Protobuf データソース ガイド

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()

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()

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();