiple batches.
For example,
```python
>>> sqlContext.sql("select double(double(1))").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [pythonUDF#10 AS double(double(1))#9]
: +- INPUT
+- !BatchPythonEvaluation double(double(1)), [pythonUDF#10]
+- Scan OneRowRelation[]
>>> sqlContext.sql("select double(double(1) + double(2))").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [pythonUDF#19 AS double((double(1) + double(2)))#16]
: +- INPUT
+- !BatchPythonEvaluation double((pythonUDF#17 + pythonUDF#18)), [pythonUDF#17,pythonUDF#18,pythonUDF#19]
+- !BatchPythonEvaluation double(2), [pythonUDF#17,pythonUDF#18]
+- !BatchPythonEvaluation double(1), [pythonUDF#17]
+- Scan OneRowRelation[]
```
TODO: will support multiple unrelated Python UDFs in one batch (another PR).
## How was this patch tested?
Added new unit tests for chained UDFs.
Author: Davies Liu <davies@databricks.com>
Closes#12014 from davies/py_udfs.
## What changes were proposed in this pull request?
This PR is a simple fix for an exception message to print `string[]` content correctly.
```java
String[] colPath = requestedSchema.getPaths().get(i);
...
- throw new IOException("Required column is missing in data file. Col: " + colPath);
+ throw new IOException("Required column is missing in data file. Col: " + Arrays.toString(colPath));
```
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12041 from dongjoon-hyun/fix_exception_message_with_string_array.
## What changes were proposed in this pull request?
This PR fixes two trivial typos: 'does not **much**' --> 'does not **match**'.
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12042 from dongjoon-hyun/fix_typo_by_replacing_much_with_match.
Add a new api endpoint `/api/v1/version` to retrieve various version info. This PR only adds support for finding the current spark version, however other version info such as jvm or scala versions can easily be added.
Author: Jakob Odersky <jodersky@gmail.com>
Closes#10760 from jodersky/SPARK-10570.
## What changes were proposed in this pull request?
The event timeline doesn't show on job page if an executor is removed with a multiple line reason. This PR replaces all new line characters in the reason string with spaces.
## How was this patch tested?
Verified on the Web UI.
Author: Carson Wang <carson.wang@intel.com>
Closes#12029 from carsonwang/eventTimeline.
## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-14154
I just read the code for KolmogorovSmirnovTest and find it could be much simplified following the original definition.
Send a PR for discussion
## How was this patch tested?
unit test
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#11954 from hhbyyh/ksoptimize.
## What changes were proposed in this pull request?
Renames SQL option `spark.sql.parquet.fileScan` since now all `HadoopFsRelation` based data sources are being migrated to `FileScanRDD` code path.
## How was this patch tested?
None.
Author: Cheng Lian <lian@databricks.com>
Closes#12003 from liancheng/spark-14208-option-renaming.
## What changes were proposed in this pull request?
Adding binary toggle parameter to ml.feature.HashingTF, as well as mllib.feature.HashingTF since the former wraps this functionality. This parameter, if true, will set non-zero valued term counts to 1 to transform term count features to binary values that are well suited for discrete probability models.
## How was this patch tested?
Added unit tests for ML and MLlib
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#11832 from BryanCutler/binary-param-HashingTF-SPARK-13963.
## What changes were proposed in this pull request?
This PR implements buildReader for json data source and enable it in the new data source code path.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11960 from cloud-fan/json.
Add property to MLWritable.write method, so we can use .write instead of .write()
Add a new test to ml/test.py to check whether the write is a property.
./python/run-tests --python-executables=python2.7 --modules=pyspark-ml
Will test against the following Python executables: ['python2.7']
Will test the following Python modules: ['pyspark-ml']
Finished test(python2.7): pyspark.ml.evaluation (11s)
Finished test(python2.7): pyspark.ml.clustering (16s)
Finished test(python2.7): pyspark.ml.classification (24s)
Finished test(python2.7): pyspark.ml.recommendation (24s)
Finished test(python2.7): pyspark.ml.feature (39s)
Finished test(python2.7): pyspark.ml.regression (26s)
Finished test(python2.7): pyspark.ml.tuning (15s)
Finished test(python2.7): pyspark.ml.tests (30s)
Tests passed in 55 seconds
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#11945 from wangmiao1981/fix_property.
