mz_compute/sink/correction_v2.rs
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// Copyright Materialize, Inc. and contributors. All rights reserved.
//
// Use of this software is governed by the Business Source License
// included in the LICENSE file.
//
// As of the Change Date specified in that file, in accordance with
// the Business Source License, use of this software will be governed
// by the Apache License, Version 2.0.
//! An implementation of the `Correction` data structure used by the MV sink's `write_batches`
//! operator to stash updates before they are written.
//!
//! The `Correction` data structure provides methods to:
//! * insert new updates
//! * advance the compaction frontier (called `since`)
//! * obtain an iterator over consolidated updates before some `upper`
//! * force consolidation of updates before some `upper`
//!
//! The goal is to provide good performance for each of these operations, even in the presence of
//! future updates. MVs downstream of temporal filters might have to deal with large amounts of
//! retractions for future times and we want those to be handled efficiently as well.
//!
//! Note that `Correction` does not provide a method to directly remove updates. Instead updates
//! are removed by inserting their retractions so that they consolidate away to nothing.
//!
//! ## Storage of Updates
//!
//! Stored updates are of the form `(data, time, diff)`, where `time` and `diff` are fixed to
//! [`mz_repr::Timestamp`] and [`mz_repr::Diff`], respectively.
//!
//! [`CorrectionV2`] holds onto a list of [`Chain`]s containing [`Chunk`]s of stashed updates. Each
//! [`Chunk`] is a columnation region containing a fixed maximum number of updates. All updates in
//! a chunk, and all updates in a chain, are ordered by (time, data) and consolidated.
//!
//! ```text
//! chain[0] | chain[1] | chain[2]
//! | |
//! chunk[0] | chunk[0] | chunk[0]
//! (a, 1, +1) | (a, 1, +1) | (d, 3, +1)
//! (b, 1, +1) | (b, 2, -1) | (d, 4, -1)
//! chunk[1] | chunk[1] |
//! (c, 1, +1) | (c, 2, -2) |
//! (a, 2, -1) | (c, 4, -1) |
//! chunk[2] | |
//! (b, 2, +1) | |
//! (c, 2, +1) | |
//! chunk[3] | |
//! (b, 3, -1) | |
//! (c, 3, +1) | |
//! ```
//!
//! The "chain invariant" states that each chain has at least [`CHAIN_PROPORTIONALITY`] times as
//! many chunks as the next one. This means that chain sizes will often be powers of
//! `CHAIN_PROPORTIONALITY`, but they don't have to be. For example, for a proportionality of 2,
//! the chain sizes `[11, 5, 2, 1]` would satisfy the chain invariant.
//!
//! Choosing the `CHAIN_PROPORTIONALITY` value allows tuning the trade-off between memory and CPU
//! resources required to maintain corrections. A higher proportionality forces more frequent chain
//! merges, and therefore consolidation, reducing memory usage but increasing CPU usage.
//!
//! ## Inserting Updates
//!
//! A batch of updates is appended as a new chain. Then chains are merged at the end of the chain
//! list until the chain invariant is restored.
//!
//! Inserting an update into the correction buffer can be expensive: It involves allocating a new
//! chunk, copying the update in, and then likely merging with an existing chain to restore the
//! chain invariant. If updates trickle in in small batches, this can cause a considerable
//! overhead. The amortize this overhead, new updates aren't immediately inserted into the sorted
//! chains but instead stored in a [`Stage`] buffer. Once enough updates have been staged to fill a
//! [`Chunk`], they are sorted an inserted into the chains.
//!
//! The insert operation has an amortized complexity of O(log N), with N being the current number
//! of updates stored.
//!
//! ## Retrieving Consolidated Updates
//!
//! Retrieving consolidated updates before a given `upper` works by first consolidating all updates
//! at times before the `upper`, merging them all into one chain, then returning an iterator over
//! that chain.
//!
//! Because each chain contains updates ordered by time first, consolidation of all updates before
//! an `upper` is possible without touching updates at future times. It works by merging the chains
//! only up to the `upper`, producing a merged chain containing consolidated times before the
//! `upper` and leaving behind the chain parts containing later times. The complexity of this
//! operation is O(U log K), with U being the number of updates before `upper` and K the number
//! of chains.
//!
//! Unfortunately, performing consolidation as described above can break the chain invariant and we
//! might need to restore it by merging chains, including ones containing future updates. This is
//! something that would be great to fix! In the meantime the hope is that in steady state it
//! doesn't matter too much because either there are no future retractions and U is approximately
//! equal to N, or the amount of future retractions is much larger than the amount of current
//! changes, in which case removing the current changes has a good chance of leaving the chain
//! invariant intact.
