Aggregate Functions

proposals/0021-aggregate-functions.md

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+- Start Date: 2026-02-26
+- RFC PR: [vortex-data/rfcs#0020](https://github.com/vortex-data/rfcs/pull/0021)
+- Tracking Issue: [vortex-data/vortex#6719](https://github.com/vortex-data/vortex/issues/6719)
+
+## Summary
+
+Introduce first-class aggregate functions to Vortex via an `AggregateFnVTable` trait and
+`Accumulator` interface. A single `ListAggregate` scalar function bridges list columns to the
+aggregate system, replacing what would otherwise be N separate list scalar functions.
+
+## Motivation
+
+Vortex has aggregate operations (`sum`, `min_max`, `is_constant`, `is_sorted`) implemented as
+standalone `ComputeFn` kernels. These cannot participate in expression trees, benefit from lazy
+evaluation, or be optimized via reduce/reduce_parent rules. Meanwhile, list scalar functions
+like `list_sum`, `list_min`, etc. don't yet exist — and implementing each one separately would
+duplicate the underlying aggregation logic.
+
+The key observation is that a list column stored as `(offsets, elements)` is a pre-materialized
+grouping. Computing `list_sum(list_col)` is a grouped `sum` over the flat elements partitioned
+by offsets. Every aggregate function has a corresponding list scalar function for free:
+
+| Aggregate | List scalar            | Operation                 |
+| --------- | ---------------------- | ------------------------- |
+| `sum`     | `list_sum(list_col)`   | Sum elements per list     |
+| `min`     | `list_min(list_col)`   | Min element per list      |
+| `max`     | `list_max(list_col)`   | Max element per list      |
+| `count`   | `list_count(list_col)` | Count non-null per list   |
+| `mean`    | `list_mean(list_col)`  | Mean of elements per list |
+
+Since Vortex does not support shuffling, grouped aggregates only apply to pre-existing groups.
+These are naturally represented by List or ListView encodings as a view over the elements array.
+By implementing N aggregate functions and one `ListAggregate` scalar function, we get both
+ungrouped column-level aggregation and grouped list-scalar operations from a single framework.
+
+## Design
+
+### `Accumulator`
+
+The `Accumulator` trait is the core aggregation primitive. It processes one group at a time:
+the caller feeds element batches via `accumulate`, then calls `flush` to finalize the group
+and begin the next. The accumulator owns an output buffer and returns all results at the end.
+
+```rust
+pub trait Accumulator: Send + Sync {
+    /// Feed a batch of elements for the currently open group.
+    /// May be called multiple times per group (e.g., chunked elements).
+    fn accumulate(&mut self, batch: &ArrayRef) -> VortexResult<()>;
+
+    /// Accumulate all groups defined by a ListView in one call.
+    /// Default: for each group, accumulate its elements then flush.
+    /// Override for vectorized fast paths (e.g., segmented sum over the flat
+    /// elements + offsets without per-group slicing).
+    fn accumulate_list(&mut self, list: &ListViewArray) -> VortexResult<()> {
+        for i in 0..list.len() {
+            self.accumulate(&list.list_elements_at(i)?)?;
+            self.flush()?;
+        }
+        Ok(())
+    }
+
+    /// Merge pre-computed partial state into the currently open group.
+    /// The scalar's dtype must match the aggregate's `state_dtype`.
+    /// This is equivalent to having processed raw elements that would produce
+    /// this state — used by encoding-specific optimizations (see aggregate).
+    fn merge(&mut self, state: &Scalar) -> VortexResult<()>;
+
+    /// Merge an array of pre-computed states, one per group, flushing each.
+    /// The array's dtype must match the aggregate's `state_dtype`.
+    /// Default: merge + flush for each element.
+    fn merge_list(&mut self, states: &ArrayRef) -> VortexResult<()> {
+        for i in 0..states.len() {
+            self.merge(&states.scalar_at(i)?)?;
+            self.flush()?;
+        }
+        Ok(())
+    }
+
+    /// Whether the currently open group's result is fully determined.
+    /// When true, callers may skip further accumulate/merge calls and proceed
+    /// directly to flush. Resets to false after flush().
+    /// Examples: IsConstant after seeing two distinct values, All after seeing false.
+    fn is_saturated(&self) -> bool { false }
+
+    /// Finalize the currently open group: push its result to the output buffer
+    /// and reset internal state for the next group.
