[SPARK-17142][SQL] Complex query triggers binding error in HashAggregateExec [BACKPORT 2.0] #15092
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…lause #### What changes were proposed in this pull request? When doing a CTAS with a Partition By clause, we got a wrong error message. For example, ```SQL CREATE TABLE gen__tmp PARTITIONED BY (key string) AS SELECT key, value FROM mytable1 ``` The error message we get now is like ``` Operation not allowed: Schema may not be specified in a Create Table As Select (CTAS) statement(line 2, pos 0) ``` However, based on the code, the message we should get is like ``` Operation not allowed: A Create Table As Select (CTAS) statement is not allowed to create a partitioned table using Hive's file formats. Please use the syntax of "CREATE TABLE tableName USING dataSource OPTIONS (...) PARTITIONED BY ...\" to create a partitioned table through a CTAS statement.(line 2, pos 0) ``` Currently, partitioning columns is part of the schema. This PR fixes the bug by changing the detection orders. #### How was this patch tested? Added test cases. Author: gatorsmile <gatorsmile@gmail.com> Closes apache#14113 from gatorsmile/ctas.
### What changes were proposed in this pull request? Currently, the `refreshTable` API is always case sensitive. When users use the view name without the exact case match, the API silently ignores the call. Users might expect the command has been successfully completed. However, when users run the subsequent SQL commands, they might still get the exception, like ``` Job aborted due to stage failure: Task 1 in stage 4.0 failed 1 times, most recent failure: Lost task 1.0 in stage 4.0 (TID 7, localhost): java.io.FileNotFoundException: File file:/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-bd4b9ea6-9aec-49c5-8f05-01cff426211e/part-r-00000-0c84b915-c032-4f2e-abf5-1d48fdbddf38.snappy.parquet does not exist ``` This PR is to fix the issue. ### How was this patch tested? Added a test case. Author: gatorsmile <gatorsmile@gmail.com> Closes apache#14523 from gatorsmile/refreshTempTable.
Some very rare JVM errors are printed to stdout, and that confuses the code in spark-class. So add a check so that those cases are detected and the proper error message is shown to the user. Tested by running spark-submit after setting "ulimit -v 32000". Closes apache#14231 Author: Marcelo Vanzin <vanzin@cloudera.com> Closes apache#14508 from vanzin/SPARK-16586.
…onsistent with requestExecutors/killExecutors ## What changes were proposed in this pull request? RequestExecutors and killExecutor are public developer APIs for managing the number of executors allocated to the SparkContext. For consistency, requestTotalExecutors should also be a public Developer API, as it provides similar functionality. In fact, using requestTotalExecutors is more convenient that requestExecutors as the former is idempotent and the latter is not. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes apache#14541 from tdas/SPARK-16953.
…t add much and remove whitelisting ## What changes were proposed in this pull request? Avoid using postfix operation for command execution in SQLQuerySuite where it wasn't whitelisted and audit existing whitelistings removing postfix operators from most places. Some notable places where postfix operation remains is in the XML parsing & time units (seconds, millis, etc.) where it arguably can improve readability. ## How was this patch tested? Existing tests. Author: Holden Karau <holden@us.ibm.com> Closes apache#14407 from holdenk/SPARK-16779.
## What changes were proposed in this pull request? Update docs to include SASL support for RPC Evidence: https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/rpc/netty/NettyRpcEnv.scala#L63 ## How was this patch tested? Docs change only Author: Michael Gummelt <mgummelt@mesosphere.io> Closes apache#14549 from mgummelt/sasl.
## What changes were proposed in this pull request? The logic for LEAD/LAG processing is more complex that it needs to be. This PR fixes that. ## How was this patch tested? Existing tests. Author: Herman van Hovell <hvanhovell@databricks.com> Closes apache#14376 from hvanhovell/SPARK-16749.
…lan like MapElements, TypedFilter, and AppendColumn ## What changes were proposed in this pull request? This PR adds argument type information for typed logical plan like MapElements, TypedFilter, and AppendColumn, so that we can use these info in customized optimizer rule. ## How was this patch tested? Existing test. Author: Sean Zhong <seanzhong@databricks.com> Closes apache#14494 from clockfly/add_more_info_for_typed_operator.
## What changes were proposed in this pull request? Add a constant iterator which point to head of result. The header will be used to reset iterator when fetch result from first row repeatedly. JIRA ticket https://issues.apache.org/jira/browse/SPARK-16563 ## How was this patch tested? This bug was found when using Cloudera HUE connecting to spark sql thrift server, currently SQL statement result can be only fetched for once. The fix was tested manually with Cloudera HUE, With this fix, HUE can fetch spark SQL results repeatedly through thrift server. Author: Alice <alice.gugu@gmail.com> Author: Alice <guhq@garena.com> Closes apache#14218 from alicegugu/SparkSQLFetchResultsBug.
