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Posted to reviews@spark.apache.org by sohum2002 <gi...@git.apache.org> on 2017/10/06 11:32:58 UTC
[GitHub] spark pull request #19445: Dataset select all columns
GitHub user sohum2002 opened a pull request:
https://github.com/apache/spark/pull/19445
Dataset select all columns
The proposed two new additional functions is to help select all the columns in a Dataset except for given columns.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/sohum2002/spark dataset_selectAllColumns
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/19445.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #19445
----
commit d35a1268d784a268e6137eff54eb8f83c981a289
Author: Burak Yavuz <br...@gmail.com>
Date: 2017-02-01T00:52:53Z
[SPARK-19378][SS] Ensure continuity of stateOperator and eventTime metrics even if there is no new data in trigger
In StructuredStreaming, if a new trigger was skipped because no new data arrived, we suddenly report nothing for the metrics `stateOperator`. We could however easily report the metrics from `lastExecution` to ensure continuity of metrics.
Regression test in `StreamingQueryStatusAndProgressSuite`
Author: Burak Yavuz <br...@gmail.com>
Closes #16716 from brkyvz/state-agg.
(cherry picked from commit 081b7addaf9560563af0ce25912972e91a78cee6)
Signed-off-by: Tathagata Das <ta...@gmail.com>
commit 61cdc8c7cc8cfc57646a30da0e0df874a14e3269
Author: Zheng RuiFeng <ru...@foxmail.com>
Date: 2017-02-01T13:27:20Z
[SPARK-19410][DOC] Fix brokens links in ml-pipeline and ml-tuning
## What changes were proposed in this pull request?
Fix brokens links in ml-pipeline and ml-tuning
`<div data-lang="scala">` -> `<div data-lang="scala" markdown="1">`
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ru...@foxmail.com>
Closes #16754 from zhengruifeng/doc_api_fix.
(cherry picked from commit 04ee8cf633e17b6bf95225a8dd77bf2e06980eb3)
Signed-off-by: Sean Owen <so...@cloudera.com>
commit f946464155bb907482dc8d8a1b0964a925d04081
Author: Devaraj K <de...@apache.org>
Date: 2017-02-01T20:55:11Z
[SPARK-19377][WEBUI][CORE] Killed tasks should have the status as KILLED
## What changes were proposed in this pull request?
Copying of the killed status was missing while getting the newTaskInfo object by dropping the unnecessary details to reduce the memory usage. This patch adds the copying of the killed status to newTaskInfo object, this will correct the display of the status from wrong status to KILLED status in Web UI.
## How was this patch tested?
Current behaviour of displaying tasks in stage UI page,
| Index | ID | Attempt | Status | Locality Level | Executor ID / Host | Launch Time | Duration | GC Time | Input Size / Records | Write Time | Shuffle Write Size / Records | Errors |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|143 |10 |0 |SUCCESS |NODE_LOCAL |6 / x.xx.x.x stdout stderr|2017/01/25 07:49:27 |0 ms | |0.0 B / 0 | |0.0 B / 0 |TaskKilled (killed intentionally)|
|156 |11 |0 |SUCCESS |NODE_LOCAL |5 / x.xx.x.x stdout stderr|2017/01/25 07:49:27 |0 ms | |0.0 B / 0 | |0.0 B / 0 |TaskKilled (killed intentionally)|
Web UI display after applying the patch,
| Index | ID | Attempt | Status | Locality Level | Executor ID / Host | Launch Time | Duration | GC Time | Input Size / Records | Write Time | Shuffle Write Size / Records | Errors |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|143 |10 |0 |KILLED |NODE_LOCAL |6 / x.xx.x.x stdout stderr|2017/01/25 07:49:27 |0 ms | |0.0 B / 0 | | 0.0 B / 0 | TaskKilled (killed intentionally)|
|156 |11 |0 |KILLED |NODE_LOCAL |5 / x.xx.x.x stdout stderr|2017/01/25 07:49:27 |0 ms | |0.0 B / 0 | |0.0 B / 0 | TaskKilled (killed intentionally)|
Author: Devaraj K <de...@apache.org>
Closes #16725 from devaraj-kavali/SPARK-19377.
(cherry picked from commit df4a27cc5cae8e251ba2a883bcc5f5ce9282f649)
Signed-off-by: Shixiong Zhu <sh...@databricks.com>
commit 7c23bd49e826fc2b7f132ffac2e55a71905abe96
Author: Shixiong Zhu <sh...@databricks.com>
Date: 2017-02-02T05:39:21Z
[SPARK-19432][CORE] Fix an unexpected failure when connecting timeout
## What changes were proposed in this pull request?
When connecting timeout, `ask` may fail with a confusing message:
```
17/02/01 23:15:19 INFO Worker: Connecting to master ...
java.lang.IllegalArgumentException: requirement failed: TransportClient has not yet been set.
at scala.Predef$.require(Predef.scala:224)
at org.apache.spark.rpc.netty.RpcOutboxMessage.onTimeout(Outbox.scala:70)
at org.apache.spark.rpc.netty.NettyRpcEnv$$anonfun$ask$1.applyOrElse(NettyRpcEnv.scala:232)
at org.apache.spark.rpc.netty.NettyRpcEnv$$anonfun$ask$1.applyOrElse(NettyRpcEnv.scala:231)
at scala.concurrent.Future$$anonfun$onFailure$1.apply(Future.scala:138)
at scala.concurrent.Future$$anonfun$onFailure$1.apply(Future.scala:136)
at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
```
It's better to provide a meaningful message.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <sh...@databricks.com>
Closes #16773 from zsxwing/connect-timeout.
