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Author SHA1 Message Date
itholic d2bdd6595e [SPARK-35025][SQL][PYTHON][DOCS] Move Parquet data source options from Python and Scala into a single page
### What changes were proposed in this pull request?

This PR proposes move Parquet data source options from Python, Scala and Java into a single page.

### Why are the changes needed?

So far, the documentation for Parquet data source options is separated into different pages for each language API documents. However, this makes managing many options inconvenient, so it is efficient to manage all options in a single page and provide a link to that page in the API of each language.

### Does this PR introduce _any_ user-facing change?

Yes, the documents will be shown below after this change:

- "Parquet Files" page
![Screen Shot 2021-05-21 at 1 35 08 PM](https://user-images.githubusercontent.com/44108233/119082866-e7375f00-ba39-11eb-9ade-a931a5957b34.png)

- Python
![Screen Shot 2021-05-21 at 1 38 27 PM](https://user-images.githubusercontent.com/44108233/119082879-eef70380-ba39-11eb-9e8e-ee50eed98dbe.png)

- Scala
![Screen Shot 2021-05-21 at 1 36 52 PM](https://user-images.githubusercontent.com/44108233/119082884-f1595d80-ba39-11eb-98d5-966657df65f7.png)

- Java
![Screen Shot 2021-05-21 at 1 37 19 PM](https://user-images.githubusercontent.com/44108233/119082888-f4544e00-ba39-11eb-8bf8-47ce78ec0b01.png)

### How was this patch tested?

Manually build docs and confirm the page.

Closes #32161 from itholic/SPARK-34491.

Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
2021-05-21 18:05:49 +09:00
itholic 419ddcb2a4 [SPARK-34494][SQL][DOCS] Move JSON data source options from Python and Scala into a single page
### What changes were proposed in this pull request?

This PR proposes move JSON data source options from Python, Scala and Java into a single page.

### Why are the changes needed?

So far, the documentation for JSON data source options is separated into different pages for each language API documents. However, this makes managing many options inconvenient, so it is efficient to manage all options in a single page and provide a link to that page in the API of each language.

### Does this PR introduce _any_ user-facing change?

Yes, the documents will be shown below after this change:

- "JSON Files" page
<img width="876" alt="Screen Shot 2021-05-20 at 8 48 27 PM" src="https://user-images.githubusercontent.com/44108233/118973662-ddb3e580-b9ac-11eb-987c-8139aa9c3fe2.png">

- Python
<img width="714" alt="Screen Shot 2021-04-16 at 5 04 11 PM" src="https://user-images.githubusercontent.com/44108233/114992491-ca0cef00-9ed5-11eb-9d0f-4de60d8b2516.png">

- Scala
<img width="726" alt="Screen Shot 2021-04-16 at 5 04 54 PM" src="https://user-images.githubusercontent.com/44108233/114992594-e315a000-9ed5-11eb-8bd3-af7e568fcfe1.png">

- Java
<img width="911" alt="Screen Shot 2021-04-16 at 5 06 11 PM" src="https://user-images.githubusercontent.com/44108233/114992751-10624e00-9ed6-11eb-888c-8668d3c74289.png">

### How was this patch tested?

Manually build docs and confirm the page.

Closes #32204 from itholic/SPARK-35081.

Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
2021-05-21 18:05:13 +09:00
itholic 0fe65b5365 [SPARK-35395][DOCS] Move ORC data source options from Python and Scala into a single page
### What changes were proposed in this pull request?

This PR proposes move ORC data source options from Python, Scala and Java into a single page.

### Why are the changes needed?

So far, the documentation for ORC data source options is separated into different pages for each language API documents. However, this makes managing many options inconvenient, so it is efficient to manage all options in a single page and provide a link to that page in the API of each language.

### Does this PR introduce _any_ user-facing change?

Yes, the documents will be shown below after this change:

- "ORC Files" page
![Screen Shot 2021-05-21 at 2 07 14 PM](https://user-images.githubusercontent.com/44108233/119085078-f4564d00-ba3d-11eb-8990-3ba031d809da.png)

- Python
![Screen Shot 2021-05-21 at 2 06 46 PM](https://user-images.githubusercontent.com/44108233/119085097-00daa580-ba3e-11eb-8017-ac5a95a7c053.png)

- Scala
![Screen Shot 2021-05-21 at 2 06 09 PM](https://user-images.githubusercontent.com/44108233/119085135-164fcf80-ba3e-11eb-9cac-78dded523f38.png)

- Java
![Screen Shot 2021-05-21 at 2 06 30 PM](https://user-images.githubusercontent.com/44108233/119085125-118b1b80-ba3e-11eb-9434-f26612d7da13.png)

### How was this patch tested?

Manually build docs and confirm the page.

Closes #32546 from itholic/SPARK-35395.

Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
2021-05-21 18:03:57 +09:00
itholic 6b912e4179 [SPARK-35364][PYTHON] Renaming the existing Koalas related codes
### What changes were proposed in this pull request?

There are still naming related to Koalas in test and function name. This PR addressed them to fit pandas-on-spark.
- kdf -> psdf
- kser -> psser
- kidx -> psidx
- kmidx -> psmidx
- to_koalas() -> to_pandas_on_spark()

### Why are the changes needed?

This is because the name Koalas is no longer used in PySpark.

### Does this PR introduce _any_ user-facing change?

`to_koalas()` function is renamed to `to_pandas_on_spark()`

### How was this patch tested?

Tested in local manually.
After changing the related naming, I checked them one by one.

Closes #32516 from itholic/SPARK-35364.

Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
2021-05-20 15:08:30 -07:00
Xinrong Meng a970f8505d [SPARK-35338][PYTHON] Separate arithmetic operations into data type based structures
### What changes were proposed in this pull request?

The PR is proposed for **pandas APIs on Spark**, in order to separate arithmetic operations shown as below into data-type-based structures.
`__add__, __sub__, __mul__, __truediv__, __floordiv__, __pow__, __mod__,
__radd__, __rsub__, __rmul__, __rtruediv__, __rfloordiv__, __rpow__,__rmod__`

DataTypeOps and subclasses are introduced.

The existing behaviors of each arithmetic operation should be preserved.

### Why are the changes needed?

Currently, the same arithmetic operation of all data types is defined in one function, so it’s difficult to extend the behavior change based on the data types.

Introducing DataTypeOps would be the foundation for [pandas APIs on Spark: Separate basic operations into data type based structures.](https://docs.google.com/document/d/12MS6xK0hETYmrcl5b9pX5lgV4FmGVfpmcSKq--_oQlc/edit?usp=sharing).

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Tests are introduced under pyspark.pandas.tests.data_type_ops. One test file per DataTypeOps class.

Closes #32596 from xinrong-databricks/datatypeop_arith_fix.

Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
2021-05-19 19:47:00 -07:00
Hyukjin Kwon 7eaabf4df5 [SPARK-35408][PYTHON][FOLLOW-UP] Avoid unnecessary f-string format
### What changes were proposed in this pull request?

This PR avoids using f-string format that's a new feature in Python 3.6. Although it's legitimate to use this syntax because Apache Spark supports Python 3.6+, this breaks unofficial support of Python 3.5.

This specific f-string format looks something unnecessary, and doesn't look worth enough to remove such unofficial support because of one string format in an error message.

**NOTE** that this PR doesn't mean that we're maintaining Python 3.5 since we dropped. It just looks like too much to remove that unofficial support only because of one string format and error message.

### Why are the changes needed?

To keep unofficial Python 3.5 support

### Does this PR introduce _any_ user-facing change?

Officially nope.

