docs/sql-data-sources-protobuf.md

---
layout: global
title: Protobuf Data Source Guide
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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](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="python" markdown="1">

<div class="d-none">
This div is only used to make markdown editor/viewer happy and does not display on web

```python
</div>

{% highlight python %}

from pyspark.sql.protobuf.functions import from_protobuf, to_protobuf

# from_protobuf and to_protobuf provide two schema choices. Via Protobuf descriptor file,
# or via shaded Java class.
# 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 class="d-none">
```
</div>

</div>

<div data-lang="scala" markdown="1">

<div class="d-none">
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```scala
</div>

{% highlight scala %}
import org.apache.spark.sql.protobuf.functions._

// `from_protobuf` and `to_protobuf` provides two schema choices. Via the protobuf descriptor file,
// or via shaded Java class.
// 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 class="d-none">
```
</div>
</div>

<div data-lang="java" markdown="1">

<div class="d-none">
This div is only used to make markdown editor/viewer happy and does not display on web

```java
</div>

{% 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. Via the protobuf descriptor file,
// or via shaded Java class.
// 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 class="d-none">
```
</div>
</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>
  <thead><tr><th><b>Protobuf type</b></th><th><b>Spark SQL type</b></th></tr></thead>
  <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>
  <thead><tr><th><b>Protobuf logical type</b></th><th><b>Protobuf schema</b></th><th><b>Spark SQL type</b></th></tr></thead>
  <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>
  <thead><tr><th><b>Spark SQL type</b></th><th><b>Protobuf type</b></th></tr></thead>
  <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 1 to 10 if needed.

Setting `recursive.fields.max.depth` to 1 drops all recursive fields, setting it to 2 allows it to be recursed once, and setting it to 3 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="proto" markdown="1">
<div class="d-none">
This div is only used to make markdown editor/viewer happy and does not display on web

```protobuf
</div>

{% highlight protobuf %}
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.

1: struct<name: string>
2: struct<name: string, bff: struct<name: string>>
3: struct<name: string, bff: struct<name: string, bff: struct<name: string>>>
...

{% endhighlight %}
<div class="d-none">
```
</div>
</div>

## Data Source Option

Data source options of Protobuf can be set via:
* the built-in functions below
  * `from_protobuf`
  * `to_protobuf`

<table>
  <thead><tr><th><b>Property Name</b></th><th><b>Default</b></th><th><b>Meaning</b></th><th><b>Scope</b></th></tr></thead>
  <tr>
    <td><code>mode</code></td>
    <td><code>FAILFAST</code></td>
    <td>Allows a mode for dealing with corrupt records during parsing.<br>
    <ul>
      <li><code>PERMISSIVE</code>: when it meets a corrupted record, sets all fields to <code>null</code>.</li>
      <li><code>DROPMALFORMED</code>: ignores the whole corrupted records. This mode is unsupported in the Protobuf built-in functions.</li>
      <li><code>FAILFAST</code>: throws an exception when it meets corrupted records.</li>
    </ul>
    </td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>recursive.fields.max.depth</code></td>
    <td><code>-1</code></td>
    <td>Specifies the maximum number of recursion levels to allow when parsing the schema. For more details refers to the section <a href="https://spark.apache.org/docs/latest/sql-data-sources-protobuf.html#handling-circular-references-protobuf-fields">Handling circular references protobuf fields</a>.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>convert.any.fields.to.json</code></td>
    <td><code>false</code></td>
    <td>Enables converting Protobuf <code>Any</code> fields to JSON. This option should be enabled carefully. JSON conversion and processing are inefficient. In addition, schema safety is also reduced making downstream processing error-prone.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>emit.default.values</code></td>
    <td><code>false</code></td>
    <td>Whether to render fields with zero values when deserializing Protobuf to a Spark struct. When a field is empty in the serialized Protobuf, this library will deserialize them as <code>null</code> by default, this option can control whether to render the type-specific zero values.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>enums.as.ints</code></td>
    <td><code>false</code></td>
    <td>Whether to render enum fields as their integer values. When this option set to <code>false</code>, an enum field will be mapped to <code>StringType</code>, and the value is the name of enum; when set to <code>true</code>, an enum field will be mapped to <code>IntegerType</code>, the value is its integer value.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>upcast.unsigned.ints</code></td>
    <td><code>false</code></td>
    <td>Whether to upcast unsigned integers into a larger type. Setting this option to <code>true</code>, <code>LongType</code> is used for <code>uint32</code> and <code>Decimal(20, 0)</code> is used for <code>uint64</code>, so their representation can contain large unsigned values without overflow.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>unwrap.primitive.wrapper.types</code></td>
    <td><code>false</code></td>
    <td>Whether to unwrap the struct representation for well-known primitive wrapper types when deserializing. By default, the wrapper types for primitives (i.e. google.protobuf.Int32Value, google.protobuf.Int64Value, etc.) will get deserialized as structs.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>retain.empty.message.types</code></td>
    <td><code>false</code></td>
    <td>Whether to retain fields of the empty proto message type in Schema. Since Spark doesn't allow writing empty <code>StructType</code>, the empty proto message type will be dropped by default. Setting this option to <code>true</code> will insert a dummy column(<code>__dummy_field_in_empty_struct</code>) to the empty proto message so that the empty message fields will be retained.</td>
    <td>read</td>
  </tr>
</table>