(cherry picked from commit 8303e20c45153f91e585e230caa29b728a4d8c6c)
Signed-off-by: Shixiong Zhu <sh...@databricks.com>
commit f55bd4c736b01f1fe3df0ca2f4582c8d2b4d77f9
Author: Herman van Hovell <hv...@databricks.com>
Date: 2017-02-06T20:28:13Z
[SPARK-19472][SQL] Parser should not mistake CASE WHEN(...) for a function call
## What changes were proposed in this pull request?
The SQL parser can mistake a `WHEN (...)` used in `CASE` for a function call. This happens in cases like the following:
```sql
select case when (1) + case when 1 > 0 then 1 else 0 end = 2 then 1 else 0 end
from tb
```
This PR fixes this by re-organizing the case related parsing rules.
## How was this patch tested?
Added a regression test to the `ExpressionParserSuite`.
Author: Herman van Hovell <hv...@databricks.com>
Closes #16821 from hvanhovell/SPARK-19472.
(cherry picked from commit cb2677b86039a75fcd8a4e567ab06055f054a19a)
Signed-off-by: gatorsmile <ga...@gmail.com>
commit 62fab5beee147c90d8b7f8092b4ee76ba611ee8e
Author: uncleGen <hu...@gmail.com>
Date: 2017-02-07T05:03:20Z
[SPARK-19407][SS] defaultFS is used FileSystem.get instead of getting it from uri scheme
## What changes were proposed in this pull request?
```
Caused by: java.lang.IllegalArgumentException: Wrong FS: s3a://**************/checkpoint/7b2231a3-d845-4740-bfa3-681850e5987f/metadata, expected: file:///
at org.apache.hadoop.fs.FileSystem.checkPath(FileSystem.java:649)
at org.apache.hadoop.fs.RawLocalFileSystem.pathToFile(RawLocalFileSystem.java:82)
at org.apache.hadoop.fs.RawLocalFileSystem.deprecatedGetFileStatus(RawLocalFileSystem.java:606)
at org.apache.hadoop.fs.RawLocalFileSystem.getFileLinkStatusInternal(RawLocalFileSystem.java:824)
at org.apache.hadoop.fs.RawLocalFileSystem.getFileStatus(RawLocalFileSystem.java:601)
at org.apache.hadoop.fs.FilterFileSystem.getFileStatus(FilterFileSystem.java:421)
at org.apache.hadoop.fs.FileSystem.exists(FileSystem.java:1426)
at org.apache.spark.sql.execution.streaming.StreamMetadata$.read(StreamMetadata.scala:51)
at org.apache.spark.sql.execution.streaming.StreamExecution.<init>(StreamExecution.scala:100)
at org.apache.spark.sql.streaming.StreamingQueryManager.createQuery(StreamingQueryManager.scala:232)
at org.apache.spark.sql.streaming.StreamingQueryManager.startQuery(StreamingQueryManager.scala:269)
at org.apache.spark.sql.streaming.DataStreamWriter.start(DataStreamWriter.scala:262)
```
Can easily replicate on spark standalone cluster by providing checkpoint location uri scheme anything other than "file://" and not overriding in config.
WorkAround --conf spark.hadoop.fs.defaultFS=s3a://somebucket or set it in sparkConf or spark-default.conf
## How was this patch tested?
existing ut
Author: uncleGen <hu...@gmail.com>
Closes #16815 from uncleGen/SPARK-19407.
(cherry picked from commit 7a0a630e0f699017c7d0214923cd4aa0227e62ff)
Signed-off-by: Shixiong Zhu <sh...@databricks.com>
commit dd1abef138581f30ab7a8dfacb616fe7dd64b421
Author: Aseem Bansal <an...@users.noreply.github.com>
Date: 2017-02-07T11:44:14Z
[SPARK-19444][ML][DOCUMENTATION] Fix imports not being present in documentation
## What changes were proposed in this pull request?
SPARK-19444 imports not being present in documentation
## How was this patch tested?
Manual
## Disclaimer
Contribution is original work and I license the work to the project under the project’s open source license
Author: Aseem Bansal <an...@users.noreply.github.com>
Closes #16789 from anshbansal/patch-1.
(cherry picked from commit aee2bd2c7ee97a58f0adec82ec52e5625b39e804)
Signed-off-by: Sean Owen <so...@cloudera.com>
commit e642a07d57798f98b25ba08ed7ae3abe0f597941
Author: Tyson Condie <tc...@gmail.com>
Date: 2017-02-07T22:31:23Z
[SPARK-18682][SS] Batch Source for Kafka
Today, you can start a stream that reads from kafka. However, given kafka's configurable retention period, it seems like sometimes you might just want to read all of the data that is available now. As such we should add a version that works with spark.read as well.
The options should be the same as the streaming kafka source, with the following differences:
startingOffsets should default to earliest, and should not allow latest (which would always be empty).
endingOffsets should also be allowed and should default to latest. the same assign json format as startingOffsets should also be accepted.
