我有一个简单的 Spark 应用程序生成 Kafka 消息
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions.{col, struct}
import org.apache.spark.sql.avro.functions.to_avro
import org.apache.spark.sql.types.{DoubleType, LongType, StructType}
object IngestFromS3ToKafka {
def main(args: Array[String]): Unit = {
val spark: SparkSession = SparkSession
.builder()
.master("local[*]")
.appName("ingest-from-s3-to-kafka")
.config("spark.ui.port", "4040")
.getOrCreate()
val folderPath = "s3a://hongbomiao-bucket/iot/"
val parquet_schema = new StructType()
.add("timestamp", DoubleType)
.add("current", DoubleType, nullable = true)
.add("voltage", DoubleType, nullable = true)
.add("temperature", DoubleType, nullable = true)
val df = spark.readStream
.schema(parquet_schema)
.option("maxFilesPerTrigger", 1)
.parquet(folderPath)
.withColumn("timestamp", (col("timestamp") * 1000).cast(LongType))
.select(to_avro(struct("*")).alias("value"))
val query = df.writeStream
.format("kafka")
.option(
"kafka.bootstrap.servers",
"hm-kafka-kafka-bootstrap.hm-kafka.svc:9092"
)
.option("topic", "hm.motor")
.option("checkpointLocation", "/tmp/checkpoint")
.start()
query.awaitTermination()
}
}
我在 Apicurio 注册表中有一个 Avro 模式,由
创建curl --location 'http://apicurio-registry-apicurio-registry.hm-apicurio-registry.svc:8080/apis/registry/v2/groups/hm-group/artifacts' \
--header 'Content-type: application/json; artifactType=AVRO' \
--header 'X-Registry-ArtifactId: hm-iot' \
--data '{
"type": "record",
"namespace": "com.hongbomiao",
"name": "hm.motor",
"fields": [
{
"name": "timestamp",
"type": "long"
},
{
"name": "current",
"type": "double"
},
{
"name": "voltage",
"type": "double"
},
{
"name": "temperature",
"type": "double"
}
]
}'
我正在尝试使用 Apicurio Registry 的 Confluent 兼容 REST API 端点。目前正在使用 Content ID 26 检索
curl --location 'http://apicurio-registry-apicurio-registry.hm-apicurio-registry.svc:8080/apis/ccompat/v6/schemas/ids/26' \
--header 'Content-type: application/json; artifactType=AVRO' \
--header 'X-Registry-ArtifactId: hm-iot'
打印
{
"schema": "{\n \"type\": \"record\",\n \"namespace\": \"com.hongbomiao\",\n \"name\": \"hm.motor\",\n \"fields\": [\n {\n \"name\": \"timestamp\",\n \"type\": \"long\"\n },\n {\n \"name\": \"current\",\n \"type\": \"double\"\n },\n {\n \"name\": \"voltage\",\n \"type\": \"double\"\n },\n {\n \"name\": \"temperature\",\n \"type\": \"double\"\n }\n ]\n}",
"references": []
}
看起来不错。
基于 Aiven 的 JDBC 连接器文档,我写了我的 JDBC sink 连接器配置:
{
"name": "hm-motor-jdbc-sink-kafka-connector",
"config": {
"connector.class": "io.aiven.connect.jdbc.JdbcSinkConnector",
"tasks.max": 1,
"topics": "hm.motor",
"connection.url": "jdbc:postgresql://timescale.hm-timescale.svc:5432/hm_iot_db",
"connection.user": "${file:/opt/kafka/external-configuration/hm-iot-db-credentials-volume/iot-db-credentials.properties:timescaledb_user}",
"connection.password": "${file:/opt/kafka/external-configuration/hm-iot-db-credentials-volume/iot-db-credentials.properties:timescaledb_password}",
"insert.mode": "upsert",
"table.name.format": "motor",
"value.converter": "io.confluent.connect.avro.AvroConverter",
"value.converter.schema.registry.url": "http://apicurio-registry-apicurio-registry.hm-apicurio-registry.svc:8080/apis/ccompat/v6",
"transforms": "convertTimestamp",
"transforms.convertTimestamp.type": "org.apache.kafka.connect.transforms.TimestampConverter$Value",
"transforms.convertTimestamp.field": "timestamp",
"transforms.convertTimestamp.target.type": "Timestamp"
}
}
但是,我在 Kafka Connect 日志中收到此错误
2023-05-01 19:01:11,291 ERROR [hm-motor-jdbc-sink-kafka-connector|task-0] WorkerSinkTask{id=hm-motor-jdbc-sink-kafka-connector-0} Task threw an uncaught and unrecoverable exception. Task is being killed and will not recover until manually restarted (org.apache.kafka.connect.runtime.WorkerTask) [task-thread-hm-motor-jdbc-sink-kafka-connector-0]
