[SPARK-20763][SQL][Backport-2.1] The function of month and day return the value which is not we expected.
#18053
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…ed createStream and use Builders ## What changes were proposed in this pull request? The spark-kinesis testcases use the KinesisUtils.createStream which are deprecated now. Modify the testcases to use the recommended KinesisInputDStream.builder instead. This change will also enable the testcases to automatically use the session tokens automatically. ## How was this patch tested? All the existing testcases work fine as expected with the changes. https://issues.apache.org/jira/browse/SPARK-20189 Author: Yash Sharma <[email protected]> Closes apache#17506 from yssharma/ysharma/cleanup_kinesis_testcases.
## What changes were proposed in this pull request? Improve PrefixSpan pre-processing efficency by preventing sequences of zero in the cleaned database. The efficiency gain is reflected in the following graph : https://postimg.org/image/9x6ireuvn/ ## How was this patch tested? Using MLlib's PrefixSpan existing tests and tests of my own on the 8 datasets shown in the graph. All result obtained were stricly the same as the original implementation (without this change). dev/run-tests was also runned, no error were found. Author : Cyril de Vogelaere <cyril.devogelaeregmail.com> Author: Syrux <[email protected]> Closes apache#17575 from Syrux/SPARK-20265.
## What changes were proposed in this pull request? This PR allows to use `SerializationStream` and `DeserializationStream` in try-with-resources. ## How was this patch tested? `core` unit tests. Author: Sergei Lebedev <[email protected]> Closes apache#17598 from superbobry/compression-stream-closeable.
…amming join enumeration
## What changes were proposed in this pull request?
Implements star-join filter to reduce the search space for dynamic programming join enumeration. Consider the following join graph:
```
T1 D1 - T2 - T3
\ /
F1
|
D2
star-join: {F1, D1, D2}
non-star: {T1, T2, T3}
```
The following join combinations will be generated:
```
level 0: (F1), (D1), (D2), (T1), (T2), (T3)
level 1: {F1, D1}, {F1, D2}, {T2, T3}
level 2: {F1, D1, D2}
level 3: {F1, D1, D2, T1}, {F1, D1, D2, T2}
level 4: {F1, D1, D2, T1, T2}, {F1, D1, D2, T2, T3 }
level 6: {F1, D1, D2, T1, T2, T3}
```
## How was this patch tested?
New test suite ```StarJOinCostBasedReorderSuite.scala```.
Author: Ioana Delaney <[email protected]>
Closes apache#17546 from ioana-delaney/starSchemaCBOv3.
## What changes were proposed in this pull request? Improve combineByKey documentation: * Add note on memory allocation * Change example code to use different mergeValue and mergeCombiners ## How was this patch tested? Doctest. ## Legal This is my original work and I license the work to the project under the project’s open source license. Author: David Gingrich <[email protected]> Closes apache#17545 from dgingrich/topic-spark-20232-combinebykey-docs.
…() implementations to be re-entrant ## What changes were proposed in this pull request? have the`FileFormatWriter.ExecuteWriteTask.releaseResources()` implementations set `currentWriter=null` in a finally clause. This guarantees that if the first call to `currentWriter()` throws an exception, the second releaseResources() call made during the task cancel process will not trigger a second attempt to close the stream. ## How was this patch tested? Tricky. I've been fixing the underlying cause when I saw the problem [HADOOP-14204](https://issues.apache.org/jira/browse/HADOOP-14204), but SPARK-10109 shows I'm not the first to have seen this. I can't replicate it locally any more, my code no longer being broken. code review, however, should be straightforward Author: Steve Loughran <[email protected]> Closes apache#17364 from steveloughran/stevel/SPARK-20038-close.
…se of estimation ## What changes were proposed in this pull request? Currently when estimating predicates like col > literal or col = literal, we will update min or max in column stats based on literal value. However, literal value is of Catalyst type (internal type), while min/max is of external type. Then for the next predicate, we again need to do type conversion to compare and update column stats. This is awkward and causes many unnecessary conversions in estimation. To solve this, we use Catalyst type for min/max in `ColumnStat`. Note that the persistent format in metastore is still of external type, so there's no inconsistency for statistics in metastore. This pr also fixes a bug for boolean type in `IN` condition. ## How was this patch tested? The changes for ColumnStat are covered by existing tests. For bug fix, a new test for boolean type in IN condition is added Author: wangzhenhua <[email protected]> Closes apache#17630 from wzhfy/refactorColumnStat.
## What changes were proposed in this pull request? val and var should strictly follow the Scala syntax ## How was this patch tested? manual test and exisiting test cases Author: ouyangxiaochen <[email protected]> Closes apache#17628 from ouyangxiaochen/spark-413.
…erializable ## What changes were proposed in this pull request? In apache#17398 we introduced `UnresolvedMapObjects` as a placeholder of `MapObjects`. Unfortunately `UnresolvedMapObjects` is not serializable as its `function` may reference Scala `Type` which is not serializable. Ideally this is fine, as we will never serialize and send unresolved expressions to executors. However users may accidentally do this, e.g. mistakenly reference an encoder instance when implementing `Aggregator`, we should fix it so that it's just a performance issue(more network traffic) and should not fail the query. ## How was this patch tested? N/A Author: Wenchen Fan <[email protected]> Closes apache#17639 from cloud-fan/minor.
…minism of Hive UDF ### What changes were proposed in this pull request? ```JAVA /** * Certain optimizations should not be applied if UDF is not deterministic. * Deterministic UDF returns same result each time it is invoked with a * particular input. This determinism just needs to hold within the context of * a query. * * return true if the UDF is deterministic */ boolean deterministic() default true; ``` Based on the definition of [UDFType](https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/udf/UDFType.java#L42-L50), when Hive UDF's children are non-deterministic, Hive UDF is also non-deterministic. ### How was this patch tested? Added test cases. Author: Xiao Li <[email protected]> Closes apache#17635 from gatorsmile/udfDeterministic.
…s into docker when running on mesos with docker containerizer ## What changes were proposed in this pull request? Allow passing in arbitrary parameters into docker when launching spark executors on mesos with docker containerizer tnachen ## How was this patch tested? Manually built and tested with passed in parameter Author: Ji Yan <[email protected]> Closes apache#17109 from yanji84/ji/allow_set_docker_user.
… failure in SBT Hadoop 2.6 master on Jenkins
## What changes were proposed in this pull request?
This PR proposes to add
```
<dependency>
<groupId>org.apache.avro</groupId>
<artifactId>avro</artifactId>
</dependency>
```
in core POM to see if it resolves the build failure as below:
```
[error] /home/jenkins/workspace/spark-master-test-sbt-hadoop-2.6/core/src/main/scala/org/apache/spark/serializer/GenericAvroSerializer.scala:123: value createDatumWriter is not a member of org.apache.avro.generic.GenericData
[error] writerCache.getOrElseUpdate(schema, GenericData.get.createDatumWriter(schema))
[error]
```
https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-sbt-hadoop-2.6/2770/consoleFull
## How was this patch tested?
I tried many ways but I was unable to reproduce this in my local. Sean also tried the way I did but he was also unable to reproduce this.