It would be really good, if things like .limit(n) were enough to prevent all the data from being read (this might just work).
KafkaRelationSuite was added for testing batch queries via KafkaUtils.
Author: Tyson Condie <tc...@gmail.com>
Closes #16686 from tcondie/SPARK-18682.
(cherry picked from commit 8df444403489aec0d68f7d930afdc4f7d50e0b41)
Signed-off-by: Shixiong Zhu <sh...@databricks.com>
commit 706d6c154d2471c00253bf9b0c4e867752f841fe
Author: CodingCat <zh...@gmail.com>
Date: 2017-02-08T04:25:18Z
[SPARK-19499][SS] Add more notes in the comments of Sink.addBatch()
## What changes were proposed in this pull request?
addBatch method in Sink trait is supposed to be a synchronous method to coordinate with the fault-tolerance design in StreamingExecution (being different with the compute() method in DStream)
We need to add more notes in the comments of this method to remind the developers
## How was this patch tested?
existing tests
Author: CodingCat <zh...@gmail.com>
Closes #16840 from CodingCat/SPARK-19499.
(cherry picked from commit d4cd975718716be11a42ce92a47c45be1a46bd60)
Signed-off-by: Shixiong Zhu <sh...@databricks.com>
commit 4d040297f55243703463ea71d5302bb46ea0bf3f
Author: manugarri <ma...@gmail.com>
Date: 2017-02-08T05:45:33Z
[MINOR][DOC] Remove parenthesis in readStream() on kafka structured streaming doc
There is a typo in http://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html#creating-a-kafka-source-stream , python example n1 uses `readStream()` instead of `readStream`
Just removed the parenthesis.
Author: manugarri <ma...@gmail.com>
Closes #16836 from manugarri/fix_kafka_python_doc.
(cherry picked from commit 5a0569ce693c635c5fa12b2de33ed3643ce888e3)
Signed-off-by: Shixiong Zhu <sh...@databricks.com>
commit 71b6eacf72fb50862d33a2bf6a0662d6c4e73bbd
Author: Herman van Hovell <hv...@databricks.com>
Date: 2017-02-08T07:35:15Z
[SPARK-18609][SPARK-18841][SQL][BACKPORT-2.1] Fix redundant Alias removal in the optimizer
This is a backport of https://github.com/apache/spark/commit/73ee73945e369a862480ef4ac64e55c797bd7d90
## What changes were proposed in this pull request?
The optimizer tries to remove redundant alias only projections from the query plan using the `RemoveAliasOnlyProject` rule. The current rule identifies removes such a project and rewrites the project's attributes in the **entire** tree. This causes problems when parts of the tree are duplicated (for instance a self join on a temporary view/CTE) and the duplicated part contains the alias only project, in this case the rewrite will break the tree.
This PR fixes these problems by using a blacklist for attributes that are not to be moved, and by making sure that attribute remapping is only done for the parent tree, and not for unrelated parts of the query plan.
The current tree transformation infrastructure works very well if the transformation at hand requires little or a global contextual information. In this case we need to know both the attributes that were not to be moved, and we also needed to know which child attributes were modified. This cannot be done easily using the current infrastructure, and solutions typically involves transversing the query plan multiple times (which is super slow). I have moved around some code in `TreeNode`, `QueryPlan` and `LogicalPlan`to make this much more straightforward; this basically allows you to manually traverse the tree.
## How was this patch tested?
I have added unit tests to `RemoveRedundantAliasAndProjectSuite` and I have added integration tests to the `SQLQueryTestSuite.union` and `SQLQueryTestSuite.cte` test cases.
Author: Herman van Hovell <hv...@databricks.com>
Closes #16843 from hvanhovell/SPARK-18609-2.1.
commit 502c927b8c8a99ef2adf4e6e1d7a6d9232d45ef5
Author: Tathagata Das <ta...@gmail.com>
Date: 2017-02-08T19:33:59Z
[SPARK-19413][SS] MapGroupsWithState for arbitrary stateful operations for branch-2.1
This is a follow up PR for merging #16758 to spark 2.1 branch
## What changes were proposed in this pull request?
`mapGroupsWithState` is a new API for arbitrary stateful operations in Structured Streaming, similar to `DStream.mapWithState`
*Requirements*
- Users should be able to specify a function that can do the following
- Access the input row corresponding to a key
- Access the previous state corresponding to a key
- Optionally, update or remove the state
- Output any number of new rows (or none at all)
*Proposed API*
```
// ------------ New methods on KeyValueGroupedDataset ------------
class KeyValueGroupedDataset[K, V] {
// Scala friendly
def mapGroupsWithState[S: Encoder, U: Encoder](func: (K, Iterator[V], KeyedState[S]) => U)
def flatMapGroupsWithState[S: Encode, U: Encoder](func: (K, Iterator[V], KeyedState[S]) => Iterator[U])
// Java friendly
def mapGroupsWithState[S, U](func: MapGroupsWithStateFunction[K, V, S, R], stateEncoder: Encoder[S], resultEncoder: Encoder[U])
def flatMapGroupsWithState[S, U](func: FlatMapGroupsWithStateFunction[K, V, S, R], stateEncoder: Encoder[S], resultEncoder: Encoder[U])
}
// ------------------- New Java-friendly function classes -------------------
public interface MapGroupsWithStateFunction<K, V, S, R> extends Serializable {
R call(K key, Iterator<V> values, state: KeyedState<S>) throws Exception;
}
public interface FlatMapGroupsWithStateFunction<K, V, S, R> extends Serializable {
Iterator<R> call(K key, Iterator<V> values, state: KeyedState<S>) throws Exception;
}
// ---------------------- Wrapper class for state data ----------------------
trait KeyedState[S] {
def exists(): Boolean
def get(): S // throws Exception is state does not exist
def getOption(): Option[S]
def update(newState: S): Unit
def remove(): Unit // exists() will be false after this
}
```
Key Semantics of the State class
- The state can be null.