org.apache.kafka.connect.errors.ConnectException: Tolerance exceeded in error handler
at org.apache.kafka.connect.runtime.errors.RetryWithToleranceOperator.execAndHandleError(RetryWithToleranceOperator.java:230)
at org.apache.kafka.connect.runtime.errors.RetryWithToleranceOperator.execute(RetryWithToleranceOperator.java:156)
at org.apache.kafka.connect.runtime.WorkerSinkTask.convertAndTransformRecord(WorkerSinkTask.java:518)
at org.apache.kafka.connect.runtime.WorkerSinkTask.convertMessages(WorkerSinkTask.java:495)
at org.apache.kafka.connect.runtime.WorkerSinkTask.poll(WorkerSinkTask.java:335)
at org.apache.kafka.connect.runtime.WorkerSinkTask.iteration(WorkerSinkTask.java:237)
at org.apache.kafka.connect.runtime.WorkerSinkTask.execute(WorkerSinkTask.java:206)
at org.apache.kafka.connect.runtime.WorkerTask.doRun(WorkerTask.java:202)
at org.apache.kafka.connect.runtime.WorkerTask.run(WorkerTask.java:257)
at org.apache.kafka.connect.runtime.isolation.Plugins.lambda$withClassLoader$1(Plugins.java:177)
at java.base/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
at java.base/java.lang.Thread.run(Thread.java:833)
Caused by: org.apache.kafka.connect.errors.DataException: Failed to deserialize data for topic hm.motor to Avro:
at io.confluent.connect.avro.AvroConverter.toConnectData(AvroConverter.java:124)
at org.apache.kafka.connect.storage.Converter.toConnectData(Converter.java:88)
at org.apache.kafka.connect.runtime.WorkerSinkTask.lambda$convertAndTransformRecord$4(WorkerSinkTask.java:518)
at org.apache.kafka.connect.runtime.errors.RetryWithToleranceOperator.execAndRetry(RetryWithToleranceOperator.java:180)
at org.apache.kafka.connect.runtime.errors.RetryWithToleranceOperator.execAndHandleError(RetryWithToleranceOperator.java:214)
... 14 more
Caused by: org.apache.kafka.common.errors.SerializationException: Error retrieving Avro value schema for id -1330532454
at io.confluent.kafka.serializers.AbstractKafkaSchemaSerDe.toKafkaException(AbstractKafkaSchemaSerDe.java:253)
at io.confluent.kafka.serializers.AbstractKafkaAvroDeserializer$DeserializationContext.schemaForDeserialize(AbstractKafkaAvroDeserializer.java:372)
at io.confluent.kafka.serializers.AbstractKafkaAvroDeserializer.deserializeWithSchemaAndVersion(AbstractKafkaAvroDeserializer.java:203)
at io.confluent.connect.avro.AvroConverter$Deserializer.deserialize(AvroConverter.java:172)
at io.confluent.connect.avro.AvroConverter.toConnectData(AvroConverter.java:107)
... 18 more
Caused by: io.confluent.kafka.schemaregistry.client.rest.exceptions.RestClientException: No content with id/hash 'contentId--1330532454' was found.; error code: 40403
at io.confluent.kafka.schemaregistry.client.rest.RestService.sendHttpRequest(RestService.java:314)
at io.confluent.kafka.schemaregistry.client.rest.RestService.httpRequest(RestService.java:384)
at io.confluent.kafka.schemaregistry.client.rest.RestService.getId(RestService.java:853)
at io.confluent.kafka.schemaregistry.client.rest.RestService.getId(RestService.java:826)
at io.confluent.kafka.schemaregistry.client.CachedSchemaRegistryClient.getSchemaByIdFromRegistry(CachedSchemaRegistryClient.java:311)
at io.confluent.kafka.schemaregistry.client.CachedSchemaRegistryClient.getSchemaBySubjectAndId(CachedSchemaRegistryClient.java:433)
at io.confluent.kafka.serializers.AbstractKafkaAvroDeserializer$DeserializationContext.schemaForDeserialize(AbstractKafkaAvroDeserializer.java:361)
... 21 more
它试图获取内容 ID
-1330532454
,但显然我没有这个。我的在26
。 JDBC 是如何寻找对应的 AVRO schema 的?
