## What changes were proposed in this pull request?
This PR implements `posexplode` table generating function. Currently, master branch raises the following exception for `map` argument. It's different from Hive.
**Before**
```scala
scala> sql("select posexplode(map('a', 1, 'b', 2))").show
org.apache.spark.sql.AnalysisException: No handler for Hive UDF ... posexplode() takes an array as a parameter; line 1 pos 7
```
**After**
```scala
scala> sql("select posexplode(map('a', 1, 'b', 2))").show
+---+---+-----+
|pos|key|value|
+---+---+-----+
| 0| a| 1|
| 1| b| 2|
+---+---+-----+
```
For `array` argument, `after` is the same with `before`.
```
scala> sql("select posexplode(array(1, 2, 3))").show
+---+---+
|pos|col|
+---+---+
| 0| 1|
| 1| 2|
| 2| 3|
+---+---+
```
## How was this patch tested?
Pass the Jenkins tests with newly added testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13971 from dongjoon-hyun/SPARK-16289.
## What changes were proposed in this pull request?
`a` -> `an`
I use regex to generate potential error lines:
`grep -in ' a [aeiou]' mllib/src/main/scala/org/apache/spark/ml/*/*scala`
and review them line by line.
## How was this patch tested?
local build
`lint-java` checking
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#13317 from zhengruifeng/a_an.
## What changes were proposed in this pull request?
in hive, `locate("aa", "aaa", 0)` would yield 0, `locate("aa", "aaa", 1)` would yield 1 and `locate("aa", "aaa", 2)` would yield 2, while in Spark, `locate("aa", "aaa", 0)` would yield 1, `locate("aa", "aaa", 1)` would yield 2 and `locate("aa", "aaa", 2)` would yield 0. This results from the different understanding of the third parameter in udf `locate`. It means the starting index and starts from 1, so when we use 0, the return would always be 0.
## How was this patch tested?
tested with modified `StringExpressionsSuite` and `StringFunctionsSuite`
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#13186 from adrian-wang/locate.
## What changes were proposed in this pull request?
Replace SQLContext and SparkContext with SparkSession using builder pattern in python test code.
## How was this patch tested?
Existing test.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#13242 from WeichenXu123/python_doctest_update_sparksession.
## What changes were proposed in this pull request?
Spark assumes that UDF functions are deterministic. This PR adds explicit notes about that.
## How was this patch tested?
It's only about docs.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13087 from dongjoon-hyun/SPARK-15282.
## What changes were proposed in this pull request?
This issue aims to expose Scala `bround` function in Python/R API.
`bround` function is implemented in SPARK-14614 by extending current `round` function.
We used the following semantics from Hive.
```java
public static double bround(double input, int scale) {
if (Double.isNaN(input) || Double.isInfinite(input)) {
return input;
}
return BigDecimal.valueOf(input).setScale(scale, RoundingMode.HALF_EVEN).doubleValue();
}
```
After this PR, `pyspark` and `sparkR` also support `bround` function.
**PySpark**
```python
>>> from pyspark.sql.functions import bround
>>> sqlContext.createDataFrame([(2.5,)], ['a']).select(bround('a', 0).alias('r')).collect()
[Row(r=2.0)]
```
**SparkR**
```r
> df = createDataFrame(sqlContext, data.frame(x = c(2.5, 3.5)))
> head(collect(select(df, bround(df$x, 0))))
bround(x, 0)
1 2
2 4
```
## How was this patch tested?
Pass the Jenkins tests (including new testcases).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12509 from dongjoon-hyun/SPARK-14639.
## What changes were proposed in this pull request?
The `window` function was added to Dataset with [this PR](https://github.com/apache/spark/pull/12008).
This PR adds the Python, and SQL, API for this function.
With this PR, SQL, Java, and Scala will share the same APIs as in users can use:
- `window(timeColumn, windowDuration)`
- `window(timeColumn, windowDuration, slideDuration)`
- `window(timeColumn, windowDuration, slideDuration, startTime)`
In Python, users can access all APIs above, but in addition they can do
- In Python:
`window(timeColumn, windowDuration, startTime=...)`
that is, they can provide the startTime without providing the `slideDuration`. In this case, we will generate tumbling windows.
