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c205f411582bc79bc3ec570d089806aa91bf6e98
turso/core/incremental/aggregate_operator.rs
Pekka Enberg d808db6af9 core: Switch to parking_lot::Mutex 
It's faster and we eliminate bunch of unwrap() calls.
2025-11-20 10:42:02 +02:00

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// Aggregate operator for DBSP-style incremental computation
use crate::function::{AggFunc, Func};
use crate::incremental::dbsp::Hash128;
use crate::incremental::dbsp::{Delta, DeltaPair, HashableRow};
use crate::incremental::operator::{
generate_storage_id, ComputationTracker, DbspStateCursors, EvalState, IncrementalOperator,
};
use crate::incremental::persistence::{ReadRecord, WriteRow};
use crate::storage::btree::CursorTrait;
use crate::types::{IOResult, ImmutableRecord, SeekKey, SeekOp, SeekResult, ValueRef};
use crate::{return_and_restore_if_io, return_if_io, LimboError, Result, Value};
use parking_lot::Mutex;
use std::collections::{BTreeMap, HashMap, HashSet};
use std::fmt::{self, Display};
use std::sync::Arc;
// Architecture of the Aggregate Operator
// ========================================
//
// This operator implements SQL aggregations (GROUP BY, DISTINCT, COUNT, SUM, AVG, MIN, MAX)
// using DBSP-style incremental computation. The key insight is that all these operations
// can be expressed as operations on weighted sets (Z-sets) stored in persistent BTrees.
//
// ## Storage Strategy
//
// We use three different storage encodings (identified by 2-bit type codes in storage IDs):
// - **Regular aggregates** (COUNT/SUM/AVG): Store accumulated state as a blob
// - **MIN/MAX aggregates**: Store individual values; BTree ordering gives us min/max efficiently
// - **DISTINCT tracking**: Store distinct values with weights (positive = present, zero = deleted)
//
// ## MIN/MAX Handling
//
// MIN/MAX are special because they're not fully incrementalizable:
// - **Inserts**: Can be computed incrementally (new_min = min(old_min, new_value))
// - **Deletes**: Must recompute from the BTree when the current min/max is deleted
//
// Our approach:
// 1. Store each value with its weight in a BTree (leveraging natural ordering)
// 2. On insert: Simply compare with current min/max (incremental)
// 3. On delete of current min/max: Scan the BTree to find the next min/max
// - For MIN: scan forward from the beginning to find first value with positive weight
// - For MAX: scan backward from the end to find last value with positive weight
//
// ## DISTINCT Handling
//
// DISTINCT operations (COUNT(DISTINCT), SUM(DISTINCT), etc.) are implemented using the
// weighted set pattern:
// - Each distinct value is stored with a weight (occurrence count)
// - Weight > 0 means the value exists in the current dataset
// - Weight = 0 means the value has been deleted (we may clean these up)
// - We track transitions: when a value's weight crosses zero (appears/disappears)
//
// ## Plain DISTINCT (SELECT DISTINCT)
//
// A clever reuse of infrastructure: SELECT DISTINCT x, y, z is compiled to:
// - GROUP BY x, y, z (making each unique row combination a group)
// - Empty aggregates vector (no actual aggregations to compute)
// - The groups themselves become the distinct rows
//
// This allows us to reuse all the incremental machinery for DISTINCT without special casing.
// The `is_distinct_only` flag indicates this pattern, where the groups ARE the output rows.
//
// ## State Machines
//
// The operator uses async-ready state machines to handle I/O operations:
// - **Eval state machine**: Fetches existing state, applies deltas, recomputes MIN/MAX
// - **Commit state machine**: Persists updated state back to storage
// - Each state represents a resumption point for when I/O operations yield
/// Constants for aggregate type encoding in storage IDs (2 bits)
pub const AGG_TYPE_REGULAR: u8 = 0b00; // COUNT/SUM/AVG
pub const AGG_TYPE_MINMAX: u8 = 0b01; // MIN/MAX (BTree ordering gives both)
pub const AGG_TYPE_DISTINCT: u8 = 0b10; // DISTINCT values tracking
/// Hash a Value to generate an element_id for DISTINCT storage
/// Uses HashableRow with column_idx as rowid for consistent hashing
fn hash_value(value: &Value, column_idx: usize) -> Hash128 {
// Use column_idx as rowid to ensure different columns with same value get different hashes
let row = HashableRow::new(column_idx as i64, vec![value.clone()]);
row.cached_hash()
}
// Serialization type codes for aggregate functions
const AGG_FUNC_COUNT: i64 = 0;
const AGG_FUNC_SUM: i64 = 1;
const AGG_FUNC_AVG: i64 = 2;
const AGG_FUNC_MIN: i64 = 3;
const AGG_FUNC_MAX: i64 = 4;
const AGG_FUNC_COUNT_DISTINCT: i64 = 5;
const AGG_FUNC_SUM_DISTINCT: i64 = 6;
const AGG_FUNC_AVG_DISTINCT: i64 = 7;
#[derive(Debug, Clone, PartialEq)]
pub enum AggregateFunction {
Count,
CountDistinct(usize), // COUNT(DISTINCT column_index)
Sum(usize), // Column index
SumDistinct(usize), // SUM(DISTINCT column_index)
Avg(usize), // Column index
AvgDistinct(usize), // AVG(DISTINCT column_index)
Min(usize), // Column index
Max(usize), // Column index
}
impl Display for AggregateFunction {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
match self {
AggregateFunction::Count => write!(f, "COUNT(*)"),
AggregateFunction::CountDistinct(idx) => write!(f, "COUNT(DISTINCT col{idx})"),
AggregateFunction::Sum(idx) => write!(f, "SUM(col{idx})"),
AggregateFunction::SumDistinct(idx) => write!(f, "SUM(DISTINCT col{idx})"),
AggregateFunction::Avg(idx) => write!(f, "AVG(col{idx})"),
AggregateFunction::AvgDistinct(idx) => write!(f, "AVG(DISTINCT col{idx})"),
AggregateFunction::Min(idx) => write!(f, "MIN(col{idx})"),
AggregateFunction::Max(idx) => write!(f, "MAX(col{idx})"),
}
}
}
impl AggregateFunction {
/// Get the default output column name for this aggregate function
#[inline]
pub fn default_output_name(&self) -> String {
self.to_string()
}
/// Serialize this aggregate function to a Value
/// Returns a vector of values: [type_code, optional_column_index]
pub fn to_values(&self) -> Vec<Value> {
match self {
AggregateFunction::Count => vec![Value::Integer(AGG_FUNC_COUNT)],
AggregateFunction::CountDistinct(idx) => {
vec![
Value::Integer(AGG_FUNC_COUNT_DISTINCT),
Value::Integer(*idx as i64),
]
}
AggregateFunction::Sum(idx) => {
vec![Value::Integer(AGG_FUNC_SUM), Value::Integer(*idx as i64)]
}
AggregateFunction::SumDistinct(idx) => {
vec![
Value::Integer(AGG_FUNC_SUM_DISTINCT),
Value::Integer(*idx as i64),
]
}
AggregateFunction::Avg(idx) => {
vec![Value::Integer(AGG_FUNC_AVG), Value::Integer(*idx as i64)]
}
AggregateFunction::AvgDistinct(idx) => {
vec![
Value::Integer(AGG_FUNC_AVG_DISTINCT),
Value::Integer(*idx as i64),
]
}
AggregateFunction::Min(idx) => {
vec![Value::Integer(AGG_FUNC_MIN), Value::Integer(*idx as i64)]
}
AggregateFunction::Max(idx) => {
vec![Value::Integer(AGG_FUNC_MAX), Value::Integer(*idx as i64)]
}
}
}
/// Deserialize an aggregate function from values
/// Consumes values from the cursor and returns the aggregate function
pub fn from_values(values: &[Value], cursor: &mut usize) -> Result<Self> {
let type_code = values
.get(*cursor)
.ok_or_else(|| LimboError::InternalError("Missing aggregate type code".into()))?;
let agg_fn = match type_code {
Value::Integer(AGG_FUNC_COUNT) => {
*cursor += 1;
AggregateFunction::Count
}
Value::Integer(AGG_FUNC_COUNT_DISTINCT) => {
*cursor += 1;
let idx = values.get(*cursor).ok_or_else(|| {
LimboError::InternalError("Missing COUNT(DISTINCT) column index".into())
})?;
if let Value::Integer(idx) = idx {
*cursor += 1;
AggregateFunction::CountDistinct(*idx as usize)
} else {
return Err(LimboError::InternalError(format!(
"Expected Integer for COUNT(DISTINCT) column index, got {idx:?}"
)));
}
}
Value::Integer(AGG_FUNC_SUM) => {
*cursor += 1;
let idx = values
.get(*cursor)
.ok_or_else(|| LimboError::InternalError("Missing SUM column index".into()))?;
if let Value::Integer(idx) = idx {
*cursor += 1;
AggregateFunction::Sum(*idx as usize)
} else {
return Err(LimboError::InternalError(format!(
"Expected Integer for SUM column index, got {idx:?}"
)));
}
}
Value::Integer(AGG_FUNC_SUM_DISTINCT) => {
*cursor += 1;
let idx = values.get(*cursor).ok_or_else(|| {
LimboError::InternalError("Missing SUM(DISTINCT) column index".into())
})?;
if let Value::Integer(idx) = idx {
*cursor += 1;
AggregateFunction::SumDistinct(*idx as usize)
} else {
return Err(LimboError::InternalError(format!(
"Expected Integer for SUM(DISTINCT) column index, got {idx:?}"
)));
}
}
Value::Integer(AGG_FUNC_AVG) => {
*cursor += 1;
let idx = values
.get(*cursor)
.ok_or_else(|| LimboError::InternalError("Missing AVG column index".into()))?;
if let Value::Integer(idx) = idx {
*cursor += 1;
AggregateFunction::Avg(*idx as usize)
} else {
return Err(LimboError::InternalError(format!(
"Expected Integer for AVG column index, got {idx:?}"
)));
}
}
Value::Integer(AGG_FUNC_AVG_DISTINCT) => {
*cursor += 1;
let idx = values.get(*cursor).ok_or_else(|| {
LimboError::InternalError("Missing AVG(DISTINCT) column index".into())
})?;
if let Value::Integer(idx) = idx {
*cursor += 1;
AggregateFunction::AvgDistinct(*idx as usize)
} else {
return Err(LimboError::InternalError(format!(
"Expected Integer for AVG(DISTINCT) column index, got {idx:?}"
)));
}
}
Value::Integer(AGG_FUNC_MIN) => {
*cursor += 1;
let idx = values
.get(*cursor)
.ok_or_else(|| LimboError::InternalError("Missing MIN column index".into()))?;
if let Value::Integer(idx) = idx {
*cursor += 1;
AggregateFunction::Min(*idx as usize)
} else {
return Err(LimboError::InternalError(format!(
"Expected Integer for MIN column index, got {idx:?}"
)));
}
}
Value::Integer(AGG_FUNC_MAX) => {
*cursor += 1;
let idx = values
.get(*cursor)
.ok_or_else(|| LimboError::InternalError("Missing MAX column index".into()))?;
if let Value::Integer(idx) = idx {
*cursor += 1;
AggregateFunction::Max(*idx as usize)
} else {
return Err(LimboError::InternalError(format!(
"Expected Integer for MAX column index, got {idx:?}"
)));
}
}
_ => {
return Err(LimboError::InternalError(format!(
"Unknown aggregate type code: {type_code:?}"
)))
}
};
Ok(agg_fn)
}
/// Create an AggregateFunction from a SQL function and its arguments
/// Returns None if the function is not a supported aggregate
pub fn from_sql_function(
func: &crate::function::Func,
input_column_idx: Option<usize>,
) -> Option<Self> {
match func {
Func::Agg(agg_func) => {
match agg_func {
AggFunc::Count | AggFunc::Count0 => Some(AggregateFunction::Count),
AggFunc::Sum => input_column_idx.map(AggregateFunction::Sum),
AggFunc::Avg => input_column_idx.map(AggregateFunction::Avg),
AggFunc::Min => input_column_idx.map(AggregateFunction::Min),
AggFunc::Max => input_column_idx.map(AggregateFunction::Max),
_ => None, // Other aggregate functions not yet supported in DBSP
}
}
_ => None, // Not an aggregate function
}
}
}
/// Information about a column that has MIN/MAX aggregations
#[derive(Debug, Clone)]
pub struct AggColumnInfo {
/// Index used for storage key generation
pub index: usize,
/// Whether this column has a MIN aggregate
pub has_min: bool,
/// Whether this column has a MAX aggregate
pub has_max: bool,
}
// group_key_str -> (group_key, state)
type ComputedStates = HashMap<String, (Vec<Value>, AggregateState)>;
// group_key_str -> (column_index, value_as_hashable_row) -> accumulated_weight
pub type MinMaxDeltas = HashMap<String, HashMap<(usize, HashableRow), isize>>;
/// Type for tracking distinct values within a batch
/// Maps: group_key_str -> (column_idx, HashableRow) -> accumulated_weight
/// HashableRow contains the value with column_idx as rowid for proper hashing
type DistinctDeltas = HashMap<String, HashMap<(usize, HashableRow), isize>>;
/// Return type for merge_delta_with_existing function
type MergeResult = (Delta, HashMap<String, (Vec<Value>, AggregateState)>);
/// Information about distinct value transitions for a single column
#[derive(Debug, Clone)]
pub struct DistinctTransition {
pub transition_type: TransitionType,
pub transitioned_value: Value, // The value that was added/removed
}
#[derive(Debug, Clone, PartialEq)]
pub enum TransitionType {
