Hi Pierre, The serialization/deserialization of sparse Row in flink is specially optimized. The principle is that each Row will have a leading mask when serializing to identify whether the field at the specified position is NULL, and one field corresponds to one bit. For example, if you have 10k fields, then there will be a mask of 10k bit / 8 = 1250 byte. In this way, the serialization/deserialization overhead can be omitted for those field values that are NULL.
For specific code optimization logic, you can refer to java logic[1], or python logic[2] and cython logic[3]. [1] https://github.com/apache/flink/blob/master/flink-core/src/main/java/org/apache/flink/api/java/typeutils/runtime/RowSerializer.java#L185 [2] https://github.com/apache/flink/blob/master/flink-python/pyflink/fn_execution/beam/beam_coder_impl_slow.py#L100 [3] https://github.com/apache/flink/blob/master/flink-python/pyflink/fn_execution/coder_impl_fast.pyx#L697 Best, Xingbo Pierre Oberholzer <pierre.oberhol...@gmail.com> 于2020年12月3日周四 下午3:08写道： > Hi Xingbo, Community, > > Thanks a lot for your support. > May I finally ask to conclude this thread, including wider audience: > - Are serious performance issues to be expected with 100k fields per ROW > (i.e. due solely to metadata overhead and independently of queries logic) ? > - In sparse population (say 99% sparsity) already optimized in the ROW > object or are sparse types on your roadmap ? > Any experience with sparse Table from other users (including benchmarks > vs. other frameworks) are also highly welcome. > > Thanks ! > > Best > > > Le jeu. 3 déc. 2020 à 02:53, Xingbo Huang <hxbks...@gmail.com> a écrit : > >> Hi Pierre, >> >> This example is written based on the syntax of release-1.12 that is about >> to be released, and the test passed. In release-1.12, input_type can be >> omitted and expression can be used directly. If you are using release-1.11, >> you only need to modify the grammar of udf used slightly according to the >> udf documentation[1]. >> >> The flink table connector supports avro format, please refer to the >> document[2]. >> >> [1] >> https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/python/table-api-users-guide/udfs/python_udfs.html#scalar-functions >> [2] >> https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/table/connectors/formats/avro.html#avro-format >> >> Best, >> Xingbo >> >> Pierre Oberholzer <pierre.oberhol...@gmail.com> 于2020年12月3日周四 上午2:57写道： >> >>> Hi Xingbo, >>> >>> Nice ! This looks a bit hacky, but shows that it can be done ;) >>> >>> I just got an exception preventing me running your code, apparently from >>> udf.py: >>> >>> TypeError: Invalid input_type: input_type should be DataType but >>> contains None >>> >>> Can you pls check again ? >>> If the schema is defined is a .avsc file, do we have to parse it and >>> rebuild those syntax (ddl and udf) and or is there an existing component >>> that could be used ? >>> >>> Thanks a lot ! >>> >>> Best, >>> >>> >>> Le mer. 2 déc. 2020 à 04:50, Xingbo Huang <hxbks...