We switched to TorrentBroadcast in Spark 1.1, and HttpBroadcast has been undocumented since then. It's time to remove it in Spark 2.0.
Author: Reynold Xin <rxin@databricks.com>
Closes#10531 from rxin/SPARK-12588.
I got an exception when accessing the below REST API with an unknown application Id.
`http://<server-url>:18080/api/v1/applications/xxx/jobs`
Instead of an exception, I expect an error message "no such app: xxx" which is a similar error message when I access `/api/v1/applications/xxx`
```
org.spark-project.guava.util.concurrent.UncheckedExecutionException: java.util.NoSuchElementException: no app with key xxx
at org.spark-project.guava.cache.LocalCache$Segment.get(LocalCache.java:2263)
at org.spark-project.guava.cache.LocalCache.get(LocalCache.java:4000)
at org.spark-project.guava.cache.LocalCache.getOrLoad(LocalCache.java:4004)
at org.spark-project.guava.cache.LocalCache$LocalLoadingCache.get(LocalCache.java:4874)
at org.apache.spark.deploy.history.HistoryServer.getSparkUI(HistoryServer.scala:116)
at org.apache.spark.status.api.v1.UIRoot$class.withSparkUI(ApiRootResource.scala:226)
at org.apache.spark.deploy.history.HistoryServer.withSparkUI(HistoryServer.scala:46)
at org.apache.spark.status.api.v1.ApiRootResource.getJobs(ApiRootResource.scala:66)
```
Author: Carson Wang <carson.wang@intel.com>
Closes#10352 from carsonwang/unknownAppFix.
This is rework from #10386 and add more tests and LIKE push-down support.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#10468 from maropu/SupportMorePushdownInJdbc.
This patch adds a new build check which enumerates Spark's resolved runtime classpath and saves it to a file, then diffs against that file to detect whether pull requests have introduced dependency changes. The aim of this check is to make it simpler to reason about whether pull request which modify the build have introduced new dependencies or changed transitive dependencies in a way that affects the final classpath.
This supplants the checks added in SPARK-4123 / #5093, which are currently disabled due to bugs.
This patch is based on pwendell's work in #8531.
Closes#8531.
Author: Josh Rosen <joshrosen@databricks.com>
Author: Patrick Wendell <patrick@databricks.com>
Closes#10461 from JoshRosen/SPARK-10359.
Current schema inference for local python collections halts as soon as there are no NullTypes. This is different than when we specify a sampling ratio of 1.0 on a distributed collection. This could result in incomplete schema information.
Author: Holden Karau <holden@us.ibm.com>
Closes#10275 from holdenk/SPARK-12300-fix-schmea-inferance-on-local-collections.
Most of cases we should propagate null when call `NewInstance`, and so far there is only one case we should stop null propagation: create product/java bean. So I think it makes more sense to propagate null by dafault.
This also fixes a bug when encode null array/map, which is firstly discovered in https://github.com/apache/spark/pull/10401
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10443 from cloud-fan/encoder.
Updated the Worker Unit IllegalStateException message to indicate no values less than 1MB instead of 0 to help solve this.
Requesting review
Author: Neelesh Srinivas Salian <nsalian@cloudera.com>
Closes#10483 from nssalian/SPARK-12263.
```
org.apache.spark.sql.AnalysisException: cannot resolve 'value' given input columns text;
```
lets put a `:` after `columns` and put the columns in `[]` so that they match the toString of DataFrame.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#10518 from gatorsmile/improveAnalysisExceptionMsg.
This is a WIP. The PR has been taken over from nongli (see https://github.com/apache/spark/pull/10420). I have removed some additional dead code, and fixed a few issues which were caused by the fact that the inlined Hive parser is newer than the Hive parser we currently use in Spark.
I am submitting this PR in order to get some feedback and testing done. There is quite a bit of work to do:
- [ ] Get it to pass jenkins build/test.
- [ ] Aknowledge Hive-project for using their parser.
- [ ] Refactorings between HiveQl and the java classes.
- [ ] Create our own ASTNode and integrate the current implicit extentions.
- [ ] Move remaining ```SemanticAnalyzer``` and ```ParseUtils``` functionality to ```HiveQl```.
- [ ] Removing Hive dependencies from the parser. This will require some edits in the grammar files.
- [ ] Introduce our own context which needs to contain a ```TokenRewriteStream```.