+    fn flush(&mut self) -> VortexResult<()>;
+
+    /// Return all flushed results as a single array.
+    /// Length = number of flush() calls made over the accumulator's lifetime.
+    fn finish(self: Box<Self>) -> VortexResult<ArrayRef>;
+}
+```
+
+Usage across all aggregation patterns:
+
+```rust
+// Grouped (list scalar): fast path processes all groups at once
+let mut acc = aggregate.accumulator(element_dtype)?;
+acc.accumulate_list(&list_view)?;
+acc.finish()  // ArrayRef of length n_lists
+
+// Ungrouped (full-column): single group, fold across chunks
+let mut acc = aggregate.accumulator(dtype)?;
+for chunk in chunked_array.chunks() {
+    if acc.is_saturated() { break; }
+    acc.accumulate(&chunk)?;
+}
+acc.flush()?;
+acc.finish()  // 1-element ArrayRef
+```
+
+#### Accumulator state
+
+Each aggregate declares a `state_dtype` — the type of its intermediate accumulator state.
+State is a single `Scalar` whose dtype matches this declaration. For aggregates with multiple
+fields, use a struct dtype:
+
+| Aggregate    | `state_dtype`                            | Example state value                     |
+| ------------ | ---------------------------------------- | --------------------------------------- |
+| `Sum`        | `i64` (or widened input type)            | `Scalar(42)`                            |
+| `Count`      | `u64`                                    | `Scalar(7)`                             |
+| `Min`        | input element type                       | `Scalar(3)`                             |
+| `Mean`       | `Struct { sum: f64, count: u64 }`        | `Scalar({sum: 10.0, count: 5})`         |
+| `IsConstant` | `Struct { value: T, is_constant: bool }` | `Scalar({value: 5, is_constant: true})` |
+| `IsSorted`   | `Struct { last: T, is_sorted: bool }`    | `Scalar({last: 9, is_sorted: true})`    |
+
+The `merge` method on `Accumulator` combines a partial state scalar into the currently open
+group. For Sum, this is addition. For IsConstant, this checks whether the incoming value
+matches the seen value. The `merge_list` method handles multiple groups at once.
+
+This enables encoding-specific optimization (see below) and also lays the groundwork for
+partial/distributed aggregation where intermediate state must be serialized and merged
+across nodes.
+
+### `AggregateFnVTable`
+
+A new trait parallel to `ScalarFnVTable`:
+
+```rust
+pub trait AggregateFnVTable: 'static + Sized + Clone + Send + Sync {
+    type Options: 'static + Send + Sync + Clone + Debug + Display + PartialEq + Eq + Hash;
+
+    fn id(&self) -> AggregateFnId;
+
+    fn serialize(&self, options: &Self::Options) -> VortexResult<Option<Vec<u8>>>;
+    fn deserialize(&self, metadata: &[u8], session: &VortexSession) -> VortexResult<Self::Options>;
+
+    /// Result dtype per group.
+    fn return_dtype(&self, options: &Self::Options, input_dtype: &DType) -> VortexResult<DType>;
+
+    /// DType of the intermediate accumulator state.
+    /// Use a struct dtype when multiple fields are needed (e.g., Mean: {sum: f64, count: u64}).
+    fn state_dtype(&self, options: &Self::Options, input_dtype: &DType) -> VortexResult<DType>;
+
+    /// Create an accumulator for streaming aggregation.
+    fn accumulator(
+        &self,
+        options: &Self::Options,
+        input_dtype: &DType,
+    ) -> VortexResult<Box<dyn Accumulator>>;
+}
+```
+
+All execution flows through the `Accumulator`. Grouped aggregation uses `accumulate_list`;
+ungrouped aggregation uses `accumulate`/`flush`/`finish` directly. There is no need for
+`execute_grouped` or `execute_scalar` methods on the vtable — the accumulator is the single
+entry point, and its `accumulate_list` override is where vectorized fast paths live.
+
+### Built-in aggregates
+
+The initial set, each implementing `AggregateFnVTable`:
+
+```rust
+pub struct Sum;       // sum of elements per group
+pub struct Count;     // count of non-null elements per group
+pub struct Min;       // minimum element per group
+pub struct Max;       // maximum element per group
+pub struct Mean;      // mean of elements per group (returns f64)
+pub struct Any;       // logical OR per group (bool input)
+pub struct All;       // logical AND per group (bool input)
+```
+
+These replace the standalone `ComputeFn` kernels (e.g., `Sum` replaces `compute::sum()`).
