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530 lines
25 KiB
C++
530 lines
25 KiB
C++
#pragma once
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#include <Core/ColumnNumbers.h>
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#include <Core/ColumnsWithTypeAndName.h>
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#include <Core/Names.h>
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#include <DataTypes/IDataType.h>
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#include "config_core.h"
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#include <memory>
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/// This file contains user interface for functions.
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namespace llvm
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{
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class LLVMContext;
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class Value;
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class IRBuilderBase;
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}
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namespace DB
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{
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namespace ErrorCodes
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{
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extern const int NOT_IMPLEMENTED;
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extern const int ILLEGAL_TYPE_OF_ARGUMENT;
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}
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class Field;
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/// The simplest executable object.
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/// Motivation:
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/// * Prepare something heavy once before main execution loop instead of doing it for each columns.
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/// * Provide const interface for IFunctionBase (later).
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/// * Create one executable function per thread to use caches without synchronization (later).
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class IExecutableFunction
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{
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public:
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virtual ~IExecutableFunction() = default;
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/// Get the main function name.
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virtual String getName() const = 0;
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ColumnPtr execute(const ColumnsWithTypeAndName & arguments, const DataTypePtr & result_type, size_t input_rows_count, bool dry_run) const;
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protected:
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virtual ColumnPtr executeImpl(const ColumnsWithTypeAndName & arguments, const DataTypePtr & result_type, size_t input_rows_count) const = 0;
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virtual ColumnPtr executeDryRunImpl(const ColumnsWithTypeAndName & arguments, const DataTypePtr & result_type, size_t input_rows_count) const
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{
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return executeImpl(arguments, result_type, input_rows_count);
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}
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/** Default implementation in presence of Nullable arguments or NULL constants as arguments is the following:
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* if some of arguments are NULL constants then return NULL constant,
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* if some of arguments are Nullable, then execute function as usual for columns,
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* where Nullable columns are substituted with nested columns (they have arbitrary values in rows corresponding to NULL value)
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* and wrap result in Nullable column where NULLs are in all rows where any of arguments are NULL.
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*/
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virtual bool useDefaultImplementationForNulls() const { return true; }
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/** Default implementation in presence of arguments with type Nothing is the following:
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* If some of arguments have type Nothing then default implementation is to return constant column with type Nothing
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*/
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virtual bool useDefaultImplementationForNothing() const { return true; }
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/** If the function have non-zero number of arguments,
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* and if all arguments are constant, that we could automatically provide default implementation:
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* arguments are converted to ordinary columns with single value, then function is executed as usual,
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* and then the result is converted to constant column.
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*/
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virtual bool useDefaultImplementationForConstants() const { return false; }
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/** If function arguments has single low cardinality column and all other arguments are constants, call function on nested column.
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* Otherwise, convert all low cardinality columns to ordinary columns.
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* Returns ColumnLowCardinality if at least one argument is ColumnLowCardinality.
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*/
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virtual bool useDefaultImplementationForLowCardinalityColumns() const { return true; }
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/** If function arguments has single sparse column and all other arguments are constants, call function on nested column.
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* Otherwise, convert all sparse columns to ordinary columns.
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* If default value doesn't change after function execution, returns sparse column as a result.
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* Otherwise, result column is converted to full.
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*/
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virtual bool useDefaultImplementationForSparseColumns() const { return true; }
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/** Some arguments could remain constant during this implementation.
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*/
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virtual ColumnNumbers getArgumentsThatAreAlwaysConstant() const { return {}; }
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/** True if function can be called on default arguments (include Nullable's) and won't throw.