- [ ] Add ```useSQL11ReservedKeywordsForIdentifier``` and ```allowQuotedId``` to the catalyst or sql configuration.
- [ ] Remove ```HiveConf``` from grammar files &HiveQl, and pass in our own configuration.
- [ ] Moving the parser into sql/core.
cc nongli rxin
Author: Herman van Hovell <hvanhovell@questtec.nl>
Author: Nong Li <nong@databricks.com>
Author: Nong Li <nongli@gmail.com>
Closes#10509 from hvanhovell/SPARK-12362.
In Spark we allow UDFs to declare its expected input types in order to apply type coercion. The expected input type parameter takes a Seq[DataType] and uses Nil when no type coercion is applied. It makes more sense to take Option[Seq[DataType]] instead, so we can differentiate a no-arg function vs function with no expected input type specified.
Author: Reynold Xin <rxin@databricks.com>
Closes#10504 from rxin/SPARK-12549.
* Changes api.r.SQLUtils to use ```SQLContext.getOrCreate``` instead of creating a new context.
* Adds a simple test
[SPARK-11199] #comment link with JIRA
Author: Hossein <hossein@databricks.com>
Closes#9185 from falaki/SPARK-11199.
`ifelse`, `when`, `otherwise` is unable to take `Column` typed S4 object as values.
For example:
```r
ifelse(lit(1) == lit(1), lit(2), lit(3))
ifelse(df$mpg > 0, df$mpg, 0)
```
will both fail with
```r
attempt to replicate an object of type 'environment'
```
The PR replaces `ifelse` calls with `if ... else ...` inside the function implementations to avoid attempt to vectorize(i.e. `rep()`). It remains to be discussed whether we should instead support vectorization in these functions for consistency because `ifelse` in base R is vectorized but I cannot foresee any scenarios these functions will want to be vectorized in SparkR.
For reference, added test cases which trigger failures:
```r
. Error: when(), otherwise() and ifelse() with column on a DataFrame ----------
error in evaluating the argument 'x' in selecting a method for function 'collect':
error in evaluating the argument 'col' in selecting a method for function 'select':
attempt to replicate an object of type 'environment'
Calls: when -> when -> ifelse -> ifelse
1: withCallingHandlers(eval(code, new_test_environment), error = capture_calls, message = function(c) invokeRestart("muffleMessage"))
2: eval(code, new_test_environment)
3: eval(expr, envir, enclos)
4: expect_equal(collect(select(df, when(df$a > 1 & df$b > 2, lit(1))))[, 1], c(NA, 1)) at test_sparkSQL.R:1126
5: expect_that(object, equals(expected, label = expected.label, ...), info = info, label = label)
6: condition(object)
7: compare(actual, expected, ...)
8: collect(select(df, when(df$a > 1 & df$b > 2, lit(1))))
Error: Test failures
Execution halted
```
Author: Forest Fang <forest.fang@outlook.com>
Closes#10481 from saurfang/spark-12526.
If DataFrame has BYTE types, throws an exception:
org.postgresql.util.PSQLException: ERROR: type "byte" does not exist
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#9350 from maropu/FixBugInPostgreJdbc.
We use scalastyle:off to turn off style checks in certain places where it is not possible to follow the style guide. This is usually ok. However, in udf registration, we disable the checker for a large amount of code simply because some of them exceed 100 char line limit. It is better to just disable the line limit check rather than everything.
In this pull request, I only disabled line length check, and fixed a problem (lack explicit types for public methods).
Author: Reynold Xin <rxin@databricks.com>
Closes#10501 from rxin/SPARK-12547.
Fixing the missing the document for the configuration. We can see the missing messages "TODO" when issuing the command "SET -V".
```
spark.sql.columnNameOfCorruptRecord
spark.sql.hive.verifyPartitionPath
spark.sql.sources.parallelPartitionDiscovery.threshold
spark.sql.hive.convertMetastoreParquet.mergeSchema
spark.sql.hive.convertCTAS
spark.sql.hive.thriftServer.async
```
Author: gatorsmile <gatorsmile@gmail.com>
Closes#10471 from gatorsmile/commandDesc.
The web UI's paginated table uses Javascript to implement certain navigation controls, such as table sorting and the "go to page" form. This is unnecessary and should be simplified to use plain HTML form controls and links.
/cc zsxwing, who wrote this original code, and yhuai.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10441 from JoshRosen/simplify-paginated-table-sorting.
