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…r mode ## What changes were proposed in this pull request? Enabled SparkR with Mesos client mode and cluster mode. Just a few changes were required to get this working on Mesos: (1) removed the SparkR on Mesos error checks and (2) do not require "--class" to be specified for R apps. The logic to check spark.mesos.executor.home was already in there. sun-rui ## How was this patch tested? 1. SparkSubmitSuite 2. On local mesos cluster (on laptop): ran SparkR shell, spark-submit client mode, and spark-submit cluster mode, with the "examples/src/main/R/dataframe.R" example application. 3. On multi-node mesos cluster: ran SparkR shell, spark-submit client mode, and spark-submit cluster mode, with the "examples/src/main/R/dataframe.R" example application. I tested with the following --conf values set: spark.mesos.executor.docker.image and spark.mesos.executor.home This contribution is my original work and I license the work to the project under the project's open source license. Author: Susan X. Huynh <[email protected]> Closes #15700 from susanxhuynh/susan-r-branch. (cherry picked from commit 9a87c31) Signed-off-by: Sean Owen <[email protected]>
…stently with expressions ## What changes were proposed in this pull request? This PR proposes to improve documentation and fix some descriptions equivalent to several minor fixes identified in #15677 Also, this suggests to change `Note:` and `NOTE:` to `.. note::` consistently with the others which marks up pretty. ## How was this patch tested? Jenkins tests and manually. For PySpark, `Note:` and `NOTE:` to `.. note::` make the document as below: **From**      **To**      Author: hyukjinkwon <[email protected]> Closes #15765 from HyukjinKwon/minor-function-doc. (cherry picked from commit 15d3926) Signed-off-by: Felix Cheung <[email protected]>
…parent ## What changes were proposed in this pull request? Only some of the models which contain a training summary currently set the summaries in the copy method. Linear/Logistic regression do, GLR, GMM, KM, and BKM do not. Additionally, these copy methods did not set the parent pointer of the copied model. This patch modifies the copy methods of the four models mentioned above to copy the training summary and set the parent. ## How was this patch tested? Add unit tests in Linear/Logistic/GeneralizedLinear regression and GaussianMixture/KMeans/BisectingKMeans to check the parent pointer of the copied model and check that the copied model has a summary. Author: sethah <[email protected]> Closes #15773 from sethah/SPARK-18276. (cherry picked from commit 23ce0d1) Signed-off-by: Yanbo Liang <[email protected]>
## What changes were proposed in this pull request? This PR proposes `rand`/`randn` accept `null` as input in Scala/SQL and `LongType` as input in SQL. In this case, it treats the values as `0`. So, this PR includes both changes below: - `null` support It seems MySQL also accepts this. ``` sql mysql> select rand(0); +---------------------+ | rand(0) | +---------------------+ | 0.15522042769493574 | +---------------------+ 1 row in set (0.00 sec) mysql> select rand(NULL); +---------------------+ | rand(NULL) | +---------------------+ | 0.15522042769493574 | +---------------------+ 1 row in set (0.00 sec) ``` and also Hive does according to [HIVE-14694](https://issues.apache.org/jira/browse/HIVE-14694) So the codes below: ``` scala spark.range(1).selectExpr("rand(null)").show() ``` prints.. **Before** ``` Input argument to rand must be an integer literal.;; line 1 pos 0 org.apache.spark.sql.AnalysisException: Input argument to rand must be an integer literal.;; line 1 pos 0 at org.apache.spark.sql.catalyst.analysis.FunctionRegistry$$anonfun$5.apply(FunctionRegistry.scala:465) at org.apache.spark.sql.catalyst.analysis.FunctionRegistry$$anonfun$5.apply(FunctionRegistry.scala:444) ``` **After** ``` +-----------------------+ |rand(CAST(NULL AS INT))| +-----------------------+ | 0.13385709732307427| +-----------------------+ ``` - `LongType` support in SQL. In addition, it make the function allows to take `LongType` consistently within Scala/SQL. In more details, the codes below: ``` scala spark.range(1).select(rand(1), rand(1L)).show() spark.range(1).selectExpr("rand(1)", "rand(1L)").show() ``` prints.. **Before** ``` +------------------+------------------+ | rand(1)| rand(1)| +------------------+------------------+ |0.2630967864682161|0.2630967864682161| +------------------+------------------+ Input argument to rand must be an integer literal.;; line 1 pos 0 org.apache.spark.sql.AnalysisException: Input argument to rand must be an integer literal.;; line 1 pos 0 at org.apache.spark.sql.catalyst.analysis.FunctionRegistry$$anonfun$5.apply(FunctionRegistry.scala:465) at ``` **After** ``` +------------------+------------------+ | rand(1)| rand(1)| +------------------+------------------+ |0.2630967864682161|0.2630967864682161| +------------------+------------------+ +------------------+------------------+ | rand(1)| rand(1)| +------------------+------------------+ |0.2630967864682161|0.2630967864682161| +------------------+------------------+ ``` ## How was this patch tested? Unit tests in `DataFrameSuite.scala` and `RandomSuite.scala`. Author: hyukjinkwon <[email protected]> Closes #15432 from HyukjinKwon/SPARK-17854. (cherry picked from commit 340f09d) Signed-off-by: Sean Owen <[email protected]>
…same UID ## What changes were proposed in this pull request? Motivation: `org.apache.spark.ml.Pipeline.copy(extra: ParamMap)` does not create an instance with the same UID. It does not conform to the method specification from its base class `org.apache.spark.ml.param.Params.copy(extra: ParamMap)` Solution: - fix for Pipeline UID - introduced new tests for `org.apache.spark.ml.Pipeline.copy` - minor improvements in test for `org.apache.spark.ml.PipelineModel.copy` ## How was this patch tested? Introduced new unit test: `org.apache.spark.ml.PipelineSuite."Pipeline.copy"` Improved existing unit test: `org.apache.spark.ml.PipelineSuite."PipelineModel.copy"` Author: Wojciech Szymanski <[email protected]> Closes #15759 from wojtek-szymanski/SPARK-18210. (cherry picked from commit b89d055) Signed-off-by: Yanbo Liang <[email protected]>
…ma is lager than parsed tokens
## What changes were proposed in this pull request?
Currently, there are the three cases when reading CSV by datasource when it is `PERMISSIVE` parse mode.
- schema == parsed tokens (from each line)
No problem to cast the value in the tokens to the field in the schema as they are equal.
- schema < parsed tokens (from each line)
It slices the tokens into the number of fields in schema.
- schema > parsed tokens (from each line)
It appends `null` into parsed tokens so that safely values can be casted with the schema.
However, when `null` is appended in the third case, we should take `null` into account when casting the values.
In case of `StringType`, it is fine as `UTF8String.fromString(datum)` produces `null` when the input is `null`. Therefore, this case will happen only when schema is explicitly given and schema includes data types that are not `StringType`.
The codes below:
```scala
val path = "/tmp/a"
Seq("1").toDF().write.text(path.getAbsolutePath)
val schema = StructType(
StructField("a", IntegerType, true) ::
StructField("b", IntegerType, true) :: Nil)
spark.read.schema(schema).option("header", "false").csv(path).show()
```
prints
**Before**
```
java.lang.NumberFormatException: null
at java.lang.Integer.parseInt(Integer.java:542)
at java.lang.Integer.parseInt(Integer.java:615)
at scala.collection.immutable.StringLike$class.toInt(StringLike.scala:272)
at scala.collection.immutable.StringOps.toInt(StringOps.scala:29)
at org.apache.spark.sql.execution.datasources.csv.CSVTypeCast$.castTo(CSVInferSchema.scala:24)
```
**After**
```
+---+----+
| a| b|
+---+----+
| 1|null|
+---+----+
```
## How was this patch tested?
Unit test in `CSVSuite.scala` and `CSVTypeCastSuite.scala`
Author: hyukjinkwon <[email protected]>
Closes #15767 from HyukjinKwon/SPARK-18269.
