hi，
了解了，我重新整理一下：
streamTableEnv
.connect(
new Kafka()
.version("0.11")
.topic("mysql_binlog_test")
.startFromEarliest()
.property("zookeeper.connect",
"localhost:2181")
.property("bootstrap.servers",
"localhost:9092")
)
.withFormat(
new Json()
)
.withSchema(
new Schema()
.field("business", DataTypes.STRING())
.field("data", DataTypes.ARRAY(
DataTypes.ROW(DataTypes.FIELD("id", DataTypes.BIGINT()),
DataTypes.FIELD("vendor_id",
DataTypes.DOUBLE()),
DataTypes.FIELD("status",
DataTypes.BIGINT()),
DataTypes.FIELD("create_time",
DataTypes.BIGINT()),
DataTypes.FIELD("tracking_number",
DataTypes.STRING()),
DataTypes.FIELD("invoice_no",
DataTypes.STRING()),
DataTypes.FIELD("parent_id",
DataTypes.BIGINT()))))
.field("database", DataTypes.STRING())
.field("old",
DataTypes.ARRAY(DataTypes.ROW(DataTypes.FIELD("logistics_status",
DataTypes.DECIMAL(38,18)))))
.field("table", DataTypes.STRING())
.field("ts", DataTypes.BIGINT())
.field("type", DataTypes.STRING())
.field("putRowNum", DataTypes.BIGINT())
)
.createTemporaryTable("Test");
这里面old复合字段里面子字段的类型使用DECIMAL时抛出异常，采用其他类型是可以的；
异常：
Exception in thread "main" org.apache.flink.table.api.ValidationException:
Type ARRAY<ROW<`logistics_status` DECIMAL(38, 18)>> of table field 'old'
does not match with the physical type ARRAY<ROW<`logistics_status`
LEGACY('DECIMAL', 'DECIMAL')>> of the 'old' field of the TableSource return
type.
at
org.apache.flink.table.utils.TypeMappingUtils.lambda$checkPhysicalLogicalTypeCompatible$4(TypeMappingUtils.java:164)
at
org.apache.flink.table.utils.TypeMappingUtils$1.defaultMethod(TypeMappingUtils.java:277)
at
org.apache.flink.table.utils.TypeMappingUtils$1.defaultMethod(TypeMappingUtils.java:254)
at
org.apache.flink.table.types.logical.utils.LogicalTypeDefaultVisitor.visit(LogicalTypeDefaultVisitor.java:157)
at org.apache.flink.table.types.logical.ArrayType.accept(ArrayType.java:110)
at
org.apache.flink.table.utils.TypeMappingUtils.checkIfCompatible(TypeMappingUtils.java:254)
at
org.apache.flink.table.utils.TypeMappingUtils.checkPhysicalLogicalTypeCompatible(TypeMappingUtils.java:160)
at
org.apache.flink.table.utils.TypeMappingUtils.lambda$computeInCompositeType$8(TypeMappingUtils.java:232)
at java.util.stream.Collectors.lambda$toMap$58(Collectors.java:1321)
at java.util.stream.ReduceOps$3ReducingSink.accept(ReduceOps.java:169)
at
java.util.ArrayList$ArrayListSpliterator.forEachRemaining(ArrayList.java:1382)
at java.util.stream.AbstractPipeline.copyInto(AbstractPipeline.java:481)
at
java.util.stream.AbstractPipeline.wrapAndCopyInto(AbstractPipeline.java:471)
at
java.util.stream.ReduceOps$ReduceOp.evaluateSequential(ReduceOps.java:708)
at java.util.stream.AbstractPipeline.evaluate(AbstractPipeline.java:234)
at java.util.stream.ReferencePipeline.collect(ReferencePipeline.java:499)
at
org.apache.flink.table.utils.TypeMappingUtils.computeInCompositeType(TypeMappingUtils.java:214)
at
org.apache.flink.table.utils.TypeMappingUtils.computePhysicalIndices(TypeMappingUtils.java:192)
at
org.apache.flink.table.utils.TypeMappingUtils.computePhysicalIndicesOrTimeAttributeMarkers(TypeMappingUtils.java:112)
at
org.apache.flink.table.planner.plan.nodes.physical.stream.StreamExecTableSourceScan.computeIndexMapping(StreamExecTableSourceScan.scala:212)
at
org.apache.flink.table.planner.plan.nodes.physical.stream.StreamExecTableSourceScan.translateToPlanInternal(StreamExecTableSourceScan.scala:107)
at
org.apache.flink.table.planner.plan.nodes.physical.stream.StreamExecTableSourceScan.translateToPlanInternal(StreamExecTableSourceScan.scala:62)