### How was this patch tested?

Ran the linters.

Closes #32598 from HyukjinKwon/SPARK-35408=followup.

Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
2021-05-20 10:47:31 +09:00
Takuya UESHIN d44e6c7f10 Revert "[SPARK-35338][PYTHON] Separate arithmetic operations into data type based structures"
This reverts commit d1b24d8aba.
2021-05-19 16:49:47 -07:00
Xinrong Meng d1b24d8aba [SPARK-35338][PYTHON] Separate arithmetic operations into data type based structures
### What changes were proposed in this pull request?

The PR is proposed for **pandas APIs on Spark**, in order to separate arithmetic operations shown as below into data-type-based structures.
`__add__, __sub__, __mul__, __truediv__, __floordiv__, __pow__, __mod__,
__radd__, __rsub__, __rmul__, __rtruediv__, __rfloordiv__, __rpow__,__rmod__`

DataTypeOps and subclasses are introduced.

The existing behaviors of each arithmetic operation should be preserved.

### Why are the changes needed?

Currently, the same arithmetic operation of all data types is defined in one function, so it’s difficult to extend the behavior change based on the data types.

Introducing DataTypeOps would be the foundation for [pandas APIs on Spark: Separate basic operations into data type based structures.](https://docs.google.com/document/d/12MS6xK0hETYmrcl5b9pX5lgV4FmGVfpmcSKq--_oQlc/edit?usp=sharing).

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Tests are introduced under pyspark.pandas.tests.data_type_ops. One test file per DataTypeOps class.

Closes #32469 from xinrong-databricks/datatypeop_arith.

Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
2021-05-19 15:05:32 -07:00
Kousuke Saruta 9283bebbbd [SPARK-35418][SQL] Add sentences function to functions.{scala,py}
### What changes were proposed in this pull request?

This PR adds `sentences`, a string function, which is present as of `2.0.0` but missing in `functions.{scala,py}`.

### Why are the changes needed?

This function can be only used from SQL for now.
It's good if we can use this function from Scala/Python code as well as SQL.

### Does this PR introduce _any_ user-facing change?

Yes. Users can use this function from Scala and Python.

### How was this patch tested?

New test.

Closes #32566 from sarutak/sentences-function.

Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com>
2021-05-19 20:07:28 +09:00
Takuya UESHIN 2a335f2d7d [SPARK-34941][PYTHON] Fix mypy errors and enable mypy check for pandas-on-Spark
### What changes were proposed in this pull request?

Fixes `mypy` errors and enables `mypy` check for pandas-on-Spark.

### Why are the changes needed?

The `mypy` check for pandas-on-Spark was disabled when the initial porting.
It should be enabled again; otherwise we will miss type checking errors.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

The enabled `mypy` check and existing unit tests should pass.

Closes #32540 from ueshin/issues/SPARK-34941/pandas_mypy.

Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
2021-05-17 10:46:59 -07:00
Gera Shegalov 9eb45ecb4f [SPARK-35408][PYTHON] Improve parameter validation in DataFrame.show
### What changes were proposed in this pull request?
Provide clearer error message tied to the user's Python code if incorrect parameters are passed to `DataFrame.show` rather than the message about a missing JVM method the user is not calling directly.

```
py4j.Py4JException: Method showString([class java.lang.Boolean, class java.lang.Integer, class java.lang.Boolean]) does not exist
	at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:318)
	at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:326)
	at py4j.Gateway.invoke(Gateway.java:274)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:238)
	at java.lang.Thread.run(Thread.java:748
```

### Why are the changes needed?
For faster debugging through actionable error message.

### Does this PR introduce _any_ user-facing change?
No change for the correct parameters but different error messages for the parameters triggering an exception.

### How was this patch tested?
- unit test
- manually in PySpark REPL

Closes #32555 from gerashegalov/df_show_validation.

Authored-by: Gera Shegalov <gera@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
2021-05-17 16:22:46 +09:00
Sean Owen a37cce95c2 [MINOR][DOCS] Add required imports to CV, train validation split Pyspark ML examples
### What changes were proposed in this pull request?

Add required imports to Pyspark ML examples in CrossValidator, TrainValidationSplit

### Why are the changes needed?

The examples pass doctests because of previous imports, but as they appear in Pyspark documentation, are incomplete. The additional imports are required to make the example work.

### Does this PR introduce _any_ user-facing change?

No, docs only change.

### How was this patch tested?

Existing tests.

Closes #32554 from srowen/TuningImports.

Authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
2021-05-15 08:13:54 -05:00
Ruifeng Zheng f7704ece40 [SPARK-35392][ML][PYTHON] Fix flaky tests in ml/clustering.py and ml/feature.py
### What changes were proposed in this pull request?

This PR removes the check of `summary.logLikelihood` in  ml/clustering.py - this GMM test is quite flaky. It fails easily e.g., if:
- change number of partitions;
- just change the way to compute the sum of weights;
- change the underlying BLAS impl

Also uses more permissive precision on `Word2Vec` test case.

### Why are the changes needed?

To recover the build and tests.

### Does this PR introduce _any_ user-facing change?

No

### How was this patch tested?

Existing test cases.

Closes #32533 from zhengruifeng/SPARK_35392_disable_flaky_gmm_test.

Lead-authored-by: Ruifeng Zheng <ruifengz@foxmail.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
2021-05-13 22:23:51 +09:00
Takuya UESHIN 17b59a9970 [SPARK-35382][PYTHON] Fix lambda variable name issues in nested DataFrame functions in Python APIs
### What changes were proposed in this pull request?

This PR fixes the same issue as #32424.

```py
from pyspark.sql.functions import flatten, struct, transform
df = spark.sql("SELECT array(1, 2, 3) as numbers, array('a', 'b', 'c') as letters")
df.select(flatten(
    transform(
        "numbers",
        lambda number: transform(
            "letters",
            lambda letter: struct(number.alias("n"), letter.alias("l"))
        )
    )
).alias("zipped")).show(truncate=False)
```

**Before:**

```
+------------------------------------------------------------------------+
|zipped                                                                  |
+------------------------------------------------------------------------+
|[{a, a}, {b, b}, {c, c}, {a, a}, {b, b}, {c, c}, {a, a}, {b, b}, {c, c}]|
+------------------------------------------------------------------------+
```

**After:**

```
+------------------------------------------------------------------------+
|zipped                                                                  |
+------------------------------------------------------------------------+
|[{1, a}, {1, b}, {1, c}, {2, a}, {2, b}, {2, c}, {3, a}, {3, b}, {3, c}]|
+------------------------------------------------------------------------+
```

### Why are the changes needed?

To produce the correct results.

### Does this PR introduce _any_ user-facing change?

Yes, it fixes the results to be correct as mentioned above.

### How was this patch tested?

Added a unit test as well as manually.

Closes #32523 from ueshin/issues/SPARK-35382/nested_higher_order_functions.

Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
2021-05-13 14:58:01 +09:00
Sean Owen a189be8754 [MINOR][DOCS] Avoid some python docs where first sentence has "e.g." or similar
### What changes were proposed in this pull request?

Avoid some python docs where first sentence has "e.g." or similar as the period causes the docs to show only half of the first sentence as the summary.

### Why are the changes needed?

See for example https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.ml.regression.LinearRegressionModel.html?highlight=linearregressionmodel#pyspark.ml.regression.LinearRegressionModel.summary where the method description is clearly truncated.

### Does this PR introduce _any_ user-facing change?

Only changes docs.

