Since we rename the column name from ```text``` to ```value``` for DataFrame load by ```SQLContext.read.text```, we need to update doc.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10349 from yanboliang/text-value.
when invFunc is None, `reduceByKeyAndWindow(func, None, winsize, slidesize)` is equivalent to
reduceByKey(func).window(winsize, slidesize).reduceByKey(winsize, slidesize)
and no checkpoint is necessary. The corresponding Scala code does exactly that, but Python code always creates a windowed stream with obligatory checkpointing. The patch fixes this.
I do not know how to unit-test this.
Author: David Tolpin <david.tolpin@gmail.com>
Closes#9888 from dtolpin/master.
MLlib should use SQLContext.getOrCreate() instead of creating new SQLContext.
Author: Davies Liu <davies@databricks.com>
Closes#10338 from davies/create_context.
Extend CrossValidator with HasSeed in PySpark.
This PR replaces [https://github.com/apache/spark/pull/7997]
CC: yanboliang thunterdb mmenestret Would one of you mind taking a look? Thanks!
Author: Joseph K. Bradley <joseph@databricks.com>
Author: Martin MENESTRET <mmenestret@ippon.fr>
Closes#10268 from jkbradley/pyspark-cv-seed.
JIRA: https://issues.apache.org/jira/browse/SPARK-12016
We should not directly use Word2VecModel in pyspark. We need to wrap it in a Word2VecModelWrapper when loading it in pyspark.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#10100 from viirya/fix-load-py-wordvecmodel.
Adding ability to define an initial state RDD for use with updateStateByKey PySpark. Added unit test and changed stateful_network_wordcount example to use initial RDD.
Author: Bryan Cutler <bjcutler@us.ibm.com>
Closes#10082 from BryanCutler/initial-rdd-updateStateByKey-SPARK-11713.
This PR adds a `private[sql]` method `metadata` to `SparkPlan`, which can be used to describe detail information about a physical plan during visualization. Specifically, this PR uses this method to provide details of `PhysicalRDD`s translated from a data source relation. For example, a `ParquetRelation` converted from Hive metastore table `default.psrc` is now shown as the following screenshot:
And here is the screenshot for a regular `ParquetRelation` (not converted from Hive metastore table) loaded from a really long path:
Author: Cheng Lian <lian@databricks.com>
Closes#10004 from liancheng/spark-12012.physical-rdd-metadata.
In SPARK-11946 the API for pivot was changed a bit and got updated doc, the doc changes were not made for the python api though. This PR updates the python doc to be consistent.
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#10176 from aray/sql-pivot-python-doc.
Currently, the current line is not cleared by Cltr-C
After this patch
```
>>> asdfasdf^C
Traceback (most recent call last):
File "~/spark/python/pyspark/context.py", line 225, in signal_handler
raise KeyboardInterrupt()
KeyboardInterrupt
```
It's still worse than 1.5 (and before).
Author: Davies Liu <davies@databricks.com>
Closes#10134 from davies/fix_cltrc.
Python tests require access to the `KinesisTestUtils` file. When this file exists under src/test, python can't access it, since it is not available in the assembly jar.
However, if we move KinesisTestUtils to src/main, we need to add the KinesisProducerLibrary as a dependency. In order to avoid this, I moved KinesisTestUtils to src/main, and extended it with ExtendedKinesisTestUtils which is under src/test that adds support for the KPL.
cc zsxwing tdas
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#10050 from brkyvz/kinesis-py.
Use ```coefficients``` replace ```weights```, I wish they are the last two.
mengxr
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10065 from yanboliang/coefficients.
Fixed a minor race condition in #10017Closes#10017
Author: jerryshao <sshao@hortonworks.com>
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10074 from zsxwing/review-pr10017.
Added Python test cases for the function `isnan`, `isnull`, `nanvl` and `json_tuple`.
Fixed a bug in the function `json_tuple`
rxin , could you help me review my changes? Please let me know anything is missing.
Thank you! Have a good Thanksgiving day!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#9977 from gatorsmile/json_tuple.
The Python exception track in TransformFunction and TransformFunctionSerializer is not sent back to Java. Py4j just throws a very general exception, which is hard to debug.
This PRs adds `getFailure` method to get the failure message in Java side.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#9922 from zsxwing/SPARK-11935.
Currently, we does not have visualization for SQL query from Python, this PR fix that.
cc zsxwing
Author: Davies Liu <davies@databricks.com>
Closes#9949 from davies/pyspark_sql_ui.
This patch makes it consistent to use varargs in all DataFrameReader methods, including Parquet, JSON, text, and the generic load function.
Also added a few more API tests for the Java API.
Author: Reynold Xin <rxin@databricks.com>
Closes#9945 from rxin/SPARK-11967.
Currently pivot's signature looks like
```scala
scala.annotation.varargs
def pivot(pivotColumn: Column, values: Column*): GroupedData
scala.annotation.varargs
def pivot(pivotColumn: String, values: Any*): GroupedData
```
I think we can remove the one that takes "Column" types, since callers should always be passing in literals. It'd also be more clear if the values are not varargs, but rather Seq or java.util.List.
I also made similar changes for Python.
Author: Reynold Xin <rxin@databricks.com>
Closes#9929 from rxin/SPARK-11946.
This is to bring the API documentation of StreamingLogisticReressionWithSGD and StreamingLinearRegressionWithSGC in line with the Scala versions.
