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## What changes were proposed in this pull request? add header ## How was this patch tested? Manual run to check vignettes html is created properly Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16709 from felixcheung/rfilelicense. (cherry picked from commit 385d738) Signed-off-by: Felix Cheung <felixcheung@apache.org>
…parkR ## What changes were proposed in this pull request? This affects mostly running job from the driver in client mode when results are expected to be through stdout (which should be somewhat rare, but possible) Before: ``` > a <- as.DataFrame(cars) > b <- group_by(a, "dist") > c <- count(b) > sparkR.callJMethod(c$countjc, "explain", TRUE) NULL ``` After: ``` > a <- as.DataFrame(cars) > b <- group_by(a, "dist") > c <- count(b) > sparkR.callJMethod(c$countjc, "explain", TRUE) count#11L NULL ``` Now, `column.explain()` doesn't seem very useful (we can get more extensive output with `DataFrame.explain()`) but there are other more complex examples with calls of `println` in Scala/JVM side, that are getting dropped. ## How was this patch tested? manual Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16670 from felixcheung/rjvmstdout. (cherry picked from commit a7ab6f9) Signed-off-by: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
### What changes were proposed in this pull request? The case are not sensitive in JDBC options, after the PR #15884 is merged to Spark 2.1. ### How was this patch tested? N/A Author: gatorsmile <gatorsmile@gmail.com> Closes #16734 from gatorsmile/fixDocCaseInsensitive. (cherry picked from commit c0eda7e) Signed-off-by: gatorsmile <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Currently, the function `to_json` allows users to provide options for generating JSON. However, it does not pass it to `JacksonGenerator`. Thus, it ignores the user-provided options. This PR is to fix it. Below is an example.
```Scala
val df = Seq(Tuple1(Tuple1(java.sql.Timestamp.valueOf("2015-08-26 18:00:00.0")))).toDF("a")
val options = Map("timestampFormat" -> "dd/MM/yyyy HH:mm")
df.select(to_json($"a", options)).show(false)
```
The current output is like
```
+--------------------------------------+
|structtojson(a) |
+--------------------------------------+
|{"_1":"2015-08-26T18:00:00.000-07:00"}|
+--------------------------------------+
```
After the fix, the output is like
```
+-------------------------+
|structtojson(a) |
+-------------------------+
|{"_1":"26/08/2015 18:00"}|
+-------------------------+
```
### How was this patch tested?
Added test cases for both `from_json` and `to_json`
Author: gatorsmile <gatorsmile@gmail.com>
Closes #16745 from gatorsmile/toJson.
(cherry picked from commit f9156d2)
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
…trics 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 <brkyvz@gmail.com> Closes #16716 from brkyvz/state-agg. (cherry picked from commit 081b7ad) Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
## 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 <ruifengz@foxmail.com> Closes #16754 from zhengruifeng/doc_api_fix. (cherry picked from commit 04ee8cf) Signed-off-by: Sean Owen <sowen@cloudera.com>
## 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 <devaraj@apache.org> Closes #16725 from devaraj-kavali/SPARK-19377. (cherry picked from commit df4a27c) Signed-off-by: Shixiong Zhu <shixiong@databricks.com>
## 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$1.apply(Future.scala:136)
at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
```
It's better to provide a meaningful message.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes #16773 from zsxwing/connect-timeout.