Added, // Value added to distinct set
Removed, // Value removed from distinct set
}
#[derive(Debug)]
enum AggregateCommitState {
Idle,
Eval {
eval_state: EvalState,
},
PersistDelta {
delta: Delta,
computed_states: ComputedStates,
old_states: HashMap<String, i64>, // Track old counts for plain DISTINCT
current_idx: usize,
write_row: WriteRow,
min_max_deltas: MinMaxDeltas,
distinct_deltas: DistinctDeltas,
input_delta: Delta, // Keep original input delta for distinct processing
},
PersistMinMax {
delta: Delta,
min_max_persist_state: MinMaxPersistState,
distinct_deltas: DistinctDeltas,
},
PersistDistinctValues {
delta: Delta,
distinct_persist_state: DistinctPersistState,
},
Done {
delta: Delta,
},
Invalid,
}
// Aggregate-specific eval states
#[derive(Debug)]
pub enum AggregateEvalState {
FetchKey {
delta: Delta, // Keep original delta for merge operation
current_idx: usize,
groups_to_read: Vec<(String, Vec<Value>)>, // Changed to Vec for index-based access
existing_groups: HashMap<String, AggregateState>,
old_values: HashMap<String, Vec<Value>>,
pre_existing_groups: HashSet<String>, // Track groups that existed before this delta
},
FetchAggregateState {
delta: Delta, // Keep original delta for merge operation
current_idx: usize,
groups_to_read: Vec<(String, Vec<Value>)>, // Changed to Vec for index-based access
existing_groups: HashMap<String, AggregateState>,
old_values: HashMap<String, Vec<Value>>,
rowid: Option<i64>, // Rowid found by FetchKey (None if not found)
read_record_state: Box<ReadRecord>,
pre_existing_groups: HashSet<String>, // Track groups that existed before this delta
},
FetchDistinctValues {
delta: Delta, // Keep original delta for merge operation
current_idx: usize,
groups_to_read: Vec<(String, Vec<Value>)>, // Changed to Vec for index-based access
existing_groups: HashMap<String, AggregateState>,
old_values: HashMap<String, Vec<Value>>,
fetch_distinct_state: Box<FetchDistinctState>,
pre_existing_groups: HashSet<String>, // Track groups that existed before this delta
},
RecomputeMinMax {
delta: Delta,
existing_groups: HashMap<String, AggregateState>,
old_values: HashMap<String, Vec<Value>>,
recompute_state: Box<RecomputeMinMax>,
pre_existing_groups: HashSet<String>, // Track groups that existed before this delta
},
Done {
output: (Delta, ComputedStates),
},
}
/// Note that the AggregateOperator essentially implements a ZSet, even
/// though the ZSet structure is never used explicitly. The on-disk btree
/// plays the role of the set!
#[derive(Debug)]
pub struct AggregateOperator {
// Unique operator ID for indexing in persistent storage
pub operator_id: i64,
// GROUP BY column indices
group_by: Vec<usize>,
// Aggregate functions to compute (including MIN/MAX)
pub aggregates: Vec<AggregateFunction>,
// Column names from input
pub input_column_names: Vec<String>,
// Map from column index to aggregate info for quick lookup
pub column_min_max: HashMap<usize, AggColumnInfo>,
// Set of column indices that have distinct aggregates
pub distinct_columns: HashSet<usize>,
tracker: Option<Arc<Mutex<ComputationTracker>>>,
// State machine for commit operation
commit_state: AggregateCommitState,
// SELECT DISTINCT x,y,z.... with no aggregations.
is_distinct_only: bool,
}
/// State for a single group's aggregates
#[derive(Debug, Clone, Default)]
pub struct AggregateState {
// For COUNT: just the count
pub count: i64,
// For SUM: column_index -> sum value
pub sums: HashMap<usize, f64>,
// For AVG: column_index -> (sum, count) for computing average
pub avgs: HashMap<usize, (f64, i64)>,
// For MIN: column_index -> minimum value
pub mins: HashMap<usize, Value>,
// For MAX: column_index -> maximum value
pub maxs: HashMap<usize, Value>,
// For DISTINCT aggregates: column_index -> computed value
// These are populated during eval when we scan the BTree (or in-memory map)
pub distinct_counts: HashMap<usize, i64>,
pub distinct_sums: HashMap<usize, f64>,
// Weights of specific distinct values needed for current delta processing
// (column_index, value) -> weight
// Populated during FetchKey for values mentioned in the delta
pub(crate) distinct_value_weights: HashMap<(usize, HashableRow), i64>,
}
impl AggregateEvalState {
/// Process a delta through the aggregate state machine.
///
/// Control flow is strictly linear for maintainability:
/// 1. FetchKey → FetchAggregateState (always)
/// 2. FetchAggregateState → FetchKey (always, loops until all groups processed)
/// 3. FetchKey (when done) → FetchDistinctValues (always)
/// 4. FetchDistinctValues → RecomputeMinMax (always)
/// 5. RecomputeMinMax → Done (always)
///
/// Some states may be no-ops depending on the operator configuration:
/// - FetchAggregateState: For plain DISTINCT, skips reading aggregate blob (no aggregates to fetch)
/// - FetchDistinctValues: No-op if no distinct columns exist (distinct_columns is empty)
/// - RecomputeMinMax: No-op if no MIN/MAX aggregates exist (has_min_max() returns false)
///
/// This deterministic flow ensures each state always transitions to the same next state,
/// making the state machine easier to understand and debug.
fn process_delta(
&mut self,
operator: &mut AggregateOperator,
cursors: &mut DbspStateCursors,
) -> Result<IOResult<(Delta, ComputedStates)>> {
loop {
match self {
AggregateEvalState::FetchKey {
delta,
current_idx,
groups_to_read,
existing_groups,
old_values,
pre_existing_groups,
} => {
if *current_idx >= groups_to_read.len() {
// All groups have been fetched, move to FetchDistinctValues
// Create FetchDistinctState based on the delta and existing groups
let fetch_distinct_state = FetchDistinctState::new(
delta,
&operator.distinct_columns,
|values| operator.extract_group_key(values),
AggregateOperator::group_key_to_string,
existing_groups,
operator.is_distinct_only,
);
*self = AggregateEvalState::FetchDistinctValues {
delta: std::mem::take(delta),
current_idx: 0,
groups_to_read: std::mem::take(groups_to_read),
existing_groups: std::mem::take(existing_groups),
old_values: std::mem::take(old_values),
fetch_distinct_state: Box::new(fetch_distinct_state),
pre_existing_groups: std::mem::take(pre_existing_groups),
};
} else {
// Get the current group to read
let (group_key_str, _group_key) = &groups_to_read[*current_idx];
// For plain DISTINCT, we still need to transition to FetchAggregateState
// to add the group to existing_groups, but we won't read any aggregate blob
// Build the key for regular aggregate state: (operator_id, zset_hash, element_id=0)
let operator_storage_id =
generate_storage_id(operator.operator_id, 0, AGG_TYPE_REGULAR);
let zset_hash = operator.generate_group_hash(group_key_str);
let element_id = Hash128::new(0, 0); // Always zeros for aggregate state
// Create index key values
let index_key_values = vec![
Value::Integer(operator_storage_id),
zset_hash.to_value(),
element_id.to_value(),
];
// Create an immutable record for the index key
let index_record =
ImmutableRecord::from_values(&index_key_values, index_key_values.len());
// Seek in the index to find if this row exists
let seek_result = return_if_io!(cursors.index_cursor.seek(
SeekKey::IndexKey(&index_record),
SeekOp::GE { eq_only: true }
));
let rowid = if matches!(seek_result, SeekResult::Found) {
// Found in index, get the table rowid
// The btree code handles extracting the rowid from the index record for has_rowid indexes
return_if_io!(cursors.index_cursor.rowid())
} else {
// Not found in index, no existing state
None
};
// Always transition to FetchAggregateState
let taken_existing = std::mem::take(existing_groups);
let taken_old_values = std::mem::take(old_values);
let next_state = AggregateEvalState::FetchAggregateState {
delta: std::mem::take(delta),
current_idx: *current_idx,
groups_to_read: std::mem::take(groups_to_read),
existing_groups: taken_existing,
old_values: taken_old_values,
rowid,
read_record_state: Box::new(ReadRecord::new()),
pre_existing_groups: std::mem::take(pre_existing_groups), // Pass through existing
};
*self = next_state;
}
}
AggregateEvalState::FetchAggregateState {
delta,
current_idx,
groups_to_read,
existing_groups,
old_values,
rowid,
read_record_state,
pre_existing_groups,
} => {
// Get the current group to read
let (group_key_str, group_key) = &groups_to_read[*current_idx];
// For plain DISTINCT, skip aggregate state fetch entirely
// The distinct values are handled separately in FetchDistinctValues
if operator.is_distinct_only {
// Always insert the group key so FetchDistinctState will process it
// The count will be set properly when we fetch distinct values
existing_groups.insert(group_key_str.clone(), AggregateState::default());
} else if let Some(rowid) = rowid {
let key = SeekKey::TableRowId(*rowid);
// Regular aggregates - read the blob
let state = return_if_io!(
read_record_state.read_record(key, &mut cursors.table_cursor)
);
// Process the fetched state
if let Some(state) = state {
let mut old_row = group_key.clone();
old_row.extend(state.to_values(&operator.aggregates));
old_values.insert(group_key_str.clone(), old_row);
existing_groups.insert(group_key_str.clone(), state);
// Track that this group exists in storage
pre_existing_groups.insert(group_key_str.clone());
}
}
// If no rowid, there's no existing state for this group
// Always move to next group via FetchKey
let next_idx = *current_idx + 1;
let taken_existing = std::mem::take(existing_groups);
let taken_old_values = std::mem::take(old_values);
let taken_pre_existing_groups = std::mem::take(pre_existing_groups);
let next_state = AggregateEvalState::FetchKey {
delta: std::mem::take(delta),
current_idx: next_idx,
groups_to_read: std::mem::take(groups_to_read),
existing_groups: taken_existing,
old_values: taken_old_values,
pre_existing_groups: taken_pre_existing_groups,
};
*self = next_state;
}
AggregateEvalState::FetchDistinctValues {
delta,
current_idx: _,
groups_to_read: _,
existing_groups,
old_values,
fetch_distinct_state,
pre_existing_groups,
} => {
// Use FetchDistinctState to read distinct values from BTree storage
return_if_io!(fetch_distinct_state.fetch_distinct_values(
operator.operator_id,
existing_groups,
cursors,
|group_key| operator.generate_group_hash(group_key),
operator.is_distinct_only
));
// For plain DISTINCT, mark groups as "from storage" if they have distinct values
if operator.is_distinct_only {
for (group_key_str, state) in existing_groups.iter() {
// Check if this group has any distinct values with positive weight
let has_values = state.distinct_value_weights.values().any(|&w| w > 0);
if has_values {
pre_existing_groups.insert(group_key_str.clone());
}
}
}
// Extract MIN/MAX deltas for recomputation
let min_max_deltas = operator.extract_min_max_deltas(delta);
// Create RecomputeMinMax before moving existing_groups
let recompute_state = Box::new(RecomputeMinMax::new(
min_max_deltas,
existing_groups,
operator,
));
// Transition to RecomputeMinMax
let next_state = AggregateEvalState::RecomputeMinMax {
delta: std::mem::take(delta),
existing_groups: std::mem::take(existing_groups),
old_values: std::mem::take(old_values),
recompute_state,
pre_existing_groups: std::mem::take(pre_existing_groups),
};
*self = next_state;
}
AggregateEvalState::RecomputeMinMax {
delta,
existing_groups,
old_values,
recompute_state,
pre_existing_groups,
} => {
if operator.has_min_max() {
// Process MIN/MAX recomputation - this will update existing_groups with correct MIN/MAX
return_if_io!(recompute_state.process(existing_groups, operator, cursors));
}
// Now compute final output with updated MIN/MAX values
let (output_delta, computed_states) = operator.merge_delta_with_existing(
delta,
existing_groups,
old_values,
pre_existing_groups,
);
*self = AggregateEvalState::Done {
output: (output_delta, computed_states),
};
}
AggregateEvalState::Done { output } => {
let (delta, computed_states) = output.clone();
return Ok(IOResult::Done((delta, computed_states)));
}
}
}
}
}
impl AggregateState {
pub fn new() -> Self {
Self::default()
}
/// Convert the aggregate state to a vector of Values for unified serialization
/// Format: [count, num_aggregates, (agg_metadata, agg_state)...]