…ption. ## What changes were proposed in this pull request? For ORC source, Spark SQL has a writer option `compression`, which is used to set the codec and its value will be also set to `orc.compress` (the orc conf used for codec). However, if a user only set `orc.compress` in the writer option, we should not use the default value of `compression` (snappy) as the codec. Instead, we should respect the value of `orc.compress`. This PR makes ORC data source not ignoring `orc.compress` when `comperssion` is unset. So, here is the behaviour, 1. Check `compression` and use this if it is set. 2. If `compression` is not set, check `orc.compress` and use it. 3. If `compression` and `orc.compress` are not set, then use the default snappy. ## How was this patch tested? Unit test in `OrcQuerySuite`. Author: hyukjinkwon <gurwls223@gmail.com> Closes apache#14518 from HyukjinKwon/SPARK-16610.
…WARN SparkContext: Use an existing SparkContext, some configuration may not take effect." ## What changes were proposed in this pull request? SparkContext.getOrCreate shouldn't warn about ignored config if - it wasn't ignored because a new context is created with it or - no config was actually provided ## How was this patch tested? Jenkins + existing tests. Author: Sean Owen <sowen@cloudera.com> Closes apache#14533 from srowen/SPARK-16606.
## What changes were proposed in this pull request? Spark applications running on Mesos throw exception upon exit. For details, refer to https://issues.apache.org/jira/browse/SPARK-16522. I am not sure if there is any better fix, so wait for review comments. ## How was this patch tested? Manual test. Observed that the exception is gone upon application exit. Author: Sun Rui <sunrui2016@gmail.com> Closes apache#14175 from sun-rui/SPARK-16522.
…or wrong results
## What changes were proposed in this pull request?
This PR fixes the following to make `checkAnswer` raise `TestFailedException` again instead of `java.util.NoSuchElementException: key not found: TZ` in the environments without `TZ` variable. Also, this PR adds `QueryTestSuite` class for testing `QueryTest` itself.
```scala
- |Timezone Env: ${sys.env("TZ")}
+ |Timezone Env: ${sys.env.getOrElse("TZ", "")}
```
## How was this patch tested?
Pass the Jenkins tests with a new test suite.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes apache#14528 from dongjoon-hyun/SPARK-16940.
## What changes were proposed in this pull request? Links the Spark Mesos Dispatcher UI to the history server UI - adds spark.mesos.dispatcher.historyServer.url - explicitly generates frameworkIDs for the launched drivers, so the dispatcher knows how to correlate drivers and frameworkIDs ## How was this patch tested? manual testing Author: Michael Gummelt <mgummelt@mesosphere.io> Author: Sergiusz Urbaniak <sur@mesosphere.io> Closes apache#14414 from mgummelt/history-server.
…ecution package ## What changes were proposed in this pull request? This package is meant to be internal, and as a result it does not make sense to mark things as private[sql] or private[spark]. It simply makes debugging harder when Spark developers need to inspect the plans at runtime. This patch removes all private[sql] and private[spark] visibility modifiers in org.apache.spark.sql.execution. ## How was this patch tested? N/A - just visibility changes. Author: Reynold Xin <rxin@databricks.com> Closes apache#14554 from rxin/remote-private.
…es unnecessary data. ## What changes were proposed in this pull request? Similar to ```LeastSquaresAggregator``` in apache#14109, ```AFTAggregator``` used for ```AFTSurvivalRegression``` ends up serializing the ```parameters``` and ```featuresStd```, which is not necessary and can cause performance issues for high dimensional data. This patch removes this serialization. This PR is highly inspired by apache#14109. ## How was this patch tested? I tested this locally and verified the serialization reduction. Before patch  After patch  Author: Yanbo Liang <ybliang8@gmail.com> Closes apache#14519 from yanboliang/spark-16933.
…reateDirectStream for python3 ## What changes were proposed in this pull request? Ability to use KafkaUtils.createDirectStream with starting offsets in python 3 by using java.lang.Number instead of Long during param mapping in scala helper. This allows py4j to pass Integer or Long to the map and resolves ClassCastException problems. ## How was this patch tested? unit tests jerryshao - could you please look at this PR? Author: Mariusz Strzelecki <mariusz.strzelecki@allegrogroup.com> Closes apache#14540 from szczeles/kafka_pyspark.
## What changes were proposed in this pull request? MSCK REPAIR TABLE could be used to recover the partitions in external catalog based on partitions in file system. Another syntax is: ALTER TABLE table RECOVER PARTITIONS The implementation in this PR will only list partitions (not the files with a partition) in driver (in parallel if needed). ## How was this patch tested? Added unit tests for it and Hive compatibility test suite. Author: Davies Liu <davies@databricks.com> Closes apache#14500 from davies/repair_table.
