Examples with SqlOperator org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator used on opensource projects

Example 46 with SqlOperator

use of org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator in project drill by apache.

the class PreProcessLogicalRel method getConvertFunctionException.

private UserException getConvertFunctionException(final String functionName, final String typeName) {
    final String newFunctionName = functionName + typeName;
    final String typeNameToPrint = typeName.length() == 0 ? "<empty_string>" : typeName;
    final UserException.Builder exceptionBuilder = UserException.unsupportedError().message("%s does not support conversion %s type '%s'.", functionName, functionName.substring(8).toLowerCase(), typeNameToPrint);
    // Build a nice error message
    if (typeName.length() > 0) {
        List<String> ops = new ArrayList<>();
        for (SqlOperator op : table.getOperatorList()) {
            ops.add(op.getName());
        }
        final String bestMatch = ApproximateStringMatcher.getBestMatch(ops, newFunctionName);
        if (bestMatch != null && bestMatch.length() > functionName.length() && bestMatch.toLowerCase().startsWith("convert")) {
            final StringBuilder s = new StringBuilder("Did you mean ").append(bestMatch.substring(functionName.length())).append("?");
            exceptionBuilder.addContext(s.toString());
        }
    }
    return exceptionBuilder.build(logger);
}

Example 47 with SqlOperator

use of org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator in project drill by apache.

the class LocalFunctionRegistry method registerOperatorsWithoutInference.

private void registerOperatorsWithoutInference(DrillOperatorTable operatorTable, Map<String, Collection<DrillFuncHolder>> registeredFunctions) {
    SqlOperator op;
    for (Entry<String, Collection<DrillFuncHolder>> function : registeredFunctions.entrySet()) {
        Set<Integer> argCounts = new HashSet<>();
        String name = function.getKey().toUpperCase();
        for (DrillFuncHolder func : function.getValue()) {
            if (argCounts.add(func.getParamCount())) {
                if (func.isAggregating()) {
                    op = new DrillSqlAggOperatorWithoutInference(name, func.getParamCount(), func.isVarArg());
                } else {
                    boolean isDeterministic;
                    // into literals
                    if (DrillConstExecutor.NON_REDUCIBLE_TYPES.contains(func.getReturnType().getMinorType()) || func.isComplexWriterFuncHolder()) {
                        isDeterministic = false;
                    } else {
                        isDeterministic = func.isDeterministic();
                    }
                    op = new DrillSqlOperatorWithoutInference(name, func.getParamCount(), func.getReturnType(), isDeterministic, func.isNiladic(), func.isVarArg());
                }
                operatorTable.addOperatorWithoutInference(function.getKey(), op);
            }
        }
    }
}

Example 48 with SqlOperator

use of org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator in project drill by apache.

the class RewriteAsBinaryOperators method visitCall.

@Override
public RexNode visitCall(RexCall call) {
    SqlOperator op = call.getOperator();
    SqlKind kind = op.getKind();
    RelDataType type = call.getType();
    if (kind == SqlKind.OR || kind == SqlKind.AND) {
        if (call.getOperands().size() > 2) {
            List<RexNode> children = new ArrayList<>(call.getOperands());
            RexNode left = children.remove(0).accept(this);
            RexNode right = builder.makeCall(type, op, children).accept(this);
            return builder.makeCall(type, op, ImmutableList.of(left, right));
        }
    }
    return builder.makeCall(type, op, visitChildren(call));
}

Example 49 with SqlOperator

use of org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator in project flink by apache.

the class HiveParserCalcitePlanner method genUDTFPlan.