(cherry picked from commit 8303e20)
Signed-off-by: Shixiong Zhu <shixiong@databricks.com>
…ction call ## What changes were proposed in this pull request? The SQL parser can mistake a `WHEN (...)` used in `CASE` for a function call. This happens in cases like the following: ```sql select case when (1) + case when 1 > 0 then 1 else 0 end = 2 then 1 else 0 end from tb ``` This PR fixes this by re-organizing the case related parsing rules. ## How was this patch tested? Added a regression test to the `ExpressionParserSuite`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #16821 from hvanhovell/SPARK-19472. (cherry picked from commit cb2677b) Signed-off-by: gatorsmile <gatorsmile@gmail.com>
… it from uri scheme ## What changes were proposed in this pull request? ``` Caused by: java.lang.IllegalArgumentException: Wrong FS: s3a://**************/checkpoint/7b2231a3-d845-4740-bfa3-681850e5987f/metadata, expected: file:/// at org.apache.hadoop.fs.FileSystem.checkPath(FileSystem.java:649) at org.apache.hadoop.fs.RawLocalFileSystem.pathToFile(RawLocalFileSystem.java:82) at org.apache.hadoop.fs.RawLocalFileSystem.deprecatedGetFileStatus(RawLocalFileSystem.java:606) at org.apache.hadoop.fs.RawLocalFileSystem.getFileLinkStatusInternal(RawLocalFileSystem.java:824) at org.apache.hadoop.fs.RawLocalFileSystem.getFileStatus(RawLocalFileSystem.java:601) at org.apache.hadoop.fs.FilterFileSystem.getFileStatus(FilterFileSystem.java:421) at org.apache.hadoop.fs.FileSystem.exists(FileSystem.java:1426) at org.apache.spark.sql.execution.streaming.StreamMetadata$.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 <hustyugm@gmail.com> Closes #16815 from uncleGen/SPARK-19407. (cherry picked from commit 7a0a630) Signed-off-by: Shixiong Zhu <shixiong@databricks.com>
…umentation ## 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 <anshbansal@users.noreply.github.com> Closes #16789 from anshbansal/patch-1. (cherry picked from commit aee2bd2) Signed-off-by: Sean Owen <sowen@cloudera.com>
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 <tcondie@gmail.com> Closes #16686 from tcondie/SPARK-18682. (cherry picked from commit 8df4444) Signed-off-by: Shixiong Zhu <shixiong@databricks.com>
## 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 <zhunansjtu@gmail.com> Closes #16840 from CodingCat/SPARK-19499. (cherry picked from commit d4cd975) Signed-off-by: Shixiong Zhu <shixiong@databricks.com>
…treaming 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 <manuel.garrido.pena@gmail.com> Closes #16836 from manugarri/fix_kafka_python_doc. (cherry picked from commit 5a0569c) Signed-off-by: Shixiong Zhu <shixiong@databricks.com>
…oval in the optimizer This is a backport of 73ee739 ## 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 <hvanhovell@databricks.com> Closes #16843 from hvanhovell/SPARK-18609-2.1.
…s 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 <tathagata.das1565@gmail.com> Closes #16850 from tdas/mapWithState-branch-2.1.
…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 <shixiong@databricks.com> Closes #16825 from zsxwing/SPARK-19481. (cherry picked from commit 303f00a) Signed-off-by: Davies Liu <davies.liu@gmail.com>
…umns ## 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 #16874 ## How was this patch tested? Added a regression test to `SQLQueryTestSuite.grouping_set`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #16873 from hvanhovell/SPARK-19509.
…6852 ## 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 <bogdan.rdc@gmail.com> Closes #16875 from bogdanrdc/SPARK-19512-2.1.
## 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 <brkyvz@gmail.com> Closes #16881 from brkyvz/json-fix. (cherry picked from commit d5593f7) Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
…able.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 <ray.andrew@gmail.com> Closes #16161 from aray/fix-map-codegen. (cherry picked from commit 46d30ac) Signed-off-by: Cheng Lian <lian@databricks.com>
…stamp 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 <fangzhou.yang@hotmail.com>
Closes #16689 from titicaca/sparkr-dev.