Please refer the comments in apache#17477 (comment)
Author: hyukjinkwon <[email protected]>
Closes apache#17642 from HyukjinKwon/SPARK-20343.
…inst project's code style
## What changes were proposed in this pull request?
Currently, multi-dot separated variables in R is not allowed. For example,
```diff
setMethod("from_json", signature(x = "Column", schema = "structType"),
- function(x, schema, asJsonArray = FALSE, ...) {
+ function(x, schema, as.json.array = FALSE, ...) {
if (asJsonArray) {
jschema <- callJStatic("org.apache.spark.sql.types.DataTypes",
"createArrayType",
```
produces an error as below:
```
R/functions.R:2462:31: style: Words within variable and function names should be separated by '_' rather than '.'.
function(x, schema, as.json.array = FALSE, ...) {
^~~~~~~~~~~~~
```
This seems against https://google.github.io/styleguide/Rguide.xml#identifiers which says
> The preferred form for variable names is all lower case letters and words separated with dots
This looks because lintr by default https://github.com/jimhester/lintr follows http://r-pkgs.had.co.nz/style.html as written in the README.md. Few cases seems not following Google's one as "a few tweaks".
Per [SPARK-6813](https://issues.apache.org/jira/browse/SPARK-6813), we follow Google's R Style Guide with few exceptions https://google.github.io/styleguide/Rguide.xml. This is also merged into Spark's website - apache/spark-website#43
Also, it looks we have no limit on function name. This rule also looks affecting to the name of functions as written in the README.md.
> `multiple_dots_linter`: check that function and variable names are separated by _ rather than ..
## How was this patch tested?
Manually tested `./dev/lint-r`with the manual change below in `R/functions.R`:
```diff
setMethod("from_json", signature(x = "Column", schema = "structType"),
- function(x, schema, asJsonArray = FALSE, ...) {
+ function(x, schema, as.json.array = FALSE, ...) {
if (asJsonArray) {
jschema <- callJStatic("org.apache.spark.sql.types.DataTypes",
"createArrayType",
```
**Before**
```R
R/functions.R:2462:31: style: Words within variable and function names should be separated by '_' rather than '.'.
function(x, schema, as.json.array = FALSE, ...) {
^~~~~~~~~~~~~
```
**After**
```
lintr checks passed.
```
Author: hyukjinkwon <[email protected]>
Closes apache#17590 from HyukjinKwon/disable-dot-in-name.
…m_json function in R ## What changes were proposed in this pull request? This was suggested to be `as.json.array` at the first place in the PR to SPARK-19828 but we could not do this as the lint check emits an error for multiple dots in the variable names. After SPARK-20278, now we are able to use `multiple.dots.in.names`. `asJsonArray` in `from_json` function is still able to be changed as 2.2 is not released yet. So, this PR proposes to rename `asJsonArray` to `as.json.array`. ## How was this patch tested? Jenkins tests, local tests with `./R/run-tests.sh` and manual `./dev/lint-r`. Existing tests should cover this. Author: hyukjinkwon <[email protected]> Closes apache#17653 from HyukjinKwon/SPARK-19828-followup.
…ing persistent functions ### What changes were proposed in this pull request? The session catalog caches some persistent functions in the `FunctionRegistry`, so there can be duplicates. Our Catalog API `listFunctions` does not handle it. It would be better if `SessionCatalog` API can de-duplciate the records, instead of doing it by each API caller. In `FunctionRegistry`, our functions are identified by the unquoted string. Thus, this PR is try to parse it using our parser interface and then de-duplicate the names. ### How was this patch tested? Added test cases. Author: Xiao Li <[email protected]> Closes apache#17646 from gatorsmile/showFunctions.
## What changes were proposed in this pull request? This patch fixes a bug in the way LIKE patterns are translated to Java regexes. The bug causes any character following an escaped backslash to be escaped, i.e. there is double-escaping. A concrete example is the following pattern:`'%\\%'`. The expected Java regex that this pattern should correspond to (according to the behavior described below) is `'.*\\.*'`, however the current situation leads to `'.*\\%'` instead. --- Update: in light of the discussion that ensued, we should explicitly define the expected behaviour of LIKE expressions, especially in certain edge cases. With the help of gatorsmile, we put together a list of different RDBMS and their variations wrt to certain standard features. | RDBMS\Features | Wildcards | Default escape [1] | Case sensitivity | | --- | --- | --- | --- | | [MS SQL Server](https://msdn.microsoft.com/en-us/library/ms179859.aspx) | _, %, [], [^] | none | no | | [Oracle](https://docs.oracle.com/cd/B12037_01/server.101/b10759/conditions016.htm) | _, % | none | yes | | [DB2 z/OS](http://www.ibm.com/support/knowledgecenter/SSEPEK_11.0.0/sqlref/src/tpc/db2z_likepredicate.html) | _, % | none | yes | | [MySQL](http://dev.mysql.com/doc/refman/5.7/en/string-comparison-functions.html) | _, % | none | no | | [PostreSQL](https://www.postgresql.org/docs/9.0/static/functions-matching.html) | _, % | \ | yes | | [Hive](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF) | _, % | none | yes | | Current Spark | _, % | \ | yes | [1] Default escape character: most systems do not have a default escape character, instead the user can specify one by calling a like expression with an escape argument [A] LIKE [B] ESCAPE [C]. This syntax is currently not supported by Spark, however I would volunteer to implement this feature in a separate ticket. The specifications are often quite terse and certain scenarios are undocumented, so here is a list of scenarios that I am uncertain about and would appreciate any input. Specifically I am looking for feedback on whether or not Spark's current behavior should be changed. 1. [x] Ending a pattern with the escape sequence, e.g. `like 'a\'`. PostreSQL gives an error: 'LIKE pattern must not end with escape character', which I personally find logical. Currently, Spark allows "non-terminated" escapes and simply ignores them as part of the pattern. According to [DB2's documentation](http://www.ibm.com/support/knowledgecenter/SSEPGG_9.7.0/com.ibm.db2.luw.messages.sql.doc/doc/msql00130n.html), ending a pattern in an escape character is invalid. _Proposed new behaviour in Spark: throw AnalysisException_ 2. [x] Empty input, e.g. `'' like ''` Postgres and DB2 will match empty input only if the pattern is empty as well, any other combination of empty input will not match. Spark currently follows this rule. 3. [x] Escape before a non-special character, e.g. `'a' like '\a'`. Escaping a non-wildcard character is not really documented but PostgreSQL just treats it verbatim, which I also find the least surprising behavior. Spark does the same. According to [DB2's documentation](http://www.ibm.com/support/knowledgecenter/SSEPGG_9.7.0/com.ibm.db2.luw.messages.sql.doc/doc/msql00130n.html), it is invalid to follow an escape character with anything other than an escape character, an underscore or a percent sign. _Proposed new behaviour in Spark: throw AnalysisException_ The current specification is also described in the operator's source code in this patch. ## How was this patch tested? Extra case in regex unit tests. Author: Jakob Odersky <[email protected]> This patch had conflicts when merged, resolved by Committer: Reynold Xin <[email protected]> Closes apache#15398 from jodersky/SPARK-17647.