## What changes were proposed in this pull request? This patch introduces a new configuration, `spark.deploy.maxExecutorRetries`, to let users configure an obscure behavior in the standalone master where the master will kill Spark applications which have experienced too many back-to-back executor failures. The current setting is a hardcoded constant (10); this patch replaces that with a new cluster-wide configuration. **Background:** This application-killing was added in 6b5980d (from September 2012) and I believe that it was designed to prevent a faulty application whose executors could never launch from DOS'ing the Spark cluster via an infinite series of executor launch attempts. In a subsequent patch (apache#1360), this feature was refined to prevent applications which have running executors from being killed by this code path. **Motivation for making this configurable:** Previously, if a Spark Standalone application experienced more than `ApplicationState.MAX_NUM_RETRY` executor failures and was left with no executors running then the Spark master would kill that application, but this behavior is problematic in environments where the Spark executors run on unstable infrastructure and can all simultaneously die. For instance, if your Spark driver runs on an on-demand EC2 instance while all workers run on ephemeral spot instances then it's possible for all executors to die at the same time while the driver stays alive. In this case, it may be desirable to keep the Spark application alive so that it can recover once new workers and executors are available. In order to accommodate this use-case, this patch modifies the Master to never kill faulty applications if `spark.deploy.maxExecutorRetries` is negative. I'd like to merge this patch into master, branch-2.0, and branch-1.6. ## How was this patch tested? I tested this manually using `spark-shell` and `local-cluster` mode. This is a tricky feature to unit test and historically this code has not changed very often, so I'd prefer to skip the additional effort of adding a testing framework and would rather rely on manual tests and review for now. Author: Josh Rosen <joshrosen@databricks.com> Closes apache#14544 from JoshRosen/add-setting-for-max-executor-failures.
In many terminals double-clicking and dragging also includes the trailing period. Simply remove this to make the value more easily copy/pasteable. Example value: `hdfs://mybox-123.net.example.com:8020/spark-events.` Author: Andrew Ash <andrew@andrewash.com> Closes apache#14566 from ash211/patch-9.
## What changes were proposed in this pull request? Fixed small typo - "value ... ~~in~~ is null" ## How was this patch tested? Still compiles! Author: Michał Kiełbowicz <jupblb@users.noreply.github.com> Closes apache#14569 from jupblb/typo-fix.
## What changes were proposed in this pull request? R API documentation for "coltypes" is confusing, found when working on another ticket. Current version http://spark.apache.org/docs/2.0.0/api/R/coltypes.html, where parameters have 2 "x" which is a duplicate, and also the example is not very clear   ## How was this patch tested? Tested manually on local machine. And the screenshots are like below:   Author: Xin Ren <iamshrek@126.com> Closes apache#14489 from keypointt/rExample.
…m Hive Metastore ### What changes were proposed in this pull request? The `comment` in `CatalogTable` returned from Hive is always empty. We store it in the table property when creating a table. However, when we try to retrieve the table metadata from Hive metastore, we do not rebuild it. The `comment` is always empty. This PR is to fix the issue. ### How was this patch tested? Fixed the test case to verify the change. Author: gatorsmile <gatorsmile@gmail.com> Closes apache#14550 from gatorsmile/tableComment.
## What changes were proposed in this pull request? This PR adds `MINUS` set operator which is equivalent `EXCEPT DISTINCT`. This will slightly improve the compatibility with Oracle. ## How was this patch tested? Pass the Jenkins with newly added testcases. Author: Dongjoon Hyun <dongjoon@apache.org> Closes apache#14570 from dongjoon-hyun/SPARK-10601.
…t Without Enabling Hive Support
#### What changes were proposed in this pull request?
When we do not turn on the Hive Support, the following query generates a confusing error message by Planner:
```Scala
sql("CREATE TABLE t2 SELECT a, b from t1")
```
```
assertion failed: No plan for CreateTable CatalogTable(
Table: `t2`
Created: Tue Aug 09 23:45:32 PDT 2016
Last Access: Wed Dec 31 15:59:59 PST 1969
Type: MANAGED
Provider: hive
Storage(InputFormat: org.apache.hadoop.mapred.TextInputFormat, OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat)), ErrorIfExists
+- Relation[a#19L,b#20L] parquet
java.lang.AssertionError: assertion failed: No plan for CreateTable CatalogTable(
Table: `t2`
Created: Tue Aug 09 23:45:32 PDT 2016
Last Access: Wed Dec 31 15:59:59 PST 1969
Type: MANAGED
Provider: hive
Storage(InputFormat: org.apache.hadoop.mapred.TextInputFormat, OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat)), ErrorIfExists
+- Relation[a#19L,b#20L] parquet
```
This PR is to issue a better error message:
```
Hive support is required to use CREATE Hive TABLE AS SELECT
```
#### How was this patch tested?
Added test cases in `DDLSuite.scala`
Author: gatorsmile <gatorsmile@gmail.com>
Closes apache#13886 from gatorsmile/createCatalogedTableAsSelect.
Author: Andrew Ash <andrew@andrewash.com> Closes apache#14563 from ash211/patch-8.