@gmail.com> a écrit : >>> >>>> Hi Pierre, >>>> >>>> I wrote a PyFlink implementation, you can see if it meets your needs: >>>> >>>> >>>> from pyflink.datastream import StreamExecutionEnvironment >>>> from pyflink.table import StreamTableEnvironment, EnvironmentSettings, >>>> DataTypes >>>> from pyflink.table.udf import udf >>>> >>>> >>>> def test(): >>>> env = StreamExecutionEnvironment.get_execution_environment() >>>> env.set_parallelism(1) >>>> t_env = StreamTableEnvironment.create(env, >>>> >>>> environment_settings=EnvironmentSettings.new_instance() >>>> >>>> .in_streaming_mode().use_blink_planner().build()) >>>> >>>> t_env.get_config().get_configuration().set_string("taskmanager.memory.task.off-heap.size", >>>> '80m') >>>> >>>> # 10k nested columns >>>> num_field = 10_000 >>>> fields = ['f%s INT' % i for i in range(num_field)] >>>> field_str = ','.join(fields) >>>> t_env.execute_sql(f""" >>>> CREATE TABLE source_table ( >>>> f0 BIGINT, >>>> f1 DECIMAL(32,2), >>>> f2 ROW<${field_str}>, >>>> f3 TIMESTAMP(3) >>>> ) WITH ( >>>> 'connector' = 'datagen', >>>> 'number-of-rows' = '2' >>>> ) >>>> """) >>>> >>>> t_env.execute_sql(f""" >>>> CREATE TABLE print_table ( >>>> f0 BIGINT, >>>> f1 DECIMAL(32,2), >>>> f2 ROW<${field_str}>, >>>> f3 TIMESTAMP(3) >>>> ) WITH ( >>>> 'connector' = 'print' >>>> ) >>>> """) >>>> result_type = DataTypes.ROW( >>>> [DataTypes.FIELD("f%s" % i, DataTypes.INT()) for i in >>>> range(num_field)]) >>>> >>>> func = udf(lambda x: x, result_type=result_type) >>>> >>>> source = t_env.from_path("source_table") >>>> result = source.select(source.f0, source.f1, func(source.f2), >>>> source.f3) >>>> result.execute_insert("print_table") >>>> >>>> >>>> if __name__ == '__main__': >>>> test() >>>> >>>> >>>> Best, >>>> Xingbo >>>> >>>> Pierre Oberholzer <pierre.oberhol...@gmail.com> 于2020年12月1日周二 下午6:10写道： >>>> >>>>> Hi Xingbo, >>>>> >>>>> That would mean giving up on using Flink (table) features on the >>>>> content of the parsed JSON objects, so definitely a big loss. Let me know >>>>> if I missed something. >>>>> >>>>> Thanks ! >>>>> >>>>> Le mar. 1 déc. 2020 à 07:26, Xingbo Huang <hxbks...@gmail.com> a >>>>> écrit : >>>>> >>>>>> Hi Pierre, >>>>>> >>>>>> Have you ever thought of declaring your entire json as a string field >>>>>> in `Table` and putting the parsing work in UDF? >>>>>> >>>>>> Best, >>>>>> Xingbo >>>>>> >>>>>> Pierre Oberholzer <pierre.oberhol...@gmail.com> 于2020年12月1日周二 >>>>>> 上午4:13写道： >>>>>> >>>>>>> Hi Xingbo, >>>>>>> >>>>>>> Many thanks for your follow up. Yes you got it right. >>>>>>> So using Table API and a ROW object for the nested output of my UDF, >>>>>>> and since types are mandatory, I guess this boils down to: >>>>>>> - How to nicely specify the types for the 100k fields : shall I use >>>>>>> TypeInformation [1] or better retrieve it from Schema Registry [2] ? >>>>>>> - Do I have to put NULL values for all the fields that don't have a >>>>>>> value in my JSON ? >>>>>>> - Will the resulting Table be "sparse" and suffer performance >>>>>>> limitations ? >>>>>>> Let me know if Table API and ROW are the right candidates here, or >>>>>>> if other better alternatives exist. >>>>>>> As said I'd be glad to apply some downstream transformations using >>>>>>> key,value access (and possibly some Table <-> Pandas operations). Hope >>>>>>> that >>>>>>> doesn't make it a too long wish list ;) >>>>>>> >>>>>>> Thanks a lot ! >>>>>>> >>>>>>> Best regards, >>>>>>> >>>>>>> [1] >>>>>>> https://stackoverflow.com/questions/48696875/how-to-attach-schema-to-a-flink-datastream-on-the-fly >>>>>>> [2] >>>>>>> https://docs.cloudera.com/csa/1.2.0/datastream-connectors/topics/csa-schema-registry.html >>>>>>> >>>>>>> Le sam. 28 nov. 2020 à 04:04, Xingbo Huang <hxbks...