//!
//! ## Merging Chains
//!
//! Merging multiple chains into a single chain is done using a k-way merge. As the input chains
//! are sorted by (time, data) and consolidated, the same properties hold for the output chain. The
//! complexity of a merge of K chains containing N updates is O(N log K).
//!
//! There is a twist though: Merging also has to respect the `since` frontier, which determines how
//! far the times of updates should be advanced. Advancing times in a sorted chain of updates
//! can make them become unsorted, so we cannot just merge the chains from top to bottom.
//!
//! For example, consider these two chains, assuming `since = [2]`:
//! chain 1: [(c, 1, +1), (b, 2, -1), (a, 3, -1)]
//! chain 2: [(b, 1, +1), (a, 2, +1), (c, 2, -1)]
//! After time advancement, the chains look like this:
//! chain 1: [(c, 2, +1), (b, 2, -1), (a, 3, -1)]
//! chain 2: [(b, 2, +1), (a, 2, +1), (c, 2, -1)]
//! Merging them naively yields [(b, 2, +1), (a, 2, +1), (b, 2, -1), (a, 3, -1)], a chain that's
//! neither sorted nor consolidated.
//!
//! Instead we need to merge sub-chains, one for each distinct time that's before or at the
//! `since`. Each of these sub-chains retains the (time, data) ordering after the time advancement
//! to `since`, so merging those yields the expected result.
//!
//! For the above example, the chains we would merge are:
//! chain 1.a: [(c, 2, +1)]
//! chain 1.b: [(b, 2, -1), (a, 3, -1)]
//! chain 2.a: [(b, 2, +1)],
//! chain 2.b: [(a, 2, +1), (c, 2, -1)]
use std::borrow::Borrow;
use std::cmp::Ordering;
use std::collections::{BinaryHeap, VecDeque};
use std::fmt;
use std::rc::Rc;
use differential_dataflow::trace::implementations::BatchContainer;
use mz_persist_client::metrics::{SinkMetrics, SinkWorkerMetrics, UpdateDelta};
use mz_repr::{Diff, Timestamp};
use timely::container::columnation::{Columnation, TimelyStack};
use timely::container::SizableContainer;
use timely::progress::Antichain;
use timely::{Container, PartialOrder};
use crate::sink::correction::LengthAndCapacity;
/// Determines the size factor of subsequent chains required by the chain invariant.
const CHAIN_PROPORTIONALITY: usize = 3;
/// Convenient alias for use in data trait bounds.
pub trait Data: differential_dataflow::Data + Columnation {}
impl<D: differential_dataflow::Data + Columnation> Data for D {}
/// A data structure used to store corrections in the MV sink implementation.
///
/// In contrast to `CorrectionV1`, this implementation stores updates in columnation regions,
/// allowing their memory to be transparently spilled to disk.
#[derive(Debug)]
pub(super) struct CorrectionV2<D: Data> {
/// Chains containing sorted updates.
chains: Vec<Chain<D>>,
/// A staging area for updates, to speed up small inserts.
stage: Stage<D>,
/// The frontier by which all contained times are advanced.
since: Antichain<Timestamp>,
/// Total length and capacity of chunks in `chains`.
///
/// Tracked to maintain metrics.
total_size: LengthAndCapacity,
/// Global persist sink metrics.
metrics: SinkMetrics,
/// Per-worker persist sink metrics.
worker_metrics: SinkWorkerMetrics,
}
impl<D: Data> CorrectionV2<D> {
/// Construct a new [`CorrectionV2`] instance.
pub fn new(metrics: SinkMetrics, worker_metrics: SinkWorkerMetrics) -> Self {
Self {
chains: Default::default(),
stage: Default::default(),
since: Antichain::from_elem(Timestamp::MIN),
total_size: Default::default(),
metrics,
worker_metrics,
}
}
/// Insert a batch of updates.
pub fn insert(&mut self, updates: &mut Vec<(D, Timestamp, Diff)>) {
let Some(since_ts) = self.since.as_option() else {
// If the since is the empty frontier, discard all updates.
updates.clear();
return;
};
for (_, time, _) in &mut *updates {
*time = std::cmp::max(*time, *since_ts);
}
self.insert_inner(updates);
}
/// Insert a batch of updates, after negating their diffs.
pub fn insert_negated(&mut self, updates: &mut Vec<(D, Timestamp, Diff)>) {
let Some(since_ts) = self.since.as_option() else {
// If the since is the empty frontier, discard all updates.
updates.clear();
return;
};
for (_, time, diff) in &mut *updates {
*time = std::cmp::max(*time, *since_ts);
*diff = -*diff;
}
self.insert_inner(updates);
}
/// Insert a batch of updates.