at
org.apache.flink.table.planner.plan.nodes.exec.ExecNode$class.translateToPlan(ExecNode.scala:58)
at
org.apache.flink.table.planner.plan.nodes.physical.stream.StreamExecTableSourceScan.translateToPlan(StreamExecTableSourceScan.scala:62)
at
org.apache.flink.table.planner.plan.nodes.physical.stream.StreamExecExchange.translateToPlanInternal(StreamExecExchange.scala:84)
at
org.apache.flink.table.planner.plan.nodes.physical.stream.StreamExecExchange.translateToPlanInternal(StreamExecExchange.scala:44)
at
org.apache.flink.table.planner.plan.nodes.exec.ExecNode$class.translateToPlan(ExecNode.scala:58)
at
org.apache.flink.table.planner.plan.nodes.physical.stream.StreamExecExchange.translateToPlan(StreamExecExchange.scala:44)
at
org.apache.flink.table.planner.plan.nodes.physical.stream.StreamExecLimit.translateToPlanInternal(StreamExecLimit.scala:161)
at
org.apache.flink.table.planner.plan.nodes.physical.stream.StreamExecLimit.translateToPlanInternal(StreamExecLimit.scala:51)
at
org.apache.flink.table.planner.plan.nodes.exec.ExecNode$class.translateToPlan(ExecNode.scala:58)
at
org.apache.flink.table.planner.plan.nodes.physical.stream.StreamExecLimit.translateToPlan(StreamExecLimit.scala:51)
at
org.apache.flink.table.planner.plan.nodes.physical.stream.StreamExecSink.translateToTransformation(StreamExecSink.scala:184)
at
org.apache.flink.table.planner.plan.nodes.physical.stream.StreamExecSink.translateToPlanInternal(StreamExecSink.scala:153)
at
org.apache.flink.table.planner.plan.nodes.physical.stream.StreamExecSink.translateToPlanInternal(StreamExecSink.scala:48)
at
org.apache.flink.table.planner.plan.nodes.exec.ExecNode$class.translateToPlan(ExecNode.scala:58)
at
org.apache.flink.table.planner.plan.nodes.physical.stream.StreamExecSink.translateToPlan(StreamExecSink.scala:48)
at
org.apache.flink.table.planner.delegation.StreamPlanner$$anonfun$translateToPlan$1.apply(StreamPlanner.scala:60)
at
org.apache.flink.table.planner.delegation.StreamPlanner$$anonfun$translateToPlan$1.apply(StreamPlanner.scala:59)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.Iterator$class.foreach(Iterator.scala:891)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1334)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.AbstractTraversable.map(Traversable.scala:104)
at
org.apache.flink.table.planner.delegation.StreamPlanner.translateToPlan(StreamPlanner.scala:59)
at
org.apache.flink.table.planner.delegation.PlannerBase.translate(PlannerBase.scala:153)
at
org.apache.flink.table.api.java.internal.StreamTableEnvironmentImpl.toDataStream(StreamTableEnvironmentImpl.java:351)
at
org.apache.flink.table.api.java.internal.StreamTableEnvironmentImpl.toAppendStream(StreamTableEnvironmentImpl.java:259)
at com.akulaku.data.main.StreamMain.main(StreamMain.java:58)
On Fri, Mar 20, 2020 at 4:43 PM Jark Wu <[email protected]> wrote:
> Hi,
>
> 你发的图片都裂开了。。。 建议直接贴文本或者先上传到某个图床服务，然后将链接贴过来。
>
> 1. 使用 DECIMAL 抛什么错误呢？
> 2. 如果保留jsonSchema的话，要保证 table schema 和 json schema 是一致的，也就是不仅 table schema
> 要正确，json schema 也得要正确。
> 这其实多了很多额外的成本，所以一般建议不配置 jsonSchema。理论上 table schema 能映射出所有的复杂的格式。
>
> Best,
> Jark
>
>
> On Fri, 20 Mar 2020 at 14:48, 宇张 <[email protected]> wrote:
>
> > hi、
> > 好吧，测试发现Decimal用不了，即使是DECIMAL(38, 18)，换成其他类型就好了，不知道是不是bug
> > [image: image.png]
> >
> > On Fri, Mar 20, 2020 at 2:17 PM 宇张 <[email protected]> wrote:
> >
> >> hi，我这面再次进行了尝试，当json数据中有数字类型的时候，即使按照将 data 的schema定义需要改成
> >> ARRAY(ROW(...))