## What changes were proposed in this pull request? - enable setting default properties for all jobs submitted through the dispatcher [SPARK-16927] - remove duplication of conf vars on cluster submitted jobs [SPARK-16923] (this is a small fix, so I'm including in the same PR) ## How was this patch tested? mesos/spark integration test suite manual testing Author: Timothy Chen <tnachen@gmail.com> Closes apache#14511 from mgummelt/override-props.
…ring when match fails ## What changes were proposed in this pull request? Doc that regexp_extract returns empty string when regex or group does not match ## How was this patch tested? Jenkins test, with a few new test cases Author: Sean Owen <sowen@cloudera.com> Closes apache#14525 from srowen/SPARK-16324.
## What changes were proposed in this pull request? This patch introduces SQLQueryTestSuite, a basic framework for end-to-end SQL test cases defined in spark/sql/core/src/test/resources/sql-tests. This is a more standard way to test SQL queries end-to-end in different open source database systems, because it is more manageable to work with files. This is inspired by HiveCompatibilitySuite, but simplified for general Spark SQL tests. Once this is merged, I can work towards porting SQLQuerySuite over, and eventually also move the existing HiveCompatibilitySuite to use this framework. Unlike HiveCompatibilitySuite, SQLQueryTestSuite compares both the output schema and the output data (in string form). When there is a mismatch, the error message looks like the following: ``` [info] - blacklist.sql !!! IGNORED !!! [info] - number-format.sql *** FAILED *** (2 seconds, 405 milliseconds) [info] Expected "...147483648 -214748364[8]", but got "...147483648 -214748364[9]" Result should match for query #1 (SQLQueryTestSuite.scala:171) [info] org.scalatest.exceptions.TestFailedException: [info] at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:495) [info] at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555) [info] at org.scalatest.Assertions$class.assertResult(Assertions.scala:1171) ``` ## How was this patch tested? This is a test infrastructure change. Author: petermaxlee <petermaxlee@gmail.com> Closes apache#14472 from petermaxlee/SPARK-16866.
…Path ## What changes were proposed in this pull request? Fix the construction of the file path. Previous way of construction caused the creation of incorrect path on Windows. ## How was this patch tested? Run SQL unit tests on Windows Author: avulanov <nashb@yandex.ru> Closes apache#13868 from avulanov/SPARK-15899-file.
## What changes were proposed in this pull request? Clean up unused variables and unused import statements, unnecessary `return` and `toArray`, and some more style improvement, when I walk through the code examples. ## How was this patch tested? Testet manually on local laptop. Author: Xin Ren <iamshrek@126.com> Closes apache#14836 from keypointt/codeWalkThroughML.
This patch is using Apache Commons Crypto library to enable shuffle encryption support. Author: Ferdinand Xu <cheng.a.xu@intel.com> Author: kellyzly <kellyzly@126.com> Closes apache#8880 from winningsix/SPARK-10771.
…elyBlacklisted This patch addresses a minor scheduler performance issue that was introduced in apache#13603. If you run ``` sc.parallelize(1 to 100000, 100000).map(identity).count() ``` then most of the time ends up being spent in `TaskSetManager.abortIfCompletelyBlacklisted()`:  When processing resource offers, the scheduler uses a nested loop which considers every task set at multiple locality levels: ```scala for (taskSet <- sortedTaskSets; maxLocality <- taskSet.myLocalityLevels) { do { launchedTask = resourceOfferSingleTaskSet( taskSet, maxLocality, shuffledOffers, availableCpus, tasks) } while (launchedTask) } ``` In order to prevent jobs with globally blacklisted tasks from hanging, apache#13603 added a `taskSet.abortIfCompletelyBlacklisted` call inside of `resourceOfferSingleTaskSet`; if a call to `resourceOfferSingleTaskSet` fails to schedule any tasks, then `abortIfCompletelyBlacklisted` checks whether the tasks are completely blacklisted in order to figure out whether they will ever be schedulable. The problem with this placement of the call is that the last call to `resourceOfferSingleTaskSet` in the `while` loop will return `false`, implying that `resourceOfferSingleTaskSet` will call `abortIfCompletelyBlacklisted`, so almost every call to `resourceOffers` will trigger the `abortIfCompletelyBlacklisted` check for every task set. Instead, I think that this call should be moved out of the innermost loop and should be called _at most_ once per task set in case none of the task set's tasks can be scheduled at any locality level. Before this patch's changes, the microbenchmark example that I posted above took 35 seconds to run, but it now only takes 15 seconds after this change. /cc squito and kayousterhout for review. Author: Josh Rosen <joshrosen@databricks.com> Closes apache#14871 from JoshRosen/bail-early-if-no-cpus.