///
/// All times are expected to be >= the `since`.
fn insert_inner(&mut self, updates: &mut Vec<(D, Timestamp, Diff)>) {
debug_assert!(updates.iter().all(|(_, t, _)| self.since.less_equal(t)));
if let Some(chain) = self.stage.insert(updates) {
self.chains.push(chain);
// Restore the chain invariant.
let merge_needed = |chains: &[Chain<_>]| match chains {
[.., prev, last] => last.len() * CHAIN_PROPORTIONALITY > prev.len(),
_ => false,
};
while merge_needed(&self.chains) {
let a = self.chains.pop().unwrap();
let b = self.chains.pop().unwrap();
let merged = merge_chains([a, b], &self.since);
self.chains.push(merged);
}
};
self.update_metrics();
}
/// Return consolidated updates before the given `upper`.
pub fn updates_before<'a>(
&'a mut self,
upper: &Antichain<Timestamp>,
) -> impl Iterator<Item = (D, Timestamp, Diff)> + 'a {
let mut result = None;
if !PartialOrder::less_than(&self.since, upper) {
// All contained updates are beyond the upper.
return result.into_iter().flatten();
}
self.consolidate_before(upper);
// There is at most one chain that contains updates before `upper` now.
result = self
.chains
.iter()
.find(|c| c.first().is_some_and(|(_, t, _)| !upper.less_equal(&t)))
.map(move |c| {
let upper = upper.clone();
c.iter().take_while(move |(_, t, _)| !upper.less_equal(t))
});
result.into_iter().flatten()
}
/// Consolidate all updates before the given `upper`.
///
/// Once this method returns, all remaining updates before `upper` are contained in a single
/// chain. Note that this chain might also contain updates beyond `upper` though!
fn consolidate_before(&mut self, upper: &Antichain<Timestamp>) {
if self.chains.is_empty() && self.stage.is_empty() {
return;
}
let mut chains = std::mem::take(&mut self.chains);
chains.extend(self.stage.flush());
if chains.is_empty() {
// We can only get here if the stage contained updates but they all got consolidated
// away by `flush`, so we need to update the metrics before we return.
self.update_metrics();
return;
}
let (merged, remains) = merge_chains_up_to(chains, &self.since, upper);
self.chains = remains;
if !merged.is_empty() {
// We put the merged chain at the end, assuming that its contents are likely to
// consolidate with retractions that will arrive soon.
self.chains.push(merged);
}
// Restore the chain invariant.
//
// This part isn't great. We've taken great care so far to only look at updates with times
// before `upper`, but now we might end up merging all chains anyway in the worst case.
// There might be something smarter we could do to avoid merging as much as possible. For
// example, we could consider sorting chains by length first, or inspect the contained
// times and prefer merging chains that have a chance at consolidating with one another.
let mut i = self.chains.len().saturating_sub(1);
while i > 0 {
let needs_merge = self.chains.get(i).is_some_and(|a| {
let b = &self.chains[i - 1];
a.len() * CHAIN_PROPORTIONALITY > b.len()
});
if needs_merge {
let a = self.chains.remove(i);
let b = std::mem::take(&mut self.chains[i - 1]);
let merged = merge_chains([a, b], &self.since);
self.chains[i - 1] = merged;
} else {
// Only advance the index if we didn't merge. A merge can reduce the size of the
// chain at `i - 1`, causing an violation of the chain invariant with the next
// chain, so we might need to merge the two before proceeding to lower indexes.
i -= 1;
}
}
self.update_metrics();
}
/// Advance the since frontier.
///
/// # Panics
///
/// Panics if the given `since` is less than the current since frontier.
pub fn advance_since(&mut self, since: Antichain<Timestamp>) {
assert!(PartialOrder::less_equal(&self.since, &since));
self.stage.advance_times(&since);
self.since = since;
}
/// Consolidate all updates at the current `since`.
pub fn consolidate_at_since(&mut self) {
let upper_ts = self.since.as_option().and_then(|t| t.try_step_forward());
if let Some(upper_ts) = upper_ts {
let upper = Antichain::from_elem(upper_ts);
self.consolidate_before(&upper);
}
}
/// Update persist sink metrics.
fn update_metrics(&mut self) {
let mut new_size = self.stage.get_size();
for chain in &mut self.chains {
new_size += chain.get_size();
}
let old_size = self.total_size;
let len_delta = UpdateDelta::new(new_size.length, old_size.length);
let cap_delta = UpdateDelta::new(new_size.capacity, old_size.capacity);
self.metrics
.report_correction_update_deltas(len_delta, cap_delta);
self.worker_metrics
.report_correction_update_totals(new_size.length, new_size.capacity);
self.total_size = new_size;
}
}
/// A chain of [`Chunk`]s containing updates.
