## What changes were proposed in this pull request?
This PR proposes to add `to_json` function in contrast with `from_json` in Scala, Java and Python.
It'd be useful if we can convert a same column from/to json. Also, some datasources do not support nested types. If we are forced to save a dataframe into those data sources, we might be able to work around by this function.
The usage is as below:
``` scala
val df = Seq(Tuple1(Tuple1(1))).toDF("a")
df.select(to_json($"a").as("json")).show()
```
``` bash
+--------+
| json|
+--------+
|{"_1":1}|
+--------+
```
## How was this patch tested?
Unit tests in `JsonFunctionsSuite` and `JsonExpressionsSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15354 from HyukjinKwon/SPARK-17764.
## What changes were proposed in this pull request?
API and programming guide doc changes for Scala, Python and R.
## How was this patch tested?
manual test
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15629 from felixcheung/jsondoc.
## What changes were proposed in this pull request?
StreamingQueryStatus exposed through StreamingQueryListener often needs to be recorded (similar to SparkListener events). This PR adds `.json` and `.prettyJson` to `StreamingQueryStatus`, `SourceStatus` and `SinkStatus`.
## How was this patch tested?
New unit tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#15476 from tdas/SPARK-17926.
## What changes were proposed in this pull request?
Add a crossJoin function to the DataFrame API similar to that in Scala. Joins with no condition (cartesian products) must be specified with the crossJoin API
## How was this patch tested?
Added python tests to ensure that an AnalysisException if a cartesian product is specified without crossJoin(), and that cartesian products can execute if specified via crossJoin()
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request.
Author: Srinath Shankar <srinath@databricks.com>
Closes#15493 from srinathshankar/crosspython.
Currently pyspark can only call the builtin java UDF, but can not call custom java UDF. It would be better to allow that. 2 benefits:
* Leverage the power of rich third party java library
* Improve the performance. Because if we use python UDF, python daemons will be started on worker which will affect the performance.
Author: Jeff Zhang <zjffdu@apache.org>
Closes#9766 from zjffdu/SPARK-11775.
[SPARK-11905](https://issues.apache.org/jira/browse/SPARK-11905) added support for `persist`/`cache` for `Dataset`. However, there is no user-facing API to check if a `Dataset` is cached and if so what the storage level is. This PR adds `getStorageLevel` to `Dataset`, analogous to `RDD.getStorageLevel`.
Updated `DatasetCacheSuite`.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#13780 from MLnick/ds-storagelevel.
Signed-off-by: Michael Armbrust <michael@databricks.com>
## What changes were proposed in this pull request?
Metrics are needed for monitoring structured streaming apps. Here is the design doc for implementing the necessary metrics.
https://docs.google.com/document/d/1NIdcGuR1B3WIe8t7VxLrt58TJB4DtipWEbj5I_mzJys/edit?usp=sharing
Specifically, this PR adds the following public APIs changes.
### New APIs
- `StreamingQuery.status` returns a `StreamingQueryStatus` object (renamed from `StreamingQueryInfo`, see later)
- `StreamingQueryStatus` has the following important fields
- inputRate - Current rate (rows/sec) at which data is being generated by all the sources
- processingRate - Current rate (rows/sec) at which the query is processing data from
all the sources
- ~~outputRate~~ - *Does not work with wholestage codegen*
- latency - Current average latency between the data being available in source and the sink writing the corresponding output
- sourceStatuses: Array[SourceStatus] - Current statuses of the sources
- sinkStatus: SinkStatus - Current status of the sink
- triggerStatus - Low-level detailed status of the last completed/currently active trigger
- latencies - getOffset, getBatch, full trigger, wal writes
- timestamps - trigger start, finish, after getOffset, after getBatch
- numRows - input, output, state total/updated rows for aggregations
- `SourceStatus` has the following important fields
- inputRate - Current rate (rows/sec) at which data is being generated by the source
- processingRate - Current rate (rows/sec) at which the query is processing data from the source
- triggerStatus - Low-level detailed status of the last completed/currently active trigger
- Python API for `StreamingQuery.status()`
### Breaking changes to existing APIs
**Existing direct public facing APIs**
- Deprecated direct public-facing APIs `StreamingQuery.sourceStatuses` and `StreamingQuery.sinkStatus` in favour of `StreamingQuery.status.sourceStatuses/sinkStatus`.
- Branch 2.0 should have it deprecated, master should have it removed.
**Existing advanced listener APIs**
- `StreamingQueryInfo` renamed to `StreamingQueryStatus` for consistency with `SourceStatus`, `SinkStatus`
- Earlier StreamingQueryInfo was used only in the advanced listener API, but now it is used in direct public-facing API (StreamingQuery.status)
- Field `queryInfo` in listener events `QueryStarted`, `QueryProgress`, `QueryTerminated` changed have name `queryStatus` and return type `StreamingQueryStatus`.
- Field `offsetDesc` in `SourceStatus` was Option[String], converted it to `String`.
- For `SourceStatus` and `SinkStatus` made constructor private instead of private[sql] to make them more java-safe. Instead added `private[sql] object SourceStatus/SinkStatus.apply()` which are harder to accidentally use in Java.
## How was this patch tested?
Old and new unit tests.
- Rate calculation and other internal logic of StreamMetrics tested by StreamMetricsSuite.
- New info in statuses returned through StreamingQueryListener is tested in StreamingQueryListenerSuite.
- New and old info returned through StreamingQuery.status is tested in StreamingQuerySuite.
- Source-specific tests for making sure input rows are counted are is source-specific test suites.
- Additional tests to test minor additions in LocalTableScanExec, StateStore, etc.
Metrics also manually tested using Ganglia sink
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#15307 from tdas/SPARK-17731.
## What changes were proposed in this pull request?
This patch improves the window function frame boundary API to make it more obvious to read and to use. The two high level changes are:
1. Create Window.currentRow, Window.unboundedPreceding, Window.unboundedFollowing to indicate the special values in frame boundaries. These methods map to the special integral values so we are not breaking backward compatibility here. This change makes the frame boundaries more self-evident (instead of Long.MinValue, it becomes Window.unboundedPreceding).
2. In Python, for any value less than or equal to JVM's Long.MinValue, treat it as Window.unboundedPreceding. For any value larger than or equal to JVM's Long.MaxValue, treat it as Window.unboundedFollowing. Before this change, if the user specifies any value that is less than Long.MinValue but not -sys.maxsize (e.g. -sys.maxsize + 1), the number we pass over to the JVM would overflow, resulting in a frame that does not make sense.
Code example required to specify a frame before this patch:
```
Window.rowsBetween(-Long.MinValue, 0)
```
While the above code should still work, the new way is more obvious to read:
```
Window.rowsBetween(Window.unboundedPreceding, Window.currentRow)
```
## How was this patch tested?
- Updated DataFrameWindowSuite (for Scala/Java)
- Updated test_window_functions_cumulative_sum (for Python)
- Renamed DataFrameWindowSuite DataFrameWindowFunctionsSuite to better reflect its purpose
Author: Reynold Xin <rxin@databricks.com>
Closes#15438 from rxin/SPARK-17845.
## What changes were proposed in this pull request?
In PySpark, the invalid join type will not throw error for the following join:
```df1.join(df2, how='not-a-valid-join-type')```
The signature of the join is:
```def join(self, other, on=None, how=None):```
The existing code completely ignores the `how` parameter when `on` is `None`. This patch will process the arguments passed to join and pass in to JVM Spark SQL Analyzer, which will validate the join type passed.
## How was this patch tested?
Used manual and existing test suites.
Author: Bijay Pathak <bkpathak@mtu.edu>
Closes#15409 from bkpathak/SPARK-14761.
## What changes were proposed in this pull request?
SQLConf is session-scoped and mutable. However, we do have the requirement for a static SQL conf, which is global and immutable, e.g. the `schemaStringThreshold` in `HiveExternalCatalog`, the flag to enable/disable hive support, the global temp view database in https://github.com/apache/spark/pull/14897.
Actually we've already implemented static SQL conf implicitly via `SparkConf`, this PR just make it explicit and expose it to users, so that they can see the config value via SQL command or `SparkSession.conf`, and forbid users to set/unset static SQL conf.
## How was this patch tested?
new tests in SQLConfSuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15295 from cloud-fan/global-conf.
## 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?
Upgraded to a newer version of Pyrolite which supports serialization of a BinaryType StructField for PySpark.SQL
## How was this patch tested?
Added a unit test which fails with a raised ValueError when using the previous version of Pyrolite 4.9 and Python3
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#15386 from BryanCutler/pyrolite-upgrade-SPARK-17808.
## What changes were proposed in this pull request?
When I was creating the example code for SPARK-10496, I realized it was pretty convoluted to define the frame boundaries for window functions when there is no partition column or ordering column. The reason is that we don't provide a way to create a WindowSpec directly with the frame boundaries. We can trivially improve this by adding rowsBetween and rangeBetween to Window object.
As an example, to compute cumulative sum using the natural ordering, before this pr:
```
df.select('key, sum("value").over(Window.partitionBy(lit(1)).rowsBetween(Long.MinValue, 0)))
```
After this pr:
```
df.select('key, sum("value").over(Window.rowsBetween(Long.MinValue, 0)))
```
Note that you could argue there is no point specifying a window frame without partitionBy/orderBy -- but it is strange that only rowsBetween and rangeBetween are not the only two APIs not available.
This also fixes https://issues.apache.org/jira/browse/SPARK-17656 (removing _root_.scala).
## How was this patch tested?
Added test cases to compute cumulative sum in DataFrameWindowSuite for Scala/Java and tests.py for Python.
Author: Reynold Xin <rxin@databricks.com>
Closes#15412 from rxin/SPARK-17844.
## 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?
If given a list of paths, `pyspark.sql.readwriter.text` will attempt to use an undefined variable `paths`. This change checks if the param `paths` is a basestring and then converts it to a list, so that the same variable `paths` can be used for both cases
## How was this patch tested?
Added unit test for reading list of files
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#15379 from BryanCutler/sql-readtext-paths-SPARK-17805.