> >> 另外删除
> >> .jsonSchema(...)后，程序仍然无法运行，当没有数字类型的时候是可以的；而报错信息输出来看，这两个结构是对的上的，但是貌似校验未通过
> >> [image: image.png]
> >>
> >>
> >> On Fri, Mar 20, 2020 at 12:08 PM 宇张 <[email protected]> wrote:
> >>
> >>> hi，
> >>> 好的，我这面进行了尝试，将 data 的schema定义需要改成
> >>> ARRAY(ROW(FIELD("tracking_numbrer", STRING), FIELD("invoice_no",
> >>> STRING)))
> >>> 另外删除 .jsonSchema(...)后，程序数据解析已经没问题了；但是如果保留
> >>> .jsonSchema(...)的话会抛出如下异常信息：Exception in thread "main"
> >>> org.apache.flink.table.api.ValidationException: Type
> >>> ARRAY<ROW<`tracking_number` STRING, `invoice_no` STRING>> of table
> field
> >>> 'data' does not match with the physical type ROW<`f0`
> ROW<`tracking_number`
> >>> STRING, `invoice_no` STRING>> of the 'data' field of the TableSource
> return
> >>> type.
> >>>
> >>>
> 而之所以保留这个jsonschema是因为我想尝试将这种复杂的json源的元数据保存到hive，进而通过这种方式推断出下面语句的格式，因为我不知道对于上述的复杂json在定义下面sql的时候字段信息怎么映射，或者说有这种复杂json的sql映射案例吗，感谢
> >>> [image: image.png]
> >>>
> >>> On Fri, Mar 20, 2020 at 11:42 AM Jark Wu <[email protected]> wrote:
> >>>
> >>>> Hi,
> >>>>
> >>>> 看了你的数据，"data" 是一个 array<row> 的类型，所以 data 的schema定义需要改成
> >>>> ARRAY(ROW(FIELD("tracking_numbrer", STRING), FIELD("invoice_no",
> >>>> STRING)))
> >>>> 另外建议删除 .jsonSchema(...)， 1.10 开始 flink-json 已经支持自动从 table schema 中推断
> >>>> json
> >>>> schema 了。
> >>>>
> >>>> Best,
> >>>> Jark
> >>>>
> >>>> On Fri, 20 Mar 2020 at 11:34, 宇张 <[email protected]> wrote:
> >>>>
> >>>> > hi：
> >>>> > 1、在Json数据解析的时候，请问这里面为什么用的是decimal，而不是bigint
> >>>> > [image: image.png]
> >>>> > 2、我在使用connect的时候，发现解析Json数组元素出现异常，这是误用导致的还是一个bug
> >>>> >
> >>>> >
> >>>>
> json：{"business":"riskt","data":[{"tracking_number":"0180024020920","invoice_no":"2020021025"}],"database":"installmentdb","table":"t_sales_order","ts":1581576074069,"type":"UPDATE","putRowNum":19999}
> >>>> >
> >>>> >
> >>>>
> jsonSchema：{"type":"object","properties":{"business":{"type":"string"},"data":{"type":"array","items":[{"type":"object","properties":{"tracking_number":{"type":"string"},"invoice_no":{"type":"string"}}}]},"database":{"type":"string"},"table":{"type":"string"},"ts":{"type":"integer"},"type":{"type":"string"},"putRowNum":{"type":"integer"}}}
> >>>> > connect：
> >>>> >
> >>>> > streamTableEnv
> >>>> > .connect(
> >>>> > new Kafka()
> >>>> > .version("0.11")
> >>>> > .topic("mysql_binlog_test_str")
> >>>> > .startFromEarliest()
> >>>> > .property("zookeeper.connect",
> >>>> "localhost:2181")
> >>>> > .property("bootstrap.servers",
> >>>> "localhost:9092")
> >>>> > )
> >>>> > .withFormat(
> >>>> > new Json()
> >>>> >
> >>>>
> .jsonSchema("{\"type\":\"object\",\"properties\":{\"business\":{\"type\":\"string\"},\"data\":{\"type\":\"array\",\"items\":[{\"type\":\"object\",\"properties\":{\"tracking_number\":{\"type\":\"string\"},\"invoice_no\":{\"type\":\"string\"}}}]},\"database\":{\"type\":\"string\"},\"table\":{\"type\":\"string\"},\"ts\":{\"type\":\"integer\"},\"type\":{\"type\":\"string\"},\"putRowNum\":{\"type\":\"integer\"}}}")
