ODIn/spark-instrumented-optimizer
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity
spark-instrumented-optimizer/python
History
HyukjinKwon 657e39a334 [SPARK-32897][PYTHON] Don't show a deprecation warning at SparkSession.builder.getOrCreate 
### What changes were proposed in this pull request?

In PySpark shell, if you call `SparkSession.builder.getOrCreate` as below:

```python
import warnings
from pyspark.sql import SparkSession, SQLContext
warnings.simplefilter('always', DeprecationWarning)
spark.stop()
SparkSession.builder.getOrCreate()
```

it shows the deprecation warning as below:

```
/.../spark/python/pyspark/sql/context.py:72: DeprecationWarning: Deprecated in 3.0.0. Use SparkSession.builder.getOrCreate() instead.
  DeprecationWarning)
```

via d3304268d3/python/pyspark/sql/session.py (L222)

We shouldn't print the deprecation warning from it. This is the only place ^.

### Why are the changes needed?

To prevent to inform users that `SparkSession.builder.getOrCreate` is deprecated mistakenly.

### Does this PR introduce _any_ user-facing change?

Yes, it won't show a deprecation warning to end users for calling `SparkSession.builder.getOrCreate`.

### How was this patch tested?

Manually tested as above.

Closes #29768 from HyukjinKwon/SPARK-32897.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
2020-09-16 10:13:47 -07:00
..
docs [SPARK-32180][FOLLOWUP] Fix .rst error in new Pyspark installation guide 2020-09-11 20:08:22 -07:00
lib [SPARK-30884][PYSPARK] Upgrade to Py4J 0.10.9 2020-02-20 09:09:30 -08:00
pyspark [SPARK-32897][PYTHON] Don't show a deprecation warning at SparkSession.builder.getOrCreate 2020-09-16 10:13:47 -07:00
test_coverage [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
test_support [SPARK-23094][SPARK-23723][SPARK-23724][SQL] Support custom encoding for json files 2018-04-29 11:25:31 +08:00
.coveragerc [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
.gitignore [SPARK-3946] gitignore in /python includes wrong directory 2014-10-14 14:09:39 -07:00
MANIFEST.in [SPARK-26803][PYTHON] Add sbin subdirectory to pyspark 2019-02-27 08:39:55 -06:00
pylintrc [SPARK-32435][PYTHON] Remove heapq3 port from Python 3 2020-07-27 20:10:13 +09:00
README.md [SPARK-30884][PYSPARK] Upgrade to Py4J 0.10.9 2020-02-20 09:09:30 -08:00
run-tests [SPARK-29672][PYSPARK] update spark testing framework to use python3 2019-11-14 10:18:55 -08:00
run-tests-with-coverage [SPARK-26252][PYTHON] Add support to run specific unittests and/or doctests in python/run-tests script 2018-12-05 15:22:08 +08:00
run-tests.py [SPARK-32812][PYTHON][TESTS] Avoid initiating a process during the main process for run-tests.py 2020-09-08 12:22:13 +09:00
setup.cfg [SPARK-1267][SPARK-18129] Allow PySpark to be pip installed 2016-11-16 14:22:15 -08:00
setup.py [SPARK-32312][SQL][PYTHON][TEST-JAVA11] Upgrade Apache Arrow to version 1.0.1 2020-09-10 14:16:19 +09:00

README.md

Apache Spark

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, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.

https://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page

Python Packaging

This README file only contains basic information related to pip installed PySpark. This packaging is currently experimental and may change in future versions (although we will do our best to keep compatibility). Using PySpark requires the Spark JARs, and if you are building this from source please see the builder instructions at "Building Spark".

The Python packaging for Spark is not intended to replace all of the other use cases. This Python packaged version of Spark is suitable for interacting with an existing cluster (be it Spark standalone, YARN, or Mesos) - but does not contain the tools required to set up your own standalone Spark cluster. You can download the full version of Spark from the Apache Spark downloads page.

NOTE: If you are using this with a Spark standalone cluster you must ensure that the version (including minor version) matches or you may experience odd errors.

Python Requirements

At its core PySpark depends on Py4J, but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).