Spark SQL has great support for reading text files that contain JSON data. However, in many cases the JSON data is just one column amongst others. This is particularly true when reading from sources such as Kafka. This PR adds a new functions `from_json` that converts a string column into a nested `StructType` with a user specified schema.
Example usage:
```scala
val df = Seq("""{"a": 1}""").toDS()
val schema = new StructType().add("a", IntegerType)
df.select(from_json($"value", schema) as 'json) // => [json: <a: int>]
```
This PR adds support for java, scala and python. I leveraged our existing JSON parsing support by moving it into catalyst (so that we could define expressions using it). I left SQL out for now, because I'm not sure how users would specify a schema.
Author: Michael Armbrust <michael@databricks.com>
Closes#15274 from marmbrus/jsonParser.
## What changes were proposed in this pull request?
This PR includes the changes below:
1. Upgrade Univocity library from 2.1.1 to 2.2.1
This includes some performance improvement and also enabling auto-extending buffer in `maxCharsPerColumn` option in CSV. Please refer the [release notes](https://github.com/uniVocity/univocity-parsers/releases).
2. Remove useless `rowSeparator` variable existing in `CSVOptions`
We have this unused variable in [CSVOptions.scala#L127](29952ed096/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVOptions.scala (L127)) but it seems possibly causing confusion that it actually does not care of `\r\n`. For example, we have an issue open about this, [SPARK-17227](https://issues.apache.org/jira/browse/SPARK-17227), describing this variable.
This variable is virtually not being used because we rely on `LineRecordReader` in Hadoop which deals with only both `\n` and `\r\n`.
3. Set the default value of `maxCharsPerColumn` to auto-expending.
We are setting 1000000 for the length of each column. It'd be more sensible we allow auto-expending rather than fixed length by default.
To make sure, using `-1` is being described in the release note, [2.2.0](https://github.com/uniVocity/univocity-parsers/releases/tag/v2.2.0).
## How was this patch tested?
N/A
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15138 from HyukjinKwon/SPARK-17583.
## What changes were proposed in this pull request?
In optimizer, we try to evaluate the condition to see whether it's nullable or not, but some expressions are not evaluable, we should check that before evaluate it.
## How was this patch tested?
Added regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#15103 from davies/udf_join.
## Problem
CSV in Spark 2.0.0:
- does not read null values back correctly for certain data types such as `Boolean`, `TimestampType`, `DateType` -- this is a regression comparing to 1.6;
- does not read empty values (specified by `options.nullValue`) as `null`s for `StringType` -- this is compatible with 1.6 but leads to problems like SPARK-16903.
## What changes were proposed in this pull request?
This patch makes changes to read all empty values back as `null`s.
## How was this patch tested?
New test cases.
Author: Liwei Lin <lwlin7@gmail.com>
Closes#14118 from lw-lin/csv-cast-null.
## What changes were proposed in this pull request?
In PySpark, `df.take(1)` runs a single-stage job which computes only one partition of the DataFrame, while `df.limit(1).collect()` computes all partitions and runs a two-stage job. This difference in performance is confusing.
The reason why `limit(1).collect()` is so much slower is that `collect()` internally maps to `df.rdd.<some-pyspark-conversions>.toLocalIterator`, which causes Spark SQL to build a query where a global limit appears in the middle of the plan; this, in turn, ends up being executed inefficiently because limits in the middle of plans are now implemented by repartitioning to a single task rather than by running a `take()` job on the driver (this was done in #7334, a patch which was a prerequisite to allowing partition-local limits to be pushed beneath unions, etc.).
In order to fix this performance problem I think that we should generalize the fix from SPARK-10731 / #8876 so that `DataFrame.collect()` also delegates to the Scala implementation and shares the same performance properties. This patch modifies `DataFrame.collect()` to first collect all results to the driver and then pass them to Python, allowing this query to be planned using Spark's `CollectLimit` optimizations.
## How was this patch tested?
Added a regression test in `sql/tests.py` which asserts that the expected number of jobs, stages, and tasks are run for both queries.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#15068 from JoshRosen/pyspark-collect-limit.
## What changes were proposed in this pull request?
When there is any Python UDF in the Project between Sort and Limit, it will be collected into TakeOrderedAndProjectExec, ExtractPythonUDFs failed to pull the Python UDFs out because QueryPlan.expressions does not include the expression inside Option[Seq[Expression]].
Ideally, we should fix the `QueryPlan.expressions`, but tried with no luck (it always run into infinite loop). In PR, I changed the TakeOrderedAndProjectExec to no use Option[Seq[Expression]] to workaround it. cc JoshRosen
## How was this patch tested?
Added regression test.
Author: Davies Liu <davies@databricks.com>
Closes#15030 from davies/all_expr.
## What changes were proposed in this pull request?
Require the use of CROSS join syntax in SQL (and a new crossJoin
DataFrame API) to specify explicit cartesian products between relations.
By cartesian product we mean a join between relations R and S where
there is no join condition involving columns from both R and S.
If a cartesian product is detected in the absence of an explicit CROSS
join, an error must be thrown. Turning on the
"spark.sql.crossJoin.enabled" configuration flag will disable this check
and allow cartesian products without an explicit CROSS join.
The new crossJoin DataFrame API must be used to specify explicit cross
joins. The existing join(DataFrame) method will produce a INNER join
that will require a subsequent join condition.
That is df1.join(df2) is equivalent to select * from df1, df2.
## How was this patch tested?
Added cross-join.sql to the SQLQueryTestSuite to test the check for cartesian products. Added a couple of tests to the DataFrameJoinSuite to test the crossJoin API. Modified various other test suites to explicitly specify a cross join where an INNER join or a comma-separated list was previously used.
Author: Srinath Shankar <srinath@databricks.com>
Closes#14866 from srinathshankar/crossjoin.
## What changes were proposed in this pull request?
Set SparkSession._instantiatedContext as None so that we can recreate SparkSession again.
## How was this patch tested?
Tested manually using the following command in pyspark shell
```
spark.stop()
spark = SparkSession.builder.enableHiveSupport().getOrCreate()
spark.sql("show databases").show()
```
Author: Jeff Zhang <zjffdu@apache.org>
Closes#14857 from zjffdu/SPARK-17261.
## What changes were proposed in this pull request?
Clarify that only parquet files are supported by DataStreamWriter now
## How was this patch tested?
(Doc build -- no functional changes to test)
Author: Sean Owen <sowen@cloudera.com>
Closes#14860 from srowen/SPARK-17264.
## What changes were proposed in this pull request?
Method `SQLContext.parseDataType(dataTypeString: String)` could be removed, we should use `SparkSession.parseDataType(dataTypeString: String)` instead.
This require updating PySpark.
## How was this patch tested?
Existing test cases.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#14790 from jiangxb1987/parseDataType.
## What changes were proposed in this pull request?
### Default - ISO 8601
Currently, CSV datasource is writing `Timestamp` and `Date` as numeric form and JSON datasource is writing both as below:
- CSV
```
// TimestampType
1414459800000000
// DateType
16673
```
- Json
```
// TimestampType
1970-01-01 11:46:40.0
// DateType
1970-01-01
```
So, for CSV we can't read back what we write and for JSON it becomes ambiguous because the timezone is being missed.
So, this PR make both **write** `Timestamp` and `Date` in ISO 8601 formatted string (please refer the [ISO 8601 specification](https://www.w3.org/TR/NOTE-datetime)).
- For `Timestamp` it becomes as below: (`yyyy-MM-dd'T'HH:mm:ss.SSSZZ`)
```
1970-01-01T02:00:01.000-01:00
```
- For `Date` it becomes as below (`yyyy-MM-dd`)
```
1970-01-01
```
### Custom date format option - `dateFormat`
This PR also adds the support to write and read dates and timestamps in a formatted string as below:
- **DateType**
- With `dateFormat` option (e.g. `yyyy/MM/dd`)
```
+----------+
| date|
+----------+
|2015/08/26|
|2014/10/27|
|2016/01/28|
+----------+
```
### Custom date format option - `timestampFormat`
- **TimestampType**
- With `dateFormat` option (e.g. `dd/MM/yyyy HH:mm`)
```
+----------------+
| date|
+----------------+
|2015/08/26 18:00|
|2014/10/27 18:30|
|2016/01/28 20:00|
+----------------+
```
## How was this patch tested?
Unit tests were added in `CSVSuite` and `JsonSuite`. For JSON, existing tests cover the default cases.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#14279 from HyukjinKwon/SPARK-16216-json-csv.
## What changes were proposed in this pull request?
This should be credited to mvervuurt. The main purpose of this PR is
- simply to include the change for the same instance in `DataFrameReader` just to match up.
- just avoid duplicately verifying the PR (as I already did).
The documentation for both should be the same because both assume the `properties` should be the same `dict` for the same option.
## How was this patch tested?
Manually building Python documentation.
This will produce the output as below:
- `DataFrameReader`
- `DataFrameWriter`
Closes#14624
Author: hyukjinkwon <gurwls223@gmail.com>
Author: mvervuurt <m.a.vervuurt@gmail.com>
Closes#14677 from HyukjinKwon/typo-python.
## What changes were proposed in this pull request?
`PySpark` loses `microsecond` precision for some corner cases during converting `Timestamp` into `Long`. For example, for the following `datetime.max` value should be converted a value whose last 6 digits are '999999'. This PR improves the logic not to lose precision for all cases.
