### What changes were proposed in this pull request?
Based on the discussion in the mailing list [[Proposal] Modification to Spark's Semantic Versioning Policy](http://apache-spark-developers-list.1001551.n3.nabble.com/Proposal-Modification-to-Spark-s-Semantic-Versioning-Policy-td28938.html) , this PR is to add back the following APIs whose maintenance cost are relatively small.
- HiveContext
- createExternalTable APIs
### Why are the changes needed?
Avoid breaking the APIs that are commonly used.
### Does this PR introduce any user-facing change?
Adding back the APIs that were removed in 3.0 branch does not introduce the user-facing changes, because Spark 3.0 has not been released.
### How was this patch tested?
add a new test suite for createExternalTable APIs.
Closes#27815 from gatorsmile/addAPIsBack.
Lead-authored-by: gatorsmile <gatorsmile@gmail.com>
Co-authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
- Remove SQLContext.createExternalTable and Catalog.createExternalTable, deprecated in favor of createTable since 2.2.0, plus tests of deprecated methods
- Remove HiveContext, deprecated in 2.0.0, in favor of `SparkSession.builder.enableHiveSupport`
- Remove deprecated KinesisUtils.createStream methods, plus tests of deprecated methods, deprecate in 2.2.0
- Remove deprecated MLlib (not Spark ML) linear method support, mostly utility constructors and 'train' methods, and associated docs. This includes methods in LinearRegression, LogisticRegression, Lasso, RidgeRegression. These have been deprecated since 2.0.0
- Remove deprecated Pyspark MLlib linear method support, including LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD
- Remove 'runs' argument in KMeans.train() method, which has been a no-op since 2.0.0
- Remove deprecated ChiSqSelector isSorted protected method
- Remove deprecated 'yarn-cluster' and 'yarn-client' master argument in favor of 'yarn' and deploy mode 'cluster', etc
Notes:
- I was not able to remove deprecated DataFrameReader.json(RDD) in favor of DataFrameReader.json(Dataset); the former was deprecated in 2.2.0, but, it is still needed to support Pyspark's .json() method, which can't use a Dataset.
- Looks like SQLContext.createExternalTable was not actually deprecated in Pyspark, but, almost certainly was meant to be? Catalog.createExternalTable was.
- I afterwards noted that the toDegrees, toRadians functions were almost removed fully in SPARK-25908, but Felix suggested keeping just the R version as they hadn't been technically deprecated. I'd like to revisit that. Do we really want the inconsistency? I'm not against reverting it again, but then that implies leaving SQLContext.createExternalTable just in Pyspark too, which seems weird.
- I *kept* LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD, RidgeRegressionWithSGD in Pyspark, though deprecated, as it is hard to remove them (still used by StreamingLogisticRegressionWithSGD?) and they are not fully removed in Scala. Maybe should not have been deprecated.
### Why are the changes needed?
Deprecated items are easiest to remove in a major release, so we should do so as much as possible for Spark 3. This does not target items deprecated 'recently' as of Spark 2.3, which is still 18 months old.
### Does this PR introduce any user-facing change?
Yes, in that deprecated items are removed from some public APIs.
### How was this patch tested?
Existing tests.
Closes#25684 from srowen/SPARK-28980.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Follow up for SPARK-24665, find some others hard code during code review.
## How was this patch tested?
Existing UT.
Closes#22122 from xuanyuanking/SPARK-24665-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
The exit() builtin is only for interactive use. applications should use sys.exit().
## What changes were proposed in this pull request?
All usage of the builtin `exit()` function is replaced by `sys.exit()`.
## How was this patch tested?
I ran `python/run-tests`.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Benjamin Peterson <benjamin@python.org>
Closes#20682 from benjaminp/sys-exit.
## What changes were proposed in this pull request?
This PR proposes to deprecate `register*` for UDFs in `SQLContext` and `Catalog` in Spark 2.3.0.
These are inconsistent with Scala / Java APIs and also these basically do the same things with `spark.udf.register*`.
Also, this PR moves the logcis from `[sqlContext|spark.catalog].register*` to `spark.udf.register*` and reuse the docstring.
This PR also handles minor doc corrections. It also includes https://github.com/apache/spark/pull/20158
## How was this patch tested?
Manually tested, manually checked the API documentation and tests added to check if deprecated APIs call the aliases correctly.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20288 from HyukjinKwon/deprecate-udf.
## What changes were proposed in this pull request?
Register Vectorized UDFs for SQL Statement. For example,
```Python
>>> from pyspark.sql.functions import pandas_udf, PandasUDFType
>>> pandas_udf("integer", PandasUDFType.SCALAR)
... def add_one(x):
... return x + 1
...
>>> _ = spark.udf.register("add_one", add_one)
>>> spark.sql("SELECT add_one(id) FROM range(3)").collect()
[Row(add_one(id)=1), Row(add_one(id)=2), Row(add_one(id)=3)]
```
## How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20171 from gatorsmile/supportVectorizedUDF.
## What changes were proposed in this pull request?
```Python
import random
from pyspark.sql.functions import udf
from pyspark.sql.types import IntegerType, StringType
random_udf = udf(lambda: int(random.random() * 100), IntegerType()).asNondeterministic()
spark.catalog.registerFunction("random_udf", random_udf, StringType())
spark.sql("SELECT random_udf()").collect()
```
We will get the following error.
```
Py4JError: An error occurred while calling o29.__getnewargs__. Trace:
py4j.Py4JException: Method __getnewargs__([]) 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:214)
at java.lang.Thread.run(Thread.java:745)
```
This PR is to support it.
## How was this patch tested?
WIP
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20137 from gatorsmile/registerFunction.