@gmail.com> a >>>>>>> écrit : >>>>>>> >>>>>>>> Hi Pierre, >>>>>>>> >>>>>>>> Sorry for the late reply. >>>>>>>> Your requirement is that your `Table` has a `field` in `Json` >>>>>>>> format and its key has reached 100k, and then you want to use such a >>>>>>>> `field` as the input/output of `udf`, right? As to whether there is a >>>>>>>> limit >>>>>>>> on the number of nested key, I am not quite clear. Other contributors >>>>>>>> with >>>>>>>> experience in this area may have answers. On the part of `Python UDF`, >>>>>>>> if >>>>>>>> the type of key or value of your `Map` is `Any`, we do not support it >>>>>>>> now. >>>>>>>> You need to specify a specific type. For more information, please >>>>>>>> refer to >>>>>>>> the related document[1]. >>>>>>>> >>>>>>>> [1] >>>>>>>> https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/python/table-api-users-guide/python_types.html >>>>>>>> >>>>>>>> Best, >>>>>>>> Xingbo >>>>>>>> >>>>>>>> 2020年11月28日 上午12:49，Pierre Oberholzer <pierre.oberhol...@gmail.com> >>>>>>>> 写道： >>>>>>>> >>>>>>>> Hello Wei, Dian, Xingbo, >>>>>>>> >>>>>>>> Not really sure when it is appropriate to knock on the door of the >>>>>>>> community ;) >>>>>>>> I just wanted to mention that your feedback on the above topic will >>>>>>>> be highly appreciated as it will condition the choice of framework on >>>>>>>> our >>>>>>>> side for the months to come, and potentially help the community to >>>>>>>> cover >>>>>>>> sparse data with Flink. >>>>>>>> >>>>>>>> Thanks a lot ! >>>>>>>> >>>>>>>> Have a great week-end >>>>>>>> >>>>>>>> Best, >>>>>>>> >>>>>>>> Le ven. 20 nov. 2020 à 10:11, Pierre Oberholzer < >>>>>>>> pierre.oberhol...@gmail.com> a écrit : >>>>>>>> >>>>>>>>> Hi Wei, >>>>>>>>> >>>>>>>>> Thanks for the hint. May I please follow up by adding more context >>>>>>>>> and ask for your guidance. >>>>>>>>> >>>>>>>>> In case the bespoken Map[String,Any] object returned by Scala: >>>>>>>>> >>>>>>>>> - Has a defined schema (incl. nested) with up to 100k (!) >>>>>>>>> different possible keys >>>>>>>>> - Has only some portion of the keys populated for each record >>>>>>>>> - Is convertible to JSON >>>>>>>>> - Has to undergo downstream processing in Flink and/or Python UDF >>>>>>>>> with key value access >>>>>>>>> - Has to be ultimately stored in a Kafka/AVRO sink >>>>>>>>> >>>>>>>>> How would you declare the types explicitly in such a case ? >>>>>>>>> >>>>>>>>> Thanks for your support ! >>>>>>>>> >>>>>>>>> Pierre >>>>>>>>> >>>>>>>>> Le jeu. 19 nov. 2020 à 03:54, Wei Zhong <weizhong0...@gmail.com> >>>>>>>>> a écrit : >>>>>>>>> >>>>>>>>>> Hi Pierre, >>>>>>>>>> >>>>>>>>>> Currently there is no type hint like ‘Map[String, Any]’. The >>>>>>>>>> recommended way is declaring your type more explicitly. >>>>>>>>>> >>>>>>>>>> If you insist on doing this, you can try to declaring a RAW data >>>>>>>>>> type for java.util.HashMap [1], but you may encounter some troubles >>>>>>>>>> [2] >>>>>>>>>> related to the kryo serializers. >>>>>>>>>> >>>>>>>>>> Best, >>>>>>>>>> Wei >>>>>>>>>> >>>>>>>>>> [1] >>>>>>>>>> https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/table/types.html#raw >>>>>>>>>> [2] >>>>>>>>>> https://stackoverflow.com/questions/28157236/kryo-serialization-with-nested-hashmap-with-custom-class >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> 在 2020年11月19日，04:31，Pierre Oberholzer < >>>>>>>>>> pierre.oberhol...