## How was this patch tested?
Unit tests + manual tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#12136 from brkyvz/python-windows.
## What changes were proposed in this pull request?
This PR support multiple Python UDFs within single batch, also improve the performance.
```python
>>> from pyspark.sql.types import IntegerType
>>> sqlContext.registerFunction("double", lambda x: x * 2, IntegerType())
>>> sqlContext.registerFunction("add", lambda x, y: x + y, IntegerType())
>>> sqlContext.sql("SELECT double(add(1, 2)), add(double(2), 1)").explain(True)
== Parsed Logical Plan ==
'Project [unresolvedalias('double('add(1, 2)), None),unresolvedalias('add('double(2), 1), None)]
+- OneRowRelation$
== Analyzed Logical Plan ==
double(add(1, 2)): int, add(double(2), 1): int
Project [double(add(1, 2))#14,add(double(2), 1)#15]
+- Project [double(add(1, 2))#14,add(double(2), 1)#15]
+- Project [pythonUDF0#16 AS double(add(1, 2))#14,pythonUDF0#18 AS add(double(2), 1)#15]
+- EvaluatePython [add(pythonUDF1#17, 1)], [pythonUDF0#18]
+- EvaluatePython [double(add(1, 2)),double(2)], [pythonUDF0#16,pythonUDF1#17]
+- OneRowRelation$
== Optimized Logical Plan ==
Project [pythonUDF0#16 AS double(add(1, 2))#14,pythonUDF0#18 AS add(double(2), 1)#15]
+- EvaluatePython [add(pythonUDF1#17, 1)], [pythonUDF0#18]
+- EvaluatePython [double(add(1, 2)),double(2)], [pythonUDF0#16,pythonUDF1#17]
+- OneRowRelation$
== Physical Plan ==
WholeStageCodegen
: +- Project [pythonUDF0#16 AS double(add(1, 2))#14,pythonUDF0#18 AS add(double(2), 1)#15]
: +- INPUT
+- !BatchPythonEvaluation [add(pythonUDF1#17, 1)], [pythonUDF0#16,pythonUDF1#17,pythonUDF0#18]
+- !BatchPythonEvaluation [double(add(1, 2)),double(2)], [pythonUDF0#16,pythonUDF1#17]
+- Scan OneRowRelation[]
```
## How was this patch tested?
Added new tests.
Using the following script to benchmark 1, 2 and 3 udfs,
```
df = sqlContext.range(1, 1 << 23, 1, 4)
double = F.udf(lambda x: x * 2, LongType())
print df.select(double(df.id)).count()
print df.select(double(df.id), double(df.id + 1)).count()
print df.select(double(df.id), double(df.id + 1), double(df.id + 2)).count()
```
Here is the results:
N | Before | After | speed up
---- |------------ | -------------|------
1 | 22 s | 7 s | 3.1X
2 | 38 s | 13 s | 2.9X
3 | 58 s | 16 s | 3.6X
This benchmark ran locally with 4 CPUs. For 3 UDFs, it launched 12 Python before before this patch, 4 process after this patch. After this patch, it will use less memory for multiple UDFs than before (less buffering).
Author: Davies Liu <davies@databricks.com>
Closes#12057 from davies/multi_udfs.
## What changes were proposed in this pull request?
This PR brings the support for chained Python UDFs, for example
```sql
select udf1(udf2(a))
select udf1(udf2(a) + 3)
select udf1(udf2(a) + udf3(b))
```
Also directly chained unary Python UDFs are put in single batch of Python UDFs, others may require multiple batches.
