cannot be used as a POJO type because not all fields are valid POJO fields, and must be processed asGenericType.据我所知,LocalDate不能归类为POJO,所以flink不使用POJO序列化程序,而是使用Kryo,效率较低。然而,从1.9.0版本开始,flink已经为java.time类(例如LocalDateSerializer)提供了
更新:env.getConfig().registerTypeWithKryoSerializer(DataElement.class, DataElementKryoSerializer.classtype because not all fields are valid POJO fields, and must be processed as GenericType.Please read the Flink documentation on "Data Type
不是一个错误,但我确实看到了这一行,根据消息可能会影响性能: 2019-01-02 14:44:44,879 INFO org.apache.flink.api.java.typeutils.TypeExtractor- class org.apache.flink.streaming.connectors.kafka.internals.KafkaTopicPartition
does- Class class org.apache.flink.streaming.connectors.kafka.internals.KafkaTop
我使用flink动态地分析json类型数据,并使用给定列进行keyby和sum,在我的mapFunction中,我将json转换为case类,但是结果流没有在keyBy函数中得到编译器,得到了这样的错误Exception in thread "main" org.apache.flink.api.common.InvalidProgramException: This type (GenericType
在与Cassandra一起进行Flink流实验时,我在MapFunction中生成INSERT语句时遇到了一个有趣的问题。:1683) at org.apache.flink.api.java.typeutils.TypeExtractor.privateGetForClass(TypeExtractor.java:
我改变了我在分析数据中的持续时间,我把所有flink数据管道(我们使用的是flink 1.13)从Long表示方式拖到java.time.Duration。seconds
2022-11-07T23:34:46,247 INFO TypeExtractor - Class class java.time.Duration cannot be used as a POJOtype because not all fields are valid POJO fields, and must be processed as <