@gmail.com> 写道： >>>>>>>>>> >>>>>>>>>> Hi Wei, >>>>>>>>>> >>>>>>>>>> It works ! Thanks a lot for your support. >>>>>>>>>> I hadn't tried this last combination for option 1, and I had >>>>>>>>>> wrong syntax for option 2. >>>>>>>>>> >>>>>>>>>> So to summarize.. >>>>>>>>>> >>>>>>>>>> Methods working: >>>>>>>>>> - Current: DataTypeHint in UDF definition + SQL for UDF >>>>>>>>>> registering >>>>>>>>>> - Outdated: override getResultType in UDF definition >>>>>>>>>> + t_env.register_java_function for UDF registering >>>>>>>>>> >>>>>>>>>> Type conversions working: >>>>>>>>>> - scala.collection.immutable.Map[String,String] => >>>>>>>>>> org.apache.flink.types.Row => ROW<STRING,STRING> >>>>>>>>>> - scala.collection.immutable.Map[String,String] => >>>>>>>>>> java.util.Map[String,String] => MAP<STRING,STRING> >>>>>>>>>> >>>>>>>>>> Any hint for Map[String,Any] ? >>>>>>>>>> >>>>>>>>>> Best regards, >>>>>>>>>> >>>>>>>>>> Le mer. 18 nov. 2020 à 03:26, Wei Zhong <weizhong0...@gmail.com> >>>>>>>>>> a écrit : >>>>>>>>>> >>>>>>>>>>> Hi Pierre, >>>>>>>>>>> >>>>>>>>>>> Those 2 approaches all work in my local machine, this is my code: >>>>>>>>>>> >>>>>>>>>>> Scala UDF: >>>>>>>>>>> >>>>>>>>>>> package com.dummy >>>>>>>>>>> >>>>>>>>>>> import org.apache.flink.api.common.typeinfo.TypeInformation >>>>>>>>>>> import org.apache.flink.table.annotation.DataTypeHint >>>>>>>>>>> import org.apache.flink.table.api.Types >>>>>>>>>>> import org.apache.flink.table.functions.ScalarFunction >>>>>>>>>>> import org.apache.flink.types.Row >>>>>>>>>>> >>>>>>>>>>> /** >>>>>>>>>>> * The scala UDF. >>>>>>>>>>> */ >>>>>>>>>>> class dummyMap extends ScalarFunction { >>>>>>>>>>> >>>>>>>>>>> // If the udf would be registered by the SQL statement, you need >>>>>>>>>>> add this typehint >>>>>>>>>>> @DataTypeHint("ROW<s STRING,t STRING>") >>>>>>>>>>> def eval(): Row = { >>>>>>>>>>> >>>>>>>>>>> Row.of(java.lang.String.valueOf("foo"), >>>>>>>>>>> java.lang.String.valueOf("bar")) >>>>>>>>>>> >>>>>>>>>>> } >>>>>>>>>>> >>>>>>>>>>> // If the udf would be registered by the method >>>>>>>>>>> 'register_java_function', you need override this >>>>>>>>>>> // method. >>>>>>>>>>> override def getResultType(signature: Array[Class[_]]): >>>>>>>>>>> TypeInformation[_] = { >>>>>>>>>>> // The type of the return values should be TypeInformation >>>>>>>>>>> Types.ROW(Array("s", "t"), >>>>>>>>>>> Array[TypeInformation[_]](Types.STRING(), Types.STRING())) >>>>>>>>>>> } >>>>>>>>>>> } >>>>>>>>>>> >>>>>>>>>>> Python code: >>>>>>>>>>> >>>>>>>>>>> from pyflink.datastream import StreamExecutionEnvironment >>>>>>>>>>> from pyflink.table import StreamTableEnvironment >>>>>>>>>>> >>>>>>>>>>> s_env = StreamExecutionEnvironment.get_execution_environment() >>>>>>>>>>> st_env = StreamTableEnvironment.create(s_env) >>>>>>>>>>> >>>>>>>>>>> # load the scala udf jar file, the path should be modified to >>>>>>>>>>> yours >>>>>>>>>>> # or your can also load the jar