### How was this patch tested?

Manual testing of docs.

Closes #32508 from srowen/TruncatedPythonDesc.

Authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
2021-05-12 10:38:59 +09:00
HyukjinKwon 8aaa9e890a [SPARK-35250][SQL][DOCS] Fix duplicated STOP_AT_DELIMITER to SKIP_VALUE at CSV's unescapedQuoteHandling option documentation
### What changes were proposed in this pull request?

This is rather a followup of https://github.com/apache/spark/pull/30518 that should be ported back to `branch-3.1` too.
`STOP_AT_DELIMITER` was mistakenly used twice. The duplicated `STOP_AT_DELIMITER` should be `SKIP_VALUE` in the documentation.

### Why are the changes needed?

To correctly document.

### Does this PR introduce _any_ user-facing change?

Yes, it fixes the user-facing documentation.

### How was this patch tested?

I checked them via running linters.

Closes #32423 from HyukjinKwon/SPARK-35250.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-05-04 08:44:18 +09:00
Yikun Jiang 44b7931936 [SPARK-35176][PYTHON] Standardize input validation error type
### What changes were proposed in this pull request?
This PR corrects some exception type when the function input params are failed to validate due to TypeError.
In order to convenient to review, there are 3 commits in this PR:
- Standardize input validation error type on sql
- Standardize input validation error type on ml
- Standardize input validation error type on pandas

### Why are the changes needed?
As suggestion from Python exception doc [1]: "Raised when an operation or function is applied to an object of inappropriate type.", but there are many Value error are raised in some pyspark code, this patch fix them.

[1] https://docs.python.org/3/library/exceptions.html#TypeError

Note that: this patch only addresses the exsiting some wrong raise type for input validation, the input validation decorator/framework which mentioned in [SPARK-35176](https://issues.apache.org/jira/browse/SPARK-35176), would be submited in a speparated patch.

### Does this PR introduce _any_ user-facing change?
Yes, code can raise the right TypeError instead of ValueError.

### How was this patch tested?
Existing test case and UT

Closes #32368 from Yikun/SPARK-35176.

Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-05-03 15:34:24 +09:00
Ludovic Henry 5b77ebb57b [SPARK-35150][ML] Accelerate fallback BLAS with dev.ludovic.netlib
### What changes were proposed in this pull request?

Following https://github.com/apache/spark/pull/30810, I've continued looking for ways to accelerate the usage of BLAS in Spark. With this PR, I integrate work done in the [`dev.ludovic.netlib`](https://github.com/luhenry/netlib/) Maven package.

The `dev.ludovic.netlib` library wraps the original `com.github.fommil.netlib` library and focus on accelerating the linear algebra routines in use in Spark. When running the `org.apache.spark.ml.linalg.BLASBenchmark` benchmarking suite, I get the results at [1] on an Intel machine. Moreover, this library is thoroughly tested to return the exact same results as the reference implementation.

Under the hood, it reimplements the necessary algorithms in pure autovectorization-friendly Java 8, as well as takes advantage of the Vector API and Foreign Linker API introduced in JDK 16 when available.

A table summarising which version gets loaded in which case:

```
|                       | BLAS.nativeBLAS                                    | BLAS.javaBLAS                                      |
| --------------------- | -------------------------------------------------- | -------------------------------------------------- |
| with -Pnetlib-lgpl    | 1. dev.ludovic.netlib.blas.NetlibNativeBLAS, a     | 1. dev.ludovic.netlib.blas.VectorizedBLAS          |
|                       |     wrapper for com.github.fommil:all              |    (JDK16+, relies on the Vector API, requires     |
|                       | 2. dev.ludovic.netlib.blas.ForeignBLAS (JDK16+,    |     `--add-modules=jdk.incubator.vector` on JDK16) |
|                       |    relies on the Foreign Linker API, requires      | 2. dev.ludovic.netlib.blas.Java11BLAS (JDK11+)     |
|                       |    `--add-modules=jdk.incubator.foreign            | 3. dev.ludovic.netlib.blas.JavaBLAS                |
|                       |     -Dforeign.restricted=warn`)                    | 4. dev.ludovic.netlib.blas.NetlibF2jBLAS, a        |
|                       | 3. fails to load, falls back to BLAS.javaBLAS in   |     wrapper for com.github.fommil:core             |
|                       |     org.apache.spark.ml.linalg.BLAS                |                                                    |
| --------------------- | -------------------------------------------------- | -------------------------------------------------- |
| without -Pnetlib-lgpl | 1. dev.ludovic.netlib.blas.ForeignBLAS (JDK16+,    | 1. dev.ludovic.netlib.blas.VectorizedBLAS          |
|                       |    relies on the Foreign Linker API, requires      |    (JDK16+, relies on the Vector API, requires     |
|                       |    `--add-modules=jdk.incubator.foreign            |     `--add-modules=jdk.incubator.vector` on JDK16) |
|                       |     -Dforeign.restricted=warn`)                    | 2. dev.ludovic.netlib.blas.Java11BLAS (JDK11+)     |
|                       | 2. fails to load, falls back to BLAS.javaBLAS in   | 3. dev.ludovic.netlib.blas.JavaBLAS                |
|                       |     org.apache.spark.ml.linalg.BLAS                | 4. dev.ludovic.netlib.blas.NetlibF2jBLAS, a        |
|                       |                                                    |     wrapper for com.github.fommil:core             |
| --------------------- | -------------------------------------------------- | -------------------------------------------------- |
```

### Why are the changes needed?

Accelerates linear algebra operations when the pure-java fallback method is in use. Transparently falls back to native implementation (OpenBLAS, MKL) when available.

### Does this PR introduce _any_ user-facing change?

No, all changes are transparent to the user.

### How was this patch tested?

The `dev.ludovic.netlib` library has its own test suite [2]. It has also been validated by running the Spark test suite and benchmarking suite.