## What changes were proposed in this pull request?
* Add a "function type" argument to pandas_udf.
* Add a new public enum class `PandasUdfType` in pyspark.sql.functions
* Refactor udf related code from pyspark.sql.functions to pyspark.sql.udf
* Merge "PythonUdfType" and "PythonEvalType" into a single enum class "PythonEvalType"
Example:
```
from pyspark.sql.functions import pandas_udf, PandasUDFType
pandas_udf('double', PandasUDFType.SCALAR):
def plus_one(v):
return v + 1
```
## Design doc
https://docs.google.com/document/d/1KlLaa-xJ3oz28xlEJqXyCAHU3dwFYkFs_ixcUXrJNTc/edit
## How was this patch tested?
Added PandasUDFTests
## TODO:
* [x] Implement proper enum type for `PandasUDFType`
* [x] Update documentation
* [x] Add more tests in PandasUDFTests
Author: Li Jin <ice.xelloss@gmail.com>
Closes#19630 from icexelloss/spark-22409-pandas-udf-type.
## What changes were proposed in this pull request?
- Move udf wrapping code from `functions.udf` to `functions.UserDefinedFunction`.
- Return wrapped udf from `catalog.registerFunction` and dependent methods.
- Update docstrings in `catalog.registerFunction` and `SQLContext.registerFunction`.
- Unit tests.
## How was this patch tested?
- Existing unit tests and docstests.
- Additional tests covering new feature.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17831 from zero323/SPARK-18777.
## What changes were proposed in this pull request?
Update doc to remove external for createTable, add refreshByPath in python
## How was this patch tested?
manual
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17512 from felixcheung/catalogdoc.
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/16296 , we reached a consensus that we should hide the external/managed table concept to users and only expose custom table path.
This PR renames `Catalog.createExternalTable` to `createTable`(still keep the old versions for backward compatibility), and only set the table type to EXTERNAL if `path` is specified in options.
## How was this patch tested?
new tests in `CatalogSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16528 from cloud-fan/create-table.
### What changes were proposed in this pull request?
Currently, we only have a SQL interface for recovering all the partitions in the directory of a table and update the catalog. `MSCK REPAIR TABLE` or `ALTER TABLE table RECOVER PARTITIONS`. (Actually, very hard for me to remember `MSCK` and have no clue what it means)
After the new "Scalable Partition Handling", the table repair becomes much more important for making visible the data in the created data source partitioned table.
Thus, this PR is to add it into the Catalog interface. After this PR, users can repair the table by
```Scala
spark.catalog.recoverPartitions("testTable")
```
### How was this patch tested?
Modified the existing test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16356 from gatorsmile/repairTable.
## What changes were proposed in this pull request?
address post hoc review comments for https://github.com/apache/spark/pull/14897
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15424 from cloud-fan/global-temp-view.
## What changes were proposed in this pull request?
Global temporary view is a cross-session temporary view, which means it's shared among all sessions. Its lifetime is the lifetime of the Spark application, i.e. it will be automatically dropped when the application terminates. It's tied to a system preserved database `global_temp`(configurable via SparkConf), and we must use the qualified name to refer a global temp view, e.g. SELECT * FROM global_temp.view1.
changes for `SessionCatalog`:
1. add a new field `gloabalTempViews: GlobalTempViewManager`, to access the shared global temp views, and the global temp db name.
2. `createDatabase` will fail if users wanna create `global_temp`, which is system preserved.
3. `setCurrentDatabase` will fail if users wanna set `global_temp`, which is system preserved.
4. add `createGlobalTempView`, which is used in `CreateViewCommand` to create global temp views.
5. add `dropGlobalTempView`, which is used in `CatalogImpl` to drop global temp view.
6. add `alterTempViewDefinition`, which is used in `AlterViewAsCommand` to update the view definition for local/global temp views.
7. `renameTable`/`dropTable`/`isTemporaryTable`/`lookupRelation`/`getTempViewOrPermanentTableMetadata`/`refreshTable` will handle global temp views.
changes for SQL commands:
1. `CreateViewCommand`/`AlterViewAsCommand` is updated to support global temp views
2. `ShowTablesCommand` outputs a new column `database`, which is used to distinguish global and local temp views.
3. other commands can also handle global temp views if they call `SessionCatalog` APIs which accepts global temp views, e.g. `DropTableCommand`, `AlterTableRenameCommand`, `ShowColumnsCommand`, etc.
changes for other public API
1. add a new method `dropGlobalTempView` in `Catalog`
2. `Catalog.findTable` can find global temp view
3. add a new method `createGlobalTempView` in `Dataset`
## How was this patch tested?
new tests in `SQLViewSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14897 from cloud-fan/global-temp-view.
## What changes were proposed in this pull request?
Add Catalog.refreshTable API into python interface for Spark-SQL.
## How was this patch tested?
Existing test.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#13558 from WeichenXu123/update_python_sql_interface_refreshTable.
## 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?
Deprecates registerTempTable and add dataset.createTempView, dataset.createOrReplaceTempView.
## How was this patch tested?
Unit tests.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#12945 from clockfly/spark-15171.
## What changes were proposed in this pull request?
Addresses comments in #12765.
## How was this patch tested?
Python tests.
Author: Andrew Or <andrew@databricks.com>
Closes#12784 from andrewor14/python-followup.
## What changes were proposed in this pull request?
The `catalog` and `conf` APIs were exposed in `SparkSession` in #12713 and #12669. This patch adds those to the python API.
## How was this patch tested?
Python tests.
Author: Andrew Or <andrew@databricks.com>
Closes#12765 from andrewor14/python-spark-session-more.
