Examples with ExpressionType org.apache.druid.math.expr.ExpressionType used on opensource projects

Example 26 with ExpressionType

use of org.apache.druid.math.expr.ExpressionType in project druid by druid-io.

the class VectorMathProcessors method doublePower.

public static <T> ExprVectorProcessor<T> doublePower(Expr.VectorInputBindingInspector inspector, Expr left, Expr right) {
    final ExpressionType leftType = left.getOutputType(inspector);
    final ExpressionType rightType = right.getOutputType(inspector);
    BivariateFunctionVectorValueProcessor<?, ?, ?> processor = null;
    if ((Types.is(leftType, ExprType.LONG) && Types.isNullOr(rightType, ExprType.LONG)) || (leftType == null && Types.is(rightType, ExprType.LONG))) {
        processor = new DoubleOutLongsInFunctionVectorValueProcessor(left.buildVectorized(inspector), right.buildVectorized(inspector), inspector.getMaxVectorSize()) {

            @Override
            public double apply(long left, long right) {
                return Math.pow(left, right);
            }
        };
    }
    if (processor != null) {
        return (ExprVectorProcessor<T>) processor;
    }
    return power(inspector, left, right);
}

Example 27 with ExpressionType

use of org.apache.druid.math.expr.ExpressionType in project druid by druid-io.

the class VectorMathProcessors method bitwiseConvertLongBitsToDouble.

public static <T> ExprVectorProcessor<T> bitwiseConvertLongBitsToDouble(Expr.VectorInputBindingInspector inspector, Expr arg) {
    final ExpressionType inputType = arg.getOutputType(inspector);
    ExprVectorProcessor<?> processor = null;
    if (Types.is(inputType, ExprType.LONG)) {
        processor = new DoubleOutLongInFunctionVectorValueProcessor(arg.buildVectorized(inspector), inspector.getMaxVectorSize()) {

            @Override
            public double apply(long input) {
                return Double.longBitsToDouble(input);
            }
        };
    } else if (Types.is(inputType, ExprType.DOUBLE)) {
        processor = new DoubleOutDoubleInFunctionVectorValueProcessor(arg.buildVectorized(inspector), inspector.getMaxVectorSize()) {

            @Override
            public double apply(double input) {
                return Double.longBitsToDouble((long) input);
            }
        };
    }
    if (processor == null) {
        throw Exprs.cannotVectorize();
    }
    return (ExprVectorProcessor<T>) processor;
}

Example 28 with ExpressionType

use of org.apache.druid.math.expr.ExpressionType in project druid by druid-io.

the class VectorMathProcessors method makeDoubleMathProcessor.

/**
 * Make a 2 argument, math processor with the following type rules
 *    long, long      -> double
 *    long, double    -> double
 *    double, long    -> double
 *    double, double  -> double
 */
public static <T> ExprVectorProcessor<T> makeDoubleMathProcessor(Expr.VectorInputBindingInspector inspector, Expr left, Expr right, Supplier<DoubleOutLongsInFunctionVectorValueProcessor> doubleOutLongsInProcessor, Supplier<DoubleOutLongDoubleInFunctionVectorValueProcessor> doubleOutLongDoubleInProcessor, Supplier<DoubleOutDoubleLongInFunctionVectorValueProcessor> doubleOutDoubleLongInProcessor, Supplier<DoubleOutDoublesInFunctionVectorValueProcessor> doubleOutDoublesInProcessor) {
    final ExpressionType leftType = left.getOutputType(inspector);
    final ExpressionType rightType = right.getOutputType(inspector);
    ExprVectorProcessor<?> processor = null;
    if (Types.is(leftType, ExprType.LONG)) {
        if (Types.is(rightType, ExprType.LONG)) {
            processor = doubleOutLongsInProcessor.get();
        } else if (Types.isNullOr(rightType, ExprType.DOUBLE)) {
            processor = doubleOutLongDoubleInProcessor.get();
        }
    } else if (Types.is(leftType, ExprType.DOUBLE)) {
        if (Types.is(rightType, ExprType.LONG)) {
            processor = doubleOutDoubleLongInProcessor.get();
        } else if (Types.isNullOr(rightType, ExprType.DOUBLE)) {
            processor = doubleOutDoublesInProcessor.get();
        }
    } else if (leftType == null) {
        if (Types.is(rightType, ExprType.LONG)) {
            processor = doubleOutDoubleLongInProcessor.get();
        } else if (Types.is(rightType, ExprType.DOUBLE)) {
            processor = doubleOutDoublesInProcessor.get();
        }
    }
    if (processor == null) {
        throw Exprs.cannotVectorize();
    }
    return (ExprVectorProcessor<T>) processor;
}

