Examples with ExecEdge org.apache.flink.table.planner.plan.nodes.exec.ExecEdge used on opensource projects

Example 46 with ExecEdge

use of org.apache.flink.table.planner.plan.nodes.exec.ExecEdge in project flink by apache.

the class BatchExecSortAggregate method translateToPlanInternal.

@SuppressWarnings("unchecked")
@Override
protected Transformation<RowData> translateToPlanInternal(PlannerBase planner, ExecNodeConfig config) {
    final ExecEdge inputEdge = getInputEdges().get(0);
    final Transformation<RowData> inputTransform = (Transformation<RowData>) inputEdge.translateToPlan(planner);
    final RowType inputRowType = (RowType) inputEdge.getOutputType();
    final RowType outputRowType = (RowType) getOutputType();
    final CodeGeneratorContext ctx = new CodeGeneratorContext(config.getTableConfig());
    final AggregateInfoList aggInfos = AggregateUtil.transformToBatchAggregateInfoList(aggInputRowType, JavaScalaConversionUtil.toScala(Arrays.asList(aggCalls)), null, null);
    final GeneratedOperator<OneInputStreamOperator<RowData, RowData>> generatedOperator;
    if (grouping.length == 0) {
        generatedOperator = AggWithoutKeysCodeGenerator.genWithoutKeys(ctx, planner.getRelBuilder(), aggInfos, inputRowType, outputRowType, isMerge, isFinal, "NoGrouping");
    } else {
        generatedOperator = SortAggCodeGenerator.genWithKeys(ctx, planner.getRelBuilder(), aggInfos, inputRowType, outputRowType, grouping, auxGrouping, isMerge, isFinal);
    }
    return ExecNodeUtil.createOneInputTransformation(inputTransform, createTransformationName(config), createTransformationDescription(config), new CodeGenOperatorFactory<>(generatedOperator), InternalTypeInfo.of(outputRowType), inputTransform.getParallelism());
}

Example 47 with ExecEdge

use of org.apache.flink.table.planner.plan.nodes.exec.ExecEdge in project flink by apache.

the class BatchExecSortWindowAggregate method translateToPlanInternal.

@SuppressWarnings("unchecked")
@Override
protected Transformation<RowData> translateToPlanInternal(PlannerBase planner, ExecNodeConfig config) {
    final ExecEdge inputEdge = getInputEdges().get(0);
    final Transformation<RowData> inputTransform = (Transformation<RowData>) inputEdge.translateToPlan(planner);
    final AggregateInfoList aggInfos = AggregateUtil.transformToBatchAggregateInfoList(aggInputRowType, JavaScalaConversionUtil.toScala(Arrays.asList(aggCalls)), // aggCallNeedRetractions
    null, // orderKeyIndexes
    null);
    final int groupBufferLimitSize = config.get(ExecutionConfigOptions.TABLE_EXEC_WINDOW_AGG_BUFFER_SIZE_LIMIT);
    final Tuple2<Long, Long> windowSizeAndSlideSize = WindowCodeGenerator.getWindowDef(window);
    final SortWindowCodeGenerator windowCodeGenerator = new SortWindowCodeGenerator(new CodeGeneratorContext(config.getTableConfig()), planner.getRelBuilder(), window, inputTimeFieldIndex, inputTimeIsDate, JavaScalaConversionUtil.toScala(Arrays.asList(namedWindowProperties)), aggInfos, (RowType) inputEdge.getOutputType(), (RowType) getOutputType(), groupBufferLimitSize, // windowStart
    0L, windowSizeAndSlideSize.f0, windowSizeAndSlideSize.f1, grouping, auxGrouping, enableAssignPane, isMerge, isFinal);
    final GeneratedOperator<OneInputStreamOperator<RowData, RowData>> generatedOperator;
    if (grouping.length == 0) {
        generatedOperator = windowCodeGenerator.genWithoutKeys();
    } else {
        generatedOperator = windowCodeGenerator.genWithKeys();
    }
    return ExecNodeUtil.createOneInputTransformation(inputTransform, createTransformationName(config), createTransformationDescription(config), new CodeGenOperatorFactory<>(generatedOperator), InternalTypeInfo.of(getOutputType()), inputTransform.getParallelism());
}

Example 48 with ExecEdge

use of org.apache.flink.table.planner.plan.nodes.exec.ExecEdge in project flink by apache.

the class CommonExecCalc method translateToPlanInternal.

@SuppressWarnings("unchecked")
@Override
protected Transformation<RowData> translateToPlanInternal(PlannerBase planner, ExecNodeConfig config) {
    final ExecEdge inputEdge = getInputEdges().get(0);
    final Transformation<RowData> inputTransform = (Transformation<RowData>) inputEdge.translateToPlan(planner);
    final CodeGeneratorContext ctx = new CodeGeneratorContext(config.getTableConfig()).setOperatorBaseClass(operatorBaseClass);
    final CodeGenOperatorFactory<RowData> substituteStreamOperator = CalcCodeGenerator.generateCalcOperator(ctx, inputTransform, (RowType) getOutputType(), JavaScalaConversionUtil.toScala(projection), JavaScalaConversionUtil.toScala(Optional.ofNullable(this.condition)), retainHeader, getClass().getSimpleName());
    return ExecNodeUtil.createOneInputTransformation(inputTransform, createTransformationMeta(CALC_TRANSFORMATION, config), substituteStreamOperator, InternalTypeInfo.of(getOutputType()), inputTransform.getParallelism());
}

Example 49 with ExecEdge

use of org.apache.flink.table.planner.plan.nodes.exec.ExecEdge in project flink by apache.

the class BatchExecNestedLoopJoin method translateToPlanInternal.