-Fixed the algorithm descriptions
-Added default values to parameter descriptions
-Changed StreamingLogisticRegressionWithSGD regParam to default to 0, as in the Scala version
Author: Bryan Cutler <bjcutler@us.ibm.com>
Closes#9141 from BryanCutler/StreamingLogisticRegressionWithSGD-python-api-sync.
TransformFunction and TransformFunctionSerializer don't rethrow the exception, so when any exception happens, it just return None. This will cause some weird NPE and confuse people.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#9847 from zsxwing/pyspark-streaming-exception.
* Update doc for PySpark ```HasCheckpointInterval``` that users can understand how to disable checkpoint.
* Update doc for PySpark ```cacheNodeIds``` of ```DecisionTreeParams``` to notify the relationship between ```cacheNodeIds``` and ```checkpointInterval```.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9856 from yanboliang/spark-11875.
invFunc is optional and can be None. Instead of invFunc (the parameter) invReduceFunc (a local function) was checked for trueness (that is, not None, in this context). A local function is never None,
thus the case of invFunc=None (a common one when inverse reduction is not defined) was treated incorrectly, resulting in loss of data.
In addition, the docstring used wrong parameter names, also fixed.
Author: David Tolpin <david.tolpin@gmail.com>
Closes#9775 from dtolpin/master.
return Double.NaN for mean/average when count == 0 for all numeric types that is converted to Double, Decimal type continue to return null.
Author: JihongMa <linlin200605@gmail.com>
Closes#9705 from JihongMA/SPARK-11720.
We will do checkpoint when generating a batch and completing a batch. When the processing time of a batch is greater than the batch interval, checkpointing for completing an old batch may run after checkpointing for generating a new batch. If this happens, checkpoint of an old batch actually has the latest information, so we want to recovery from it. This PR will use the latest checkpoint time as the file name, so that we can always recovery from the latest checkpoint file.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#9707 from zsxwing/fix-checkpoint.
This patch adds the following options to the JSON data source, for dealing with non-standard JSON files:
* `allowComments` (default `false`): ignores Java/C++ style comment in JSON records
* `allowUnquotedFieldNames` (default `false`): allows unquoted JSON field names
* `allowSingleQuotes` (default `true`): allows single quotes in addition to double quotes
* `allowNumericLeadingZeros` (default `false`): allows leading zeros in numbers (e.g. 00012)
To avoid passing a lot of options throughout the json package, I introduced a new JSONOptions case class to define all JSON config options.
Also updated documentation to explain these options.
Scala
Python
Author: Reynold Xin <rxin@databricks.com>
Closes#9724 from rxin/SPARK-11745.
This PR adds pivot to the python api of GroupedData with the same syntax as Scala/Java.
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#9653 from aray/sql-pivot-python.
This PR just checks the test results and returns 1 if the test fails, so that `run-tests.py` can mark it fail.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#9669 from zsxwing/streaming-python-tests.
switched stddev support from DeclarativeAggregate to ImperativeAggregate.
Author: JihongMa <linlin200605@gmail.com>
Closes#9380 from JihongMA/SPARK-11420.
Only install signal in main thread, or it will fail to create context in not-main thread.
Author: Davies Liu <davies@databricks.com>
Closes#9574 from davies/python_signal.
https://issues.apache.org/jira/browse/SPARK-9830
This PR contains the following main changes.
* Removing `AggregateExpression1`.
* Removing `Aggregate` operator, which is used to evaluate `AggregateExpression1`.
* Removing planner rule used to plan `Aggregate`.
* Linking `MultipleDistinctRewriter` to analyzer.
* Renaming `AggregateExpression2` to `AggregateExpression` and `AggregateFunction2` to `AggregateFunction`.
* Updating places where we create aggregate expression. The way to create aggregate expressions is `AggregateExpression(aggregateFunction, mode, isDistinct)`.
* Changing `val`s in `DeclarativeAggregate`s that touch children of this function to `lazy val`s (when we create aggregate expression in DataFrame API, children of an aggregate function can be unresolved).
Author: Yin Huai <yhuai@databricks.com>
Closes#9556 from yhuai/removeAgg1.
For now they are thin wrappers around the corresponding Hive UDAFs.
One limitation with these in Hive 0.13.0 is they only support aggregating primitive types.
I chose snake_case here instead of camelCase because it seems to be used in the majority of the multi-word fns.
Do we also want to add these to `functions.py`?
This approach was recommended here: https://github.com/apache/spark/pull/8592#issuecomment-154247089
marmbrus rxin
Author: Nick Buroojy <nick.buroojy@civitaslearning.com>
Closes#9526 from nburoojy/nick/udaf-alias.
(cherry picked from commit a6ee4f989d)
Signed-off-by: Michael Armbrust <michael@databricks.com>
Could jkbradley and davies review it?
- Create a wrapper class: `LDAModelWrapper` for `LDAModel`. Because we can't deal with the return value of`describeTopics` in Scala from pyspark directly. `Array[(Array[Int], Array[Double])]` is too complicated to convert it.
- Add `loadLDAModel` in `PythonMLlibAPI`. Since `LDAModel` in Scala is an abstract class and we need to call `load` of `DistributedLDAModel`.
[[SPARK-8467] Add LDAModel.describeTopics() in Python - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-8467)
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes#8643 from yu-iskw/SPARK-8467-2.