+
+### Encoding-specific optimization
+
+Arrays can short-circuit accumulation by producing partial state directly, avoiding
+decompression. This follows the `execute_parent` pattern: the array sees the aggregate
+being applied and returns pre-computed state.
+
+Two new methods on the Array VTable:
+
+```rust
+/// Produce partial accumulator state for the given aggregate, treating the
+/// entire array as a single group.
+/// Returns None to fall back to element-by-element accumulation.
+fn aggregate(
+    &self,
+    array: &Self::Array,
+    aggregate_fn: &AggregateFnRef,
+) -> VortexResult<Option<Scalar>>;
+
+/// Produce partial accumulator state for each group defined by a ListView
+/// over this array. Returns an array of state values (one per group) with
+/// dtype = aggregate_fn.state_dtype() and length = list.len().
+/// Returns None to fall back to per-group accumulation.
+fn aggregate_list(
+    &self,
+    elements: &Self::Array,
+    list: &ListViewArray,
+    aggregate_fn: &AggregateFnRef,
+) -> VortexResult<Option<ArrayRef>>;
+```
+
+**Ungrouped examples** (`aggregate` returns `Option<Scalar>`):
+
+| Encoding                 | Aggregate  | Returns                                |
+| ------------------------ | ---------- | -------------------------------------- |
+| Constant(5, n=100)       | Sum        | `Some(Scalar(500))` — value \* len     |
+| Constant(5, n=100)       | IsConstant | `Some({value: 5, is_constant: true})`  |
+| RunEnd([1,5,3], [2,5,8]) | Sum        | `Some(Scalar(26))` — weighted sum      |
+| RunEnd(...)              | Min        | `Some(Scalar(1))` — min of run values  |
+| Primitive                | Sum        | `None` — no shortcut, process elements |
+
+**Grouped examples** (`aggregate_list` returns `Option<ArrayRef>`):
+
+| Elements encoding   | Aggregate  | Optimization                             |
+| ------------------- | ---------- | ---------------------------------------- |
+| Constant(5)         | Sum        | `constant * list.sizes()` — one multiply |
+| Constant(5)         | IsConstant | All groups constant with same value      |
+| Dict(codes, values) | Min        | Min code per group → look up value       |
+| Dict(codes, values) | Max        | Max code per group → look up value       |
+
+The accumulator wires these into its methods:
+
+```rust
+// In accumulate():
+if let Some(state) = batch.aggregate(&self.aggregate_fn)? {
+    return self.merge(&state);
+}
+// ... fall back to canonical processing
+
+// In accumulate_list() default:
+if let Some(states) = list.elements().aggregate_list(list, &self.aggregate_fn)? {
+    return self.merge_list(&states);
+}
+// ... fall back to per-group slice + accumulate + flush
+```
+
+The encoding doesn't need to know accumulator internals — it produces state matching the
+aggregate's declared `state_dtype`. The accumulator knows how to merge it.
+
+### `ListAggregate` scalar function
+
+A single `ScalarFnVTable` that bridges list columns to the aggregate system. Because it is a
+scalar function, wrapping it in an expression produces a `ScalarFnArray` — reusing the
+existing lazy evaluation, slicing, and reduce infrastructure with no new array type.
+
+```rust
+pub struct ListAggregate;
+
+pub struct ListAggregateOptions {
+    pub aggregate_fn: AggregateFnRef,
+}
+
+impl ScalarFnVTable for ListAggregate {
+    type Options = ListAggregateOptions;
+
+    fn execute(&self, options: &Self::Options, args: ExecutionArgs) -> VortexResult<ArrayRef> {
+        let list = args.inputs[0].to_listview()?;
+        let mut acc = options.aggregate_fn.accumulator(list.elements().dtype())?;
+        acc.accumulate_list(&list)?;
+        acc.finish()
+    }
+
+    // return_dtype delegates to aggregate_fn.return_dtype over the list element type.
