---
layout: global
title: Protobuf Data Source Guide
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  this work for additional information regarding copyright ownership.
  The ASF licenses this file to You under the Apache License, Version 2.0
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* This will become a table of contents (this text will be scraped).
{:toc}

Since Spark 3.4.0 release, [Spark SQL](https://spark.apache.org/docs/latest/sql-programming-guide.html) provides built-in support for reading and writing protobuf data.

## Deploying
The `spark-protobuf` module is external and not included in `spark-submit` or `spark-shell` by default.

As with any Spark applications, `spark-submit` is used to launch your application. `spark-protobuf_{{site.SCALA_BINARY_VERSION}}`
and its dependencies can be directly added to `spark-submit` using `--packages`, such as,

    ./bin/spark-submit --packages org.apache.spark:spark-protobuf_{{site.SCALA_BINARY_VERSION}}:{{site.SPARK_VERSION_SHORT}} ...

For experimenting on `spark-shell`, you can also use `--packages` to add `org.apache.spark:spark-protobuf_{{site.SCALA_BINARY_VERSION}}` and its dependencies directly,

    ./bin/spark-shell --packages org.apache.spark:spark-protobuf_{{site.SCALA_BINARY_VERSION}}:{{site.SPARK_VERSION_SHORT}} ...

See [Application Submission Guide](submitting-applications.html) for more details about submitting applications with external dependencies.

## to_protobuf() and from_protobuf()
The spark-protobuf package provides function `to_protobuf` to encode a column as binary in protobuf
format, and `from_protobuf()` to decode protobuf binary data into a column. Both functions transform one column to
another column, and the input/output SQL data type can be a complex type or a primitive type.

Using protobuf message as columns is useful when reading from or writing to a streaming source like Kafka. Each
Kafka key-value record will be augmented with some metadata, such as the ingestion timestamp into Kafka, the offset in Kafka, etc.
* If the "value" field that contains your data is in protobuf, you could use `from_protobuf()` to extract your data, enrich it, clean it, and then push it downstream to Kafka again or write it out to a different sink.
* `to_protobuf()` can be used to turn structs into protobuf message. This method is particularly useful when you would like to re-encode multiple columns into a single one when writing data out to Kafka.

Spark SQL schema is generated based on the protobuf descriptor file or protobuf class passed to `from_protobuf` and `to_protobuf`. The specified protobuf class or protobuf descriptor file must match the data, otherwise, the behavior is undefined: it may fail or return arbitrary results.

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}
import org.apache.spark.sql.protobuf.functions._

// `from_protobuf` and `to_protobuf` provides two schema choices. First, via the protobuf descriptor
// file, and then via the protobuf message class name.
// give input .proto protobuf schema
// syntax  = "proto3"
// message AppEvent {
//    string name = 1;
//    int64 id = 2;
//    string context = 3;
// }

val df = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribe", "topic1")
.load()

// 1. Decode the Protobuf data of schema `AppEvent` into a struct;
// 2. Filter by column `name`;
// 3. Encode the column `event` in Protobuf format.
// The Protobuf protoc command can be used to generate a protobuf descriptor file for give .proto file.
val output = df
.select(from_protobuf($"value", "AppEvent", descriptorFilePath) as $"event")
.where("event.name == \"alice\"")
.select(to_protobuf($"user", "AppEvent", descriptorFilePath) as $"event")

val query = output
.writeStream
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("topic", "topic2")
.start()

// Alternatively, you can decode and encode the SQL columns into protobuf format using protobuf
// class name. The specified Protobuf class must match the data, otherwise the behavior is undefined:
// it may fail or return arbitrary result. To avoid conflicts, the jar file containing the
// 'com.google.protobuf.*' classes should be shaded. An example of shading can be found at
// https://github.com/rangadi/shaded-protobuf-classes.
var output = df
.select(from_protobuf($"value", "org.example.protos..AppEvent") as $"event")
.where("event.name == \"alice\"")

output.printSchema()
// root
//  |--event: struct (nullable = true)
//  |    |-- name : string (nullable = true)
//  |    |-- id: long (nullable = true)
//  |    |-- context: string (nullable = true)

output = output.select(to_protobuf($"event", "org.sparkproject.spark_protobuf.protobuf.AppEvent") as $"event")

val query = output
.writeStream
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("topic", "topic2")
.start()

{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
{% highlight java %}
import static org.apache.spark.sql.functions.col;
import static org.apache.spark.sql.protobuf.functions.*;

// `from_protobuf` and `to_protobuf` provides two schema choices. First, via the protobuf descriptor
// file, and then via the protobuf message class name.
// give input .proto protobuf schema
// syntax  = "proto3"
// message AppEvent {
//    string name = 1;
//    int64 id = 2;
//    string context = 3;
// }