**Corner case**
```python
>>> datetime.datetime.max
datetime.datetime(9999, 12, 31, 23, 59, 59, 999999)
```
**Before**
```python
>>> from datetime import datetime
>>> from pyspark.sql import Row
>>> from pyspark.sql.types import StructType, StructField, TimestampType
>>> schema = StructType([StructField("dt", TimestampType(), False)])
>>> [schema.toInternal(row) for row in [{"dt": datetime.max}]]
[(253402329600000000,)]
```
**After**
```python
>>> [schema.toInternal(row) for row in [{"dt": datetime.max}]]
[(253402329599999999,)]
```
## How was this patch tested?
Pass the Jenkins test with a new test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14631 from dongjoon-hyun/SPARK-17035.
## What changes were proposed in this pull request?
In 2.0, we verify the data type against schema for every row for safety, but with performance cost, this PR make it optional.
When we verify the data type for StructType, it does not support all the types we support in infer schema (for example, dict), this PR fix that to make them consistent.
For Row object which is created using named arguments, the order of fields are sorted by name, they may be not different than the order in provided schema, this PR fix that by ignore the order of fields in this case.
## How was this patch tested?
Created regression tests for them.
Author: Davies Liu <davies@databricks.com>
Closes#14469 from davies/py_dict.
## What changes were proposed in this pull request?
Doc that regexp_extract returns empty string when regex or group does not match
## How was this patch tested?
Jenkins test, with a few new test cases
Author: Sean Owen <sowen@cloudera.com>
Closes#14525 from srowen/SPARK-16324.
## What changes were proposed in this pull request?
regexp_extract actually returns null when it shouldn't when a regex matches but the requested optional group did not. This makes it return an empty string, as apparently designed.
## How was this patch tested?
Additional unit test
Author: Sean Owen <sowen@cloudera.com>
Closes#14504 from srowen/SPARK-16409.
## Proposed Changes
* Update the list of "important classes" in `pyspark.sql` to match 2.0.
* Fix references to `UDFRegistration` so that the class shows up in the docs. It currently [doesn't](http://spark.apache.org/docs/latest/api/python/pyspark.sql.html).
* Remove some unnecessary whitespace in the Python RST doc files.
I reused the [existing JIRA](https://issues.apache.org/jira/browse/SPARK-16772) I created last week for similar API doc fixes.
## How was this patch tested?
* I ran `lint-python` successfully.
* I ran `make clean build` on the Python docs and confirmed the results are as expected locally in my browser.
Author: Nicholas Chammas <nicholas.chammas@gmail.com>
Closes#14496 from nchammas/SPARK-16772-UDFRegistration.
## What changes were proposed in this pull request?
There are two related bugs of Python-only UDTs. Because the test case of second one needs the first fix too. I put them into one PR. If it is not appropriate, please let me know.
### First bug: When MapObjects works on Python-only UDTs
`RowEncoder` will use `PythonUserDefinedType.sqlType` for its deserializer expression. If the sql type is `ArrayType`, we will have `MapObjects` working on it. But `MapObjects` doesn't consider `PythonUserDefinedType` as its input data type. It causes error like:
import pyspark.sql.group
from pyspark.sql.tests import PythonOnlyPoint, PythonOnlyUDT
from pyspark.sql.types import *
schema = StructType().add("key", LongType()).add("val", PythonOnlyUDT())
df = spark.createDataFrame([(i % 3, PythonOnlyPoint(float(i), float(i))) for i in range(10)], schema=schema)
df.show()
File "/home/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line 312, in get_return_value py4j.protocol.Py4JJavaError: An error occurred while calling o36.showString.
: java.lang.RuntimeException: Error while decoding: scala.MatchError: org.apache.spark.sql.types.PythonUserDefinedTypef4ceede8 (of class org.apache.spark.sql.types.PythonUserDefinedType)
...
### Second bug: When Python-only UDTs is the element type of ArrayType
import pyspark.sql.group
from pyspark.sql.tests import PythonOnlyPoint, PythonOnlyUDT
from pyspark.sql.types import *
schema = StructType().add("key", LongType()).add("val", ArrayType(PythonOnlyUDT()))
df = spark.createDataFrame([(i % 3, [PythonOnlyPoint(float(i), float(i))]) for i in range(10)], schema=schema)
df.show()
## How was this patch tested?
PySpark's sql tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#13778 from viirya/fix-pyudt.
## What changes were proposed in this pull request?
This PR corrects [an error made in an earlier PR](https://github.com/apache/spark/pull/14393/files#r72843069).
## How was this patch tested?
```sh
$ ./dev/lint-python
PEP8 checks passed.
rm -rf _build/*
pydoc checks passed.
```
I also built the docs and confirmed that they looked good in my browser.
Author: Nicholas Chammas <nicholas.chammas@gmail.com>
Closes#14408 from nchammas/SPARK-16772.
## What changes were proposed in this pull request?
move the `HiveContext` deprecate warning printing statement into `HiveContext` constructor.
so that this warning will appear only when we use `HiveContext`
otherwise this warning will always appear if we reference the pyspark.ml.context code file.
## How was this patch tested?
Manual.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#14301 from WeichenXu123/hiveContext_python_warning_update.
## What changes were proposed in this pull request?
`withColumnRenamed` and `drop` is a no-op if the given column name does not exists. Python documentation also describe that, but this PR adds more explicit line consistently with Scala to reduce the ambiguity.
## How was this patch tested?
It's about docs.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14288 from dongjoon-hyun/SPARK-16651.
## What changes were proposed in this pull request?
doc change only
## How was this patch tested?
doc change only
Author: Mortada Mehyar <mortada.mehyar@gmail.com>
Closes#14253 from mortada/histogram_typos.
## What changes were proposed in this pull request?
Make `dataframe.drop` API in python support multi-columns parameters,
so that it is the same with scala API.
## How was this patch tested?
The doc test.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#14203 from WeichenXu123/drop_python_api.
## What changes were proposed in this pull request?
This patch enables SparkSession to provide spark version.
## How was this patch tested?
Manual test:
```
scala> sc.version
res0: String = 2.1.0-SNAPSHOT
scala> spark.version
res1: String = 2.1.0-SNAPSHOT
```
```
>>> sc.version
u'2.1.0-SNAPSHOT'
>>> spark.version
u'2.1.0-SNAPSHOT'
```
Author: Liwei Lin <lwlin7@gmail.com>
Closes#14165 from lw-lin/add-version.
## What changes were proposed in this pull request?
Adds an quoteAll option for writing CSV which will quote all fields.
See https://issues.apache.org/jira/browse/SPARK-13638
## How was this patch tested?
Added a test to verify the output columns are quoted for all fields in the Dataframe
Author: Jurriaan Pruis <email@jurriaanpruis.nl>
Closes#13374 from jurriaan/csv-quote-all.
## What changes were proposed in this pull request?
In structured streaming, Spark does not report errors when the specified directory does not exist. This is a behavior different from the batch mode. This patch changes the behavior to fail if the directory does not exist (when the path is not a glob pattern).
## How was this patch tested?
Updated unit tests to reflect the new behavior.
Author: Reynold Xin <rxin@databricks.com>
Closes#14002 from rxin/SPARK-16335.
## What changes were proposed in this pull request?
This patch introduces a flag to disable loading test tables in TestHiveSparkSession and disables that in Python. This fixes an issue in which python/run-tests would fail due to failure to load test tables.
Note that these test tables are not used outside of HiveCompatibilitySuite. In the long run we should probably decouple the loading of test tables from the test Hive setup.
## How was this patch tested?
This is a test only change.
Author: Reynold Xin <rxin@databricks.com>
Closes#14005 from rxin/SPARK-15954.
## What changes were proposed in this pull request?
Spark silently drops exceptions during file listing. This is a very bad behavior because it can mask legitimate errors and the resulting plan will silently have 0 rows. This patch changes it to not silently drop the errors.
## How was this patch tested?
Manually verified.
Author: Reynold Xin <rxin@databricks.com>
Closes#13987 from rxin/SPARK-16313.
## What changes were proposed in this pull request?
This PR implements `posexplode` table generating function. Currently, master branch raises the following exception for `map` argument. It's different from Hive.
**Before**
```scala
scala> sql("select posexplode(map('a', 1, 'b', 2))").show
org.apache.spark.sql.AnalysisException: No handler for Hive UDF ... posexplode() takes an array as a parameter; line 1 pos 7
```
**After**
```scala
scala> sql("select posexplode(map('a', 1, 'b', 2))").show
+---+---+-----+
|pos|key|value|
+---+---+-----+
| 0| a| 1|
| 1| b| 2|
+---+---+-----+
```
For `array` argument, `after` is the same with `before`.
```
scala> sql("select posexplode(array(1, 2, 3))").show
+---+---+
|pos|col|
+---+---+
| 0| 1|
| 1| 2|
| 2| 3|
+---+---+
```
## How was this patch tested?
Pass the Jenkins tests with newly added testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13971 from dongjoon-hyun/SPARK-16289.
## What changes were proposed in this pull request?
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?
This PR corrects ORC compression option for PySpark as well. I think this was missed mistakenly in https://github.com/apache/spark/pull/13948.
## How was this patch tested?
N/A
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#13963 from HyukjinKwon/minor-orc-compress.
#### What changes were proposed in this pull request?
In Python API, we have the same issue. Thanks for identifying this issue, zsxwing ! Below is an example:
```Python
spark.read.format('json').load('python/test_support/sql/people.json')
```
#### How was this patch tested?
Existing test cases cover the changes by this PR
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13965 from gatorsmile/optionPaths.
## What changes were proposed in this pull request?
- Moved DataStreamReader/Writer from pyspark.sql to pyspark.sql.streaming to make them consistent with scala packaging
- Exposed the necessary classes in sql.streaming package so that they appear in the docs
- Added pyspark.sql.streaming module to the docs
## How was this patch tested?
- updated unit tests.
- generated docs for testing visibility of pyspark.sql.streaming classes.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13955 from tdas/SPARK-16266.
## What changes were proposed in this pull request?
Fixed the following error:
```
>>> sqlContext.readStream
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "...", line 442, in readStream
return DataStreamReader(self._wrapped)
NameError: global name 'DataStreamReader' is not defined
```
## How was this patch tested?
The added test.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13958 from zsxwing/fix-import.
