```withStd``` and ```withMean``` should be params of ```StandardScaler``` and ```StandardScalerModel```.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9839 from yanboliang/standardScaler-refactor.
This patch fixes an issue where the `spark.sql.TungstenAggregate.testFallbackStartsAt` SQLConf setting was not properly reset / cleared at the end of `TungstenAggregationQueryWithControlledFallbackSuite`. This ended up causing test failures in HiveCompatibilitySuite in Maven builds by causing spilling to occur way too frequently.
This configuration leak was inadvertently introduced during test cleanup in #9618.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9857 from JoshRosen/clear-fallback-prop-in-test-teardown.
Need to remove parent directory (```className```) rather than just tempDir (```className/random_name```)
I tested this with IDFSuite, which has 2 read/write tests, and it fixes the problem.
CC: mengxr Can you confirm this is fine? I believe it is since the same ```random_name``` is used for all tests in a suite; we basically have an extra unneeded level of nesting.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9851 from jkbradley/tempdir-cleanup.
* Update doc for PySpark ```HasCheckpointInterval``` that users can understand how to disable checkpoint.
* Update doc for PySpark ```cacheNodeIds``` of ```DecisionTreeParams``` to notify the relationship between ```cacheNodeIds``` and ```checkpointInterval```.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9856 from yanboliang/spark-11875.
Add read/write support to the following estimators under spark.ml:
* ChiSqSelector
* PCA
* VectorIndexer
* Word2Vec
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9838 from yanboliang/spark-11829.
Apply the user supplied pathfilter while retrieving the files from fs.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#9830 from dilipbiswal/spark-11544.
This PR has the following optimization:
1) The greatest/least already does the null-check, so the `If` and `IsNull` are not necessary.
2) In greatest/least, it should initialize the result using the first child (removing one block).
3) For primitive types, the generated greater expression is too complicated (`a > b ? 1 : (a < b) ? -1 : 0) > 0`), should be as simple as `a > b`
Combine these optimization, this could improve the performance of `ss_max` query by 30%.
Author: Davies Liu <davies@databricks.com>
Closes#9846 from davies/improve_max.
To make sure that all lineage is correctly truncated for TrackStateRDD when checkpointed.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#9831 from tdas/SPARK-11845.
Don't log ERROR messages when executors are explicitly killed or when
the exit reason is not yet known.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#9780 from vanzin/SPARK-11789.
Fixes bug with grouping sets (including cube/rollup) where aggregates that included grouping expressions would return the wrong (null) result.
Also simplifies the analyzer rule a bit and leaves column pruning to the optimizer.
Added multiple unit tests to DataFrameAggregateSuite and verified it passes hive compatibility suite:
```
build/sbt -Phive -Dspark.hive.whitelist='groupby.*_grouping.*' 'test-only org.apache.spark.sql.hive.execution.HiveCompatibilitySuite'
```
This is an alternative to pr https://github.com/apache/spark/pull/9419 but I think its better as it simplifies the analyzer rule instead of adding another special case to it.
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#9815 from aray/groupingset-agg-fix.
…xceptions are thrown during executor shutdown
This commit will make sure that when uncaught exceptions are prepended with [Container in shutdown] when JVM is shutting down.
Author: Srinivasa Reddy Vundela <vsr@cloudera.com>
Closes#9809 from vundela/master_11799.
In addition, tightened visibility of a lot of classes in the columnar package from private[sql] to private[columnar].
Author: Reynold Xin <rxin@databricks.com>
Closes#9842 from rxin/SPARK-11858.
invFunc is optional and can be None. Instead of invFunc (the parameter) invReduceFunc (a local function) was checked for trueness (that is, not None, in this context). A local function is never None,
thus the case of invFunc=None (a common one when inverse reduction is not defined) was treated incorrectly, resulting in loss of data.
In addition, the docstring used wrong parameter names, also fixed.
Author: David Tolpin <david.tolpin@gmail.com>
Closes#9775 from dtolpin/master.
Fix a bug in DataFrameReader.table (table with schema name such as "db_name.table" doesn't work)
Use SqlParser.parseTableIdentifier to parse the table name before lookupRelation.
