=org.apache.kafka.connect.json.JsonConverter - INTERNAL_VALUE_CONVERTER=org.apache.kafka.connect.json.JsonConverter...Hudi 管理的数据集使用开放存储格式存储在云存储桶中，而与 Presto、Apache Hive[3] 和/或 Apache Spark[4] 的集成使用熟悉的工具提供近乎实时的更新数据访问 Apache...创建实例后，我们可以在其中运行以下 Spark 作业来完成我们的管道： spark-submit \ --packages org.apache.hudi:hudi-spark3.1.2-bundle...https://hive.apache.org/ [4] Apache Spark: https://spark.apache.org/ [5] Google Cloud Dataproc: https...[8] Hudi: https://hudi.apache.org/ [9] Spark: https://spark.apache.org/ [10] Presto: https://prestodb.io
解决方法：正则表达式的字符串太长，复杂度过高，正则匹配要精练，不要枚举式匹配 90、java.lang.StackOverflowError at org.apache.spark.sql.catalyst.trees.CurrentOrigin...$.withOrigin(TreeNode.scala:53) 解决方法：sql语句的where条件过长，字符串栈溢出 91、org.apache.spark.shuffle.MetadataFetchFailedException...-Phive参数 121、User class threw exception: org.apache.spark.sql.AnalysisException: path hdfs://XXXXXX...=DEFAULT' at line 1 解决方法：用新版mysql-connector 123、org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.security.authorize.AuthorizationException...中 connector.name写错了，应该为指定的版本，以便于presto使用对应的适配器，修改为：connector.name=hive-hadoop2 129、org.apache.spark.SparkException
解决方法：正则表达式的字符串太长，复杂度过高，正则匹配要精练，不要枚举式匹配 90、java.lang.StackOverflowError at org.apache.spark.sql.catalyst.trees.CurrentOrigin...$.withOrigin(TreeNode.scala:53) 解决方法：sql语句的where条件过长，字符串栈溢出 91、org.apache.spark.shuffle.MetadataFetchFailedException...解决方法：原因有多种，去hive.log查看日志进一步定位问题 114、Exception in thread “main” java.lang.NoClassDefFoundError: org/apache...参数 121、User class threw exception: org.apache.spark.sql.AnalysisException: path hdfs://XXXXXX already...中 connector.name写错了，应该为指定的版本，以便于presto使用对应的适配器，修改为：connector.name=hive-hadoop2 129、org.apache.spark.SparkException
" %% "spark-core" % "1.6.0" % "provided", "org.apache.spark" %% "spark-sql" % "1.6.0" % "provided",..."org.apache.spark" %% "spark-streaming" % "1.6.0" % "provided", "org.apache.spark" %% "spark-streaming-kafka...% "2.0.0" % "provided", "org.apache.spark" %% "spark-sql" % "2.0.0" % "provided", "org.apache.spark..." %% "spark-streaming" % "2.0.0" % "provided", "org.apache.spark" %% "spark-streaming-kafka" % "2.0.0...: [warn] [warn] Note: Unresolved dependencies path: [warn] org.apache.spark:spark-streaming-kafka
现象 在spark-shell中执行streaming application时，频繁出现以下错误。...: org/apache/kafka/common/message/KafkaLZ4BlockOutputStream at kafka.message.ByteBufferMessageSet...skip(Iterator.scala:612) at scala.collection.Iterator$$anon$19.hasNext(Iterator.scala:615) at org.apache.spark.streaming.kafka.KafkaRDD...:56) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68) at org.apache.spark.scheduler.ShuffleMapTask.runTask...(ShuffleMapTask.scala:41) at org.apache.spark.scheduler.Task.run(Task.scala:64) at org.apache.spark.executor.Executor
$class.initializeLogIfNecessary(Logging.scala:99) at org.apache.spark.sql.kafka010.KafkaSourceProvider...$.initializeLogIfNecessary(KafkaSourceProvider.scala:369) at org.apache.spark.internal.Logging$class.log...(Logging.scala:46) at org.apache.spark.sql.kafka010.KafkaSourceProvider$.log(KafkaSourceProvider.scala...:369) at org.apache.spark.internal.Logging$class.logDebug(Logging.scala:58) at org.apache.spark.sql.kafka010...于是重新检查各个jar包，发现spark-sql-kafka的版本是2.2，而spark的版本是2.3，修改spark-sql-kafka的版本后，顺利执行。
-- spark-sql --> org.apache.spark <artifactId...{Level, Logger} import org.apache.spark.SparkConf import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribe...import org.apache.spark.streaming.kafka010.KafkaUtils import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent...import org.apache.spark.streaming....必须设置，否则Kafka数据会报无法序列化的错误 .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
添加依赖 我们使用 Maven 进行依赖管理，这个项目使用到的依赖如下： org.apache.spark org.apache.spark spark-sql_2.11...2.3.0 provided org.apache.spark...provided org.apache.spark spark-streaming-kafka...它将与我们之前创建的Kafka主题集成。
.config("spark.hadoop.fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem") \ .getOrCreate.../org/apache/kafka/kafka-clients/2.8.1/kafka-clients-2.8.1.jar> curl -O curl -O curl -O cd ..
