Improve aggregatation documentation

datafusion/expr/src/accumulator.rs

@@ -94,7 +94,7 @@ pub trait Accumulator: Send + Sync + Debug {
     ///
     /// Intermediate state is used for "multi-phase" grouping in
     /// DataFusion, where an aggregate is computed in parallel with
-    /// multiple `Accumulator` instances, as illustrated below:
+    /// multiple `Accumulator` instances, as described below:
     ///
     /// # MultiPhase Grouping
     ///
@@ -130,7 +130,7 @@ pub trait Accumulator: Send + Sync + Debug {
     ///          `───────'                        `───────'
     /// ```
     ///
-    /// The partial state is serialied as `Arrays` and then combined
+    /// The partial state is serialized as `Arrays` and then combined
     /// with other partial states from different instances of this
     /// Accumulator (that ran on different partitions, for example).
     ///
@@ -147,6 +147,107 @@ pub trait Accumulator: Send + Sync + Debug {
     /// Note that [`ScalarValue::List`] can be used to pass multiple
     /// values if the number of intermediate values is not known at
     /// planning time (e.g. for `MEDIAN`)
+    ///
+    /// # Multi-phase repartitioned Grouping
+    ///
+    /// Many multi-phase grouping plans contain a Repartition operation
+    /// as well as shown below:
+    ///
+    /// ```text
+    ///                ▲                          ▲
+    ///                │                          │
+    ///                │                          │
+    ///                │                          │
+    ///                │                          │
+    ///                │                          │
+    ///    ┌───────────────────────┐  ┌───────────────────────┐       4. Each AggregateMode::Final
+    ///    │GroupBy                │  │GroupBy                │       GroupBy has an entry for its
+    ///    │(AggregateMode::Final) │  │(AggregateMode::Final) │       subset of groups (in this case
+    ///    │                       │  │                       │       that means half the entries)
+    ///    └───────────────────────┘  └───────────────────────┘
+    ///                ▲                          ▲
+    ///                │                          │
+    ///                └─────────────┬────────────┘
+    ///                              │
+    ///                              │
+    ///                              │
+    ///                 ┌─────────────────────────┐                   3. Repartitioning by hash(group
+    ///                 │       Repartition       │                   keys) ensures that each distinct
+    ///                 │         HASH(x)         │                   group key now appears in exactly
+    ///                 └─────────────────────────┘                   one partition
+    ///                              ▲
+    ///                              │
+    ///              ┌───────────────┴─────────────┐
+    ///              │                             │
+    ///              │                             │
+    /// ┌─────────────────────────┐  ┌──────────────────────────┐     2. Each AggregateMode::Partial
+    /// │        GroubyBy         │  │         GroubyBy         │     GroupBy has an entry for *all*
+    /// │(AggregateMode::Partial) │  │ (AggregateMode::Partial) │     the groups
+    /// └─────────────────────────┘  └──────────────────────────┘
+    ///              ▲                             ▲
+    ///              │                            ┌┘
+    ///              │                            │
+    ///         .─────────.                  .─────────.
+    ///      ,─'           '─.            ,─'           '─.
+    ///     ;      Input      :          ;      Input      :          1. Since input data is
+    ///     :   Partition 0   ;          :   Partition 1   ;          arbitrarily or RoundRobin
+    ///      ╲               ╱            ╲               ╱           distributed, each partition
+    ///       '─.         ,─'              '─.         ,─'            likely has all distinct
+    ///          `───────'                    `───────'
+    /// ```
+    ///
+    /// This structure is used so that the `AggregateMode::Partial` accumulators
+    /// reduces the cardinality of the input as soon as possible. Typically,
+    /// each partial accumulator sees all groups in the input as the group keys
+    /// are evenly distributed across the input.
+    ///
+    /// The final output is computed by repartitioning the result of
+    /// [`Self::state`] from each Partial aggregate and `hash(group keys)` so
+    /// that each distinct group key appears in exactly one of the
+    /// `AggregateMode::Final` GroupBy nodes. The output of the final nodes are
+    /// then unioned together to produce the overall final output.
+    ///
+    /// Here is an example that shows the distribution of groups in the
+    /// different phases
+    ///
+    /// ```text
+    ///               ┌─────┐                ┌─────┐
+    ///               │  1  │                │  3  │
+    ///               ├─────┤                ├─────┤
+    ///               │  2  │                │  4  │                After repartitioning by
+    ///               └─────┘                └─────┘                hash(group keys), each distinct
+    ///               ┌─────┐                ┌─────┐                group key now appears in exactly
+    ///               │  1  │                │  3  │                one partition
+    ///               ├─────┤                ├─────┤
+    ///               │  2  │                │  4  │
+    ///               └─────┘                └─────┘
+    ///
+    ///
+    /// ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─
+    ///
+    ///               ┌─────┐                ┌─────┐
+    ///               │  2  │                │  2  │
+    ///               ├─────┤                ├─────┤
+    ///               │  1  │                │  2  │
+    ///               ├─────┤                ├─────┤
+    ///               │  3  │                │  3  │
+    ///               ├─────┤                ├─────┤
+    ///               │  4  │                │  1  │
+    ///               └─────┘                └─────┘                Input data is arbitrarily or
+    ///                 ...                    ...                  RoundRobin distributed, each
+    ///               ┌─────┐                ┌─────┐                partition likely has all
+    ///               │  1  │                │  4  │                distinct group keys
+    ///               ├─────┤                ├─────┤
+    ///               │  4  │                │  3  │
+    ///               ├─────┤                ├─────┤
+    ///               │  1  │                │  1  │
+    ///               ├─────┤                ├─────┤
+    ///               │  4  │                │  3  │
+    ///               └─────┘                └─────┘
+    ///
+    ///           group values           group values
+    ///           in partition 0         in partition 1
+    /// ```
     fn state(&mut self) -> Result<Vec<ScalarValue>>;
 