> >>>> > )
> >>>> > .withSchema(
> >>>> > new Schema()
> >>>> > .field("business", DataTypes.STRING())
> >>>> > .field("data",
> >>>> DataTypes.ROW(DataTypes.FIELD("f0", DataTypes.ROW(
> >>>> > DataTypes.FIELD("tracking_number",
> >>>> DataTypes.STRING()),
> >>>> > DataTypes.FIELD("invoice_no",
> >>>> DataTypes.STRING())))))
> >>>> > .field("database", DataTypes.STRING())
> >>>> > .field("table", DataTypes.STRING())
> >>>> > .field("ts", DataTypes.DECIMAL(38, 18))
> >>>> > .field("type", DataTypes.STRING())
> >>>> > .field("putRowNum", DataTypes.DECIMAL(38,
> 18))
> >>>> > )
> >>>> > .createTemporaryTable("Test");
> >>>> >
> >>>> > 异常信息：Caused by: java.io.IOException: Failed to deserialize JSON
> >>>> object.
> >>>> >
> >>>> > at
> >>>> >
> >>>>
> org.apache.flink.formats.json.JsonRowDeserializationSchema.deserialize(JsonRowDeserializationSchema.java:133)
> >>>> > at
> >>>> >
> >>>>
> org.apache.flink.formats.json.JsonRowDeserializationSchema.deserialize(JsonRowDeserializationSchema.java:76)
> >>>> > at
> >>>> >
> >>>>
> org.apache.flink.streaming.connectors.kafka.internals.KafkaDeserializationSchemaWrapper.deserialize(KafkaDeserializationSchemaWrapper.java:45)
> >>>> > at
> >>>> >
> >>>>
> org.apache.flink.streaming.connectors.kafka.internal.Kafka09Fetcher.runFetchLoop(Kafka09Fetcher.java:146)
> >>>> > 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$LegacySourceFunctionThread.run(SourceStreamTask.java:196)
> >>>> > Caused by: java.lang.ClassCastException:
> >>>> >
> >>>>
> org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.node.ArrayNode
> >>>> > cannot be cast to
> >>>> >
> >>>>
> org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.node.ObjectNode
> >>>> > at
> >>>> >
> >>>>
> org.apache.flink.formats.json.JsonRowDeserializationSchema.lambda$assembleRowConverter$77f7700$1(JsonRowDeserializationSchema.java:411)
> >>>> > at
> >>>> >
> >>>>
> org.apache.flink.formats.json.JsonRowDeserializationSchema.lambda$wrapIntoNullableConverter$d586c97$1(JsonRowDeserializationSchema.java:236)
> >>>> > at
> >>>> >
> >>>>
> org.apache.flink.formats.json.JsonRowDeserializationSchema.convertField(JsonRowDeserializationSchema.java:439)
> >>>> > at
> >>>> >
> >>>>
> org.apache.flink.formats.json.JsonRowDeserializationSchema.lambda$assembleRowConverter$77f7700$1(JsonRowDeserializationSchema.java:418)
> >>>> > at
> >>>> >
> >>>>
> org.apache.flink.formats.json.JsonRowDeserializationSchema.lambda$wrapIntoNullableConverter$d586c97$1(JsonRowDeserializationSchema.java:236)
> >>>> > at
> >>>> >
> >>>>
> org.apache.flink.formats.json.JsonRowDeserializationSchema.deserialize(JsonRowDeserializationSchema.java:131)
> >>>> > ... 7 more
> >>>> >
> >>>> >
> >>>> >
> >>>>
> >>>
>