@Override
@SuppressWarnings("unchecked")
protected Transformation<RowData> translateToPlanInternal(PlannerBase planner, ExecNodeConfig config) {
    ExecEdge leftInputEdge = getInputEdges().get(0);
    ExecEdge rightInputEdge = getInputEdges().get(1);
    Transformation<RowData> leftInputTransform = (Transformation<RowData>) leftInputEdge.translateToPlan(planner);
    Transformation<RowData> rightInputTransform = (Transformation<RowData>) rightInputEdge.translateToPlan(planner);
    // get input types
    RowType leftType = (RowType) leftInputEdge.getOutputType();
    RowType rightType = (RowType) rightInputEdge.getOutputType();
    CodeGenOperatorFactory<RowData> operator = new NestedLoopJoinCodeGenerator(new CodeGeneratorContext(config.getTableConfig()), singleRowJoin, leftIsBuild, leftType, rightType, (RowType) getOutputType(), joinType, condition).gen();
    int parallelism = leftInputTransform.getParallelism();
    if (leftIsBuild) {
        parallelism = rightInputTransform.getParallelism();
    }
    long manageMem = 0;
    if (!singleRowJoin) {
        manageMem = config.get(ExecutionConfigOptions.TABLE_EXEC_RESOURCE_EXTERNAL_BUFFER_MEMORY).getBytes();
    }
    return ExecNodeUtil.createTwoInputTransformation(leftInputTransform, rightInputTransform, createTransformationName(config), createTransformationDescription(config), operator, InternalTypeInfo.of(getOutputType()), parallelism, manageMem);
}

Example 50 with ExecEdge

use of org.apache.flink.table.planner.plan.nodes.exec.ExecEdge in project flink by apache.

the class BatchExecOverAggregate method translateToPlanInternal.

@SuppressWarnings("unchecked")
@Override
protected Transformation<RowData> translateToPlanInternal(PlannerBase planner, ExecNodeConfig config) {
    final ExecEdge inputEdge = getInputEdges().get(0);
    final Transformation<RowData> inputTransform = (Transformation<RowData>) inputEdge.translateToPlan(planner);
    final RowType inputType = (RowType) inputEdge.getOutputType();
    // The generated sort is used for generating the comparator among partitions.
    // So here not care the ASC or DESC for the grouping fields.
    // TODO just replace comparator to equaliser
    final int[] partitionFields = overSpec.getPartition().getFieldIndices();
    final GeneratedRecordComparator genComparator = ComparatorCodeGenerator.gen(config.getTableConfig(), "SortComparator", inputType, SortUtil.getAscendingSortSpec(partitionFields));
    // use aggInputType which considers constants as input instead of inputType
    final RowType inputTypeWithConstants = getInputTypeWithConstants();
    // Over operator could support different order-by keys with collation satisfied.
    // Currently, this operator requires all order keys (combined with partition keys) are
    // the same, but order-by keys may be different. Consider the following sql:
    // select *, sum(b) over partition by a order by a, count(c) over partition by a from T
    // So we can use any one from the groups. To keep the behavior with the rule, we use the
    // last one.
    final SortSpec sortSpec = overSpec.getGroups().get(overSpec.getGroups().size() - 1).getSort();
    final TableStreamOperator<RowData> operator;
    final long managedMemory;
    if (!needBufferData()) {
        // operator needn't cache data
        final int numOfGroup = overSpec.getGroups().size();
        final GeneratedAggsHandleFunction[] aggsHandlers = new GeneratedAggsHandleFunction[numOfGroup];
        final boolean[] resetAccumulators = new boolean[numOfGroup];
        for (int i = 0; i < numOfGroup; ++i) {
            GroupSpec group = overSpec.getGroups().get(i);
            AggregateInfoList aggInfoList = AggregateUtil.transformToBatchAggregateInfoList(inputTypeWithConstants, JavaScalaConversionUtil.toScala(group.getAggCalls()), // aggCallNeedRetractions
            null, sortSpec.getFieldIndices());
            AggsHandlerCodeGenerator generator = new AggsHandlerCodeGenerator(new CodeGeneratorContext(config.getTableConfig()), planner.getRelBuilder(), JavaScalaConversionUtil.toScala(inputType.getChildren()), // copyInputField
            false);
            // over agg code gen must pass the constants
            aggsHandlers[i] = generator.needAccumulate().withConstants(JavaScalaConversionUtil.toScala(getConstants())).generateAggsHandler("BoundedOverAggregateHelper", aggInfoList);
            OverWindowMode mode = inferGroupMode(group);
            resetAccumulators[i] = mode == OverWindowMode.ROW && group.getLowerBound().isCurrentRow() && group.getUpperBound().isCurrentRow();
        }
        operator = new NonBufferOverWindowOperator(aggsHandlers, genComparator, resetAccumulators);
        managedMemory = 0L;
    } else {
        List<OverWindowFrame> windowFrames = createOverWindowFrames(planner.getRelBuilder(), config, inputType, sortSpec, inputTypeWithConstants);
        operator = new BufferDataOverWindowOperator(windowFrames.toArray(new OverWindowFrame[0]), genComparator, inputType.getChildren().stream().allMatch(BinaryRowData::isInFixedLengthPart));
        managedMemory = config.get(ExecutionConfigOptions.TABLE_EXEC_RESOURCE_EXTERNAL_BUFFER_MEMORY).getBytes();
    }
    return ExecNodeUtil.createOneInputTransformation(inputTransform, createTransformationName(config), createTransformationDescription(config), SimpleOperatorFactory.of(operator), InternalTypeInfo.of(getOutputType()), inputTransform.getParallelism(), managedMemory);
}