我不确定它现在如何映射。我以为它会根据 Kafka 主题寻找一个名为
hm.motor
的模式,但事实证明不是。
谢谢!
谢谢@Ftisiot!
我找到了关于Kafka序列化器和反序列化器的文档。
Kafka 序列化器和反序列化器在注册或检索模式时默认使用
和<topicName>-key
作为相应的主题名称。<topicName>-value
同样对于
value.converter.value.subject.name.strategy
,它默认使用io.confluent.kafka.serializers.subject.TopicNameStrategy
。
我已经将我的 Avro 模式名称更新为
hm.motor-value
,但仍然有同样的错误。
我相信默认模式名称将是主题名称和
-value
或-key
的串联,具体取决于您正在解码的消息部分。
因此,在您的情况下,我会尝试使用架构名称
hm.motor-value
.
在这个视频中,您可以检查使用 flink 从 json 编码到 avro 时自动生成的模式名称。
免责声明:我为 Aiven 工作,我们应该更新文档以反映这一点
暂时忘掉连接。您应该先使用
kafka-avro-console-consumer
调试您的主题。你会在那里得到同样的错误,因为你的生产者需要正确编码数据。
Spark 的
to_avro
不会这样做。
查看这个库的
toConfluentAvro
功能-https://github.com/AbsaOSS/ABRiS
有关内部结构的更多详细信息https://docs.confluent.io/platform/current/schema-registry/fundamentals/serdes-develop/index.html#wire-format
关于您的架构问题,
name
指的是 Avro 规范定义的完全限定的 Java 类名,并且在使用 TopicNameStategy 时与注册表主题无关
这个科目名称是什么
它是 API 调用中的路径参数
POST /subjects/:name/versions/
由 Serializer 和 Deserializer 内部 HTTP 客户端使用。
前面也提到过,这里不需要Kafka Connect。 Spark 可以直接写入 JDBC 数据库。数据源可以是Parquet或者Kafka。
感谢大家的帮助,我终于明白了!这是我学到的。
Avro 数据实际上有两种主要类型:“标准”/“香草”Apache Avro 和 Confluent Avro。
首先,我通过
生成了我的 Varo 模式curl --location 'http://apicurio-registry.svc:8080/apis/registry/v2/groups/default/artifacts' \
--header 'Content-type: application/json; artifactType=AVRO' \
--header 'X-Registry-ArtifactId: hm.motor-value' \
--data '{
"type": "record",
"namespace": "com.hongbomiao",
"name": "motor",
"fields": [
{
"name": "timestamp",
"type": "long"
},
{
"name": "current",
"type": "double"
},
{
"name": "voltage",
"type": "double"
},
{
"name": "temperature",
"type": "double"
}
]
}'
在 Spark 中,与原生一起使用非常简单
org.apache.spark.sql.avro.functions.to_avro
.
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions.{col, struct}
import org.apache.spark.sql.types.{DoubleType, LongType, StructType}
import org.apache.spark.sql.avro.functions.to_avro
import sttp.client3.{HttpClientSyncBackend, UriContext, basicRequest}
object IngestFromS3ToKafka {
def main(args: Array[String]): Unit = {
val spark: SparkSession = SparkSession
.builder()
.master("local[*]")
.appName("ingest-from-s3-to-kafka")
.config("spark.ui.port", "4040")
.getOrCreate()
val folderPath = "s3a://hongbomiao-bucket/iot/"