…st ThreadLocal impl ## What changes were proposed in this pull request? When a thread is a Netty's FastThreadLocalThread, Netty will use its fast ThreadLocal implementation. It has a better performance than JDK's (See the benchmark results in netty/netty#4417, note: it's not a fix to Netty's FastThreadLocal. It just fixed an issue in Netty's benchmark codes) This PR just changed the ThreadFactory to Netty's DefaultThreadFactory which will use FastThreadLocalThread. There is also a minor change to the thread names. See https://github.com/netty/netty/blob/netty-4.0.22.Final/common/src/main/java/io/netty/util/concurrent/DefaultThreadFactory.java#L94 ## How was this patch tested? Author: Shixiong Zhu <shixiong@databricks.com> Closes apache#14879 from zsxwing/netty-thread.
…arge application history ## What changes were proposed in this pull request? With the new History Server the summary page loads the application list via the the REST API, this makes it very slow to impossible to load with large (10K+) application history. This pr fixes this by adding the `spark.history.ui.maxApplications` conf to limit the number of applications the History Server displays. This is accomplished using a new optional `limit` param for the `applications` api. (Note this only applies to what the summary page displays, all the Application UI's are still accessible if the user knows the App ID and goes to the Application UI directly.) I've also added a new test for the `limit` param in `HistoryServerSuite.scala` ## How was this patch tested? Manual testing and dev/run-tests Author: Alex Bozarth <ajbozart@us.ibm.com> Closes apache#14835 from ajbozarth/spark17243.
…class defined in repl ## What changes were proposed in this pull request? There are a lot of failures recently: http://spark-tests.appspot.com/tests/org.apache.spark.repl.ReplSuite/replicating%20blocks%20of%20object%20with%20class%20defined%20in%20repl This PR just changed the persist level to `MEMORY_AND_DISK_2` to avoid blocks being evicted from memory. ## How was this patch tested? Jenkins unit tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes apache#14884 from zsxwing/SPARK-17318.
…l or a map without null
## What changes were proposed in this pull request?
This PR eliminates redundant cast from an `ArrayType` with `containsNull = false` or a `MapType` with `containsNull = false`.
For example, in `ArrayType` case, current implementation leaves a cast `cast(value#63 as array<double>).toDoubleArray`. However, we can eliminate `cast(value#63 as array<double>)` if we know `value#63` does not include `null`. This PR apply this elimination for `ArrayType` and `MapType` in `SimplifyCasts` at a plan optimization phase.
In summary, we got 1.2-1.3x performance improvements over the code before applying this PR.
Here are performance results of benchmark programs:
```
test("Read array in Dataset") {
import sparkSession.implicits._
val iters = 5
val n = 1024 * 1024
val rows = 15
val benchmark = new Benchmark("Read primnitive array", n)
val rand = new Random(511)
val intDS = sparkSession.sparkContext.parallelize(0 until rows, 1)
.map(i => Array.tabulate(n)(i => i)).toDS()
intDS.count() // force to create ds
val lastElement = n - 1
val randElement = rand.nextInt(lastElement)
benchmark.addCase(s"Read int array in Dataset", numIters = iters)(iter => {
val idx0 = randElement
val idx1 = lastElement
intDS.map(a => a(0) + a(idx0) + a(idx1)).collect
})
val doubleDS = sparkSession.sparkContext.parallelize(0 until rows, 1)
.map(i => Array.tabulate(n)(i => i.toDouble)).toDS()
doubleDS.count() // force to create ds
benchmark.addCase(s"Read double array in Dataset", numIters = iters)(iter => {
val idx0 = randElement
val idx1 = lastElement
doubleDS.map(a => a(0) + a(idx0) + a(idx1)).collect
})
benchmark.run()
}
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.10.4
Intel(R) Core(TM) i5-5257U CPU 2.70GHz
without this PR
Read primnitive array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Read int array in Dataset 525 / 690 2.0 500.9 1.0X
Read double array in Dataset 947 / 1209 1.1 902.7 0.6X
with this PR
Read primnitive array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Read int array in Dataset 400 / 492 2.6 381.5 1.0X
Read double array in Dataset 788 / 870 1.3 751.4 0.5X
```
An example program that originally caused this performance issue.