/// Each aggregate includes its type and column index for proper deserialization
pub fn to_value_vector(&self, aggregates: &[AggregateFunction]) -> Vec<Value> {
let mut values = Vec::new();
// Include count first
values.push(Value::Integer(self.count));
// Store number of aggregates
values.push(Value::Integer(aggregates.len() as i64));
// Add each aggregate's metadata and state
for agg in aggregates {
// First, add the aggregate function metadata (type and column index)
values.extend(agg.to_values());
// Then add the state for this aggregate
match agg {
AggregateFunction::Count => {
// Count state is already stored at the beginning
}
AggregateFunction::CountDistinct(col_idx) => {
// Store the distinct count for this column
let count = self.distinct_counts.get(col_idx).copied().unwrap_or(0);
values.push(Value::Integer(count));
}
AggregateFunction::Sum(col_idx) => {
let sum = self.sums.get(col_idx).copied().unwrap_or(0.0);
values.push(Value::Float(sum));
}
AggregateFunction::SumDistinct(col_idx) => {
// Store both the distinct count and sum for this column
let count = self.distinct_counts.get(col_idx).copied().unwrap_or(0);
let sum = self.distinct_sums.get(col_idx).copied().unwrap_or(0.0);
values.push(Value::Integer(count));
values.push(Value::Float(sum));
}
AggregateFunction::Avg(col_idx) => {
let (sum, count) = self.avgs.get(col_idx).copied().unwrap_or((0.0, 0));
values.push(Value::Float(sum));
values.push(Value::Integer(count));
}
AggregateFunction::AvgDistinct(col_idx) => {
// Store both the distinct count and sum for this column
let count = self.distinct_counts.get(col_idx).copied().unwrap_or(0);
let sum = self.distinct_sums.get(col_idx).copied().unwrap_or(0.0);
values.push(Value::Integer(count));
values.push(Value::Float(sum));
}
AggregateFunction::Min(col_idx) => {
if let Some(min_val) = self.mins.get(col_idx) {
values.push(Value::Integer(1)); // Has value
values.push(min_val.clone());
} else {
values.push(Value::Integer(0)); // No value
}
}
AggregateFunction::Max(col_idx) => {
if let Some(max_val) = self.maxs.get(col_idx) {
values.push(Value::Integer(1)); // Has value
values.push(max_val.clone());
} else {
values.push(Value::Integer(0)); // No value
}
}
}
}
values
}
/// Reconstruct aggregate state from a vector of Values
pub fn from_value_vector(values: &[Value]) -> Result<Self> {
let mut cursor = 0;
let mut state = Self::new();
// Read count
let count = values
.get(cursor)
.ok_or_else(|| LimboError::InternalError("Aggregate state missing count".into()))?;
if let Value::Integer(count) = count {
state.count = *count;
cursor += 1;
} else {
return Err(LimboError::InternalError(format!(
"Expected Integer for count, got {count:?}"
)));
}
// Read number of aggregates
let num_aggregates = values
.get(cursor)
.ok_or_else(|| LimboError::InternalError("Missing number of aggregates".into()))?;
let num_aggregates = match num_aggregates {
Value::Integer(n) => *n as usize,
_ => {
return Err(LimboError::InternalError(format!(
"Expected Integer for aggregate count, got {num_aggregates:?}"
)))
}
};
cursor += 1;
// Read each aggregate's state with type and column index
for _ in 0..num_aggregates {
// Deserialize the aggregate function metadata
let agg_fn = AggregateFunction::from_values(values, &mut cursor)?;
// Read the state for this aggregate
match agg_fn {
AggregateFunction::Count => {
// Count state is already stored at the beginning
}
AggregateFunction::CountDistinct(col_idx) => {
let count = values.get(cursor).ok_or_else(|| {
LimboError::InternalError("Missing COUNT(DISTINCT) value".into())
})?;
if let Value::Integer(count) = count {
state.distinct_counts.insert(col_idx, *count);
cursor += 1;
} else {
return Err(LimboError::InternalError(format!(
"Expected Integer for COUNT(DISTINCT) value, got {count:?}"
)));
}
}
AggregateFunction::SumDistinct(col_idx) => {
let count = values.get(cursor).ok_or_else(|| {
LimboError::InternalError("Missing SUM(DISTINCT) count".into())
})?;
if let Value::Integer(count) = count {
state.distinct_counts.insert(col_idx, *count);
cursor += 1;
} else {
return Err(LimboError::InternalError(format!(
"Expected Integer for SUM(DISTINCT) count, got {count:?}"
)));
}
let sum = values.get(cursor).ok_or_else(|| {
LimboError::InternalError("Missing SUM(DISTINCT) sum".into())
})?;
if let Value::Float(sum) = sum {
state.distinct_sums.insert(col_idx, *sum);
cursor += 1;
} else {
return Err(LimboError::InternalError(format!(
"Expected Float for SUM(DISTINCT) sum, got {sum:?}"
)));
}
}
AggregateFunction::AvgDistinct(col_idx) => {
let count = values.get(cursor).ok_or_else(|| {
LimboError::InternalError("Missing AVG(DISTINCT) count".into())
})?;
if let Value::Integer(count) = count {
state.distinct_counts.insert(col_idx, *count);
cursor += 1;
} else {
return Err(LimboError::InternalError(format!(
"Expected Integer for AVG(DISTINCT) count, got {count:?}"
)));
}
let sum = values.get(cursor).ok_or_else(|| {
LimboError::InternalError("Missing AVG(DISTINCT) sum".into())
})?;
if let Value::Float(sum) = sum {
state.distinct_sums.insert(col_idx, *sum);
cursor += 1;
} else {
return Err(LimboError::InternalError(format!(
"Expected Float for AVG(DISTINCT) sum, got {sum:?}"
)));
}
}
AggregateFunction::Sum(col_idx) => {
let sum = values
.get(cursor)
.ok_or_else(|| LimboError::InternalError("Missing SUM value".into()))?;
if let Value::Float(sum) = sum {
state.sums.insert(col_idx, *sum);
cursor += 1;
} else {
return Err(LimboError::InternalError(format!(
"Expected Float for SUM value, got {sum:?}"
)));
}
}
AggregateFunction::Avg(col_idx) => {
let sum = values
.get(cursor)
.ok_or_else(|| LimboError::InternalError("Missing AVG sum value".into()))?;
let sum = match sum {
Value::Float(f) => *f,
_ => {
return Err(LimboError::InternalError(format!(
"Expected Float for AVG sum, got {sum:?}"
)))
}
};
cursor += 1;
let count = values.get(cursor).ok_or_else(|| {
LimboError::InternalError("Missing AVG count value".into())
})?;
let count = match count {
Value::Integer(i) => *i,
_ => {
return Err(LimboError::InternalError(format!(
"Expected Integer for AVG count, got {count:?}"
)))
}
};
cursor += 1;
state.avgs.insert(col_idx, (sum, count));
}
AggregateFunction::Min(col_idx) => {
let has_value = values.get(cursor).ok_or_else(|| {
LimboError::InternalError("Missing MIN has_value flag".into())
})?;
if let Value::Integer(has_value) = has_value {
cursor += 1;
if *has_value == 1 {
let min_val = values
.get(cursor)
.ok_or_else(|| {
LimboError::InternalError("Missing MIN value".into())
})?
.clone();
cursor += 1;
state.mins.insert(col_idx, min_val);
}
} else {
return Err(LimboError::InternalError(format!(
"Expected Integer for MIN has_value flag, got {has_value:?}"
)));
}
}
AggregateFunction::Max(col_idx) => {
let has_value = values.get(cursor).ok_or_else(|| {
LimboError::InternalError("Missing MAX has_value flag".into())
})?;
if let Value::Integer(has_value) = has_value {
cursor += 1;
if *has_value == 1 {
let max_val = values
.get(cursor)
.ok_or_else(|| {
LimboError::InternalError("Missing MAX value".into())
})?