(cherry picked from commit bc0a0e6)
Signed-off-by: Felix Cheung <felixcheung@apache.org>
…hould 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 <lwlin7@gmail.com> Closes #16902 from lw-lin/SPARK-19564-. (cherry picked from commit 2bdbc87) Signed-off-by: Shixiong Zhu <shixiong@databricks.com>
…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 #16789 This PR is to fix it. ## How was this patch tested? Manual Author: Xiao Li <gatorsmile@gmail.com> Closes #16908 from gatorsmile/docMLFix. (cherry picked from commit 855a1b7) Signed-off-by: Sean Owen <sowen@cloudera.com>
## What changes were proposed in this pull request? Add missing `warnings` import. ## How was this patch tested? Manual tests. Author: zero323 <zero323@users.noreply.github.com> Closes #16846 from zero323/SPARK-19506. (cherry picked from commit 5e7cd33) Signed-off-by: Holden Karau <holden@us.ibm.com>
…thout 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 <shixiong@databricks.com> Closes #16880 from zsxwing/delete-temp-checkpoint. (cherry picked from commit 3dbff9b) Signed-off-by: Burak Yavuz <brkyvz@gmail.com>
…lassLoader 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 <shixiong@databricks.com> Closes #16859 from zsxwing/SPARK-17714. (cherry picked from commit 905fdf0) Signed-off-by: Shixiong Zhu <shixiong@databricks.com>
## What changes were proposed in this pull request? We are explicitly calling release on the byteBuf's used to encode the string to Base64 to suppress the memory leak error message reported by netty. This is to make it less confusing for the user. ### Changes proposed in this fix By explicitly invoking release on the byteBuf's we are decrement the internal reference counts for the wrappedByteBuf's. Now, when the GC kicks in, these would be reclaimed as before, just that netty wouldn't report any memory leak error messages as the internal ref. counts are now 0. ## How was this patch tested? Ran a few spark-applications and examined the logs. The error message no longer appears. Original PR was opened against branch-2.1 => #18392 Author: Dhruve Ashar <dhruveashar@gmail.com> Closes #18407 from dhruve/master. (cherry picked from commit 1ebe7ff) Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request? * Following the first few examples in this file, the remaining methods should also be methods of `df.na` not `df`. * Filled in some missing parentheses ## How was this patch tested? N/A Author: Ong Ming Yang <me@ongmingyang.com> Closes #18398 from ongmingyang/master. (cherry picked from commit 4cc6295) Signed-off-by: Xiao Li <gatorsmile@gmail.com>
… in read path This PR is to revert some code changes in the read path of #14377. The original fix is #17830 When merging this PR, please give the credit to gaborfeher Added a test case to OracleIntegrationSuite.scala Author: Gabor Feher <gabor.feher@lynxanalytics.com> Author: gatorsmile <gatorsmile@gmail.com> Closes #18408 from gatorsmile/OracleType.
…uster mode. Monitoring for standalone cluster mode is not implemented (see SPARK-11033), but the same scheduler implementation is used, and if it tries to connect to the launcher it will fail. So fix the scheduler so it only tries that in client mode; cluster mode applications will be correctly launched and will work, but monitoring through the launcher handle will not be available. Tested by running a cluster mode app with "SparkLauncher.startApplication". Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #18397 from vanzin/SPARK-21159. (cherry picked from commit bfd73a7) Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
```SQL
CREATE TABLE `tab1`
(`custom_fields` ARRAY<STRUCT<`id`: BIGINT, `value`: STRING>>)
USING parquet
INSERT INTO `tab1`
SELECT ARRAY(named_struct('id', 1, 'value', 'a'), named_struct('id', 2, 'value', 'b'))
SELECT custom_fields.id, custom_fields.value FROM tab1
```
The above query always return the last struct of the array, because the rule `SimplifyCasts` incorrectly rewrites the query. The underlying cause is we always use the same `GenericInternalRow` object when doing the cast.
### How was this patch tested?
Author: gatorsmile <gatorsmile@gmail.com>
Closes #18412 from gatorsmile/castStruct.
(cherry picked from commit 2e1586f)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
…stant Pool Limit - Class Splitting" This reverts commit 6b37c86.