Example 29 with ExpressionType

use of org.apache.druid.math.expr.ExpressionType in project druid by druid-io.

the class ExpressionSelectors method createBindings.

/**
 * Create {@link Expr.ObjectBinding} given a {@link ColumnSelectorFactory} and {@link ExpressionPlan} which
 * provides the set of identifiers which need a binding (list of required columns), and context of whether or not they
 * are used as array or scalar inputs
 */
public static Expr.ObjectBinding createBindings(ColumnSelectorFactory columnSelectorFactory, ExpressionPlan plan) {
    final List<String> columns = plan.getAnalysis().getRequiredBindingsList();
    final Map<String, Pair<ExpressionType, Supplier<Object>>> suppliers = new HashMap<>();
    for (String columnName : columns) {
        final ColumnCapabilities capabilities = columnSelectorFactory.getColumnCapabilities(columnName);
        final boolean multiVal = capabilities != null && capabilities.hasMultipleValues().isTrue();
        final Supplier<Object> supplier;
        final ExpressionType expressionType = ExpressionType.fromColumnType(capabilities);
        final boolean useObjectSupplierForMultiValueStringArray = capabilities != null && // multi-value rows, we can just use the dimension selector, which has the homogenization behavior built-in
        ((!capabilities.is(ValueType.STRING)) || (capabilities.is(ValueType.STRING) && !ExpressionProcessing.isHomogenizeNullMultiValueStringArrays() && !plan.is(ExpressionPlan.Trait.NEEDS_APPLIED))) && // expression has array output
        plan.is(ExpressionPlan.Trait.NON_SCALAR_OUTPUT);
        final boolean homogenizeNullMultiValueStringArrays = plan.is(ExpressionPlan.Trait.NEEDS_APPLIED) || ExpressionProcessing.isHomogenizeNullMultiValueStringArrays();
        if (capabilities == null || capabilities.isArray() || useObjectSupplierForMultiValueStringArray) {
            // Unknown type, array type, or output array uses an Object selector and see if that gives anything useful
            supplier = supplierFromObjectSelector(columnSelectorFactory.makeColumnValueSelector(columnName), homogenizeNullMultiValueStringArrays);
        } else if (capabilities.is(ValueType.FLOAT)) {
            ColumnValueSelector<?> selector = columnSelectorFactory.makeColumnValueSelector(columnName);
            supplier = makeNullableNumericSupplier(selector, selector::getFloat);
        } else if (capabilities.is(ValueType.LONG)) {
            ColumnValueSelector<?> selector = columnSelectorFactory.makeColumnValueSelector(columnName);
            supplier = makeNullableNumericSupplier(selector, selector::getLong);
        } else if (capabilities.is(ValueType.DOUBLE)) {
            ColumnValueSelector<?> selector = columnSelectorFactory.makeColumnValueSelector(columnName);
            supplier = makeNullableNumericSupplier(selector, selector::getDouble);
        } else if (capabilities.is(ValueType.STRING)) {
            supplier = supplierFromDimensionSelector(columnSelectorFactory.makeDimensionSelector(new DefaultDimensionSpec(columnName, columnName)), multiVal, homogenizeNullMultiValueStringArrays);
        } else {
            // complex type just pass straight through
            ColumnValueSelector<?> selector = columnSelectorFactory.makeColumnValueSelector(columnName);
            if (!(selector instanceof NilColumnValueSelector)) {
                supplier = selector::getObject;
            } else {
                supplier = null;
            }
        }
        if (supplier != null) {
            suppliers.put(columnName, new Pair<>(expressionType, supplier));
        }
    }
    if (suppliers.isEmpty()) {
        return InputBindings.nilBindings();
    } else if (suppliers.size() == 1 && columns.size() == 1) {
        // If there's only one column (and it has a supplier), we can skip the Map and just use that supplier when
        // asked for something.
        final String column = Iterables.getOnlyElement(suppliers.keySet());
        final Pair<ExpressionType, Supplier<Object>> supplier = Iterables.getOnlyElement(suppliers.values());
        return new Expr.ObjectBinding() {