file via other approaches >>>>>>>>>>> st_env.get_config().get_configuration().set_string("pipeline.jars", >>>>>>>>>>> "file:///Users/zhongwei/the-dummy-udf.jar") >>>>>>>>>>> >>>>>>>>>>> # register the udf via >>>>>>>>>>> st_env.execute_sql("CREATE FUNCTION dummyMap AS >>>>>>>>>>> 'com.dummy.dummyMap' LANGUAGE SCALA") >>>>>>>>>>> # or register via the method >>>>>>>>>>> # st_env.register_java_function("dummyMap", "com.dummy.dummyMap") >>>>>>>>>>> >>>>>>>>>>> # prepare source and sink >>>>>>>>>>> t = st_env.from_elements([(1, 'hi', 'hello'), (2, 'hi', >>>>>>>>>>> 'hello')], ['a', 'b', 'c']) >>>>>>>>>>> st_env.execute_sql("""create table mySink ( >>>>>>>>>>> output_of_my_scala_udf ROW<s STRING,t STRING> >>>>>>>>>>> ) with ( >>>>>>>>>>> 'connector' = 'print' >>>>>>>>>>> )""") >>>>>>>>>>> >>>>>>>>>>> # execute query >>>>>>>>>>> >>>>>>>>>>> t.select("dummyMap()").execute_insert("mySink").get_job_client().get_job_execution_result().result() >>>>>>>>>>> >>>>>>>>>>> Best, >>>>>>>>>>> Wei >>>>>>>>>>> >>>>>>>>>>> 在 2020年11月18日，03:28，Pierre Oberholzer < >>>>>>>>>>> pierre.oberhol...@gmail.com> 写道： >>>>>>>>>>> >>>>>>>>>>> Hi Wei, >>>>>>>>>>> >>>>>>>>>>> True, I'm using the method you mention, but glad to change. >>>>>>>>>>> I tried your suggestion instead, but got a similar error. >>>>>>>>>>> >>>>>>>>>>> Thanks for your support. That is much more tedious than I >>>>>>>>>>> thought. >>>>>>>>>>> >>>>>>>>>>> *Option 1 - SQL UDF* >>>>>>>>>>> >>>>>>>>>>> *SQL UDF* >>>>>>>>>>> create_func_ddl = """ >>>>>>>>>>> CREATE FUNCTION dummyMap >>>>>>>>>>> AS 'com.dummy.dummyMap' LANGUAGE SCALA >>>>>>>>>>> """ >>>>>>>>>>> >>>>>>>>>>> t_env.execute_sql(create_func_ddl) >>>>>>>>>>> >>>>>>>>>>> *Error* >>>>>>>>>>> Py4JJavaError: An error occurred while calling o672.execute. >>>>>>>>>>> : org.apache.flink.table.api.TableException: Result field does >>>>>>>>>>> not match requested type. Requested: Row(s: String, t: String); >>>>>>>>>>> Actual: >>>>>>>>>>> GenericType<org.apache.flink.types.Row> >>>>>>>>>>> >>>>>>>>>>> *Option 2 *- *Overriding getResultType* >>>>>>>>>>> >>>>>>>>>>> Back to the old registering method, but overriding getResultType >>>>>>>>>>> : >>>>>>>>>>> >>>>>>>>>>> t_env.register_java_function("dummyMap","com.dummy.dummyMap") >>>>>>>>>>> >>>>>>>>>>> *Scala UDF* >>>>>>>>>>> class dummyMap() extends ScalarFunction { >>>>>>>>>>> >>>>>>>>>>> def eval(): Row = { >>>>>>>>>>> >>>>>>>>>>> Row.of(java.lang.String.valueOf("foo"), >>>>>>>>>>> java.lang.String.valueOf("bar")) >>>>>>>>>>> >>>>>>>>>>> } >>>>>>>>>>> >>>>>>>>>>> override def getResultType(signature: Array[Class[_]]): >>>>>>>>>>> TypeInformation[_] = >>>>>>>>>>> DataTypes.ROW(DataTypes.STRING,DataTypes.STRING) >>>>>>>>>>> } >>>>>>>>>>> >>>>>>>>>>> *Error (on compilation)* >>>>>>>>>>> >>>>>>>>>>> [error] dummyMap.scala:66:90: overloaded method value ROW with >>>>>>>>>>> alternatives: >>>>>>>>>>> [error] (x$1: >>>>>>>>>>> org.apache.flink.table.api.DataTypes.AbstractField*)org.apache.flink.table.types.UnresolvedDataType >>>>>>>>>>> <and> >>>>>>>>>>> [error] ()org.apache.flink.table.types.DataType <and> >>>>>>>>>>> [error] (x$1: >>>>>>>>>>> org.apache.flink.table.api.DataTypes.Field*)org.apache.flink.table.types.DataType >>>>>>>>>>> [error] cannot be applied to >>>>>>>>>>> (org.apache.flink.table.types.DataType, >>>>>>>>>>> org.apache.flink.table.types.DataType) >>>>>>>>>>> [error] override def getResultType(signature: >>>>>>>>>>> Array[Class[_]]): TypeInformation[_] = >>>>>>>>>>> DataTypes.ROW(DataTypes.STRING,DataTypes.STRING) >>>>>>>>>>> [error] >>>>>>>>>>> ^ >>>>>>>>>>> [error] one error found >>>>>>>>>>> [error] (Compile / compileIncremental) Compilation failed >>>>>>>>>>> [error] Total time: 3 s, completed 17 nov. 2020 à 20:00:01 >>>>>>>>>>> >>>>>>>>>>> Le mar. 17 nov. 2020 à 14:01, Wei Zhong <weizhong0...@gmail.com> >>>>>>>>>>> a écrit : >>>>>>>>>>> >>>>>>>>>>>> Hi Pierre, >>>>>>>>>>>> >>>>>>>>>>>> I guess your UDF is registered by the method >>>>>>>>>>>> 'register_java_function' which uses the old type system. In this >>>>>>>>>>>> situation >>>>>>>>>>>> you need to override the 'getResultType' method instead of adding >>>>>>>>>>>> type >>>>>>>>>>>> hint. >>>>>>>>>>>> >>>>>>>>>>>> You can also try to register your UDF via the "CREATE FUNCTION" >>>>>>>>>>>> sql statement, which accepts the type hint. >>>>>>>>>>>> >>>>>>>>>>>> Best, >>>>>>>>>>>> Wei >>>>>>>>>>>> >>>>>>>>>>>> 在 2020年11月17日，19:29，Pierre Oberholzer < >>>>>>>>>>>> pierre.oberhol...@gmail.com> 写道： >>>>>>>>>>>> >>>>>>>>>>>> Hi Wei, >>>>>>>>>>>> >>>>>>>>>>>> Thanks for your suggestion. Same error. >>>>>>>>>>>> >>>>>>>>>>>> *Scala UDF* >>>>>>>>>>>> >>>>>>>>>>>> @FunctionHint(output = new DataTypeHint("ROW<s STRING,t >>>>>>>>>>>> STRING>")) >>>>>>>>>>>> class dummyMap() extends ScalarFunction { >>>>>>>>>>>> def eval(): Row = { >>>>>>>>>>>> Row.of(java.lang.String.valueOf("foo"), >>>>>>>>>>>> java.lang.String.valueOf("bar")) >>>>>>>>>>>> } >>>>>>>>>>>> } >>>>>>>>>>>> >>>>>>>>>>>> Best regards, >>>>>>>>>>>> >>>>>>>>>>>> Le mar. 17 nov. 2020 à 10:04, Wei Zhong <weizhong0...@gmail.com> >>>>>>>>>>>> a écrit : >>>>>>>>>>>> >>>>>>>>>>>>> Hi Pierre, >>>>>>>>>>>>> >>>>>>>>>>>>> You can try to replace the '@DataTypeHint("ROW<s STRING,t >>>>>>>>>>>>> STRING>")' with '@FunctionHint(output = new DataTypeHint("ROW<s >>>>>>>>>>>>> STRING,t >>>>>>>>>>>>> STRING>”))' >>>>>>>>>>>>> >>>>>>>>>>>>> Best, >>>>>>>>>>>>> Wei >>>>>>>>>>>>> >>>>>>>>>>>>> 在 2020年11月17日，15:45，Pierre Oberholzer < >>>>>>>>>>>>> pierre.oberhol...@gmail.com> 写道： >>>>>>>>>>>>> >>>>>>>>>>>>> Hi Dian, Community, >>>>>>>>>>>>> >>>>>>>>>>>>> (bringing the thread back to wider audience) >>>>>>>>>>>>> >>>>>>>>>>>>> As you suggested, I've tried to use DataTypeHint with Row instead >>>>>>>>>>>>> of Map but also this simple case leads to a type mismatch >>>>>>>>>>>>> between UDF and Table API. >>>>>>>>>>>>> I've also tried other Map objects from Flink >>>>>>>>>>>>> (table.data.MapData, flink.types.MapValue, >>>>>>>>>>>>> flink.table.api.DataTypes.MAP) >>>>>>>>>>>>> in addition to Java (java.util.Map) in combination with >>>>>>>>>>>>> DataTypeHint, without success. >>>>>>>>>>>>> N.B. I'm using version 1.11. >>>>>>>>>>>>> >>>>>>>>>>>>> Am I doing something wrong or am I facing limitations in the >>>>>>>>>>>>> toolkit ? >>>>>>>>>>>>> >>>>>>>>>>>>> Thanks in advance for your support ! >>>>>>>>>>>>> >>>>>>>>>>>>> Best regards, >>>>>>>>>>>>> >>>>>>>>>>>>> *Scala UDF* >>>>>>>>>>>>> >>>>>>>>>>>>> class dummyMap() extends ScalarFunction { >>>>>>>>>>>>> >>>>>>>>>>>>> @DataTypeHint("ROW<s STRING,t