For example,
```python
>>> sqlContext.sql("select double(double(1))").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [pythonUDF#10 AS double(double(1))#9]
: +- INPUT
+- !BatchPythonEvaluation double(double(1)), [pythonUDF#10]
+- Scan OneRowRelation[]
>>> sqlContext.sql("select double(double(1) + double(2))").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [pythonUDF#19 AS double((double(1) + double(2)))#16]
: +- INPUT
+- !BatchPythonEvaluation double((pythonUDF#17 + pythonUDF#18)), [pythonUDF#17,pythonUDF#18,pythonUDF#19]
+- !BatchPythonEvaluation double(2), [pythonUDF#17,pythonUDF#18]
+- !BatchPythonEvaluation double(1), [pythonUDF#17]
+- Scan OneRowRelation[]
```
TODO: will support multiple unrelated Python UDFs in one batch (another PR).
## How was this patch tested?
Added new unit tests for chained UDFs.
Author: Davies Liu <davies@databricks.com>
Closes#12014 from davies/py_udfs.
## What changes were proposed in this pull request?
As we have `CreateArray` and `CreateStruct`, we should also have `CreateMap`. This PR adds the `CreateMap` expression, and the DataFrame API, and python API.
## How was this patch tested?
various new tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11879 from cloud-fan/create_map.
Minor typo: docstring for pyspark.sql.functions: hypot has extra characters
N/A
Author: Tristan Reid <treid@netflix.com>
Closes#11616 from tristanreid/master.
#### What changes were proposed in this pull request?
This PR is for supporting SQL generation for cube, rollup and grouping sets.
For example, a query using rollup:
```SQL
SELECT count(*) as cnt, key % 5, grouping_id() FROM t1 GROUP BY key % 5 WITH ROLLUP
```
Original logical plan:
```
Aggregate [(key#17L % cast(5 as bigint))#47L,grouping__id#46],
[(count(1),mode=Complete,isDistinct=false) AS cnt#43L,
(key#17L % cast(5 as bigint))#47L AS _c1#45L,
grouping__id#46 AS _c2#44]
+- Expand [List(key#17L, value#18, (key#17L % cast(5 as bigint))#47L, 0),
List(key#17L, value#18, null, 1)],
[key#17L,value#18,(key#17L % cast(5 as bigint))#47L,grouping__id#46]
+- Project [key#17L,
value#18,
(key#17L % cast(5 as bigint)) AS (key#17L % cast(5 as bigint))#47L]
+- Subquery t1
+- Relation[key#17L,value#18] ParquetRelation
```
Converted SQL:
```SQL
SELECT count( 1) AS `cnt`,
(`t1`.`key` % CAST(5 AS BIGINT)),
grouping_id() AS `_c2`
FROM `default`.`t1`
GROUP BY (`t1`.`key` % CAST(5 AS BIGINT))
GROUPING SETS (((`t1`.`key` % CAST(5 AS BIGINT))), ())
```
#### How was the this patch tested?
Added eight test cases in `LogicalPlanToSQLSuite`.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#11283 from gatorsmile/groupingSetsToSQL.
## What changes were proposed in this pull request?
Remove `map`, `flatMap`, `mapPartitions` from python DataFrame, to prepare for Dataset API in the future.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11445 from cloud-fan/python-clean.
## What changes were proposed in this pull request?
When we pass a Python function to JVM side, we also need to send its context, e.g. `envVars`, `pythonIncludes`, `pythonExec`, etc. However, it's annoying to pass around so many parameters at many places. This PR abstract python function along with its context, to simplify some pyspark code and make the logic more clear.
## How was the this patch tested?
by existing unit tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11342 from cloud-fan/python-clean.
## What changes were proposed in this pull request?
This PR tries to fix all typos in all markdown files under `docs` module,
and fixes similar typos in other comments, too.
## How was the this patch tested?
manual tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11300 from dongjoon-hyun/minor_fix_typos.
This PR introduces several major changes:
1. Replacing `Expression.prettyString` with `Expression.sql`
The `prettyString` method is mostly an internal, developer faced facility for debugging purposes, and shouldn't be exposed to users.