…roxy servlets to 8 ## What changes were proposed in this pull request? Please see also https://issues.apache.org/jira/browse/SPARK-21176 This change limits the number of selector threads that jetty creates to maximum 8 per proxy servlet (Jetty default is number of processors / 2). The newHttpClient for Jettys ProxyServlet class is overwritten to avoid the Jetty defaults (which are designed for high-performance http servers). Once jetty/jetty.project#1643 is available, the code could be cleaned up to avoid the method override. I really need this on v2.1.1 - what is the best way for a backport automatic merge works fine)? Shall I create another PR? ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) The patch was tested manually on a Spark cluster with a head node that has 88 processors using JMX to verify that the number of selector threads is now limited to 8 per proxy. gurvindersingh zsxwing can you please review the change? Author: IngoSchuster <ingo.schuster@de.ibm.com> Author: Ingo Schuster <ingo.schuster@de.ibm.com> Closes #18437 from IngoSchuster/master. (cherry picked from commit 88a536b) Signed-off-by: Wenchen Fan <wenchen@databricks.com>
…lling ## What changes were proposed in this pull request? `WindowExec` currently improperly stores complex objects (UnsafeRow, UnsafeArrayData, UnsafeMapData, UTF8String) during aggregation by keeping a reference in the buffer used by `GeneratedMutableProjections` to the actual input data. Things go wrong when the input object (or the backing bytes) are reused for other things. This could happen in window functions when it starts spilling to disk. When reading the back the spill files the `UnsafeSorterSpillReader` reuses the buffer to which the `UnsafeRow` points, leading to weird corruption scenario's. Note that this only happens for aggregate functions that preserve (parts of) their input, for example `FIRST`, `LAST`, `MIN` & `MAX`. This was not seen before, because the spilling logic was not doing actual spills as much and actually used an in-memory page. This page was not cleaned up during window processing and made sure unsafe objects point to their own dedicated memory location. This was changed by #16909, after this PR Spark spills more eagerly. This PR provides a surgical fix because we are close to releasing Spark 2.2. This change just makes sure that there cannot be any object reuse at the expensive of a little bit of performance. We will follow-up with a more subtle solution at a later point. ## How was this patch tested? Added a regression test to `DataFrameWindowFunctionsSuite`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #18470 from hvanhovell/SPARK-21258. (cherry picked from commit e2f32ee) Signed-off-by: Wenchen Fan <wenchen@databricks.com>
…with spilling" This reverts commit d995dac.
…zily ## What changes were proposed in this pull request? `SessionState` is designed to be created lazily. However, in reality, it created immediately in `SparkSession.Builder.getOrCreate` ([here](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala#L943)). This PR aims to recover the lazy behavior by keeping the options into `initialSessionOptions`. The benefit is like the following. Users can start `spark-shell` and use RDD operations without any problems. **BEFORE** ```scala $ bin/spark-shell java.lang.IllegalArgumentException: Error while instantiating 'org.apache.spark.sql.hive.HiveSessionStateBuilder' ... Caused by: org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.security.AccessControlException: Permission denied: user=spark, access=READ, inode="/apps/hive/warehouse":hive:hdfs:drwx------ ``` As reported in SPARK-20256, this happens when the warehouse directory is not allowed for this user. **AFTER** ```scala $ bin/spark-shell ... Welcome to ____ __ / __/__ ___ _____/ /__ _\ \/ _ \/ _ `/ __/ '_/ /___/ .__/\_,_/_/ /_/\_\ version 2.1.2-SNAPSHOT /_/ Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_131) Type in expressions to have them evaluated. Type :help for more information. scala> sc.range(0, 10, 1).count() res0: Long = 10 ``` ## How was this patch tested? Manual. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #18530 from dongjoon-hyun/SPARK-20256-BRANCH-2.1.