Dataset<Row> df = spark
.readStream()
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribe", "topic1")
.load();

// 1. Decode the Protobuf data of schema `AppEvent` into a struct;
// 2. Filter by column `name`;
// 3. Encode the column `event` in Protobuf format.
// The Protobuf protoc command can be used to generate a protobuf descriptor file for give .proto file.
Dataset<Row> output = df
.select(from_protobuf(col("value"), "AppEvent", descriptorFilePath).as("event"))
.where("event.name == \"alice\"")
.select(to_protobuf(col("event"), "AppEvent", descriptorFilePath).as("event"));

// Alternatively, you can decode and encode the SQL columns into protobuf format using protobuf
// class name. The specified Protobuf class must match the data, otherwise the behavior is undefined:
// it may fail or return arbitrary result. To avoid conflicts, the jar file containing the
// 'com.google.protobuf.*' classes should be shaded. An example of shading can be found at
// https://github.com/rangadi/shaded-protobuf-classes.
Dataset<Row> output = df
.select(
  from_protobuf(col("value"),
  "org.sparkproject.spark_protobuf.protobuf.AppEvent").as("event"))
.where("event.name == \"alice\"")

output.printSchema()
// root
//  |--event: struct (nullable = true)
//  |    |-- name : string (nullable = true)
//  |    |-- id: long (nullable = true)
//  |    |-- context: string (nullable = true)

output = output.select(
  to_protobuf(col("event"),
  "org.sparkproject.spark_protobuf.protobuf.AppEvent").as("event"));

StreamingQuery query = output
.writeStream()
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("topic", "topic2")
.start();

{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
{% highlight python %}
from pyspark.sql.protobuf.functions import from_protobuf, to_protobuf

# `from_protobuf` and `to_protobuf` provides two schema choices. First, via the protobuf descriptor
# file, and then via the protobuf message class name.
# give input .proto protobuf schema
# syntax  = "proto3"
# message AppEvent {
#    string name = 1;
#    int64 id = 2;
#    string context = 3;
# }

df = spark\
.readStream\
.format("kafka")\
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")\
.option("subscribe", "topic1")\
.load()

# 1. Decode the Protobuf data of schema `AppEvent` into a struct;
# 2. Filter by column `name`;
# 3. Encode the column `event` in Protobuf format.
# The Protobuf protoc command can be used to generate a protobuf descriptor file for give .proto file.
output = df\
.select(from_protobuf("value", "AppEvent", descriptorFilePath).alias("event"))\
.where('event.name == "alice"')\
.select(to_protobuf("event", "AppEvent", descriptorFilePath).alias("event"))

# Alternatively, you can decode and encode the SQL columns into protobuf format using protobuf
# class name. The specified Protobuf class must match the data, otherwise the behavior is undefined:
# it may fail or return arbitrary result. To avoid conflicts, the jar file containing the
# 'com.google.protobuf.*' classes should be shaded. An example of shading can be found at
# https://github.com/rangadi/shaded-protobuf-classes.

output = df\
.select(from_protobuf("value", "org.sparkproject.spark_protobuf.protobuf.AppEvent").alias("event"))\
.where('event.name == "alice"')

output.printSchema()
# root
#  |--event: struct (nullable = true)
#  |   |-- name : string (nullable = true)
#  |   |-- id: long (nullable = true)
#  |   |-- context: string (nullable = true)

output = output
.select(to_protobuf("event", "org.sparkproject.spark_protobuf.protobuf.AppEvent").alias("event"))

query = output\
.writeStream\
.format("kafka")\
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")\
.option("topic", "topic2")\
.start()

{% endhighlight %}
</div>
</div>

## Supported types for Protobuf -> Spark SQL conversion
Currently Spark supports reading [protobuf scalar types](https://developers.google.com/protocol-buffers/docs/proto3#scalar), [enum types](https://developers.google.com/protocol-buffers/docs/proto3#enum), [nested type](https://developers.google.com/protocol-buffers/docs/proto3#nested), and [maps type](https://developers.google.com/protocol-buffers/docs/proto3#maps) under messages of Protobuf.
In addition to the these types, `spark-protobuf` also introduces support for Protobuf `OneOf` fields. which allows you to handle messages that can have multiple possible sets of fields, but only one set can be present at a time. This is useful for situations where the data you are working with is not always in the same format, and you need to be able to handle messages with different sets of fields without encountering errors.