(cherry picked from commit 556a3b7)
Signed-off-by: Reynold Xin <[email protected]>
…tion ## What changes were proposed in this pull request? Previously `TRUNCATE TABLE ... PARTITION` will always truncate the whole table for data source tables, this PR fixes it and improve `InMemoryCatalog` to make this command work with it. ## How was this patch tested? existing tests Author: Wenchen Fan <[email protected]> Closes #15688 from cloud-fan/truncate. (cherry picked from commit 46b2e49) Signed-off-by: Reynold Xin <[email protected]>
(cherry picked from commit 07ac3f0) Signed-off-by: Reynold Xin <[email protected]>
## What changes were proposed in this pull request? We have an undocumented naming convention to call expression unit tests ExpressionsSuite, and the end-to-end tests FunctionsSuite. It'd be great to make all test suites consistent with this naming convention. ## How was this patch tested? This is a test-only naming change. Author: Reynold Xin <[email protected]> Closes #15793 from rxin/SPARK-18296. (cherry picked from commit 9db06c4) Signed-off-by: Reynold Xin <[email protected]>
…veFunctionRegistry ### What changes were proposed in this pull request? Currently, the Hive built-in `hash` function is not being used in Spark since Spark 2.0. The public interface does not allow users to unregister the Spark built-in functions. Thus, users will never use Hive's built-in `hash` function. The only exception here is `TestHiveFunctionRegistry`, which allows users to unregister the built-in functions. Thus, we can load Hive's hash function in the test cases. If we disable it, 10+ test cases will fail because the results are different from the Hive golden answer files. This PR is to remove `hash` from the list of `hiveFunctions` in `HiveSessionCatalog`. It will also remove `TestHiveFunctionRegistry`. This removal makes us easier to remove `TestHiveSessionState` in the future. ### How was this patch tested? N/A Author: gatorsmile <[email protected]> Closes #14498 from gatorsmile/removeHash. (cherry picked from commit 57626a5) Signed-off-by: Reynold Xin <[email protected]>
…ression
## What changes were proposed in this pull request?
As reported in the jira, sometimes the generated java code in codegen will cause compilation error.
Code snippet to test it:
case class Route(src: String, dest: String, cost: Int)
case class GroupedRoutes(src: String, dest: String, routes: Seq[Route])
val ds = sc.parallelize(Array(
Route("a", "b", 1),
Route("a", "b", 2),
Route("a", "c", 2),
Route("a", "d", 10),
Route("b", "a", 1),
Route("b", "a", 5),
Route("b", "c", 6))
).toDF.as[Route]
val grped = ds.map(r => GroupedRoutes(r.src, r.dest, Seq(r)))
.groupByKey(r => (r.src, r.dest))
.reduceGroups { (g1: GroupedRoutes, g2: GroupedRoutes) =>
GroupedRoutes(g1.src, g1.dest, g1.routes ++ g2.routes)
}.map(_._2)
The problem here is, in `ReferenceToExpressions` we evaluate the children vars to local variables. Then the result expression is evaluated to use those children variables. In the above case, the result expression code is too long and will be split by `CodegenContext.splitExpression`. So those local variables cannot be accessed and cause compilation error.
## How was this patch tested?
Jenkins tests.
Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request.
Author: Liang-Chi Hsieh <[email protected]>
Closes #15693 from viirya/fix-codege-compilation-error.
(cherry picked from commit a814eea)
Signed-off-by: Herman van Hovell <[email protected]>
…abel when family = binomial.
## What changes were proposed in this pull request?
SparkR ```spark.glm``` predict should output original label when family = "binomial".
## How was this patch tested?
Add unit test.
You can also run the following code to test:
```R
training <- suppressWarnings(createDataFrame(iris))
training <- training[training$Species %in% c("versicolor", "virginica"), ]
model <- spark.glm(training, Species ~ Sepal_Length + Sepal_Width,family = binomial(link = "logit"))
showDF(predict(model, training))
```
Before this change:
```
+------------+-----------+------------+-----------+----------+-----+-------------------+
|Sepal_Length|Sepal_Width|Petal_Length|Petal_Width| Species|label| prediction|
+------------+-----------+------------+-----------+----------+-----+-------------------+
| 7.0| 3.2| 4.7| 1.4|versicolor| 0.0| 0.8271421517601544|
| 6.4| 3.2| 4.5| 1.5|versicolor| 0.0| 0.6044595910413112|
| 6.9| 3.1| 4.9| 1.5|versicolor| 0.0| 0.7916340858281998|
| 5.5| 2.3| 4.0| 1.3|versicolor| 0.0|0.16080518180591158|
| 6.5| 2.8| 4.6| 1.5|versicolor| 0.0| 0.6112229217050189|
| 5.7| 2.8| 4.5| 1.3|versicolor| 0.0| 0.2555087295500885|
| 6.3| 3.3| 4.7| 1.6|versicolor| 0.0| 0.5681507664364834|
| 4.9| 2.4| 3.3| 1.0|versicolor| 0.0|0.05990570219972002|
| 6.6| 2.9| 4.6| 1.3|versicolor| 0.0| 0.6644434078306246|
| 5.2| 2.7| 3.9| 1.4|versicolor| 0.0|0.11293577405862379|
| 5.0| 2.0| 3.5| 1.0|versicolor| 0.0|0.06152372321585971|
| 5.9| 3.0| 4.2| 1.5|versicolor| 0.0|0.35250697207602555|
| 6.0| 2.2| 4.0| 1.0|versicolor| 0.0|0.32267018290814303|
| 6.1| 2.9| 4.7| 1.4|versicolor| 0.0| 0.433391153814592|
| 5.6| 2.9| 3.6| 1.3|versicolor| 0.0| 0.2280744262436993|
| 6.7| 3.1| 4.4| 1.4|versicolor| 0.0| 0.7219848389339459|
| 5.6| 3.0| 4.5| 1.5|versicolor| 0.0|0.23527698971404695|
| 5.8| 2.7| 4.1| 1.0|versicolor| 0.0| 0.285024533520016|
| 6.2| 2.2| 4.5| 1.5|versicolor| 0.0| 0.4107047877447493|
| 5.6| 2.5| 3.9| 1.1|versicolor| 0.0|0.20083561961645083|
+------------+-----------+------------+-----------+----------+-----+-------------------+
```
After this change:
```
+------------+-----------+------------+-----------+----------+-----+----------+
|Sepal_Length|Sepal_Width|Petal_Length|Petal_Width| Species|label|prediction|
+------------+-----------+------------+-----------+----------+-----+----------+
| 7.0| 3.2| 4.7| 1.4|versicolor| 0.0| virginica|
| 6.4| 3.2| 4.5| 1.5|versicolor| 0.0| virginica|
| 6.9| 3.1| 4.9| 1.5|versicolor| 0.0| virginica|
| 5.5| 2.3| 4.0| 1.3|versicolor| 0.0|versicolor|
| 6.5| 2.8| 4.6| 1.5|versicolor| 0.0| virginica|
| 5.7| 2.8| 4.5| 1.3|versicolor| 0.0|versicolor|
| 6.3| 3.3| 4.7| 1.6|versicolor| 0.0| virginica|
| 4.9| 2.4| 3.3| 1.0|versicolor| 0.0|versicolor|
| 6.6| 2.9| 4.6| 1.3|versicolor| 0.0| virginica|
| 5.2| 2.7| 3.9| 1.4|versicolor| 0.0|versicolor|
| 5.0| 2.0| 3.5| 1.0|versicolor| 0.0|versicolor|
| 5.9| 3.0| 4.2| 1.5|versicolor| 0.0|versicolor|
| 6.0| 2.2| 4.0| 1.0|versicolor| 0.0|versicolor|
| 6.1| 2.9| 4.7| 1.4|versicolor| 0.0|versicolor|
| 5.6| 2.9| 3.6| 1.3|versicolor| 0.0|versicolor|
| 6.7| 3.1| 4.4| 1.4|versicolor| 0.0| virginica|
| 5.6| 3.0| 4.5| 1.5|versicolor| 0.0|versicolor|
| 5.8| 2.7| 4.1| 1.0|versicolor| 0.0|versicolor|
| 6.2| 2.2| 4.5| 1.5|versicolor| 0.0|versicolor|
| 5.6| 2.5| 3.9| 1.1|versicolor| 0.0|versicolor|
+------------+-----------+------------+-----------+----------+-----+----------+
```
Author: Yanbo Liang <[email protected]>
Closes #15788 from yanboliang/spark-18291.
(cherry picked from commit daa975f)
Signed-off-by: Yanbo Liang <[email protected]>
…r default starting offset in latest ## What changes were proposed in this pull request? Added test to check whether default starting offset in latest ## How was this patch tested? new unit test Author: Tathagata Das <[email protected]> Closes #15778 from tdas/SPARK-18283. (cherry picked from commit b06c23d) Signed-off-by: Shixiong Zhu <[email protected]>
## What changes were proposed in this pull request?
Add a function to check if two integers are compatible when invoking `acceptsType()` in `DataType`.