- If the state.remove() is called, then state.exists() will return false, and getOption will returm None.
- After that state.update(newState) is called, then state.exists() will return true, and getOption will return Some(...).
- None of the operations are thread-safe. This is to avoid memory barriers.
*Usage*
```
val stateFunc = (word: String, words: Iterator[String, runningCount: KeyedState[Long]) => {
val newCount = words.size + runningCount.getOption.getOrElse(0L)
runningCount.update(newCount)
(word, newCount)
}
dataset // type is Dataset[String]
.groupByKey[String](w => w) // generates KeyValueGroupedDataset[String, String]
.mapGroupsWithState[Long, (String, Long)](stateFunc) // returns Dataset[(String, Long)]
```
## How was this patch tested?
New unit tests.
Author: Tathagata Das <ta...@gmail.com>
Closes #16850 from tdas/mapWithState-branch-2.1.
commit b3fd36a15a0924b9de88dadc6e0acbe504ba4b96
Author: Shixiong Zhu <sh...@databricks.com>
Date: 2017-02-09T19:16:51Z
[SPARK-19481] [REPL] [MAVEN] Avoid to leak SparkContext in Signaling.cancelOnInterrupt
## What changes were proposed in this pull request?
`Signaling.cancelOnInterrupt` leaks a SparkContext per call and it makes ReplSuite unstable.
This PR adds `SparkContext.getActive` to allow `Signaling.cancelOnInterrupt` to get the active `SparkContext` to avoid the leak.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <sh...@databricks.com>
Closes #16825 from zsxwing/SPARK-19481.
(cherry picked from commit 303f00a4bf6660dd83c8bd9e3a107bb3438a421b)
Signed-off-by: Davies Liu <da...@gmail.com>
commit a3d5300a030fb5f1c275e671603e0745b6466735
Author: Stan Zhai <ma...@zhaishidan.cn>
Date: 2017-02-09T20:01:25Z
[SPARK-19509][SQL] Grouping Sets do not respect nullable grouping columns
## What changes were proposed in this pull request?
The analyzer currently does not check if a column used in grouping sets is actually nullable itself. This can cause the nullability of the column to be incorrect, which can cause null pointer exceptions down the line. This PR fixes that by also consider the nullability of the column.
This is only a problem for Spark 2.1 and below. The latest master uses a different approach.
Closes https://github.com/apache/spark/pull/16874
## How was this patch tested?
Added a regression test to `SQLQueryTestSuite.grouping_set`.
Author: Herman van Hovell <hv...@databricks.com>
Closes #16873 from hvanhovell/SPARK-19509.
commit ff5818b8cee7c718ef5bdef125c8d6971d64acde
Author: Bogdan Raducanu <bo...@databricks.com>
Date: 2017-02-10T09:50:07Z
[SPARK-19512][BACKPORT-2.1][SQL] codegen for compare structs fails #16852
## What changes were proposed in this pull request?
Set currentVars to null in GenerateOrdering.genComparisons before genCode is called. genCode ignores INPUT_ROW if currentVars is not null and in genComparisons we want it to use INPUT_ROW.
## How was this patch tested?
Added test with 2 queries in WholeStageCodegenSuite
Author: Bogdan Raducanu <bo...@gmail.com>
Closes #16875 from bogdanrdc/SPARK-19512-2.1.
commit 7b5ea000e246f7052e7324fd7f2e99f32aaece17
Author: Burak Yavuz <br...@gmail.com>
Date: 2017-02-10T11:55:06Z
[SPARK-19543] from_json fails when the input row is empty
## What changes were proposed in this pull request?
Using from_json on a column with an empty string results in: java.util.NoSuchElementException: head of empty list.
This is because `parser.parse(input)` may return `Nil` when `input.trim.isEmpty`
## How was this patch tested?
Regression test in `JsonExpressionsSuite`
Author: Burak Yavuz <br...@gmail.com>
Closes #16881 from brkyvz/json-fix.
(cherry picked from commit d5593f7f5794bd0343e783ac4957864fed9d1b38)
Signed-off-by: Herman van Hovell <hv...@databricks.com>
commit e580bb035236dd92ade126af6bb98288d88179c4
Author: Andrew Ray <ra...@gmail.com>
Date: 2016-12-13T07:49:22Z
[SPARK-18717][SQL] Make code generation for Scala Map work with immutable.Map also
## What changes were proposed in this pull request?
Fixes compile errors in generated code when user has case class with a `scala.collections.immutable.Map` instead of a `scala.collections.Map`. Since ArrayBasedMapData.toScalaMap returns the immutable version we can make it work with both.