private RelNode genUDTFPlan(SqlOperator sqlOperator, String genericUDTFName, String outputTableAlias, List<String> colAliases, HiveParserQB qb, List<RexNode> operands, List<ColumnInfo> opColInfos, RelNode input, boolean inSelect, boolean isOuter) throws SemanticException {
    Preconditions.checkState(!isOuter || !inSelect, "OUTER is not supported for SELECT UDTF");
    // No GROUP BY / DISTRIBUTE BY / SORT BY / CLUSTER BY
    HiveParserQBParseInfo qbp = qb.getParseInfo();
    if (inSelect && !qbp.getDestToGroupBy().isEmpty()) {
        throw new SemanticException(ErrorMsg.UDTF_NO_GROUP_BY.getMsg());
    }
    if (inSelect && !qbp.getDestToDistributeBy().isEmpty()) {
        throw new SemanticException(ErrorMsg.UDTF_NO_DISTRIBUTE_BY.getMsg());
    }
    if (inSelect && !qbp.getDestToSortBy().isEmpty()) {
        throw new SemanticException(ErrorMsg.UDTF_NO_SORT_BY.getMsg());
    }
    if (inSelect && !qbp.getDestToClusterBy().isEmpty()) {
        throw new SemanticException(ErrorMsg.UDTF_NO_CLUSTER_BY.getMsg());
    }
    if (inSelect && !qbp.getAliasToLateralViews().isEmpty()) {
        throw new SemanticException(ErrorMsg.UDTF_LATERAL_VIEW.getMsg());
    }
    LOG.debug("Table alias: " + outputTableAlias + " Col aliases: " + colAliases);
    // Create the object inspector for the input columns and initialize the UDTF
    RelDataType relDataType = HiveParserUtils.inferReturnTypeForOperands(sqlOperator, operands, cluster.getTypeFactory());
    DataType dataType = HiveParserUtils.toDataType(relDataType);
    StructObjectInspector outputOI = (StructObjectInspector) HiveInspectors.getObjectInspector(HiveTypeUtil.toHiveTypeInfo(dataType, false));
    // this should only happen for select udtf
    if (outputTableAlias == null) {
        Preconditions.checkState(inSelect, "Table alias not specified for lateral view");
        String prefix = "select_" + genericUDTFName + "_alias_";
        int i = 0;
        while (qb.getAliases().contains(prefix + i)) {
            i++;
        }
        outputTableAlias = prefix + i;
    }
    if (colAliases.isEmpty()) {
        // user did not specify alias names, infer names from outputOI
        for (StructField field : outputOI.getAllStructFieldRefs()) {
            colAliases.add(field.getFieldName());
        }
    }
    // Make sure that the number of column aliases in the AS clause matches the number of
    // columns output by the UDTF
    int numOutputCols = outputOI.getAllStructFieldRefs().size();
    int numSuppliedAliases = colAliases.size();
    if (numOutputCols != numSuppliedAliases) {
        throw new SemanticException(ErrorMsg.UDTF_ALIAS_MISMATCH.getMsg("expected " + numOutputCols + " aliases " + "but got " + numSuppliedAliases));
    }
    // Generate the output column info's / row resolver using internal names.
    ArrayList<ColumnInfo> udtfOutputCols = new ArrayList<>();
    Iterator<String> colAliasesIter = colAliases.iterator();
    for (StructField sf : outputOI.getAllStructFieldRefs()) {
        String colAlias = colAliasesIter.next();
        assert (colAlias != null);
        // Since the UDTF operator feeds into a LVJ operator that will rename all the internal
        // names,
        // we can just use field name from the UDTF's OI as the internal name
        ColumnInfo col = new ColumnInfo(sf.getFieldName(), TypeInfoUtils.getTypeInfoFromObjectInspector(sf.getFieldObjectInspector()), outputTableAlias, false);
        udtfOutputCols.add(col);
    }
    // Create the row resolver for the table function scan
    HiveParserRowResolver udtfOutRR = new HiveParserRowResolver();
    for (int i = 0; i < udtfOutputCols.size(); i++) {
        udtfOutRR.put(outputTableAlias, colAliases.get(i), udtfOutputCols.get(i));
    }
    // Build row type from field <type, name>