     /// Updates the accumulator's state from an `Array` containing one

datafusion/expr/src/groups_accumulator.rs

@@ -128,18 +128,23 @@ pub trait GroupsAccumulator: Send {
     /// Returns the intermediate aggregate state for this accumulator,
     /// used for multi-phase grouping, resetting its internal state.
     ///
+    /// See [`Accumulator::state`] for more information on multi-phase
+    /// aggregation.
+    ///
     /// For example, `AVG` might return two arrays: `SUM` and `COUNT`
     /// but the `MIN` aggregate would just return a single array.
     ///
     /// Note more sophisticated internal state can be passed as
     /// single `StructArray` rather than multiple arrays.
     ///
     /// See [`Self::evaluate`] for details on the required output
-    /// order and  `emit_to`.
+    /// order and `emit_to`.
+    ///
+    /// [`Accumulator::state`]: crate::Accumulator::state
     fn state(&mut self, emit_to: EmitTo) -> Result<Vec<ArrayRef>>;
 
     /// Merges intermediate state (the output from [`Self::state`])
-    /// into this accumulator's values.
+    /// into this accumulator's current state.
     ///
     /// For some aggregates (such as `SUM`), `merge_batch` is the same
     /// as `update_batch`, but for some aggregates (such as `COUNT`,
@@ -158,9 +163,41 @@ pub trait GroupsAccumulator: Send {
         total_num_groups: usize,
     ) -> Result<()>;
 