# For below `parquet_schema`, you can
# 1. hard code like current code
# 2. read from one file `val parquet_schema = spark.read.parquet("s3a://hongbomiao-bucket/iot/motor.parquet").schema`
# 3. Maybe possible also from Avro, I will try in future!
val parquet_schema = new StructType()
.add("timestamp", DoubleType)
.add("current", DoubleType, nullable = true)
.add("voltage", DoubleType, nullable = true)
.add("temperature", DoubleType, nullable = true)
val backend = HttpClientSyncBackend()
val response = basicRequest
.get(
uri"http://apicurio-registry.svc:8080/apis/registry/v2/groups/hm-group/artifacts/hm.motor-value"
)
.send(backend)
val schemaString = response.body.fold(identity, identity)
val df = spark.readStream
.schema(parquet_schema)
.option("maxFilesPerTrigger", 1)
.parquet(folderPath)
.withColumn("timestamp", (col("timestamp") * 1000).cast(LongType))
.select(to_avro(struct("*"), schemaString).alias("value"))
val query = df.writeStream
.format("kafka")
.option(
"kafka.bootstrap.servers",
"hm-kafka-kafka-bootstrap.hm-kafka.svc:9092"
)
.option("topic", "hm.motor")
.option("checkpointLocation", "/tmp/checkpoint")
.start()
query.awaitTermination()
}
}
built.sbt
name := "IngestFromS3ToKafka"
version := "1.0"
scalaVersion := "2.12.17"
libraryDependencies ++= Seq(
"org.apache.spark" %% "spark-core" % "3.3.2" % "provided",
"org.apache.spark" %% "spark-sql" % "3.3.2" % "provided",
"org.apache.spark" %% "spark-sql-kafka-0-10" % "3.3.2" % "provided",
"org.apache.spark" %% "spark-avro" % "3.3.2" % "provided",
"org.apache.hadoop" % "hadoop-common" % "3.3.5" % "provided",
"org.apache.hadoop" % "hadoop-aws" % "3.3.5" % "provided",
"com.amazonaws" % "aws-java-sdk-bundle" % "1.12.461" % "provided",
"com.softwaremill.sttp.client3" %% "core" % "3.8.15"
)
我从这篇文章中得到了很多想法.
Confluent Avro 不是“标准”/“香草”Avro,这给 Spark 和其他工具带来了一些不便。
有一个库 ABRiS 可以帮助生成 Confluent Avro 格式的 Kafka 消息 (
toConfluentAvro
)。
然而,
sbt assembly
对于ABRiS来说又是一场噩梦。你必须处理 assemblyMergeStrategy。 🥲
(没有往这个方向走)
非常简单,只需使用
io.apicurio.registry.utils.converter.AvroConverter
.
我的 JDBC 连接器配置:
{
"name": "hm-motor-jdbc-sink-kafka-connector",
"config": {
"connector.class": "io.aiven.connect.jdbc.JdbcSinkConnector",
"tasks.max": 1,
"topics": "hm.motor",
"connection.url": "jdbc:postgresql://timescale.hm-timescale.svc:5432/hm_iot_db",
"connection.user": "${file:/opt/kafka/external-configuration/hm-iot-db-credentials-volume/iot-db-credentials.properties:timescaledb_user}",
"connection.password": "${file:/opt/kafka/external-configuration/hm-iot-db-credentials-volume/iot-db-credentials.properties:timescaledb_password}",
"insert.mode": "upsert",
"table.name.format": "motor",
"transforms": "convertTimestamp",
"transforms.convertTimestamp.type": "org.apache.kafka.connect.transforms.TimestampConverter$Value",
"transforms.convertTimestamp.field": "timestamp",
"transforms.convertTimestamp.target.type": "Timestamp",
"value.converter": "io.apicurio.registry.utils.converter.AvroConverter",
"value.converter.apicurio.registry.url": "http://apicurio-registry.svc:8080/apis/registry/v2"
"value.converter.apicurio.registry.fallback.group-id": "hm-group",
"value.converter.apicurio.registry.fallback.artifact-id": "hm.motor-value"
}
}
也许将来我能想出摆脱
value.converter.apicurio.registry.fallback
相关领域的方法。
有关
io.apicurio.registry.utils.converter.AvroConverter
的更多信息可以在这里找到。
io.confluent.connect.avro.AvroConverter
与 Apicurio Registry这里我们使用 Apicurio Registry 的 Confluent 兼容 REST API:
"value.converter": "io.confluent.connect.avro.AvroConverter",
"value.converter.schema.registry.url": "http://apicurio-registry.svc:8080/apis/ccompat/v6",
(没有往这个方向走)
io.apicurio.registry.utils.converter.AvroConverter
和 Confluent Schema Registry这里我们使用Confluent Registry REST API:
"value.converter": "io.confluent.connect.avro.AvroConverter",
"value.converter.schema.registry.url": "http://confluent-schema-registry.svc:8081",
(没有往这个方向走)