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+---
+feature: user_defined_aggregate_functions
+authors:
+ - "Runji Wang"
+start_date: "2023/08/02"
+---
+
+# User Defined Aggregate Functions (UDAF)
+
+## Summary
+
+This RFC proposes the user interface and internal design of user defined aggregate functions (UDAF).
+
+## User Interfaces
+
+This section describes how users create UDAFs in Python, Java, and SQL through examples.
+
+### Python API
+
+Similar to scalar functions and table functions, we provide an `@udaf` decorator to define aggregate functions. The difference is that below the decorator, we define a class instead of a function. The class has an associated type for **intermediate state**.
+
+```python
+from risingwave.udf import udaf
+
+# The intermediate state can be arbitrary class.
+# It will be serialized into bytes before sending to kernel,
+# and deserialized from bytes after receiving from kernel.
+class State:
+ sum: int = 0
+ count: int = 0
+
+# The aggregate function is defined as a class.
+# Specify the schema of the aggregate function in the `udaf` decorator.
+@udaf(input_types=['BIGINT', 'INT'], result_type='BIGINT')
+class WeightedAvg:
+ # Create an empty state.
+ def create_state(self) -> State:
+ return State()
+
+ # Get the aggregate result.
+ # The return value should match `result_type`.
+ def get_result(self, state: State) -> int:
+ if state.count == 0:
+ return None
+ else:
+ return state.sum / state.count
+
+ # Accumulate a row to the state.
+ # The last arguments should match `input_types`.
+ def accumulate(self, state: State, value: int, weight: int):
+ state.sum += value * weight
+ state.count += weight
+
+ # Retract a row from the state.
+ # This method is optional. If not defined, the function is append-only and not retractable.
+ def retract(self, state: State, value: int, weight: int):
+ state.sum -= value * weight
+ state.count -= weight
+
+ # Merge the state with another one.
+ # This method is optional. If defined, the function can be optimized with two-phase aggregation.
+ def merge(self, state: State, other: State):
+ state.count += other.count
+ state.sum += other.sum
+
+ # Serialize the state into bytes.
+ # This method is optional. If not defined, the state would be serialized using pickle.
+ def serialize(self, state: State) -> bytes:
+ # default implementation
+ return pickle.dumps(state)
+
+ # Deserialize the bytes into an state.
+ # This method is optional. If not defined, the state would be deserialized using pickle.
+ def deserialize(self, serialized: bytes) -> State:
+ # default implementation
+ return pickle.loads(serialized)
+```
+
+Reference: [UDAF Python API in Flink](https://nightlies.apache.org/flink/flink-docs-release-1.17/docs/dev/python/table/udfs/python_udfs/#aggregate-functions)
+
+### Java API
+
+The Java API is similar to Python. One of the differences is that users don't need to specify the input and output types, because we can infer them from the function signatures.
+
+```java
+import java.io.Serializable;
+import com.risingwave.functions.AggregateFunction;
+
+// Mutable intermediate state for the aggregate function.
+// This class should be Serializable.
+public class WeightedAvgState implements Serializable {
+ public long sum = 0;
+ public int count = 0;
+}
+
+// The aggregate function is defined as a class, which implements the `AggregateFunction` interface.
+public class WeightedAvg implements AggregateFunction {
+ // Create a new state.
+ public WeightedAvgState createState() {
+ return new WeightedAvgState();
+ }
+
+ // Get the aggregate result.
+ // The result type is inferred from the signature. (BIGINT)
+ // If a Java type can not infer to an unique SQL type, it should be annotated with `@DataTypeHint`.
+ public Long getResult(WeightedAvgState acc) {
+ if (acc.count == 0) {
+ return null;
+ } else {
+ return acc.sum / acc.count;
+ }
+ }
+
+ // Accumulate a row to the state.
+ // The input types are inferred from the signatures. (BIGINT, INT)
+ // If a Java type can not infer to an unique SQL type, it should be annotated with `@DataTypeHint`.
+ public void accumulate(WeightedAvgState acc, long iValue, int iWeight) {
+ acc.sum += iValue * iWeight;
+ acc.count += iWeight;
+ }
+
+ // Retract a row from the state. (optional)
+ // The function signature should match `accumulate`.
+ public void retract(WeightedAvgState acc, long iValue, int iWeight) {
+ acc.sum -= iValue * iWeight;
+ acc.count -= iWeight;
+ }
+
+ // Merge the state with another one. (optional)
+ public void merge(WeightedAvgState acc, WeightedAvgState other) {
+ acc.count += other.count;
+ acc.sum += other.sum;
+ }
+}
+```
+
+Reference: [UDAF Java API in Flink](https://nightlies.apache.org/flink/flink-docs-release-1.17/docs/dev/table/functions/udfs/#aggregate-functions)
+
+### SQL API
+
+After setting up the UDF server, users can register the function through `create aggregate` in RisingWave.
+
+```sql
+create aggregate weighted_avg(value bigint, weight int) returns bigint
+as 'weighted_avg' using link 'http://localhost:8815';
+```
+
+The full syntax is:
+
+```sql
+CREATE [ OR REPLACE ] AGGREGATE name ( [ argname ] arg_data_type [ , ... ] )
+[ RETURNS result_data_type ] [ APPEND ONLY ]
+[ LANGUAGE language ] [ AS identifier ] [ USING LINK link ];
+```
+
+The `RETURNS` clause is optional if the function has exactly one input argument.