[1] Results for `org.apache.spark.ml.linalg.BLASBenchmark`:
#### JDK8:
```
[info] OpenJDK 64-Bit Server VM 1.8.0_292-b10 on Linux 5.8.0-50-generic
[info] Intel(R) Xeon(R) E-2276G CPU  3.80GHz
[info]
[info] f2jBLAS    = dev.ludovic.netlib.blas.NetlibF2jBLAS
[info] javaBLAS   = dev.ludovic.netlib.blas.Java8BLAS
[info] nativeBLAS = dev.ludovic.netlib.blas.Java8BLAS
[info]
[info] daxpy:                                    Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 223            232           8        448.0           2.2       1.0X
[info] java                                                221            228           7        453.0           2.2       1.0X
[info]
[info] saxpy:                                    Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 122            128           4        821.2           1.2       1.0X
[info] java                                                122            128           4        822.3           1.2       1.0X
[info]
[info] ddot:                                     Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 109            112           2        921.4           1.1       1.0X
[info] java                                                 70             74           3       1423.5           0.7       1.5X
[info]
[info] sdot:                                     Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                  96             98           2       1046.1           1.0       1.0X
[info] java                                                 47             49           2       2121.7           0.5       2.0X
[info]
[info] dscal:                                    Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 184            195           8        544.3           1.8       1.0X
[info] java                                                185            196           7        539.5           1.9       1.0X
[info]
[info] sscal:                                    Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                  99            104           4       1011.9           1.0       1.0X
[info] java                                                 99            104           4       1010.4           1.0       1.0X
[info]
[info] dspmv[U]:                                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   1              1           0        947.2           1.1       1.0X
[info] java                                                  0              0           0       1584.8           0.6       1.7X
[info]
[info] dspr[U]:                                  Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   1              1           0        867.4           1.2       1.0X
[info] java                                                  1              1           0        865.0           1.2       1.0X
[info]
[info] dsyr[U]:                                  Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   1              1           0        485.9           2.1       1.0X
[info] java                                                  1              1           0        486.8           2.1       1.0X
[info]
[info] dgemv[N]:                                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   1              1           0       1843.0           0.5       1.0X
[info] java                                                  0              0           0       2690.6           0.4       1.5X
[info]
[info] dgemv[T]:                                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   1              1           0       1214.7           0.8       1.0X
[info] java                                                  0              0           0       2536.8           0.4       2.1X
[info]
[info] sgemv[N]:                                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   1              1           0       1895.9           0.5       1.0X
[info] java                                                  0              0           0       2961.1           0.3       1.6X
[info]
[info] sgemv[T]:                                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   1              1           0       1223.4           0.8       1.0X
[info] java                                                  0              0           0       3091.4           0.3       2.5X
[info]
[info] dgemm[N,N]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 560            575          20       1787.1           0.6       1.0X
[info] java                                                226            232           5       4432.4           0.2       2.5X
[info]
[info] dgemm[N,T]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 570            586          23       1755.2           0.6       1.0X
[info] java                                                227            232           4       4410.1           0.2       2.5X
[info]
[info] dgemm[T,N]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 863            879          17       1158.4           0.9       1.0X
[info] java                                                227            231           3       4407.9           0.2       3.8X
[info]
[info] dgemm[T,T]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                1282           1305          23        780.0           1.3       1.0X
[info] java                                                227            232           4       4413.4           0.2       5.7X
[info]
[info] sgemm[N,N]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 538            548           8       1858.6           0.5       1.0X
[info] java                                                221            226           3       4521.1           0.2       2.4X
[info]
[info] sgemm[N,T]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 549            558          10       1819.9           0.5       1.0X
[info] java                                                222            229           7       4503.5           0.2       2.5X
[info]
[info] sgemm[T,N]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 838            852          12       1193.0           0.8       1.0X
[info] java                                                222            229           5       4500.5           0.2       3.8X
[info]
[info] sgemm[T,T]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 905            919          18       1104.8           0.9       1.0X
[info] java                                                221            228           5       4521.3           0.2       4.1X
```

#### JDK11:
```
[info] OpenJDK 64-Bit Server VM 11.0.11+9-LTS on Linux 5.8.0-50-generic
[info] Intel(R) Xeon(R) E-2276G CPU  3.80GHz
[info]
[info] f2jBLAS    = dev.ludovic.netlib.blas.NetlibF2jBLAS
[info] javaBLAS   = dev.ludovic.netlib.blas.Java11BLAS
[info] nativeBLAS = dev.ludovic.netlib.blas.Java11BLAS
[info]
[info] daxpy:                                    Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 195            204          10        512.7           2.0       1.0X
[info] java                                                195            202           7        512.4           2.0       1.0X
[info]
[info] saxpy:                                    Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 108            113           4        923.3           1.1       1.0X
[info] java                                                102            107           4        984.4           1.0       1.1X
[info]
[info] ddot:                                     Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 107            110           3        938.1           1.1       1.0X
[info] java                                                 69             72           3       1447.1           0.7       1.5X
[info]
[info] sdot:                                     Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                  96             98           2       1046.5           1.0       1.0X
[info] java                                                 43             45           2       2317.1           0.4       2.2X
[info]
[info] dscal:                                    Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 155            168           8        644.2           1.6       1.0X
[info] java                                                158            169           8        632.8           1.6       1.0X
[info]
[info] sscal:                                    Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                  85             90           4       1178.1           0.8       1.0X
[info] java                                                 86             90           4       1167.7           0.9       1.0X
[info]
[info] dspmv[U]:                                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   0              0           0       1182.1           0.8       1.0X
[info] java                                                  0              0           0       1432.1           0.7       1.2X
[info]
[info] dspr[U]:                                  Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   1              1           0        898.7           1.1       1.0X
[info] java                                                  1              1           0        891.5           1.1       1.0X
[info]
[info] dsyr[U]:                                  Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   1              1           0        495.4           2.0       1.0X
[info] java                                                  1              1           0        495.7           2.0       1.0X
[info]
[info] dgemv[N]:                                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   0              0           0       2271.6           0.4       1.0X
[info] java                                                  0              0           0       3648.1           0.3       1.6X
[info]
[info] dgemv[T]:                                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   1              1           0       1229.3           0.8       1.0X
[info] java                                                  0              0           0       2711.3           0.4       2.2X
[info]
[info] sgemv[N]:                                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   0              0           0       2677.5           0.4       1.0X
[info] java                                                  0              0           0       3288.2           0.3       1.2X
[info]
[info] sgemv[T]:                                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   1              1           0       1233.0           0.8       1.0X
[info] java                                                  0              0           0       2766.3           0.4       2.2X
[info]
[info] dgemm[N,N]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 520            536          16       1923.6           0.5       1.0X
[info] java                                                214            221           7       4669.5           0.2       2.4X
[info]
[info] dgemm[N,T]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 593            612          17       1686.5           0.6       1.0X
[info] java                                                215            219           3       4643.3           0.2       2.8X
[info]
[info] dgemm[T,N]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 853            870          16       1172.8           0.9       1.0X
[info] java                                                215            218           3       4659.7           0.2       4.0X
[info]
[info] dgemm[T,T]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                1350           1370          23        740.8           1.3       1.0X
[info] java                                                215            219           4       4656.6           0.2       6.3X
[info]
[info] sgemm[N,N]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 460            468           6       2173.2           0.5       1.0X
[info] java                                                210            213           2       4752.7           0.2       2.2X
[info]
[info] sgemm[N,T]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 535            544           8       1869.3           0.5       1.0X
[info] java                                                210            215           5       4761.8           0.2       2.5X
[info]
[info] sgemm[T,N]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 843            853          11       1186.8           0.8       1.0X
[info] java                                                209            214           4       4793.4           0.2       4.0X
[info]
[info] sgemm[T,T]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 891            904          15       1122.0           0.9       1.0X
[info] java                                                209            214           4       4777.2           0.2       4.3X
```