## What changes were proposed in this pull request? Corrects offsetInBytes calculation in UnsafeRow.writeToStream. Known failures include writes to some DataSources that have own SparkPlan implementations and cause EXCHANGE in writes. ## How was this patch tested? Extended UnsafeRowSuite.writeToStream to include an UnsafeRow over byte array having non-zero offset. Author: Sumedh Wale <swale@snappydata.io> Closes #18535 from sumwale/SPARK-21312. (cherry picked from commit 14a3bb3) Signed-off-by: Wenchen Fan <wenchen@databricks.com>
…uite should clean up stopped sessions. ## What changes were proposed in this pull request? `SparkSessionBuilderSuite` should clean up stopped sessions. Otherwise, it leaves behind some stopped `SparkContext`s interfereing with other test suites using `ShardSQLContext`. Recently, master branch fails consequtively. - https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/ ## How was this patch tested? Pass the Jenkins with a updated suite. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #18572 from dongjoon-hyun/SPARK-21345-BRANCH-2.1.
…lyzing empty table ## What changes were proposed in this pull request? We should be able to store zero size and row count after analyzing empty table. This is a backport for 9fccc36. ## How was this patch tested? Added new test. Author: Zhenhua Wang <wzh_zju@163.com> Closes #18577 from wzhfy/analyzeEmptyTable-2.1.
…rison ## What changes were proposed in this pull request? This PR fixes a wrong comparison for `BinaryType`. This PR enables unsigned comparison and unsigned prefix generation for an array for `BinaryType`. Previous implementations uses signed operations. ## How was this patch tested? Added a test suite in `OrderingSuite`. Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com> Closes #18571 from kiszk/SPARK-21344. (cherry picked from commit ac5d5d7) Signed-off-by: gatorsmile <gatorsmile@gmail.com>
…alyst should be global ## What changes were proposed in this pull request? This PR is backport of #18418 to Spark 2.1. [SPARK-21391](https://issues.apache.org/jira/browse/SPARK-21391) reported this problem in Spark 2.1. The issue happens in `ExternalMapToCatalyst`. For example, the following codes create ExternalMap`ExternalMapToCatalyst`ToCatalyst to convert Scala Map to catalyst map format. ``` val data = Seq.tabulate(10)(i => NestedData(1, Map("key" -> InnerData("name", i + 100)))) val ds = spark.createDataset(data) ``` The `valueConverter` in `ExternalMapToCatalyst` looks like: ``` if (isnull(lambdavariable(ExternalMapToCatalyst_value52, ExternalMapToCatalyst_value_isNull52, ObjectType(class org.apache.spark.sql.InnerData), true))) null else named_struct(name, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, assertnotnull(lambdavariable(ExternalMapToCatalyst_value52, ExternalMapToCatalyst_value_isNull52, ObjectType(class org.apache.spark.sql.InnerData), true)).name, true), value, assertnotnull(lambdavariable(ExternalMapToCatalyst_value52, ExternalMapToCatalyst_value_isNull52, ObjectType(class org.apache.spark.sql.InnerData), true)).value) ``` There is a `CreateNamedStruct` expression (`named_struct`) to create a row of `InnerData.name` and `InnerData.value` that are referred by `ExternalMapToCatalyst_value52`. Because `ExternalMapToCatalyst_value52` are local variable, when `CreateNamedStruct` splits expressions to individual functions, the local variable can't be accessed anymore. ## How was this patch tested? Added a new test suite into `DatasetPrimitiveSuite` Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com> Closes #18627 from kiszk/SPARK-21391.