            @Nullable
            @Override
            public Object get(String name) {
                // There's only one binding, and it must be the single column, so it can safely be ignored in production.
                assert column.equals(name);
                return supplier.rhs.get();
            }

            @Nullable
            @Override
            public ExpressionType getType(String name) {
                return supplier.lhs;
            }
        };
    } else {
        return InputBindings.withTypedSuppliers(suppliers);
    }
}

Example 30 with ExpressionType

use of org.apache.druid.math.expr.ExpressionType in project druid by druid-io.

the class ExpressionPlanner method plan.

/**
 * Druid tries to be chill to expressions to make up for not having a well defined table schema across segments. This
 * method performs some analysis to determine what sort of selectors can be constructed on top of an expression,
 * whether or not the expression will need implicitly mapped across multi-valued inputs, if the expression produces
 * multi-valued outputs, is vectorizable, and everything else interesting when making a selector.
 *
 * Results are stored in a {@link ExpressionPlan}, which can be examined to do whatever is necessary to make things
 * function properly.
 */
public static ExpressionPlan plan(ColumnInspector inspector, Expr expression) {
    final Expr.BindingAnalysis analysis = expression.analyzeInputs();
    Parser.validateExpr(expression, analysis);
    EnumSet<ExpressionPlan.Trait> traits = EnumSet.noneOf(ExpressionPlan.Trait.class);
    Set<String> noCapabilities = new HashSet<>();
    Set<String> maybeMultiValued = new HashSet<>();
    List<String> needsApplied = ImmutableList.of();
    ColumnType singleInputType = null;
    ExpressionType outputType = null;
    final Set<String> columns = analysis.getRequiredBindings();
    // check and set traits which allow optimized selectors to be created
    if (columns.isEmpty()) {
        traits.add(ExpressionPlan.Trait.CONSTANT);
    } else if (expression.isIdentifier()) {
        traits.add(ExpressionPlan.Trait.IDENTIFIER);
    } else if (columns.size() == 1) {
        final String column = Iterables.getOnlyElement(columns);
        final ColumnCapabilities capabilities = inspector.getColumnCapabilities(column);
        // (i.e. the expression is not treating its input as an array and not wanting to output an array)
        if (capabilities != null && !analysis.hasInputArrays() && !analysis.isOutputArray()) {
            boolean isSingleInputMappable = false;
            boolean isSingleInputScalar = capabilities.hasMultipleValues().isFalse();
            if (capabilities.is(ValueType.STRING)) {
                isSingleInputScalar &= capabilities.isDictionaryEncoded().isTrue();
                isSingleInputMappable = capabilities.isDictionaryEncoded().isTrue() && !capabilities.hasMultipleValues().isUnknown();
            }
            // if satisfied, set single input output type and flags
            if (isSingleInputScalar || isSingleInputMappable) {
                singleInputType = capabilities.toColumnType();
                if (isSingleInputScalar) {
                    traits.add(ExpressionPlan.Trait.SINGLE_INPUT_SCALAR);
                }
                if (isSingleInputMappable) {
                    traits.add(ExpressionPlan.Trait.SINGLE_INPUT_MAPPABLE);
                }
            }
        }
    }
    // automatic transformation to map across multi-valued inputs (or row by row detection in the worst case)
    if (ExpressionPlan.none(traits, ExpressionPlan.Trait.SINGLE_INPUT_SCALAR, ExpressionPlan.Trait.CONSTANT, ExpressionPlan.Trait.IDENTIFIER)) {