## What changes were proposed in this pull request? Typo fix: distitrbuted -> distributed ## How was this patch tested? Existing tests Author: Andrew Ash <[email protected]> Closes apache#17664 from ash211/patch-1.
…itionSuite ## What changes were proposed in this pull request? Replace non-existent `repartitionBy` with `distribute` in `CollapseRepartitionSuite`. ## How was this patch tested? local build and `catalyst/testOnly *CollapseRepartitionSuite` Author: Jacek Laskowski <[email protected]> Closes apache#17657 from jaceklaskowski/CollapseRepartitionSuite.
## What changes were proposed in this pull request? fix typo ## How was this patch tested? manual Author: Felix Cheung <[email protected]> Closes apache#17663 from felixcheung/likedoctypo.
…TaskSetManager ## What changes were proposed in this pull request? Eliminate the duplicate call to `Pool.getSchedulableByName()` in `FairSchedulableBuilder.addTaskSetManager` ## How was this patch tested? ./dev/run-tests Author: Robert Stupp <[email protected]> Closes apache#17647 from snazy/20344-dup-call-master.
…ailure in SBT Hadoop 2.6 master on Jenkins ## What changes were proposed in this pull request? This PR proposes to force Avro's version to 1.7.7 in core to resolve the build failure as below: ``` [error] /home/jenkins/workspace/spark-master-test-sbt-hadoop-2.6/core/src/main/scala/org/apache/spark/serializer/GenericAvroSerializer.scala:123: value createDatumWriter is not a member of org.apache.avro.generic.GenericData [error] writerCache.getOrElseUpdate(schema, GenericData.get.createDatumWriter(schema)) [error] ``` https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-sbt-hadoop-2.6/2770/consoleFull Note that this is a hack and should be removed in the future. ## How was this patch tested? I only tested this actually overrides the dependency. I tried many ways but I was unable to reproduce this in my local. Sean also tried the way I did but he was also unable to reproduce this. Please refer the comments in apache#17477 (comment) Author: hyukjinkwon <[email protected]> Closes apache#17651 from HyukjinKwon/SPARK-20343-sbt.
…t reordered
## What changes were proposed in this pull request?
If a plan has multi-level successive joins, e.g.:
```
Join
/ \
Union t5
/ \
Join t4
/ \
Join t3
/ \
t1 t2
```
Currently we fail to reorder the inside joins, i.e. t1, t2, t3.
In join reorder, we use `OrderedJoin` to indicate a join has been ordered, such that when transforming down the plan, these joins don't need to be rerodered again.
But there's a problem in the definition of `OrderedJoin`:
The real join node is a parameter, but not a child. This breaks the transform procedure because `mapChildren` applies transform function on parameters which should be children.
In this patch, we change `OrderedJoin` to a class having the same structure as a join node.
## How was this patch tested?
Add a corresponding test case.
Author: wangzhenhua <[email protected]>
Closes apache#17668 from wzhfy/recursiveReorder.
…p:port/api/v1/applications' link, return 'sparkUser' is empty in REST API.
## What changes were proposed in this pull request?
When I request access to the 'http: //ip:port/api/v1/applications' link, get the json. I need the 'sparkUser' field specific value, because my Spark big data management platform needs to filter through this field which user submits the application to facilitate my administration and query, but the current return of the json string is empty, causing me this Function can not be achieved, that is, I do not know who the specific application is submitted by this REST Api.
**current return json:**
[ {
"id" : "app-20170417152053-0000",
"name" : "KafkaWordCount",
"attempts" : [ {
"startTime" : "2017-04-17T07:20:51.395GMT",
"endTime" : "1969-12-31T23:59:59.999GMT",
"lastUpdated" : "2017-04-17T07:20:51.395GMT",
"duration" : 0,
**"sparkUser" : "",**
"completed" : false,
"endTimeEpoch" : -1,
"startTimeEpoch" : 1492413651395,
"lastUpdatedEpoch" : 1492413651395
} ]
} ]
**When I fix this question, return json:**
[ {
"id" : "app-20170417154201-0000",
"name" : "KafkaWordCount",
"attempts" : [ {
"startTime" : "2017-04-17T07:41:57.335GMT",
"endTime" : "1969-12-31T23:59:59.999GMT",
"lastUpdated" : "2017-04-17T07:41:57.335GMT",
"duration" : 0,
**"sparkUser" : "mr",**
"completed" : false,
"startTimeEpoch" : 1492414917335,
"endTimeEpoch" : -1,
"lastUpdatedEpoch" : 1492414917335
} ]
} ]
## How was this patch tested?
manual tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: 郭小龙 10207633 <[email protected]>
Author: guoxiaolong <[email protected]>
Author: guoxiaolongzte <[email protected]>
Closes apache#17656 from guoxiaolongzte/SPARK-20354.
## What changes were proposed in this pull request? Establishes a very minimal `_repr_html_` for PySpark's `SparkContext`. ## How was this patch tested? nteract:  Jupyter:  Hydrogen:  Author: Kyle Kelley <[email protected]> Closes apache#17662 from rgbkrk/repr.
## What changes were proposed in this pull request? Extra accessors in java bean class causes incorrect encoder generation, which corrupted the state when using timeouts. ## How was this patch tested? manually ran the example Author: Tathagata Das <[email protected]> Closes apache#17676 from tdas/SPARK-20377.
…h primitive array
## What changes were proposed in this pull request?
This PR elminates unnecessary data conversion, which is introduced by SPARK-19716, for Dataset with primitve array in the generated Java code.
When we run the following example program, now we get the Java code "Without this PR". In this code, lines 56-82 are unnecessary since the primitive array in ArrayData can be converted into Java primitive array by using ``toDoubleArray()`` method. ``GenericArrayData`` is not required.