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* Counterexample: modulo(0, 0)
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*/
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virtual bool canBeExecutedOnDefaultArguments() const { return true; }
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private:
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ColumnPtr defaultImplementationForConstantArguments(
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const ColumnsWithTypeAndName & args, const DataTypePtr & result_type, size_t input_rows_count, bool dry_run) const;
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ColumnPtr defaultImplementationForNulls(
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const ColumnsWithTypeAndName & args, const DataTypePtr & result_type, size_t input_rows_count, bool dry_run) const;
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ColumnPtr defaultImplementationForNothing(
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const ColumnsWithTypeAndName & args, const DataTypePtr & result_type, size_t input_rows_count) const;
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ColumnPtr executeWithoutLowCardinalityColumns(
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const ColumnsWithTypeAndName & args, const DataTypePtr & result_type, size_t input_rows_count, bool dry_run) const;
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ColumnPtr executeWithoutSparseColumns(
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const ColumnsWithTypeAndName & arguments, const DataTypePtr & result_type, size_t input_rows_count, bool dry_run) const;
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};
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using ExecutableFunctionPtr = std::shared_ptr<IExecutableFunction>;
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using Values = std::vector<llvm::Value *>;
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/** Function with known arguments and return type (when the specific overload was chosen).
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* It is also the point where all function-specific properties are known.
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*/
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class IFunctionBase
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{
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public:
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virtual ~IFunctionBase() = default;
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virtual ColumnPtr execute( /// NOLINT
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const ColumnsWithTypeAndName & arguments, const DataTypePtr & result_type, size_t input_rows_count, bool dry_run = false) const
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{
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return prepare(arguments)->execute(arguments, result_type, input_rows_count, dry_run);
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}
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/// Get the main function name.
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virtual String getName() const = 0;
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virtual const DataTypes & getArgumentTypes() const = 0;
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virtual const DataTypePtr & getResultType() const = 0;
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/// Do preparations and return executable.
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/// sample_columns should contain data types of arguments and values of constants, if relevant.
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virtual ExecutableFunctionPtr prepare(const ColumnsWithTypeAndName & arguments) const = 0;
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#if USE_EMBEDDED_COMPILER
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virtual bool isCompilable() const { return false; }
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/** Produce LLVM IR code that operates on scalar values. See `toNativeType` in DataTypes/Native.h
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* for supported value types and how they map to LLVM types.
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*
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* NOTE: the builder is actually guaranteed to be exactly `llvm::IRBuilder<>`, so you may safely
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* downcast it to that type. This method is specified with `IRBuilderBase` because forward-declaring
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* templates with default arguments is impossible and including LLVM in such a generic header
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* as this one is a major pain.
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*/
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virtual llvm::Value * compile(llvm::IRBuilderBase & /*builder*/, Values /*values*/) const
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{
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throw Exception(getName() + " is not JIT-compilable", ErrorCodes::NOT_IMPLEMENTED);
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}
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#endif
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virtual bool isStateful() const { return false; }
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/** Should we evaluate this function while constant folding, if arguments are constants?
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* Usually this is true. Notable counterexample is function 'sleep'.
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* If we will call it during query analysis, we will sleep extra amount of time.
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*/
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virtual bool isSuitableForConstantFolding() const { return true; }
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/** If function isSuitableForConstantFolding then, this method will be called during query analyzis
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* if some arguments are constants. For example logical functions (AndFunction, OrFunction) can
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* return they result based on some constant arguments.
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* Arguments are passed without modifications, useDefaultImplementationForNulls, useDefaultImplementationForNothing,
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* useDefaultImplementationForConstants, useDefaultImplementationForLowCardinality are not applied.
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*/
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virtual ColumnPtr getConstantResultForNonConstArguments(
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const ColumnsWithTypeAndName & /* arguments */, const DataTypePtr & /* result_type */) const { return nullptr; }
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/** Function is called "injective" if it returns different result for different values of arguments.
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* Example: hex, negate, tuple...
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*
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* Function could be injective with some arguments fixed to some constant values.
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* Examples:
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* plus(const, x);
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* multiply(const, x) where x is an integer and constant is not divisible by two;
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* concat(x, 'const');
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* concat(x, 'const', y) where const contain at least one non-numeric character;
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* concat with FixedString
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* dictGet... functions takes name of dictionary as its argument,
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* and some dictionaries could be explicitly defined as injective.