        final Set<String> definitelyMultiValued = new HashSet<>();
        final Set<String> definitelyArray = new HashSet<>();
        for (String column : analysis.getRequiredBindings()) {
            final ColumnCapabilities capabilities = inspector.getColumnCapabilities(column);
            if (capabilities != null) {
                if (capabilities.isArray()) {
                    definitelyArray.add(column);
                } else if (capabilities.is(ValueType.STRING) && capabilities.hasMultipleValues().isTrue()) {
                    definitelyMultiValued.add(column);
                } else if (capabilities.is(ValueType.STRING) && capabilities.hasMultipleValues().isMaybeTrue() && !analysis.getArrayBindings().contains(column)) {
                    maybeMultiValued.add(column);
                }
            } else {
                noCapabilities.add(column);
            }
        }
        // find any inputs which will need implicitly mapped across multi-valued rows
        needsApplied = columns.stream().filter(c -> !definitelyArray.contains(c) && definitelyMultiValued.contains(c) && !analysis.getArrayBindings().contains(c)).collect(Collectors.toList());
        // if any multi-value inputs, set flag for non-scalar inputs
        if (analysis.hasInputArrays()) {
            traits.add(ExpressionPlan.Trait.NON_SCALAR_INPUTS);
        }
        if (!noCapabilities.isEmpty()) {
            traits.add(ExpressionPlan.Trait.UNKNOWN_INPUTS);
        }
        if (!maybeMultiValued.isEmpty()) {
            traits.add(ExpressionPlan.Trait.INCOMPLETE_INPUTS);
        }
        // if expression needs transformed, lets do it
        if (!needsApplied.isEmpty()) {
            traits.add(ExpressionPlan.Trait.NEEDS_APPLIED);
        }
    }
    // only set output type if we are pretty confident about input types
    final boolean shouldComputeOutput = ExpressionPlan.none(traits, ExpressionPlan.Trait.UNKNOWN_INPUTS, ExpressionPlan.Trait.INCOMPLETE_INPUTS);
    if (shouldComputeOutput) {
        outputType = expression.getOutputType(inspector);
    }
    // if analysis predicts output, or inferred output type, is array, output will be arrays
    if (analysis.isOutputArray() || (outputType != null && outputType.isArray())) {
        traits.add(ExpressionPlan.Trait.NON_SCALAR_OUTPUT);
        // single input mappable may not produce array output explicitly, only through implicit mapping
        traits.remove(ExpressionPlan.Trait.SINGLE_INPUT_SCALAR);
        traits.remove(ExpressionPlan.Trait.SINGLE_INPUT_MAPPABLE);
    }
    // vectorized expressions do not support incomplete, multi-valued inputs or outputs, or implicit mapping
    // they also do not support unknown inputs, but they also do not currently have to deal with them, as missing
    // capabilites is indicative of a non-existent column instead of an unknown schema. If this ever changes,
    // this check should also change
    boolean supportsVector = ExpressionPlan.none(traits, ExpressionPlan.Trait.INCOMPLETE_INPUTS, ExpressionPlan.Trait.NEEDS_APPLIED, ExpressionPlan.Trait.NON_SCALAR_INPUTS, ExpressionPlan.Trait.NON_SCALAR_OUTPUT);
    if (supportsVector && expression.canVectorize(inspector)) {
        // make sure to compute the output type for a vector expression though, because we might have skipped it earlier
        // due to unknown inputs, but that's ok here since it just means it doesnt exist
        outputType = expression.getOutputType(inspector);
        traits.add(ExpressionPlan.Trait.VECTORIZABLE);
    }
    return new ExpressionPlan(inspector, expression, analysis, traits, outputType, singleInputType, Sets.union(noCapabilities, maybeMultiValued), needsApplied);
}