## How was this patch tested?
Manually.
E.g.
```
spark.sql("create table t3(a map<bigint, array<string>>)")
spark.sql("select * from t3 where a[1] is not null")
```
Before:
```
cannot resolve 't.`a`[1]' due to data type mismatch: argument 2 requires bigint type, however, '1' is of int type.; line 1 pos 22
org.apache.spark.sql.AnalysisException: cannot resolve 't.`a`[1]' due to data type mismatch: argument 2 requires bigint type, however, '1' is of int type.; line 1 pos 22
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:82)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:307)
```
After:
Run the sql queries above. No errors.
Author: Weiqing Yang <[email protected]>
Closes #15448 from weiqingy/SPARK_17108.
(cherry picked from commit 0d95662)
Signed-off-by: Herman van Hovell <[email protected]>
Waiting for merging #13680 This PR optimizes `SerializeFromObject()` for an primitive array. This is derived from #13758 to address one of problems by using a simple way in #13758. The current implementation always generates `GenericArrayData` from `SerializeFromObject()` for any type of an array in a logical plan. This involves a boxing at a constructor of `GenericArrayData` when `SerializedFromObject()` has an primitive array. This PR enables to generate `UnsafeArrayData` from `SerializeFromObject()` for a primitive array. It can avoid boxing to create an instance of `ArrayData` in the generated code by Catalyst. This PR also generate `UnsafeArrayData` in a case for `RowEncoder.serializeFor` or `CatalystTypeConverters.createToCatalystConverter`. Performance improvement of `SerializeFromObject()` is up to 2.0x ``` OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.4.11-200.fc22.x86_64 Intel Xeon E3-12xx v2 (Ivy Bridge) Without this PR Write an array in Dataset: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 556 / 608 15.1 66.3 1.0X Double 1668 / 1746 5.0 198.8 0.3X with this PR Write an array in Dataset: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 352 / 401 23.8 42.0 1.0X Double 821 / 885 10.2 97.9 0.4X ``` Here is an example program that will happen in mllib as described in [SPARK-16070](https://issues.apache.org/jira/browse/SPARK-16070). ``` sparkContext.parallelize(Seq(Array(1, 2)), 1).toDS.map(e => e).show ``` Generated code before applying this PR ``` java /* 039 */ protected void processNext() throws java.io.IOException { /* 040 */ while (inputadapter_input.hasNext()) { /* 041 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next(); /* 042 */ int[] inputadapter_value = (int[])inputadapter_row.get(0, null); /* 043 */ /* 044 */ Object mapelements_obj = ((Expression) references[0]).eval(null); /* 045 */ scala.Function1 mapelements_value1 = (scala.Function1) mapelements_obj; /* 046 */ /* 047 */ boolean mapelements_isNull = false || false; /* 048 */ int[] mapelements_value = null; /* 049 */ if (!mapelements_isNull) { /* 050 */ Object mapelements_funcResult = null; /* 051 */ mapelements_funcResult = mapelements_value1.apply(inputadapter_value); /* 052 */ if (mapelements_funcResult == null) { /* 053 */ mapelements_isNull = true; /* 054 */ } else { /* 055 */ mapelements_value = (int[]) mapelements_funcResult; /* 056 */ } /* 057 */ /* 058 */ } /* 059 */ mapelements_isNull = mapelements_value == null; /* 060 */ /* 061 */ serializefromobject_argIsNulls[0] = mapelements_isNull; /* 062 */ serializefromobject_argValue = mapelements_value; /* 063 */ /* 064 */ boolean serializefromobject_isNull = false; /* 065 */ for (int idx = 0; idx < 1; idx++) { /* 066 */ if (serializefromobject_argIsNulls[idx]) { serializefromobject_isNull = true; break; } /* 067 */ } /* 068 */ /* 069 */ final ArrayData serializefromobject_value = serializefromobject_isNull ? null : new org.apache.spark.sql.catalyst.util.GenericArrayData(serializefromobject_argValue); /* 070 */ serializefromobject_holder.reset(); /* 071 */ /* 072 */ serializefromobject_rowWriter.zeroOutNullBytes(); /* 073 */ /* 074 */ if (serializefromobject_isNull) { /* 075 */ serializefromobject_rowWriter.setNullAt(0); /* 076 */ } else { /* 077 */ // Remember the current cursor so that we can calculate how many bytes are /* 078 */ // written later. /* 079 */ final int serializefromobject_tmpCursor = serializefromobject_holder.cursor; /* 080 */ /* 081 */ if (serializefromobject_value instanceof UnsafeArrayData) { /* 082 */ final int serializefromobject_sizeInBytes = ((UnsafeArrayData) serializefromobject_value).getSizeInBytes(); /* 083 */ // grow the global buffer before writing data. /* 084 */ serializefromobject_holder.grow(serializefromobject_sizeInBytes); /* 085 */ ((UnsafeArrayData) serializefromobject_value).writeToMemory(serializefromobject_holder.buffer, serializefromobject_holder.cursor); /* 086 */ serializefromobject_holder.cursor += serializefromobject_sizeInBytes; /* 087 */ /* 088 */ } else { /* 089 */ final int serializefromobject_numElements = serializefromobject_value.numElements(); /* 090 */ serializefromobject_arrayWriter.initialize(serializefromobject_holder, serializefromobject_numElements, 4); /* 091 */ /* 092 */ for (int serializefromobject_index = 0; serializefromobject_index < serializefromobject_numElements; serializefromobject_index++) { /* 093 */ if (serializefromobject_value.isNullAt(serializefromobject_index)) { /* 094 */ serializefromobject_arrayWriter.setNullInt(serializefromobject_index); /* 095 */ } else { /* 096 */ final int serializefromobject_element = serializefromobject_value.getInt(serializefromobject_index); /* 097 */ serializefromobject_arrayWriter.write(serializefromobject_index, serializefromobject_element); /* 098 */ } /* 099 */ } /* 100 */ } /* 101 */ /* 102 */ serializefromobject_rowWriter.setOffsetAndSize(0, serializefromobject_tmpCursor, serializefromobject_holder.cursor - serializefromobject_tmpCursor); /* 103 */ } /* 104 */ serializefromobject_result.setTotalSize(serializefromobject_holder.totalSize()); /* 105 */ append(serializefromobject_result); /* 106 */ if (shouldStop()) return; /* 107 */ } /* 108 */ } /* 109 */ } ``` Generated code after applying this PR ``` java /* 035 */ protected void processNext() throws java.io.IOException { /* 036 */ while (inputadapter_input.hasNext()) { /* 037 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next(); /* 038 */ int[] inputadapter_value = (int[])inputadapter_row.get(0, null); /* 039 */ /* 040 */ Object mapelements_obj = ((Expression) references[0]).eval(null); /* 041 */ scala.Function1 mapelements_value1 = (scala.Function1) mapelements_obj; /* 042 */ /* 043 */ boolean mapelements_isNull = false || false; /* 044 */ int[] mapelements_value = null; /* 045 */ if (!mapelements_isNull) { /* 046 */ Object mapelements_funcResult = null; /* 047 */ mapelements_funcResult = mapelements_value1.apply(inputadapter_value); /* 048 */ if (mapelements_funcResult == null) { /* 049 */ mapelements_isNull = true; /* 050 */ } else { /* 051 */ mapelements_value = (int[]) mapelements_funcResult; /* 052 */ } /* 053 */ /* 054 */ } /* 055 */ mapelements_isNull = mapelements_value == null; /* 056 */ /* 057 */ boolean serializefromobject_isNull = mapelements_isNull; /* 058 */ final ArrayData serializefromobject_value = serializefromobject_isNull ? null : org.apache.spark.sql.catalyst.expressions.UnsafeArrayData.fromPrimitiveArray(mapelements_value); /* 059 */ serializefromobject_isNull = serializefromobject_value == null; /* 060 */ serializefromobject_holder.reset(); /* 061 */ /* 062 */ serializefromobject_rowWriter.zeroOutNullBytes(); /* 063 */ /* 064 */ if (serializefromobject_isNull) { /* 065 */ serializefromobject_rowWriter.setNullAt(0); /* 066 */ } else { /* 067 */ // Remember the current cursor so that we can calculate how many bytes are /* 068 */ // written later. /* 069 */ final int serializefromobject_tmpCursor = serializefromobject_holder.cursor; /* 070 */ /* 071 */ if (serializefromobject_value instanceof UnsafeArrayData) { /* 072 */ final int serializefromobject_sizeInBytes = ((UnsafeArrayData) serializefromobject_value).getSizeInBytes(); /* 073 */ // grow the global buffer before writing data. /* 074 */ serializefromobject_holder.grow(serializefromobject_sizeInBytes); /* 075 */ ((UnsafeArrayData) serializefromobject_value).writeToMemory(serializefromobject_holder.buffer, serializefromobject_holder.cursor); /* 076 */ serializefromobject_holder.cursor += serializefromobject_sizeInBytes; /* 077 */ /* 078 */ } else { /* 079 */ final int serializefromobject_numElements = serializefromobject_value.numElements(); /* 080 */ serializefromobject_arrayWriter.initialize(serializefromobject_holder, serializefromobject_numElements, 4); /* 081 */ /* 082 */ for (int serializefromobject_index = 0; serializefromobject_index < serializefromobject_numElements; serializefromobject_index++) { /* 083 */ if (serializefromobject_value.isNullAt(serializefromobject_index)) { /* 084 */ serializefromobject_arrayWriter.setNullInt(serializefromobject_index); /* 085 */ } else { /* 086 */ final int serializefromobject_element = serializefromobject_value.getInt(serializefromobject_index); /* 087 */ serializefromobject_arrayWriter.write(serializefromobject_index, serializefromobject_element); /* 088 */ } /* 089 */ } /* 090 */ } /* 091 */ /* 092 */ serializefromobject_rowWriter.setOffsetAndSize(0, serializefromobject_tmpCursor, serializefromobject_holder.cursor - serializefromobject_tmpCursor); /* 093 */ } /* 094 */ serializefromobject_result.setTotalSize(serializefromobject_holder.totalSize()); /* 095 */ append(serializefromobject_result); /* 096 */ if (shouldStop()) return; /* 097 */ } /* 098 */ } /* 099 */ } ``` Added a test in `DatasetSuite`, `RowEncoderSuite`, and `CatalystTypeConvertersSuite` Author: Kazuaki Ishizaki <[email protected]> Closes #15044 from kiszk/SPARK-17490. (cherry picked from commit 19cf208) Signed-off-by: Herman van Hovell <[email protected]>
…om_json)