.clone();
cursor += 1;
state.maxs.insert(col_idx, max_val);
}
} else {
return Err(LimboError::InternalError(format!(
"Expected Integer for MAX has_value flag, got {has_value:?}"
)));
}
}
}
}
Ok(state)
}
fn to_blob(&self, aggregates: &[AggregateFunction], group_key: &[Value]) -> Vec<u8> {
let mut all_values = Vec::new();
// Store the group key size first
all_values.push(Value::Integer(group_key.len() as i64));
all_values.extend_from_slice(group_key);
all_values.extend(self.to_value_vector(aggregates));
let record = ImmutableRecord::from_values(&all_values, all_values.len());
record.as_blob().clone()
}
pub fn from_blob(blob: &[u8]) -> Result<(Self, Vec<Value>)> {
let record = ImmutableRecord::from_bin_record(blob.to_vec());
let ref_values = record.get_values();
let mut all_values: Vec<Value> = ref_values.into_iter().map(|rv| rv.to_owned()).collect();
if all_values.is_empty() {
return Err(LimboError::InternalError(
"Aggregate state blob is empty".into(),
));
}
// Read the group key size
let group_key_count = match &all_values[0] {
Value::Integer(n) if *n >= 0 => *n as usize,
Value::Integer(n) => {
return Err(LimboError::InternalError(format!(
"Negative group key count: {n}"
)))
}
other => {
return Err(LimboError::InternalError(format!(
"Expected Integer for group key count, got {other:?}"
)))
}
};
// Remove the group key count from the values
all_values.remove(0);
if all_values.len() < group_key_count {
return Err(LimboError::InternalError(format!(
"Blob too short: expected at least {} values for group key, got {}",
group_key_count,
all_values.len()
)));
}
// Split into group key and state values
let group_key = all_values[..group_key_count].to_vec();
let state_values = &all_values[group_key_count..];
// Reconstruct the aggregate state
let state = Self::from_value_vector(state_values)?;
Ok((state, group_key))
}
/// Apply a delta to this aggregate state
fn apply_delta(
&mut self,
values: &[Value],
weight: isize,
aggregates: &[AggregateFunction],
_column_names: &[String], // No longer needed
distinct_transitions: &HashMap<usize, DistinctTransition>,
) {
// Update COUNT
self.count += weight as i64;
// Track which columns have had their distinct counts/sums updated
// This prevents double-counting when multiple distinct aggregates
// operate on the same column (e.g., COUNT(DISTINCT col), SUM(DISTINCT col), AVG(DISTINCT col))
let mut processed_counts: HashSet<usize> = HashSet::new();
let mut processed_sums: HashSet<usize> = HashSet::new();
// Update distinct aggregate state
for agg in aggregates {
match agg {
AggregateFunction::Count => {
// Already handled above
}
AggregateFunction::CountDistinct(col_idx) => {
// Only update count if we haven't processed this column yet
if !processed_counts.contains(col_idx) {
if let Some(transition) = distinct_transitions.get(col_idx) {
let current_count =
self.distinct_counts.get(col_idx).copied().unwrap_or(0);
let new_count = match transition.transition_type {
TransitionType::Added => current_count + 1,
TransitionType::Removed => current_count - 1,
};
self.distinct_counts.insert(*col_idx, new_count);
processed_counts.insert(*col_idx);
}
}
}
AggregateFunction::SumDistinct(col_idx)
| AggregateFunction::AvgDistinct(col_idx) => {
if let Some(transition) = distinct_transitions.get(col_idx) {
// Update count if not already processed (needed for AVG)
if !processed_counts.contains(col_idx) {
let current_count =
self.distinct_counts.get(col_idx).copied().unwrap_or(0);
let new_count = match transition.transition_type {
TransitionType::Added => current_count + 1,
TransitionType::Removed => current_count - 1,
};
self.distinct_counts.insert(*col_idx, new_count);
processed_counts.insert(*col_idx);
}
// Update sum if not already processed
if !processed_sums.contains(col_idx) {
let current_sum =
self.distinct_sums.get(col_idx).copied().unwrap_or(0.0);
let value_as_float = match &transition.transitioned_value {
Value::Integer(i) => *i as f64,
Value::Float(f) => *f,
_ => 0.0,
};
let new_sum = match transition.transition_type {
TransitionType::Added => current_sum + value_as_float,
TransitionType::Removed => current_sum - value_as_float,
};
self.distinct_sums.insert(*col_idx, new_sum);
processed_sums.insert(*col_idx);
}
}
}
AggregateFunction::Sum(col_idx) => {
if let Some(val) = values.get(*col_idx) {
let num_val = match val {
Value::Integer(i) => *i as f64,
Value::Float(f) => *f,
_ => 0.0,
};
*self.sums.entry(*col_idx).or_insert(0.0) += num_val * weight as f64;
}
}
AggregateFunction::Avg(col_idx) => {
if let Some(val) = values.get(*col_idx) {
let num_val = match val {
Value::Integer(i) => *i as f64,
Value::Float(f) => *f,
_ => 0.0,
};
let (sum, count) = self.avgs.entry(*col_idx).or_insert((0.0, 0));
*sum += num_val * weight as f64;
*count += weight as i64;
}
}
AggregateFunction::Min(_col_name) | AggregateFunction::Max(_col_name) => {
// MIN/MAX cannot be handled incrementally in apply_delta because:
//
// 1. For insertions: We can't just keep the minimum/maximum value.
// We need to track ALL values to handle future deletions correctly.
//
// 2. For deletions (retractions): If we delete the current MIN/MAX,
// we need to find the next best value, which requires knowing all
// other values in the group.
//
// Example: Consider MIN(price) with values [10, 20, 30]
// - Current MIN = 10
// - Delete 10 (weight = -1)
// - New MIN should be 20, but we can't determine this without
// having tracked all values [20, 30]
//
// Therefore, MIN/MAX processing is handled separately:
// - All input values are persisted to the index via persist_min_max()
// - When aggregates have MIN/MAX, we unconditionally transition to
// the RecomputeMinMax state machine (see EvalState::RecomputeMinMax)
// - RecomputeMinMax checks if the current MIN/MAX was deleted, and if so,
// scans the index to find the new MIN/MAX from remaining values
//
// This ensures correctness for incremental computation at the cost of
// additional I/O for MIN/MAX operations.
}
}
}
}
/// Convert aggregate state to output values
///
/// Note: SQLite returns INTEGER for SUM when all inputs are integers, and REAL when any input is REAL.
/// However, in an incremental system like DBSP, we cannot track whether all current values are integers
/// after deletions. For example:
/// - Initial: SUM(10, 20, 30.5) = 60.5 (REAL)
/// - After DELETE 30.5: SUM(10, 20) = 30 (SQLite returns INTEGER, but we only know the sum is 30.0)
///
/// Therefore, we always return REAL for SUM operations.
pub fn to_values(&self, aggregates: &[AggregateFunction]) -> Vec<Value> {
let mut result = Vec::new();
for agg in aggregates {
match agg {
AggregateFunction::Count => {
result.push(Value::Integer(self.count));
}
AggregateFunction::CountDistinct(col_idx) => {
// Return the computed DISTINCT count
let count = self.distinct_counts.get(col_idx).copied().unwrap_or(0);
result.push(Value::Integer(count));
}
AggregateFunction::Sum(col_idx) => {
let sum = self.sums.get(col_idx).copied().unwrap_or(0.0);
result.push(Value::Float(sum));
}
AggregateFunction::SumDistinct(col_idx) => {
// Return the computed SUM(DISTINCT)
let sum = self.distinct_sums.get(col_idx).copied().unwrap_or(0.0);
result.push(Value::Float(sum));
}
AggregateFunction::Avg(col_idx) => {
if let Some((sum, count)) = self.avgs.get(col_idx) {
if *count > 0 {
result.push(Value::Float(sum / *count as f64));
} else {
result.push(Value::Null);
}
} else {
result.push(Value::Null);
}
}
AggregateFunction::AvgDistinct(col_idx) => {
// Compute AVG from SUM(DISTINCT) / COUNT(DISTINCT)
let count = self.distinct_counts.get(col_idx).copied().unwrap_or(0);
if count > 0 {
let sum = self.distinct_sums.get(col_idx).copied().unwrap_or(0.0);
let avg = sum / count as f64;
// AVG always returns a float value for consistency with SQLite
result.push(Value::Float(avg));
} else {
result.push(Value::Null);
}
}
AggregateFunction::Min(col_idx) => {
// Return the MIN value from our state
result.push(self.mins.get(col_idx).cloned().unwrap_or(Value::Null));
}
AggregateFunction::Max(col_idx) => {
// Return the MAX value from our state
result.push(self.maxs.get(col_idx).cloned().unwrap_or(Value::Null));
}
}
}
result
}
}
impl AggregateOperator {
/// Detect if a distinct value crosses the zero boundary (using pre-fetched weights and batch-accumulated weights)
fn detect_distinct_value_transition(
col_idx: usize,
val: &Value,
weight: isize,
existing_state: &AggregateState,
group_distinct_deltas: Option<&HashMap<(usize, HashableRow), isize>>,
) -> Option<DistinctTransition> {
let hashable_row = HashableRow::new(col_idx as i64, vec![val.clone()]);
// Get the weight from storage (pre-fetched in AggregateState)
let storage_count = existing_state
.distinct_value_weights
.get(&(col_idx, hashable_row.clone()))
.copied()
.unwrap_or(0);
// Get the accumulated weight from the current batch (before this row)
let batch_accumulated = if let Some(deltas) = group_distinct_deltas {
deltas
.get(&(col_idx, hashable_row.clone()))
.copied()
.unwrap_or(0)
} else {
0
};
// The old count is storage + batch accumulated so far (before this row)
let old_count = storage_count + batch_accumulated as i64;
// The new count includes the current weight
let new_count = old_count + weight as i64;
// Detect transitions
if old_count <= 0 && new_count > 0 {
// Value added to distinct set
Some(DistinctTransition {
transition_type: TransitionType::Added,
transitioned_value: val.clone(),
})
} else if old_count > 0 && new_count <= 0 {
// Value removed from distinct set
Some(DistinctTransition {
transition_type: TransitionType::Removed,
transitioned_value: val.clone(),
})
} else {
// No transition
None
}
}
/// Detect distinct value transitions for a single row
fn detect_distinct_transitions(
&self,
row_values: &[Value],
weight: isize,
existing_state: &AggregateState,
group_distinct_deltas: Option<&HashMap<(usize, HashableRow), isize>>,
) -> HashMap<usize, DistinctTransition> {
let mut transitions = HashMap::new();
// Plain Distinct doesn't track individual values, so no transitions needed
if self.is_distinct_only {
// Distinct is handled by the count alone in apply_delta
return transitions;
}
// Process each distinct column
for &col_idx in &self.distinct_columns {
let val = match row_values.get(col_idx) {
Some(v) => v,
None => continue,
};
// Skip null values
if val == &Value::Null {
continue;
}
if let Some(transition) = Self::detect_distinct_value_transition(
col_idx,
val,
weight,
existing_state,
group_distinct_deltas,
) {
transitions.insert(col_idx, transition);
}
}
transitions
}
pub fn new(
operator_id: i64,
group_by: Vec<usize>,
aggregates: Vec<AggregateFunction>,
input_column_names: Vec<String>,
) -> Self {
// Precompute flags for runtime efficiency
// Plain DISTINCT is indicated by empty aggregates vector
let is_distinct_only = aggregates.is_empty();
// Build map of column indices to their MIN/MAX info
let mut column_min_max = HashMap::new();
let mut storage_indices = HashMap::new();
let mut current_index = 0;
// First pass: assign storage indices to unique MIN/MAX columns
for agg in &aggregates {
match agg {
AggregateFunction::Min(col_idx) | AggregateFunction::Max(col_idx) => {
storage_indices.entry(*col_idx).or_insert_with(|| {
let idx = current_index;
current_index += 1;
idx
});
}
_ => {}
}
}
// Second pass: build the column info map for MIN/MAX
for agg in &aggregates {
match agg {
AggregateFunction::Min(col_idx) => {
let storage_index = *storage_indices.get(col_idx).unwrap();
let entry = column_min_max.entry(*col_idx).or_insert(AggColumnInfo {
index: storage_index,
has_min: false,
has_max: false,
});
entry.has_min = true;
}
AggregateFunction::Max(col_idx) => {
let storage_index = *storage_indices.get(col_idx).unwrap();
let entry = column_min_max.entry(*col_idx).or_insert(AggColumnInfo {
index: storage_index,
has_min: false,
has_max: false,
});
entry.has_max = true;
}
_ => {}
}
}
// Build the distinct columns set
let mut distinct_columns = HashSet::new();
for agg in &aggregates {
match agg {
AggregateFunction::CountDistinct(col_idx)
| AggregateFunction::SumDistinct(col_idx)
| AggregateFunction::AvgDistinct(col_idx) => {
distinct_columns.insert(*col_idx);
}
_ => {}
}
}
Self {
operator_id,
group_by,
aggregates,
input_column_names,
column_min_max,
distinct_columns,
tracker: None,
commit_state: AggregateCommitState::Idle,
is_distinct_only,
}
}
pub fn has_min_max(&self) -> bool {
!self.column_min_max.is_empty()
}
/// Check if this operator has any DISTINCT aggregates or plain DISTINCT
pub fn has_distinct(&self) -> bool {
!self.distinct_columns.is_empty() || self.is_distinct_only
}
fn eval_internal(
&mut self,
state: &mut EvalState,
cursors: &mut DbspStateCursors,
) -> Result<IOResult<(Delta, ComputedStates)>> {
match state {
EvalState::Uninitialized => {
panic!("Cannot eval AggregateOperator with Uninitialized state");
}
EvalState::Init { deltas } => {
// Aggregate operators only use left_delta, right_delta must be empty
assert!(
deltas.right.is_empty(),
"AggregateOperator expects right_delta to be empty"
);
if deltas.left.changes.is_empty() {
*state = EvalState::Done;
return Ok(IOResult::Done((Delta::new(), HashMap::new())));
}
let mut groups_to_read = BTreeMap::new();
for (row, _weight) in &deltas.left.changes {
let group_key = self.extract_group_key(&row.values);
let group_key_str = Self::group_key_to_string(&group_key);
groups_to_read.insert(group_key_str, group_key);
}
let delta = std::mem::take(&mut deltas.left);
*state = EvalState::Aggregate(Box::new(AggregateEvalState::FetchKey {
delta,
current_idx: 0,
groups_to_read: groups_to_read.into_iter().collect(),
existing_groups: HashMap::new(),
old_values: HashMap::new(),
pre_existing_groups: HashSet::new(), // Initialize empty
}));
}
EvalState::Aggregate(_agg_state) => {
// Already in progress, continue processing below.
