Relational Data - Functions
There are at least* two types of functions - regular functions (they are just called “functions”) and aggregate functions. These are completely different concepts. Regular functions work as if they are applied to each row separately (for each row, the result of the function does not depend on the other rows). Aggregate functions accumulate a set of values from various rows (i.e. they depend on the entire set of rows).
In this section we discuss regular functions. For aggregate functions, see the section “Aggregate functions”.
There is a third type of function that the ‘arrayJoin’ function belongs to. And table functions can also be mentioned separately.
Strong Typing
In contrast to standard SQL, ClickHouse has strong typing. In other words, it does not make implicit conversions between types. Each function works for a specific set of types. This means that sometimes you need to use type conversion functions.
Common Subexpression Elimination
All expressions in a query that have the same AST (the same record or same result of syntactic parsing) are considered to have identical values. Such expressions are concatenated and executed once. Identical subqueries are also eliminated this way.
Types of Results
All functions return a single return as the result (not several values, and not zero values). The type of result is usually defined only by the types of arguments, not by the values. Exceptions are the tupleElement function (the a.N operator), and the toFixedString function.
Constants
For simplicity, certain functions can only work with constants for some arguments. For example, the right argument of the LIKE operator must be a constant. Almost all functions return a constant for constant arguments. The exception is functions that generate random numbers. The ‘now’ function returns different values for queries that were run at different times, but the result is considered a constant, since constancy is only important within a single query. A constant expression is also considered a constant (for example, the right half of the LIKE operator can be constructed from multiple constants).
Functions can be implemented in different ways for constant and non-constant arguments (different code is executed). But the results for a constant and for a true column containing only the same value should match each other.
NULL Processing
Functions have the following behaviors:
- If at least one of the arguments of the function is
NULL, the function result is alsoNULL. - Special behavior that is specified individually in the description of each function. In the ClickHouse source code, these functions have
UseDefaultImplementationForNulls=false.
Constancy
Functions can’t change the values of their arguments – any changes are returned as the result. Thus, the result of calculating separate functions does not depend on the order in which the functions are written in the query.
Higher-order functions, -> operator and lambda(params, expr) function
Higher-order functions can only accept lambda functions as their functional argument. To pass a lambda function to a higher-order function use -> operator. The left side of the arrow has a formal parameter, which is any ID, or multiple formal parameters – any IDs in a tuple. The right side of the arrow has an expression that can use these formal parameters, as well as any table columns.
Examples:
x -> 2 * x str -> str != Referer
A lambda function that accepts multiple arguments can also be passed to a higher-order function. In this case, the higher-order function is passed several arrays of identical length that these arguments will correspond to.
For some functions the first argument (the lambda function) can be omitted. In this case, identical mapping is assumed.
User Defined Functions (UDFs)
ClickHouse supports user-defined functions. See UDFs.