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*
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* It could be used, for example, to remove useless function applications from GROUP BY.
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*
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* Sometimes, function is not really injective, but considered as injective, for purpose of query optimization.
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* For example, toString function is not injective for Float64 data type,
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* as it returns 'nan' for many different representation of NaNs.
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* But we assume, that it is injective. This could be documented as implementation-specific behaviour.
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*
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* sample_columns should contain data types of arguments and values of constants, if relevant.
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* NOTE: to check is function injective with any arguments, you can pass
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* empty columns as sample_columns (since most of the time function will
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* ignore it anyway, and creating arguments just for checking is
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* function injective or not is overkill).
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*/
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virtual bool isInjective(const ColumnsWithTypeAndName & /*sample_columns*/) const { return false; }
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/** Function is called "deterministic", if it returns same result for same values of arguments.
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* Most of functions are deterministic. Notable counterexample is rand().
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* Sometimes, functions are "deterministic" in scope of single query
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* (even for distributed query), but not deterministic it general.
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* Example: now(). Another example: functions that work with periodically updated dictionaries.
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*/
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virtual bool isDeterministic() const { return true; }
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virtual bool isDeterministicInScopeOfQuery() const { return true; }
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/** Lets you know if the function is monotonic in a range of values.
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* This is used to work with the index in a sorted chunk of data.
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* And allows to use the index not only when it is written, for example `date >= const`, but also, for example, `toMonth(date) >= 11`.
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* All this is considered only for functions of one argument.
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*/
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virtual bool hasInformationAboutMonotonicity() const { return false; }
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struct ShortCircuitSettings
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{
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/// Should we enable lazy execution for the first argument of short-circuit function?
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/// Example: if(cond, then, else), we don't need to execute cond lazily.
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bool enable_lazy_execution_for_first_argument;
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/// Should we enable lazy execution for functions, that are common descendants of
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/// different short-circuit function arguments?
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/// Example 1: if (cond, expr1(..., expr, ...), expr2(..., expr, ...)), we don't need
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/// to execute expr lazily, because it's used in both branches.
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/// Example 2: and(expr1, expr2(..., expr, ...), expr3(..., expr, ...)), here we
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/// should enable lazy execution for expr, because it must be filtered by expr1.
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bool enable_lazy_execution_for_common_descendants_of_arguments;
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/// Should we enable lazy execution without checking isSuitableForShortCircuitArgumentsExecution?
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/// Example: toTypeName(expr), even if expr contains functions that are not suitable for
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/// lazy execution (because of their simplicity), we shouldn't execute them at all.
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bool force_enable_lazy_execution;
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};
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/** Function is called "short-circuit" if it's arguments can be evaluated lazily
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* (examples: and, or, if, multiIf). If function is short circuit, it should be
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* able to work with lazy executed arguments,
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* this method will be called before function execution.
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* If function is short circuit, it must define all fields in settings for
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* appropriate preparations. Number of arguments is provided because some settings might depend on it.
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* Example: multiIf(cond, else, then) and multiIf(cond1, else1, cond2, else2, ...), the first
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* version can enable enable_lazy_execution_for_common_descendants_of_arguments setting, the second - not.
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*/
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virtual bool isShortCircuit(ShortCircuitSettings & /*settings*/, size_t /*number_of_arguments*/) const { return false; }
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/** Should we evaluate this function lazily in short-circuit function arguments?
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* If function can throw an exception or it's computationally heavy, then
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* it's suitable, otherwise it's not (due to the overhead of lazy execution).
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* Suitability may depend on function arguments.
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*/
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virtual bool isSuitableForShortCircuitArgumentsExecution(const DataTypesWithConstInfo & /*arguments*/) const = 0;
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/// The property of monotonicity for a certain range.
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struct Monotonicity
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{
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bool is_monotonic = false; /// Is the function monotonous (non-decreasing or non-increasing).
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bool is_positive = true; /// true if the function is non-decreasing, false if non-increasing. If is_monotonic = false, then it does not matter.