Include the following changes:
1. Close `java.sql.Statement`
2. Fix incorrect `asInstanceOf`.
3. Remove unnecessary `synchronized` and `ReentrantLock`.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10440 from zsxwing/findbugs.
The Scala 2.11 SBT build currently fails for Spark 1.6.0 and master due to warnings about the `transient` annotation:
```
[error] [warn] /Users/joshrosen/Documents/spark/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala:73: no valid targets for annotation on value sc - it is discarded unused. You may specify targets with meta-annotations, e.g. (transient param)
[error] [warn] transient sc: SparkContext,
```
This fix implemented here is the same as what we did in #8433: remove the `transient` annotations when they are not necessary and replace use `transient private val` in the remaining cases.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10479 from JoshRosen/fix-sbt-2.11.
Since we only need to implement `def skipBytes(n: Int)`,
code in #10213 could be simplified.
davies scwf
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#10253 from adrian-wang/kryo.
When explain any plan with Generate, we will see an exclamation mark in the plan. Normally, when we see this mark, it means the plan has an error. This PR is to correct the `missingInput` in `Generate`.
For example,
```scala
val df = Seq((1, "a b c"), (2, "a b"), (3, "a")).toDF("number", "letters")
val df2 =
df.explode('letters) {
case Row(letters: String) => letters.split(" ").map(Tuple1(_)).toSeq
}
df2.explain(true)
```
Before the fix, the plan is like
```
== Parsed Logical Plan ==
'Generate UserDefinedGenerator('letters), true, false, None
+- Project [_1#0 AS number#2,_2#1 AS letters#3]
+- LocalRelation [_1#0,_2#1], [[1,a b c],[2,a b],[3,a]]
== Analyzed Logical Plan ==
number: int, letters: string, _1: string
Generate UserDefinedGenerator(letters#3), true, false, None, [_1#8]
+- Project [_1#0 AS number#2,_2#1 AS letters#3]
+- LocalRelation [_1#0,_2#1], [[1,a b c],[2,a b],[3,a]]
== Optimized Logical Plan ==
Generate UserDefinedGenerator(letters#3), true, false, None, [_1#8]
+- LocalRelation [number#2,letters#3], [[1,a b c],[2,a b],[3,a]]
== Physical Plan ==
!Generate UserDefinedGenerator(letters#3), true, false, [number#2,letters#3,_1#8]
+- LocalTableScan [number#2,letters#3], [[1,a b c],[2,a b],[3,a]]
```
**Updates**: The same issues are also found in the other four Dataset operators: `MapPartitions`/`AppendColumns`/`MapGroups`/`CoGroup`. Fixed all these four.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#10393 from gatorsmile/generateExplain.
Moved (case) classes Strategy, Once, FixedPoint and Batch to the companion object. This is necessary if we want to have the Optimizer easily extendable in the following sense: Usually a user wants to add additional rules, and just take the ones that are already there. However, inner classes made that impossible since the code did not compile
This allows easy extension of existing Optimizers see the DefaultOptimizerExtendableSuite for a corresponding test case.
Author: Stephan Kessler <stephan.kessler@sap.com>
Closes#10174 from stephankessler/SPARK-7727.
ParamMap#filter uses `mutable.Map#filterKeys`. The return type of `filterKey` is collection.Map, not mutable.Map but the result is casted to mutable.Map using `asInstanceOf` so we get `ClassCastException`.
Also, the return type of Map#filterKeys is not Serializable. It's the issue of Scala (https://issues.scala-lang.org/browse/SI-6654).
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#10381 from sarutak/SPARK-12424.
Support Unsafe Row in MapPartitions/MapGroups/CoGroup.
Added a test case for MapPartitions. Since MapGroups and CoGroup are built on AppendColumns, all the related dataset test cases already can verify the correctness when MapGroups and CoGroup processing unsafe rows.
davies cloud-fan Not sure if my understanding is right, please correct me. Thank you!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#10398 from gatorsmile/unsafeRowMapGroup.
The feature was first added at commit: 7b877b2705 but was later removed (probably by mistake) at commit: fc8b58195a.
This change sets the default path of RDDs created via sc.textFile(...) to the path argument.