}
EvalState::Done => {
panic!("unreachable state! should have returned");
}
EvalState::Join(_) => {
panic!("Join state should not appear in aggregate operator");
}
}
// Process the delta through the aggregate state machine
match state {
EvalState::Aggregate(agg_state) => {
let result = return_if_io!(agg_state.process_delta(self, cursors));
Ok(IOResult::Done(result))
}
_ => panic!("Invalid state for aggregate processing"),
}
}
fn merge_delta_with_existing(
&mut self,
delta: &Delta,
existing_groups: &mut HashMap<String, AggregateState>,
old_values: &mut HashMap<String, Vec<Value>>,
pre_existing_groups: &HashSet<String>,
) -> MergeResult {
let mut output_delta = Delta::new();
let mut temp_keys: HashMap<String, Vec<Value>> = HashMap::new();
// Track distinct value weights as we process the batch
let mut batch_distinct_weights: HashMap<String, HashMap<(usize, HashableRow), isize>> =
HashMap::new();
// Process each change in the delta
for (row, weight) in delta.changes.iter() {
if let Some(tracker) = &self.tracker {
tracker.lock().record_aggregation();
}
// Extract group key
let group_key = self.extract_group_key(&row.values);
let group_key_str = Self::group_key_to_string(&group_key);
// Get or create the state for this group
let state = existing_groups.entry(group_key_str.clone()).or_default();
// Get batch weights for this group
let group_batch_weights = batch_distinct_weights.get(&group_key_str);
// Detect distinct transitions using the existing state and batch-accumulated weights
let distinct_transitions = if self.has_distinct() {
self.detect_distinct_transitions(&row.values, *weight, state, group_batch_weights)
} else {
HashMap::new()
};
// Update batch weights after detecting transitions
if self.has_distinct() {
for &col_idx in &self.distinct_columns {
if let Some(val) = row.values.get(col_idx) {
if val != &Value::Null {
let hashable_row = HashableRow::new(col_idx as i64, vec![val.clone()]);
let group_entry = batch_distinct_weights
.entry(group_key_str.clone())
.or_default();
let weight_entry =
group_entry.entry((col_idx, hashable_row)).or_insert(0);
*weight_entry += weight;
}
}
}
}
temp_keys.insert(group_key_str.clone(), group_key.clone());
// Apply the delta to the state with pre-computed transitions
state.apply_delta(
&row.values,
*weight,
&self.aggregates,
&self.input_column_names,
&distinct_transitions,
);
}
// Generate output delta from temporary states and collect final states
let mut final_states = HashMap::new();
for (group_key_str, state) in existing_groups.iter() {
let group_key = if let Some(key) = temp_keys.get(group_key_str) {
key.clone()
} else if let Some(old_row) = old_values.get(group_key_str) {
// Extract group key from old row (first N columns where N = group_by.len())
old_row[0..self.group_by.len()].to_vec()
} else {
vec![]
};
// Generate synthetic rowid for this group
let result_key = self.generate_group_rowid(group_key_str);
// Always store the state for persistence (even if count=0, we need to delete it)
final_states.insert(group_key_str.clone(), (group_key.clone(), state.clone()));
// Check if we only have DISTINCT (no other aggregates)
if self.is_distinct_only {
// For plain DISTINCT, we output each distinct VALUE (not group)
// state.count tells us how many distinct values have positive weight
// Check if this group had any values before
let old_existed = pre_existing_groups.contains(group_key_str);
let new_exists = state.count > 0;
if old_existed && !new_exists {
// All distinct values removed: output deletion
if let Some(old_row_values) = old_values.get(group_key_str) {
let old_row = HashableRow::new(result_key, old_row_values.clone());
output_delta.changes.push((old_row, -1));
} else {
// For plain DISTINCT, the old row is just the group key itself
let old_row = HashableRow::new(result_key, group_key.clone());
output_delta.changes.push((old_row, -1));
}
} else if !old_existed && new_exists {
// First distinct value added: output insertion
let output_values = group_key.clone();
// DISTINCT doesn't add aggregate values - just the group key
let output_row = HashableRow::new(result_key, output_values.clone());
output_delta.changes.push((output_row, 1));
}
// No output if staying positive or staying at zero
} else {
// Normal aggregates: output deletions and insertions as before
if let Some(old_row_values) = old_values.get(group_key_str) {
let old_row = HashableRow::new(result_key, old_row_values.clone());
output_delta.changes.push((old_row, -1));
}
// Only include groups with count > 0 in the output delta
if state.count > 0 {
// Build output row: group_by columns + aggregate values
let mut output_values = group_key.clone();
let aggregate_values = state.to_values(&self.aggregates);
output_values.extend(aggregate_values);
let output_row = HashableRow::new(result_key, output_values.clone());
output_delta.changes.push((output_row, 1));
}
}
}
(output_delta, final_states)
}
/// Extract distinct values from delta changes for batch tracking
fn extract_distinct_deltas(&self, delta: &Delta) -> DistinctDeltas {
let mut distinct_deltas: DistinctDeltas = HashMap::new();
for (row, weight) in &delta.changes {
let group_key = self.extract_group_key(&row.values);
let group_key_str = Self::group_key_to_string(&group_key);
// Get or create entry for this group
let group_entry = distinct_deltas.entry(group_key_str.clone()).or_default();
if self.is_distinct_only {
// For plain DISTINCT, the group itself is what we're tracking
// We store a single entry that represents "this group exists N times"
// Use column index 0 with the group_key_str as the value
// For group key, use 0 as column index
let key = (
0,
HashableRow::new(0, vec![Value::Text(group_key_str.clone().into())]),
);
let value_entry = group_entry.entry(key).or_insert(0);
*value_entry += weight;
} else {
// For DISTINCT aggregates, track individual column values
for &col_idx in &self.distinct_columns {
if let Some(val) = row.values.get(col_idx) {
// Skip NULL values
if val == &Value::Null {
continue;
}
let key = (col_idx, HashableRow::new(col_idx as i64, vec![val.clone()]));
let value_entry = group_entry.entry(key).or_insert(0);
*value_entry += weight;
}
}
}
}
distinct_deltas
}
/// Extract MIN/MAX values from delta changes for persistence to index
fn extract_min_max_deltas(&self, delta: &Delta) -> MinMaxDeltas {
let mut min_max_deltas: MinMaxDeltas = HashMap::new();
for (row, weight) in &delta.changes {
let group_key = self.extract_group_key(&row.values);
let group_key_str = Self::group_key_to_string(&group_key);
for agg in &self.aggregates {
match agg {
AggregateFunction::Min(col_idx) | AggregateFunction::Max(col_idx) => {
if let Some(val) = row.values.get(*col_idx) {
// Skip NULL values - they don't participate in MIN/MAX
if val == &Value::Null {
continue;
}
// Create a HashableRow with just this value
// Use 0 as rowid since we only care about the value for comparison
let hashable_value = HashableRow::new(0, vec![val.clone()]);
let key = (*col_idx, hashable_value);
let group_entry =
min_max_deltas.entry(group_key_str.clone()).or_default();
let value_entry = group_entry.entry(key).or_insert(0);
// Accumulate the weight
*value_entry += weight;
}
}
_ => {} // Ignore non-MIN/MAX aggregates
}
}
}
min_max_deltas
}
pub fn set_tracker(&mut self, tracker: Arc<Mutex<ComputationTracker>>) {
self.tracker = Some(tracker);
}
/// Generate a hash for a group
/// For no GROUP BY: returns a zero hash
/// For GROUP BY: returns a 128-bit hash of the group key string
pub fn generate_group_hash(&self, group_key_str: &str) -> Hash128 {
if self.group_by.is_empty() {
Hash128::new(0, 0)
} else {
Hash128::hash_str(group_key_str)
}
}
/// Generate a rowid for a group (for output rows)
/// This is NOT the hash used for storage (that's generate_group_hash which returns full 128-bit).
/// This is a synthetic rowid used in place of SQLite's rowid for aggregate output rows.
/// We truncate the 128-bit hash to 64 bits for SQLite rowid compatibility.
pub fn generate_group_rowid(&self, group_key_str: &str) -> i64 {
let hash = self.generate_group_hash(group_key_str);
hash.as_i64()
}
/// Extract group key values from a row
pub fn extract_group_key(&self, values: &[Value]) -> Vec<Value> {
let mut key = Vec::new();
for &idx in &self.group_by {
if let Some(val) = values.get(idx) {
key.push(val.clone());
} else {
key.push(Value::Null);
}
}
key
}
/// Convert group key to string for indexing (since Value doesn't implement Hash)
pub fn group_key_to_string(key: &[Value]) -> String {
key.iter()
.map(|v| format!("{v:?}"))
.collect::<Vec<_>>()
.join(",")
}
}
impl IncrementalOperator for AggregateOperator {
fn eval(
&mut self,
state: &mut EvalState,
cursors: &mut DbspStateCursors,
) -> Result<IOResult<Delta>> {
let (delta, _) = return_if_io!(self.eval_internal(state, cursors));
Ok(IOResult::Done(delta))
}
fn commit(
&mut self,
mut deltas: DeltaPair,
cursors: &mut DbspStateCursors,
) -> Result<IOResult<Delta>> {
// Aggregate operator only uses left delta, right must be empty
assert!(
deltas.right.is_empty(),
"AggregateOperator expects right delta to be empty in commit"
);
let delta = std::mem::take(&mut deltas.left);
loop {
// Note: because we std::mem::replace here (without it, the borrow checker goes nuts,
// because we call self.eval_interval, which requires a mutable borrow), we have to
// restore the state if we return I/O. So we can't use return_if_io!