```java
val ds = sparkContext.parallelize(Seq(Array(1.1, 2.2)), 1).toDS.cache
ds.count
ds.map(e => e).show
```
Without this PR
```
== Parsed Logical Plan ==
'SerializeFromObject [staticinvoke(class org.apache.spark.sql.catalyst.expressions.UnsafeArrayData, ArrayType(DoubleType,false), fromPrimitiveArray, input[0, [D, true], true) AS value#25]
+- 'MapElements <function1>, class [D, [StructField(value,ArrayType(DoubleType,false),true)], obj#24: [D
+- 'DeserializeToObject unresolveddeserializer(unresolvedmapobjects(<function1>, getcolumnbyordinal(0, ArrayType(DoubleType,false)), None).toDoubleArray), obj#23: [D
+- SerializeFromObject [staticinvoke(class org.apache.spark.sql.catalyst.expressions.UnsafeArrayData, ArrayType(DoubleType,false), fromPrimitiveArray, input[0, [D, true], true) AS value#2]
+- ExternalRDD [obj#1]
== Analyzed Logical Plan ==
value: array<double>
SerializeFromObject [staticinvoke(class org.apache.spark.sql.catalyst.expressions.UnsafeArrayData, ArrayType(DoubleType,false), fromPrimitiveArray, input[0, [D, true], true) AS value#25]
+- MapElements <function1>, class [D, [StructField(value,ArrayType(DoubleType,false),true)], obj#24: [D
+- DeserializeToObject mapobjects(MapObjects_loopValue5, MapObjects_loopIsNull5, DoubleType, assertnotnull(lambdavariable(MapObjects_loopValue5, MapObjects_loopIsNull5, DoubleType, true), - array element class: "scala.Double", - root class: "scala.Array"), value#2, None, MapObjects_builderValue5).toDoubleArray, obj#23: [D
+- SerializeFromObject [staticinvoke(class org.apache.spark.sql.catalyst.expressions.UnsafeArrayData, ArrayType(DoubleType,false), fromPrimitiveArray, input[0, [D, true], true) AS value#2]
+- ExternalRDD [obj#1]
== Optimized Logical Plan ==
SerializeFromObject [staticinvoke(class org.apache.spark.sql.catalyst.expressions.UnsafeArrayData, ArrayType(DoubleType,false), fromPrimitiveArray, input[0, [D, true], true) AS value#25]
+- MapElements <function1>, class [D, [StructField(value,ArrayType(DoubleType,false),true)], obj#24: [D
+- DeserializeToObject mapobjects(MapObjects_loopValue5, MapObjects_loopIsNull5, DoubleType, assertnotnull(lambdavariable(MapObjects_loopValue5, MapObjects_loopIsNull5, DoubleType, true), - array element class: "scala.Double", - root class: "scala.Array"), value#2, None, MapObjects_builderValue5).toDoubleArray, obj#23: [D
+- InMemoryRelation [value#2], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas)
+- *SerializeFromObject [staticinvoke(class org.apache.spark.sql.catalyst.expressions.UnsafeArrayData, ArrayType(DoubleType,false), fromPrimitiveArray, input[0, [D, true], true) AS value#2]
+- Scan ExternalRDDScan[obj#1]
== Physical Plan ==
*SerializeFromObject [staticinvoke(class org.apache.spark.sql.catalyst.expressions.UnsafeArrayData, ArrayType(DoubleType,false), fromPrimitiveArray, input[0, [D, true], true) AS value#25]
+- *MapElements <function1>, obj#24: [D
+- *DeserializeToObject mapobjects(MapObjects_loopValue5, MapObjects_loopIsNull5, DoubleType, assertnotnull(lambdavariable(MapObjects_loopValue5, MapObjects_loopIsNull5, DoubleType, true), - array element class: "scala.Double", - root class: "scala.Array"), value#2, None, MapObjects_builderValue5).toDoubleArray, obj#23: [D
+- InMemoryTableScan [value#2]
+- InMemoryRelation [value#2], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas)
+- *SerializeFromObject [staticinvoke(class org.apache.spark.sql.catalyst.expressions.UnsafeArrayData, ArrayType(DoubleType,false), fromPrimitiveArray, input[0, [D, true], true) AS value#2]
+- Scan ExternalRDDScan[obj#1]
```
```java
/* 050 */ protected void processNext() throws java.io.IOException {
/* 051 */ while (inputadapter_input.hasNext() && !stopEarly()) {
/* 052 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next();
/* 053 */ boolean inputadapter_isNull = inputadapter_row.isNullAt(0);
/* 054 */ ArrayData inputadapter_value = inputadapter_isNull ? null : (inputadapter_row.getArray(0));
/* 055 */
/* 056 */ ArrayData deserializetoobject_value1 = null;
/* 057 */
/* 058 */ if (!inputadapter_isNull) {
/* 059 */ int deserializetoobject_dataLength = inputadapter_value.numElements();
/* 060 */
/* 061 */ Double[] deserializetoobject_convertedArray = null;
/* 062 */ deserializetoobject_convertedArray = new Double[deserializetoobject_dataLength];
/* 063 */
/* 064 */ int deserializetoobject_loopIndex = 0;
/* 065 */ while (deserializetoobject_loopIndex < deserializetoobject_dataLength) {
/* 066 */ MapObjects_loopValue2 = (double) (inputadapter_value.getDouble(deserializetoobject_loopIndex));
/* 067 */ MapObjects_loopIsNull2 = inputadapter_value.isNullAt(deserializetoobject_loopIndex);
/* 068 */
/* 069 */ if (MapObjects_loopIsNull2) {
/* 070 */ throw new RuntimeException(((java.lang.String) references[0]));
/* 071 */ }
/* 072 */ if (false) {
/* 073 */ deserializetoobject_convertedArray[deserializetoobject_loopIndex] = null;
/* 074 */ } else {
/* 075 */ deserializetoobject_convertedArray[deserializetoobject_loopIndex] = MapObjects_loopValue2;
/* 076 */ }
/* 077 */
/* 078 */ deserializetoobject_loopIndex += 1;
/* 079 */ }
/* 080 */
/* 081 */ deserializetoobject_value1 = new org.apache.spark.sql.catalyst.util.GenericArrayData(deserializetoobject_convertedArray); /*###*/
/* 082 */ }
/* 083 */ boolean deserializetoobject_isNull = true;
/* 084 */ double[] deserializetoobject_value = null;
/* 085 */ if (!inputadapter_isNull) {
/* 086 */ deserializetoobject_isNull = false;
/* 087 */ if (!deserializetoobject_isNull) {
/* 088 */ Object deserializetoobject_funcResult = null;
/* 089 */ deserializetoobject_funcResult = deserializetoobject_value1.toDoubleArray();
/* 090 */ if (deserializetoobject_funcResult == null) {
/* 091 */ deserializetoobject_isNull = true;
/* 092 */ } else {
/* 093 */ deserializetoobject_value = (double[]) deserializetoobject_funcResult;
/* 094 */ }
/* 095 */
/* 096 */ }
/* 097 */ deserializetoobject_isNull = deserializetoobject_value == null;
/* 098 */ }
/* 099 */
/* 100 */ boolean mapelements_isNull = true;
/* 101 */ double[] mapelements_value = null;
/* 102 */ if (!false) {
/* 103 */ mapelements_resultIsNull = false;
/* 104 */
/* 105 */ if (!mapelements_resultIsNull) {
/* 106 */ mapelements_resultIsNull = deserializetoobject_isNull;
/* 107 */ mapelements_argValue = deserializetoobject_value;
/* 108 */ }
/* 109 */