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bool is_always_monotonic = false; /// Is true if function is monotonic on the whole input range I
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};
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/** Get information about monotonicity on a range of values. Call only if hasInformationAboutMonotonicity.
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* NULL can be passed as one of the arguments. This means that the corresponding range is unlimited on the left or on the right.
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*/
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virtual Monotonicity getMonotonicityForRange(const IDataType & /*type*/, const Field & /*left*/, const Field & /*right*/) const
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{
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throw Exception("Function " + getName() + " has no information about its monotonicity", ErrorCodes::NOT_IMPLEMENTED);
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}
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};
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using FunctionBasePtr = std::shared_ptr<IFunctionBase>;
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/** Creates IFunctionBase from argument types list (chooses one function overload).
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*/
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class IFunctionOverloadResolver
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{
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public:
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virtual ~IFunctionOverloadResolver() = default;
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virtual FunctionBasePtr build(const ColumnsWithTypeAndName & arguments) const;
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void getLambdaArgumentTypes(DataTypes & arguments) const;
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void checkNumberOfArguments(size_t number_of_arguments) const;
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/// Get the main function name.
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virtual String getName() const = 0;
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/// For non-variadic functions, return number of arguments; otherwise return zero (that should be ignored).
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virtual size_t getNumberOfArguments() const = 0;
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/// TODO: This method should not be duplicated here and in IFunctionBase
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/// See the comment for the same method in IFunctionBase
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virtual bool isDeterministic() const { return true; }
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virtual bool isDeterministicInScopeOfQuery() const { return true; }
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virtual bool isInjective(const ColumnsWithTypeAndName &) const { return false; }
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/// Override and return true if function needs to depend on the state of the data.
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virtual bool isStateful() const { return false; }
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/// Override and return true if function could take different number of arguments.
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virtual bool isVariadic() const { return false; }
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/// For non-variadic functions, return number of arguments; otherwise return zero (that should be ignored).
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/// For higher-order functions (functions, that have lambda expression as at least one argument).
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/// You pass data types with empty DataTypeFunction for lambda arguments.
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/// This function will replace it with DataTypeFunction containing actual types.
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virtual void getLambdaArgumentTypesImpl(DataTypes & arguments [[maybe_unused]]) const
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{
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throw Exception("Function " + getName() + " can't have lambda-expressions as arguments", ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT);
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}
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/// Returns indexes of arguments, that must be ColumnConst
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virtual ColumnNumbers getArgumentsThatAreAlwaysConstant() const { return {}; }
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/// Returns indexes if arguments, that can be Nullable without making result of function Nullable
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/// (for functions like isNull(x))
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virtual ColumnNumbers getArgumentsThatDontImplyNullableReturnType(size_t number_of_arguments [[maybe_unused]]) const { return {}; }
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protected:
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virtual FunctionBasePtr buildImpl(const ColumnsWithTypeAndName & /* arguments */, const DataTypePtr & /* result_type */) const
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{
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throw Exception("buildImpl is not implemented for " + getName(), ErrorCodes::NOT_IMPLEMENTED);
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}
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virtual DataTypePtr getReturnTypeImpl(const DataTypes & /*arguments*/) const
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{
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throw Exception("getReturnType is not implemented for " + getName(), ErrorCodes::NOT_IMPLEMENTED);
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}
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/// This function will be called in default implementation. You can overload it or the previous one.