## What changes were proposed in this pull request?
This PR proposes to match up the behaviour of `to_json` to `from_json` function for null-safety.
Currently, it throws `NullPointException` but this PR fixes this to produce `null` instead.
with the data below:
```scala
import spark.implicits._
val df = Seq(Some(Tuple1(Tuple1(1))), None).toDF("a")
df.show()
```
```
+----+
| a|
+----+
| [1]|
|null|
+----+
```
the codes below
```scala
import org.apache.spark.sql.functions._
df.select(to_json($"a")).show()
```
produces..
**Before**
throws `NullPointException` as below:
```
java.lang.NullPointerException
at org.apache.spark.sql.catalyst.json.JacksonGenerator.org$apache$spark$sql$catalyst$json$JacksonGenerator$$writeFields(JacksonGenerator.scala:138)
at org.apache.spark.sql.catalyst.json.JacksonGenerator$$anonfun$write$1.apply$mcV$sp(JacksonGenerator.scala:194)
at org.apache.spark.sql.catalyst.json.JacksonGenerator.org$apache$spark$sql$catalyst$json$JacksonGenerator$$writeObject(JacksonGenerator.scala:131)
at org.apache.spark.sql.catalyst.json.JacksonGenerator.write(JacksonGenerator.scala:193)
at org.apache.spark.sql.catalyst.expressions.StructToJson.eval(jsonExpressions.scala:544)
at org.apache.spark.sql.catalyst.expressions.Alias.eval(namedExpressions.scala:142)
at org.apache.spark.sql.catalyst.expressions.InterpretedProjection.apply(Projection.scala:48)
at org.apache.spark.sql.catalyst.expressions.InterpretedProjection.apply(Projection.scala:30)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
```
**After**
```
+---------------+
|structtojson(a)|
+---------------+
| {"_1":1}|
| null|
+---------------+
```
## How was this patch tested?
Unit test in `JsonExpressionsSuite.scala` and `JsonFunctionsSuite.scala`.
Author: hyukjinkwon <[email protected]>
Closes #15792 from HyukjinKwon/SPARK-18295.
(cherry picked from commit 3eda057)
Signed-off-by: Michael Armbrust <[email protected]>
## What changes were proposed in this pull request? This adds support for Hive variables: * Makes values set via `spark-sql --hivevar name=value` accessible * Adds `getHiveVar` and `setHiveVar` to the `HiveClient` interface * Adds a SessionVariables trait for sessions like Hive that support variables (including Hive vars) * Adds SessionVariables support to variable substitution * Adds SessionVariables support to the SET command ## How was this patch tested? * Adds a test to all supported Hive versions for accessing Hive variables * Adds HiveVariableSubstitutionSuite Author: Ryan Blue <[email protected]> Closes #15738 from rdblue/SPARK-18086-add-hivevar-support. (cherry picked from commit 9b0593d) Signed-off-by: Reynold Xin <[email protected]>
…joining ## What changes were proposed in this pull request? Right now, there is no way to join the output of a memory sink with any table: > UnsupportedOperationException: LeafNode MemoryPlan must implement statistics This patch adds statistics to MemorySink, making joining snapshots of memory streams with tables possible. ## How was this patch tested? Added a test case. Author: Liwei Lin <[email protected]> Closes #15786 from lw-lin/memory-sink-stat. (cherry picked from commit c1a0c66) Signed-off-by: Reynold Xin <[email protected]>
…ry views or UDFs ### What changes were proposed in this pull request? Based on the discussion in [SPARK-18209](https://issues.apache.org/jira/browse/SPARK-18209). It doesn't really make sense to create permanent views based on temporary views or temporary UDFs. To disallow the supports and issue the exceptions, this PR needs to detect whether a temporary view/UDF is being used when defining a permanent view. Basically, this PR can be split to two sub-tasks: **Task 1:** detecting a temporary view from the query plan of view definition. When finding an unresolved temporary view, Analyzer replaces it by a `SubqueryAlias` with the corresponding logical plan, which is stored in an in-memory HashMap. After replacement, it is impossible to detect whether the `SubqueryAlias` is added/generated from a temporary view. Thus, to detect the usage of a temporary view in view definition, this PR traverses the unresolved logical plan and uses the name of an `UnresolvedRelation` to detect whether it is a (global) temporary view. **Task 2:** detecting a temporary UDF from the query plan of view definition. Detecting usage of a temporary UDF in view definition is not straightfoward. First, in the analyzed plan, we are having different forms to represent the functions. More importantly, some classes (e.g., `HiveGenericUDF`) are not accessible from `CreateViewCommand`, which is part of `sql/core`. Thus, we used the unanalyzed plan `child` of `CreateViewCommand` to detect the usage of a temporary UDF. Because the plan has already been successfully analyzed, we can assume the functions have been defined/registered. Second, in Spark, the functions have four forms: Spark built-in functions, built-in hash functions, permanent UDFs and temporary UDFs. We do not have any direct way to determine whether a function is temporary or not. Thus, we introduced a function `isTemporaryFunction` in `SessionCatalog`. This function contains the detailed logics to determine whether a function is temporary or not. ### How was this patch tested? Added test cases. Author: gatorsmile <[email protected]> Closes #15764 from gatorsmile/blockTempFromPermViewCreation. (cherry picked from commit 1da64e1) Signed-off-by: Reynold Xin <[email protected]>
## What changes were proposed in this pull request? This Pull request comprises of the critical bug SPARK-16575 changes. This change rectifies the issue with BinaryFileRDD partition calculations as upon creating an RDD with sc.binaryFiles, the resulting RDD always just consisted of two partitions only. ## How was this patch tested? The original issue ie. getNumPartitions on binary Files RDD (always having two partitions) was first replicated and then tested upon the changes. Also the unit tests have been checked and passed. This contribution is my original work and I licence the work to the project under the project's open source license srowen hvanhovell rxin vanzin skyluc kmader zsxwing datafarmer Please have a look . Author: fidato <[email protected]> Closes #15327 from fidato13/SPARK-16575. (cherry picked from commit 6f36971) Signed-off-by: Reynold Xin <[email protected]>
…nCode
This PR avoids a compilation error due to more than 64KB Java byte code size. This error occur since generate java code for computing a hash value for a row is too big. This PR fixes this compilation error by splitting a big code chunk into multiple methods by calling `CodegenContext.splitExpression` at `HashExpression.doGenCode`
The test case requires a calculation of hash code for a row that includes 1000 String fields. `HashExpression.doGenCode` generate a lot of Java code for this computation into one function. As a result, the size of the corresponding Java bytecode is more than 64 KB.
