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[SPARK-14127][SQL] "DESC <table>": Extracts schema information from table...

Cheng Lian authored 8 years ago

[SPARK-14127][SQL] "DESC <table>": Extracts schema information from table properties for data source tables

## What changes were proposed in this pull request?

This is a follow-up of #12934 and #12844. This PR adds a set of utility methods in `DDLUtils` to help extract schema information (user-defined schema, partition columns, and bucketing information) from data source table properties. These utility methods are then used in `DescribeTableCommand` to refine output for data source tables. Before this PR, the aforementioned schema information are only shown as table properties, which are hard to read.

Sample output:

```
+----------------------------+---------------------------------------------------------+-------+
|col_name                    |data_type                                                |comment|
+----------------------------+---------------------------------------------------------+-------+
|a                           |bigint                                                   |       |
|b                           |bigint                                                   |       |
|c                           |bigint                                                   |       |
|d                           |bigint                                                   |       |
|# Partition Information     |                                                         |       |
|# col_name                  |                                                         |       |
|d                           |                                                         |       |
|                            |                                                         |       |
|# Detailed Table Information|                                                         |       |
|Database:                   |default                                                  |       |
|Owner:                      |lian                                                     |       |
|Create Time:                |Tue May 10 03:20:34 PDT 2016                             |       |
|Last Access Time:           |Wed Dec 31 16:00:00 PST 1969                             |       |
|Location:                   |file:/Users/lian/local/src/spark/workspace-a/target/...  |       |
|Table Type:                 |MANAGED                                                  |       |
|Table Parameters:           |                                                         |       |
|  rawDataSize               |-1                                                       |       |
|  numFiles                  |1                                                        |       |
|  transient_lastDdlTime     |1462875634                                               |       |
|  totalSize                 |684                                                      |       |
|  spark.sql.sources.provider|parquet                                                  |       |
|  EXTERNAL                  |FALSE                                                    |       |
|  COLUMN_STATS_ACCURATE     |false                                                    |       |
|  numRows                   |-1                                                       |       |
|                            |                                                         |       |
|# Storage Information       |                                                         |       |
|SerDe Library:              |org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe       |       |
|InputFormat:                |org.apache.hadoop.mapred.SequenceFileInputFormat         |       |
|OutputFormat:               |org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat|       |
|Compressed:                 |No                                                       |       |
|Num Buckets:                |2                                                        |       |
|Bucket Columns:             |[b]                                                      |       |
|Sort Columns:               |[c]                                                      |       |
|Storage Desc Parameters:    |                                                         |       |
|  path                      |file:/Users/lian/local/src/spark/workspace-a/target/...  |       |
|  serialization.format      |1                                                        |       |
+----------------------------+---------------------------------------------------------+-------+
```

## How was this patch tested?

Test cases are added in `HiveDDLSuite` to check command output.

Author: Cheng Lian <lian@databricks.com>

Closes #13025 from liancheng/spark-14127-extract-schema-info.

8a12580d

8a12580d 8 years ago

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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.

http://spark.apache.org/

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.