## What changes were proposed in this pull request?
Now that GBTs have been moved to ML, they can use the implementation of feature importance for random forests. This patch simply adds a `featureImportances` attribute to `GBTClassifier` and `GBTRegressor` and adds tests for each.
GBT feature importances here simply average the feature importances for each tree in its ensemble. This follows the implementation from scikit-learn. This method is also suggested by J Friedman in [this paper](https://statweb.stanford.edu/~jhf/ftp/trebst.pdf).
## How was this patch tested?
Unit tests were added to `GBTClassifierSuite` and `GBTRegressorSuite` to validate feature importances.
Author: sethah <seth.hendrickson16@gmail.com>
Closes#11961 from sethah/SPARK-11730.
## What changes were proposed in this pull request?
Refactor RRDD by separating the common logic interacting with the R worker to a new class RRunner, which can be used to evaluate R UDFs.
Now RRDD relies on RRuner for RDD computation and RRDD could be reomved if we want to remove RDD API in SparkR later.
## How was this patch tested?
dev/lint-r
SparkR unit tests
Author: Sun Rui <rui.sun@intel.com>
Closes#12024 from sun-rui/SPARK-12792_new.
## What changes were proposed in this pull request?
This adds a metric to parquet scans that measures the time in just the scan phase. This is
only possible when the scan returns ColumnarBatches, otherwise the overhead is too high.
This combined with the pipeline metric lets us easily see what percent of the time was
in the scan.
Author: Nong Li <nong@databricks.com>
Closes#12007 from nongli/spark-14210.
## What changes were proposed in this pull request?
This improves the Filter codegen for NULLs by deferring loading the values for IsNotNull.
Instead of generating code like:
boolean isNull = ...
int value = ...
if (isNull) continue;
we will generate:
boolean isNull = ...
if (isNull) continue;
int value = ...
This is useful since retrieving the values can be non-trivial (they can be dictionary encoded
among other things). This currently only works when the attribute comes from the column batch
but could be extended to other cases in the future.
## How was this patch tested?
On tpcds q55, this fixes the regression from introducing the IsNotNull predicates.
```
TPCDS Snappy: Best/Avg Time(ms) Rate(M/s) Per Row(ns)
--------------------------------------------------------------------------------
q55 4564 / 5036 25.2 39.6
q55 4064 / 4340 28.3 35.3
```
Author: Nong Li <nong@databricks.com>
Closes#11792 from nongli/spark-13981.
### What changes were proposed in this pull request?
This PR migrates all HiveQl parsing to the new ANTLR4 parser. This PR is build on top of https://github.com/apache/spark/pull/12011, and we should wait with merging until that one is in (hence the WIP tag).
As soon as this PR is merged we can start removing much of the old parser infrastructure.
### How was this patch tested?
Exisiting Hive unit tests.
cc rxin andrewor14 yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12015 from hvanhovell/SPARK-14213.
## What changes were proposed in this pull request?
After DataFrame and Dataset are merged, the trait `Queryable` becomes unnecessary as it has only one implementation. We should remove it.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12001 from cloud-fan/df-ds.
## What changes were proposed in this pull request?
Currently, `GraphOps.pickRandomVertex()` falls into infinite loops for graphs having only one vertex. This PR fixes it by modifying the following termination-checking condition.
```scala
- if (selectedVertices.count > 1) {
+ if (selectedVertices.count > 0) {
```
## How was this patch tested?
Pass the Jenkins tests (including new test case).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12018 from dongjoon-hyun/SPARK-14219.
## What changes were proposed in this pull request?
This is a follow-up fix of #9963, in #9963 we handle this stale states clean-up work only for dynamic allocation enabled scenario. Here we should also clean the states in `CoarseGrainedSchedulerBackend` for dynamic allocation disabled scenario.
Please review, CC andrewor14 lianhuiwang , thanks a lot.
