Examples with PythonFunction org.apache.flink.table.functions.python.PythonFunction used on opensource projects

Example 1 with PythonFunction

use of org.apache.flink.table.functions.python.PythonFunction in project flink by apache.

the class CommonPythonUtil method extractPythonAggregateFunctionInfosFromAggregateCall.

public static Tuple2<int[], PythonFunctionInfo[]> extractPythonAggregateFunctionInfosFromAggregateCall(AggregateCall[] aggCalls) {
    Map<Integer, Integer> inputNodes = new LinkedHashMap<>();
    List<PythonFunctionInfo> pythonFunctionInfos = new ArrayList<>();
    for (AggregateCall aggregateCall : aggCalls) {
        List<Integer> inputs = new ArrayList<>();
        List<Integer> argList = aggregateCall.getArgList();
        for (Integer arg : argList) {
            if (inputNodes.containsKey(arg)) {
                inputs.add(inputNodes.get(arg));
            } else {
                Integer inputOffset = inputNodes.size();
                inputs.add(inputOffset);
                inputNodes.put(arg, inputOffset);
            }
        }
        PythonFunction pythonFunction = null;
        SqlAggFunction aggregateFunction = aggregateCall.getAggregation();
        if (aggregateFunction instanceof AggSqlFunction) {
            pythonFunction = (PythonFunction) ((AggSqlFunction) aggregateFunction).aggregateFunction();
        } else if (aggregateFunction instanceof BridgingSqlAggFunction) {
            pythonFunction = (PythonFunction) ((BridgingSqlAggFunction) aggregateFunction).getDefinition();
        }
        PythonFunctionInfo pythonFunctionInfo = new PythonAggregateFunctionInfo(pythonFunction, inputs.toArray(), aggregateCall.filterArg, aggregateCall.isDistinct());
        pythonFunctionInfos.add(pythonFunctionInfo);
    }
    int[] udafInputOffsets = inputNodes.keySet().stream().mapToInt(i -> i).toArray();
    return Tuple2.of(udafInputOffsets, pythonFunctionInfos.toArray(new PythonFunctionInfo[0]));
}

Example 2 with PythonFunction

use of org.apache.flink.table.functions.python.PythonFunction in project flink by apache.

the class CommonPythonUtil method extractPythonAggregateFunctionInfos.

public static Tuple2<PythonAggregateFunctionInfo[], DataViewSpec[][]> extractPythonAggregateFunctionInfos(AggregateInfoList pythonAggregateInfoList, AggregateCall[] aggCalls) {
    List<PythonAggregateFunctionInfo> pythonAggregateFunctionInfoList = new ArrayList<>();
    List<DataViewSpec[]> dataViewSpecList = new ArrayList<>();
    AggregateInfo[] aggInfos = pythonAggregateInfoList.aggInfos();
    for (int i = 0; i < aggInfos.length; i++) {
        AggregateInfo aggInfo = aggInfos[i];
        UserDefinedFunction function = aggInfo.function();
        if (function instanceof PythonFunction) {
            pythonAggregateFunctionInfoList.add(new PythonAggregateFunctionInfo((PythonFunction) function, Arrays.stream(aggInfo.argIndexes()).boxed().toArray(), aggCalls[i].filterArg, aggCalls[i].isDistinct()));
            TypeInference typeInference = function.getTypeInference(null);
            dataViewSpecList.add(extractDataViewSpecs(i, typeInference.getAccumulatorTypeStrategy().get().inferType(null).get()));
        } else {
            int filterArg = -1;
            boolean distinct = false;
            if (i < aggCalls.length) {
                filterArg = aggCalls[i].filterArg;
                distinct = aggCalls[i].isDistinct();
            }
            pythonAggregateFunctionInfoList.add(new PythonAggregateFunctionInfo(getBuiltInPythonAggregateFunction(function), Arrays.stream(aggInfo.argIndexes()).boxed().toArray(), filterArg, distinct));
            // The data views of the built in Python Aggregate Function are different from Java
            // side, we will create the spec at Python side.
            dataViewSpecList.add(new DataViewSpec[0]);
        }
    }
    return Tuple2.of(pythonAggregateFunctionInfoList.toArray(new PythonAggregateFunctionInfo[0]), dataViewSpecList.toArray(new DataViewSpec[0][0]));
}