Author: Huaxin Gao <huaxing@oc0558782468.ibm.com>
Closes#9773 from huaxingao/spark-11778.
After some experiment, I found it's not convenient to have separate encoder builders: `FlatEncoder` and `ProductEncoder`. For example, when create encoders for `ScalaUDF`, we have no idea if the type `T` is flat or not. So I revert the splitting change in https://github.com/apache/spark/pull/9693, while still keeping the bug fixes and tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9726 from cloud-fan/follow.
When debugging DataSet API, I always need to print the logical and physical plans.
I am wondering if we should provide a simple API for EXPLAIN?
Author: gatorsmile <gatorsmile@gmail.com>
Closes#9832 from gatorsmile/explainDS.
When handling self joins, the implementation did not consider the case insensitivity of HiveContext. It could cause an exception as shown in the JIRA:
```
TreeNodeException: Failed to copy node.
```
The fix is low risk. It avoids unnecessary attribute replacement. It should not affect the existing behavior of self joins. Also added the test case to cover this case.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#9762 from gatorsmile/joinMakeCopy.
This PR includes the following change:
1. Bind NettyRpcEnv to the specified host
2. Fix the port information in the log for NettyRpcEnv.
3. Fix the service name of NettyRpcEnv.
Author: zsxwing <zsxwing@gmail.com>
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#9821 from zsxwing/SPARK-11830.
The impact of this change is for a query that has a single distinct column and does not have any grouping expression like
`SELECT COUNT(DISTINCT a) FROM table`
The plan will be changed from
```
AGG-2 (count distinct)
Shuffle to a single reducer
Partial-AGG-2 (count distinct)
AGG-1 (grouping on a)
Shuffle by a
Partial-AGG-1 (grouping on 1)
```
to the following one (1.5 uses this)
```
AGG-2
AGG-1 (grouping on a)
Shuffle to a single reducer
Partial-AGG-1(grouping on a)
```
The first plan is more robust. However, to better benchmark the impact of this change, we should use 1.5's plan and use the conf of `spark.sql.specializeSingleDistinctAggPlanning` to control the plan.
Author: Yin Huai <yhuai@databricks.com>
Closes#9828 from yhuai/distinctRewriter.
We currently rely on the optimizer's constant folding to replace current_timestamp and current_date. However, this can still result in different values for different instances of current_timestamp/current_date if the optimizer is not running fast enough.
A better solution is to replace these functions in the analyzer in one shot.
Author: Reynold Xin <rxin@databricks.com>
Closes#9833 from rxin/SPARK-11849.
Added tests for function that are reported as masked, to make sure the base:: or stats:: function can be called.
For those we can't call, added them to SparkR programming guide.
It would seem to me `table, sample, subset, filter, cov` not working are not actually expected - I investigated/experimented with them but couldn't get them to work. It looks like as they are defined in base or stats they are missing the S3 generic, eg.
```
> methods("transform")
[1] transform,ANY-method transform.data.frame
[3] transform,DataFrame-method transform.default
see '?methods' for accessing help and source code
> methods("subset")
[1] subset.data.frame subset,DataFrame-method subset.default
[4] subset.matrix
see '?methods' for accessing help and source code
Warning message:
In .S3methods(generic.function, class, parent.frame()) :
function 'subset' appears not to be S3 generic; found functions that look like S3 methods
```
Any idea?
More information on masking:
http://www.ats.ucla.edu/stat/r/faq/referencing_objects.htmhttp://www.sfu.ca/~sweldon/howTo/guide4.pdf
This is what the output doc looks like (minus css):
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#9785 from felixcheung/rmasked.
Updates:
* Add repartition(1) to save() methods' saving of data for LogisticRegressionModel, LinearRegressionModel.
* Strengthen privacy to class and companion object for Writers and Readers
* Change LogisticRegressionSuite read/write test to fit intercept
* Add Since versions for read/write methods in Pipeline, LogisticRegression
* Switch from hand-written class names in Readers to using getClass
CC: mengxr
CC: yanboliang Would you mind taking a look at this PR? mengxr might not be able to soon. Thank you!
