如何将基本身份验证传递给Confluent Schema Registry?

问题描述

我想从融合的云主题中读取数据，然后再写入另一个主题.

I want to read data from a confluent cloud topic and then write in another topic.

在本地主机上，我没有遇到任何重大问题.但是融合云的架构注册表需要传递一些我不知道如何输入的身份验证数据:

At localhost, I haven't had any major problems. But the schema registry of confluent cloud requires to pass some authentication data that I don't know how to enter them:

schema.registry.basic.auth.user.info =:

schema.registry.basic.auth.user.info=:

schema.registry.url = https://xxxxxxxxxx.confluent.cloudBlockquote

schema.registry.url=https://xxxxxxxxxx.confluent.cloudBlockquote

下面是当前代码:

import com.databricks.spark.avro.SchemaConverters
import io.confluent.kafka.schemaregistry.client.{CachedSchemaRegistryClient, SchemaRegistryClient}
import io.confluent.kafka.serializers.AbstractKafkaAvroDeserializer
import org.apache.avro.Schema
import org.apache.avro.generic.GenericRecord
import org.apache.spark.sql.SparkSession

object AvroConsumer {
  private val topic = "transactions"
  private val kafkaUrl = "http://localhost:9092"
  private val schemaRegistryUrl = "http://localhost:8081"

  private val schemaRegistryClient = new CachedSchemaRegistryClient(schemaRegistryUrl, 128)
  private val kafkaAvroDeserializer = new AvroDeserializer(schemaRegistryClient)

  private val avroSchema = schemaRegistryClient.getLatestSchemaMetadata(topic + "-value").getSchema
  private var sparkSchema = SchemaConverters.toSqlType(new Schema.Parser().parse(avroSchema))

  def main(args: Array[String]): Unit = {
    val spark = SparkSession
      .builder
      .appName("ConfluentConsumer")
      .master("local[*]")
      .getOrCreate()

    spark.sparkContext.setLogLevel("ERROR")

    spark.udf.register("deserialize", (bytes: Array[Byte]) =>
      DeserializerWrapper.deserializer.deserialize(bytes)
    )

    val kafkaDataFrame = spark
      .readStream
      .format("kafka")
      .option("kafka.bootstrap.servers", kafkaUrl)
      .option("subscribe", topic)
      .load()

    val valueDataFrame = kafkaDataFrame.selectExpr("""deserialize(value) AS message""")

    import org.apache.spark.sql.functions._

    val formattedDataFrame = valueDataFrame.select(
      from_json(col("message"), sparkSchema.dataType).alias("parsed_value"))
      .select("parsed_value.*")

    formattedDataFrame
      .writeStream
      .format("console")
      .option("truncate", false)
      .start()
      .awaitTermination()
  }

  object DeserializerWrapper {
    val deserializer = kafkaAvroDeserializer
  }

  class AvroDeserializer extends AbstractKafkaAvroDeserializer {
    def this(client: SchemaRegistryClient) {
      this()
      this.schemaRegistry = client
    }

    override def deserialize(bytes: Array[Byte]): String = {
      val genericRecord = super.deserialize(bytes).asInstanceOf[GenericRecord]
      genericRecord.toString
    }
  }

}

我认为我必须将此身份验证数据传递给CachedSchemaRegistryClient，但不确定是否这样以及如何传递.

I think I have to pass this authentication data to CachedSchemaRegistryClient but I'm not sure if so and how.

推荐答案

我终于能够传递属性.

我留下给出解决方案的那一行.

I leave the lines that gave the solution.

val restService = new RestService(schemaRegistryURL)

  val props = Map(
    "basic.auth.credentials.source" -> "USER_INFO",
    "schema.registry.basic.auth.user.info" -> "secret:secret"
  ).asJava

  var schemaRegistryClient = new CachedSchemaRegistryClient(restService, 100, props)

这篇关于如何将基本身份验证传递给Confluent Schema Registry?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！