…pressions
## What changes were proposed in this pull request?
This PR changes the direction of expression transformation in the DecimalPrecision rule. Previously, the expressions were transformed down, which led to incorrect result types when decimal expressions had other decimal expressions as their operands. The root cause of this issue was in visiting outer nodes before their children. Consider the example below:
```
val inputSchema = StructType(StructField("col", DecimalType(26, 6)) :: Nil)
val sc = spark.sparkContext
val rdd = sc.parallelize(1 to 2).map(_ => Row(BigDecimal(12)))
val df = spark.createDataFrame(rdd, inputSchema)
// Works correctly since no nested decimal expression is involved
// Expected result type: (26, 6) * (26, 6) = (38, 12)
df.select($"col" * $"col").explain(true)
df.select($"col" * $"col").printSchema()
// Gives a wrong result since there is a nested decimal expression that should be visited first
// Expected result type: ((26, 6) * (26, 6)) * (26, 6) = (38, 12) * (26, 6) = (38, 18)
df.select($"col" * $"col" * $"col").explain(true)
df.select($"col" * $"col" * $"col").printSchema()
```
The example above gives the following output:
```
// Correct result without sub-expressions
== Parsed Logical Plan ==
'Project [('col * 'col) AS (col * col)#4]
+- LogicalRDD [col#1]
== Analyzed Logical Plan ==
(col * col): decimal(38,12)
Project [CheckOverflow((promote_precision(cast(col#1 as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) AS (col * col)#4]
+- LogicalRDD [col#1]
== Optimized Logical Plan ==
Project [CheckOverflow((col#1 * col#1), DecimalType(38,12)) AS (col * col)#4]
+- LogicalRDD [col#1]
== Physical Plan ==
*Project [CheckOverflow((col#1 * col#1), DecimalType(38,12)) AS (col * col)#4]
+- Scan ExistingRDD[col#1]
// Schema
root
|-- (col * col): decimal(38,12) (nullable = true)
// Incorrect result with sub-expressions
== Parsed Logical Plan ==
'Project [(('col * 'col) * 'col) AS ((col * col) * col)#11]
+- LogicalRDD [col#1]
== Analyzed Logical Plan ==
((col * col) * col): decimal(38,12)
Project [CheckOverflow((promote_precision(cast(CheckOverflow((promote_precision(cast(col#1 as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) AS ((col * col) * col)#11]
+- LogicalRDD [col#1]
== Optimized Logical Plan ==
Project [CheckOverflow((cast(CheckOverflow((col#1 * col#1), DecimalType(38,12)) as decimal(26,6)) * col#1), DecimalType(38,12)) AS ((col * col) * col)#11]
+- LogicalRDD [col#1]
== Physical Plan ==
*Project [CheckOverflow((cast(CheckOverflow((col#1 * col#1), DecimalType(38,12)) as decimal(26,6)) * col#1), DecimalType(38,12)) AS ((col * col) * col)#11]
+- Scan ExistingRDD[col#1]
// Schema
root
|-- ((col * col) * col): decimal(38,12) (nullable = true)
```
## How was this patch tested?
This PR was tested with available unit tests. Moreover, there are tests to cover previously failing scenarios.
Author: aokolnychyi <anton.okolnychyi@sap.com>
Closes #18583 from aokolnychyi/spark-21332.