## What changes were proposed in this pull request?
Fixes a couple old references to `DataFrameWriter.startStream` to `DataStreamWriter.start
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#13952 from brkyvz/minor-doc-fix.
## What changes were proposed in this pull request?
Scala UDT will bypass all the null and will not pass them into serialize() and deserialize() of UDT, this PR update the Python UDT to do this as well.
## How was this patch tested?
Added tests.
Author: Davies Liu <davies@databricks.com>
Closes#13878 from davies/udt_null.
## What changes were proposed in this pull request?
There are some duplicated code for options in DataFrame reader/writer API, this PR clean them up, it also fix a bug for `escapeQuotes` of csv().
## How was this patch tested?
Existing tests.
Author: Davies Liu <davies@databricks.com>
Closes#13948 from davies/csv_options.
## What changes were proposed in this pull request?
When we create a SparkSession at the Python side, it is possible that a SparkContext has been created. For this case, we need to set configs of the SparkSession builder to the Scala SparkContext's SparkConf (we need to do so because conf changes on a active Python SparkContext will not be propagated to the JVM side). Otherwise, we may create a wrong SparkSession (e.g. Hive support is not enabled even if enableHiveSupport is called).
## How was this patch tested?
New tests and manual tests.
Author: Yin Huai <yhuai@databricks.com>
Closes#13931 from yhuai/SPARK-16224.
## What changes were proposed in this pull request?
Allowing truncate to a specific number of character is convenient at times, especially while operating from the REPL. Sometimes those last few characters make all the difference, and showing everything brings in whole lot of noise.
## How was this patch tested?
Existing tests. + 1 new test in DataFrameSuite.
For SparkR and pyspark, existing tests and manual testing.
Author: Prashant Sharma <prashsh1@in.ibm.com>
Author: Prashant Sharma <prashant@apache.org>
Closes#13839 from ScrapCodes/add_truncateTo_DF.show.
## What changes were proposed in this pull request?
- Fix tests regarding show functions functionality
- Revert `catalog.ListFunctions` and `SHOW FUNCTIONS` to return to `Spark 1.X` functionality.
Cherry picked changes from this PR: https://github.com/apache/spark/pull/13413/files
## How was this patch tested?
Unit tests.
Author: Bill Chambers <bill@databricks.com>
Author: Bill Chambers <wchambers@ischool.berkeley.edu>
Closes#13916 from anabranch/master.
## What changes were proposed in this pull request?
This PR fix the bug when Python UDF is used in explode (generator), GenerateExec requires that all the attributes in expressions should be resolvable from children when creating, we should replace the children first, then replace it's expressions.
```
>>> df.select(explode(f(*df))).show()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/vlad/dev/spark/python/pyspark/sql/dataframe.py", line 286, in show
print(self._jdf.showString(n, truncate))
File "/home/vlad/dev/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", line 933, in __call__
File "/home/vlad/dev/spark/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/home/vlad/dev/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line 312, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o52.showString.
: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: makeCopy, tree:
Generate explode(<lambda>(_1#0L)), false, false, [col#15L]
+- Scan ExistingRDD[_1#0L]
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:50)
at org.apache.spark.sql.catalyst.trees.TreeNode.makeCopy(TreeNode.scala:387)
at org.apache.spark.sql.execution.SparkPlan.makeCopy(SparkPlan.scala:69)
at org.apache.spark.sql.execution.SparkPlan.makeCopy(SparkPlan.scala:45)
at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsDown(QueryPlan.scala:177)
at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressions(QueryPlan.scala:144)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract(ExtractPythonUDFs.scala:153)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:114)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:113)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:301)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:301)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:300)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:298)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:298)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:321)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:179)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:319)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:298)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.apply(ExtractPythonUDFs.scala:113)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.apply(ExtractPythonUDFs.scala:93)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$prepareForExecution$1.apply(QueryExecution.scala:95)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$prepareForExecution$1.apply(QueryExecution.scala:95)
at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
at scala.collection.immutable.List.foldLeft(List.scala:84)
at org.apache.spark.sql.execution.QueryExecution.prepareForExecution(QueryExecution.scala:95)
at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:85)
at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:85)
at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2557)
at org.apache.spark.sql.Dataset.head(Dataset.scala:1923)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2138)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:239)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:211)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.reflect.InvocationTargetException
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$makeCopy$1$$anonfun$apply$13.apply(TreeNode.scala:413)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$makeCopy$1$$anonfun$apply$13.apply(TreeNode.scala:413)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$makeCopy$1.apply(TreeNode.scala:412)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$makeCopy$1.apply(TreeNode.scala:387)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:49)
... 42 more
Caused by: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: pythonUDF0#20
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:50)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:88)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:87)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:279)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:279)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:278)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:284)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:284)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:321)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:179)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:319)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:284)
at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:268)
at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:87)
at org.apache.spark.sql.execution.GenerateExec.<init>(GenerateExec.scala:63)
... 52 more
Caused by: java.lang.RuntimeException: Couldn't find pythonUDF0#20 in [_1#0L]
at scala.sys.package$.error(package.scala:27)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:94)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:88)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:49)
... 67 more
```
## How was this patch tested?
Added regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#13883 from davies/udf_in_generate.
## What changes were proposed in this pull request?
This PR allows us to create a Row without any fields.
## How was this patch tested?
Added a test for empty row and udf without arguments.
Author: Davies Liu <davies@databricks.com>
Closes#13812 from davies/no_argus.
## What changes were proposed in this pull request?
This is a follow-up to https://github.com/apache/spark/pull/13795 to properly set CSV options in Python API. As part of this, I also make the Python option setting for both CSV and JSON more robust against positional errors.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#13800 from rxin/SPARK-13792-2.
## What changes were proposed in this pull request?
This pull request adds a new option (maxMalformedLogPerPartition) in CSV reader to limit the maximum of logging message Spark generates per partition for malformed records.
The error log looks something like
```
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: More than 10 malformed records have been found on this partition. Malformed records from now on will not be logged.
```
Closes#12173
## How was this patch tested?
Manually tested.
Author: Reynold Xin <rxin@databricks.com>
Closes#13795 from rxin/SPARK-13792.
Fix the bug for Python UDF that does not have any arguments.
Added regression tests.
Author: Davies Liu <davies.liu@gmail.com>
Closes#13793 from davies/fix_no_arguments.
(cherry picked from commit abe36c53d1)
Signed-off-by: Davies Liu <davies.liu@gmail.com>
*This contribution is my original work and that I license the work to the project under the project's open source license.*
## What changes were proposed in this pull request?
Documentation updates to PySpark's GroupedData
## How was this patch tested?
Manual Tests
Author: Josh Howes <josh.howes@gmail.com>
Author: Josh Howes <josh.howes@maxpoint.com>
Closes#13724 from josh-howes/bugfix/SPARK-15973.
## What changes were proposed in this pull request?
Support with statement syntax for SparkSession in pyspark
## How was this patch tested?
Manually verify it. Although I can add unit test for it, it would affect other unit test because the SparkContext is stopped after the with statement.
Author: Jeff Zhang <zjffdu@apache.org>
Closes#13541 from zjffdu/SPARK-15803.
## What changes were proposed in this pull request?
- Fixed bug in Python API of DataStreamReader. Because a single path was being converted to a array before calling Java DataStreamReader method (which takes a string only), it gave the following error.
```
File "/Users/tdas/Projects/Spark/spark/python/pyspark/sql/readwriter.py", line 947, in pyspark.sql.readwriter.DataStreamReader.json
Failed example:
json_sdf = spark.readStream.json(os.path.join(tempfile.mkdtemp(), 'data'), schema = sdf_schema)
Exception raised:
Traceback (most recent call last):
File "/System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/doctest.py", line 1253, in __run
compileflags, 1) in test.globs
File "<doctest pyspark.sql.readwriter.DataStreamReader.json[0]>", line 1, in <module>
json_sdf = spark.readStream.json(os.path.join(tempfile.mkdtemp(), 'data'), schema = sdf_schema)
File "/Users/tdas/Projects/Spark/spark/python/pyspark/sql/readwriter.py", line 963, in json
return self._df(self._jreader.json(path))
File "/Users/tdas/Projects/Spark/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", line 933, in __call__
answer, self.gateway_client, self.target_id, self.name)
File "/Users/tdas/Projects/Spark/spark/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/Users/tdas/Projects/Spark/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line 316, in get_return_value
format(target_id, ".", name, value))
Py4JError: An error occurred while calling o121.json. Trace:
py4j.Py4JException: Method json([class java.util.ArrayList]) 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:272)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:211)
at java.lang.Thread.run(Thread.java:744)
```
- Reduced code duplication between DataStreamReader and DataFrameWriter
- Added missing Python doctests
## How was this patch tested?
New tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13703 from tdas/SPARK-15981.
## What changes were proposed in this pull request?
After we move the ExtractPythonUDF rule into physical plan, Python UDF can't work on top of aggregate anymore, because they can't be evaluated before aggregate, should be evaluated after aggregate. This PR add another rule to extract these kind of Python UDF from logical aggregate, create a Project on top of Aggregate.