## How was this patch tested?
Additional unit tests.
Author: Andrew Ray <ra...@gmail.com>
Closes #16161 from aray/fix-map-codegen.
(cherry picked from commit 46d30ac4846b3ec94426cc482c42cff72ebd6d92)
Signed-off-by: Cheng Lian <li...@databricks.com>
commit 173c2387a38b260b46d7646b332e404f6ebe1a17
Author: titicaca <fa...@hotmail.com>
Date: 2017-02-12T18:42:15Z
[SPARK-19342][SPARKR] bug fixed in collect method for collecting timestamp column
## What changes were proposed in this pull request?
Fix a bug in collect method for collecting timestamp column, the bug can be reproduced as shown in the following codes and outputs:
```
library(SparkR)
sparkR.session(master = "local")
df <- data.frame(col1 = c(0, 1, 2),
col2 = c(as.POSIXct("2017-01-01 00:00:01"), NA, as.POSIXct("2017-01-01 12:00:01")))
sdf1 <- createDataFrame(df)
print(dtypes(sdf1))
df1 <- collect(sdf1)
print(lapply(df1, class))
sdf2 <- filter(sdf1, "col1 > 0")
print(dtypes(sdf2))
df2 <- collect(sdf2)
print(lapply(df2, class))
```
As we can see from the printed output, the column type of col2 in df2 is converted to numeric unexpectedly, when NA exists at the top of the column.
This is caused by method `do.call(c, list)`, if we convert a list, i.e. `do.call(c, list(NA, as.POSIXct("2017-01-01 12:00:01"))`, the class of the result is numeric instead of POSIXct.
Therefore, we need to cast the data type of the vector explicitly.
## How was this patch tested?
The patch can be tested manually with the same code above.
Author: titicaca <fa...@hotmail.com>
Closes #16689 from titicaca/sparkr-dev.
(cherry picked from commit bc0a0e6392c4e729d8f0e4caffc0bd05adb0d950)
Signed-off-by: Felix Cheung <fe...@apache.org>
commit 06e77e0097c6fa0accc5d9d6ce08a65a3828b878
Author: wm624@hotmail.com <wm...@hotmail.com>
Date: 2017-02-12T18:48:55Z
[SPARK-19319][BACKPORT-2.1][SPARKR] SparkR Kmeans summary returns error when the cluster size doesn't equal to k
## What changes were proposed in this pull request?
Backport fix of #16666
## How was this patch tested?
Backport unit tests
Author: wm624@hotmail.com <wm...@hotmail.com>
Closes #16761 from wangmiao1981/kmeansport.
commit fe4fcc5701cbd3f2e698e00f1cc7d49d5c7c702b
Author: Liwei Lin <lw...@gmail.com>
Date: 2017-02-13T07:00:22Z
[SPARK-19564][SPARK-19559][SS][KAFKA] KafkaOffsetReader's consumers should not be in the same group
## What changes were proposed in this pull request?
In `KafkaOffsetReader`, when error occurs, we abort the existing consumer and create a new consumer. In our current implementation, the first consumer and the second consumer would be in the same group (which leads to SPARK-19559), **_violating our intention of the two consumers not being in the same group._**
The cause is that, in our current implementation, the first consumer is created before `groupId` and `nextId` are initialized in the constructor. Then even if `groupId` and `nextId` are increased during the creation of that first consumer, `groupId` and `nextId` would still be initialized to default values in the constructor for the second consumer.
We should make sure that `groupId` and `nextId` are initialized before any consumer is created.
## How was this patch tested?
Ran 100 times of `KafkaSourceSuite`; all passed
Author: Liwei Lin <lw...@gmail.com>
Closes #16902 from lw-lin/SPARK-19564-.
(cherry picked from commit 2bdbc87052389ff69404347fbc69457132dbcafd)
Signed-off-by: Shixiong Zhu <sh...@databricks.com>
commit a3b6751375cf301dec156b85fe79e32b0797a24f
Author: Xiao Li <ga...@gmail.com>
Date: 2017-02-13T11:18:31Z
[SPARK-19574][ML][DOCUMENTATION] Fix Liquid Exception: Start indices amount is not equal to end indices amount
### What changes were proposed in this pull request?
```
Liquid Exception: Start indices amount is not equal to end indices amount, see /Users/xiao/IdeaProjects/sparkDelivery/docs/../examples/src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java. in ml-features.md
```
So far, the build is broken after merging https://github.com/apache/spark/pull/16789
This PR is to fix it.
## How was this patch tested?
Manual
Author: Xiao Li <ga...@gmail.com>
Closes #16908 from gatorsmile/docMLFix.
(cherry picked from commit 855a1b7551c71b26ce7d9310342fefe0a87281ec)
Signed-off-by: Sean Owen <so...@cloudera.com>
commit ef4fb7ebca963eb95d6a8bf7543e05aa375edc23
Author: zero323 <ze...@users.noreply.github.com>
Date: 2017-02-13T17:26:49Z
[SPARK-19506][ML][PYTHON] Import warnings in pyspark.ml.util
## What changes were proposed in this pull request?
Add missing `warnings` import.