Here is the symptom:
* Using spark-1.5.2-bin-hadoop2.6:
scala> sc.textFile("/home/root/.bashrc").name
res5: String = null
scala> sc.binaryFiles("/home/root/.bashrc").name
res6: String = /home/root/.bashrc
* while using Spark 1.3.1:
scala> sc.textFile("/home/root/.bashrc").name
res0: String = /home/root/.bashrc
scala> sc.binaryFiles("/home/root/.bashrc").name
res1: String = /home/root/.bashrc
Author: Yaron Weinsberg <wyaron@gmail.com>
Author: yaron <yaron@il.ibm.com>
Closes#10456 from wyaron/master.
Hello Michael & All:
We have some issues to submit the new codes in the other PR(#10299), so we closed that PR and open this one with the fix.
The reason for the previous failure is that the projection for the scan when there is a filter that is not pushed down (the "left-over" filter) could be different, in elements or ordering, from the original projection.
With this new codes, the approach to solve this problem is:
Insert a new Project if the "left-over" filter is nonempty and (the original projection is not empty and the projection for the scan has more than one elements which could otherwise cause different ordering in projection).
We create 3 test cases to cover the otherwise failure cases.
Author: Kevin Yu <qyu@us.ibm.com>
Closes#10388 from kevinyu98/spark-12231.
This patch fixes a handful of minor bugs in the `dev/tests/pr_public_classes.sh` script, which is used by the `run_tests_jenkins` script to detect the addition of new public classes:
- Account for differences between BSD and GNU `sed` in order to allow the script to run on OS X.
- Diff `$ghprbActualCommit^...$ghprbActualCommit ` instead of `master...$ghprbActualCommit`: since `ghprbActualCommit` is a merge commit which results from merging the PR into the target branch, this will give us the desired diff and will avoid certain race-conditions which could lead to false-positives.
- Use `echo -e` instead of `echo` so that newline characters are handled correctly in output. This should fix a formatting glitch which caused the output to appear on a single line in the GitHub comment (see [the SC2028 page](https://github.com/koalaman/shellcheck/wiki/SC2028) on the Shellcheck wiki for more details).
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10455 from JoshRosen/fix-pr-public-classes-test.
This PR is a follow-up of PR #10362.
Two major changes:
1. The fix introduced in #10362 is OK for Parquet, but may disable ORC PPD in many cases
PR #10362 stops converting an `AND` predicate if any branch is inconvertible. On the other hand, `OrcFilters` combines all filters into a single big conjunction first and then tries to convert it into ORC `SearchArgument`. This means, if any filter is inconvertible, no filters can be pushed down. This PR fixes this issue by finding out all convertible filters first before doing the actual conversion.
The reason behind the current implementation is mostly due to the limitation of ORC `SearchArgument` builder, which is documented in this PR in detail.
1. Copied the `AND` predicate fix for ORC from #10362 to avoid merge conflict.
Same as #10362, this PR targets master (2.0.0-SNAPSHOT), branch-1.6, and branch-1.5.
Author: Cheng Lian <lian@databricks.com>
Closes#10377 from liancheng/spark-12218.fix-orc-conjunction-ppd.
The semantics of Python countByValue is different from Scala API, it is more like countDistinctValue, so here change to make it consistent with Scala/Java API.
Author: jerryshao <sshao@hortonworks.com>
Closes#10350 from jerryshao/SPARK-12353.
After reading the JIRA https://issues.apache.org/jira/browse/SPARK-12520, I double checked the code.
For example, users can do the Equi-Join like
```df.join(df2, 'name', 'outer').select('name', 'height').collect()```
- There exists a bug in 1.5 and 1.4. The code just ignores the third parameter (join type) users pass. However, the join type we called is `Inner`, even if the user-specified type is the other type (e.g., `Outer`).
- After a PR: https://github.com/apache/spark/pull/8600, the 1.6 does not have such an issue, but the description has not been updated.
Plan to submit another PR to fix 1.5 and issue an error message if users specify a non-inner join type when using Equi-Join.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#10477 from gatorsmile/pyOuterJoin.
Instead of just cancel the registrationRetryTimer to avoid driver retry connect to master, change the function to schedule.
It is no need to register to master iteratively.
Author: echo2mei <534384876@qq.com>
Closes#10447 from echoTomei/master.
In SparkContext method `setCheckpointDir`, a warning is issued when spark master is not local and the passed directory for the checkpoint dir appears to be local.
In practice, when relying on HDFS configuration file and using a relative path for the checkpoint directory (using an incomplete URI without HDFS scheme, ...), this warning should not be issued and might be confusing.
In fact, in this case, the checkpoint directory is successfully created, and the checkpointing mechanism works as expected.
This PR uses the `FileSystem` instance created with the given directory, and checks whether it is local or not.