1. Using SQL-like representation as column names for selected fields that are not named expression (back-ticks and double quotes should be removed)
Before, we were using `prettyString` as column names when possible, and sometimes the result column names can be weird. Here are several examples:
Expression | `prettyString` | `sql` | Note
------------------ | -------------- | ---------- | ---------------
`a && b` | `a && b` | `a AND b` |
`a.getField("f")` | `a[f]` | `a.f` | `a` is a struct
1. Adding trait `NonSQLExpression` extending from `Expression` for expressions that don't have a SQL representation (e.g. Scala UDF/UDAF and Java/Scala object expressions used for encoders)
`NonSQLExpression.sql` may return an arbitrary user facing string representation of the expression.
Author: Cheng Lian <lian@databricks.com>
Closes#10757 from liancheng/spark-12799.simplify-expression-string-methods.
This pull request has the following changes:
1. Moved UserDefinedFunction into expressions package. This is more consistent with how we structure the packages for window functions and UDAFs.
2. Moved UserDefinedPythonFunction into execution.python package, so we don't have a random private class in the top level sql package.
3. Move everything in execution/python.scala into the newly created execution.python package.
Most of the diffs are just straight copy-paste.
Author: Reynold Xin <rxin@databricks.com>
Closes#11181 from rxin/SPARK-13296.
PySpark support ```covar_samp``` and ```covar_pop```.
cc rxin davies marmbrus
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10876 from yanboliang/spark-12962.
Grouping() returns a column is aggregated or not, grouping_id() returns the aggregation levels.
grouping()/grouping_id() could be used with window function, but does not work in having/sort clause, will be fixed by another PR.
The GROUPING__ID/grouping_id() in Hive is wrong (according to docs), we also did it wrongly, this PR change that to match the behavior in most databases (also the docs of Hive).
Author: Davies Liu <davies@databricks.com>
Closes#10677 from davies/grouping.
This PR adds the ability to specify the ```ignoreNulls``` option to the functions dsl, e.g:
```df.select($"id", last($"value", ignoreNulls = true).over(Window.partitionBy($"id").orderBy($"other"))```
This PR is some where between a bug fix (see the JIRA) and a new feature. I am not sure if we should backport to 1.6.
cc yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10957 from hvanhovell/SPARK-13049.
address comments in #10435
This makes the API easier to use if user programmatically generate the call to hash, and they will get analysis exception if the arguments of hash is empty.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10588 from cloud-fan/hash.
Added Python test cases for the function `isnan`, `isnull`, `nanvl` and `json_tuple`.
Fixed a bug in the function `json_tuple`
rxin , could you help me review my changes? Please let me know anything is missing.
Thank you! Have a good Thanksgiving day!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#9977 from gatorsmile/json_tuple.
https://issues.apache.org/jira/browse/SPARK-9830
This PR contains the following main changes.
* Removing `AggregateExpression1`.
* Removing `Aggregate` operator, which is used to evaluate `AggregateExpression1`.
* Removing planner rule used to plan `Aggregate`.
* Linking `MultipleDistinctRewriter` to analyzer.
* Renaming `AggregateExpression2` to `AggregateExpression` and `AggregateFunction2` to `AggregateFunction`.
* Updating places where we create aggregate expression. The way to create aggregate expressions is `AggregateExpression(aggregateFunction, mode, isDistinct)`.
* Changing `val`s in `DeclarativeAggregate`s that touch children of this function to `lazy val`s (when we create aggregate expression in DataFrame API, children of an aggregate function can be unresolved).
Author: Yin Huai <yhuai@databricks.com>
Closes#9556 from yhuai/removeAgg1.
For now they are thin wrappers around the corresponding Hive UDAFs.
One limitation with these in Hive 0.13.0 is they only support aggregating primitive types.
I chose snake_case here instead of camelCase because it seems to be used in the majority of the multi-word fns.
Do we also want to add these to `functions.py`?
This approach was recommended here: https://github.com/apache/spark/pull/8592#issuecomment-154247089
marmbrus rxin
Author: Nick Buroojy <nick.buroojy@civitaslearning.com>
Closes#9526 from nburoojy/nick/udaf-alias.
