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How to Write an Aggregate Function

An aggregate function, or aggregation function, is a function that combines values from multiple rows to form a single summary value. Common aggregate functions include sum, count, avg, and others.

Databend allows you to customize your own aggregate functions with Rust. It's not an easy way because you need to be a rustacean first.

Databend has a plan to support writing UDAFs in other languages(like js, web assembly) in the future.

This section shows you how to write an aggregate function in Databend.

Function Registration

The register function for aggregate functions takes a lowercase function name and an AggregateFunctionDescription object as parameters. Each registered function is stored in case_insensitive_desc (HashMap data structure).

The case_insensitive_combinator_desc is used to store combined functions, such as count_if and sum_if that are combined with the _if suffix.

pub struct AggregateFunctionFactory {
case_insensitive_desc: HashMap<String, AggregateFunctionDescription>,
case_insensitive_combinator_desc: Vec<(String, CombinatorDescription)>,
impl AggregateFunctionFactory {
pub fn register(&mut self, name: &str, desc: AggregateFunctionDescription) {
let case_insensitive_desc = &mut self.case_insensitive_desc;
case_insensitive_desc.insert(name.to_lowercase(), desc);

Each registered function must implement both the AggregateFunction and AggregateFunctionFeatures traits,and the AggregateFunctionFeatures is similar to FunctionProperty in Scalar because they both store some properties of functions.

pub type AggregateFunctionRef = Arc<dyn AggregateFunction>;
pub type AggregateFunctionCreator =
Box<dyn Fn(&str, Vec<Scalar>, Vec<DataType>) -> Result<AggregateFunctionRef> + Sync + Send>;
pub struct AggregateFunctionDescription {
pub(crate) aggregate_function_creator: AggregateFunctionCreator,
pub(crate) features: AggregateFunctionFeatures,

Function Composition

Unlike Scalar directly using a Struct, AggregateFunction is a trait because aggregate functions accumulate data from the block and generate some intermediate results during the accumulation process.

Therefore, Aggregate Function must have an initial state, and the results generated during the aggregation process must be mergeable and serializable.

The main functions are:

  • name represents the name of the function being registered, such as avg, sum, etc.
  • return_type represents the return type of the registered function. The return value of the same function may change due to different parameter types. For example, the parameter of sum(int8) is of type i8, but the return value may be int64.
  • init_state is used to initialize the state of the aggregate function.
  • state_layout is used to represent the size of the state in memory and the arrangement of memory blocks.
  • accumulate is used for SingleStateAggregator. That is, the entire block can be aggregated under a single state without any keys. For example, when selecting count(*) from t, there is no grouping column in the query, and the accumulate function will be called.
  • accumulate_keys is used for PartialAggregator. Here, key and offset need to be considered, where each key represents a unique memory address, denoted as the function parameter place.
  • serialize serializes the state in the aggregation process into binary.
  • deserialize deserializes the binary into state.
  • merge is used to* merge other state into the current state.
  • merge_result can merge the Aggregate Function state into a single value.


Take avg as an example

The implementation details can be found in

Because we need to accumulate each value and divide them by the total number of non-null rows, the avg function is defined as a struct AggregateAvgFunction<T, SumT> where T and SumT are logical types that implement Number.

During the aggregation process, the avg function will get the sum of accumulated values and the number of non-null rows already scanned. Therefore, AggregateAvgState can be defined as the following structure:

#[derive(Serialize, Deserialize)]
struct AggregateAvgState<T: Number> {
#[serde(bound(deserialize = "T: DeserializeOwned"))]
pub value: T,
pub count: u64,
  • return_type is set to Float64Type. For example, value = 3, count = 2, and avg = value/count.
  • init_state initializes the state with the value of T's default and count set to 0.
  • accumulate accumulates count and value of AggregateAvgState for non-null rows in the block.
  • accumulate_keys gets and updates the corresponding state value with place.get::<AggregateAvgState<SumT>>().
fn accumulate_keys(
places: &[StateAddr],
offset: usize,
columns: &[Column],
_input_rows: usize,
) -> Result<()> {
let darray = NumberType::<T>::try_downcast_column(&columns[0]).unwrap();
darray.iter().zip(places.iter()).for_each(|(c, place)| {
let place =;
let state = place.get::<AggregateAvgState<SumT>>();
state.add(c.as_(), 1);

Refer to other examples

As you see, adding a new aggregate function in Databend is not as hard as you think. Before you start to add one, please refer to other aggregate function examples, such as sum, count.


As a good developer, you always test your code, don't you? Please add unit tests and logic tests after you complete the new aggregate functions.

Unit Test

The unit tests for aggregate functions are located in

Logic Test

The logic tests for functions are located in tests/logictest/suites/base/02_function/.

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