/* 110 */ mapelements_isNull = mapelements_resultIsNull;
/* 111 */ if (!mapelements_isNull) {
/* 112 */ Object mapelements_funcResult = null;
/* 113 */ mapelements_funcResult = ((scala.Function1) references[1]).apply(mapelements_argValue);
/* 114 */ if (mapelements_funcResult == null) {
/* 115 */ mapelements_isNull = true;
/* 116 */ } else {
/* 117 */ mapelements_value = (double[]) mapelements_funcResult;
/* 118 */ }
/* 119 */
/* 120 */ }
/* 121 */ mapelements_isNull = mapelements_value == null;
/* 122 */ }
/* 123 */
/* 124 */ serializefromobject_resultIsNull = false;
/* 125 */
/* 126 */ if (!serializefromobject_resultIsNull) {
/* 127 */ serializefromobject_resultIsNull = mapelements_isNull;
/* 128 */ serializefromobject_argValue = mapelements_value;
/* 129 */ }
/* 130 */
/* 131 */ boolean serializefromobject_isNull = serializefromobject_resultIsNull;
/* 132 */ final ArrayData serializefromobject_value = serializefromobject_resultIsNull ? null : org.apache.spark.sql.catalyst.expressions.UnsafeArrayData.fromPrimitiveArray(serializefromobject_argValue);
/* 133 */ serializefromobject_isNull = serializefromobject_value == null;
/* 134 */ serializefromobject_holder.reset();
/* 135 */
/* 136 */ serializefromobject_rowWriter.zeroOutNullBytes();
/* 137 */
/* 138 */ if (serializefromobject_isNull) {
/* 139 */ serializefromobject_rowWriter.setNullAt(0);
/* 140 */ } else {
/* 141 */ // Remember the current cursor so that we can calculate how many bytes are
/* 142 */ // written later.
/* 143 */ final int serializefromobject_tmpCursor = serializefromobject_holder.cursor;
/* 144 */
/* 145 */ if (serializefromobject_value instanceof UnsafeArrayData) {
/* 146 */ final int serializefromobject_sizeInBytes = ((UnsafeArrayData) serializefromobject_value).getSizeInBytes();
/* 147 */ // grow the global buffer before writing data.
/* 148 */ serializefromobject_holder.grow(serializefromobject_sizeInBytes);
/* 149 */ ((UnsafeArrayData) serializefromobject_value).writeToMemory(serializefromobject_holder.buffer, serializefromobject_holder.cursor);
/* 150 */ serializefromobject_holder.cursor += serializefromobject_sizeInBytes;
/* 151 */
/* 152 */ } else {
/* 153 */ final int serializefromobject_numElements = serializefromobject_value.numElements();
/* 154 */ serializefromobject_arrayWriter.initialize(serializefromobject_holder, serializefromobject_numElements, 8);
/* 155 */
/* 156 */ for (int serializefromobject_index = 0; serializefromobject_index < serializefromobject_numElements; serializefromobject_index++) {
/* 157 */ if (serializefromobject_value.isNullAt(serializefromobject_index)) {
/* 158 */ serializefromobject_arrayWriter.setNullDouble(serializefromobject_index);
/* 159 */ } else {
/* 160 */ final double serializefromobject_element = serializefromobject_value.getDouble(serializefromobject_index);
/* 161 */ serializefromobject_arrayWriter.write(serializefromobject_index, serializefromobject_element);
/* 162 */ }
/* 163 */ }
/* 164 */ }
/* 165 */
/* 166 */ serializefromobject_rowWriter.setOffsetAndSize(0, serializefromobject_tmpCursor, serializefromobject_holder.cursor - serializefromobject_tmpCursor);
/* 167 */ }
/* 168 */ serializefromobject_result.setTotalSize(serializefromobject_holder.totalSize());
/* 169 */ append(serializefromobject_result);
/* 170 */ if (shouldStop()) return;
/* 171 */ }
/* 172 */ }
```
With this PR (eliminated lines 56-62 in the above code)
```java
/* 047 */ protected void processNext() throws java.io.IOException {
/* 048 */ while (inputadapter_input.hasNext() && !stopEarly()) {
/* 049 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next();
/* 050 */ boolean inputadapter_isNull = inputadapter_row.isNullAt(0);
/* 051 */ ArrayData inputadapter_value = inputadapter_isNull ? null : (inputadapter_row.getArray(0));
/* 052 */
/* 053 */ boolean deserializetoobject_isNull = true;
/* 054 */ double[] deserializetoobject_value = null;
/* 055 */ if (!inputadapter_isNull) {
/* 056 */ deserializetoobject_isNull = false;
/* 057 */ if (!deserializetoobject_isNull) {
/* 058 */ Object deserializetoobject_funcResult = null;
/* 059 */ deserializetoobject_funcResult = inputadapter_value.toDoubleArray();
/* 060 */ if (deserializetoobject_funcResult == null) {
/* 061 */ deserializetoobject_isNull = true;
/* 062 */ } else {
/* 063 */ deserializetoobject_value = (double[]) deserializetoobject_funcResult;
/* 064 */ }
/* 065 */
/* 066 */ }
/* 067 */ deserializetoobject_isNull = deserializetoobject_value == null;
/* 068 */ }
/* 069 */
/* 070 */ boolean mapelements_isNull = true;
/* 071 */ double[] mapelements_value = null;
/* 072 */ if (!false) {
/* 073 */ mapelements_resultIsNull = false;
/* 074 */
/* 075 */ if (!mapelements_resultIsNull) {
/* 076 */ mapelements_resultIsNull = deserializetoobject_isNull;
/* 077 */ mapelements_argValue = deserializetoobject_value;
/* 078 */ }
/* 079 */
/* 080 */ mapelements_isNull = mapelements_resultIsNull;
/* 081 */ if (!mapelements_isNull) {
/* 082 */ Object mapelements_funcResult = null;
/* 083 */ mapelements_funcResult = ((scala.Function1) references[0]).apply(mapelements_argValue);
/* 084 */ if (mapelements_funcResult == null) {
/* 085 */ mapelements_isNull = true;
/* 086 */ } else {
/* 087 */ mapelements_value = (double[]) mapelements_funcResult;
/* 088 */ }
/* 089 */
/* 090 */ }
/* 091 */ mapelements_isNull = mapelements_value == null;
/* 092 */ }
/* 093 */
/* 094 */ serializefromobject_resultIsNull = false;
/* 095 */
/* 096 */ if (!serializefromobject_resultIsNull) {
/* 097 */ serializefromobject_resultIsNull = mapelements_isNull;
/* 098 */ serializefromobject_argValue = mapelements_value;
/* 099 */ }
/* 100 */
/* 101 */ boolean serializefromobject_isNull = serializefromobject_resultIsNull;
/* 102 */ final ArrayData serializefromobject_value = serializefromobject_resultIsNull ? null : org.apache.spark.sql.catalyst.expressions.UnsafeArrayData.fromPrimitiveArray(serializefromobject_argValue);
/* 103 */ serializefromobject_isNull = serializefromobject_value == null;
/* 104 */ serializefromobject_holder.reset();
/* 105 */
/* 106 */ serializefromobject_rowWriter.zeroOutNullBytes();
/* 107 */
/* 108 */ if (serializefromobject_isNull) {
/* 109 */ serializefromobject_rowWriter.setNullAt(0);
/* 110 */ } else {
/* 111 */ // Remember the current cursor so that we can calculate how many bytes are
/* 112 */ // written later.
