Hi Zijie, 应该是你的 sqlTimestamp 字段中有 null 的数据，在去取 ts 的时候报 NPE 了。 目前 watermark assigner 要求每条数据的 ts 都是有值的。
Best, Jark > 在 2019年10月17日，20:25，Zijie Lu <[email protected]> 写道： > > CREATE TABLE requests( > `rowtime` TIMESTAMP, > `requestId` VARCHAR, > `algoExtent` ROW(`mAdId` VARCHAR)) > with ( > 'connector.type' = 'kafka', > 'connector.version' = 'universal', > 'connector.topic' = 'test_request', > 'connector.startup-mode' = 'latest-offset', > 'connector.properties.0.key' = 'zookeeper.connect', > 'connector.properties.0.value' = '10.107.116.42:2181', > 'connector.properties.1.key' = 'bootstrap.servers', > 'connector.properties.1.value' = '10.107.116.42:9092', > 'connector.properties.2.key' = 'group.id', > 'connector.properties.2.value' = 'test_request', > 'update-mode' = 'append','format.type' = 'json', > 'format.json-schema': '{type: "object", properties: {sqlTimestamp: { > type: "string"}, requestId: { type: "string"}, "algoExtent": {type: > "object", "properties": {"mAdId": {type: "string"}}}}}' > 'schema.0.rowtime.timestamps.type' = 'from-field', > 'schema.0.rowtime.timestamps.from' = 'sqlTimestamp', > 'schema.0.rowtime.watermarks.type' = 'periodic-ascending') > 尝试过这样的定义也是报同样的错 > > On Thu, 17 Oct 2019 at 20:22, Zijie Lu <[email protected]> wrote: > >> 而这个定义在old planner里是可以用的 >> >> On Thu, 17 Oct 2019 at 19:49, Zijie Lu <[email protected]> wrote: >> >>> 我使用blink planner来定义了下面的表 >>> CREATE TABLE requests( >>> `rowtime` TIMESTAMP, >>> `requestId` VARCHAR, >>> `algoExtent` ROW(`mAdId` VARCHAR)) >>> with ( >>> 'connector.type' = 'kafka', >>> 'connector.version' = 'universal', >>> 'connector.topic' = 'test_request', >>> 'connector.startup-mode' = 'latest-offset', >>> 'connector.properties.0.key' = 'zookeeper.connect', >>> 'connector.properties.0.value' = '10.107.116.42:2181', >>> 'connector.properties.1.key' = 'bootstrap.servers', >>> 'connector.properties.1.value' = '10.107.116.42:9092', >>> 'connector.properties.2.key' = 'group.id', >>> 'connector.properties.2.value' = 'test_request', >>> 'update-mode' = 'append','format.type' = 'json', >>> 'format.derive-schema' = 'true', >>> 'schema.0.rowtime.timestamps.type' = 'from-field', >>> 'schema.0.rowtime.timestamps.from' = 'sqlTimestamp', >>> 'schema.0.rowtime.watermarks.type' = 'periodic-ascending') >>> 然后kafka里消息的格式如下 >>> {"requestId": "rrrr","algoExtent": {"duration": 12,"adType ": >>> "FEED_568_320","mAdId": "1910141050233527", "sqlTimestamp":"2019-10-17 >>> 19:08:01" }} >>> 但是运行时报错 >>> Caused by: >>> org.apache.flink.streaming.runtime.tasks.ExceptionInChainedOperatorException: >>> Could not forward element to next operator >>> at >>> org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:654) >>> at >>> org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:612) >>> at >>> org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:592) >>> at >>> org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:727) >>> at >>> org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:705) >>> at >>> org.apache.flink.streaming.api.operators.StreamSourceContexts$ManualWatermarkContext.processAndCollectWithTimestamp(StreamSourceContexts.java:310) >>> at >>> org.apache.flink.streaming.api.operators.StreamSourceContexts$WatermarkContext.collectWithTimestamp(StreamSourceContexts.java:409) >>> at >>> org.apache.flink.streaming.connectors.kafka.internals.AbstractFetcher.emitRecordWithTimestamp(AbstractFetcher.java:398) >>> at >>> org.apache.flink.streaming.connectors.kafka.internal.KafkaFetcher.emitRecord(KafkaFetcher.java:185) >>> at >>> org.apache.flink.streaming.connectors.kafka.internal.KafkaFetcher.runFetchLoop(KafkaFetcher.java:150) >>> at >>> org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.run(FlinkKafkaConsumerBase.java:715) >>> at >>> org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:100) >>> at >>> org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:63) >>> at >>> org.apache.flink.streaming.runtime.tasks.SourceStreamTask$LegacySourceFunctionThread.run(SourceStreamTask.java:202) >>> Caused by: >>> org.apache.flink.streaming.runtime.tasks.ExceptionInChainedOperatorException: >>> Could not forward element to next operator >>> at >>> org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:654) >>> at >>> org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:612) >>> at >>> org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:592) >>> at >>> org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:727) >>> at >>> org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:705) >>> at SourceConversion$4.processElement(Unknown Source) >>> at >>> org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:637) >>> ... 13 more >>> Caused by: java.lang.NullPointerException >>> at >>> org.apache.flink.table.dataformat.GenericRow.getLong(GenericRow.java:58) >>> at >>> org.apache.flink.table.planner.plan.nodes.physical.stream.PeriodicWatermarkAssignerWrapper.extractTimestamp(StreamExecTableSourceScan.scala:202) >>> at >>> org.apache.flink.table.planner.plan.nodes.physical.stream.PeriodicWatermarkAssignerWrapper.extractTimestamp(StreamExecTableSourceScan.scala:194) >>> at >>> org.apache.flink.streaming.runtime.operators.TimestampsAndPeriodicWatermarksOperator.processElement(TimestampsAndPeriodicWatermarksOperator.java:64) >>> at >>> org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:637) >>> ... 19 more >>> 请问在blink里应该如何定义rowtime呢？ >>> >>