```
val ds = Seq(Array(1.0, 2.0, 3.0), Array(4.0, 5.0, 6.0)).toDS()
val ds2 = ds.map(p => {
var s = 0.0
for (i <- 0 to 2) { s += p(i) }
s
})
ds2.show
ds2.explain(true)
```
Plans before this PR
```
== Parsed Logical Plan ==
'SerializeFromObject [input[0, double, true] AS value#68]
+- 'MapElements <function1>, obj#67: double
+- 'DeserializeToObject unresolveddeserializer(upcast(getcolumnbyordinal(0, ArrayType(DoubleType,false)), ArrayType(DoubleType,false), - root class: "scala.Array").toDoubleArray), obj#66: [D
+- LocalRelation [value#63]
== Analyzed Logical Plan ==
value: double
SerializeFromObject [input[0, double, true] AS value#68]
+- MapElements <function1>, obj#67: double
+- DeserializeToObject cast(value#63 as array<double>).toDoubleArray, obj#66: [D
+- LocalRelation [value#63]
== Optimized Logical Plan ==
SerializeFromObject [input[0, double, true] AS value#68]
+- MapElements <function1>, obj#67: double
+- DeserializeToObject cast(value#63 as array<double>).toDoubleArray, obj#66: [D
+- LocalRelation [value#63]
== Physical Plan ==
*SerializeFromObject [input[0, double, true] AS value#68]
+- *MapElements <function1>, obj#67: double
+- *DeserializeToObject cast(value#63 as array<double>).toDoubleArray, obj#66: [D
+- LocalTableScan [value#63]
```
Plans after this PR
```
== Parsed Logical Plan ==
'SerializeFromObject [input[0, double, true] AS value#6]
+- 'MapElements <function1>, obj#5: double
+- 'DeserializeToObject unresolveddeserializer(upcast(getcolumnbyordinal(0, ArrayType(DoubleType,false)), ArrayType(DoubleType,false), - root class: "scala.Array").toDoubleArray), obj#4: [D
+- LocalRelation [value#1]
== Analyzed Logical Plan ==
value: double
SerializeFromObject [input[0, double, true] AS value#6]
+- MapElements <function1>, obj#5: double
+- DeserializeToObject cast(value#1 as array<double>).toDoubleArray, obj#4: [D
+- LocalRelation [value#1]
== Optimized Logical Plan ==
SerializeFromObject [input[0, double, true] AS value#6]
+- MapElements <function1>, obj#5: double
+- DeserializeToObject value#1.toDoubleArray, obj#4: [D
+- LocalRelation [value#1]
== Physical Plan ==
*SerializeFromObject [input[0, double, true] AS value#6]
+- *MapElements <function1>, obj#5: double
+- *DeserializeToObject value#1.toDoubleArray, obj#4: [D
+- LocalTableScan [value#1]
```
## How was this patch tested?
Tested by new test cases in `SimplifyCastsSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes apache#13704 from kiszk/SPARK-15985.
…through --conf ## What changes were proposed in this pull request? Allow user to set sparkr shell command through --conf spark.r.shell.command ## How was this patch tested? Unit test is added and also verify it manually through ``` bin/sparkr --master yarn-client --conf spark.r.shell.command=/usr/local/bin/R ``` Author: Jeff Zhang <zjffdu@apache.org> Closes apache#14744 from zjffdu/SPARK-17178.
…d to handle ALTER VIEW AS ## What changes were proposed in this pull request? Currently we use `CreateViewCommand` to implement ALTER VIEW AS, which has 3 bugs: 1. SPARK-17180: ALTER VIEW AS should alter temp view if view name has no database part and temp view exists 2. SPARK-17309: ALTER VIEW AS should issue exception if view does not exist. 3. SPARK-17323: ALTER VIEW AS should keep the previous table properties, comment, create_time, etc. The root cause is, ALTER VIEW AS is quite different from CREATE VIEW, we need different code path to handle them. However, in `CreateViewCommand`, there is no way to distinguish ALTER VIEW AS and CREATE VIEW, we have to introduce extra flag. But instead of doing this, I think a more natural way is to separate the ALTER VIEW AS logic into a new command. ## How was this patch tested? new tests in SQLViewSuite Author: Wenchen Fan <wenchen@databricks.com> Closes apache#14874 from cloud-fan/minor4.
…ting default build without Hive ## What changes were proposed in this pull request? This PR fixes `WINDOWS.md` to imply referring other profiles in http://spark.apache.org/docs/latest/building-spark.html#building-with-buildmvn rather than directly pointing to run `mvn -DskipTests -Psparkr package` without Hive supports. ## How was this patch tested? Manually, <img width="626" alt="2016-08-31 6 01 08" src="https://cloud.githubusercontent.com/assets/6477701/18122549/f6297b2c-6fa4-11e6-9b5e-fd4347355d87.png"> Author: hyukjinkwon <gurwls223@gmail.com> Closes apache#14890 from HyukjinKwon/minor-build-r.
## What changes were proposed in this pull request? add build_profile_flags entry to mesos build module ## How was this patch tested? unit tests Author: Michael Gummelt <mgummelt@mesosphere.io> Closes apache#14885 from mgummelt/mesos-profile.
… non-blocking ## What changes were proposed in this pull request? StandaloneSchedulerBackend.executorRemoved is a blocking call right now. It may cause some deadlock since it's called inside StandaloneAppClient.ClientEndpoint. This PR just changed CoarseGrainedSchedulerBackend.removeExecutor to be non-blocking. It's safe since the only two usages (StandaloneSchedulerBackend and YarnSchedulerEndpoint) don't need the return value). ## How was this patch tested? Jenkins unit tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes apache#14882 from zsxwing/SPARK-17316.
## What changes were proposed in this pull request? Make all Java Loggers static members ## How was this patch tested? Jenkins Author: Sean Owen <sowen@cloudera.com> Closes apache#14896 from srowen/SPARK-17332.
