| commit | ecca1bf6453e5e0042e1b56d4c35fb0b4d0f3121 | [log] [tgz] |
|---|---|---|
| author | Ian Cook <ianmcook@gmail.com> | Thu May 09 17:25:34 2024 +0900 |
| committer | Hyukjin Kwon <gurwls223@apache.org> | Thu May 09 17:25:34 2024 +0900 |
| tree | 69f85b7be36f5473950a2795d16fc1c4a27d4bc0 | |
| parent | 027327d94b3413ffb228ac482d51e75856c88d02 [diff] |
[SPARK-47365][PYTHON] Add toArrow() DataFrame method to PySpark ### What changes were proposed in this pull request? - Add a PySpark DataFrame method `toArrow()` which returns the contents of the DataFrame as a [PyArrow Table](https://arrow.apache.org/docs/python/generated/pyarrow.Table.html), for both local Spark and Spark Connect. - Add a new entry to the **Apache Arrow in PySpark** user guide page describing usage of the `toArrow()` method. - Add a new option to the method `_collect_as_arrow()` to provide more useful output when there are zero records returned. (This keeps the implementation of `toArrow()` simpler.) ### Why are the changes needed? In the Apache Arrow community, we hear from a lot of users who want to return the contents of a PySpark DataFrame as a PyArrow Table. Currently the only documented way to do this is to return the contents as a pandas DataFrame, then use PyArrow (`pa`) to convert that to a PyArrow Table. ```py pa.Table.from_pandas(df.toPandas()) ``` But going through pandas adds significant overhead which is easily avoided since internally `toPandas()` already converts the contents of Spark DataFrame to Arrow format as an intermediate step when `spark.sql.execution.arrow.pyspark.enabled` is `true`. Currently it is also possible to use the experimental `_collect_as_arrow()` method to return the contents of a PySpark DataFrame as a list of PyArrow RecordBatches. This PR adds a new non-experimental method `toArrow()` which returns the more user-friendly PyArrow Table object. This PR also adds a new argument `empty_list_if_zero_records` to the experimental method `_collect_as_arrow()` to control what the method returns in the case when the result data has zero rows. If set to `True` (the default), the existing behavior is preserved, and the method returns an empty Python list. If set to `False`, the method returns returns a length-one list containing an empty Arrow RecordBatch which includes the schema. This is used by `toArrow()` which requires the schema even if the data has zero rows. For Spark Connect, there is already a `SparkSession.client.to_table()` method that returns a PyArrow table. This PR uses that to expose `toArrow()` for Spark Connect. ### Does this PR introduce _any_ user-facing change? - It adds a DataFrame method `toArrow()` to the PySpark SQL DataFrame API. - It adds a new argument `empty_list_if_zero_records` to the experimental DataFrame method `_collect_as_arrow()` with a default value which preserves the method's existing behavior. - It exposes `toArrow()` for Spark Connect, via the existing `SparkSession.client.to_table()` method. - It does not introduce any other user-facing changes. ### How was this patch tested? This adds a new test and a new helper function for the test in `pyspark/sql/tests/test_arrow.py`. ### Was this patch authored or co-authored using generative AI tooling? No Closes #45481 from ianmcook/SPARK-47365. Lead-authored-by: Ian Cook <ianmcook@gmail.com> Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
Spark is a unified analytics engine for large-scale data processing. 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, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
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.)
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”.
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1,000,000,000:
scala> spark.range(1000 * 1000 * 1000).count()
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1,000,000,000:
>>> spark.range(1000 * 1000 * 1000).count()
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 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.
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.
There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md
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 and Enabling YARN” for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Please review the Contribution to Spark guide for information on how to get started contributing to the project.