如果希望使用与Atlas更松散耦合的集成来实现更好的可伸缩性，可靠性等，则消息传递接口特别有用.Atlas使用Apache Kafka作为通知服务器，用于钩子和元数据通知事件的下游消费者之间的通信。...数据血缘 打包spark-atlas-connector atlas 官方文档中并不支持 spark sql 的解析，需要使用第三方的包。...二、打包后在 spark-atlas-connector/spark-atlas-connector-assembly/target 目录有一个 spark-atlas-connector-assembly...需要注意的是不要上传 spark-atlas-connector/spark-atlas-connector/target 这个目录内的 jar ，缺少相关依赖包 三、将 spark-atlas-connector-assembly...-${version}.jar 放到一个固定目录 比如/opt/resource 测试spark hook 首先进入spark-sql client spark-sql --master yarn \
-- 导入spark sql的依赖 --> org.apache.spark...-- https://mvnrepository.com/artifact/org.apache.kafka/kafka --> org.apache.kafka kafka_2.12 0.10.2.1...-- 导入spark streaming 与kafka的依赖包--> org.apache.spark</groupId
如果希望使用与Atlas更松散耦合的集成来实现更好的可伸缩性，可靠性等，则消息传递接口特别有用.Atlas使用Apache Kafka作为通知服务器，用于钩子和元数据通知事件的下游消费者之间的通信。...数据血缘 打包spark-atlas-connector atlas 官方文档中并不支持 spark sql 的解析，需要使用第三方的包。...二、打包后在 spark-atlas-connector/spark-atlas-connector-assembly/target 目录有一个 spark-atlas-connector-assembly...需要注意的是不要上传 spark-atlas-connector/spark-atlas-connector/target 这个目录内的 jar ，缺少相关依赖包 三、将 spark-atlas-connector-assembly...-${version}.jar 放到一个固定目录 比如/opt/resource 测试spark hook 首先进入spark-sql client spark-sql --master yarn
•相比Flink纯内存的计算模型，在延迟不敏感的场景Spark更友好 这里举一个例子，比如批流一体引擎SS与Flink分别创建Kafka table并写入到ClickHouse，语法分别如下 Spark...的中间流程解决了，但链路过长这并不是我们预期内的）•还有一点是任务存在丢数据的风险，对比Spark方案发现Flink会有丢数据的风险 标注：这个case并非Flink集成Hudi不够，国内已经有很多使用...SS集成的说明，一开始笔者快速实现了SS与Hudi的集成，但是在通读Hudi代码之后发现其实社区早已有了SS的完整实现，另外咨询社区同学leesf之后给出的反馈是当前SS的实现也很稳定。...hoodie.datasource.write.partitionpath.field' = 'dt,hour', 'hoodie.datasource.write.keygenerator.class'= 'org.apache.hudi.keygen.ComplexKeyGenerator...hoodie.datasource.hive_sync.partition_fields'='dt,hour', -- 'hoodie.datasource.hive_sync.partition_extractor_class'='org.apache.hudi.hive.MultiPartKeysValueExtractor
{Date, Properties} import org.apache.spark.rdd.RDD import org.apache.spark.sql....cn.itcast.spark.config.ApplicationConfig import org.apache.spark.sql.types.StringType import org.apache.spark.sql...import java.util.Properties import org.apache.spark.sql....--spark-streaming+Kafka依赖--> org.apache.spark...-- Spark Streaming 与Kafka 0.10.0 集成依赖--> org.apache.spark</
刚才写入的数据 python kafka_consumer.py 2、spark-streaming 1）先解决依赖 其中比较核心的是spark-streaming和kafka集成包spark-streaming-kafka...; import org.apache.spark.api.java.JavaPairRDD; import org.apache.spark.api.java.JavaSparkContext; import...org.apache.spark.api.java.function.Function; import org.apache.spark.api.java.function.Function2; import...org.apache.spark.api.java.function.PairFunction; import org.apache.spark.api.java.function.VoidFunction...; import org.apache.spark.streaming.kafka.KafkaUtils; import scala.Tuple2; import java.sql.Connection
刚才写入的数据 python kafka_consumer.py 2、spark-streaming 1）先解决依赖 其中比较核心的是spark-streaming和kafka集成包spark-streaming-kafka...; import org.apache.spark.api.java.JavaPairRDD; import org.apache.spark.api.java.JavaSparkContext; import... org.apache.spark.api.java.function.Function; import org.apache.spark.api.java.function.Function2; import... org.apache.spark.api.java.function.PairFunction; import org.apache.spark.api.java.function.VoidFunction...; import org.apache.spark.streaming.kafka.KafkaUtils; import scala.Tuple2; import java.sql.Connection
-- org.apache.spark spark-streaming-kafka... org.apache.spark spark-streaming-kafka...-- org.apache.spark spark-streaming-kafka... org.apache.spark spark-streaming-kafka... org.apache.spark spark-sql-kafka-
org.apache.spark.sql.streaming....{DataFrame, SparkSession} import org.apache.spark.sql.functions._ import org.apache.spark.sql.types....11-[掌握]-集成Kafka之Kafka Source StructuredStreaming集成Kafka，官方文档如下：http://spark.apache.org/docs/2.4.5/structured-streaming-kafka-integration.html...package cn.itcast.spark.kafka.source import org.apache.spark.sql.streaming....package cn.itcast.spark.kafka.sink import org.apache.spark.sql.streaming.
代码 import org.apache.spark.sql.SparkSession import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.flume.FlumeUtils...+SparkStreaming.conf ---SparkStreaming集成flume+kafka的flume配置 #flume+kafka+sparkStreaming集成 a1.sources...org.apache.kafka.clients.consumer.ConsumerRecord import org.apache.kafka.common.serialization.StringDeserializer...import org.apache.spark.sql.SparkSession import org.apache.spark.streaming. import org.apache.spark.streaming.kafka010...._ import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent import org.apache.spark.streaming.kafka010
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