…skipped always ## What changes were proposed in this pull request? Currently, `HiveContext` in SparkR is not being tested and always skipped. This is because the initiation of `TestHiveContext` is being failed due to trying to load non-existing data paths (test tables). This is introduced from apache#14005 This enables the tests with SparkR. ## How was this patch tested? Manually, **Before** (on Mac OS) ``` ... Skipped ------------------------------------------------------------------------ 1. create DataFrame from RDD (test_sparkSQL.R#200) - Hive is not build with SparkSQL, skipped 2. test HiveContext (test_sparkSQL.R#1041) - Hive is not build with SparkSQL, skipped 3. read/write ORC files (test_sparkSQL.R#1748) - Hive is not build with SparkSQL, skipped 4. enableHiveSupport on SparkSession (test_sparkSQL.R#2480) - Hive is not build with SparkSQL, skipped 5. sparkJars tag in SparkContext (test_Windows.R#21) - This test is only for Windows, skipped ... ``` **After** (on Mac OS) ``` ... Skipped ------------------------------------------------------------------------ 1. sparkJars tag in SparkContext (test_Windows.R#21) - This test is only for Windows, skipped ... ``` Please refer the tests below (on Windows) - Before: https://ci.appveyor.com/project/HyukjinKwon/spark/build/45-test123 - After: https://ci.appveyor.com/project/HyukjinKwon/spark/build/46-test123 Author: hyukjinkwon <gurwls223@gmail.com> Closes apache#14889 from HyukjinKwon/SPARK-17326.
## What changes were proposed in this pull request? The master is broken because apache#14882 didn't run mesos tests. ## How was this patch tested? Jenkins unit tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes apache#14902 from zsxwing/hotfix.
## What changes were proposed in this pull request? Remove cleanup.jobj test. Use JVM wrapper API for other test cases. ## How was this patch tested? Run R unit tests with testthat 1.0 Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu> Closes apache#14904 from shivaram/sparkr-jvm-tests-fix.
## What changes were proposed in this pull request? The usage in the original example is incorrect. This PR fixes it. ## How was this patch tested? Manual test. Author: Junyang Qian <junyangq@databricks.com> Closes apache#14903 from junyangq/SPARKR-FixWindowPartitionByDoc.
…e regularization parameter https://issues.apache.org/jira/browse/SPARK-17241 ## What changes were proposed in this pull request? Spark has configurable L2 regularization parameter for generalized linear regression. It is very important to have them in SparkR so that users can run ridge regression. ## How was this patch tested? Test manually on local laptop. Author: Xin Ren <iamshrek@126.com> Closes apache#14856 from keypointt/SPARK-17241.
## What changes were proposed in this pull request? according to the discussion in the original PR apache#10896 and the new approach PR apache#14876 , we decided to revert these 2 PRs and go with the new approach. ## How was this patch tested? N/A Author: Wenchen Fan <wenchen@databricks.com> Closes apache#14909 from cloud-fan/revert.
…class defined in repl again ## What changes were proposed in this pull request? After digging into the logs, I noticed the failure is because in this test, it starts a local cluster with 2 executors. However, when SparkContext is created, executors may be still not up. When one of the executor is not up during running the job, the blocks won't be replicated. This PR just adds a wait loop before running the job to fix the flaky test. ## How was this patch tested? Jenkins Author: Shixiong Zhu <shixiong@databricks.com> Closes apache#14905 from zsxwing/SPARK-17318-2.
…nged ## What changes were proposed in this pull request? Only build PRs with -Pyarn if YARN code was modified. ## How was this patch tested? Jenkins tests (will look to verify whether -Pyarn was included in the PR builder for this one.) Author: Sean Owen <sowen@cloudera.com> Closes apache#14892 from srowen/SPARK-17329.