Generated code without this PR
````java
/* 027 */ public UnsafeRow apply(InternalRow i) {
/* 028 */ boolean isNull = false;
/* 029 */
/* 030 */ int value1 = 42;
/* 031 */
/* 032 */ boolean isNull2 = i.isNullAt(0);
/* 033 */ UTF8String value2 = isNull2 ? null : (i.getUTF8String(0));
/* 034 */ if (!isNull2) {
/* 035 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value2.getBaseObject(), value2.getBaseOffset(), value2.numBytes(), value1);
/* 036 */ }
/* 037 */
/* 038 */
/* 039 */ boolean isNull3 = i.isNullAt(1);
/* 040 */ UTF8String value3 = isNull3 ? null : (i.getUTF8String(1));
/* 041 */ if (!isNull3) {
/* 042 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value3.getBaseObject(), value3.getBaseOffset(), value3.numBytes(), value1);
/* 043 */ }
/* 044 */
/* 045 */
...
/* 7024 */
/* 7025 */ boolean isNull1001 = i.isNullAt(999);
/* 7026 */ UTF8String value1001 = isNull1001 ? null : (i.getUTF8String(999));
/* 7027 */ if (!isNull1001) {
/* 7028 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value1001.getBaseObject(), value1001.getBaseOffset(), value1001.numBytes(), value1);
/* 7029 */ }
/* 7030 */
/* 7031 */
/* 7032 */ boolean isNull1002 = i.isNullAt(1000);
/* 7033 */ UTF8String value1002 = isNull1002 ? null : (i.getUTF8String(1000));
/* 7034 */ if (!isNull1002) {
/* 7035 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value1002.getBaseObject(), value1002.getBaseOffset(), value1002.numBytes(), value1);
/* 7036 */ }
````
Generated code with this PR
````java
/* 3807 */ private void apply_249(InternalRow i) {
/* 3808 */
/* 3809 */ boolean isNull998 = i.isNullAt(996);
/* 3810 */ UTF8String value998 = isNull998 ? null : (i.getUTF8String(996));
/* 3811 */ if (!isNull998) {
/* 3812 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value998.getBaseObject(), value998.getBaseOffset(), value998.numBytes(), value1);
/* 3813 */ }
/* 3814 */
/* 3815 */ boolean isNull999 = i.isNullAt(997);
/* 3816 */ UTF8String value999 = isNull999 ? null : (i.getUTF8String(997));
/* 3817 */ if (!isNull999) {
/* 3818 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value999.getBaseObject(), value999.getBaseOffset(), value999.numBytes(), value1);
/* 3819 */ }
/* 3820 */
/* 3821 */ boolean isNull1000 = i.isNullAt(998);
/* 3822 */ UTF8String value1000 = isNull1000 ? null : (i.getUTF8String(998));
/* 3823 */ if (!isNull1000) {
/* 3824 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value1000.getBaseObject(), value1000.getBaseOffset(), value1000.numBytes(), value1);
/* 3825 */ }
/* 3826 */
/* 3827 */ boolean isNull1001 = i.isNullAt(999);
/* 3828 */ UTF8String value1001 = isNull1001 ? null : (i.getUTF8String(999));
/* 3829 */ if (!isNull1001) {
/* 3830 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value1001.getBaseObject(), value1001.getBaseOffset(), value1001.numBytes(), value1);
/* 3831 */ }
/* 3832 */
/* 3833 */ }
/* 3834 */
...
/* 4532 */ private void apply_0(InternalRow i) {
/* 4533 */
/* 4534 */ boolean isNull2 = i.isNullAt(0);
/* 4535 */ UTF8String value2 = isNull2 ? null : (i.getUTF8String(0));
/* 4536 */ if (!isNull2) {
/* 4537 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value2.getBaseObject(), value2.getBaseOffset(), value2.numBytes(), value1);
/* 4538 */ }
/* 4539 */
/* 4540 */ boolean isNull3 = i.isNullAt(1);
/* 4541 */ UTF8String value3 = isNull3 ? null : (i.getUTF8String(1));
/* 4542 */ if (!isNull3) {
/* 4543 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value3.getBaseObject(), value3.getBaseOffset(), value3.numBytes(), value1);
/* 4544 */ }
/* 4545 */
/* 4546 */ boolean isNull4 = i.isNullAt(2);
/* 4547 */ UTF8String value4 = isNull4 ? null : (i.getUTF8String(2));
/* 4548 */ if (!isNull4) {
/* 4549 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value4.getBaseObject(), value4.getBaseOffset(), value4.numBytes(), value1);
/* 4550 */ }
/* 4551 */
/* 4552 */ boolean isNull5 = i.isNullAt(3);
/* 4553 */ UTF8String value5 = isNull5 ? null : (i.getUTF8String(3));
/* 4554 */ if (!isNull5) {
/* 4555 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value5.getBaseObject(), value5.getBaseOffset(), value5.numBytes(), value1);
/* 4556 */ }
/* 4557 */
/* 4558 */ }
...
/* 7344 */ public UnsafeRow apply(InternalRow i) {
/* 7345 */ boolean isNull = false;
/* 7346 */
/* 7347 */ value1 = 42;
/* 7348 */ apply_0(i);
/* 7349 */ apply_1(i);
...
/* 7596 */ apply_248(i);
/* 7597 */ apply_249(i);
/* 7598 */ apply_250(i);
/* 7599 */ apply_251(i);
...
````
Add a new test in `DataFrameSuite`
Author: Kazuaki Ishizaki <[email protected]>
Closes #15745 from kiszk/SPARK-18207.
(cherry picked from commit 47731e1)
Signed-off-by: Herman van Hovell <[email protected]>
…when a UDAF has a foldable TypeCheck ## What changes were proposed in this pull request? In RewriteDistinctAggregates rewrite funtion,after the UDAF's childs are mapped to AttributeRefference, If the UDAF(such as ApproximatePercentile) has a foldable TypeCheck for the input, It will failed because the AttributeRefference is not foldable,then the UDAF is not resolved, and then nullify on the unresolved object will throw a Exception. In this PR, only map Unfoldable child to AttributeRefference, this can avoid the UDAF's foldable TypeCheck. and then only Expand Unfoldable child, there is no need to Expand a static value(foldable value). **Before sql result** > select percentile_approxy(key,0.99999),count(distinct key),sume(distinc key) from src limit 1 > org.apache.spark.sql.catalyst.analysis.UnresolvedException: Invalid call to dataType on unresolved object, tree: 'percentile_approx(CAST(src.`key` AS DOUBLE), CAST(0.99999BD AS DOUBLE), 10000) > at org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute.dataType(unresolved.scala:92) > at org.apache.spark.sql.catalyst.optimizer.RewriteDistinctAggregates$.org$apache$spark$sql$catalyst$optimizer$RewriteDistinctAggregates$$nullify(RewriteDistinctAggregates.scala:261) **After sql result** > select percentile_approxy(key,0.99999),count(distinct key),sume(distinc key) from src limit 1 > [498.0,309,79136] ## How was this patch tested? Add a test case in HiveUDFSuit. Author: root <root@iZbp1gsnrlfzjxh82cz80vZ.(none)> Closes #15668 from windpiger/RewriteDistinctUDAFUnresolveExcep. (cherry picked from commit c291bd2) Signed-off-by: Herman van Hovell <[email protected]>
I created Scala and Java example and added documentation. Author: chie8842 <[email protected]> Closes #15658 from hayashidac/SPARK-13770. (cherry picked from commit ee2e741) Signed-off-by: Sean Owen <[email protected]>
…hed for the given non-partial partition spec ## What changes were proposed in this pull request? a follow up of #15688 ## How was this patch tested? updated test in `DDLSuite` Author: Wenchen Fan <[email protected]> Closes #15805 from cloud-fan/truncate. (cherry picked from commit 73feaa3) Signed-off-by: Wenchen Fan <[email protected]>
## What changes were proposed in this pull request? The #15627 broke functionality with yarn --files --archives does not accept any files. This patch ensures that --files and --archives accept unique files. ## How was this patch tested? A. I added unit tests. B. Also, manually tested --files with --archives to throw exception if duplicate files are specified and continue if unique files are specified. Author: Kishor Patil <[email protected]> Closes #15810 from kishorvpatil/SPARK18357. (cherry picked from commit 245e5a2) Signed-off-by: Tom Graves <[email protected]>
…lastic net ## What changes were proposed in this pull request? * Made SingularMatrixException private ml * WeightedLeastSquares: Changed to allow tol >= 0 instead of only tol > 0 ## How was this patch tested? existing tests Author: Joseph K. Bradley <[email protected]> Closes #15779 from jkbradley/wls-cleanups. (cherry picked from commit 26e1c53) Signed-off-by: Joseph K. Bradley <[email protected]>
…kend.dead` ## What changes were proposed in this pull request? "StandaloneSchedulerBackend.dead" is called in a RPC thread, so it should not call "SparkContext.stop" in the same thread. "SparkContext.stop" will block until all RPC threads exit, if it's called inside a RPC thread, it will be dead-lock. This PR add a thread local flag inside RPC threads. `SparkContext.stop` uses it to decide if launching a new thread to stop the SparkContext. ## How was this patch tested? Jenkins Author: Shixiong Zhu <[email protected]> Closes #15775 from zsxwing/SPARK-18280.