(cherry picked from commit a6ee4f989d)
Signed-off-by: Michael Armbrust <michael@databricks.com>
Add Python API for stddev/stddev_pop/stddev_samp/variance/var_pop/var_samp/skewness/kurtosis
Author: Davies Liu <davies@databricks.com>
Closes#9424 from davies/py_var.
https://issues.apache.org/jira/browse/SPARK-9691
jkbradley rxin
Author: Yin Huai <yhuai@databricks.com>
Closes#7999 from yhuai/pythonRand and squashes the following commits:
4187e0c [Yin Huai] Regression test.
a985ef9 [Yin Huai] Use "if seed is not None" instead "if seed" because "if seed" returns false when seed is 0.
Author: zhichao.li <zhichao.li@intel.com>
Closes#7709 from zhichao-li/translate and squashes the following commits:
9418088 [zhichao.li] refine checking condition
f2ab77a [zhichao.li] clone string
9d88f2d [zhichao.li] fix indent
6aa2962 [zhichao.li] style
e575ead [zhichao.li] add python api
9d4bab0 [zhichao.li] add special case for fodable and refactor unittest
eda7ad6 [zhichao.li] update to use TernaryExpression
cdfd4be [zhichao.li] add function translate
This PR is based on #7580 , thanks to EntilZha
PR for work on https://issues.apache.org/jira/browse/SPARK-8231
Currently, I have an initial implementation for contains. Based on discussion on JIRA, it should behave same as Hive: https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDFArrayContains.java#L102-L128
Main points are:
1. If the array is empty, null, or the value is null, return false
2. If there is a type mismatch, throw error
3. If comparison is not supported, throw error
Closes#7580
Author: Pedro Rodriguez <prodriguez@trulia.com>
Author: Pedro Rodriguez <ski.rodriguez@gmail.com>
Author: Davies Liu <davies@databricks.com>
Closes#7949 from davies/array_contains and squashes the following commits:
d3c08bc [Davies Liu] use foreach() to avoid copy
bc3d1fe [Davies Liu] fix array_contains
719e37d [Davies Liu] Merge branch 'master' of github.com:apache/spark into array_contains
e352cf9 [Pedro Rodriguez] fixed diff from master
4d5b0ff [Pedro Rodriguez] added docs and another type check
ffc0591 [Pedro Rodriguez] fixed unit test
7a22deb [Pedro Rodriguez] Changed test to use strings instead of long/ints which are different between python 2 an 3
b5ffae8 [Pedro Rodriguez] fixed pyspark test
4e7dce3 [Pedro Rodriguez] added more docs
3082399 [Pedro Rodriguez] fixed unit test
46f9789 [Pedro Rodriguez] reverted change
d3ca013 [Pedro Rodriguez] Fixed type checking to match hive behavior, then added tests to insure this
8528027 [Pedro Rodriguez] added more tests
686e029 [Pedro Rodriguez] fix scala style
d262e9d [Pedro Rodriguez] reworked type checking code and added more tests
2517a58 [Pedro Rodriguez] removed unused import
28b4f71 [Pedro Rodriguez] fixed bug with type conversions and re-added tests
12f8795 [Pedro Rodriguez] fix scala style checks
e8a20a9 [Pedro Rodriguez] added python df (broken atm)
65b562c [Pedro Rodriguez] made array_contains nullable false
33b45aa [Pedro Rodriguez] reordered test
9623c64 [Pedro Rodriguez] fixed test
4b4425b [Pedro Rodriguez] changed Arrays in tests to Seqs
72cb4b1 [Pedro Rodriguez] added checkInputTypes and docs
69c46fb [Pedro Rodriguez] added tests and codegen
9e0bfc4 [Pedro Rodriguez] initial attempt at implementation
This adds Python API for those DataFrame functions that is introduced in 1.5.