(The rationale is that since this same `FileSystem` instance is used to create the checkpoint dir anyway and can therefore be reliably used to determine if it is local or not).
The warning is only issued if the directory is not local, on top of the existing conditions.
Author: pierre-borckmans <pierre.borckmans@realimpactanalytics.com>
Closes#10392 from pierre-borckmans/SPARK-12440_CheckpointDir_Warning_NonLocal.
In the past Spark JDBC write only worked with technologies which support the following INSERT statement syntax (JdbcUtils.scala: insertStatement()):
INSERT INTO $table VALUES ( ?, ?, ..., ? )
But some technologies require a list of column names:
INSERT INTO $table ( $colNameList ) VALUES ( ?, ?, ..., ? )
This was blocking the use of e.g. the Progress JDBC Driver for Cassandra.
Another limitation is that syntax 1 relies no the dataframe field ordering match that of the target table. This works fine, as long as the target table has been created by writer.jdbc().
If the target table contains more columns (not created by writer.jdbc()), then the insert fails due mismatch of number of columns or their data types.
This PR switches to the recommended second INSERT syntax. Column names are taken from datafram field names.
Author: CK50 <christian.kurz@oracle.com>
Closes#10380 from CK50/master-SPARK-12010-2.
Restore the original value of os.arch property after each test
Since some of tests forced to set the specific value to os.arch property, we need to set the original value.
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#10289 from kiszk/SPARK-12311.
fix an exception with IBM JDK by removing update field from a JavaVersion tuple. This is because IBM JDK does not have information on update '_xx'
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#10463 from kiszk/SPARK-12502.
allow the user to override MAVEN_OPTS (2GB wasn't sufficient for me)
Author: Adrian Bridgett <adrian@smop.co.uk>
Closes#10448 from abridgett/feature/do_not_force_maven_opts.
Fix Tachyon deprecations; pull Tachyon dependency into `TachyonBlockManager` only
CC calvinjia as I probably need a double-check that the usage of the new API is correct.
Author: Sean Owen <sowen@cloudera.com>
Closes#10449 from srowen/SPARK-12500.
Accessing null elements in an array field fails when tungsten is enabled.
It works in Spark 1.3.1, and in Spark > 1.5 with Tungsten disabled.
This PR solves this by checking if the accessed element in the array field is null, in the generated code.
Example:
```
// Array of String
case class AS( as: Seq[String] )
val dfAS = sc.parallelize( Seq( AS ( Seq("a",null,"b") ) ) ).toDF
dfAS.registerTempTable("T_AS")
for (i <- 0 to 2) { println(i + " = " + sqlContext.sql(s"select as[$i] from T_AS").collect.mkString(","))}
```
With Tungsten disabled:
```
0 = [a]
1 = [null]
2 = [b]
```
With Tungsten enabled:
```
0 = [a]
15/12/22 09:32:50 ERROR Executor: Exception in task 7.0 in stage 1.0 (TID 15)
java.lang.NullPointerException
at org.apache.spark.sql.catalyst.expressions.UnsafeRowWriters$UTF8StringWriter.getSize(UnsafeRowWriters.java:90)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
at org.apache.spark.sql.execution.TungstenProject$$anonfun$3$$anonfun$apply$3.apply(basicOperators.scala:90)
at org.apache.spark.sql.execution.TungstenProject$$anonfun$3$$anonfun$apply$3.apply(basicOperators.scala:88)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
```
Author: pierre-borckmans <pierre.borckmans@realimpactanalytics.com>
Closes#10429 from pierre-borckmans/SPARK-12477_Tungsten-Projection-Null-Element-In-Array.
When the filter is ```"b in ('1', '2')"```, the filter is not pushed down to Parquet. Thanks!
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#10278 from gatorsmile/parquetFilterNot.
When creating extractors for product types (i.e. case classes and tuples), a null check is missing, thus we always assume input product values are non-null.
This PR adds a null check in the extractor expression for product types. The null check is stripped off for top level product fields, which are mapped to the outermost `Row`s, since they can't be null.
Thanks cloud-fan for helping investigating this issue!
Author: Cheng Lian <lian@databricks.com>
Closes#10431 from liancheng/spark-12478.top-level-null-field.
This PR adds Scala, Java and Python examples to show how to use Accumulator and Broadcast in Spark Streaming to support checkpointing.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10385 from zsxwing/accumulator-broadcast-example.
