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sparksql-protobuf

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Read SparkSQL parquet file as RDD[Protobuf]

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# sparksql-protobuf

This library provides utilities to work with Protobuf objects in SparkSQL.
It provides a way to read parquet file written by SparkSQL back as an RDD of compatible protobuf object.
It can also converts RDD of protobuf objects into DataFrame.

[![Build Status](https://travis-ci.org/saurfang/sparksql-protobuf.svg?branch=master)](https://travis-ci.org/saurfang/sparksql-protobuf)
[![codecov.io](https://codecov.io/github/saurfang/sparksql-protobuf/coverage.svg?branch=master)](https://codecov.io/github/saurfang/sparksql-protobuf?branch=master)

For sbt 0.13.6+

```scala
resolvers += Resolver.jcenterRepo

libraryDependencies ++= Seq(
    "com.github.saurfang" %% "sparksql-protobuf" % "0.1.3",
    "org.apache.parquet" % "parquet-protobuf" % "1.8.3"
)
```

## Motivation

SparkSQL is very powerful and easy to use. However it has a few limitations and schema is only detected during runtime
makes developers a lot less confident that they will get things right at first time. _Static_ typing helps a lot! This is where protobuf comes in:

1. Protobuf defines nested data structure easily
2. It doesn't constraint you to the 22 fields limit in case class (no longer true once we upgrade to 2.11+)
3. It is language agnostic and generates code that gives you native objects
   hence you get all the benefit of type checking and code completion unlike operating `Row` in Spark/SparkSQL

## Features

### Read Parquet file as `RDD[Protobuf]`

```scala
val personsPB = new ProtoParquetRDD(sc, "persons.parquet", classOf[Person])
```

where we need `SparkContext`, parquet path and protobuf class.

This converts the existing workflow:

1. Ingest raw data as DataFrame with nested data structure
2. Create awkward runtime type checking udfs
3. Transform raw DataFrame using above udfs into a tabular DataFrame for data analytics

to

1. Ingest raw data as DataFrame with nested data structure and persist as Parquet file
2. Read Parquet file back as `RDD[Protobuf]`
3. Perform any data transformation and extraction by working with compile typesafe Protobuf getters
4. Create a DataFrame out of the above transformation and perform additional downstream data analytics on the tabular DataFrame

### Infer SparkSQL Schema from Protobuf Definition

```scala
val personSchema = ProtoReflection.schemaFor[Person].dataType.asInstanceOf[StructType]
```

### Convert `RDD[Protobuf]` to `DataFrame`

```scala
import com.github.saurfang.parquet.proto.spark.sql._
val personsDF = sqlContext.createDataFrame(protoPersons)
```

_For more information, please see test cases._

## Under the hood

1. `ProtoMessageConverter` has been improved to read from [LIST specification](https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#lists)
   according to latest parquet documentation. This implementation should be backwards compatible and is able to read repeated
   fields generated by writers like SparkSQL.
2. `ProtoMessageParquetInputFormat` helps the above process by correctly returning the built protobuf object as value.
3. `ProtoParquetRDD` abstract the Hadoop input format and returns an RDD of your protobuf objects from parquet files directly.
4. `ProtoReflection` infers SparkSQL schema from any Protobuf message class.
5. `ProtoRDDConversions` converts Protobuf objects into SparkSQL rows.

## Related Work

[Elephant Bird](https://github.com/twitter/elephant-bird)

[Spark-9999](https://issues.apache.org/jira/browse/SPARK-9999)

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