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ASF GitHub Bot commented on FLINK-3226: --------------------------------------- Github user fhueske commented on a diff in the pull request: https://github.com/apache/flink/pull/1624#discussion_r52740335 --- Diff: flink-libraries/flink-table/src/main/scala/org/apache/flink/api/table/codegen/CodeGenerator.scala --- @@ -231,29 +252,40 @@ class CodeGenerator( * be reused, they will be added to reusable code sections internally. The evaluation result * may be stored in the global result variable (see [[outRecordTerm]]). * - * @param fieldExprs + * @param fieldExprs field expressions to be converted * @param returnType conversion target type. Type must have the same arity than fieldExprs. + * @param resultFieldNames result field names necessary for a mapping to POJO fields. * @return instance of GeneratedExpression */ def generateResultExpression( fieldExprs: Seq[GeneratedExpression], - returnType: TypeInformation[_ <: Any]) + returnType: TypeInformation[_ <: Any], + resultFieldNames: Seq[String]) : GeneratedExpression = { - // TODO disable arity check for Rows and derive row arity from fieldExprs // initial type check if (returnType.getArity != fieldExprs.length) { throw new CodeGenException("Arity of result type does not match number of expressions.") } // type check returnType match { + case pt: PojoTypeInfo[_] => + fieldExprs.zipWithIndex foreach { --- End diff -- Add a check that both `fieldExpr` and `resultFieldName` have the same length. > Translate optimized logical Table API plans into physical plans representing > DataSet programs > --------------------------------------------------------------------------------------------- > > Key: FLINK-3226 > URL: https://issues.apache.org/jira/browse/FLINK-3226 > Project: Flink > Issue Type: Sub-task > Components: Table API > Reporter: Fabian Hueske > Assignee: Chengxiang Li > > This issue is about translating an (optimized) logical Table API (see > FLINK-3225) query plan into a physical plan. The physical plan is a 1-to-1 > representation of the DataSet program that will be executed. This means: > - Each Flink RelNode refers to exactly one Flink DataSet or DataStream > operator. > - All (join and grouping) keys of Flink operators are correctly specified. > - The expressions which are to be executed in user-code are identified. > - All fields are referenced with their physical execution-time index. > - Flink type information is available. > - Optional: Add physical execution hints for joins > The translation should be the final part of Calcite's optimization process. > For this task we need to: > - implement a set of Flink DataSet RelNodes. Each RelNode corresponds to one > Flink DataSet operator (Map, Reduce, Join, ...). The RelNodes must hold all > relevant operator information (keys, user-code expression, strategy hints, > parallelism). > - implement rules to translate optimized Calcite RelNodes into Flink > RelNodes. We start with a straight-forward mapping and later add rules that > merge several relational operators into a single Flink operator, e.g., merge > a join followed by a filter. Timo implemented some rules for the first SQL > implementation which can be used as a starting point. > - Integrate the translation rules into the Calcite optimization process -- This message was sent by Atlassian JIRA (v6.3.4#6332)