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virtual DataTypePtr getReturnTypeImpl(const ColumnsWithTypeAndName & arguments) const
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{
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DataTypes data_types(arguments.size());
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for (size_t i = 0; i < arguments.size(); ++i)
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data_types[i] = arguments[i].type;
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return getReturnTypeImpl(data_types);
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}
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/** If useDefaultImplementationForNulls() is true, then change arguments for getReturnType() and build():
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* if some of arguments are Nullable(Nothing) then don't call getReturnType(), call build() with return_type = Nullable(Nothing),
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* if some of arguments are Nullable, then:
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* - Nullable types are substituted with nested types for getReturnType() function
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* - wrap getReturnType() result in Nullable type and pass to build
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*
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* Otherwise build returns build(arguments, getReturnType(arguments));
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*/
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virtual bool useDefaultImplementationForNulls() const { return true; }
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/** If useDefaultImplementationForNothing() is true, then change arguments for getReturnType() and build():
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* if some of arguments are Nothing then don't call getReturnType(), call build() with return_type = Nothing,
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* Otherwise build returns build(arguments, getReturnType(arguments));
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*/
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virtual bool useDefaultImplementationForNothing() const { return true; }
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/** If useDefaultImplementationForLowCardinalityColumns() is true, then change arguments for getReturnType() and build().
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* If function arguments has low cardinality types, convert them to ordinary types.
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* getReturnType returns ColumnLowCardinality if at least one argument type is ColumnLowCardinality.
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*/
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virtual bool useDefaultImplementationForLowCardinalityColumns() const { return true; }
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/** If function arguments has single sparse column and all other arguments are constants, call function on nested column.
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* Otherwise, convert all sparse columns to ordinary columns.
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* If default value doesn't change after function execution, returns sparse column as a result.
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* Otherwise, result column is converted to full.
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*/
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virtual bool useDefaultImplementationForSparseColumns() const { return true; }
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// /// If it isn't, will convert all ColumnLowCardinality arguments to full columns.
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virtual bool canBeExecutedOnLowCardinalityDictionary() const { return true; }
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private:
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DataTypePtr getReturnType(const ColumnsWithTypeAndName & arguments) const;
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DataTypePtr getReturnTypeWithoutLowCardinality(const ColumnsWithTypeAndName & arguments) const;
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};
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using FunctionOverloadResolverPtr = std::shared_ptr<IFunctionOverloadResolver>;
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/// Old function interface. Check documentation in IFunction.h.
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/// If client do not need statefull properties it can implement this interface.
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class IFunction
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{
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public:
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virtual ~IFunction() = default;
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virtual String getName() const = 0;
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virtual ColumnPtr executeImpl(const ColumnsWithTypeAndName & arguments, const DataTypePtr & result_type, size_t input_rows_count) const = 0;
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virtual ColumnPtr executeImplDryRun(const ColumnsWithTypeAndName & arguments, const DataTypePtr & result_type, size_t input_rows_count) const
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{
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return executeImpl(arguments, result_type, input_rows_count);
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}
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/** Default implementation in presence of Nullable arguments or NULL constants as arguments is the following:
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* if some of arguments are NULL constants then return NULL constant,
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* if some of arguments are Nullable, then execute function as usual for columns,
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* where Nullable columns are substituted with nested columns (they have arbitrary values in rows corresponding to NULL value)
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* and wrap result in Nullable column where NULLs are in all rows where any of arguments are NULL.
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*/
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virtual bool useDefaultImplementationForNulls() const { return true; }
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/** Default implementation in presence of arguments with type Nothing is the following:
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* If some of arguments have type Nothing then default implementation is to return constant column with type Nothing
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*/
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virtual bool useDefaultImplementationForNothing() const { return true; }
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/** If the function have non-zero number of arguments,
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* and if all arguments are constant, that we could automatically provide default implementation:
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* arguments are converted to ordinary columns with single value, then function is executed as usual,
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* and then the result is converted to constant column.
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*/
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virtual bool useDefaultImplementationForConstants() const { return false; }
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/** Some arguments could remain constant during this implementation.
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*/
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virtual ColumnNumbers getArgumentsThatAreAlwaysConstant() const { return {}; }
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/** If function arguments has single low cardinality column and all other arguments are constants, call function on nested column.
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* Otherwise, convert all low cardinality columns to ordinary columns.
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* Returns ColumnLowCardinality if at least one argument is ColumnLowCardinality.