    RelDataType retType = HiveParserTypeConverter.getType(cluster, udtfOutRR, null);
    List<RelDataType> argTypes = new ArrayList<>();
    RelDataTypeFactory dtFactory = cluster.getRexBuilder().getTypeFactory();
    for (ColumnInfo ci : opColInfos) {
        argTypes.add(HiveParserUtils.toRelDataType(ci.getType(), dtFactory));
    }
    SqlOperator calciteOp = HiveParserSqlFunctionConverter.getCalciteFn(genericUDTFName, argTypes, retType, false);
    RexNode rexNode = cluster.getRexBuilder().makeCall(calciteOp, operands);
    // convert the rex call
    TableFunctionConverter udtfConverter = new TableFunctionConverter(cluster, input, frameworkConfig.getOperatorTable(), catalogReader.nameMatcher());
    RexCall convertedCall = (RexCall) rexNode.accept(udtfConverter);
    SqlOperator convertedOperator = convertedCall.getOperator();
    Preconditions.checkState(convertedOperator instanceof SqlUserDefinedTableFunction, "Expect operator to be " + SqlUserDefinedTableFunction.class.getSimpleName() + ", actually got " + convertedOperator.getClass().getSimpleName());
    // TODO: how to decide this?
    Type elementType = Object[].class;
    // create LogicalTableFunctionScan
    RelNode tableFunctionScan = LogicalTableFunctionScan.create(input.getCluster(), Collections.emptyList(), convertedCall, elementType, retType, null);
    // remember the table alias for the UDTF so that we can reference the cols later
    qb.addAlias(outputTableAlias);
    RelNode correlRel;
    RexBuilder rexBuilder = cluster.getRexBuilder();
    // find correlation in the converted call
    Pair<List<CorrelationId>, ImmutableBitSet> correlUse = getCorrelationUse(convertedCall);
    // create correlate node
    if (correlUse == null) {
        correlRel = plannerContext.createRelBuilder(catalogManager.getCurrentCatalog(), catalogManager.getCurrentDatabase()).push(input).push(tableFunctionScan).join(isOuter ? JoinRelType.LEFT : JoinRelType.INNER, rexBuilder.makeLiteral(true)).build();
    } else {
        if (correlUse.left.size() > 1) {
            tableFunctionScan = DeduplicateCorrelateVariables.go(rexBuilder, correlUse.left.get(0), Util.skip(correlUse.left), tableFunctionScan);
        }
        correlRel = LogicalCorrelate.create(input, tableFunctionScan, correlUse.left.get(0), correlUse.right, isOuter ? JoinRelType.LEFT : JoinRelType.INNER);
    }
    // Add new rel & its RR to the maps
    relToHiveColNameCalcitePosMap.put(tableFunctionScan, buildHiveToCalciteColumnMap(udtfOutRR));
    relToRowResolver.put(tableFunctionScan, udtfOutRR);
    HiveParserRowResolver correlRR = HiveParserRowResolver.getCombinedRR(relToRowResolver.get(input), relToRowResolver.get(tableFunctionScan));
    relToHiveColNameCalcitePosMap.put(correlRel, buildHiveToCalciteColumnMap(correlRR));
    relToRowResolver.put(correlRel, correlRR);
    if (!inSelect) {
        return correlRel;
    }
    // create project node
    List<RexNode> projects = new ArrayList<>();
    HiveParserRowResolver projectRR = new HiveParserRowResolver();
    int j = 0;
    for (int i = input.getRowType().getFieldCount(); i < correlRel.getRowType().getFieldCount(); i++) {
        projects.add(cluster.getRexBuilder().makeInputRef(correlRel, i));
        ColumnInfo inputColInfo = correlRR.getRowSchema().getSignature().get(i);
        String colAlias = inputColInfo.getAlias();
        ColumnInfo colInfo = new ColumnInfo(getColumnInternalName(j++), inputColInfo.getObjectInspector(), null, false);
        projectRR.put(null, colAlias, colInfo);
    }
    RelNode projectNode = LogicalProject.create(correlRel, Collections.emptyList(), projects, tableFunctionScan.getRowType());
    relToHiveColNameCalcitePosMap.put(projectNode, buildHiveToCalciteColumnMap(projectRR));
    relToRowResolver.put(projectNode, projectRR);
    return projectNode;
}