## How was this patch tested?
Manual tests.
Author: zero323 <ze...@users.noreply.github.com>
Closes #16846 from zero323/SPARK-19506.
(cherry picked from commit 5e7cd3322b04f1dd207829b70546bc7ffdd63363)
Signed-off-by: Holden Karau <ho...@us.ibm.com>
commit c5a7cb0225ed4ed0d1ede5da0593b258c5dfd79f
Author: Shixiong Zhu <sh...@databricks.com>
Date: 2017-02-13T19:54:54Z
[SPARK-19542][SS] Delete the temp checkpoint if a query is stopped without errors
## What changes were proposed in this pull request?
When a query uses a temp checkpoint dir, it's better to delete it if it's stopped without errors.
## How was this patch tested?
New unit tests.
Author: Shixiong Zhu <sh...@databricks.com>
Closes #16880 from zsxwing/delete-temp-checkpoint.
(cherry picked from commit 3dbff9be06c2007fdb2ad4a1e113f3bc7fc06529)
Signed-off-by: Burak Yavuz <br...@gmail.com>
commit 328b229840d6e87c7faf7ee3cd5bf66a905c9a7d
Author: Shixiong Zhu <sh...@databricks.com>
Date: 2017-02-13T20:03:36Z
[SPARK-17714][CORE][TEST-MAVEN][TEST-HADOOP2.6] Avoid using ExecutorClassLoader to load Netty generated classes
## What changes were proposed in this pull request?
Netty's `MessageToMessageEncoder` uses [Javassist](https://github.com/netty/netty/blob/91a0bdc17a8298437d6de08a8958d753799bd4a6/common/src/main/java/io/netty/util/internal/JavassistTypeParameterMatcherGenerator.java#L62) to generate a matcher class and the implementation calls `Class.forName` to check if this class is already generated. If `MessageEncoder` or `MessageDecoder` is created in `ExecutorClassLoader.findClass`, it will cause `ClassCircularityError`. This is because loading this Netty generated class will call `ExecutorClassLoader.findClass` to search this class, and `ExecutorClassLoader` will try to use RPC to load it and cause to load the non-exist matcher class again. JVM will report `ClassCircularityError` to prevent such infinite recursion.
##### Why it only happens in Maven builds
It's because Maven and SBT have different class loader tree. The Maven build will set a URLClassLoader as the current context class loader to run the tests and expose this issue. The class loader tree is as following:
```
bootstrap class loader ------ ... ----- REPL class loader ---- ExecutorClassLoader
|
|
URLClasssLoader
```
The SBT build uses the bootstrap class loader directly and `ReplSuite.test("propagation of local properties")` is the first test in ReplSuite, which happens to load `io/netty/util/internal/__matchers__/org/apache/spark/network/protocol/MessageMatcher` into the bootstrap class loader (Note: in maven build, it's loaded into URLClasssLoader so it cannot be found in ExecutorClassLoader). This issue can be reproduced in SBT as well. Here are the produce steps:
- Enable `hadoop.caller.context.enabled`.
- Replace `Class.forName` with `Utils.classForName` in `object CallerContext`.
- Ignore `ReplSuite.test("propagation of local properties")`.
- Run `ReplSuite` using SBT.
This PR just creates a singleton MessageEncoder and MessageDecoder and makes sure they are created before switching to ExecutorClassLoader. TransportContext will be created when creating RpcEnv and that happens before creating ExecutorClassLoader.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <sh...@databricks.com>
Closes #16859 from zsxwing/SPARK-17714.
(cherry picked from commit 905fdf0c243e1776c54c01a25b17878361400225)
Signed-off-by: Shixiong Zhu <sh...@databricks.com>
commit 2968d8c0666801fb6a363dfca3c5a85ee8a1cc0c
Author: Shixiong Zhu <sh...@databricks.com>
Date: 2017-02-13T20:35:56Z
[HOTFIX][SPARK-19542][SS]Fix the missing import in DataStreamReaderWriterSuite
commit 5db23473008a58fb9a7f77ad8b01bcdc2c5f2d9c
Author: Josh Rosen <jo...@databricks.com>
Date: 2017-02-13T19:04:27Z
[SPARK-19529] TransportClientFactory.createClient() shouldn't call awaitUninterruptibly()
This patch replaces a single `awaitUninterruptibly()` call with a plain `await()` call in Spark's `network-common` library in order to fix a bug which may cause tasks to be uncancellable.
In Spark's Netty RPC layer, `TransportClientFactory.createClient()` calls `awaitUninterruptibly()` on a Netty future while waiting for a connection to be established. This creates problem when a Spark task is interrupted while blocking in this call (which can happen in the event of a slow connection which will eventually time out). This has bad impacts on task cancellation when `interruptOnCancel = true`.