+In this case the return type is same as the input type. For example:
+
+```sql
+create aggregate max(int) as ...;
+```
+
+The `APPEND ONLY` clause is required if the function doesn't have `retract` method.
+
+The remaining clauses are defined the same as for `create function` statement.
+
+## Design
+
+### Dataflow
+
+User defined functions are running in a separate process called UDF server. The RisingWave kernel communicates with the UDF server through Arrow Flight RPC to exchange data.
+
+To avoid trouble in fault tolerance, the UDF server should be **stateless** even if the aggregate function is stateful. This means the aggregate state should be maintained by the kernel. However, exchanging the state in each RPC call (batch aggregation) is not efficient, especially when the state is large. On the other hand, kernel doesn't need to know the state or the aggregate result before a barrier arrives. Therefore, we **create a streaming RPC `doExchange` to call the aggregate function, and sync the state every time a barrier arrives**. When the connection to the UDF server is broken, the executor raises an error and may retry or pause the stream.
+
+```mermaid
+sequenceDiagram
+ participant UP as Upstream
Downstream
+ participant AG as Agg Executor
+ participant CP as UDF Server
+ UP ->> AG: init(epoch)
+ Note over AG: load state
from state table
+ AG ->>+ CP: doExchange(fid, state)
+ CP -->> AG: output @ epoch
+ Note right of CP: decode state,
create state
+ loop each epoch
+ loop each chunk
+ UP ->> AG: input chunk
+ AG ->> CP: input chunk
+ Note right of CP: accumulate,
retract
+ end
+ UP ->> AG: barrier(epoch+1)
+ AG ->> CP: finish
+ Note right of CP: encode state,
get output
+ CP -->> AG: state + output @ epoch+1
+ Note over AG: store state
into state table
+ AG ->> UP: - output @ epoch
+ output @ epoch+1
+ AG ->> UP: barrier(epoch+1)
+ end
+ deactivate CP
+```
+
+### State Storage
+
+The state of UDAF is managed by the compute node as a single encoded BYTEA value.
+
+Currently, each aggregate operator has a **result table** to store the aggregate result. For most of our built-in aggregate functions, they have the same output as their state, so the result table is actually being used as the **intermediate state table**. However, for general UDAFs, their state may not be the same as their output. Such functions are not supported for now.
+
+Therefore, we propose to **transform the result table into intermediate state table**. The content of the table remains the same for existing functions. But for new functions whose state is different from output, only the state is stored. The output can be computed from the state when needed.
+
+For example, given the input:
+
+| Epoch | op | id (pk) | v0 | w0 | v1 |
+| ----- | ---- | ------- | ---- | ---- | ----- |
+| 1 | + | 0 | 1 | 2 | false |
+| 2 | U- | 0 | 1 | 2 | false |
+| 2 | U+ | 0 | 2 | 1 | true |
+
+The new **intermediate state table** (inherited from the old result table) of the agg operator would be like:
+
+| Epoch | id | sum(v0) | bool_and(v1) | weighted_avg(v0, w0) | max(v0)* |
+| ----- | ---- | ------- | ------------------- | -------------------- | ---------- |
+| 1 | 0 | sum = 1 | false = 1, true = 0 | encode(1,2) = b'XXX' | output = 1 |
+| 2 | 0 | sum = 2 | false = 0, true = 1 | encode(2,1) = b'YYY' | output = 2 |
+
+Note: for **append-only** aggregate functions (e.g. max, min, first, last, string_agg...), their states are all input values maintained in seperate "materialized input" tables. For backward compatibility, their values in the state table are still aggregate results.
+
+The output would be:
+
+| Epoch | op | id | sum(v0) | bool_and(v1) | weighted_avg(v0, w0) | max(v0) |
+| ----- | ---- | ---- | ------- | ------------ | -------------------- | ------- |
+| 1 | + | 0 | 1 | false | 1 | 1 |
+| 2 | U- | 0 | 1 | false | 1 | 1 |
+| 2 | U+ | 0 | 2 | true | 2 | 2 |
+
+## Unresolved questions
+
+## Alternatives
+
+### Aggregate Function Class as the State
+
+This proposal follows the Flink API, which defines a separate class as the intermediate state. There is an alternative design to define the state as the aggregate function class itself. For example:
+
+```python
+@udaf(input_types=['BIGINT', 'INT'], result_type='BIGINT')
+class WeightedAvg:
+ sum: int
+ count: int
+
+ def __init__(self):
+ self.sum = 0
+ self.count = 0
+
+ def get_result(self) -> int:
+ if self.count == 0:
+ return None
+ else:
+ return self.sum / self.count
+
+ def accumulate(self, value: int, weight: int):
+ self.sum += value * weight
+ self.count += weight
+```
+
+This is simpler and more intuitive, but is also less flexible than the previous design. The previous design allows direct arguments (e.g. `percentile_cont(fraction)`, `rank(n)`) to be passed to the aggregate function, while this alternative requires the arguments to be stored in the state.
+
+## Future possibilities