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Cheng Lian authored
PR #8341 is a valid fix for SPARK-10136, but it didn't catch the real root cause.  The real problem can be rather tricky to explain, and requires audiences to be pretty familiar with parquet-format spec, especially details of `LIST` backwards-compatibility rules.  Let me have a try to give an explanation here.

The structure of the problematic Parquet schema generated by parquet-avro is something like this:

```
message m {
  <repetition> group f (LIST) {         // Level 1
    repeated group array (LIST) {       // Level 2
      repeated <primitive-type> array;  // Level 3
    }
  }
}
```

(The schema generated by parquet-thrift is structurally similar, just replace the `array` at level 2 with `f_tuple`, and the other one at level 3 with `f_tuple_tuple`.)

This structure consists of two nested legacy 2-level `LIST`-like structures:

1. The repeated group type at level 2 is the element type of the outer array defined at level 1

   This group should map to an `CatalystArrayConverter.ElementConverter` when building converters.

2. The repeated primitive type at level 3 is the element type of the inner array defined at level 2

   This group should also map to an `CatalystArrayConverter.ElementConverter`.

The root cause of SPARK-10136 is that, the group at level 2 isn't properly recognized as the element type of level 1.  Thus, according to parquet-format spec, the repeated primitive at level 3 is left as a so called "unannotated repeated primitive type", and is recognized as a required list of required primitive type, thus a `RepeatedPrimitiveConverter` instead of a `CatalystArrayConverter.ElementConverter` is created for it.

According to  parquet-format spec, unannotated repeated type shouldn't appear in a `LIST`- or `MAP`-annotated group.  PR #8341 fixed this issue by allowing such unannotated repeated type appear in `LIST`-annotated groups, which is a non-standard, hacky, but valid fix.  (I didn't realize this when authoring #8341 though.)

As for the reason why level 2 isn't recognized as a list element type, it's because of the following `LIST` backwards-compatibility rule defined in the parquet-format spec:

> If the repeated field is a group with one field and is named either `array` or uses the `LIST`-annotated group's name with `_tuple` appended then the repeated type is the element type and elements are required.

(The `array` part is for parquet-avro compatibility, while the `_tuple` part is for parquet-thrift.)

This rule is implemented in [`CatalystSchemaConverter.isElementType`] [1], but neglected in [`CatalystRowConverter.isElementType`] [2].  This PR delivers a more robust fix by adding this rule in the latter method.

Note that parquet-avro 1.7.0 also suffers from this issue. Details can be found at [PARQUET-364] [3].

[1]: https://github.com/apache/spark/blob/85f9a61357994da5023b08b0a8a2eb09388ce7f8/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala#L259-L305
[2]: https://github.com/apache/spark/blob/85f9a61357994da5023b08b0a8a2eb09388ce7f8/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala#L456-L463
[3]: https://issues.apache.org/jira/browse/PARQUET-364

Author: Cheng Lian <lian@databricks.com>

Closes #8361 from liancheng/spark-10136/proper-version.
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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, and Python, 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".

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-cluster" or "yarn-client" 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. See also "Third Party Hadoop Distributions" for guidance on building a Spark application that works with a particular distribution.

Configuration

Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.