STRING>") >>>>>>>>>>>>> def eval(): Row = { >>>>>>>>>>>>> >>>>>>>>>>>>> Row.of(java.lang.String.valueOf("foo"), >>>>>>>>>>>>> java.lang.String.valueOf("bar")) >>>>>>>>>>>>> >>>>>>>>>>>>> } >>>>>>>>>>>>> } >>>>>>>>>>>>> >>>>>>>>>>>>> *Table DDL* >>>>>>>>>>>>> >>>>>>>>>>>>> my_sink_ddl = f""" >>>>>>>>>>>>> create table mySink ( >>>>>>>>>>>>> output_of_my_scala_udf ROW<s STRING,t STRING> >>>>>>>>>>>>> ) with ( >>>>>>>>>>>>> ... >>>>>>>>>>>>> ) >>>>>>>>>>>>> """ >>>>>>>>>>>>> >>>>>>>>>>>>> *Error* >>>>>>>>>>>>> >>>>>>>>>>>>> Py4JJavaError: An error occurred while calling o2.execute. >>>>>>>>>>>>> : org.apache.flink.table.api.ValidationException: Field types >>>>>>>>>>>>> of query result and registered TableSink >>>>>>>>>>>>> `default_catalog`.`default_database`.`mySink` do not match. >>>>>>>>>>>>> Query result schema: [output_of_my_scala_udf: >>>>>>>>>>>>> GenericType<org.apache.flink.types.Row>] >>>>>>>>>>>>> TableSink schema: [output_of_my_scala_udf: Row(s: String, >>>>>>>>>>>>> t: String)] >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>>> Le ven. 13 nov. 2020 à 11:59, Pierre Oberholzer < >>>>>>>>>>>>> pierre.oberhol...@gmail.com> a écrit : >>>>>>>>>>>>> >>>>>>>>>>>>>> Thanks Dian, but same error when using explicit returned type: >>>>>>>>>>>>>> >>>>>>>>>>>>>> class dummyMap() extends ScalarFunction { >>>>>>>>>>>>>> >>>>>>>>>>>>>> def eval() : util.Map[java.lang.String,java.lang.String] = >>>>>>>>>>>>>> { >>>>>>>>>>>>>> >>>>>>>>>>>>>> val states = Map("key1" -> "val1", "key2" -> "val2") >>>>>>>>>>>>>> >>>>>>>>>>>>>> states.asInstanceOf[util.Map[java.lang.String,java.lang.String]] >>>>>>>>>>>>>> >>>>>>>>>>>>>> } >>>>>>>>>>>>>> } >>>>>>>>>>>>>> >>>>>>>>>>>>>> Le ven. 13 nov. 2020 à 10:34, Dian Fu <dian0511...@gmail.com> >>>>>>>>>>>>>> a écrit : >>>>>>>>>>>>>> >>>>>>>>>>>>>>> You need to explicitly defined the result type the UDF. You >>>>>>>>>>>>>>> could refer to [1] for more details if you are using Flink >>>>>>>>>>>>>>> 1.11. If you are >>>>>>>>>>>>>>> using other versions of Flink, you need to refer to the >>>>>>>>>>>>>>> corresponding >>>>>>>>>>>>>>> documentation. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> [1] >>>>>>>>>>>>>>> https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/table/functions/udfs.html#implementation-guide >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> 在 2020年11月13日，下午4:56，Pierre Oberholzer < >>>>>>>>>>>>>>> pierre.oberhol...@gmail.com> 写道： >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> ScalarFunction >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>> >>>>>>>>>>>>>> -- >>>>>>>>>>>>>> Pierre >>>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>>> -- >>>>>>>>>>>>> Pierre >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> -- >>>>>>>>>>>> Pierre >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> -- >>>>>>>>>>> Pierre >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>> >>>>>>>>>> -- >>>>>>>>>> Pierre >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>> >>>>>>>>> -- >>>>>>>>> Pierre >>>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> -- >>>>>>>> Pierre >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>> >>>>>>> -- >>>>>>> Pierre >>>>>>> >>>>>> -- >>>>> Pierre >>>>> >>>> >>> >>> -- >>> Pierre >>> >> -- > Pierre >