<table class="table">
  <tr><th><b>Protobuf type</b></th><th><b>Spark SQL type</b></th></tr>
  <tr>
    <td>boolean</td>
    <td>BooleanType</td>
  </tr>
  <tr>
    <td>int</td>
    <td>IntegerType</td>
  </tr>
  <tr>
    <td>long</td>
    <td>LongType</td>
  </tr>
  <tr>
    <td>float</td>
    <td>FloatType</td>
  </tr>
  <tr>
    <td>double</td>
    <td>DoubleType</td>
  </tr>
  <tr>
    <td>string</td>
    <td>StringType</td>
  </tr>
  <tr>
    <td>enum</td>
    <td>StringType</td>
  </tr>
  <tr>
    <td>bytes</td>
    <td>BinaryType</td>
  </tr>
  <tr>
    <td>Message</td>
    <td>StructType</td>
  </tr>
  <tr>
    <td>repeated</td>
    <td>ArrayType</td>
  </tr>
  <tr>
    <td>map</td>
    <td>MapType</td>
  </tr>
  <tr>
    <td>OneOf</td>
    <td>Struct</td>
  </tr>
</table>

It also supports reading the following Protobuf types [Timestamp](https://developers.google.com/protocol-buffers/docs/reference/google.protobuf#timestamp) and [Duration](https://developers.google.com/protocol-buffers/docs/reference/google.protobuf#duration)

<table class="table">
  <tr><th><b>Protobuf logical type</b></th><th><b>Protobuf schema</b></th><th><b>Spark SQL type</b></th></tr>
  <tr>
    <td>duration</td>
    <td>MessageType{seconds: Long, nanos: Int}</td>
    <td>DayTimeIntervalType</td>
  </tr>
  <tr>
    <td>timestamp</td>
    <td>MessageType{seconds: Long, nanos: Int}</td>
    <td>TimestampType</td>
  </tr>
</table>

## Supported types for Spark SQL -> Protobuf conversion
Spark supports the writing of all Spark SQL types into Protobuf. For most types, the mapping from Spark types to Protobuf types is straightforward (e.g. IntegerType gets converted to int);

<table class="table">
  <tr><th><b>Spark SQL type</b></th><th><b>Protobuf type</b></th></tr>
  <tr>
    <td>BooleanType</td>
    <td>boolean</td>
  </tr>
  <tr>
    <td>IntegerType</td>
    <td>int</td>
  </tr>
  <tr>
    <td>LongType</td>
    <td>long</td>
  </tr>
  <tr>
    <td>FloatType</td>
    <td>float</td>
  </tr>
  <tr>
    <td>DoubleType</td>
    <td>double</td>
  </tr>
  <tr>
    <td>StringType</td>
    <td>string</td>
  </tr>
  <tr>
    <td>StringType</td>
    <td>enum</td>
  </tr>
  <tr>
    <td>BinaryType</td>
    <td>bytes</td>
  </tr>
  <tr>
    <td>StructType</td>
    <td>message</td>
  </tr>
  <tr>
    <td>ArrayType</td>
    <td>repeated</td>
  </tr>
  <tr>
    <td>MapType</td>
    <td>map</td>
  </tr>
</table>

## Handling circular references protobuf fields
One common issue that can arise when working with Protobuf data is the presence of circular references. In Protobuf, a circular reference occurs when a field refers back to itself or to another field that refers back to the original field. This can cause issues when parsing the data, as it can result in infinite loops or other unexpected behavior.
To address this issue, the latest version of spark-protobuf introduces a new feature: the ability to check for circular references through field types. This allows users use the `recursive.fields.max.depth` option to specify the maximum number of levels of recursion to allow when parsing the schema. By default, `spark-protobuf` will not permit recursive fields by setting `recursive.fields.max.depth` to -1. However, you can set this option to 0 to 10 if needed. 

Setting `recursive.fields.max.depth` to 0 drops all recursive fields, setting it to 1 allows it to be recursed once, and setting it to 2 allows it to be recursed twice. A `recursive.fields.max.depth` value greater than 10 is not allowed, as it can lead to performance issues and even stack overflows.

SQL Schema for the below protobuf message will vary based on the value of `recursive.fields.max.depth`.

<div data-lang="scala" markdown="1">
{% highlight scala %}
syntax  = "proto3"
message Person {
  string name = 1;
  Person bff = 2
}

// The protobuf schema defined above, would be converted into a Spark SQL columns with the following
// structure based on `recursive.fields.max.depth` value.

0: struct<name: string, bff: null>
1: struct<name string, bff: <name: string, bff: null>>
2: struct<name string, bff: <name: string, bff: struct<name: string, bff: null>>> ...

{% endhighlight %}
</div>