let mut state =
std::mem::replace(&mut self.commit_state, AggregateCommitState::Invalid);
match &mut state {
AggregateCommitState::Invalid => {
panic!("Reached invalid state! State was replaced, and not replaced back");
}
AggregateCommitState::Idle => {
let eval_state = EvalState::from_delta(delta.clone());
self.commit_state = AggregateCommitState::Eval { eval_state };
}
AggregateCommitState::Eval { ref mut eval_state } => {
// Clone the delta for MIN/MAX processing before eval consumes it
// We need to get the delta from the eval_state if it's still in Init
let input_delta = match eval_state {
EvalState::Init { deltas } => deltas.left.clone(),
_ => Delta::new(), // Empty delta if already processed
};
// Extract MIN/MAX and DISTINCT deltas before any I/O operations
let min_max_deltas = self.extract_min_max_deltas(&input_delta);
// For plain DISTINCT, we need to extract deltas too
let distinct_deltas = if self.has_distinct() || self.is_distinct_only {
self.extract_distinct_deltas(&input_delta)
} else {
HashMap::new()
};
// Get old counts before eval modifies the states
// We need to extract this from the eval_state before it's consumed
let old_states = HashMap::new(); // TODO: Extract from eval_state
let (output_delta, computed_states) = return_and_restore_if_io!(
&mut self.commit_state,
state,
self.eval_internal(eval_state, cursors)
);
self.commit_state = AggregateCommitState::PersistDelta {
delta: output_delta,
computed_states,
old_states,
current_idx: 0,
write_row: WriteRow::new(),
min_max_deltas, // Store for later use
distinct_deltas, // Store for distinct processing
input_delta, // Store original input
};
}
AggregateCommitState::PersistDelta {
delta,
computed_states,
old_states,
current_idx,
write_row,
min_max_deltas,
distinct_deltas,
input_delta,
} => {
let states_vec: Vec<_> = computed_states.iter().collect();
if *current_idx >= states_vec.len() {
// Use the min_max_deltas we extracted earlier from the input delta
self.commit_state = AggregateCommitState::PersistMinMax {
delta: delta.clone(),
min_max_persist_state: MinMaxPersistState::new(min_max_deltas.clone()),
distinct_deltas: distinct_deltas.clone(),
};
} else {
let (group_key_str, (group_key, agg_state)) = states_vec[*current_idx];
// Skip aggregate state persistence for plain DISTINCT
// Plain DISTINCT only uses the distinct value weights, not aggregate state
if self.is_distinct_only {
// Skip to next - distinct values are handled in PersistDistinctValues
// We still need to transition states properly
let next_idx = *current_idx + 1;
if next_idx >= states_vec.len() {
// Done with all groups, move to PersistMinMax
self.commit_state = AggregateCommitState::PersistMinMax {
delta: std::mem::take(delta),
min_max_persist_state: MinMaxPersistState::new(std::mem::take(
min_max_deltas,
)),
distinct_deltas: std::mem::take(distinct_deltas),
};
} else {
// Move to next group
self.commit_state = AggregateCommitState::PersistDelta {
delta: std::mem::take(delta),
computed_states: std::mem::take(computed_states),
old_states: std::mem::take(old_states),
current_idx: next_idx,
write_row: WriteRow::new(),
min_max_deltas: std::mem::take(min_max_deltas),
distinct_deltas: std::mem::take(distinct_deltas),
input_delta: std::mem::take(input_delta),
};
}
continue;
}
// Build the key components for regular aggregates
let operator_storage_id =
generate_storage_id(self.operator_id, 0, AGG_TYPE_REGULAR);
let zset_hash = self.generate_group_hash(group_key_str);
let element_id = Hash128::new(0, 0); // Always zeros for regular aggregates
// Determine weight: 1 if exists, -1 if deleted
let weight = if agg_state.count == 0 { -1 } else { 1 };
// Serialize the aggregate state (only for regular aggregates, not plain DISTINCT)
let state_blob = agg_state.to_blob(&self.aggregates, group_key);
let blob_value = Value::Blob(state_blob);
// Build the aggregate storage format: [operator_id, zset_hash, element_id, value, weight]
let operator_id_val = Value::Integer(operator_storage_id);
let zset_hash_val = zset_hash.to_value();
let element_id_val = element_id.to_value();
let blob_val = blob_value.clone();
// Create index key - the first 3 columns of our primary key
let index_key = vec![
operator_id_val.clone(),
zset_hash_val.clone(),
element_id_val.clone(),
];
// Record values (without weight)
let record_values =
vec![operator_id_val, zset_hash_val, element_id_val, blob_val];
return_and_restore_if_io!(
&mut self.commit_state,
state,
write_row.write_row(cursors, index_key, record_values, weight)
);
let delta = std::mem::take(delta);
let computed_states = std::mem::take(computed_states);
let min_max_deltas = std::mem::take(min_max_deltas);
let distinct_deltas = std::mem::take(distinct_deltas);
let input_delta = std::mem::take(input_delta);
self.commit_state = AggregateCommitState::PersistDelta {
delta,
computed_states,
old_states: std::mem::take(old_states),
current_idx: *current_idx + 1,
write_row: WriteRow::new(), // Reset for next write
min_max_deltas,
distinct_deltas,
input_delta,
};
}
}
AggregateCommitState::PersistMinMax {
delta,
min_max_persist_state,
distinct_deltas,
} => {
if self.has_min_max() {
return_and_restore_if_io!(
&mut self.commit_state,
state,
min_max_persist_state.persist_min_max(
self.operator_id,
&self.column_min_max,
cursors,
|group_key_str| self.generate_group_hash(group_key_str)
)
);
}
// Transition to PersistDistinctValues
let delta = std::mem::take(delta);
let distinct_deltas = std::mem::take(distinct_deltas);
let distinct_persist_state = DistinctPersistState::new(distinct_deltas);
self.commit_state = AggregateCommitState::PersistDistinctValues {
delta,
distinct_persist_state,
};
}
AggregateCommitState::PersistDistinctValues {
delta,
distinct_persist_state,
} => {
if self.has_distinct() {
// Use the state machine to persist distinct values to BTree
return_and_restore_if_io!(
&mut self.commit_state,
state,
distinct_persist_state.persist_distinct_values(
self.operator_id,
cursors,
|group_key_str| self.generate_group_hash(group_key_str)
)
);
}
// Transition to Done
let delta = std::mem::take(delta);
self.commit_state = AggregateCommitState::Done { delta };
}
AggregateCommitState::Done { delta } => {
self.commit_state = AggregateCommitState::Idle;
let delta = std::mem::take(delta);
return Ok(IOResult::Done(delta));
}
}
}
}
fn set_tracker(&mut self, tracker: Arc<Mutex<ComputationTracker>>) {
self.tracker = Some(tracker);
}
}
/// State machine for recomputing MIN/MAX values after deletion
#[derive(Debug)]
pub enum RecomputeMinMax {
ProcessElements {
/// Current column being processed
current_column_idx: usize,
/// Columns to process (combined MIN and MAX)
columns_to_process: Vec<(String, usize, bool)>, // (group_key, column_name, is_min)
/// MIN/MAX deltas for checking values and weights
min_max_deltas: MinMaxDeltas,
},
Scan {
/// Columns still to process
columns_to_process: Vec<(String, usize, bool)>,
/// Current index in columns_to_process (will resume from here)
current_column_idx: usize,
/// MIN/MAX deltas for checking values and weights
min_max_deltas: MinMaxDeltas,
/// Current group key being processed
group_key: String,
/// Current column name being processed
column_name: usize,
/// Whether we're looking for MIN (true) or MAX (false)
is_min: bool,
/// The scan state machine for finding the new MIN/MAX
scan_state: Box<ScanState>,
},
Done,
}
impl RecomputeMinMax {
pub fn new(
min_max_deltas: MinMaxDeltas,
existing_groups: &HashMap<String, AggregateState>,
operator: &AggregateOperator,
) -> Self {
let mut groups_to_check: HashSet<(String, usize, bool)> = HashSet::new();
// Remember the min_max_deltas are essentially just the only column that is affected by
// this min/max, in delta (actually ZSet - consolidated delta) format. This makes it easier
// for us to consume it in here.
//
// The most challenging case is the case where there is a retraction, since we need to go
// back to the index.
for (group_key_str, values) in &min_max_deltas {
for ((col_name, hashable_row), weight) in values {
let col_info = operator.column_min_max.get(col_name);
let value = &hashable_row.values[0];
if *weight < 0 {
// Deletion detected - check if it's the current MIN/MAX
if let Some(state) = existing_groups.get(group_key_str) {
// Check for MIN
if let Some(current_min) = state.mins.get(col_name) {
if current_min == value {
groups_to_check.insert((group_key_str.clone(), *col_name, true));
}
}
// Check for MAX
if let Some(current_max) = state.maxs.get(col_name) {
if current_max == value {
groups_to_check.insert((group_key_str.clone(), *col_name, false));
}
}
}
} else if *weight > 0 {
// If it is not found in the existing groups, then we only need to care
// about this if this is a new record being inserted
if let Some(info) = col_info {
if info.has_min {
groups_to_check.insert((group_key_str.clone(), *col_name, true));
}
if info.has_max {
groups_to_check.insert((group_key_str.clone(), *col_name, false));
}
}
}
}
}
if groups_to_check.is_empty() {
// No recomputation or initialization needed
Self::Done
} else {
// Convert HashSet to Vec for indexed processing
let groups_to_check_vec: Vec<_> = groups_to_check.into_iter().collect();
Self::ProcessElements {
current_column_idx: 0,
columns_to_process: groups_to_check_vec,
min_max_deltas,
}
}
}
pub fn process(
&mut self,
existing_groups: &mut HashMap<String, AggregateState>,
operator: &AggregateOperator,
cursors: &mut DbspStateCursors,
) -> Result<IOResult<()>> {
loop {
match self {
RecomputeMinMax::ProcessElements {
current_column_idx,
columns_to_process,
min_max_deltas,
} => {
if *current_column_idx >= columns_to_process.len() {
*self = RecomputeMinMax::Done;
return Ok(IOResult::Done(()));
}
let (group_key, column_name, is_min) =
columns_to_process[*current_column_idx].clone();
// Column name is already the index
// Get the storage index from column_min_max map
let column_info = operator
.column_min_max
.get(&column_name)
.expect("Column should exist in column_min_max map");
let storage_index = column_info.index;
// Get current value from existing state
let current_value = existing_groups.get(&group_key).and_then(|state| {
if is_min {
state.mins.get(&column_name).cloned()
} else {
state.maxs.get(&column_name).cloned()
}
});
// Create storage keys for index lookup
let storage_id =
generate_storage_id(operator.operator_id, storage_index, AGG_TYPE_MINMAX);
let zset_hash = operator.generate_group_hash(&group_key);
// Get the values for this group from min_max_deltas
let group_values = min_max_deltas.get(&group_key).cloned().unwrap_or_default();
let columns_to_process = std::mem::take(columns_to_process);
let min_max_deltas = std::mem::take(min_max_deltas);
let scan_state = if is_min {
Box::new(ScanState::new_for_min(
current_value,
group_key.clone(),
column_name,
storage_id,
zset_hash,
group_values,
))
} else {
Box::new(ScanState::new_for_max(
current_value,
group_key.clone(),
column_name,
storage_id,
zset_hash,
group_values,
))
};
*self = RecomputeMinMax::Scan {
columns_to_process,
current_column_idx: *current_column_idx,
min_max_deltas,
group_key,
column_name,
is_min,
scan_state,
};
}
RecomputeMinMax::Scan {
columns_to_process,
current_column_idx,
min_max_deltas,
group_key,
column_name,
is_min,
scan_state,
} => {
// Find new value using the scan state machine
let new_value = return_if_io!(scan_state.find_new_value(cursors));
// Update the state with new value (create if doesn't exist)
let state = existing_groups.entry(group_key.clone()).or_default();
if *is_min {