## What changes were proposed in this pull request? If the rename operation in the state store fails (`fs.rename` returns `false`), the StateStore should throw an exception and have the task retry. Currently if renames fail, nothing happens during execution immediately. However, you will observe that snapshot operations will fail, and then any attempt at recovery (executor failure / checkpoint recovery) also fails. ## How was this patch tested? Unit test Author: Burak Yavuz <[email protected]> Closes #15804 from brkyvz/rename-state. (cherry picked from commit 6f7ecb0) Signed-off-by: Tathagata Das <[email protected]>
## What changes were proposed in this pull request? Gradient Boosted Tree in R. With a few minor improvements to RandomForest in R. Since this is relatively isolated I'd like to target this for branch-2.1 ## How was this patch tested? manual tests, unit tests Author: Felix Cheung <[email protected]> Closes #15746 from felixcheung/rgbt. (cherry picked from commit 55964c1) Signed-off-by: Felix Cheung <[email protected]>
…case insensitive resolution ## What changes were proposed in this pull request? These are no longer needed after https://issues.apache.org/jira/browse/SPARK-17183 cc cloud-fan ## How was this patch tested? Existing parquet and orc tests. Author: Eric Liang <[email protected]> Closes #15799 from ericl/sc-4929. (cherry picked from commit 4afa39e) Signed-off-by: Wenchen Fan <[email protected]>
…2.1 release ## What changes were proposed in this pull request? Update ML programming and migration guide for 2.1 release. ## How was this patch tested? Doc change, no test. Author: Yanbo Liang <[email protected]> Closes #16076 from yanboliang/spark-18324. (cherry picked from commit 2dc0d7e) Signed-off-by: Joseph K. Bradley <[email protected]>
## What changes were proposed in this pull request? Makes `Window.unboundedPreceding` and `Window.unboundedFollowing` backward compatible. ## How was this patch tested? Pyspark SQL unittests. Please review http://spark.apache.org/contributing.html before opening a pull request. Author: zero323 <[email protected]> Closes #16123 from zero323/SPARK-17845-follow-up. (cherry picked from commit a9cbfc4) Signed-off-by: Reynold Xin <[email protected]>
…ests at ExecutorClassLoaderSuite ## What changes were proposed in this pull request? This PR fixes two problems as below: - Close `BufferedSource` after `Source.fromInputStream(...)` to release resource and make the tests pass on Windows in `ExecutorClassLoaderSuite` ``` [info] Exception encountered when attempting to run a suite with class name: org.apache.spark.repl.ExecutorClassLoaderSuite *** ABORTED *** (7 seconds, 333 milliseconds) [info] java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-77b2f37b-6405-47c4-af1c-4a6a206511f2 [info] at org.apache.spark.util.Utils$.deleteRecursively(Utils.scala:1010) [info] at org.apache.spark.repl.ExecutorClassLoaderSuite.afterAll(ExecutorClassLoaderSuite.scala:76) [info] at org.scalatest.BeforeAndAfterAll$class.afterAll(BeforeAndAfterAll.scala:213) ... ``` - Fix URI correctly so that related tests can be passed on Windows. ``` [info] - child first *** FAILED *** (78 milliseconds) [info] java.net.URISyntaxException: Illegal character in authority at index 7: file://C:\projects\spark\target\tmp\spark-00b66070-0548-463c-b6f3-8965d173da9b [info] at java.net.URI$Parser.fail(URI.java:2848) [info] at java.net.URI$Parser.parseAuthority(URI.java:3186) ... [info] - parent first *** FAILED *** (15 milliseconds) [info] java.net.URISyntaxException: Illegal character in authority at index 7: file://C:\projects\spark\target\tmp\spark-00b66070-0548-463c-b6f3-8965d173da9b [info] at java.net.URI$Parser.fail(URI.java:2848) [info] at java.net.URI$Parser.parseAuthority(URI.java:3186) ... [info] - child first can fall back *** FAILED *** (0 milliseconds) [info] java.net.URISyntaxException: Illegal character in authority at index 7: file://C:\projects\spark\target\tmp\spark-00b66070-0548-463c-b6f3-8965d173da9b [info] at java.net.URI$Parser.fail(URI.java:2848) [info] at java.net.URI$Parser.parseAuthority(URI.java:3186) ... [info] - child first can fail *** FAILED *** (0 milliseconds) [info] java.net.URISyntaxException: Illegal character in authority at index 7: file://C:\projects\spark\target\tmp\spark-00b66070-0548-463c-b6f3-8965d173da9b [info] at java.net.URI$Parser.fail(URI.java:2848) [info] at java.net.URI$Parser.parseAuthority(URI.java:3186) ... [info] - resource from parent *** FAILED *** (0 milliseconds) [info] java.net.URISyntaxException: Illegal character in authority at index 7: file://C:\projects\spark\target\tmp\spark-00b66070-0548-463c-b6f3-8965d173da9b [info] at java.net.URI$Parser.fail(URI.java:2848) [info] at java.net.URI$Parser.parseAuthority(URI.java:3186) ... [info] - resources from parent *** FAILED *** (0 milliseconds) [info] java.net.URISyntaxException: Illegal character in authority at index 7: file://C:\projects\spark\target\tmp\spark-00b66070-0548-463c-b6f3-8965d173da9b [info] at java.net.URI$Parser.fail(URI.java:2848) [info] at java.net.URI$Parser.parseAuthority(URI.java:3186) ``` ## How was this patch tested? Manually tested via AppVeyor. **Before** https://ci.appveyor.com/project/spark-test/spark/build/102-rpel-ExecutorClassLoaderSuite **After** https://ci.appveyor.com/project/spark-test/spark/build/108-rpel-ExecutorClassLoaderSuite Author: hyukjinkwon <[email protected]> Closes #16116 from HyukjinKwon/close-after-open. (cherry picked from commit d1312fb) Signed-off-by: Sean Owen <[email protected]>
…ted subqueries ## What changes were proposed in this pull request? This fix puts an explicit list of operators that Spark supports for correlated subqueries. ## How was this patch tested? Run sql/test, catalyst/test and add a new test case on Generate. Author: Nattavut Sutyanyong <[email protected]> Closes #16046 from nsyca/spark18455.0. (cherry picked from commit 4a3c096) Signed-off-by: Herman van Hovell <[email protected]>
…(LSH) ## What changes were proposed in this pull request? The user guide for LSH is added to ml-features.md, with several scala/java examples in spark-examples. ## How was this patch tested? Doc has been generated through Jekyll, and checked through manual inspection. Author: Yunni <[email protected]> Author: Yun Ni <[email protected]> Author: Joseph K. Bradley <[email protected]> Author: Yun Ni <[email protected]> Closes #15795 from Yunni/SPARK-18081-lsh-guide. (cherry picked from commit 3477718) Signed-off-by: Joseph K. Bradley <[email protected]>
…Projection code to exceed JVM code size limit ## What changes were proposed in this pull request? Fix for SPARK-18091 which is a bug related to large if expressions causing generated SpecificUnsafeProjection code to exceed JVM code size limit. This PR changes if expression's code generation to place its predicate, true value and false value expressions' generated code in separate methods in context so as to never generate too long combined code. ## How was this patch tested? Added a unit test and also tested manually with the application (having transformations similar to the unit test) which caused the issue to be identified in the first place. Author: Kapil Singh <[email protected]> Closes #15620 from kapilsingh5050/SPARK-18091-IfCodegenFix. (cherry picked from commit e463678) Signed-off-by: Wenchen Fan <[email protected]>
… scan all files for table ## What changes were proposed in this pull request? Even though in 2.1 creating a partitioned datasource table will not populate the partition data by default (until the user issues MSCK REPAIR TABLE), it seems we still scan the filesystem for no good reason. We should avoid doing this when the user specifies a schema. ## How was this patch tested? Perf stat tests. Author: Eric Liang <[email protected]> Closes #16090 from ericl/spark-18661. (cherry picked from commit d9eb4c7) Signed-off-by: Wenchen Fan <[email protected]>