## How was this patch tested?
Added regression tests. The plan of added test query looks like this:
```
== Parsed Logical Plan ==
'Project [<lambda>('k, 's) AS t#26]
+- Aggregate [<lambda>(key#5L)], [<lambda>(key#5L) AS k#17, sum(cast(<lambda>(value#6) as bigint)) AS s#22L]
+- LogicalRDD [key#5L, value#6]
== Analyzed Logical Plan ==
t: int
Project [<lambda>(k#17, s#22L) AS t#26]
+- Aggregate [<lambda>(key#5L)], [<lambda>(key#5L) AS k#17, sum(cast(<lambda>(value#6) as bigint)) AS s#22L]
+- LogicalRDD [key#5L, value#6]
== Optimized Logical Plan ==
Project [<lambda>(agg#29, agg#30L) AS t#26]
+- Aggregate [<lambda>(key#5L)], [<lambda>(key#5L) AS agg#29, sum(cast(<lambda>(value#6) as bigint)) AS agg#30L]
+- LogicalRDD [key#5L, value#6]
== Physical Plan ==
*Project [pythonUDF0#37 AS t#26]
+- BatchEvalPython [<lambda>(agg#29, agg#30L)], [agg#29, agg#30L, pythonUDF0#37]
+- *HashAggregate(key=[<lambda>(key#5L)#31], functions=[sum(cast(<lambda>(value#6) as bigint))], output=[agg#29,agg#30L])
+- Exchange hashpartitioning(<lambda>(key#5L)#31, 200)
+- *HashAggregate(key=[pythonUDF0#34 AS <lambda>(key#5L)#31], functions=[partial_sum(cast(pythonUDF1#35 as bigint))], output=[<lambda>(key#5L)#31,sum#33L])
+- BatchEvalPython [<lambda>(key#5L), <lambda>(value#6)], [key#5L, value#6, pythonUDF0#34, pythonUDF1#35]
+- Scan ExistingRDD[key#5L,value#6]
```
Author: Davies Liu <davies@databricks.com>
Closes#13682 from davies/fix_py_udf.
Renamed for simplicity, so that its obvious that its related to streaming.
Existing unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13673 from tdas/SPARK-15953.
## What changes were proposed in this pull request?
A follow up PR for #13655 to fix a wrong format tag.
## How was this patch tested?
Jenkins unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13665 from zsxwing/fix.
## What changes were proposed in this pull request?
Currently, the DataFrameReader/Writer has method that are needed for streaming and non-streaming DFs. This is quite awkward because each method in them through runtime exception for one case or the other. So rather having half the methods throw runtime exceptions, its just better to have a different reader/writer API for streams.
- [x] Python API!!
## How was this patch tested?
Existing unit tests + two sets of unit tests for DataFrameReader/Writer and DataStreamReader/Writer.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13653 from tdas/SPARK-15933.
## What changes were proposed in this pull request?
This PR just enables tests for sql/streaming.py and also fixes the failures.
## How was this patch tested?
Existing unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13655 from zsxwing/python-streaming-test.
## What changes were proposed in this pull request?
SparkSession.catalog.listFunctions currently returns all functions, including the list of built-in functions. This makes the method not as useful because anytime it is run the result set contains over 100 built-in functions.
## How was this patch tested?
CatalogSuite
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#13413 from techaddict/SPARK-15663.
## What changes were proposed in this pull request?
This pr is to add doc for turning off quotations because this behavior is different from `com.databricks.spark.csv`.
## How was this patch tested?
Check behavior to put an empty string in csv options.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#13616 from maropu/SPARK-15585-2.
## What changes were proposed in this pull request?
`an -> a`
Use cmds like `find . -name '*.R' | xargs -i sh -c "grep -in ' an [^aeiou]' {} && echo {}"` to generate candidates, and review them one by one.
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#13515 from zhengruifeng/an_a.
## What changes were proposed in this pull request?
This pr fixes the behaviour of `format("csv").option("quote", null)` along with one of spark-csv.
Also, it explicitly sets default values for CSV options in python.
## How was this patch tested?
Added tests in CSVSuite.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#13372 from maropu/SPARK-15585.
## What changes were proposed in this pull request?
This patch moves all user-facing structured streaming classes into sql.streaming. As part of this, I also added some since version annotation to methods and classes that don't have them.
## How was this patch tested?
Updated tests to reflect the moves.
Author: Reynold Xin <rxin@databricks.com>
Closes#13429 from rxin/SPARK-15686.
## What changes were proposed in this pull request?
Currently structured streaming only supports append output mode. This PR adds the following.
- Added support for Complete output mode in the internal state store, analyzer and planner.
- Added public API in Scala and Python for users to specify output mode
- Added checks for unsupported combinations of output mode and DF operations
- Plans with no aggregation should support only Append mode
- Plans with aggregation should support only Update and Complete modes
- Default output mode is Append mode (**Question: should we change this to automatically set to Complete mode when there is aggregation?**)
- Added support for Complete output mode in Memory Sink. So Memory Sink internally supports append and complete, update. But from public API only Complete and Append output modes are supported.
## How was this patch tested?
Unit tests in various test suites
- StreamingAggregationSuite: tests for complete mode
- MemorySinkSuite: tests for checking behavior in Append and Complete modes.
- UnsupportedOperationSuite: tests for checking unsupported combinations of DF ops and output modes
- DataFrameReaderWriterSuite: tests for checking that output mode cannot be called on static DFs
- Python doc test and existing unit tests modified to call write.outputMode.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13286 from tdas/complete-mode.
## What changes were proposed in this pull request?
This reverts commit c24b6b679c. Sent a PR to run Jenkins tests due to the revert conflicts of `dev/deps/spark-deps-hadoop*`.
## How was this patch tested?
Jenkins unit tests, integration tests, manual tests)
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13417 from zsxwing/revert-SPARK-11753.
## What changes were proposed in this pull request?
`a` -> `an`
I use regex to generate potential error lines:
`grep -in ' a [aeiou]' mllib/src/main/scala/org/apache/spark/ml/*/*scala`
and review them line by line.
## How was this patch tested?
local build
`lint-java` checking
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#13317 from zhengruifeng/a_an.
## What changes were proposed in this pull request?
Also sets confs in the underlying sc when using SparkSession.builder.getOrCreate(). This is a bug-fix from a post-merge comment in https://github.com/apache/spark/pull/13289
## How was this patch tested?
Python doc-tests.
Author: Eric Liang <ekl@databricks.com>
Closes#13309 from ericl/spark-15520-1.
## What changes were proposed in this pull request?
This fixes the python SparkSession builder to allow setting confs correctly. This was a leftover TODO from https://github.com/apache/spark/pull/13200.
## How was this patch tested?
Python doc tests.
cc andrewor14
Author: Eric Liang <ekl@databricks.com>
Closes#13289 from ericl/spark-15520.
## What changes were proposed in this pull request?
Jackson suppprts `allowNonNumericNumbers` option to parse non-standard non-numeric numbers such as "NaN", "Infinity", "INF". Currently used Jackson version (2.5.3) doesn't support it all. This patch upgrades the library and make the two ignored tests in `JsonParsingOptionsSuite` passed.
## How was this patch tested?
`JsonParsingOptionsSuite`.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#9759 from viirya/fix-json-nonnumric.
## What changes were proposed in this pull request?
in hive, `locate("aa", "aaa", 0)` would yield 0, `locate("aa", "aaa", 1)` would yield 1 and `locate("aa", "aaa", 2)` would yield 2, while in Spark, `locate("aa", "aaa", 0)` would yield 1, `locate("aa", "aaa", 1)` would yield 2 and `locate("aa", "aaa", 2)` would yield 0. This results from the different understanding of the third parameter in udf `locate`. It means the starting index and starts from 1, so when we use 0, the return would always be 0.
## How was this patch tested?
tested with modified `StringExpressionsSuite` and `StringFunctionsSuite`
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#13186 from adrian-wang/locate.
## What changes were proposed in this pull request?
Replace SQLContext and SparkContext with SparkSession using builder pattern in python test code.
## How was this patch tested?
Existing test.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#13242 from WeichenXu123/python_doctest_update_sparksession.
## What changes were proposed in this pull request?
Spark assumes that UDF functions are deterministic. This PR adds explicit notes about that.
## How was this patch tested?
It's only about docs.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13087 from dongjoon-hyun/SPARK-15282.
## What changes were proposed in this pull request?
There is no way to use the Hive catalog in `pyspark-shell`. This is because we used to create a `SparkContext` before calling `SparkSession.enableHiveSupport().getOrCreate()`, which just gets the existing `SparkContext` instead of creating a new one. As a result, `spark.sql.catalogImplementation` was never propagated.
## How was this patch tested?
Manual.
Author: Andrew Or <andrew@databricks.com>
Closes#13203 from andrewor14/fix-pyspark-shell.
## What changes were proposed in this pull request?
Currently SparkSession.Builder use SQLContext.getOrCreate. It should probably the the other way around, i.e. all the core logic goes in SparkSession, and SQLContext just calls that. This patch does that.
This patch also makes sure config options specified in the builder are propagated to the existing (and of course the new) SparkSession.
## How was this patch tested?
Updated tests to reflect the change, and also introduced a new SparkSessionBuilderSuite that should cover all the branches.
Author: Reynold Xin <rxin@databricks.com>
Closes#13200 from rxin/SPARK-15075.
## What changes were proposed in this pull request?
We use autoBroadcastJoinThreshold + 1L as the default value of size estimation, that is not good in 2.0, because we will calculate the size based on size of schema, then the estimation could be less than autoBroadcastJoinThreshold if you have an SELECT on top of an DataFrame created from RDD.
This PR change the default value to Long.MaxValue.
## How was this patch tested?
Added regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#13183 from davies/fix_default_size.
#### What changes were proposed in this pull request?
This follow-up PR is to address the remaining comments in https://github.com/apache/spark/pull/12385
The major change in this PR is to issue better error messages in PySpark by using the mechanism that was proposed by davies in https://github.com/apache/spark/pull/7135
For example, in PySpark, if we input the following statement:
```python
>>> l = [('Alice', 1)]
>>> df = sqlContext.createDataFrame(l)
>>> df.createTempView("people")
>>> df.createTempView("people")
```
Before this PR, the exception we will get is like
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/pyspark/sql/dataframe.py", line 152, in createTempView
self._jdf.createTempView(name)
File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", line 933, in __call__
File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line 312, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o35.createTempView.