…which supports partial aggregation. ## What changes were proposed in this pull request? This PR implements aggregation function `percentile_approx`. Function `percentile_approx` returns the approximate percentile(s) of a column at the given percentage(s). A percentile is a watermark value below which a given percentage of the column values fall. For example, the percentile of column `col` at percentage 50% is the median value of column `col`. ### Syntax: ``` # Returns percentile at a given percentage value. The approximation error can be reduced by increasing parameter accuracy, at the cost of memory. percentile_approx(col, percentage [, accuracy]) # Returns percentile value array at given percentage value array percentile_approx(col, array(percentage1 [, percentage2]...) [, accuracy]) ``` ### Features: 1. This function supports partial aggregation. 2. The memory consumption is bounded. The larger `accuracy` parameter we choose, we smaller error we get. The default accuracy value is 10000, to match with Hive default setting. Choose a smaller value for smaller memory footprint. 3. This function supports window function aggregation. ### Example usages: ``` ## Returns the 25th percentile value, with default accuracy SELECT percentile_approx(col, 0.25) FROM table ## Returns an array of percentile value (25th, 50th, 75th), with default accuracy SELECT percentile_approx(col, array(0.25, 0.5, 0.75)) FROM table ## Returns 25th percentile value, with custom accuracy value 100, larger accuracy parameter yields smaller approximation error SELECT percentile_approx(col, 0.25, 100) FROM table ## Returns the 25th, and 50th percentile values, with custom accuracy value 100 SELECT percentile_approx(col, array(0.25, 0.5), 100) FROM table ``` ### NOTE: 1. The `percentile_approx` implementation is different from Hive, so the result returned on same query maybe slightly different with Hive. This implementation uses `QuantileSummaries` as the underlying probabilistic data structure, and mainly follows paper `Space-efficient Online Computation of Quantile Summaries` by Greenwald, Michael and Khanna, Sanjeev. (http://dx.doi.org/10.1145/375663.375670)` 2. The current implementation of `QuantileSummaries` doesn't support automatic compression. This PR has a rule to do compression automatically at the caller side, but it may not be optimal. ## How was this patch tested? Unit test, and Sql query test. ## Acknowledgement 1. This PR's work in based on lw-lin's PR apache#14298, with improvements like supporting partial aggregation, fixing out of memory issue. Author: Sean Zhong <seanzhong@databricks.com> Closes apache#14868 from clockfly/appro_percentile_try_2.
fixed 2 typos Author: Seigneurin, Alexis (CONT) <Alexis.Seigneurin@capitalone.com> Closes apache#14877 from aseigneurin/fix-typo-2.
…ATE TABLE LIKE command ### What changes were proposed in this pull request? The existing `CREATE TABLE LIKE` command has multiple issues: - The generated table is non-empty when the source table is a data source table. The major reason is the data source table is using the table property `path` to store the location of table contents. Currently, we keep it unchanged. Thus, we still create the same table with the same location. - The table type of the generated table is `EXTERNAL` when the source table is an external Hive Serde table. Currently, we explicitly set it to `MANAGED`, but Hive is checking the table property `EXTERNAL` to decide whether the table is `EXTERNAL` or not. (See https://github.com/apache/hive/blob/master/metastore/src/java/org/apache/hadoop/hive/metastore/ObjectStore.java#L1407-L1408) Thus, the created table is still `EXTERNAL`. - When the source table is a `VIEW`, the metadata of the generated table contains the original view text and view original text. So far, this does not break anything, but it could cause something wrong in Hive. (For example, https://github.com/apache/hive/blob/master/metastore/src/java/org/apache/hadoop/hive/metastore/ObjectStore.java#L1405-L1406) - The issue regarding the table `comment`. To follow what Hive does, the table comment should be cleaned, but the column comments should be still kept. - The `INDEX` table is not supported. Thus, we should throw an exception in this case. - `owner` should not be retained. `ToHiveTable` set it [here](https://github.com/apache/spark/blob/e679bc3c1cd418ef0025d2ecbc547c9660cac433/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClientImpl.scala#L793) no matter which value we set in `CatalogTable`. We set it to an empty string for avoiding the confusing output in Explain. - Add a support for temp tables - Like Hive, we should not copy the table properties from the source table to the created table, especially for the statistics-related properties, which could be wrong in the created table. - `unsupportedFeatures` should not be copied from the source table. The created table does not have these unsupported features. - When the type of source table is a view, the target table is using the default format of data source tables: `spark.sql.sources.default`. This PR is to fix the above issues. ### How was this patch tested? Improve the test coverage by adding more test cases Author: gatorsmile <gatorsmile@gmail.com> Closes apache#14531 from gatorsmile/createTableLike.
…ake CatalogTable ## What changes were proposed in this pull request? This is kind of a follow-up of apache#14482 . As we put `CatalogTable` in the logical plan directly, it makes sense to let physical plans take `CatalogTable` directly, instead of extracting some fields of `CatalogTable` in planner and then construct a new `CatalogTable` in physical plan. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes apache#14823 from cloud-fan/create-table.
## What changes were proposed in this pull request? Removing `semanticEquals()` from `SortOrder` because it can use the `semanticEquals()` provided by its parent class (`Expression`). This was as per suggestion by cloud-fan at https://github.com/apache/spark/pull/14841/files/7192418b3a26a14642fc04fc92bf496a954ffa5d#r77106801 ## How was this patch tested? Ran the test added in apache#14841 Author: Tejas Patil <tejasp@fb.com> Closes apache#14910 from tejasapatil/SPARK-17271_remove_semantic_ordering.
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It seems something went wrong @jiangxb1987 . Could you maybe close this and open another one to backport correctly? |
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Test build #65353 has finished for PR 15092 at commit
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What changes were proposed in this pull request?
This PR backports #14917 to branch-2.0. It fixes a expression optimize bug caused by rule
ReorderAssociativeOperator.How was this patch tested?
Add new test case in ReorderAssociativeOperatorSuite.