ef55e46c92
#### What changes were proposed in this pull request?
When we load a dataset, if we set the path to ```/path/a=1```, we will not take `a` as the partitioning column. However, if we set the path to ```/path/a=1/file.parquet```, we take `a` as the partitioning column and it shows up in the schema.
This PR is to fix the behavior inconsistency issue.
The base path contains a set of paths that are considered as the base dirs of the input datasets. The partitioning discovery logic will make sure it will stop when it reaches any base path.
By default, the paths of the dataset provided by users will be base paths. Below are three typical cases,
**Case 1**```sqlContext.read.parquet("/path/something=true/")```: the base path will be
`/path/something=true/`, and the returned DataFrame will not contain a column of `something`.
**Case 2**```sqlContext.read.parquet("/path/something=true/a.parquet")```: the base path will be
still `/path/something=true/`, and the returned DataFrame will also not contain a column of
`something`.
**Case 3**```sqlContext.read.parquet("/path/")```: the base path will be `/path/`, and the returned
DataFrame will have the column of `something`.
Users also can override the basePath by setting `basePath` in the options to pass the new base
path to the data source. For example,
```sqlContext.read.option("basePath", "/path/").parquet("/path/something=true/")```,
and the returned DataFrame will have the column of `something`.
The related PRs:
- https://github.com/apache/spark/pull/9651
- https://github.com/apache/spark/pull/10211
#### How was this patch tested?
Added a couple of test cases
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes #12828 from gatorsmile/readPartitionedTable.
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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 and project wiki. 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.) More detailed documentation is available from the project site, at "Building Spark". For developing Spark using an IDE, see Eclipse and IntelliJ.
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.