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9829 from jkbradley/ml-io-cleanups.
see HIVE-7975 and HIVE-12373
With changed semantic of setters in thrift objects in hive, setter should be called only after all parameters are set. It's not problem of current state but will be a problem in some day.
Author: navis.ryu <navis@apache.org>
Closes#9580 from navis/SPARK-11614.
jira: https://issues.apache.org/jira/browse/SPARK-11816
Currently I only fixed some obvious comments issue like
// scalastyle:off println
on the bottom.
Yet the style in examples is not quite consistent, like only half of the examples are with
// Example usage: ./bin/run-example mllib.FPGrowthExample \,
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#9808 from hhbyyh/exampleStyle.
This patch adds an alternate to the Parquet RecordReader from the parquet-mr project
that is much faster for flat schemas. Instead of using the general converter mechanism
from parquet-mr, this directly uses the lower level APIs from parquet-columnar and a
customer RecordReader that directly assembles into UnsafeRows.
This is optionally disabled and only used for supported schemas.
Using the tpcds store sales table and doing a sum of increasingly more columns, the results
are:
For 1 Column:
Before: 11.3M rows/second
After: 18.2M rows/second
For 2 Columns:
Before: 7.2M rows/second
After: 11.2M rows/second
For 5 Columns:
Before: 2.9M rows/second
After: 4.5M rows/second
Author: Nong Li <nong@databricks.com>
Closes#9774 from nongli/parquet.
Also added some nicer error messages for incompatible types (private types and primitive types) for Kryo/Java encoder.
Author: Reynold Xin <rxin@databricks.com>
Closes#9823 from rxin/SPARK-11833.
* add "ML" prefix to reader/writer/readable/writable to avoid name collision with java.util.*
* define `DefaultParamsReadable/Writable` and use them to save some code
* use `super.load` instead so people can jump directly to the doc of `Readable.load`, which documents the Java compatibility issues
jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#9827 from mengxr/SPARK-11839.
Before this PR there were two things that would blow up if you called `df.as[MyClass]` if `MyClass` was defined in the REPL:
- [x] Because `classForName` doesn't work on the munged names returned by `tpe.erasure.typeSymbol.asClass.fullName`
- [x] Because we don't have anything to pass into the constructor for the `$outer` pointer.
Note that this PR is just adding the infrastructure for working with inner classes in encoder and is not yet sufficient to make them work in the REPL. Currently, the implementation show in 95cec7d413 is causing a bug that breaks code gen due to some interaction between janino and the `ExecutorClassLoader`. This will be addressed in a follow-up PR.
Author: Michael Armbrust <michael@databricks.com>
Closes#9602 from marmbrus/dataset-replClasses.
stack trace of failure:
```
org.scalatest.exceptions.TestFailedDueToTimeoutException: The code passed to eventually never returned normally. Attempted 62 times over 1.006322071 seconds. Last failure message:
Argument(s) are different! Wanted:
writeAheadLog.write(
java.nio.HeapByteBuffer[pos=0 lim=124 cap=124],
10
);
-> at org.apache.spark.streaming.util.BatchedWriteAheadLogSuite$$anonfun$23$$anonfun$apply$mcV$sp$15.apply(WriteAheadLogSuite.scala:518)
Actual invocation has different arguments:
writeAheadLog.write(
java.nio.HeapByteBuffer[pos=0 lim=124 cap=124],
10
);
-> at org.apache.spark.streaming.util.WriteAheadLogSuite$BlockingWriteAheadLog.write(WriteAheadLogSuite.scala:756)
```
I believe the issue was that due to a race condition, the ordering of the events could be messed up in the final ByteBuffer, therefore the comparison fails.
By adding eventually between the requests, we make sure the ordering is preserved. Note that in real life situations, the ordering across threads will not matter.
Another solution would be to implement a custom mockito matcher that sorts and then compares the results, but that kind of sounds like overkill to me. Let me know what you think tdas zsxwing
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#9790 from brkyvz/fix-flaky-2.
DStream checkpoint interval is by default set at max(10 second, batch interval). That's bad for large batch intervals where the checkpoint interval = batch interval, and RDDs get checkpointed every batch.