/* 113 */ final int serializefromobject_tmpCursor = serializefromobject_holder.cursor;
/* 114 */
/* 115 */ if (serializefromobject_value instanceof UnsafeArrayData) {
/* 116 */ final int serializefromobject_sizeInBytes = ((UnsafeArrayData) serializefromobject_value).getSizeInBytes();
/* 117 */ // grow the global buffer before writing data.
/* 118 */ serializefromobject_holder.grow(serializefromobject_sizeInBytes);
/* 119 */ ((UnsafeArrayData) serializefromobject_value).writeToMemory(serializefromobject_holder.buffer, serializefromobject_holder.cursor);
/* 120 */ serializefromobject_holder.cursor += serializefromobject_sizeInBytes;
/* 121 */
/* 122 */ } else {
/* 123 */ final int serializefromobject_numElements = serializefromobject_value.numElements();
/* 124 */ serializefromobject_arrayWriter.initialize(serializefromobject_holder, serializefromobject_numElements, 8);
/* 125 */
/* 126 */ for (int serializefromobject_index = 0; serializefromobject_index < serializefromobject_numElements; serializefromobject_index++) {
/* 127 */ if (serializefromobject_value.isNullAt(serializefromobject_index)) {
/* 128 */ serializefromobject_arrayWriter.setNullDouble(serializefromobject_index);
/* 129 */ } else {
/* 130 */ final double serializefromobject_element = serializefromobject_value.getDouble(serializefromobject_index);
/* 131 */ serializefromobject_arrayWriter.write(serializefromobject_index, serializefromobject_element);
/* 132 */ }
/* 133 */ }
/* 134 */ }
/* 135 */
/* 136 */ serializefromobject_rowWriter.setOffsetAndSize(0, serializefromobject_tmpCursor, serializefromobject_holder.cursor - serializefromobject_tmpCursor);
/* 137 */ }
/* 138 */ serializefromobject_result.setTotalSize(serializefromobject_holder.totalSize());
/* 139 */ append(serializefromobject_result);
/* 140 */ if (shouldStop()) return;
/* 141 */ }
/* 142 */ }
```
## How was this patch tested?
Add test suites into `DatasetPrimitiveSuite`
Author: Kazuaki Ishizaki <[email protected]>
Closes apache#17568 from kiszk/SPARK-20254.
## What changes were proposed in this pull request? Document fpGrowth in: - vignettes - programming guide - code example ## How was this patch tested? Manual tests. Author: zero323 <[email protected]> Closes apache#17557 from zero323/SPARK-20208.
…optimization that can lead to NPE Avoid necessary execution that can lead to NPE in EliminateOuterJoin and add test in DataFrameSuite to confirm NPE is no longer thrown ## What changes were proposed in this pull request? Change leftHasNonNullPredicate and rightHasNonNullPredicate to lazy so they are only executed when needed. ## How was this patch tested? Added test in DataFrameSuite that failed before this fix and now succeeds. Note that a test in catalyst project would be better but i am unsure how to do this. Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Koert Kuipers <[email protected]> Closes apache#17660 from koertkuipers/feat-catch-npe-in-eliminate-outer-join.
…utput partitioning and ordering ## What changes were proposed in this pull request? The output of `InMemoryTableScanExec` can be pruned and mismatch with `InMemoryRelation` and its child plan's output. This causes wrong output partitioning and ordering. ## How was this patch tested? Jenkins tests. Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Liang-Chi Hsieh <[email protected]> Closes apache#17679 from viirya/SPARK-20356.
…tly set in SBT build ## What changes were proposed in this pull request? This PR proposes two things as below: - Avoid Unidoc build only if Hadoop 2.6 is explicitly set in SBT build Due to a different dependency resolution in SBT & Unidoc by an unknown reason, the documentation build fails on a specific machine & environment in Jenkins but it was unable to reproduce. So, this PR just checks an environment variable `AMPLAB_JENKINS_BUILD_PROFILE` that is set in Hadoop 2.6 SBT build against branches on Jenkins, and then disables Unidoc build. **Note that PR builder will still build it with Hadoop 2.6 & SBT.** ``` ======================================================================== Building Unidoc API Documentation ======================================================================== [info] Building Spark unidoc (w/Hive 1.2.1) using SBT with these arguments: -Phadoop-2.6 -Pmesos -Pkinesis-asl -Pyarn -Phive-thriftserver -Phive unidoc Using /usr/java/jdk1.8.0_60 as default JAVA_HOME. ... ``` I checked the environment variables from the logs (first bit) as below: - **spark-master-test-sbt-hadoop-2.6** (this one is being failed) - https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-sbt-hadoop-2.6/lastBuild/consoleFull ``` JAVA_HOME=/usr/java/jdk1.8.0_60 JAVA_7_HOME=/usr/java/jdk1.7.0_79 SPARK_BRANCH=master AMPLAB_JENKINS_BUILD_PROFILE=hadoop2.6 <- I use this variable AMPLAB_JENKINS="true" ``` - spark-master-test-sbt-hadoop-2.7 - https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-sbt-hadoop-2.7/lastBuild/consoleFull ``` JAVA_HOME=/usr/java/jdk1.8.0_60 JAVA_7_HOME=/usr/java/jdk1.7.0_79 SPARK_BRANCH=master AMPLAB_JENKINS_BUILD_PROFILE=hadoop2.7 AMPLAB_JENKINS="true" ``` - spark-master-test-maven-hadoop-2.6 - https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-2.6/lastBuild/consoleFull ``` JAVA_HOME=/usr/java/jdk1.8.0_60 JAVA_7_HOME=/usr/java/jdk1.7.0_79 HADOOP_PROFILE=hadoop-2.6 HADOOP_VERSION= SPARK_BRANCH=master AMPLAB_JENKINS="true" ``` - spark-master-test-maven-hadoop-2.7 - https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-2.7/lastBuild/consoleFull ``` JAVA_HOME=/usr/java/jdk1.8.0_60 JAVA_7_HOME=/usr/java/jdk1.7.0_79 HADOOP_PROFILE=hadoop-2.7 HADOOP_VERSION= SPARK_BRANCH=master AMPLAB_JENKINS="true" ``` - PR builder - https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/75843/consoleFull ``` JENKINS_MASTER_HOSTNAME=amp-jenkins-master JAVA_HOME=/usr/java/jdk1.8.0_60 JAVA_7_HOME=/usr/java/jdk1.7.0_79 ``` Assuming from other logs in branch-2.1 - SBT & Hadoop 2.6 against branch-2.1 https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-branch-2.1-test-sbt-hadoop-2.6/lastBuild/consoleFull ``` JAVA_HOME=/usr/java/jdk1.8.0_60 JAVA_7_HOME=/usr/java/jdk1.7.0_79 SPARK_BRANCH=branch-2.1 AMPLAB_JENKINS_BUILD_PROFILE=hadoop2.6 AMPLAB_JENKINS="true" ``` - Maven & Hadoop 2.6 against branch-2.1 https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-branch-2.1-test-maven-hadoop-2.6/lastBuild/consoleFull ``` JAVA_HOME=/usr/java/jdk1.8.0_60 JAVA_7_HOME=/usr/java/jdk1.7.0_79 HADOOP_PROFILE=hadoop-2.6 HADOOP_VERSION= SPARK_BRANCH=branch-2.1 AMPLAB_JENKINS="true" ``` We have been using the same convention for those variables. These are actually being used in `run-tests.py` script - here https://github.com/apache/spark/blob/master/dev/run-tests.py#L519-L520 - Revert the previous try After apache#17651, it seems the build still fails on SBT Hadoop 2.6 master. I am unable to reproduce this - apache#17477 (comment) and the reviewer was too. So, this got merged as it looks the only way to verify this is to merge it currently (as no one seems able to reproduce this). ## How was this patch tested? I only checked `is_hadoop_version_2_6 = os.environ.get("AMPLAB_JENKINS_BUILD_PROFILE") == "hadoop2.6"` is working fine as expected as below: ```python >>> import collections >>> os = collections.namedtuple('os', 'environ')(environ={"AMPLAB_JENKINS_BUILD_PROFILE": "hadoop2.6"}) >>> print(not os.environ.get("AMPLAB_JENKINS_BUILD_PROFILE") == "hadoop2.6") False >>> os = collections.namedtuple('os', 'environ')(environ={"AMPLAB_JENKINS_BUILD_PROFILE": "hadoop2.7"}) >>> print(not os.environ.get("AMPLAB_JENKINS_BUILD_PROFILE") == "hadoop2.6") True >>> os = collections.namedtuple('os', 'environ')(environ={}) >>> print(not os.environ.get("AMPLAB_JENKINS_BUILD_PROFILE") == "hadoop2.6") True ``` I tried many ways but I was unable to reproduce this in my local. Sean also tried the way I did but he was also unable to reproduce this. Please refer the comments in apache#17477 (comment) Author: hyukjinkwon <[email protected]> Closes apache#17669 from HyukjinKwon/revert-SPARK-20343.
…s in JSON ## What changes were proposed in this pull request? This PR is based on apache#16199 and extracts the valid change from apache#9759 to resolve SPARK-18772 This avoids additional conversion try with `toFloat` and `toDouble`. For avoiding additional conversions, please refer the codes below: **Before** ```scala scala> import org.apache.spark.sql.types._ import org.apache.spark.sql.types._ scala> spark.read.schema(StructType(Seq(StructField("a", DoubleType)))).option("mode", "FAILFAST").json(Seq("""{"a": "nan"}""").toDS).show() 17/05/12 11:30:41 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2) java.lang.NumberFormatException: For input string: "nan" ... ``` **After** ```scala scala> import org.apache.spark.sql.types._ import org.apache.spark.sql.types._ scala> spark.read.schema(StructType(Seq(StructField("a", DoubleType)))).option("mode", "FAILFAST").json(Seq("""{"a": "nan"}""").toDS).show() 17/05/12 11:44:30 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID 0) java.lang.RuntimeException: Cannot parse nan as DoubleType. ... ``` ## How was this patch tested? Unit tests added in `JsonSuite`. Closes apache#16199 Author: hyukjinkwon <[email protected]> Author: Nathan Howell <[email protected]> Closes apache#17956 from HyukjinKwon/SPARK-18772.
…Result ## What changes were proposed in this pull request? For aggregate function with `PartialMerge` or `Final` mode, the input is aggregate buffers instead of the actual children expressions. So the actual children expressions won't affect the result, we should normalize the expr id for them. ## How was this patch tested? a new regression test Author: Wenchen Fan <[email protected]> Closes apache#17964 from cloud-fan/tmp.
## What changes were proposed in this pull request?
- Change current short annotations (same as Scala `group`) to verbose names (same as Scala `groupname`).
Before:
After:
- Add missing `family` annotations.
## How was this patch tested?
`check-cran.R` (skipping tests), manual inspection.
Author: zero323 <[email protected]>
Closes apache#17976 from zero323/SPARKR-FUNCTIONS-DOCSTRINGS.
## What changes were proposed in this pull request? - Adds R wrapper for `o.a.s.sql.functions.broadcast`. - Renames `broadcast` to `broadcast_`. ## How was this patch tested? Unit tests, check `check-cran.sh`. Author: zero323 <[email protected]> Closes apache#17965 from zero323/SPARK-20726.
… page when you use Firefox or Google Chrome. ## What changes were proposed in this pull request? When you open the master page, when you use Firefox or Google Chrom, the console of Firefox or Google Chrome is wrong. But The IE is no problem. e.g.  My Firefox version is 48.0.2. My Google Chrome version is 49.0.2623.75 m. ## How was this patch tested? manual tests Please review http://spark.apache.org/contributing.html before opening a pull request. Author: guoxiaolong <[email protected]> Author: 郭小龙 10207633 <[email protected]> Author: guoxiaolongzte <[email protected]> Closes apache#17952 from guoxiaolongzte/SPARK-20705.
… 'Removed Executors' should display the specific number, in the Application Page ## What changes were proposed in this pull request? When the number of spark worker executors is large, if the specific number is displayed, will better help us to analyze and observe by spark ui. Although this is a small improvement, but it is indeed very valuable. After fix:  ## How was this patch tested? manual tests Please review http://spark.apache.org/contributing.html before opening a pull request. Author: guoxiaolong <[email protected]> Author: 郭小龙 10207633 <[email protected]> Author: guoxiaolongzte <[email protected]> Closes apache#17961 from guoxiaolongzte/SPARK-20720.