: org.apache.spark.sql.catalyst.analysis.TempTableAlreadyExistsException: Temporary table 'people' already exists;
at org.apache.spark.sql.catalyst.catalog.SessionCatalog.createTempView(SessionCatalog.scala:324)
at org.apache.spark.sql.SparkSession.createTempView(SparkSession.scala:523)
at org.apache.spark.sql.Dataset.createTempView(Dataset.scala:2328)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:211)
at java.lang.Thread.run(Thread.java:745)
```
After this PR, the exception we will get become cleaner:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/pyspark/sql/dataframe.py", line 152, in createTempView
self._jdf.createTempView(name)
File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", line 933, in __call__
File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/pyspark/sql/utils.py", line 75, in deco
raise AnalysisException(s.split(': ', 1)[1], stackTrace)
pyspark.sql.utils.AnalysisException: u"Temporary table 'people' already exists;"
```
#### How was this patch tested?
Fixed an existing PySpark test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13126 from gatorsmile/followup-14684.
## What changes were proposed in this pull request?
This patch is a follow-up to https://github.com/apache/spark/pull/13104 and adds documentation to clarify the semantics of read.text with respect to partitioning.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#13184 from rxin/SPARK-14463.
## What changes were proposed in this pull request?
The PySpark SQL `test_column_name_with_non_ascii` wants to test non-ascii column name. But it doesn't actually test it. We need to construct an unicode explicitly using `unicode` under Python 2.
## How was this patch tested?
Existing tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#13134 from viirya/correct-non-ascii-colname-pytest.
## What changes were proposed in this pull request?
Update the unit test code, examples, and documents to remove calls to deprecated method `dataset.registerTempTable`.
## How was this patch tested?
This PR only changes the unit test code, examples, and comments. It should be safe.
This is a follow up of PR https://github.com/apache/spark/pull/12945 which was merged.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#13098 from clockfly/spark-15171-remove-deprecation.
## What changes were proposed in this pull request?
**createDataFrame** returns inconsistent types for column names.
```python
>>> from pyspark.sql.types import StructType, StructField, StringType
>>> schema = StructType([StructField(u"col", StringType())])
>>> df1 = spark.createDataFrame([("a",)], schema)
>>> df1.columns # "col" is str
['col']
>>> df2 = spark.createDataFrame([("a",)], [u"col"])
>>> df2.columns # "col" is unicode
[u'col']
```
The reason is only **StructField** has the following code.
```
if not isinstance(name, str):
name = name.encode('utf-8')
```
This PR adds the same logic into **createDataFrame** for consistency.
```
if isinstance(schema, list):
schema = [x.encode('utf-8') if not isinstance(x, str) else x for x in schema]
```
## How was this patch tested?
Pass the Jenkins test (with new python doctest)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13097 from dongjoon-hyun/SPARK-15244.
## 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?
Seems db573fc743 did not remove withHiveSupport from readwrite.py
Author: Yin Huai <yhuai@databricks.com>
Closes#13069 from yhuai/fixPython.
## What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/12851
Remove `SparkSession.withHiveSupport` in PySpark and instead use `SparkSession.builder. enableHiveSupport`
## How was this patch tested?
Existing tests.
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#13063 from techaddict/SPARK-15072-followup.
## What changes were proposed in this pull request?
When a CSV begins with:
- `,,`
OR
- `"","",`
meaning that the first column names are either empty or blank strings and `header` is specified to be `true`, then the column name is replaced with `C` + the index number of that given column. For example, if you were to read in the CSV:
```
"","second column"
"hello", "there"
```
Then column names would become `"C0", "second column"`.
This behavior aligns with what currently happens when `header` is specified to be `false` in recent versions of Spark.
### Current Behavior in Spark <=1.6
In Spark <=1.6, a CSV with a blank column name becomes a blank string, `""`, meaning that this column cannot be accessed. However the CSV reads in without issue.
### Current Behavior in Spark 2.0
Spark throws a NullPointerError and will not read in the file.
#### Reproduction in 2.0
https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/346304/2828750690305044/484361/latest.html
## How was this patch tested?
A new test was added to `CSVSuite` to account for this issue. We then have asserts that test for being able to select both the empty column names as well as the regular column names.
Author: Bill Chambers <bill@databricks.com>
Author: Bill Chambers <wchambers@ischool.berkeley.edu>
Closes#13041 from anabranch/master.
This PR:
* Corrects the documentation for the `properties` parameter, which is supposed to be a dictionary and not a list.
* Generally clarifies the Python docstring for DataFrameReader.jdbc() by pulling from the [Scala docstrings](b281377647/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala (L201-L251)) and rephrasing things.
* Corrects minor Sphinx typos.
Author: Nicholas Chammas <nicholas.chammas@gmail.com>
Closes#13034 from nchammas/SPARK-15256.
## What changes were proposed in this pull request?
Earlier we removed experimental tag for Scala/Java DataFrames, but haven't done so for Python. This patch removes the experimental flag for Python and declares them stable.
## How was this patch tested?
N/A.
Author: Reynold Xin <rxin@databricks.com>
Closes#13062 from rxin/SPARK-15278.
## What changes were proposed in this pull request?
Before:
Creating a hiveContext was failing
```python
from pyspark.sql import HiveContext
hc = HiveContext(sc)
```
with
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "spark-2.0/python/pyspark/sql/context.py", line 458, in __init__
sparkSession = SparkSession.withHiveSupport(sparkContext)
File "spark-2.0/python/pyspark/sql/session.py", line 192, in withHiveSupport
jsparkSession = sparkContext._jvm.SparkSession.withHiveSupport(sparkContext._jsc.sc())
File "spark-2.0/python/lib/py4j-0.9.2-src.zip/py4j/java_gateway.py", line 1048, in __getattr__
py4j.protocol.Py4JError: org.apache.spark.sql.SparkSession.withHiveSupport does not exist in the JVM
```
Now:
```python
>>> from pyspark.sql import HiveContext
>>> hc = HiveContext(sc)
>>> hc.range(0, 100)
DataFrame[id: bigint]
>>> hc.range(0, 100).count()
100
```
## How was this patch tested?
Existing Tests, tested manually in python shell
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#13056 from techaddict/SPARK-15270.
## What changes were proposed in this pull request?
Use SparkSession instead of SQLContext in Python TestSuites
## How was this patch tested?
Existing tests
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#13044 from techaddict/SPARK-15037-python.
## What changes were proposed in this pull request?
This PR removes the old `json(path: String)` API which is covered by the new `json(paths: String*)`.
## How was this patch tested?
Jenkins tests (existing tests should cover this)
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Closes#13040 from HyukjinKwon/SPARK-15250.
## What changes were proposed in this pull request?
This patch removes experimental tag from DataFrameReader and DataFrameWriter, and explicitly tags a few methods added for structured streaming as experimental.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#13038 from rxin/SPARK-15261.
Since we cannot really trust if the underlying external catalog can throw exceptions when there is an invalid metadata operation, let's do it in SessionCatalog.
- [X] The first step is to unify the error messages issued in Hive-specific Session Catalog and general Session Catalog.
- [X] The second step is to verify the inputs of metadata operations for partitioning-related operations. This is moved to a separate PR: https://github.com/apache/spark/pull/12801
- [X] The third step is to add database existence verification in `SessionCatalog`
- [X] The fourth step is to add table existence verification in `SessionCatalog`
- [X] The fifth step is to add function existence verification in `SessionCatalog`
Add test cases and verify the error messages we issued
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#12385 from gatorsmile/verifySessionAPIs.
## What changes were proposed in this pull request?
See title.
## How was this patch tested?
PySpark tests.
Author: Andrew Or <andrew@databricks.com>
Closes#12917 from andrewor14/deprecate-hive-context-python.
## What changes were proposed in this pull request?
Currently we return RuntimeConfig itself to facilitate chaining. However, it makes the output in interactive environments (e.g. notebooks, scala repl) weird because it'd show the response of calling set as a RuntimeConfig itself.
## How was this patch tested?
Updated unit tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12902 from rxin/SPARK-15126.
## What changes were proposed in this pull request?
This is a python port of corresponding Scala builder pattern code. `sql.py` is modified as a target example case.
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12860 from dongjoon-hyun/SPARK-15084.
# What changes were proposed in this pull request?
Support partitioning in the file stream sink. This is implemented using a new, but simpler code path for writing parquet files - both unpartitioned and partitioned. This new code path does not use Output Committers, as we will eventually write the file names to the metadata log for "committing" them.
This patch duplicates < 100 LOC from the WriterContainer. But its far simpler that WriterContainer as it does not involve output committing. In addition, it introduces the new APIs in FileFormat and OutputWriterFactory in an attempt to simplify the APIs (not have Job in the `FileFormat` API, not have bucket and other stuff in the `OutputWriterFactory.newInstance()` ).
# Tests
- New unit tests to test the FileStreamSinkWriter for partitioned and unpartitioned files
- New unit test to partially test the FileStreamSink for partitioned files (does not test recovery of partition column data, as that requires change in the StreamFileCatalog, future PR).
- Updated FileStressSuite to test number of records read from partitioned output files.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#12409 from tdas/streaming-partitioned-parquet.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-15050
This PR adds function parameters for Python API for reading and writing `csv()`.
## How was this patch tested?
This was tested by `./dev/run_tests`.
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Closes#12834 from HyukjinKwon/SPARK-15050.
## What changes were proposed in this pull request?
This PR adds the explanation and documentation for CSV options for reading and writing.
## How was this patch tested?
Style tests with `./dev/run_tests` for documentation style.
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Closes#12817 from HyukjinKwon/SPARK-13425.
## What changes were proposed in this pull request?
1. Remove all the `spark.setConf` etc. Just expose `spark.conf`
2. Make `spark.conf` take in things set in the core `SparkConf` as well, otherwise users may get confused
This was done for both the Python and Scala APIs.
## How was this patch tested?
`SQLConfSuite`, python tests.
This one fixes the failed tests in #12787Closes#12787
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#12798 from yhuai/conf-api.