… a package without Spark ## What changes were proposed in this pull request? If SparkR is running as a package and it has previously downloaded Spark Jar it should be able to run as before without having to set SPARK_HOME. Basically with this bug the auto install Spark will only work in the first session. This seems to be a regression on the earlier behavior. Fix is to always try to install or check for the cached Spark if running in an interactive session. As discussed before, we should probably only install Spark iff running in an interactive session (R shell, RStudio etc) ## How was this patch tested? Manually Author: Felix Cheung <[email protected]> Closes #16077 from felixcheung/rsessioninteractive. (cherry picked from commit b019b3a) Signed-off-by: Shivaram Venkataraman <[email protected]>
…tPredictionCol ## What changes were proposed in this pull request? add `setFeaturesCol` and `setPredictionCol` for `OneVsRestModel` ## How was this patch tested? added tests Author: Zheng RuiFeng <[email protected]> Closes #16059 from zhengruifeng/ovrm_setCol. (cherry picked from commit bdfe7f6) Signed-off-by: Yanbo Liang <[email protected]>
## What changes were proposed in this pull request? Add R examples to ML programming guide for the following algorithms as POC: * spark.glm * spark.survreg * spark.naiveBayes * spark.kmeans The four algorithms were added to SparkR since 2.0.0, more docs for algorithms added during 2.1 release cycle will be addressed in a separate follow-up PR. ## How was this patch tested? This is the screenshots of generated ML programming guide for ```GeneralizedLinearRegression```:  Author: Yanbo Liang <[email protected]> Closes #16136 from yanboliang/spark-18279. (cherry picked from commit eb8dd68) Signed-off-by: Yanbo Liang <[email protected]>
…ctField` in SQL Guide. ## What changes were proposed in this pull request? In `SQL Programming Guide`, this PR uses `TRUE` instead of `True` in SparkR and adds default values of `nullable` for `StructField` in Scala/Python/R (i.e., "Note: The default value of nullable is true."). In Java API, `nullable` is not optional. **BEFORE** * SPARK 2.1.0 RC1 http://people.apache.org/~pwendell/spark-releases/spark-2.1.0-rc1-docs/sql-programming-guide.html#data-types **AFTER** * R <img width="916" alt="screen shot 2016-12-04 at 11 58 19 pm" src="https://cloud.githubusercontent.com/assets/9700541/20877443/abba19a6-ba7d-11e6-8984-afbe00333fb0.png"> * Scala <img width="914" alt="screen shot 2016-12-04 at 11 57 37 pm" src="https://cloud.githubusercontent.com/assets/9700541/20877433/99ce734a-ba7d-11e6-8bb5-e8619041b09b.png"> * Python <img width="914" alt="screen shot 2016-12-04 at 11 58 04 pm" src="https://cloud.githubusercontent.com/assets/9700541/20877440/a5c89338-ba7d-11e6-8f92-6c0ae9388d7e.png"> ## How was this patch tested? Manual. ``` cd docs SKIP_API=1 jekyll build open _site/index.html ``` Author: Dongjoon Hyun <[email protected]> Closes #16141 from dongjoon-hyun/SPARK-SQL-GUIDE. (cherry picked from commit 410b789) Signed-off-by: Shivaram Venkataraman <[email protected]>
… precise" This reverts commit fce1be6 from branch-2.1.
…aVariable ## What changes were proposed in this pull request? This is kind of a long-standing bug, it's hidden until #15780 , which may add `AssertNotNull` on top of `LambdaVariable` and thus enables subexpression elimination. However, subexpression elimination will evaluate the common expressions at the beginning, which is invalid for `LambdaVariable`. `LambdaVariable` usually represents loop variable, which can't be evaluated ahead of the loop. This PR skips expressions containing `LambdaVariable` when doing subexpression elimination. ## How was this patch tested? updated test in `DatasetAggregatorSuite` Author: Wenchen Fan <[email protected]> Closes #16143 from cloud-fan/aggregator. (cherry picked from commit 01a7d33) Signed-off-by: Herman van Hovell <[email protected]>
Looking at the distributions provided on spark.apache.org, I see that the Spark YARN shuffle jar is under `yarn/` and not `lib/`. This change is so minor I'm not sure it needs a JIRA. But let me know if so and I'll create one. Author: Nicholas Chammas <[email protected]> Closes #16130 from nchammas/yarn-doc-fix. (cherry picked from commit 5a92dc7) Signed-off-by: Marcelo Vanzin <[email protected]>
…and fix StreamingQueryException (branch 2.1) ## What changes were proposed in this pull request? Backport #16125 to branch 2.1. ## How was this patch tested? Jenkins Author: Shixiong Zhu <[email protected]> Closes #16153 from zsxwing/SPARK-18694-2.1.
…ploding Python UDFs
## What changes were proposed in this pull request?
As reported in the Jira, there are some weird issues with exploding Python UDFs in SparkSQL.
The following test code can reproduce it. Notice: the following test code is reported to return wrong results in the Jira. However, as I tested on master branch, it causes exception and so can't return any result.
>>> from pyspark.sql.functions import *
>>> from pyspark.sql.types import *
>>>
>>> df = spark.range(10)
>>>
>>> def return_range(value):
... return [(i, str(i)) for i in range(value - 1, value + 1)]
...
>>> range_udf = udf(return_range, ArrayType(StructType([StructField("integer_val", IntegerType()),
... StructField("string_val", StringType())])))
>>>
>>> df.select("id", explode(range_udf(df.id))).show()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/spark/python/pyspark/sql/dataframe.py", line 318, in show
print(self._jdf.showString(n, 20))
File "/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
File "/spark/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value py4j.protocol.Py4JJavaError: An error occurred while calling o126.showString.: java.lang.AssertionError: assertion failed
at scala.Predef$.assert(Predef.scala:156)
at org.apache.spark.sql.execution.CodegenSupport$class.consume(WholeStageCodegenExec.scala:120)
at org.apache.spark.sql.execution.GenerateExec.consume(GenerateExec.scala:57)
The cause of this issue is, in `ExtractPythonUDFs` we insert `BatchEvalPythonExec` to run PythonUDFs in batch. `BatchEvalPythonExec` will add extra outputs (e.g., `pythonUDF0`) to original plan. In above case, the original `Range` only has one output `id`. After `ExtractPythonUDFs`, the added `BatchEvalPythonExec` has two outputs `id` and `pythonUDF0`.
Because the output of `GenerateExec` is given after analysis phase, in above case, it is the combination of `id`, i.e., the output of `Range`, and `col`. But in planning phase, we change `GenerateExec`'s child plan to `BatchEvalPythonExec` with additional output attributes.
It will cause no problem in non wholestage codegen. Because when evaluating the additional attributes are projected out the final output of `GenerateExec`.
However, as `GenerateExec` now supports wholestage codegen, the framework will input all the outputs of the child plan to `GenerateExec`. Then when consuming `GenerateExec`'s output data (i.e., calling `consume`), the number of output attributes is different to the output variables in wholestage codegen.
To solve this issue, this patch only gives the generator's output to `GenerateExec` after analysis phase. `GenerateExec`'s output is the combination of its child plan's output and the generator's output. So when we change `GenerateExec`'s child, its output is still correct.
## How was this patch tested?
Added test cases to PySpark.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <[email protected]>
Closes #16120 from viirya/fix-py-udf-with-generator.