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*/
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virtual bool useDefaultImplementationForLowCardinalityColumns() const { return true; }
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/** If function arguments has single sparse column and all other arguments are constants, call function on nested column.
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* Otherwise, convert all sparse columns to ordinary columns.
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* If default value doesn't change after function execution, returns sparse column as a result.
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* Otherwise, result column is converted to full.
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*/
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virtual bool useDefaultImplementationForSparseColumns() const { return true; }
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/// If it isn't, will convert all ColumnLowCardinality arguments to full columns.
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virtual bool canBeExecutedOnLowCardinalityDictionary() const { return true; }
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/** True if function can be called on default arguments (include Nullable's) and won't throw.
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* Counterexample: modulo(0, 0)
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*/
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virtual bool canBeExecutedOnDefaultArguments() const { return true; }
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/// Properties from IFunctionBase (see IFunction.h)
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virtual bool isSuitableForConstantFolding() const { return true; }
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virtual ColumnPtr getConstantResultForNonConstArguments(const ColumnsWithTypeAndName & /*arguments*/, const DataTypePtr & /*result_type*/) const { return nullptr; }
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virtual bool isInjective(const ColumnsWithTypeAndName & /*sample_columns*/) const { return false; }
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virtual bool isDeterministic() const { return true; }
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virtual bool isDeterministicInScopeOfQuery() const { return true; }
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virtual bool isStateful() const { return false; }
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using ShortCircuitSettings = IFunctionBase::ShortCircuitSettings;
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virtual bool isShortCircuit(ShortCircuitSettings & /*settings*/, size_t /*number_of_arguments*/) const { return false; }
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virtual bool isSuitableForShortCircuitArgumentsExecution(const DataTypesWithConstInfo & /*arguments*/) const = 0;
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virtual bool hasInformationAboutMonotonicity() const { return false; }
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using Monotonicity = IFunctionBase::Monotonicity;
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virtual Monotonicity getMonotonicityForRange(const IDataType & /*type*/, const Field & /*left*/, const Field & /*right*/) const
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{
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throw Exception("Function " + getName() + " has no information about its monotonicity", ErrorCodes::NOT_IMPLEMENTED);
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}
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/// For non-variadic functions, return number of arguments; otherwise return zero (that should be ignored).
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virtual size_t getNumberOfArguments() const = 0;
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virtual DataTypePtr getReturnTypeImpl(const DataTypes & /*arguments*/) const
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{
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throw Exception("getReturnType is not implemented for " + getName(), ErrorCodes::NOT_IMPLEMENTED);
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}
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/// Get the result type by argument type. If the function does not apply to these arguments, throw an exception.
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virtual DataTypePtr getReturnTypeImpl(const ColumnsWithTypeAndName & arguments) const
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{
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DataTypes data_types(arguments.size());
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for (size_t i = 0; i < arguments.size(); ++i)
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data_types[i] = arguments[i].type;
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return getReturnTypeImpl(data_types);
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}
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virtual bool isVariadic() const { return false; }
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virtual void getLambdaArgumentTypes(DataTypes & /*arguments*/) const
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{
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throw Exception("Function " + getName() + " can't have lambda-expressions as arguments", ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT);
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}
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virtual ColumnNumbers getArgumentsThatDontImplyNullableReturnType(size_t /*number_of_arguments*/) const { return {}; }
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#if USE_EMBEDDED_COMPILER
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bool isCompilable(const DataTypes & arguments) const;
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llvm::Value * compile(llvm::IRBuilderBase &, const DataTypes & arguments, Values values) const;
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#endif
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protected:
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#if USE_EMBEDDED_COMPILER
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virtual bool isCompilableImpl(const DataTypes &) const { return false; }
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virtual llvm::Value * compileImpl(llvm::IRBuilderBase &, const DataTypes &, Values) const
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{
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throw Exception(getName() + " is not JIT-compilable", ErrorCodes::NOT_IMPLEMENTED);
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}
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#endif
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};
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using FunctionPtr = std::shared_ptr<IFunction>;
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}
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