## What changes were proposed in this pull request? This pr added a new Optimizer rule to combine nested Concat. The master supports a pipeline operator '||' to concatenate strings in apache#17711 (This pr is follow-up). Since the parser currently generates nested Concat expressions, the optimizer needs to combine the nested expressions. ## How was this patch tested? Added tests in `CombineConcatSuite` and `SQLQueryTestSuite`. Author: Takeshi Yamamuro <[email protected]> Closes apache#17970 from maropu/SPARK-20730.
…tive ## What changes were proposed in this pull request? make param `family` in LoR and `optimizer` in LDA case insensitive ## How was this patch tested? updated tests yanboliang Author: Zheng RuiFeng <[email protected]> Closes apache#17910 from zhengruifeng/lr_family_lowercase.
…il if accumulator is garbage collected ## What changes were proposed in this pull request? After apache#17596 , we do not send internal accumulator name to executor side anymore, and always look up the accumulator name in `AccumulatorContext`. This cause a regression if the accumulator is already garbage collected, this PR fixes this by still sending accumulator name for `SQLMetrics`. ## How was this patch tested? N/A Author: Wenchen Fan <[email protected]> Closes apache#17931 from cloud-fan/bug.
## What changes were proposed in this pull request? StateStore.abort() should do a best effort attempt to clean up temporary resources. It should not throw errors, especially because its called in a TaskCompletionListener, because this error could hide previous real errors in the task. ## How was this patch tested? No unit test. Author: Tathagata Das <[email protected]> Closes apache#17958 from tdas/SPARK-20716.
## What changes were proposed in this pull request? Hive allows inserting data to bucketed table without guaranteeing bucketed and sorted-ness based on these two configs : `hive.enforce.bucketing` and `hive.enforce.sorting`. What does this PR achieve ? - Spark will disallow users from writing outputs to hive bucketed tables by default (given that output won't adhere with Hive's semantics). - IF user still wants to write to hive bucketed table, the only resort is to use `hive.enforce.bucketing=false` and `hive.enforce.sorting=false` which means user does NOT care about bucketing guarantees. Changes done in this PR: - Extract table's bucketing information in `HiveClientImpl` - While writing table info to metastore, `HiveClientImpl` now populates the bucketing information in the hive `Table` object - `InsertIntoHiveTable` allows inserts to bucketed table only if both `hive.enforce.bucketing` and `hive.enforce.sorting` are `false` Ability to create bucketed tables will enable adding test cases to Spark while I add more changes related to hive bucketing support. Design doc for hive hive bucketing support : https://docs.google.com/document/d/1a8IDh23RAkrkg9YYAeO51F4aGO8-xAlupKwdshve2fc/edit# ## How was this patch tested? - Added test for creating bucketed and sorted table. - Added test to ensure that INSERTs fail if strict bucket / sort is enforced - Added test to ensure that INSERTs can go through if strict bucket / sort is NOT enforced - Added test to validate that bucketing information shows up in output of DESC FORMATTED - Added test to ensure that `SHOW CREATE TABLE` works for hive bucketed tables Author: Tejas Patil <[email protected]> Closes apache#17644 from tejasapatil/SPARK-17729_create_bucketed_table.
## What changes were proposed in this pull request? Timeout and state data are two independent entities and should be settable independently. Therefore, in the same call of the user-defined function, one should be able to set the timeout before initializing the state and also after removing the state. Whether timeouts can be set or not, should not depend on the current state, and vice versa. However, a limitation of the current implementation is that state cannot be null while timeout is set. This is checked lazily after the function call has completed. ## How was this patch tested? - Updated existing unit tests that test the behavior of GroupState.setTimeout*** wrt to the current state - Added new tests that verify the disallowed cases where state is undefined but timeout is set. Author: Tathagata Das <[email protected]> Closes apache#17957 from tdas/SPARK-20717.
## What changes were proposed in this pull request? Since [SPARK-17298](https://issues.apache.org/jira/browse/SPARK-17298), some queries (q28, q61, q77, q88, q90) in the test suites fail with a message "_Use the CROSS JOIN syntax to allow cartesian products between these relations_". This benchmark is used as a reference model for Spark TPC-DS, so this PR aims to enable the correct configuration in `TPCDSQueryBenchmark.scala`. ## How was this patch tested? Manual. (Run TPCDSQueryBenchmark) Author: Dongjoon Hyun <[email protected]> Closes apache#17977 from dongjoon-hyun/SPARK-20735.
## What changes were proposed in this pull request? Because the method `TimeZone.getTimeZone(String ID)` is synchronized on the TimeZone class, concurrent call of this method will become a bottleneck. This especially happens when casting from string value containing timezone info to timestamp value, which uses `DateTimeUtils.stringToTimestamp()` and gets TimeZone instance on the site. This pr makes a cache of the generated TimeZone instances to avoid the synchronization. ## How was this patch tested? Existing tests. Author: Takuya UESHIN <[email protected]> Closes apache#17933 from ueshin/issues/SPARK-20588.
## What changes were proposed in this pull request? Before 2.2, MLlib keep to remove APIs deprecated in last feature/minor release. But from Spark 2.2, we decide to remove deprecated APIs in a major release, so we need to change corresponding annotations to tell users those will be removed in 3.0. Meanwhile, this fixed bugs in ML documents. The original ML docs can't show deprecated annotations in ```MLWriter``` and ```MLReader``` related class, we correct it in this PR. Before:  After:  ## How was this patch tested? Existing tests. Author: Yanbo Liang <[email protected]> Closes apache#17946 from yanboliang/spark-20707.
## What changes were proposed in this pull request? Review new Scala APIs introduced in 2.2. ## How was this patch tested? Existing tests. Author: Yanbo Liang <[email protected]> Closes apache#17934 from yanboliang/spark-20501.
…hods Update ALS examples illustrating use of "recommendForAllX" methods. ## How was this patch tested? Built and ran examples locally Author: Nick Pentreath <[email protected]> Closes apache#17950 from MLnick/SPARK-20553-update-als-examples.
Small clean ups from apache#17742 and apache#17845. ## How was this patch tested? Existing unit tests. Author: Nick Pentreath <[email protected]> Closes apache#17919 from MLnick/SPARK-20677-als-perf-followup.
|
Can one of the admins verify this patch? |
Contributor
|
Wrong branch (branch-2.1 instead of master)? |
Contributor
Author
|
This change has Merged to master/2.2 in PR #17997 |
Contributor
|
@10110346 I mean you want to merge this into branch-2.1, not master, right? |
Contributor
Author
|
yes,merge this into branch-2.1 @wzhfy |
Contributor
|
So can you change this?
|
Member
|
@10110346 Hi, I guess you created a topic branch from |
Contributor
Author
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What changes were proposed in this pull request?
This PR is to backport #17997 to Spark 2.1
when the date before "1582-10-04", the function of month and day return the value which is not we expected.
How was this patch tested?
unit tests