087fb31420
## What changes were proposed in this pull request?
Add documentation about the limitations of `pandas_udf` with keyword arguments and related concepts, like `functools.partial` fn objects.
NOTE: intermediate commits on this PR show some of the steps that can be taken to fix some (but not all) of these pain points.
### Survey of problems we face today:
(Initialize) Note: python 3.6 and spark 2.4snapshot.
```
from pyspark.sql import SparkSession
import inspect, functools
from pyspark.sql.functions import pandas_udf, PandasUDFType, col, lit, udf
spark = SparkSession.builder.getOrCreate()
print(spark.version)
df = spark.range(1,6).withColumn('b', col('id') * 2)
def ok(a,b): return a+b
```
Using a keyword argument at the call site `b=...` (and yes, *full* stack trace below, haha):
```
---> 14 df.withColumn('ok', pandas_udf(f=ok, returnType='bigint')('id', b='id')).show() # no kwargs
TypeError: wrapper() got an unexpected keyword argument 'b'
```
Using partial with a keyword argument where the kw-arg is the first argument of the fn:
*(Aside: kind of interesting that lines 15,16 work great and then 17 explodes)*
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-9-e9f31b8799c1> in <module>()
15 df.withColumn('ok', pandas_udf(f=functools.partial(ok, 7), returnType='bigint')('id')).show()
16 df.withColumn('ok', pandas_udf(f=functools.partial(ok, b=7), returnType='bigint')('id')).show()
---> 17 df.withColumn('ok', pandas_udf(f=functools.partial(ok, a=7), returnType='bigint')('id')).show()
/Users/stu/ZZ/spark/python/pyspark/sql/functions.py in pandas_udf(f, returnType, functionType)
2378 return functools.partial(_create_udf, returnType=return_type, evalType=eval_type)
2379 else:
-> 2380 return _create_udf(f=f, returnType=return_type, evalType=eval_type)
2381
2382
/Users/stu/ZZ/spark/python/pyspark/sql/udf.py in _create_udf(f, returnType, evalType)
54 argspec.varargs is None:
55 raise ValueError(
---> 56 "Invalid function: 0-arg pandas_udfs are not supported. "
57 "Instead, create a 1-arg pandas_udf and ignore the arg in your function."
58 )
ValueError: Invalid function: 0-arg pandas_udfs are not supported. Instead, create a 1-arg pandas_udf and ignore the arg in your function.
```
Author: Michael (Stu) Stewart <mstewart141@gmail.com>
Closes #20900 from mstewart141/udfkw2.
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| .github | ||
| assembly | ||
| bin | ||
| build | ||
| common | ||
| conf | ||
| core | ||
| data | ||
| dev | ||
| docs | ||
| examples | ||
| external | ||
| graphx | ||
| hadoop-cloud | ||
| launcher | ||
| licenses | ||
| mllib | ||
| mllib-local | ||
| project | ||
| python | ||
| R | ||
| repl | ||
| resource-managers | ||
| sbin | ||
| sql | ||
| streaming | ||
| tools | ||
| .gitattributes | ||
| .gitignore | ||
| .travis.yml | ||
| appveyor.yml | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| NOTICE | ||
| pom.xml | ||
| README.md | ||
| scalastyle-config.xml | ||
Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
build/mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.)
You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". More detailed documentation is available from the project site, at "Building Spark".
For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1000:
scala> sc.parallelize(1 to 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1000:
>>> sc.parallelize(range(1000)).count()
Example Programs
Spark also comes with several sample programs in the examples directory.
To run one of them, use ./bin/run-example <class> [params]. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the examples
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Running Tests
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
Please see the guidance on how to run tests for a module, or individual tests.
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.
Configuration
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Contributing
Please review the Contribution to Spark guide for information on how to get started contributing to the project.