## How was this patch tested?
Run the unit test locally, also with integration test manually.
Author: jerryshao <sshao@hortonworks.com>
Closes#11366 from jerryshao/SPARK-13447.
## What changes were proposed in this pull request?
We have a streaming job using `FlumePollInputStream` always driver OOM after few days, here is some driver heap dump before OOM
```
num #instances #bytes class name
----------------------------------------------
1: 13845916 553836640 org.apache.spark.storage.BlockStatus
2: 14020324 336487776 org.apache.spark.storage.StreamBlockId
3: 13883881 333213144 scala.collection.mutable.DefaultEntry
4: 8907 89043952 [Lscala.collection.mutable.HashEntry;
5: 62360 65107352 [B
6: 163368 24453904 [Ljava.lang.Object;
7: 293651 20342664 [C
...
```
`BlockStatus` and `StreamBlockId` keep on growing, and the driver OOM in the end.
After investigated, i found the `executorIdToStorageStatus` in `StorageStatusListener` seems never remove the blocks from `StorageStatus`.
In order to fix the issue, i try to use `onBlockUpdated` replace `onTaskEnd ` , so we can update the block informations(add blocks, drop the block from memory to disk and delete the blocks) in time.
## How was this patch tested?
Existing unit tests and manual tests
Author: jeanlyn <jeanlyn92@gmail.com>
Closes#11779 from jeanlyn/fix_driver_oom.
## What changes were proposed in this pull request?
Before: We just pass all role commands to Hive even though it doesn't work.
After: We throw an `AnalysisException` that looks like this:
```
scala> sql("CREATE ROLE x")
org.apache.spark.sql.AnalysisException: Unsupported Hive operation: CREATE ROLE;
at org.apache.spark.sql.hive.HiveQl$$anonfun$parsePlan$1.apply(HiveQl.scala:213)
at org.apache.spark.sql.hive.HiveQl$$anonfun$parsePlan$1.apply(HiveQl.scala:208)
at org.apache.spark.sql.catalyst.parser.CatalystQl.safeParse(CatalystQl.scala:49)
at org.apache.spark.sql.hive.HiveQl.parsePlan(HiveQl.scala:208)
at org.apache.spark.sql.SQLContext.parseSql(SQLContext.scala:198)
```
## How was this patch tested?
`HiveQuerySuite`
Author: Andrew Or <andrew@databricks.com>
Closes#11948 from andrewor14/ddl-role-management.
## What changes were proposed in this pull request?
Session catalog was added in #11750. However, it doesn't really support temporary functions properly; right now we only store the metadata in the form of `CatalogFunction`, but this doesn't make sense for temporary functions because there is no class name.
This patch moves the `FunctionRegistry` into the `SessionCatalog`. With this, the user can call `catalog.createTempFunction` and `catalog.lookupFunction` to use the function they registered previously. This is currently still dead code, however.
## How was this patch tested?
`SessionCatalogSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#11972 from andrewor14/temp-functions.
## What changes were proposed in this pull request?
Extract the workaround for HADOOP-10622 introduced by #11940 into UninterruptibleThread so that we can test and reuse it.
## How was this patch tested?
Unit tests
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#11971 from zsxwing/uninterrupt.
## What changes were proposed in this pull request?
UserDefinedType is a developer API in Spark 1.x. With very high probability we will create a new API for user-defined type that also works well with column batches as well as encoders (datasets). In Spark 2.0, let's make `UserDefinedType` `private[spark]` first.
## How was this patch tested?
Existing unit tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#11955 from rxin/SPARK-14155.
## What changes were proposed in this pull request?
This patch addresses the remaining comments left in #11750 and #11918 after they are merged. For a full list of changes in this patch, just trace the commits.
## How was this patch tested?
`SessionCatalogSuite` and `CatalogTestCases`
Author: Andrew Or <andrew@databricks.com>
Closes#12006 from andrewor14/session-catalog-followup.
## What changes were proposed in this pull request?
Call `executor.stop` in a new thread to eliminate deadlock.