## 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.
## What changes were proposed in this pull request?
This PR adds Python APIs for:
- `ContinuousQueryManager`
- `ContinuousQueryException`
The `ContinuousQueryException` is a very basic wrapper, it doesn't provide the functionality that the Scala side provides, but it follows the same pattern for `AnalysisException`.
For `ContinuousQueryManager`, all APIs are provided except for registering listeners.
This PR also attempts to fix test flakiness by stopping all active streams just before tests.
## How was this patch tested?
Python Doc tests and unit tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#12673 from brkyvz/pyspark-cqm.
## What changes were proposed in this pull request?
```
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 2.0.0-SNAPSHOT
/_/
Using Python version 2.7.5 (default, Mar 9 2014 22:15:05)
SparkSession available as 'spark'.
>>> spark
<pyspark.sql.session.SparkSession object at 0x101f3bfd0>
>>> spark.sql("SHOW TABLES").show()
...
+---------+-----------+
|tableName|isTemporary|
+---------+-----------+
| src| false|
+---------+-----------+
>>> spark.range(1, 10, 2).show()
+---+
| id|
+---+
| 1|
| 3|
| 5|
| 7|
| 9|
+---+
```
**Note**: This API is NOT complete in its current state. In particular, for now I left out the `conf` and `catalog` APIs, which were added later in Scala. These will be added later before 2.0.
## How was this patch tested?
Python tests.
Author: Andrew Or <andrew@databricks.com>
Closes#12746 from andrewor14/python-spark-session.
## What changes were proposed in this pull request?
This removes the class `HiveContext` itself along with all code usages associated with it. The bulk of the work was already done in #12485. This is mainly just code cleanup and actually removing the class.
Note: A couple of things will break after this patch. These will be fixed separately.
- the python HiveContext
- all the documentation / comments referencing HiveContext
- there will be no more HiveContext in the REPL (fixed by #12589)
## How was this patch tested?
No change in functionality.
Author: Andrew Or <andrew@databricks.com>
Closes#12585 from andrewor14/delete-hive-context.
## What changes were proposed in this pull request?
In Python, sqlContext.getConf didn't allow getting the system default (getConf with one parameter).
Now the following are supported:
```
sqlContext.getConf(confName) # System default if not locally set, this is new
sqlContext.getConf(confName, myDefault) # myDefault if not locally set, old behavior
```
I also added doctests to this function. The original behavior does not change.
## How was this patch tested?
Manually, but doctests were added.
Author: mathieu longtin <mathieu.longtin@nuance.com>
Closes#12488 from mathieulongtin/pyfixgetconf3.
## What changes were proposed in this pull request?
In Python, the `option` and `options` method of `DataFrameReader` and `DataFrameWriter` were sending the string "None" instead of `null` when passed `None`, therefore making it impossible to send an actual `null`. This fixes that problem.
This is based on #11305 from mathieulongtin.
## How was this patch tested?
Added test to readwriter.py.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: mathieu longtin <mathieu.longtin@nuance.com>
Closes#12494 from viirya/py-df-none-option.
## What changes were proposed in this pull request?
Expand the possible ways to interact with the contents of a `pyspark.sql.types.StructType` instance.
- Iterating a `StructType` will iterate its fields
- `[field.name for field in my_structtype]`
- Indexing with a string will return a field by name
- `my_structtype['my_field_name']`
- Indexing with an integer will return a field by position
- `my_structtype[0]`
- Indexing with a slice will return a new `StructType` with just the chosen fields:
- `my_structtype[1:3]`
- The length is the number of fields (should also provide "truthiness" for free)
- `len(my_structtype) == 2`
## How was this patch tested?
Extended the unit test coverage in the accompanying `tests.py`.
Author: Sheamus K. Parkes <shea.parkes@milliman.com>
Closes#12251 from skparkes/pyspark-structtype-enhance.
## What changes were proposed in this pull request?
This patch provides a first cut of python APIs for structured streaming. This PR provides the new classes:
- ContinuousQuery
- Trigger
- ProcessingTime
in pyspark under `pyspark.sql.streaming`.
In addition, it contains the new methods added under:
- `DataFrameWriter`
a) `startStream`
b) `trigger`
c) `queryName`
- `DataFrameReader`
a) `stream`
- `DataFrame`
a) `isStreaming`
This PR doesn't contain all methods exposed for `ContinuousQuery`, for example:
- `exception`
- `sourceStatuses`
- `sinkStatus`
They may be added in a follow up.
This PR also contains some very minor doc fixes in the Scala side.
## How was this patch tested?
Python doc tests
TODO:
- [ ] verify Python docs look good
Author: Burak Yavuz <brkyvz@gmail.com>
Author: Burak Yavuz <burak@databricks.com>
Closes#12320 from brkyvz/stream-python.
## What changes were proposed in this pull request?
This issue aims to expose Scala `bround` function in Python/R API.
`bround` function is implemented in SPARK-14614 by extending current `round` function.
We used the following semantics from Hive.
```java
public static double bround(double input, int scale) {
if (Double.isNaN(input) || Double.isInfinite(input)) {
return input;
}
return BigDecimal.valueOf(input).setScale(scale, RoundingMode.HALF_EVEN).doubleValue();
}
```
After this PR, `pyspark` and `sparkR` also support `bround` function.
**PySpark**
```python
>>> from pyspark.sql.functions import bround
>>> sqlContext.createDataFrame([(2.5,)], ['a']).select(bround('a', 0).alias('r')).collect()
[Row(r=2.0)]
```
**SparkR**
```r
> df = createDataFrame(sqlContext, data.frame(x = c(2.5, 3.5)))
> head(collect(select(df, bround(df$x, 0))))
bround(x, 0)
1 2
2 4
```
## How was this patch tested?
Pass the Jenkins tests (including new testcases).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12509 from dongjoon-hyun/SPARK-14639.
## What changes were proposed in this pull request?
Change unpersist blocking parameter default value to match Scala
## How was this patch tested?
unit tests, manual tests
jkbradley davies
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#12507 from felixcheung/pyunpersist.
## What changes were proposed in this pull request?
The PyDoc Makefile used "=" rather than "?=" for setting env variables so it overwrote the user values. This ignored the environment variables we set for linting allowing warnings through. This PR also fixes the warnings that had been introduced.
## How was this patch tested?
manual local export & make
Author: Holden Karau <holden@us.ibm.com>
Closes#12336 from holdenk/SPARK-14573-fix-pydoc-makefile.
## What changes were proposed in this pull request?
The `window` function was added to Dataset with [this PR](https://github.com/apache/spark/pull/12008).
This PR adds the Python, and SQL, API for this function.
With this PR, SQL, Java, and Scala will share the same APIs as in users can use:
- `window(timeColumn, windowDuration)`
- `window(timeColumn, windowDuration, slideDuration)`
- `window(timeColumn, windowDuration, slideDuration, startTime)`
In Python, users can access all APIs above, but in addition they can do
- In Python:
`window(timeColumn, windowDuration, startTime=...)`
that is, they can provide the startTime without providing the `slideDuration`. In this case, we will generate tumbling windows.
## How was this patch tested?
Unit tests + manual tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#12136 from brkyvz/python-windows.
## What changes were proposed in this pull request?
RDD.toLocalIterator() could be used to fetch one partition at a time to reduce the memory usage. Right now, for Dataset/Dataframe we have to use df.rdd.toLocalIterator, which is super slow also requires lots of memory (because of the Java serializer or even Kyro serializer).
This PR introduce an optimized toLocalIterator for Dataset/DataFrame, which is much faster and requires much less memory. For a partition with 5 millions rows, `df.rdd.toIterator` took about 100 seconds, but df.toIterator took less than 7 seconds. For 10 millions row, rdd.toIterator will crash (not enough memory) with 4G heap, but df.toLocalIterator could finished in 12 seconds.
The JDBC server has been updated to use DataFrame.toIterator.
## How was this patch tested?
Existing tests.
Author: Davies Liu <davies@databricks.com>
Closes#12114 from davies/local_iterator.
## What changes were proposed in this pull request?
Currently we extract Python UDFs into a special logical plan EvaluatePython in analyzer, But EvaluatePython is not part of catalyst, many rules have no knowledge of it , which will break many things (for example, filter push down or column pruning).
We should treat Python UDFs as normal expressions, until we want to evaluate in physical plan, we could extract them in end of optimizer, or physical plan.
This PR extract Python UDFs in physical plan.
Closes#10935
## How was this patch tested?
Added regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#12127 from davies/py_udf.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-14231
Currently, JSON data source supports to infer `DecimalType` for big numbers and `floatAsBigDecimal` option which reads floating-point values as `DecimalType`.
But there are few restrictions in Spark `DecimalType` below:
1. The precision cannot be bigger than 38.
2. scale cannot be bigger than precision.
Currently, both restrictions are not being handled.
This PR handles the cases by inferring them as `DoubleType`. Also, the option name was changed from `floatAsBigDecimal` to `prefersDecimal` as suggested [here](https://issues.apache.org/jira/browse/SPARK-14231?focusedCommentId=15215579&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15215579).
So, the codes below:
```scala
def doubleRecords: RDD[String] =
sqlContext.sparkContext.parallelize(
s"""{"a": 1${"0" * 38}, "b": 0.01}""" ::
s"""{"a": 2${"0" * 38}, "b": 0.02}""" :: Nil)
val jsonDF = sqlContext.read
.option("prefersDecimal", "true")
.json(doubleRecords)
jsonDF.printSchema()
```
produces below:
- **Before**
```scala
org.apache.spark.sql.AnalysisException: Decimal scale (2) cannot be greater than precision (1).;
at org.apache.spark.sql.types.DecimalType.<init>(DecimalType.scala:44)
at org.apache.spark.sql.execution.datasources.json.InferSchema$.org$apache$spark$sql$execution$datasources$json$InferSchema$$inferField(InferSchema.scala:144)
at org.apache.spark.sql.execution.datasources.json.InferSchema$.org$apache$spark$sql$execution$datasources$json$InferSchema$$inferField(InferSchema.scala:108)
at
...