As an example of the impact of this problem, I experienced significant numbers of uncancellable "zombie tasks" on a production cluster where several tasks were blocked trying to connect to a dead shuffle server and then continued running as zombies after I cancelled the associated Spark stage. The zombie tasks ran for several minutes with the following stack:
```
java.lang.Object.wait(Native Method)
java.lang.Object.wait(Object.java:460)
io.netty.util.concurrent.DefaultPromise.await0(DefaultPromise.java:607)
io.netty.util.concurrent.DefaultPromise.awaitUninterruptibly(DefaultPromise.java:301)
org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:224)
org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:179) => holding Monitor(java.lang.Object1849476028})
org.apache.spark.network.shuffle.ExternalShuffleClient$1.createAndStart(ExternalShuffleClient.java:105)
org.apache.spark.network.shuffle.RetryingBlockFetcher.fetchAllOutstanding(RetryingBlockFetcher.java:140)
org.apache.spark.network.shuffle.RetryingBlockFetcher.start(RetryingBlockFetcher.java:120)
org.apache.spark.network.shuffle.ExternalShuffleClient.fetchBlocks(ExternalShuffleClient.java:114)
org.apache.spark.storage.ShuffleBlockFetcherIterator.sendRequest(ShuffleBlockFetcherIterator.scala:169)
org.apache.spark.storage.ShuffleBlockFetcherIterator.fetchUpToMaxBytes(ShuffleBlockFetcherIterator.scala:
350)
org.apache.spark.storage.ShuffleBlockFetcherIterator.initialize(ShuffleBlockFetcherIterator.scala:286)
org.apache.spark.storage.ShuffleBlockFetcherIterator.<init>(ShuffleBlockFetcherIterator.scala:120)
org.apache.spark.shuffle.BlockStoreShuffleReader.read(BlockStoreShuffleReader.scala:45)
org.apache.spark.sql.execution.ShuffledRowRDD.compute(ShuffledRowRDD.scala:169)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
[...]
```
As far as I can tell, `awaitUninterruptibly()` might have been used in order to avoid having to declare that methods throw `InterruptedException` (this code is written in Java, hence the need to use checked exceptions). This patch simply replaces this with a regular, interruptible `await()` call,.
This required several interface changes to declare a new checked exception (these are internal interfaces, though, and this change doesn't significantly impact binary compatibility).
An alternative approach would be to wrap `InterruptedException` into `IOException` in order to avoid having to change interfaces. The problem with this approach is that the `network-shuffle` project's `RetryingBlockFetcher` code treats `IOExceptions` as transitive failures when deciding whether to retry fetches, so throwing a wrapped `IOException` might cause an interrupted shuffle fetch to be retried, further prolonging the lifetime of a cancelled zombie task.
Note that there are three other `awaitUninterruptibly()` in the codebase, but those calls have a hard 10 second timeout and are waiting on a `close()` operation which is expected to complete near instantaneously, so the impact of uninterruptibility there is much smaller.
Manually.
Author: Josh Rosen <jo...@databricks.com>
Closes #16866 from JoshRosen/SPARK-19529.
(cherry picked from commit 1c4d10b10c78d138b55e381ec6828e04fef70d6f)
Signed-off-by: Cheng Lian <li...@databricks.com>
commit 7fe3543fd2cc1cf82135b4208e1391ab3a25f2d9
Author: Marcelo Vanzin <va...@cloudera.com>
Date: 2017-02-13T22:19:41Z
[SPARK-19520][STREAMING] Do not encrypt data written to the WAL.
Spark's I/O encryption uses an ephemeral key for each driver instance.
So driver B cannot decrypt data written by driver A since it doesn't
have the correct key.
The write ahead log is used for recovery, thus needs to be readable by
a different driver. So it cannot be encrypted by Spark's I/O encryption
code.
The BlockManager APIs used by the WAL code to write the data automatically
encrypt data, so changes are needed so that callers can to opt out of
encryption.
Aside from that, the "putBytes" API in the BlockManager does not do
encryption, so a separate situation arised where the WAL would write
unencrypted data to the BM and, when those blocks were read, decryption
would fail. So the WAL code needs to ask the BM to encrypt that data
when encryption is enabled; this code is not optimal since it results
in a (temporary) second copy of the data block in memory, but should be
OK for now until a more performant solution is added. The non-encryption
case should not be affected.
Tested with new unit tests, and by running streaming apps that do
recovery using the WAL data with I/O encryption turned on.
Author: Marcelo Vanzin <va...@cloudera.com>
Closes #16862 from vanzin/SPARK-19520.
(cherry picked from commit 0169360ef58891ca10a8d64d1c8637c7b873cbdd)
Signed-off-by: Marcelo Vanzin <va...@cloudera.com>
commit c8113b0ee0555efe72827a91246af2737d1d4993
Author: Sunitha Kambhampati <sk...@us.ibm.com>
Date: 2017-02-14T06:49:29Z
[SPARK-19585][DOC][SQL] Fix the cacheTable and uncacheTable api call in the doc
## What changes were proposed in this pull request?
https://spark.apache.org/docs/latest/sql-programming-guide.html#caching-data-in-memory
In the doc, the call spark.cacheTable(“tableName”) and spark.uncacheTable(“tableName”) actually needs to be spark.catalog.cacheTable and spark.catalog.uncacheTable
## How was this patch tested?
Built the docs and verified the change shows up fine.
Author: Sunitha Kambhampati <sk...@us.ibm.com>
Closes #16919 from skambha/docChange.