## How was this patch tested?
Existing unit tests
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#12012 from zsxwing/SPARK-14180.
#### What changes were proposed in this pull request?
This PR adds all the current Spark SQL DDL commands to the new ANTLR 4 based SQL parser.
I have found a few inconsistencies in the current commands:
- Function has an alias field. This is actually the class name of the function.
- Partition specifications should contain nulls in some commands, and contain `None`s in others.
- `AlterTableSkewedLocation`: Should defines which columns have skewed values, and should allow us to define storage for each skewed combination of values. We currently only allow one value per field.
- `AlterTableSetFileFormat`: Should only have one file format, it currently supports both.
I have implemented all these comments like they were, and I propose to improve them in follow-up PRs.
#### How was this patch tested?
The existing DDLCommandSuite.
cc rxin andrewor14 yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12011 from hvanhovell/SPARK-14086.
https://issues.apache.org/jira/browse/SPARK-11893
jkbradley In order to share read/write with `TrainValidationSplit`, I move the `SharedReadWrite` out of `CrossValidator` into a new trait `SharedReadWrite` in the tunning package.
To reduce the repeated tests, I move the complex tests from `CrossValidatorSuite` to `SharedReadWriteSuite`, and create a fake validator called `MyValidator` to test the shared code.
With `SharedReadWrite`, potential newly added `Validator` can share the read/write common part, and only need to implement their extra params save/load.
Author: Xusen Yin <yinxusen@gmail.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9971 from yinxusen/SPARK-11893.
## What changes were proposed in this pull request?
This PR replaces list comprehension in python_full_outer_join.dispatch with a generator expression.
## How was this patch tested?
PySpark-Core, PySpark-MLlib test suites against Python 2.7, 3.5.
Author: zero323 <matthew.szymkiewicz@gmail.com>
Closes#11998 from zero323/pyspark-join-generator-expr.
## What changes were proposed in this pull request?
This patch will ensure that we trim all path set in yarn.nodemanager.local-dirs and that the the scheme is well removed so the level db can be created.
## How was this patch tested?
manual tests.
Author: nfraison <nfraison@yahoo.fr>
Closes#11475 from ashangit/level_db_creation_issue.
## What changes were proposed in this pull request?
Currently, for the key that can not fit within a long, we build a hash map for UnsafeHashedRelation, it's converted to BytesToBytesMap after serialization and deserialization. We should build a BytesToBytesMap directly to have better memory efficiency.
In order to do that, BytesToBytesMap should support multiple (K,V) pair with the same K, Location.putNewKey() is renamed to Location.append(), which could append multiple values for the same key (same Location). `Location.newValue()` is added to find the next value for the same key.
## How was this patch tested?
Existing tests. Added benchmark for broadcast hash join with duplicated keys.
Author: Davies Liu <davies@databricks.com>
Closes#11870 from davies/map2.
### What changes were proposed in this pull request?
The current ANTLR3 parser is quite complex to maintain and suffers from code blow-ups. This PR introduces a new parser that is based on ANTLR4.
This parser is based on the [Presto's SQL parser](https://github.com/facebook/presto/blob/master/presto-parser/src/main/antlr4/com/facebook/presto/sql/parser/SqlBase.g4). The current implementation can parse and create Catalyst and SQL plans. Large parts of the HiveQl DDL and some of the DML functionality is currently missing, the plan is to add this in follow-up PRs.
This PR is a work in progress, and work needs to be done in the following area's:
- [x] Error handling should be improved.
- [x] Documentation should be improved.
- [x] Multi-Insert needs to be tested.
- [ ] Naming and package locations.
### How was this patch tested?
Catalyst and SQL unit tests.
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#11557 from hvanhovell/ngParser.
## What changes were proposed in this pull request?
JIRA: https://issues.apache.org/jira/browse/SPARK-14156
In HiveComparisonTest, when catalyst results are different to hive results, we will collect the messages for computed tables during the test. During creating the message, we use sparkPlan. But we actually run the query with executedPlan. So the error message is sometimes confusing.
For example, as wholestage codegen is enabled by default now. The shown spark plan for computed tables is the plan before wholestage codegen.