There is issue with serialize byte_array in Python 3, so some of functions (for BinaryType) does not have tests.
cc rxin
Author: Davies Liu <davies@databricks.com>
Closes#7922 from davies/python_functions and squashes the following commits:
8ad942f [Davies Liu] fix test
5fb6ec3 [Davies Liu] fix bugs
3495ed3 [Davies Liu] fix issues
ea5f7bb [Davies Liu] Add python API for DataFrame functions
This PR is based on #7208 , thanks to HuJiayin
Closes#7208
Author: HuJiayin <jiayin.hu@intel.com>
Author: Davies Liu <davies@databricks.com>
Closes#7850 from davies/initcap and squashes the following commits:
54472e9 [Davies Liu] fix python test
17ffe51 [Davies Liu] Merge branch 'master' of github.com:apache/spark into initcap
ca46390 [Davies Liu] Merge branch 'master' of github.com:apache/spark into initcap
3a906e4 [Davies Liu] implement title case in UTF8String
8b2506a [HuJiayin] Update functions.py
2cd43e5 [HuJiayin] fix python style check
b616c0e [HuJiayin] add python api
1f5a0ef [HuJiayin] add codegen
7e0c604 [HuJiayin] Merge branch 'master' of https://github.com/apache/spark into initcap
6a0b958 [HuJiayin] add column
c79482d [HuJiayin] support soundex
7ce416b [HuJiayin] support initcap rebase code
This is based on #7641, thanks to zhichao-li
Closes#7641
Author: zhichao.li <zhichao.li@intel.com>
Author: Davies Liu <davies@databricks.com>
Closes#7848 from davies/substr and squashes the following commits:
461b709 [Davies Liu] remove bytearry from tests
b45377a [Davies Liu] Merge branch 'master' of github.com:apache/spark into substr
01d795e [zhichao.li] scala style
99aa130 [zhichao.li] add substring to dataframe
4f68bfe [zhichao.li] add binary type support for substring
This PR is based on #7581 , just fix the conflict.
Author: Cheng Hao <hao.cheng@intel.com>
Author: Davies Liu <davies@databricks.com>
Closes#7851 from davies/sort_array and squashes the following commits:
a80ef66 [Davies Liu] fix conflict
7cfda65 [Davies Liu] Merge branch 'master' of github.com:apache/spark into sort_array
664c960 [Cheng Hao] update the sort_array by using the ArrayData
276d2d5 [Cheng Hao] add empty line
0edab9c [Cheng Hao] Add asending/descending support for sort_array
80fc0f8 [Cheng Hao] Add type checking
a42b678 [Cheng Hao] Add sort_array support
Add expression `sort_array` support.
Author: Cheng Hao <hao.cheng@intel.com>
This patch had conflicts when merged, resolved by
Committer: Davies Liu <davies.liu@gmail.com>
Closes#7581 from chenghao-intel/sort_array and squashes the following commits:
664c960 [Cheng Hao] update the sort_array by using the ArrayData
276d2d5 [Cheng Hao] add empty line
0edab9c [Cheng Hao] Add asending/descending support for sort_array
80fc0f8 [Cheng Hao] Add type checking
a42b678 [Cheng Hao] Add sort_array support
This PR is based on #7533 , thanks to zhichao-li
Closes#7533
Author: zhichao.li <zhichao.li@intel.com>
Author: Davies Liu <davies@databricks.com>
Closes#7843 from davies/str_index and squashes the following commits:
391347b [Davies Liu] add python api
3ce7802 [Davies Liu] fix substringIndex
f2d29a1 [Davies Liu] Merge branch 'master' of github.com:apache/spark into str_index
515519b [zhichao.li] add foldable and remove null checking
9546991 [zhichao.li] scala style
67c253a [zhichao.li] hide some apis and clean code
b19b013 [zhichao.li] add codegen and clean code
ac863e9 [zhichao.li] reduce the calling of numChars
12e108f [zhichao.li] refine unittest
d92951b [zhichao.li] add lastIndexOf
52d7b03 [zhichao.li] add substring_index function