This PR is to set the checkpoint interval of trackStateByKey to 10 * batch duration.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#9805 from tdas/SPARK-11814.
The HP Fortify Opens Source Review team (https://www.hpfod.com/open-source-review-project) reported a handful of potential resource leaks that were discovered using their static analysis tool. We should fix the issues identified by their scan.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9455 from JoshRosen/fix-potential-resource-leaks.
SparkListenerSuite's _"onTaskGettingResult() called when result fetched remotely"_ test was extremely slow (1 to 4 minutes to run) and recently became extremely flaky, frequently failing with OutOfMemoryError.
The root cause was the fact that this was using `System.setProperty` to set the Akka frame size, which was not actually modifying the frame size. As a result, this test would allocate much more data than necessary. The fix here is to simply use SparkConf in order to configure the frame size.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9822 from JoshRosen/SPARK-11649.
Add read/write support to the following estimators under spark.ml:
* CountVectorizer
* IDF
* MinMaxScaler
* StandardScaler (a little awkward because we store some params in spark.mllib model)
* StringIndexer
Added some necessary method for read/write. Maybe we should add `private[ml] trait DefaultParamsReadable` and `DefaultParamsWritable` to save some boilerplate code, though we still need to override `load` for Java compatibility.
jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#9798 from mengxr/SPARK-6787.
This patch refactors the existing Kryo encoder expressions and adds support for Java serialization.
Author: Reynold Xin <rxin@databricks.com>
Closes#9802 from rxin/SPARK-11810.
Apply the user supplied pathfilter while retrieving the files from fs.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#9652 from dilipbiswal/spark-11544.
This PR includes:
* Update SparkR:::glm, SparkR:::summary API docs.
* Update SparkR machine learning user guide and example codes to show:
* supporting feature interaction in R formula.
* summary for gaussian GLM model.
* coefficients for binomial GLM model.
mengxr
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9727 from yanboliang/spark-11684.
jira: https://issues.apache.org/jira/browse/SPARK-11813
I found the problem during training a large corpus. Avoid serialization of vocab in Word2Vec has 2 benefits.
1. Performance improvement for less serialization.
2. Increase the capacity of Word2Vec a lot.
Currently in the fit of word2vec, the closure mainly includes serialization of Word2Vec and 2 global table.
the main part of Word2vec is the vocab of size: vocab * 40 * 2 * 4 = 320 vocab
2 global table: vocab * vectorSize * 8. If vectorSize = 20, that's 160 vocab.
Their sum cannot exceed Int.max due to the restriction of ByteArrayOutputStream. In any case, avoiding serialization of vocab helps decrease the size of the closure serialization, especially when vectorSize is small, thus to allow larger vocabulary.
Actually there's another possible fix, make local copy of fields to avoid including Word2Vec in the closure. Let me know if that's preferred.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#9803 from hhbyyh/w2vVocab.
Also modifies DefaultParamsWriter.saveMetadata to take optional extra metadata.
CC: mengxr yanboliang
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9786 from jkbradley/als-io.
This replaces [https://github.com/apache/spark/pull/9656] with updates.
fayeshine should be the main author when this PR is committed.
CC: mengxr fayeshine
Author: Wenjian Huang <nextrush@163.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9814 from jkbradley/fayeshine-patch-6790.
return Double.NaN for mean/average when count == 0 for all numeric types that is converted to Double, Decimal type continue to return null.
Author: JihongMa <linlin200605@gmail.com>
Closes#9705 from JihongMA/SPARK-11720.
[SPARK-6028](https://issues.apache.org/jira/browse/SPARK-6028) uses network module to implement RPC. However, there are some configurations named with `spark.shuffle` prefix in the network module.
This PR refactors them to make sure the user can control them in shuffle and RPC separately. The user can use `spark.rpc.*` to set the configuration for netty RPC.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#9481 from zsxwing/SPARK-10745.
Based on my conversions with people, I believe the consensus is that the coarse-grained mode is more stable and easier to reason about. It is best to use that as the default rather than the more flaky fine-grained mode.
Author: Reynold Xin <rxin@databricks.com>
Closes#9795 from rxin/SPARK-11809.