-    /// Converts input batch to intermediate aggregate state,
-    /// without grouping (each input row considered as a separate
-    /// group).
+    /// Converts an input batch directly the intermediate aggregate state.
+    ///
+    /// This is the equivalent of treating each input row as its own group. It
+    /// is invoked when the Partial phase of a multi-phase aggregation is not
+    /// reducing the cardinality enough to warrant spending more effort on
+    /// pre-aggregation (see `Background` section below), and switches to
+    /// passing intermediate state directly on to the next aggregation phase.
+    ///
+    /// Examples:
+    /// * `COUNT`: an array of 1s for each row in the input batch.
+    /// * `SUM/MIN/MAX`: the input values themselves.
+    ///
+    /// # Arguments
+    /// * `values`: the input arguments to the accumulator
+    /// * `opt_filter`: if present, any row where `opt_filter[i]` is false should be ignored
+    ///
+    /// # Background
+    ///
+    /// In a multi-phase aggregation (see [`Accumulator::state`]), the initial
+    /// Partial phase reduces the cardinality of the input data as soon as
+    /// possible in the plan.
+    ///
+    /// This strategy is very effective for queries with a small number of
+    /// groups, as most of the data is aggregated immediately and only a small
+    /// amount of data must be repartitioned (see [`Accumulator::state`] for
+    /// background)
+    ///
+    /// However, for queries with a large number of groups, the Partial phase
+    /// often does not reduce the cardinality enough to warrant the memory and
+    /// CPU cost of actually performing the aggregation. For such cases, the
+    /// HashAggregate operator will dynamically switch to passing intermediate
+    /// state directly to the next aggregation phase with minimal processing
+    /// using this method.
+    ///
+    /// [`Accumulator::state`]: crate::Accumulator::state
     fn convert_to_state(
         &self,
         _values: &[ArrayRef],
@@ -169,15 +206,16 @@ pub trait GroupsAccumulator: Send {
         not_impl_err!("Input batch conversion to state not implemented")
     }
 
-    /// Returns `true` is groups accumulator supports input batch
-    /// to intermediate aggregate state conversion (`convert_to_state`
-    /// method is implemented).
+    /// Returns `true` if [`Self::convert_to_state`] is implemented to support
+    /// intermediate aggregate state conversion.
     fn supports_convert_to_state(&self) -> bool {
         false
     }
 
     /// Amount of memory used to store the state of this accumulator,
-    /// in bytes. This function is called once per batch, so it should
-    /// be `O(n)` to compute, not `O(num_groups)`
+    /// in bytes.
+    ///
+    /// This function is called once per batch, so it should be `O(n)` to
+    /// compute, not `O(num_groups)`
     fn size(&self) -> usize;
 }

datafusion/expr/src/udaf.rs

@@ -351,6 +351,8 @@ pub trait AggregateUDFImpl: Debug + Send + Sync {
 
     /// Return the fields used to store the intermediate state of this accumulator.
     ///
+    /// See [`Accumulator::state`] for background information.
+    ///
     /// args:  [`StateFieldsArgs`] contains arguments passed to the
     /// aggregate function's accumulator.
     ///
@@ -388,7 +390,7 @@ pub trait AggregateUDFImpl: Debug + Send + Sync {
     /// # Notes
     ///
     /// Even if this function returns true, DataFusion will still use
-    /// `Self::accumulator` for certain queries, such as when this aggregate is
+    /// [`Self::accumulator`] for certain queries, such as when this aggregate is
     /// used as a window function or when there no GROUP BY columns in the
     /// query.
     fn groups_accumulator_supported(&self, _args: AccumulatorArgs) -> bool {

datafusion/functions-aggregate/src/count.rs

@@ -441,6 +441,11 @@ impl GroupsAccumulator for CountGroupsAccumulator {
         Ok(vec![Arc::new(counts) as ArrayRef])
     }
 
+    /// Converts an input batch directly to a state batch
+    ///
+    /// The state of `COUNT` is always a single Int64Array:
+    /// * `1` (for non-null, non filtered values)
+    /// * `0` (for null values)
     fn convert_to_state(
         &self,
         values: &[ArrayRef],

datafusion/physical-expr-common/src/aggregate/groups_accumulator/prim_op.rs

@@ -136,6 +136,12 @@ where
         self.update_batch(values, group_indices, opt_filter, total_num_groups)
     }
 
+    /// Converts an input batch directly to a state batch
+    ///
+    /// The state is:
+    /// - self.prim_fn for all non null, non filtered values
+    /// - null otherwise
+    ///
     fn convert_to_state(
         &self,
         values: &[ArrayRef],

datafusion/physical-plan/src/aggregates/mod.rs

@@ -56,11 +56,20 @@ mod topk;
 mod topk_stream;
 