(cherry picked from commit 9b5e460a9168ab78607034434ca45ab6cb51e5a6)
Signed-off-by: Xiao Li <ga...@gmail.com>
commit f837ced4c448e918efa7bfc49becfa09a50f5147
Author: Jong Wook Kim <jo...@nyu.edu>
Date: 2017-02-14T19:33:31Z
[SPARK-19501][YARN] Reduce the number of HDFS RPCs during YARN deployment
## What changes were proposed in this pull request?
As discussed in [JIRA](https://issues.apache.org/jira/browse/SPARK-19501), this patch addresses the problem where too many HDFS RPCs are made when there are many URIs specified in `spark.yarn.jars`, potentially adding hundreds of RTTs to YARN before the application launches. This becomes significant when submitting the application to a non-local YARN cluster (where the RTT may be in order of 100ms, for example). For each URI specified, the current implementation makes at least two HDFS RPCs, for:
- [Calling `getFileStatus()` before uploading each file to the distributed cache in `ClientDistributedCacheManager.addResource()`](https://github.com/apache/spark/blob/v2.1.0/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManager.scala#L71).
- [Resolving any symbolic links in each of the file URI](https://github.com/apache/spark/blob/v2.1.0/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala#L377-L379), which repeatedly makes HDFS RPCs until the all symlinks are resolved. (see [`FileContext.resolve(Path)`](https://github.com/apache/hadoop/blob/release-2.7.1/hadoop-common-project/hadoop-common/src/main/java/org/apache/hadoop/fs/FileContext.java#L2189-L2195), [`FSLinkResolver.resolve(FileContext, Path)`](https://github.com/apache/hadoop/blob/release-2.7.1/hadoop-common-project/hadoop-common/src/main/java/org/apache/hadoop/fs/FSLinkResolver.java#L79-L112), and [`AbstractFileSystem.resolvePath()`](https://github.com/apache/hadoop/blob/release-2.7.1/hadoop-common-project/hadoop-common/src/main/java/org/apache/hadoop/fs/AbstractFileSystem.java#L464-L468).)
The first `getFileStatus` RPC can be removed, using `statCache` populated with the file statuses retrieved with [the previous `globStatus` call](https://github.com/apache/spark/blob/v2.1.0/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala#L531).
The second one can be largely reduced by caching the symlink resolution results in a mutable.HashMap. This patch adds a local variable in `yarn.Client.prepareLocalResources()` and passes it as an additional parameter to `yarn.Client.copyFileToRemote`. [The symlink resolution code was added in 2013](https://github.com/apache/spark/commit/a35472e1dd2ea1b5a0b1fb6b382f5a98f5aeba5a#diff-b050df3f55b82065803d6e83453b9706R187) and has not changed since. I am assuming that this is still required, but otherwise we can remove using `symlinkCache` and symlink resolution altogether.
## How was this patch tested?
This patch is based off 8e8afb3, currently the latest YARN patch on master. All tests except a few in spark-hive passed with `./dev/run-tests` on my machine, using JDK 1.8.0_112 on macOS 10.12.3; also tested myself with this modified version of SPARK 2.2.0-SNAPSHOT which performed a normal deployment and execution on a YARN cluster without errors.
Author: Jong Wook Kim <jo...@nyu.edu>
Closes #16916 from jongwook/SPARK-19501.
(cherry picked from commit ab9872db1f9c0f289541ec5756d1a142d85545ce)
Signed-off-by: Marcelo Vanzin <va...@cloudera.com>
commit 7763b0b8bd33b0baa99434136528efb5de261919
Author: Felix Cheung <fe...@hotmail.com>
Date: 2017-02-14T21:51:27Z
[SPARK-19387][SPARKR] Tests do not run with SparkR source package in CRAN check
## What changes were proposed in this pull request?
- this is cause by changes in SPARK-18444, SPARK-18643 that we no longer install Spark when `master = ""` (default), but also related to SPARK-18449 since the real `master` value is not known at the time the R code in `sparkR.session` is run. (`master` cannot default to "local" since it could be overridden by spark-submit commandline or spark config)
- as a result, while running SparkR as a package in IDE is working fine, CRAN check is not as it is launching it via non-interactive script
- fix is to add check to the beginning of each test and vignettes; the same would also work by changing `sparkR.session()` to `sparkR.session(master = "local")` in tests, but I think being more explicit is better.
## How was this patch tested?
Tested this by reverting version to 2.1, since it needs to download the release jar with matching version. But since there are changes in 2.2 (specifically around SparkR ML) that are incompatible with 2.1, some tests are failing in this config. Will need to port this to branch-2.1 and retest with 2.1 release jar.
manually as:
```
# modify DESCRIPTION to revert version to 2.1.0
SPARK_HOME=/usr/spark R CMD build pkg
# run cran check without SPARK_HOME
R CMD check --as-cran SparkR_2.1.0.tar.gz
```
Author: Felix Cheung <fe...@hotmail.com>
Closes #16720 from felixcheung/rcranchecktest.
(cherry picked from commit a3626ca333e6e1881e2f09ccae0fa8fa7243223e)
Signed-off-by: Shivaram Venkataraman <sh...@cs.berkeley.edu>
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[GitHub] spark pull request #19445: Dataset select all columns
Posted by sohum2002 <gi...@git.apache.org>.
Github user sohum2002 closed the pull request at:
https://github.com/apache/spark/pull/19445
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