if let Some(min_val) = new_value {
state.mins.insert(*column_name, min_val);
} else {
state.mins.remove(column_name);
}
} else if let Some(max_val) = new_value {
state.maxs.insert(*column_name, max_val);
} else {
state.maxs.remove(column_name);
}
// Move to next column
let min_max_deltas = std::mem::take(min_max_deltas);
let columns_to_process = std::mem::take(columns_to_process);
*self = RecomputeMinMax::ProcessElements {
current_column_idx: *current_column_idx + 1,
columns_to_process,
min_max_deltas,
};
}
RecomputeMinMax::Done => {
return Ok(IOResult::Done(()));
}
}
}
}
}
/// State machine for scanning through the index to find new MIN/MAX values
#[derive(Debug)]
pub enum ScanState {
CheckCandidate {
/// Current candidate value for MIN/MAX
candidate: Option<Value>,
/// Group key being processed
group_key: String,
/// Column name being processed
column_name: usize,
/// Storage ID for the index seek
storage_id: i64,
/// ZSet ID for the group
zset_hash: Hash128,
/// Group values from MinMaxDeltas: (column_name, HashableRow) -> weight
group_values: HashMap<(usize, HashableRow), isize>,
/// Whether we're looking for MIN (true) or MAX (false)
is_min: bool,
},
FetchNextCandidate {
/// Current candidate to seek past
current_candidate: Value,
/// Group key being processed
group_key: String,
/// Column name being processed
column_name: usize,
/// Storage ID for the index seek
storage_id: i64,
/// ZSet ID for the group
zset_hash: Hash128,
/// Group values from MinMaxDeltas: (column_name, HashableRow) -> weight
group_values: HashMap<(usize, HashableRow), isize>,
/// Whether we're looking for MIN (true) or MAX (false)
is_min: bool,
},
Done {
/// The final MIN/MAX value found
result: Option<Value>,
},
}
impl ScanState {
pub fn new_for_min(
current_min: Option<Value>,
group_key: String,
column_name: usize,
storage_id: i64,
zset_hash: Hash128,
group_values: HashMap<(usize, HashableRow), isize>,
) -> Self {
Self::CheckCandidate {
candidate: current_min,
group_key,
column_name,
storage_id,
zset_hash,
group_values,
is_min: true,
}
}
// Extract a new candidate from the index. It is possible that, when searching,
// we end up going into a different operator altogether. That means we have
// exhausted this operator (or group) entirely, and no good candidate was found
fn extract_new_candidate(
cursors: &mut DbspStateCursors,
index_record: &ImmutableRecord,
seek_op: SeekOp,
storage_id: i64,
zset_hash: Hash128,
) -> Result<IOResult<Option<Value>>> {
let seek_result = return_if_io!(cursors
.index_cursor
.seek(SeekKey::IndexKey(index_record), seek_op));
if !matches!(seek_result, SeekResult::Found) {
return Ok(IOResult::Done(None));
}
let record = return_if_io!(cursors.index_cursor.record()).ok_or_else(|| {
LimboError::InternalError(
"Record found on the cursor, but could not be read".to_string(),
)
})?;
let values = record.get_values();
if values.len() < 3 {
return Ok(IOResult::Done(None));
}
let Some(rec_storage_id) = values.first() else {
return Ok(IOResult::Done(None));
};
let Some(rec_zset_hash) = values.get(1) else {
return Ok(IOResult::Done(None));
};
// Check if we're still in the same group
if let ValueRef::Integer(rec_sid) = rec_storage_id {
if *rec_sid != storage_id {
return Ok(IOResult::Done(None));
}
} else {
return Ok(IOResult::Done(None));
}
// Compare zset_hash as blob
if let ValueRef::Blob(rec_zset_blob) = rec_zset_hash {
if let Some(rec_hash) = Hash128::from_blob(rec_zset_blob) {
if rec_hash != zset_hash {
return Ok(IOResult::Done(None));
}
} else {
return Ok(IOResult::Done(None));
}
} else {
return Ok(IOResult::Done(None));
}
// Get the value (3rd element)
Ok(IOResult::Done(values.get(2).map(|v| v.to_owned())))
}
pub fn new_for_max(
current_max: Option<Value>,
group_key: String,
column_name: usize,
storage_id: i64,
zset_hash: Hash128,
group_values: HashMap<(usize, HashableRow), isize>,
) -> Self {
Self::CheckCandidate {
candidate: current_max,
group_key,
column_name,
storage_id,
zset_hash,
group_values,
is_min: false,
}
}
pub fn find_new_value(
&mut self,
cursors: &mut DbspStateCursors,
) -> Result<IOResult<Option<Value>>> {
loop {
match self {
ScanState::CheckCandidate {
candidate,
group_key,
column_name,
storage_id,
zset_hash,
group_values,
is_min,
} => {
// First, check if we have a candidate
if let Some(cand_val) = candidate {
// Check if the candidate is retracted (weight <= 0)
// Create a HashableRow to look up the weight
let hashable_cand = HashableRow::new(0, vec![cand_val.clone()]);
let key = (*column_name, hashable_cand);
let is_retracted =
group_values.get(&key).is_some_and(|weight| *weight <= 0);
if is_retracted {
// Candidate is retracted, need to fetch next from index
*self = ScanState::FetchNextCandidate {
current_candidate: cand_val.clone(),
group_key: std::mem::take(group_key),
column_name: std::mem::take(column_name),
storage_id: *storage_id,
zset_hash: *zset_hash,
group_values: std::mem::take(group_values),
is_min: *is_min,
};
continue;
}
}
// Candidate is valid or we have no candidate
// Now find the best value from insertions in group_values
let mut best_from_zset = None;
for ((col, hashable_val), weight) in group_values.iter() {
if col == column_name && *weight > 0 {
let value = &hashable_val.values[0];
// Skip NULL values - they don't participate in MIN/MAX
if value == &Value::Null {
continue;
}
// This is an insertion for our column
if let Some(ref current_best) = best_from_zset {
if *is_min {
if value.cmp(current_best) == std::cmp::Ordering::Less {
best_from_zset = Some(value.clone());
}
} else if value.cmp(current_best) == std::cmp::Ordering::Greater {
best_from_zset = Some(value.clone());
}
} else {
best_from_zset = Some(value.clone());
}
}
}
// Compare candidate with best from ZSet, filtering out NULLs
let result = match (&candidate, &best_from_zset) {
(Some(cand), Some(zset_val)) if cand != &Value::Null => {
if *is_min {
if zset_val.cmp(cand) == std::cmp::Ordering::Less {
Some(zset_val.clone())
} else {
Some(cand.clone())
}
} else if zset_val.cmp(cand) == std::cmp::Ordering::Greater {
Some(zset_val.clone())
} else {
Some(cand.clone())
}
}
(Some(cand), None) if cand != &Value::Null => Some(cand.clone()),
(None, Some(zset_val)) => Some(zset_val.clone()),
(Some(cand), Some(_)) if cand == &Value::Null => best_from_zset,
_ => None,
};
*self = ScanState::Done { result };
}
ScanState::FetchNextCandidate {
current_candidate,
group_key,
column_name,
storage_id,
zset_hash,
group_values,
is_min,
} => {
// Seek to the next value in the index
let index_key = vec![
Value::Integer(*storage_id),
zset_hash.to_value(),
current_candidate.clone(),
];
let index_record = ImmutableRecord::from_values(&index_key, index_key.len());
let seek_op = if *is_min {
SeekOp::GT // For MIN, seek greater than current
} else {
SeekOp::LT // For MAX, seek less than current
};
let new_candidate = return_if_io!(Self::extract_new_candidate(
cursors,
&index_record,
seek_op,
*storage_id,
*zset_hash
));
*self = ScanState::CheckCandidate {
candidate: new_candidate,
group_key: std::mem::take(group_key),
column_name: std::mem::take(column_name),
storage_id: *storage_id,
zset_hash: *zset_hash,
group_values: std::mem::take(group_values),
is_min: *is_min,
};
}
ScanState::Done { result } => {
return Ok(IOResult::Done(result.clone()));
}
}
}
}
}
/// State machine for persisting Min/Max values to storage
#[derive(Debug)]
pub enum MinMaxPersistState {
Init {
min_max_deltas: MinMaxDeltas,
group_keys: Vec<String>,
},
ProcessGroup {
min_max_deltas: MinMaxDeltas,
group_keys: Vec<String>,
group_idx: usize,
value_idx: usize,
},
WriteValue {
min_max_deltas: MinMaxDeltas,
group_keys: Vec<String>,
group_idx: usize,
value_idx: usize,
value: Value,
column_name: usize,
weight: isize,
write_row: WriteRow,
},
Done,
}
/// State machine for fetching distinct values from BTree storage
#[derive(Debug)]
pub enum FetchDistinctState {
Init {
groups_to_fetch: Vec<(String, HashMap<usize, HashSet<HashableRow>>)>,
},
FetchGroup {
groups_to_fetch: Vec<(String, HashMap<usize, HashSet<HashableRow>>)>,
group_idx: usize,
value_idx: usize,
values_to_fetch: Vec<(usize, Value)>,
},
ReadValue {
groups_to_fetch: Vec<(String, HashMap<usize, HashSet<HashableRow>>)>,
group_idx: usize,
value_idx: usize,
values_to_fetch: Vec<(usize, Value)>,
group_key: String,
column_idx: usize,
value: Value,
},
Done,
}
impl FetchDistinctState {
/// Add fetch entry for plain DISTINCT - the group itself is the distinct value
fn add_plain_distinct_fetch(
group_entry: &mut HashMap<usize, HashSet<HashableRow>>,
group_key_str: &str,
) {
let group_value = Value::Text(group_key_str.to_string().into());
group_entry
.entry(0)
.or_default()
.insert(HashableRow::new(0, vec![group_value]));
}
/// Add fetch entries for DISTINCT aggregates - individual column values
fn add_aggregate_distinct_fetch(
group_entry: &mut HashMap<usize, HashSet<HashableRow>>,
row_values: &[Value],
distinct_columns: &HashSet<usize>,
) {
for &col_idx in distinct_columns {
if let Some(val) = row_values.get(col_idx) {
if val != &Value::Null {
group_entry
.entry(col_idx)
.or_default()
.insert(HashableRow::new(col_idx as i64, vec![val.clone()]));
}
}
}
}
pub fn new(
delta: &Delta,
distinct_columns: &HashSet<usize>,
extract_group_key: impl Fn(&[Value]) -> Vec<Value>,
group_key_to_string: impl Fn(&[Value]) -> String,
existing_groups: &HashMap<String, AggregateState>,
is_plain_distinct: bool,
) -> Self {
let mut groups_to_fetch: HashMap<String, HashMap<usize, HashSet<HashableRow>>> =
HashMap::new();
for (row, _weight) in &delta.changes {
let group_key = extract_group_key(&row.values);
let group_key_str = group_key_to_string(&group_key);
// Skip groups we don't need to fetch
// For DISTINCT aggregates, only fetch for existing groups
if !is_plain_distinct && !existing_groups.contains_key(&group_key_str) {
continue;
}
let group_entry = groups_to_fetch.entry(group_key_str.clone()).or_default();
if is_plain_distinct {
Self::add_plain_distinct_fetch(group_entry, &group_key_str);
} else {
Self::add_aggregate_distinct_fetch(group_entry, &row.values, distinct_columns);
}
}
let groups_to_fetch: Vec<_> = groups_to_fetch.into_iter().collect();
if groups_to_fetch.is_empty() {
Self::Done
} else {
Self::Init { groups_to_fetch }
}
}
pub fn fetch_distinct_values(
&mut self,
operator_id: i64,
existing_groups: &mut HashMap<String, AggregateState>,
cursors: &mut DbspStateCursors,
generate_group_hash: impl Fn(&str) -> Hash128,
is_plain_distinct: bool,
) -> Result<IOResult<()>> {
loop {
match self {
FetchDistinctState::Init { groups_to_fetch } => {
if groups_to_fetch.is_empty() {
*self = FetchDistinctState::Done;
continue;
}
let groups = std::mem::take(groups_to_fetch);
*self = FetchDistinctState::FetchGroup {
groups_to_fetch: groups,
group_idx: 0,
value_idx: 0,
values_to_fetch: Vec::new(),
};
}
FetchDistinctState::FetchGroup {
groups_to_fetch,
group_idx,
value_idx,
values_to_fetch,
} => {
if *group_idx >= groups_to_fetch.len() {
*self = FetchDistinctState::Done;
continue;
}
// Build list of values to fetch for current group if not done
if values_to_fetch.is_empty() && *group_idx < groups_to_fetch.len() {
let (_group_key, cols_values) = &groups_to_fetch[*group_idx];
for (col_idx, values) in cols_values {
for hashable_row in values {
// Extract the value from HashableRow
values_to_fetch
.push((*col_idx, hashable_row.values.first().unwrap().clone()));
}
}
}
if *value_idx >= values_to_fetch.len() {
// Move to next group
*group_idx += 1;
*value_idx = 0;
values_to_fetch.clear();
continue;