 /// Hash aggregate modes
+///
+/// See [`Accumulator::state`] for background information on multi-phase
+/// aggregation and how these modes are used.
 #[derive(Debug, Copy, Clone, PartialEq, Eq)]
 pub enum AggregateMode {
-    /// Partial aggregate that can be applied in parallel across input partitions
+    /// Partial aggregate that can be applied in parallel across input
+    /// partitions.
+    ///
+    /// This is the first phase of a multi-phase aggregation.
     Partial,
-    /// Final aggregate that produces a single partition of output
+    /// Final aggregate that produces a single partition of output by combining
+    /// the output of multiple partial aggregates.
+    ///
+    /// This is the second phase of a multi-phase aggregation.
     Final,
     /// Final aggregate that works on pre-partitioned data.
     ///
@@ -72,12 +81,15 @@ pub enum AggregateMode {
     /// Applies the entire logical aggregation operation in a single operator,
     /// as opposed to Partial / Final modes which apply the logical aggregation using
     /// two operators.
+    ///
     /// This mode requires that the input is a single partition (like Final)
     Single,
     /// Applies the entire logical aggregation operation in a single operator,
     /// as opposed to Partial / Final modes which apply the logical aggregation using
     /// two operators.
-    /// This mode requires that the input is partitioned by group key (like FinalPartitioned)
+    ///
+    /// This mode requires that the input is partitioned by group key (like
+    /// FinalPartitioned)
     SinglePartitioned,
 }
 

datafusion/physical-plan/src/aggregates/row_hash.rs

@@ -62,10 +62,12 @@ pub(crate) enum ExecutionState {
     /// When producing output, the remaining rows to output are stored
     /// here and are sliced off as needed in batch_size chunks
     ProducingOutput(RecordBatch),
-    /// Indicates that GroupedHashAggregateStream should produce
-    /// intermediate aggregate state for each input rows without
-    /// their aggregation
+    /// Produce intermediate aggregate state for each input row without
+    /// aggregation.
+    ///
+    /// See "partial aggregation" discussion on [`GroupedHashAggregateStream`]
     SkippingAggregation,
+    /// All input has been consumed and all groups have been emitted
     Done,
 }
 
@@ -94,6 +96,9 @@ struct SpillState {
     merging_group_by: PhysicalGroupBy,
 }
 
+/// Tracks if the aggregate should skip partial aggregations
+///
+/// See "partial aggregation" discussion on [`GroupedHashAggregateStream`]
 struct SkipAggregationProbe {
     /// Number of processed input rows
     input_rows: usize,
@@ -204,7 +209,7 @@ impl SkipAggregationProbe {
 /// of `x` and one accumulator for `SUM(y)`, specialized for the data
 /// type of `y`.
 ///
-/// # Description
+/// # Discussion
 ///
 /// [`group_values`] does not store any aggregate state inline. It only
 /// assigns "group indices", one for each (distinct) group value. The
@@ -222,7 +227,25 @@ impl SkipAggregationProbe {
 ///
 /// [`group_values`]: Self::group_values
 ///
-/// # Spilling
+/// # Partial Aggregate and multi-phase grouping
+///
+/// As described on [`Accumulator::state`], this operator is used in the context
+/// "multi-phase" grouping when the mode is [`AggregateMode::Partial`].
+///
+/// An important optimization for multi-phase partial aggregation is to skip
+/// partial aggregation when it is not effective enough to warrant the memory or
+/// CPU cost, as is often the case for queries many distinct groups (high
+/// cardinality group by). Memory is particularly important because each Partial
+/// aggregator must store the intermediate state for each group.
+///
+/// If the ratio of the number of groups to the number of input rows exceeds a
+/// threshold, and [`GroupsAccumulator::convert_to_state_supported`] is
+/// supported, this operator will stop applying Partial aggregation and directly
+/// pass the input rows to the next aggregation phase.
+///
+/// [`Accumulator::state`]: datafusion_expr::Accumulator::state
+///
+/// # Spilling (to disk)
 ///
 /// The sizes of group values and accumulators can become large. Before that causes out of memory,
 /// this hash aggregator outputs partial states early for partial aggregation or spills to local
@@ -344,7 +367,7 @@ pub(crate) struct GroupedHashAggregateStream {
     group_values_soft_limit: Option<usize>,
 
     /// Optional probe for skipping data aggregation, if supported by
-    /// current stream
+    /// current stream.
     skip_aggregation_probe: Option<SkipAggregationProbe>,
 }