A concrete is the following error message shown before this patch. It is the error shown when running `HiveCompatibilityTest` `auto_join26`.
auto_join26 has one SQL to create table:
INSERT OVERWRITE TABLE dest_j1
SELECT x.key, count(1) FROM src1 x JOIN src y ON (x.key = y.key) group by x.key; (1)
Then a SQL to retrieve the result:
select * from dest_j1 x order by x.key; (2)
When the above SQL (2) to retrieve the result fails, In `HiveComparisonTest` we will try to collect and show the generated data from table `dest_j1` using the SQL (1)'s spark plan. The you will see this error:
TungstenAggregate(key=[key#8804], functions=[(count(1),mode=Partial,isDistinct=false)], output=[key#8804,count#8834L])
+- Project [key#8804]
+- BroadcastHashJoin [key#8804], [key#8806], Inner, BuildRight, None
:- Filter isnotnull(key#8804)
: +- InMemoryColumnarTableScan [key#8804], [isnotnull(key#8804)], InMemoryRelation [key#8804,value#8805], true, 5, StorageLevel(true, true, false, true, 1), HiveTableScan [key#8717,value#8718], MetastoreRelation default, src1, None, Some(src1)
+- Filter isnotnull(key#8806)
+- InMemoryColumnarTableScan [key#8806], [isnotnull(key#8806)], InMemoryRelation [key#8806,value#8807], true, 5, StorageLevel(true, true, false, true, 1), HiveTableScan [key#8760,value#8761], MetastoreRelation default, src, None, Some(src)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:47)
at org.apache.spark.sql.execution.aggregate.TungstenAggregate.doExecute(TungstenAggregate.scala:82)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:121)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:121)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:140)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:137)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:120)
at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1.apply(TungstenAggregate.scala:87)
at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1.apply(TungstenAggregate.scala:82)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:46)
... 70 more
Caused by: java.lang.UnsupportedOperationException: Filter does not implement doExecuteBroadcast
at org.apache.spark.sql.execution.SparkPlan.doExecuteBroadcast(SparkPlan.scala:221)
The message is confusing because it is not the plan actually run by SparkSQL engine to create the generated table. The plan actually run is no problem. But as before this patch, we run `e.sparkPlan.collect` to retrieve and show the generated data, spark plan is not the plan we can run. So the above error will be shown.
After this patch, we won't see the error because the executed plan is no problem and works.
## How was this patch tested?
Existing tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#11957 from viirya/use-executedplan.
Refactor RRDD by separating the common logic interacting with the R worker to a new class RRunner, which can be used to evaluate R UDFs.
Now RRDD relies on RRuner for RDD computation and RRDD could be reomved if we want to remove RDD API in SparkR later.
Author: Sun Rui <rui.sun@intel.com>
Closes#10947 from sun-rui/SPARK-12792.
JIRA: https://issues.apache.org/jira/browse/SPARK-13742
## What changes were proposed in this pull request?
`RandomSampler.sample` currently accepts iterator as input and output another iterator. This makes it inappropriate to use in wholestage codegen of `Sampler` operator #11517. This change is to add non-iterator interface to `RandomSampler`.
This change adds a new method `def sample(): Int` to the trait `RandomSampler`. As we don't need to know the actual values of the sampling items, so this new method takes no arguments.
This method will decide whether to sample the next item or not. It returns how many times the next item will be sampled.
For `BernoulliSampler` and `BernoulliCellSampler`, the returned sampling times can only be 0 or 1. It simply means whether to sample the next item or not.
For `PoissonSampler`, the returned value can be more than 1, meaning the next item will be sampled multiple times.
## How was this patch tested?
Tests are added into `RandomSamplerSuite`.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: Liang-Chi Hsieh <viirya@appier.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11578 from viirya/random-sampler-no-iterator.
## What changes were proposed in this pull request?
Fix incorrect use of binarySearch in SparseMatrix
## How was this patch tested?
Unit test added.
Author: Chenliang Xu <chexu@groupon.com>
Closes#11992 from luckyrandom/SPARK-14187.