}
// Fetch current value
let (group_key, _) = groups_to_fetch[*group_idx].clone();
let (column_idx, value) = values_to_fetch[*value_idx].clone();
let groups = std::mem::take(groups_to_fetch);
let values = std::mem::take(values_to_fetch);
*self = FetchDistinctState::ReadValue {
groups_to_fetch: groups,
group_idx: *group_idx,
value_idx: *value_idx,
values_to_fetch: values,
group_key,
column_idx,
value,
};
}
FetchDistinctState::ReadValue {
groups_to_fetch,
group_idx,
value_idx,
values_to_fetch,
group_key,
column_idx,
value,
} => {
// Read the record from BTree using the same pattern as WriteRow:
// 1. Seek in index to find the entry
// 2. Get rowid from index cursor
// 3. Use rowid to read from table cursor
let storage_id =
generate_storage_id(operator_id, *column_idx, AGG_TYPE_DISTINCT);
let zset_hash = generate_group_hash(group_key);
let element_id = hash_value(value, *column_idx);
// First, seek in the index cursor
let index_key = vec![
Value::Integer(storage_id),
zset_hash.to_value(),
element_id.to_value(),
];
let index_record = ImmutableRecord::from_values(&index_key, index_key.len());
let seek_result = return_if_io!(cursors.index_cursor.seek(
SeekKey::IndexKey(&index_record),
SeekOp::GE { eq_only: true }
));
// Early exit if not found in index
if !matches!(seek_result, SeekResult::Found) {
let groups = std::mem::take(groups_to_fetch);
let values = std::mem::take(values_to_fetch);
*self = FetchDistinctState::FetchGroup {
groups_to_fetch: groups,
group_idx: *group_idx,
value_idx: *value_idx + 1,
values_to_fetch: values,
};
continue;
}
// Get the rowid from the index cursor
let rowid = return_if_io!(cursors.index_cursor.rowid());
// Early exit if no rowid
let rowid = match rowid {
Some(id) => id,
None => {
let groups = std::mem::take(groups_to_fetch);
let values = std::mem::take(values_to_fetch);
*self = FetchDistinctState::FetchGroup {
groups_to_fetch: groups,
group_idx: *group_idx,
value_idx: *value_idx + 1,
values_to_fetch: values,
};
continue;
}
};
// Now seek in the table cursor using the rowid
let table_result = return_if_io!(cursors
.table_cursor
.seek(SeekKey::TableRowId(rowid), SeekOp::GE { eq_only: true }));
// Early exit if not found in table
if !matches!(table_result, SeekResult::Found) {
let groups = std::mem::take(groups_to_fetch);
let values = std::mem::take(values_to_fetch);
*self = FetchDistinctState::FetchGroup {
groups_to_fetch: groups,
group_idx: *group_idx,
value_idx: *value_idx + 1,
values_to_fetch: values,
};
continue;
}
// Read the actual record from the table cursor
let record = return_if_io!(cursors.table_cursor.record());
if let Some(r) = record {
let values = r.get_values();
// The table has 5 columns: storage_id, zset_hash, element_id, blob, weight
// The weight is at index 4
if values.len() >= 5 {
// Get the weight directly from column 4
let weight = match values[4].to_owned() {
Value::Integer(w) => w,
_ => 0,
};
// Store the weight in the existing group's state
let state = existing_groups.entry(group_key.clone()).or_default();
state.distinct_value_weights.insert(
(
*column_idx,
HashableRow::new(*column_idx as i64, vec![value.clone()]),
),
weight,
);
}
}
// Move to next value
let groups = std::mem::take(groups_to_fetch);
let values = std::mem::take(values_to_fetch);
*self = FetchDistinctState::FetchGroup {
groups_to_fetch: groups,
group_idx: *group_idx,
value_idx: *value_idx + 1,
values_to_fetch: values,
};
}
FetchDistinctState::Done => {
// For plain DISTINCT, construct AggregateState from the weights we fetched
if is_plain_distinct {
for (_group_key_str, state) in existing_groups.iter_mut() {
// For plain DISTINCT, sum all the weights to get total count
// Each weight represents how many times the distinct value appears
let total_weight: i64 = state.distinct_value_weights.values().sum();
// Set the count based on total weight
state.count = total_weight;
}
}
return Ok(IOResult::Done(()));
}
}
}
}
}
/// State machine for persisting distinct values to BTree storage
#[derive(Debug)]
pub enum DistinctPersistState {
Init {
distinct_deltas: DistinctDeltas,
group_keys: Vec<String>,
},
ProcessGroup {
distinct_deltas: DistinctDeltas,
group_keys: Vec<String>,
group_idx: usize,
value_keys: Vec<(usize, HashableRow)>, // (col_idx, value) pairs for current group
value_idx: usize,
},
WriteValue {
distinct_deltas: DistinctDeltas,
group_keys: Vec<String>,
group_idx: usize,
value_keys: Vec<(usize, HashableRow)>,
value_idx: usize,
group_key: String,
col_idx: usize,
value: Value,
weight: isize,
write_row: WriteRow,
},
Done,
}
impl DistinctPersistState {
pub fn new(distinct_deltas: DistinctDeltas) -> Self {
let group_keys: Vec<String> = distinct_deltas.keys().cloned().collect();
Self::Init {
distinct_deltas,
group_keys,
}
}
pub fn persist_distinct_values(
&mut self,
operator_id: i64,
cursors: &mut DbspStateCursors,
generate_group_hash: impl Fn(&str) -> Hash128,
) -> Result<IOResult<()>> {
loop {
match self {
DistinctPersistState::Init {
distinct_deltas,
group_keys,
} => {
let distinct_deltas = std::mem::take(distinct_deltas);
let group_keys = std::mem::take(group_keys);
*self = DistinctPersistState::ProcessGroup {
distinct_deltas,
group_keys,
group_idx: 0,
value_keys: Vec::new(),
value_idx: 0,
};
}
DistinctPersistState::ProcessGroup {
distinct_deltas,
group_keys,
group_idx,
value_keys,
value_idx,
} => {
// Check if we're past all groups
if *group_idx >= group_keys.len() {
*self = DistinctPersistState::Done;
continue;
}
// Check if we need to get value_keys for current group
if value_keys.is_empty() && *group_idx < group_keys.len() {
let group_key_str = &group_keys[*group_idx];
if let Some(group_values) = distinct_deltas.get(group_key_str) {
*value_keys = group_values.keys().cloned().collect();
}
}
// Check if we have more values in current group
if *value_idx >= value_keys.len() {
*group_idx += 1;
*value_idx = 0;
value_keys.clear();
continue;
}
// Process current value
let group_key = group_keys[*group_idx].clone();
let (col_idx, hashable_row) = value_keys[*value_idx].clone();
let weight = distinct_deltas[&group_key][&(col_idx, hashable_row.clone())];
// Extract the value from HashableRow (it's the first element in values vector)
let value = hashable_row.values.first().unwrap().clone();
let distinct_deltas = std::mem::take(distinct_deltas);
let group_keys = std::mem::take(group_keys);
let value_keys = std::mem::take(value_keys);
*self = DistinctPersistState::WriteValue {
distinct_deltas,
group_keys,
group_idx: *group_idx,
value_keys,
value_idx: *value_idx,
group_key,
col_idx,
value,
weight,
write_row: WriteRow::new(),
};
}
DistinctPersistState::WriteValue {
distinct_deltas,
group_keys,
group_idx,
value_keys,
value_idx,
group_key,
col_idx,
value,
weight,
write_row,
} => {
// Build the key components for DISTINCT storage
let storage_id = generate_storage_id(operator_id, *col_idx, AGG_TYPE_DISTINCT);
let zset_hash = generate_group_hash(group_key);
// For DISTINCT, element_id is a hash of the value
let element_id = hash_value(value, *col_idx);
// Create index key
let index_key = vec![
Value::Integer(storage_id),
zset_hash.to_value(),
element_id.to_value(),
];
// Record values (operator_id, zset_hash, element_id, weight_blob)
// Store weight as a minimal AggregateState blob so ReadRecord can parse it
let weight_state = AggregateState {
count: *weight as i64,
..Default::default()
};
let weight_blob = weight_state.to_blob(&[], &[]);
let record_values = vec![
Value::Integer(storage_id),
zset_hash.to_value(),
element_id.to_value(),
Value::Blob(weight_blob),
];
// Write to BTree
return_if_io!(write_row.write_row(cursors, index_key, record_values, *weight));
// Move to next value
let distinct_deltas = std::mem::take(distinct_deltas);
let group_keys = std::mem::take(group_keys);
let value_keys = std::mem::take(value_keys);
*self = DistinctPersistState::ProcessGroup {
distinct_deltas,
group_keys,
group_idx: *group_idx,
value_keys,
value_idx: *value_idx + 1,
};
}
DistinctPersistState::Done => {
return Ok(IOResult::Done(()));
}
}
}
}
}
impl MinMaxPersistState {
pub fn new(min_max_deltas: MinMaxDeltas) -> Self {
let group_keys: Vec<String> = min_max_deltas.keys().cloned().collect();
Self::Init {
min_max_deltas,
group_keys,
}
}
pub fn persist_min_max(
&mut self,
operator_id: i64,
column_min_max: &HashMap<usize, AggColumnInfo>,
cursors: &mut DbspStateCursors,
generate_group_hash: impl Fn(&str) -> Hash128,
) -> Result<IOResult<()>> {
loop {
match self {
MinMaxPersistState::Init {
min_max_deltas,
group_keys,
} => {
let min_max_deltas = std::mem::take(min_max_deltas);
let group_keys = std::mem::take(group_keys);
*self = MinMaxPersistState::ProcessGroup {
min_max_deltas,
group_keys,
group_idx: 0,
value_idx: 0,
};
}
MinMaxPersistState::ProcessGroup {
min_max_deltas,
group_keys,
group_idx,
value_idx,
} => {
// Check if we're past all groups
if *group_idx >= group_keys.len() {
*self = MinMaxPersistState::Done;
continue;
}
let group_key_str = &group_keys[*group_idx];
let values = &min_max_deltas[group_key_str]; // This should always exist
// Convert HashMap to Vec for indexed access
let values_vec: Vec<_> = values.iter().collect();
// Check if we have more values in current group
if *value_idx >= values_vec.len() {
*group_idx += 1;
*value_idx = 0;
// Continue to check if we're past all groups now
continue;
}
// Process current value and extract what we need before taking ownership
let ((column_name, hashable_row), weight) = values_vec[*value_idx];
let column_name = *column_name;
let value = hashable_row.values[0].clone(); // Extract the Value from HashableRow
let weight = *weight;
let min_max_deltas = std::mem::take(min_max_deltas);
let group_keys = std::mem::take(group_keys);
*self = MinMaxPersistState::WriteValue {
min_max_deltas,
group_keys,
group_idx: *group_idx,
value_idx: *value_idx,
column_name,
value,
weight,
write_row: WriteRow::new(),
};
}
MinMaxPersistState::WriteValue {
min_max_deltas,
group_keys,
group_idx,
value_idx,
value,
column_name,
weight,
write_row,
} => {
// Should have exited in the previous state
assert!(*group_idx < group_keys.len());
let group_key_str = &group_keys[*group_idx];
// Get the column info from the pre-computed map
let column_info = column_min_max
.get(column_name)
.expect("Column should exist in column_min_max map");
let column_index = column_info.index;
// Build the key components for MinMax storage using new encoding
let storage_id =
generate_storage_id(operator_id, column_index, AGG_TYPE_MINMAX);
let zset_hash = generate_group_hash(group_key_str);
// element_id is the actual value for Min/Max
let element_id_val = value.clone();
// Create index key
let index_key = vec![
Value::Integer(storage_id),
zset_hash.to_value(),
element_id_val.clone(),
];
// Record values (operator_id, zset_hash, element_id, unused_placeholder)
// For MIN/MAX, the element_id IS the value, so we use NULL for the 4th column
let record_values = vec![
Value::Integer(storage_id),
zset_hash.to_value(),
element_id_val.clone(),
Value::Null, // Placeholder - not used for MIN/MAX
];
return_if_io!(write_row.write_row(
cursors,
index_key.clone(),
record_values,
*weight
));
// Move to next value
let min_max_deltas = std::mem::take(min_max_deltas);
let group_keys = std::mem::take(group_keys);
*self = MinMaxPersistState::ProcessGroup {
min_max_deltas,
group_keys,
group_idx: *group_idx,
value_idx: *value_idx + 1,
};
}
MinMaxPersistState::Done => {
return Ok(IOResult::Done(()));
}
}
}
}
}
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