#### JDK16:
```
[info] OpenJDK 64-Bit Server VM 16+36 on Linux 5.8.0-50-generic
[info] Intel(R) Xeon(R) E-2276G CPU  3.80GHz
[info]
[info] f2jBLAS    = dev.ludovic.netlib.blas.NetlibF2jBLAS
[info] javaBLAS   = dev.ludovic.netlib.blas.VectorizedBLAS
[info] nativeBLAS = dev.ludovic.netlib.blas.VectorizedBLAS
[info]
[info] daxpy:                                    Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 194            199           7        515.7           1.9       1.0X
[info] java                                                181            186           3        551.1           1.8       1.1X
[info]
[info] saxpy:                                    Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 109            115           4        915.0           1.1       1.0X
[info] java                                                 88             92           3       1138.8           0.9       1.2X
[info]
[info] ddot:                                     Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 108            110           2        922.6           1.1       1.0X
[info] java                                                 54             56           2       1839.2           0.5       2.0X
[info]
[info] sdot:                                     Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                  96             97           2       1046.1           1.0       1.0X
[info] java                                                 29             30           1       3393.4           0.3       3.2X
[info]
[info] dscal:                                    Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 156            165           5        643.0           1.6       1.0X
[info] java                                                150            159           5        667.1           1.5       1.0X
[info]
[info] sscal:                                    Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                  85             91           6       1171.0           0.9       1.0X
[info] java                                                 75             79           3       1340.6           0.7       1.1X
[info]
[info] dspmv[U]:                                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   1              1           0        917.0           1.1       1.0X
[info] java                                                  0              0           0       8147.2           0.1       8.9X
[info]
[info] dspr[U]:                                  Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   1              1           0        859.3           1.2       1.0X
[info] java                                                  1              1           0        859.3           1.2       1.0X
[info]
[info] dsyr[U]:                                  Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   1              1           0        482.1           2.1       1.0X
[info] java                                                  1              1           0        482.6           2.1       1.0X
[info]
[info] dgemv[N]:                                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   0              0           0       2214.2           0.5       1.0X
[info] java                                                  0              0           0       7975.8           0.1       3.6X
[info]
[info] dgemv[T]:                                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   1              1           0       1231.4           0.8       1.0X
[info] java                                                  0              0           0       8680.9           0.1       7.0X
[info]
[info] sgemv[N]:                                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   0              0           0       2684.3           0.4       1.0X
[info] java                                                  0              0           0      18527.1           0.1       6.9X
[info]
[info] sgemv[T]:                                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                   1              1           0       1235.4           0.8       1.0X
[info] java                                                  0              0           0      17347.9           0.1      14.0X
[info]
[info] dgemm[N,N]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 530            552          18       1887.5           0.5       1.0X
[info] java                                                 58             64           3      17143.9           0.1       9.1X
[info]
[info] dgemm[N,T]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 598            620          17       1671.1           0.6       1.0X
[info] java                                                 58             64           3      17196.6           0.1      10.3X
[info]
[info] dgemm[T,N]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 834            847          14       1199.4           0.8       1.0X
[info] java                                                 57             63           4      17486.9           0.1      14.6X
[info]
[info] dgemm[T,T]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                1338           1366          22        747.3           1.3       1.0X
[info] java                                                 58             63           3      17356.6           0.1      23.2X
[info]
[info] sgemm[N,N]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 489            501           9       2045.5           0.5       1.0X
[info] java                                                 36             38           2      27721.9           0.0      13.6X
[info]
[info] sgemm[N,T]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 478            488           9       2094.0           0.5       1.0X
[info] java                                                 36             38           2      27813.2           0.0      13.3X
[info]
[info] sgemm[T,N]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 825            837          10       1211.6           0.8       1.0X
[info] java                                                 35             38           2      28433.1           0.0      23.5X
[info]
[info] sgemm[T,T]:                               Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] f2j                                                 900            918          15       1111.6           0.9       1.0X
[info] java                                                 36             38           2      28073.0           0.0      25.3X
```

[2] https://github.com/luhenry/netlib/tree/master/blas/src/test/java/dev/ludovic/netlib/blas

Closes #32253 from luhenry/master.

Authored-by: Ludovic Henry <git@ludovic.dev>
Signed-off-by: Sean Owen <srowen@gmail.com>
2021-04-27 14:00:59 -05:00
Julien Lafaye 592230e47b [MINOR][DOCS][ML] Explicit return type of array_to_vector utility function
There are two types of dense vectors:
* pyspark.ml.linalg.DenseVector
* pyspark.mllib.linalg.DenseVector

In spark-3.1.1, array_to_vector returns instances of pyspark.ml.linalg.DenseVector.
The documentation is ambiguous & can lead to the false conclusion that instances of
pyspark.mllib.linalg.DenseVector will be returned.
Conversion from ml versions to mllib versions can easly be achieved with
mlutils.convertVectorColumnsToML helper.

### What changes were proposed in this pull request?
Make documentation more explicit

### Why are the changes needed?
The documentation is a bit misleading and users can lose time investigating & realizing there are two DenseVector types.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
No test were run as only the documentation was changed

Closes #32255 from jlafaye/master.

Authored-by: Julien Lafaye <jlafaye@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
2021-04-27 09:08:26 -05:00
Ruifeng Zheng 1f150b9392 [SPARK-35024][ML] Refactor LinearSVC - support virtual centering
### What changes were proposed in this pull request?
1, remove existing agg, and use a new agg supporting virtual centering
2, add related testsuites

### Why are the changes needed?
centering vectors should accelerate convergence, and generate solution more close to R

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
updated testsuites and added testsuites

Closes #32124 from zhengruifeng/svc_agg_refactor.

Authored-by: Ruifeng Zheng <ruifengz@foxmail.com>
Signed-off-by: Ruifeng Zheng <ruifengz@foxmail.com>
2021-04-25 13:16:46 +08:00
Xinrong Meng 4fcbf59079 [SPARK-35040][PYTHON] Remove Spark-version related codes from test codes
### What changes were proposed in this pull request?

Removes PySpark version dependent codes from pyspark.pandas test codes.

### Why are the changes needed?

There are several places to check the PySpark version and switch the logic, but now those are not necessary.
We should remove them.

We will do the same thing after we finish porting tests.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Existing tests.

Closes #32300 from xinrong-databricks/port.rmv_spark_version_chk_in_tests.

Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
2021-04-22 18:01:07 -07:00
Xinrong Meng 4d2b559d92 [SPARK-34999][PYTHON] Consolidate PySpark testing utils
### What changes were proposed in this pull request?
Consolidate PySpark testing utils by removing `python/pyspark/pandas/testing`, and then creating a file `pandasutils` under `python/pyspark/testing` for test utilities used in `pyspark/pandas`.

### Why are the changes needed?

`python/pyspark/pandas/testing` hold test utilites for pandas-on-spark, and `python/pyspark/testing` contain test utilities for pyspark. Consolidating them makes code cleaner and easier to maintain.

Updated import statements are as shown below:
- from pyspark.testing.sqlutils import SQLTestUtils
- from pyspark.testing.pandasutils import PandasOnSparkTestCase, TestUtils
(PandasOnSparkTestCase is the original ReusedSQLTestCase in `python/pyspark/pandas/testing/utils.py`)

Minor improvements include:
- Usage of missing library's requirement_message
- `except ImportError` rather than `except`
- import pyspark.pandas alias as `ps` rather than `pp`

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Unit tests under python/pyspark/pandas/tests.

Closes #32177 from xinrong-databricks/port.merge_utils.

Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
2021-04-22 13:07:35 -07:00
harupy b6350f5bb0 [SPARK-35142][PYTHON][ML] Fix incorrect return type for rawPredictionUDF in OneVsRestModel
### What changes were proposed in this pull request?

Fixes incorrect return type for `rawPredictionUDF` in `OneVsRestModel`.

### Why are the changes needed?
Bugfix

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
Unit test.

Closes #32245 from harupy/SPARK-35142.

Authored-by: harupy <17039389+harupy@users.noreply.github.com>
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
2021-04-21 16:29:10 +08:00
itholic 91bd38467e [SPARK-34995] Port/integrate Koalas remaining codes into PySpark
### What changes were proposed in this pull request?

There are some more changes in Koalas such as [databricks/koalas#2141](c8f803d6be), [databricks/koalas#2143](913d68868d) after the main code porting, this PR is to synchronize those changes with the `pyspark.pandas`.

### Why are the changes needed?

We should port the whole Koalas codes into PySpark and synchronize them.

### Does this PR introduce _any_ user-facing change?

Fixed some incompatible behavior with pandas 1.2.0 and added more to the `to_markdown` docstring.

### How was this patch tested?

Manually tested in local.

Closes #32197 from itholic/SPARK-34995-fix.

Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-16 17:42:03 +09:00
Xinrong Meng 4aee19efb4 [SPARK-35032][PYTHON] Port Koalas Index unit tests into PySpark
### What changes were proposed in this pull request?
Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas Index unit tests to PySpark.

### Why are the changes needed?
Currently, the pandas-on-Spark modules are not tested fully. We should enable the Index unit tests.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
Enable Index unit tests.

Closes #32139 from xinrong-databricks/port.indexes_tests.

Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-16 08:53:30 +09:00
HyukjinKwon 637f59360b Revert "[SPARK-34995] Port/integrate Koalas remaining codes into PySpark"
This reverts commit 9689c44b60.
2021-04-15 21:01:47 +09:00
itholic 9689c44b60 [SPARK-34995] Port/integrate Koalas remaining codes into PySpark
### What changes were proposed in this pull request?

There are some more changes in Koalas such as [databricks/koalas#2141](c8f803d6be), [databricks/koalas#2143](913d68868d) after the main code porting, this PR is to synchronize those changes with the `pyspark.pandas`.

### Why are the changes needed?

We should port the whole Koalas codes into PySpark and synchronize them.

### Does this PR introduce _any_ user-facing change?

Fixed some incompatible behavior with pandas 1.2.0 and added more to the `to_markdown` docstring.

### How was this patch tested?

Manually tested in local.

Closes #32154 from itholic/SPARK-34995.

Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-15 19:13:08 +09:00
HyukjinKwon 7ff9d2e3ee [SPARK-35071][PYTHON] Rename Koalas to pandas-on-Spark in main codes
### What changes were proposed in this pull request?

This PR proposes to rename Koalas to pandas-on-Spark in main codes

### Why are the changes needed?

To have the correct name in PySpark. NOTE that the official name in the main documentation will be pandas APIs on Spark to be extra clear. pandas-on-Spark is not the official term.

### Does this PR introduce _any_ user-facing change?

No, it's master-only change. It changes the docstring and class names.

### How was this patch tested?

Manually tested via:

```bash
./python/run-tests --python-executable=python3 --modules pyspark-pandas
```

Closes #32166 from HyukjinKwon/rename-koalas.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-15 12:48:59 +09:00
xinrong-databricks 58feb85145 [SPARK-35034][PYTHON] Port Koalas miscellaneous unit tests into PySpark
### What changes were proposed in this pull request?
Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas miscellaneous unit tests to PySpark.

### Why are the changes needed?
Currently, the pandas-on-Spark modules are not tested fully. We should enable miscellaneous unit tests.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
Enable miscellaneous unit tests.

Closes #32152 from xinrong-databricks/port.misc_tests.

Lead-authored-by: xinrong-databricks <47337188+xinrong-databricks@users.noreply.github.com>
Co-authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-15 11:45:15 +09:00
Yikun Jiang 31555f7779
[SPARK-34630][PYTHON][FOLLOWUP] Add __version__ into pyspark init __all__
### What changes were proposed in this pull request?
This patch add `__version__` into pyspark.__init__.__all__ to make the `__version__` as exported explicitly, see more in https://github.com/apache/spark/pull/32110#issuecomment-817331896

### Why are the changes needed?
1. make the `__version__` as exported explicitly
2. cleanup `noqa: F401` on `__version`

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
Python related CI passed

Closes #32125 from Yikun/SPARK-34629-Follow.

Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: zero323 <mszymkiewicz@gmail.com>
2021-04-14 23:36:25 +02:00
Takuya UESHIN 4ae57d5b3a [SPARK-35039][PYTHON] Remove PySpark version dependent codes
### What changes were proposed in this pull request?

Removes PySpark version dependent codes from `pyspark.pandas` main codes.

### Why are the changes needed?

There are several places to check the PySpark version and switch the logic, but now those are not necessary.
We should remove them.

We will do the same thing after we finish porting tests.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Existing tests.

Closes #32138 from ueshin/issues/SPARK-35039/pyspark_version.

Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-14 14:30:48 +09:00
Xinrong Meng 47d62af2a9 [SPARK-35035][PYTHON] Port Koalas internal implementation unit tests into PySpark
### What changes were proposed in this pull request?
Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas internal implementation unit tests to PySpark.

### Why are the changes needed?
Currently, the pandas-on-Spark modules are not tested fully. We should enable the internal implementation unit tests.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
Enable internal implementation unit tests.

Closes #32137 from xinrong-databricks/port.test_internal_impl.

Lead-authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Co-authored-by: xinrong-databricks <47337188+xinrong-databricks@users.noreply.github.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-14 13:59:33 +09:00
Xinrong Meng cd1e8e8158 [SPARK-35033][PYTHON] Port Koalas plot unit tests into PySpark
### What changes were proposed in this pull request?
Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas plot unit tests to PySpark.

### Why are the changes needed?
Currently, the pandas-on-Spark modules are not tested fully. We should enable the plot unit tests.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
Enable plot unit tests.

Closes #32151 from xinrong-databricks/port.plot_tests.

Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-14 13:20:16 +09:00
Alex Mooney faa928cefc [MINOR][PYTHON][DOCS] Fix docstring for pyspark.sql.DataFrameWriter.json lineSep param
### What changes were proposed in this pull request?

Add a new line to the `lineSep` parameter so that the doc renders correctly.

### Why are the changes needed?

> <img width="608" alt="image" src="https://user-images.githubusercontent.com/8269566/114631408-5c608900-9c71-11eb-8ded-ae1e21ae48b2.png">

The first line of the description is part of the signature and is **bolded**.

### Does this PR introduce _any_ user-facing change?

Yes, it changes how the docs for `pyspark.sql.DataFrameWriter.json` are rendered.

### How was this patch tested?

I didn't test it; I don't have the doc rendering tool chain on my machine, but the change is obvious.

Closes #32153 from AlexMooney/patch-1.

Authored-by: Alex Mooney <alexmooney@fastmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-14 13:14:51 +09:00
Xinrong Meng 8ebc3fca8c [SPARK-35012][PYTHON] Port Koalas DataFrame-related unit tests into PySpark
### What changes were proposed in this pull request?
Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas DataFrame-related unit tests to PySpark.

### Why are the changes needed?
Currently, the pandas-on-Spark modules are not fully tested. We should enable the DataFrame-related unit tests first.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
Enable DataFrame-related unit tests.

Closes #32131 from xinrong-databricks/port.test_dataframe_related.

Lead-authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Co-authored-by: xinrong-databricks <47337188+xinrong-databricks@users.noreply.github.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
2021-04-13 14:24:08 -07:00
Xinrong Meng a392633566 [SPARK-34996][PYTHON] Port Koalas Series-related unit tests into PySpark
### What changes were proposed in this pull request?
Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas Series related unit tests to PySpark.

### Why are the changes needed?
Currently, the pandas-on-Spark modules are not fully tested. We should enable the Series related unit tests first.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
Enable Series-related unit tests.

Closes #32117 from xinrong-databricks/port.test_series_related.

Lead-authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Co-authored-by: xinrong-databricks <47337188+xinrong-databricks@users.noreply.github.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-13 13:03:35 +09:00
Xinrong Meng 9c1f807549 [SPARK-35031][PYTHON] Port Koalas operations on different frames tests into PySpark
### What changes were proposed in this pull request?
Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas operations on different frames unit tests to PySpark.

### Why are the changes needed?
Currently, the pandas-on-Spark modules are not tested fully. We should enable the operations on different frames unit tests.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
Enable operations on different frames unit tests.

Closes #32133 from xinrong-databricks/port.test_ops_on_diff_frames.

Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-13 11:22:51 +09:00
Yikun Jiang b43f7e6a97 [SPARK-35019][PYTHON][SQL] Fix type hints mismatches in pyspark.sql.*
### What changes were proposed in this pull request?
Fix type hints mismatches in pyspark.sql.*

### Why are the changes needed?
There were some mismatches in pyspark.sql.*

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
dev/lint-python passed.

Closes #32122 from Yikun/SPARK-35019.

Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-13 11:21:13 +09:00
Luka Sturtewagen fd8081cd27 [SPARK-34983][PYTHON] Renaming the package alias from pp to ps
### What changes were proposed in this pull request?

This PR proposes to fix:

```python
import pyspark.pandas as pp
```

to

```python
import pyspark.pandas as ps
```

### Why are the changes needed?

`pp` might sound offensive in some contexts.

### Does this PR introduce _any_ user-facing change?

The change is in master only. We'll use `ps` as the short name instead of `pp`.

### How was this patch tested?

The CI in this PR will test it out.

Closes #32108 from LSturtew/renaming_pyspark.pandas.

Authored-by: Luka Sturtewagen <luka.sturtewagen@linkit.nl>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-12 11:18:08 +09:00
Takuya UESHIN ff1fc5ed4b [SPARK-34972][PYTHON][TEST][FOLLOWUP] Fix pyspark.pandas doctests which could be flaky
### What changes were proposed in this pull request?

This is a follow-up of #32069.

Makes some doctests which could be flaky skip.

### Why are the changes needed?

Some doctests in `pyspark.pandas` module enabled at #32069 could be flaky because the result row order is nondeterministic.

- groupby-apply with UDF which has a return type annotation will lose its index.
- `Index.symmetric_difference` uses `DataFrame.intersect` and `subtract` internally.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Existing tests.

Closes #32116 from ueshin/issues/SPARK-34972/fix_flaky_tests.

Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-11 10:42:00 +09:00
Yikun Jiang 4c1ccdabe8 [SPARK-34630][PYTHON] Add typehint for pyspark.__version__
### What changes were proposed in this pull request?
This PR adds the typehint of pyspark.__version__, which was mentioned in [SPARK-34630](https://issues.apache.org/jira/browse/SPARK-34630).

### Why are the changes needed?
There were some short discussion happened in https://github.com/apache/spark/pull/31823#discussion_r593830911 .

After further deep investigation on [1][2], we can see the `pyspark.__version__` is added by [setup.py](c06758834e/python/setup.py (L201)), it makes `__version__` embedded into pyspark module, that means the `__init__.pyi` is the right place to add the typehint for `__version__`.

So, this patch adds the type hint `__version__` in pyspark/__init__.pyi.

[1] [PEP-396 Module Version Numbers](https://www.python.org/dev/peps/pep-0396/)
[2] https://packaging.python.org/guides/single-sourcing-package-version/
### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
1. Disable the ignore_error on
ee7bf7d962/python/mypy.ini (L132)

2. Run mypy:
- Before fix
```shell
(venv) ➜  spark git:(SPARK-34629) ✗ mypy --config-file python/mypy.ini python/pyspark | grep version
python/pyspark/pandas/spark/accessors.py:884: error: Module has no attribute "__version__"
```

- After fix
```shell
(venv) ➜  spark git:(SPARK-34629) ✗ mypy --config-file python/mypy.ini python/pyspark | grep version
```
no output

Closes #32110 from Yikun/SPARK-34629.

Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-11 10:40:08 +09:00
Xinrong Meng 3af2c1bb9c [SPARK-34886][PYTHON] Port/integrate Koalas DataFrame unit test into PySpark
### What changes were proposed in this pull request?
Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas DataFrame unit test to PySpark.

### Why are the changes needed?
Currently, the pandas-on-Spark modules are not tested at all. We should enable the DataFrame unit test first.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
Enable the DataFrame unit test.

Closes #32083 from xinrong-databricks/port.test_dataframe.

Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-09 15:48:13 +09:00
Takuya UESHIN 2635c3894f [SPARK-34972][PYTHON] Make pandas-on-Spark doctests work
### What changes were proposed in this pull request?

Now that we merged the Koalas main code into PySpark code base (#32036), we should enable doctests on the Spark's infrastructure.

### Why are the changes needed?

Currently the pandas-on-Spark modules are not tested at all.
We should enable doctests first, and we will port other unit tests separately later.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Enabled the whole doctests.

Closes #32069 from ueshin/issues/SPARK-34972/pyspark-pandas_doctests.

Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-07 20:50:41 +09:00
itholic caf04f9b77 [SPARK-34890][PYTHON] Port/integrate Koalas main codes into PySpark
### What changes were proposed in this pull request?

As a first step of [SPARK-34849](https://issues.apache.org/jira/browse/SPARK-34849), this PR proposes porting the Koalas main code into PySpark.

This PR contains minimal changes to the existing Koalas code as follows:
1. `databricks.koalas` -> `pyspark.pandas`
2. `from databricks import koalas as ks` -> `from pyspark import pandas as pp`
3. `ks.xxx -> pp.xxx`

Other than them:
1. Added a line to `python/mypy.ini` in order to ignore the mypy test. See related issue at [SPARK-34941](https://issues.apache.org/jira/browse/SPARK-34941).
2. Added a comment to several lines in several files to ignore the flake8 F401. See related issue at [SPARK-34943](https://issues.apache.org/jira/browse/SPARK-34943).

When this PR is merged, all the features that were previously used in [Koalas](https://github.com/databricks/koalas) will be available in PySpark as well.

Users can access to the pandas API in PySpark as below:

```python
>>> from pyspark import pandas as pp
>>> ppdf = pp.DataFrame({"A": [1, 2, 3], "B": [15, 20, 25]})
>>> ppdf
   A   B
0  1  15
1  2  20
2  3  25
```

The existing "options and settings" in Koalas are also available in the same way:

```python
>>> from pyspark.pandas.config import set_option, reset_option, get_option
>>> ppser1 = pp.Series([1, 2, 3])
>>> ppser2 = pp.Series([3, 4, 5])
>>> ppser1 + ppser2
Traceback (most recent call last):
...
ValueError: Cannot combine the series or dataframe because it comes from a different dataframe. In order to allow this operation, enable 'compute.ops_on_diff_frames' option.

>>> set_option("compute.ops_on_diff_frames", True)
>>> ppser1 + ppser2
0    4
1    6
2    8
dtype: int64
```

Please also refer to the [API Reference](https://koalas.readthedocs.io/en/latest/reference/index.html) and [Options and Settings](https://koalas.readthedocs.io/en/latest/user_guide/options.html) for more detail.

**NOTE** that this PR intentionally ports the main codes of Koalas first almost as are with minimal changes because:
- Koalas project is fairly large. Making some changes together for PySpark will make it difficult to review the individual change.
    Koalas dev includes multiple Spark committers who will review. By doing this, the committers will be able to more easily and effectively review and drive the development.
- Koalas tests and documentation require major changes to make it look great together with PySpark whereas main codes do not require.
- We lately froze the Koalas codebase, and plan to work together on the initial porting. By porting the main codes first as are, it unblocks the Koalas dev to work on other items in parallel.

I promise and will make sure on:
- Rename Koalas to PySpark pandas APIs and/or pandas-on-Spark accordingly in documentation, and the docstrings and comments in the main codes.
- Triage APIs to remove that don’t make sense when Koalas is in PySpark

The documentation changes will be tracked in [SPARK-34885](https://issues.apache.org/jira/browse/SPARK-34885), the test code changes will be tracked in [SPARK-34886](https://issues.apache.org/jira/browse/SPARK-34886).