+}
+```
+
+Expression-level sugar:
+
+```rust
+pub fn list_sum(list: Expression) -> Expression {
+    ListAggregate.new_expr(
+        ListAggregateOptions { aggregate_fn: Sum.bind(EmptyOptions) },
+        [list],
+    )
+}
+// list_min, list_max, list_count, list_mean, list_any, list_all analogously
+```
+
+This is one scalar function parameterized by the aggregate, analogous to DuckDB's
+`list_aggregate(list, 'sum')`.
+
+### Reduce rules
+
+Since `ListAggregate` is a `ScalarFnVTable`, it participates in the existing reduce/reduce_parent
+optimization framework:
+
+**Self-reduce** (`ScalarFnVTable::reduce`): constant list folding, count from list sizes,
+min/max from statistics, sum of constant elements.
+
+**Parent-reduce** (encoding-specific): child encodings match on `ExactScalarFn<ListAggregate>`
+to optimize specific aggregate + encoding combinations. For example:
+
+- **Dict**: `ListAggregate(Min/Max, List(Dict(codes, values)))` pushes down to values.
+- **RunEnd**: `ListAggregate(Sum, List(RunEnd))` becomes a weighted sum over run values.
+
+### Aggregate push-down
+
+Aggregate reduce rules can push computation into the Scan API, allowing aggregates to be
+computed during file scanning without materializing full columns. For example, `Count` can
+be resolved from row group metadata alone; `Min`/`Max` can use column-chunk statistics.
+The details of scan-level push-down are out of scope for this RFC.
+
+## Migration
+
+Each `ComputeFn` kernel is replaced by creating an accumulator and driving it directly:
+
+| Current `ComputeFn`       | New                                                           |
+| ------------------------- | ------------------------------------------------------------- |
+| `compute::sum(array)`     | `Sum.accumulator(dtype)` -> accumulate -> flush/finish        |
+| `compute::min_max(array)` | `Min/Max.accumulator(dtype)` -> accumulate -> flush/finish    |
+| `compute::is_constant()`  | `IsConstant.accumulator(dtype)` -> accumulate -> flush/finish |
+| `compute::is_sorted()`    | `IsSorted.accumulator(dtype)` -> accumulate -> flush/finish   |
+
+Convenience functions (e.g., `compute::sum()`) can be kept as thin wrappers that create an
+accumulator, feed the array, flush, and extract the scalar result.
+
+## Compatibility
+
+No file format or wire format changes. `ListAggregate` produces a `ScalarFnArray` at runtime
+and is not persisted. Public API additions:
+
+- `Accumulator` trait
+- `AggregateFnVTable` trait and built-in implementations
+- `ListAggregate` scalar function
+- Expression constructors: `list_sum()`, `list_count()`, `list_min()`, `list_max()`,
+  `list_mean()`, `list_any()`, `list_all()`
+
+## Drawbacks
+
+- **New trait surface area.** `AggregateFnVTable` and `Accumulator` are new traits, though
+  they closely mirror existing `ScalarFnVTable` patterns.
+
+- **Reduce rule coverage.** Not all encoding x aggregate combinations will have optimized
+  reduce_parent rules initially. The fallback (canonicalize + accumulator loop) is correct
+  but slower.
+
+## Alternatives
+
+### Separate list scalar functions
+
+Implement `ListSum`, `ListMin`, etc. as individual `ScalarFnVTable` implementations.
+Rejected: duplicates logic across N functions, no shared optimization, no path to reuse
+for ungrouped aggregation.
+
+### Keep aggregates as `ComputeFn` only
+
+Rejected: no lazy evaluation, no expression tree participation, no reduce_parent optimization.
+
+### Dedicated `AggregateFnArray`
+
+A new array type wrapping an aggregate + list child, parallel to `ScalarFnArray`.
+Rejected: structurally identical to `ScalarFnArray` with one child — duplicates existing
+lazy evaluation and reduce infrastructure. `ExactScalarFn<ListAggregate>` provides the same
+typed matching without a new array type.
+
+## Future Possibilities
+
+- **Partial aggregation** (`state()` / distributed `merge`): the `state_dtype` and `merge`
+  infrastructure enables serializing intermediate state for distributed execution. A
+  `state()` export method on `Accumulator` would complete this.
+
+- **Aggregate push-down in Scan**: using reduce rules to push aggregates into `LayoutReader`,
+  computing results during file scan without materializing full columns.
+
+- **Window functions**: sliding-window operations share the "operate within boundaries"
+  property but have different execution semantics. A separate trait or extension is more
+  appropriate.