(cherry picked from commit 3ba69b6)
Signed-off-by: Herman van Hovell <[email protected]>
… MemorySink ## What changes were proposed in this pull request? Move DataFrame.collect out of synchronized block so that we can query content in MemorySink when `DataFrame.collect` is running. ## How was this patch tested? Jenkins Author: Shixiong Zhu <[email protected]> Closes #16162 from zsxwing/SPARK-18729. (cherry picked from commit 1b2785c) Signed-off-by: Tathagata Das <[email protected]>
…tart and not auto-generate StreamingQuery.name
Here are the major changes in this PR.
- Added the ability to recover `StreamingQuery.id` from checkpoint location, by writing the id to `checkpointLoc/metadata`.
- Added `StreamingQuery.runId` which is unique for every query started and does not persist across restarts. This is to identify each restart of a query separately (same as earlier behavior of `id`).
- Removed auto-generation of `StreamingQuery.name`. The purpose of name was to have the ability to define an identifier across restarts, but since id is precisely that, there is no need for a auto-generated name. This means name becomes purely cosmetic, and is null by default.
- Added `runId` to `StreamingQueryListener` events and `StreamingQueryProgress`.
Implementation details
- Renamed existing `StreamExecutionMetadata` to `OffsetSeqMetadata`, and moved it to the file `OffsetSeq.scala`, because that is what this metadata is tied to. Also did some refactoring to make the code cleaner (got rid of a lot of `.json` and `.getOrElse("{}")`).
- Added the `id` as the new `StreamMetadata`.
- When a StreamingQuery is created it gets or writes the `StreamMetadata` from `checkpointLoc/metadata`.
- All internal logging in `StreamExecution` uses `(name, id, runId)` instead of just `name`
TODO
- [x] Test handling of name=null in json generation of StreamingQueryProgress
- [x] Test handling of name=null in json generation of StreamingQueryListener events
- [x] Test python API of runId
Updated unit tests and new unit tests
Author: Tathagata Das <[email protected]>
Closes #16113 from tdas/SPARK-18657.
(cherry picked from commit bb57bfe)
Signed-off-by: Tathagata Das <[email protected]>
…gressReporter ## What changes were proposed in this pull request? Move no data rate limit from StreamExecution to ProgressReporter to make `recentProgresses` and listener events consistent. ## How was this patch tested? Jenkins Author: Shixiong Zhu <[email protected]> Closes #16155 from zsxwing/SPARK-18722. (cherry picked from commit 4af142f) Signed-off-by: Tathagata Das <[email protected]>
…log` (Link to Jira issue: https://issues.apache.org/jira/browse/SPARK-18572) ## What changes were proposed in this pull request? Currently Spark answers the `SHOW PARTITIONS` command by fetching all of the table's partition metadata from the external catalog and constructing partition names therefrom. The Hive client has a `getPartitionNames` method which is many times faster for this purpose, with the performance improvement scaling with the number of partitions in a table. To test the performance impact of this PR, I ran the `SHOW PARTITIONS` command on two Hive tables with large numbers of partitions. One table has ~17,800 partitions, and the other has ~95,000 partitions. For the purposes of this PR, I'll call the former table `table1` and the latter table `table2`. I ran 5 trials for each table with before-and-after versions of this PR. The results are as follows: Spark at bdc8153, `SHOW PARTITIONS table1`, times in seconds: 7.901 3.983 4.018 4.331 4.261 Spark at bdc8153, `SHOW PARTITIONS table2` (Timed out after 10 minutes with a `SocketTimeoutException`.) Spark at this PR, `SHOW PARTITIONS table1`, times in seconds: 3.801 0.449 0.395 0.348 0.336 Spark at this PR, `SHOW PARTITIONS table2`, times in seconds: 5.184 1.63 1.474 1.519 1.41 Taking the best times from each trial, we get a 12x performance improvement for a table with ~17,800 partitions and at least a 426x improvement for a table with ~95,000 partitions. More significantly, the latter command doesn't even complete with the current code in master. This is actually a patch we've been using in-house at VideoAmp since Spark 1.1. It's made all the difference in the practical usability of our largest tables. Even with tables with about 1,000 partitions there's a performance improvement of about 2-3x. ## How was this patch tested? I added a unit test to `VersionsSuite` which tests that the Hive client's `getPartitionNames` method returns the correct number of partitions. Author: Michael Allman <[email protected]> Closes #15998 from mallman/spark-18572-list_partition_names. (cherry picked from commit 772ddbe) Signed-off-by: Wenchen Fan <[email protected]>
## What changes were proposed in this pull request?
Right now ForeachSink creates a new physical plan, so StreamExecution cannot retrieval metrics and watermark.
This PR changes ForeachSink to manually convert InternalRows to objects without creating a new plan.
## How was this patch tested?
`test("foreach with watermark: append")`.
Author: Shixiong Zhu <[email protected]>
Closes #16160 from zsxwing/SPARK-18721.
(cherry picked from commit 7863c62)
Signed-off-by: Tathagata Das <[email protected]>
## What changes were proposed in this pull request? I jumped the gun on merging #16120, and missed a tiny potential problem. This PR fixes that by changing a val into a def; this should prevent potential serialization/initialization weirdness from happening. ## How was this patch tested? Existing tests. Author: Herman van Hovell <[email protected]> Closes #16170 from hvanhovell/SPARK-18634. (cherry picked from commit 381ef4e) Signed-off-by: Herman van Hovell <[email protected]>
## What changes were proposed in this pull request?
Many Spark developers often want to test the runtime of some function in interactive debugging and testing. This patch adds a simple time function to SparkSession:
```
scala> spark.time { spark.range(1000).count() }
Time taken: 77 ms
res1: Long = 1000
```
## How was this patch tested?
I tested this interactively in spark-shell.
Author: Reynold Xin <[email protected]>
Closes #16140 from rxin/SPARK-18714.
(cherry picked from commit cb1f10b)
Signed-off-by: Herman van Hovell <[email protected]>
…tructured Streaming log formats ## What changes were proposed in this pull request? To be able to restart StreamingQueries across Spark version, we have already made the logs (offset log, file source log, file sink log) use json. We should added tests with actual json files in the Spark such that any incompatible changes in reading the logs is immediately caught. This PR add tests for FileStreamSourceLog, FileStreamSinkLog, and OffsetSeqLog. ## How was this patch tested? new unit tests Author: Tathagata Das <[email protected]> Closes #16128 from tdas/SPARK-18671. (cherry picked from commit 1ef6b29) Signed-off-by: Shixiong Zhu <[email protected]>
…es in pyspark package. ## What changes were proposed in this pull request? Since we already include the python examples in the pyspark package, we should include the example data with it as well. We should also include the third-party licences since we distribute their jars with the pyspark package. ## How was this patch tested? Manually tested with python2.7 and python3.4 ```sh $ ./build/mvn -DskipTests -Phive -Phive-thriftserver -Pyarn -Pmesos clean package $ cd python $ python setup.py sdist $ pip install dist/pyspark-2.1.0.dev0.tar.gz $ ls -1 /usr/local/lib/python2.7/dist-packages/pyspark/data/ graphx mllib streaming $ du -sh /usr/local/lib/python2.7/dist-packages/pyspark/data/ 600K /usr/local/lib/python2.7/dist-packages/pyspark/data/ $ ls -1 /usr/local/lib/python2.7/dist-packages/pyspark/licenses/|head -5 LICENSE-AnchorJS.txt LICENSE-DPark.txt LICENSE-Mockito.txt LICENSE-SnapTree.txt LICENSE-antlr.txt ``` Author: Shuai Lin <[email protected]> Closes #16082 from lins05/include-data-in-pyspark-dist. (cherry picked from commit bd9a4a5) Signed-off-by: Sean Owen <[email protected]>
…rmatted string instead of millis ## What changes were proposed in this pull request? Easier to read while debugging as a formatted string (in ISO8601 format) than in millis ## How was this patch tested? Updated unit tests Author: Tathagata Das <[email protected]> Closes #16166 from tdas/SPARK-18734. (cherry picked from commit 539bb3c) Signed-off-by: Shixiong Zhu <[email protected]>
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@zhuangxue close this please |
zifeif2
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Nov 22, 2025
Closes apache#14537. Closes apache#16181. Closes apache#8318. Closes apache#6848. Closes apache#7265. Closes apache#9543.
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What algorithm is used in spark decision tree(is ID3, C4.5 or CART)?