### Why are the changes needed?

Please refer to:
- [[DISCUSS] Support pandas API layer on PySpark](http://apache-spark-developers-list.1001551.n3.nabble.com/DISCUSS-Support-pandas-API-layer-on-PySpark-td30945.html)
- [[VOTE] SPIP: Support pandas API layer on PySpark](http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-SPIP-Support-pandas-API-layer-on-PySpark-td30996.html)

### Does this PR introduce _any_ user-facing change?

Yes, now users can use the pandas APIs on Spark

### How was this patch tested?

Manually tested for exposed major APIs and options as described above.

### Koalas contributors

Koalas would not have been possible without the following contributors:

ueshin
HyukjinKwon
rxin
xinrong-databricks
RainFung
charlesdong1991
harupy
floscha
beobest2
thunterdb
garawalid
LucasG0
shril
deepyaman
gioa
fwani
90jam
thoo
AbdealiJK
abishekganesh72
gliptak
DumbMachine
dvgodoy
stbof
nitlev
hjoo
gatorsmile
tomspur
icexelloss
awdavidson
guyao
akhilputhiry
scook12
patryk-oleniuk
tracek
dennyglee
athena15
gstaubli
WeichenXu123
hsubbaraj
lfdversluis
ktksq
shengjh
margaret-databricks
LSturtew
sllynn
manuzhang
jijosg
sadikovi

Closes #32036 from itholic/SPARK-34890.

Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-04-06 12:42:39 +09:00
HyukjinKwon 2ca76a57be [MINOR][DOCS] Use ASCII characters when possible in PySpark documentation
### What changes were proposed in this pull request?

This PR replaces the non-ASCII characters to ASCII characters when possible in PySpark documentation

### Why are the changes needed?

To avoid unnecessarily using other non-ASCII characters which could lead to the issue such as https://github.com/apache/spark/pull/32047 or https://github.com/apache/spark/pull/22782

### Does this PR introduce _any_ user-facing change?

Virtually no.

### How was this patch tested?

Found via (Mac OS):

```bash
# In Spark root directory
cd python
pcregrep --color='auto' -n "[\x80-\xFF]" `git ls-files .`
```

Closes #32048 from HyukjinKwon/minor-fix.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
2021-04-04 09:49:36 +03:00
Kousuke Saruta 14c7bb877d [SPARK-34872][SQL] quoteIfNeeded should quote a name which contains non-word characters
### What changes were proposed in this pull request?

This PR fixes an issue that `quoteIfNeeded` quotes a name only if it contains `.` or ``` ` ```.
This method should quote it if it contains non-word characters.

### Why are the changes needed?

It's a potential bug.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

New test.

Closes #31964 from sarutak/fix-quoteIfNeeded.

Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
2021-03-29 09:31:24 +00:00
Danny Meijer ad211ccd9d
[SPARK-34630][PYTHON][SQL] Added typehint for pyspark.sql.Column.contains
### What changes were proposed in this pull request?

This PR implements the missing typehints as per SPARK-34630.

### Why are the changes needed?

To satisfy the aforementioned Jira ticket

### Does this PR introduce _any_ user-facing change?

No, just adding a missing typehint for Project Zen

### How was this patch tested?

No tests needed (just adding a typehint)

Closes #31823 from dannymeijer/feature/SPARK-34630.

Authored-by: Danny Meijer <danny.meijer@nike.com>
Signed-off-by: zero323 <mszymkiewicz@gmail.com>
2021-03-24 15:21:19 +01:00
John Ayad ddfc75ec64 [SPARK-34803][PYSPARK] Pass the raised ImportError if pandas or pyarrow fail to import
### What changes were proposed in this pull request?

Pass the raised `ImportError` on failing to import pandas/pyarrow. This will help the user identify whether pandas/pyarrow are indeed not in the environment or if they threw a different `ImportError`.

### Why are the changes needed?

This can already happen in Pandas for example where it could throw an `ImportError` on its initialisation path if `dateutil` doesn't satisfy a certain version requirement https://github.com/pandas-dev/pandas/blob/0.24.x/pandas/compat/__init__.py#L438

### Does this PR introduce _any_ user-facing change?

Yes, it will now show the root cause of the exception when pandas or arrow is missing during import.

### How was this patch tested?

Manually tested.

```python
from pyspark.sql.functions import pandas_udf
spark.range(1).select(pandas_udf(lambda x: x))
```

Before:

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/...//spark/python/pyspark/sql/pandas/functions.py", line 332, in pandas_udf
    require_minimum_pyarrow_version()
  File "/.../spark/python/pyspark/sql/pandas/utils.py", line 53, in require_minimum_pyarrow_version
    raise ImportError("PyArrow >= %s must be installed; however, "
ImportError: PyArrow >= 1.0.0 must be installed; however, it was not found.
```

After:

```
Traceback (most recent call last):
  File "/.../spark/python/pyspark/sql/pandas/utils.py", line 49, in require_minimum_pyarrow_version
    import pyarrow
ModuleNotFoundError: No module named 'pyarrow'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/.../spark/python/pyspark/sql/pandas/functions.py", line 332, in pandas_udf
    require_minimum_pyarrow_version()
  File "/.../spark/python/pyspark/sql/pandas/utils.py", line 55, in require_minimum_pyarrow_version
    raise ImportError("PyArrow >= %s must be installed; however, "
ImportError: PyArrow >= 1.0.0 must be installed; however, it was not found.
```

Closes #31902 from johnhany97/jayad/spark-34803.

Lead-authored-by: John Ayad <johnhany97@gmail.com>
Co-authored-by: John H. Ayad <johnhany97@gmail.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-03-22 23:29:28 +09:00
Sean Owen ed641fbad6 [MINOR][DOCS][ML] Doc 'mode' as a supported Imputer strategy in Pyspark
### What changes were proposed in this pull request?

Document `mode` as a supported Imputer strategy in Pyspark docs.

### Why are the changes needed?

Support was added in 3.1, and documented in Scala, but some Python docs were missed.

### Does this PR introduce _any_ user-facing change?

No

### How was this patch tested?

Existing tests.

Closes #31883 from srowen/ImputerModeDocs.

Authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
2021-03-20 01:16:49 -05:00
Kousuke Saruta 03dd33cc98 [SPARK-25769][SPARK-34636][SPARK-34626][SQL] sql method in UnresolvedAttribute, AttributeReference and Alias don't quote qualified names properly
### What changes were proposed in this pull request?

This PR fixes an issue that `sql` method in the following classes which take qualified names don't quote the qualified names properly.

* UnresolvedAttribute
* AttributeReference
* Alias

One instance caused by this issue is reported in SPARK-34626.
```
UnresolvedAttribute("a" :: "b" :: Nil).sql
`a.b` // expected: `a`.`b`
```
And other instances are like as follows.
```
UnresolvedAttribute("a`b"::"c.d"::Nil).sql
a`b.`c.d` // expected: `a``b`.`c.d`

AttributeReference("a.b", IntegerType)(qualifier = "c.d"::Nil).sql
c.d.`a.b` // expected: `c.d`.`a.b`

Alias(AttributeReference("a", IntegerType)(), "b.c")(qualifier = "d.e"::Nil).sql
`a` AS d.e.`b.c` // expected: `a` AS `d.e`.`b.c`
```

### Why are the changes needed?

This is a bug.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

New test.

Closes #31754 from sarutak/fix-qualified-names.

Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
2021-03-12 02:58:46 +00:00