Example 50 with SqlOperator

use of org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator in project flink by apache.

the class HiveParserRexNodeConverter method convertGenericFunc.

private RexNode convertGenericFunc(ExprNodeGenericFuncDesc func) throws SemanticException {
    ExprNodeDesc tmpExprNode;
    RexNode tmpRN;
    List<RexNode> childRexNodeLst = new ArrayList<>();
    List<RelDataType> argTypes = new ArrayList<>();
    // TODO: 1) Expand to other functions as needed 2) What about types other than primitive.
    TypeInfo tgtDT = null;
    GenericUDF tgtUdf = func.getGenericUDF();
    if (tgtUdf instanceof GenericUDFIn) {
        return convertIN(func);
    }
    boolean isNumeric = isNumericBinary(func);
    boolean isCompare = !isNumeric && tgtUdf instanceof GenericUDFBaseCompare;
    boolean isWhenCase = tgtUdf instanceof GenericUDFWhen || tgtUdf instanceof GenericUDFCase;
    boolean isTransformableTimeStamp = func.getGenericUDF() instanceof GenericUDFUnixTimeStamp && func.getChildren().size() != 0;
    if (isNumeric) {
        tgtDT = func.getTypeInfo();
        assert func.getChildren().size() == 2;
    // TODO: checking 2 children is useless, compare already does that.
    } else if (isCompare && (func.getChildren().size() == 2)) {
        tgtDT = FunctionRegistry.getCommonClassForComparison(func.getChildren().get(0).getTypeInfo(), func.getChildren().get(1).getTypeInfo());
    } else if (isWhenCase) {
        // functions as they are not allowed
        if (checkForStatefulFunctions(func.getChildren())) {
            throw new SemanticException("Stateful expressions cannot be used inside of CASE");
        }
    } else if (isTransformableTimeStamp) {
        func = ExprNodeGenericFuncDesc.newInstance(new GenericUDFToUnixTimeStamp(), func.getChildren());
    }
    for (ExprNodeDesc childExpr : func.getChildren()) {
        tmpExprNode = childExpr;
        if (tgtDT != null && TypeInfoUtils.isConversionRequiredForComparison(tgtDT, childExpr.getTypeInfo())) {
            if (isCompare) {
                // For compare, we will convert requisite children
                tmpExprNode = HiveASTParseUtils.createConversionCast(childExpr, (PrimitiveTypeInfo) tgtDT);
            } else if (isNumeric) {
                // For numeric, we'll do minimum necessary cast - if we cast to the type
                // of expression, bad things will happen.
                PrimitiveTypeInfo minArgType = HiveParserExprNodeDescUtils.deriveMinArgumentCast(childExpr, tgtDT);
                tmpExprNode = HiveASTParseUtils.createConversionCast(childExpr, minArgType);
            } else {
                throw new AssertionError("Unexpected " + tgtDT + " - not a numeric op or compare");
            }
        }
        argTypes.add(HiveParserTypeConverter.convert(tmpExprNode.getTypeInfo(), cluster.getTypeFactory()));
        tmpRN = convert(tmpExprNode);
        childRexNodeLst.add(tmpRN);
    }
    // process the function
    RelDataType retType = HiveParserTypeConverter.convert(func.getTypeInfo(), cluster.getTypeFactory());
    SqlOperator calciteOp = HiveParserSqlFunctionConverter.getCalciteOperator(func.getFuncText(), func.getGenericUDF(), argTypes, retType);
    if (calciteOp.getKind() == SqlKind.CASE) {
        // If it is a case operator, we need to rewrite it
        childRexNodeLst = rewriteCaseChildren(func, childRexNodeLst);
    }
    RexNode expr = cluster.getRexBuilder().makeCall(calciteOp, childRexNodeLst);
    // check whether we need a calcite cast
    RexNode cast = handleExplicitCast(func, childRexNodeLst, ((RexCall) expr).getOperator());
    if (cast != null) {
        expr = cast;
        retType = cast.getType();
    }
    // an exception
    if (flattenExpr && expr instanceof RexCall && !(((RexCall) expr).getOperator() instanceof SqlCastFunction)) {
        RexCall call = (RexCall) expr;
        expr = cluster.getRexBuilder().makeCall(retType, call.getOperator(), RexUtil.flatten(call.getOperands(), call.getOperator()));
    }
    return expr;
}