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Nicholas Chammas f540031419 [SPARK-31000][PYTHON][SQL] Add ability to set table description via Catalog.createTable() 
### What changes were proposed in this pull request?

This PR enhances `Catalog.createTable()` to allow users to set the table's description. This corresponds to the following SQL syntax:

```sql
CREATE TABLE ...
COMMENT 'this is a fancy table';
```

### Why are the changes needed?

This brings the Scala/Python catalog APIs a bit closer to what's already possible via SQL.

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

Yes, it adds a new parameter to `Catalog.createTable()`.

### How was this patch tested?

Existing unit tests:

```sh
./python/run-tests \
  --python-executables python3.7 \
  --testnames 'pyspark.sql.tests.test_catalog,pyspark.sql.tests.test_context'
```

```
$ ./build/sbt
testOnly org.apache.spark.sql.internal.CatalogSuite org.apache.spark.sql.CachedTableSuite org.apache.spark.sql.hive.MetastoreDataSourcesSuite org.apache.spark.sql.hive.execution.HiveDDLSuite
```

Closes #27908 from nchammas/SPARK-31000-table-description.

Authored-by: Nicholas Chammas <nicholas.chammas@liveramp.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-08-25 13:42:31 +09:00
..
docs [MINOR][DOCS] Add KMeansSummary and InheritableThread to documentation 2020-08-19 14:30:07 +09:00
lib [SPARK-30884][PYSPARK] Upgrade to Py4J 0.10.9 2020-02-20 09:09:30 -08:00
pyspark [SPARK-31000][PYTHON][SQL] Add ability to set table description via Catalog.createTable() 2020-08-25 13:42:31 +09:00
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-32036] Replace references to blacklist/whitelist language with more appropriate terminology, excluding the blacklisting feature 2020-07-15 11:40:55 -05:00
setup.cfg [SPARK-1267][SPARK-18129] Allow PySpark to be pip installed 2016-11-16 14:22:15 -08:00
setup.py [SPARK-29802][BUILD] Use python3 in build scripts 2020-07-19 11:02:37 +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).