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ASF GitHub Bot commented on FLINK-4469: --------------------------------------- Github user twalthr commented on a diff in the pull request: https://github.com/apache/flink/pull/2653#discussion_r88483086 --- Diff: flink-libraries/flink-table/src/main/scala/org/apache/flink/api/table/functions/utils/UserDefinedFunctionUtils.scala --- @@ -162,24 +191,107 @@ object UserDefinedFunctionUtils { } /** + * Internal method of [[ScalarFunction#getResultType()]] that does some pre-checking and uses + * [[TypeExtractor]] as default return type inference. + */ + def getResultType( + tableFunction: TableFunction[_], + signature: Array[Class[_]]) + : TypeInformation[_] = { + // find method for signature + val evalMethod = tableFunction.getEvalMethods + .find(m => signature.sameElements(m.getParameterTypes)) + .getOrElse(throw new ValidationException("Given signature is invalid.")) + + val userDefinedTypeInfo = tableFunction.getResultType + if (userDefinedTypeInfo != null) { + userDefinedTypeInfo + } else { + try { + TypeExtractor.getForClass(evalMethod.getReturnType) + } catch { + case ite: InvalidTypesException => + throw new ValidationException( + s"Return type of table function '$this' cannot be " + --- End diff -- If this is used for both methods, than `table function` is wrong. > Add support for user defined table function in Table API & SQL > -------------------------------------------------------------- > > Key: FLINK-4469 > URL: https://issues.apache.org/jira/browse/FLINK-4469 > Project: Flink > Issue Type: New Feature > Components: Table API & SQL > Reporter: Jark Wu > Assignee: Jark Wu > > Normal user-defined functions, such as concat(), take in a single input row > and output a single output row. In contrast, table-generating functions > transform a single input row to multiple output rows. It is very useful in > some cases, such as look up in HBase by rowkey and return one or more rows. > Adding a user defined table function should: > 1. inherit from UDTF class with specific generic type T > 2. define one or more evel function. > NOTE: > 1. the eval method must be public and non-static. > 2. the generic type T is the row type returned by table function. Because of > Java type erasure, we can’t extract T from the Iterable. > 3. use {{collect(T)}} to emit table row > 4. eval method can be overload. Blink will choose the best match eval method > to call according to parameter types and number. > {code} > public class Word { > public String word; > public Integer length; > } > public class SplitStringUDTF extends UDTF<Word> { > public Iterable<Word> eval(String str) { > if (str != null) { > for (String s : str.split(",")) { > collect(new Word(s, s.length())); > } > } > } > } > // in SQL > tableEnv.registerFunction("split", new SplitStringUDTF()) > tableEnv.sql("SELECT a, b, t.* FROM MyTable, LATERAL TABLE(split(c)) AS > t(w,l)") > // in Java Table API > tableEnv.registerFunction("split", new SplitStringUDTF()) > // rename split table columns to “w” and “l” > table.crossApply("split(c) as (w, l)") > .select("a, b, w, l") > // without renaming, we will use the origin field names in the POJO/case/... > table.crossApply("split(c)") > .select("a, b, word, length") > // in Scala Table API > val split = new SplitStringUDTF() > table.crossApply(split('c) as ('w, 'l)) > .select('a, 'b, 'w, 'l) > // outerApply for outer join to a UDTF > table.outerApply(split('c)) > .select('a, 'b, 'word, 'length) > {code} > See [1] for more information about UDTF design. > [1] > https://docs.google.com/document/d/15iVc1781dxYWm3loVQlESYvMAxEzbbuVFPZWBYuY1Ek/edit# -- This message was sent by Atlassian JIRA (v6.3.4#6332)