(cherry picked from commit 0be5fb4)
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
…lures in some cases ## What changes were proposed in this pull request? https://issues.apache.org/jira/projects/SPARK/issues/SPARK-21441 This issue can be reproduced by the following example: ``` val spark = SparkSession .builder() .appName("smj-codegen") .master("local") .config("spark.sql.autoBroadcastJoinThreshold", "1") .getOrCreate() val df1 = spark.createDataFrame(Seq((1, 1), (2, 2), (3, 3))).toDF("key", "int") val df2 = spark.createDataFrame(Seq((1, "1"), (2, "2"), (3, "3"))).toDF("key", "str") val df = df1.join(df2, df1("key") === df2("key")) .filter("int = 2 or reflect('java.lang.Integer', 'valueOf', str) = 1") .select("int") df.show() ``` To conclude, the issue happens when: (1) SortMergeJoin condition contains CodegenFallback expressions. (2) In PhysicalPlan tree, SortMergeJoin node is the child of root node, e.g., the Project in above example. This patch fixes the logic in `CollapseCodegenStages` rule. ## How was this patch tested? Unit test and manual verification in our cluster. Author: donnyzone <wellfengzhu@gmail.com> Closes #18656 from DonnyZone/Fix_SortMergeJoinExec. (cherry picked from commit 6b6dd68) Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request? JIRA Issue: https://issues.apache.org/jira/browse/SPARK-21446 options.asConnectionProperties can not have fetchsize，because fetchsize belongs to Spark-only options, and Spark-only options have been excluded in connection properities. So change properties of beforeFetch from options.asConnectionProperties.asScala.toMap to options.asProperties.asScala.toMap ## How was this patch tested? Author: DFFuture <albert.zhang23@gmail.com> Closes #18665 from DFFuture/sparksql_pg. (cherry picked from commit c972918) Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request? add `setWeightCol` method for OneVsRest. `weightCol` is ignored if classifier doesn't inherit HasWeightCol trait. ## How was this patch tested? + [x] add an unit test. Author: Yan Facai (颜发才) <facai.yan@gmail.com> Closes #18554 from facaiy/BUG/oneVsRest_missing_weightCol. (cherry picked from commit a5a3189) Signed-off-by: Yanbo Liang <ybliang8@gmail.com>
This reverts commit 8520d7c.
… by its canonicalized child ## What changes were proposed in this pull request? When there are aliases (these aliases were added for nested fields) as parameters in `RuntimeReplaceable`, as they are not in the children expression, those aliases can't be cleaned up in analyzer rule `CleanupAliases`. An expression `nvl(foo.foo1, "value")` can be resolved to two semantically different expressions in a group by query because they contain different aliases. Because those aliases are not children of `RuntimeReplaceable` which is an `UnaryExpression`. So we can't trim the aliases out by simple transforming the expressions in `CleanupAliases`. If we want to replace the non-children aliases in `RuntimeReplaceable`, we need to add more codes to `RuntimeReplaceable` and modify all expressions of `RuntimeReplaceable`. It makes the interface ugly IMO. Consider those aliases will be replaced later at optimization and so they're no harm, this patch chooses to simply override `canonicalized` of `RuntimeReplaceable`. One concern is about `CleanupAliases`. Because it actually cannot clean up ALL aliases inside a plan. To make caller of this rule notice that, this patch adds a comment to `CleanupAliases`. ## How was this patch tested? Added test. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #18761 from viirya/SPARK-21555. (cherry picked from commit 9c8109e) Signed-off-by: gatorsmile <gatorsmile@gmail.com>
|
branch2.1 |
|
Can one of the admins verify this patch? |
|
@xiaobing007 it looks mistakenly open. Close this please. |
Handle the case where the server closes the socket before the full message has been written by the client. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #18727 from vanzin/SPARK-21522. (cherry picked from commit b133501) Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
… registry ## What changes were proposed in this pull request? When using PySpark broadcast variables in a multi-threaded environment, `SparkContext._pickled_broadcast_vars` becomes a shared resource. A race condition can occur when broadcast variables that are pickled from one thread get added to the shared ` _pickled_broadcast_vars` and become part of the python command from another thread. This PR introduces a thread-safe pickled registry using thread local storage so that when python command is pickled (causing the broadcast variable to be pickled and added to the registry) each thread will have their own view of the pickle registry to retrieve and clear the broadcast variables used. ## How was this patch tested? Added a unit test that causes this race condition using another thread. Author: Bryan Cutler <cutlerb@gmail.com> Closes #18825 from BryanCutler/pyspark-bcast-threadsafe-SPARK-12717-2_1.
…le with extreme values on the partition column ## What changes were proposed in this pull request? An overflow of the difference of bounds on the partitioning column leads to no data being read. This patch checks for this overflow. ## How was this patch tested? New unit test. Author: Andrew Ray <ray.andrew@gmail.com> Closes #18800 from aray/SPARK-21330. (cherry picked from commit 25826c7) Signed-off-by: Sean Owen <sowen@cloudera.com>
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