```
- **After**
```scala
root
|-- a: double (nullable = true)
|-- b: double (nullable = true)
```
## How was this patch tested?
Unit tests were used and `./dev/run_tests` for coding style tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#12030 from HyukjinKwon/SPARK-14231.
## What changes were proposed in this pull request?
This PR support multiple Python UDFs within single batch, also improve the performance.
```python
>>> from pyspark.sql.types import IntegerType
>>> sqlContext.registerFunction("double", lambda x: x * 2, IntegerType())
>>> sqlContext.registerFunction("add", lambda x, y: x + y, IntegerType())
>>> sqlContext.sql("SELECT double(add(1, 2)), add(double(2), 1)").explain(True)
== Parsed Logical Plan ==
'Project [unresolvedalias('double('add(1, 2)), None),unresolvedalias('add('double(2), 1), None)]
+- OneRowRelation$
== Analyzed Logical Plan ==
double(add(1, 2)): int, add(double(2), 1): int
Project [double(add(1, 2))#14,add(double(2), 1)#15]
+- Project [double(add(1, 2))#14,add(double(2), 1)#15]
+- Project [pythonUDF0#16 AS double(add(1, 2))#14,pythonUDF0#18 AS add(double(2), 1)#15]
+- EvaluatePython [add(pythonUDF1#17, 1)], [pythonUDF0#18]
+- EvaluatePython [double(add(1, 2)),double(2)], [pythonUDF0#16,pythonUDF1#17]
+- OneRowRelation$
== Optimized Logical Plan ==
Project [pythonUDF0#16 AS double(add(1, 2))#14,pythonUDF0#18 AS add(double(2), 1)#15]
+- EvaluatePython [add(pythonUDF1#17, 1)], [pythonUDF0#18]
+- EvaluatePython [double(add(1, 2)),double(2)], [pythonUDF0#16,pythonUDF1#17]
+- OneRowRelation$
== Physical Plan ==
WholeStageCodegen
: +- Project [pythonUDF0#16 AS double(add(1, 2))#14,pythonUDF0#18 AS add(double(2), 1)#15]
: +- INPUT
+- !BatchPythonEvaluation [add(pythonUDF1#17, 1)], [pythonUDF0#16,pythonUDF1#17,pythonUDF0#18]
+- !BatchPythonEvaluation [double(add(1, 2)),double(2)], [pythonUDF0#16,pythonUDF1#17]
+- Scan OneRowRelation[]
```
## How was this patch tested?
Added new tests.
Using the following script to benchmark 1, 2 and 3 udfs,
```
df = sqlContext.range(1, 1 << 23, 1, 4)
double = F.udf(lambda x: x * 2, LongType())
print df.select(double(df.id)).count()
print df.select(double(df.id), double(df.id + 1)).count()
print df.select(double(df.id), double(df.id + 1), double(df.id + 2)).count()
```
Here is the results:
N | Before | After | speed up
---- |------------ | -------------|------
1 | 22 s | 7 s | 3.1X
2 | 38 s | 13 s | 2.9X
3 | 58 s | 16 s | 3.6X
This benchmark ran locally with 4 CPUs. For 3 UDFs, it launched 12 Python before before this patch, 4 process after this patch. After this patch, it will use less memory for multiple UDFs than before (less buffering).
Author: Davies Liu <davies@databricks.com>
Closes#12057 from davies/multi_udfs.
### What changes were proposed in this pull request?
This PR removes the ANTLR3 based parser, and moves the new ANTLR4 based parser into the `org.apache.spark.sql.catalyst.parser package`.
### How was this patch tested?
Existing unit tests.
cc rxin andrewor14 yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12071 from hvanhovell/SPARK-14211.
## What changes were proposed in this pull request?
This PR brings the support for chained Python UDFs, for example
```sql
select udf1(udf2(a))
select udf1(udf2(a) + 3)
select udf1(udf2(a) + udf3(b))
```
Also directly chained unary Python UDFs are put in single batch of Python UDFs, others may require multiple batches.
For example,
```python
>>> sqlContext.sql("select double(double(1))").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [pythonUDF#10 AS double(double(1))#9]
: +- INPUT
+- !BatchPythonEvaluation double(double(1)), [pythonUDF#10]
+- Scan OneRowRelation[]
>>> sqlContext.sql("select double(double(1) + double(2))").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [pythonUDF#19 AS double((double(1) + double(2)))#16]
: +- INPUT
+- !BatchPythonEvaluation double((pythonUDF#17 + pythonUDF#18)), [pythonUDF#17,pythonUDF#18,pythonUDF#19]
+- !BatchPythonEvaluation double(2), [pythonUDF#17,pythonUDF#18]
+- !BatchPythonEvaluation double(1), [pythonUDF#17]
+- Scan OneRowRelation[]
```
TODO: will support multiple unrelated Python UDFs in one batch (another PR).
## How was this patch tested?
Added new unit tests for chained UDFs.
Author: Davies Liu <davies@databricks.com>
Closes#12014 from davies/py_udfs.
### What changes were proposed in this pull request?
The current ANTLR3 parser is quite complex to maintain and suffers from code blow-ups. This PR introduces a new parser that is based on ANTLR4.
This parser is based on the [Presto's SQL parser](https://github.com/facebook/presto/blob/master/presto-parser/src/main/antlr4/com/facebook/presto/sql/parser/SqlBase.g4). The current implementation can parse and create Catalyst and SQL plans. Large parts of the HiveQl DDL and some of the DML functionality is currently missing, the plan is to add this in follow-up PRs.
This PR is a work in progress, and work needs to be done in the following area's:
- [x] Error handling should be improved.
- [x] Documentation should be improved.
- [x] Multi-Insert needs to be tested.
- [ ] Naming and package locations.
### How was this patch tested?
Catalyst and SQL unit tests.
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#11557 from hvanhovell/ngParser.
## What changes were proposed in this pull request?
As we have `CreateArray` and `CreateStruct`, we should also have `CreateMap`. This PR adds the `CreateMap` expression, and the DataFrame API, and python API.
## How was this patch tested?
various new tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11879 from cloud-fan/create_map.
## What changes were proposed in this pull request?
This reopens#11836, which was merged but promptly reverted because it introduced flaky Hive tests.
## How was this patch tested?
See `CatalogTestCases`, `SessionCatalogSuite` and `HiveContextSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#11938 from andrewor14/session-catalog-again.
## What changes were proposed in this pull request?
unionAll has been deprecated in SPARK-14088.
## How was this patch tested?
Should be covered by all existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#11946 from rxin/SPARK-14142.
## What changes were proposed in this pull request?
`SessionCatalog`, introduced in #11750, is a catalog that keeps track of temporary functions and tables, and delegates metastore operations to `ExternalCatalog`. This functionality overlaps a lot with the existing `analysis.Catalog`.
As of this commit, `SessionCatalog` and `ExternalCatalog` will no longer be dead code. There are still things that need to be done after this patch, namely:
- SPARK-14013: Properly implement temporary functions in `SessionCatalog`
- SPARK-13879: Decide which DDL/DML commands to support natively in Spark
- SPARK-?????: Implement the ones we do want to support through `SessionCatalog`.
- SPARK-?????: Merge SQL/HiveContext
## How was this patch tested?
This is largely a refactoring task so there are no new tests introduced. The particularly relevant tests are `SessionCatalogSuite` and `ExternalCatalogSuite`.
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#11836 from andrewor14/use-session-catalog.
## What changes were proposed in this pull request?
1. Deprecated unionAll. It is pretty confusing to have both "union" and "unionAll" when the two do the same thing in Spark but are different in SQL.
2. Rename reduce in KeyValueGroupedDataset to reduceGroups so it is more consistent with rest of the functions in KeyValueGroupedDataset. Also makes it more obvious what "reduce" and "reduceGroups" mean. Previously it was confusing because it could be reducing a Dataset, or just reducing groups.
3. Added a "name" function, which is more natural to name columns than "as" for non-SQL users.
4. Remove "subtract" function since it is just an alias for "except".
## How was this patch tested?
All changes should be covered by existing tests. Also added couple test cases to cover "name".
Author: Reynold Xin <rxin@databricks.com>
Closes#11908 from rxin/SPARK-14088.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-13953
Currently, JSON data source creates a new field in `PERMISSIVE` mode for storing malformed string.
This field can be renamed via `spark.sql.columnNameOfCorruptRecord` option but it is a global configuration.
This PR make that option can be applied per read and can be specified via `option()`. This will overwrites `spark.sql.columnNameOfCorruptRecord` if it is set.
## How was this patch tested?
Unit tests were used and `./dev/run_tests` for coding style tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#11881 from HyukjinKwon/SPARK-13953.
## What changes were proposed in this pull request?
Replaces current docstring ("Creates a :class:`WindowSpec` with the partitioning defined.") with "Creates a :class:`WindowSpec` with the ordering defined."
## How was this patch tested?
PySpark unit tests (no regression introduced). No changes to the code.
Author: zero323 <matthew.szymkiewicz@gmail.com>
Closes#11877 from zero323/order-by-description.
## What changes were proposed in this pull request?
Currently, there is no way to control the behaviour when fails to parse corrupt records in JSON data source .
This PR adds the support for parse modes just like CSV data source. There are three modes below:
- `PERMISSIVE` : When it fails to parse, this sets `null` to to field. This is a default mode when it has been this mode.
- `DROPMALFORMED`: When it fails to parse, this drops the whole record.
- `FAILFAST`: When it fails to parse, it just throws an exception.
This PR also make JSON data source share the `ParseModes` in CSV data source.
## How was this patch tested?
Unit tests were used and `./dev/run_tests` for code style tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#11756 from HyukjinKwon/SPARK-13764.