///
/// All updates in a chain are sorted by (time, data) and consolidated.
///
/// Note that, in contrast to [`Chunk`]s, chains can be empty. Though we generally try to avoid
/// keeping around empty chains.
#[derive(Debug)]
struct Chain<D: Data> {
/// The contained chunks.
chunks: Vec<Chunk<D>>,
/// Cached value of the current chain size, for efficient updating of metrics.
cached_size: Option<LengthAndCapacity>,
}
impl<D: Data> Default for Chain<D> {
fn default() -> Self {
Self {
chunks: Default::default(),
cached_size: None,
}
}
}
impl<D: Data> Chain<D> {
/// Return whether the chain is empty.
fn is_empty(&self) -> bool {
self.chunks.is_empty()
}
/// Return the length of the chain, in chunks.
fn len(&self) -> usize {
self.chunks.len()
}
/// Push an update onto the chain.
///
/// The update must sort after all updates already in the chain, in (time, data)-order, to
/// ensure the chain remains sorted.
fn push<DT: Borrow<D>>(&mut self, update: (DT, Timestamp, Diff)) {
let (d, t, r) = update;
let update = (d.borrow(), t, r);
debug_assert!(self.can_accept(update));
match self.chunks.last_mut() {
Some(c) if !c.at_capacity() => c.push(update),
Some(_) | None => {
let chunk = Chunk::from_update(update);
self.push_chunk(chunk);
}
}
self.invalidate_cached_size();
}
/// Push a chunk onto the chain.
///
/// All updates in the chunk must sort after all updates already in the chain, in
/// (time, data)-order, to ensure the chain remains sorted.
fn push_chunk(&mut self, chunk: Chunk<D>) {
debug_assert!(self.can_accept(chunk.first()));
self.chunks.push(chunk);
self.invalidate_cached_size();
}
/// Push the updates produced by a cursor onto the chain.
///
/// All updates produced by the cursor must sort after all updates already in the chain, in
/// (time, data)-order, to ensure the chain remains sorted.
fn push_cursor(&mut self, cursor: Cursor<D>) {
let mut rest = Some(cursor);
while let Some(cursor) = rest.take() {
let update = cursor.get();
self.push(update);
rest = cursor.step();
}
}
/// Return whether the chain can accept the given update.
///
/// A chain can accept an update if pushing it at the end upholds the (time, data)-order.
fn can_accept(&self, update: (&D, Timestamp, Diff)) -> bool {
self.last().is_none_or(|(dc, tc, _)| {
let (d, t, _) = update;
(tc, dc) < (t, d)
})
}
/// Return the first update in the chain, if any.
fn first(&self) -> Option<(&D, Timestamp, Diff)> {
self.chunks.first().map(|c| c.first())
}
/// Return the last update in the chain, if any.
fn last(&self) -> Option<(&D, Timestamp, Diff)> {
self.chunks.last().map(|c| c.last())
}
/// Convert the chain into a cursor over the contained updates.
fn into_cursor(self) -> Option<Cursor<D>> {
let chunks = self.chunks.into_iter().map(Rc::new).collect();
Cursor::new(chunks)
}
/// Return an iterator over the contained updates.
fn iter(&self) -> impl Iterator<Item = (D, Timestamp, Diff)> + '_ {
self.chunks
.iter()
.flat_map(|c| c.data.iter().map(|(d, t, r)| (d.clone(), *t, *r)))
}
/// Return the size of the chain, for use in metrics.
fn get_size(&mut self) -> LengthAndCapacity {
// This operation can be expensive as it requires inspecting the individual chunks and
// their backing regions. We thus cache the result to hopefully avoid the cost most of the
// time.
if self.cached_size.is_none() {
let mut size = LengthAndCapacity::default();
for chunk in &mut self.chunks {
size += chunk.get_size();
}
self.cached_size = Some(size);
}
self.cached_size.unwrap()
}
/// Invalidate the cached chain size.
///
/// This method must be called every time the size of the chain changed.
fn invalidate_cached_size(&mut self) {
self.cached_size = None;
}
}
impl<D: Data> Extend<(D, Timestamp, Diff)> for Chain<D> {
fn extend<I: IntoIterator<Item = (D, Timestamp, Diff)>>(&mut self, iter: I) {
for update in iter {
self.push(update);
}
}
}
/// A cursor over updates in a chain.
///
/// A cursor provides two guarantees:
/// * Produced updates are ordered and consolidated.
/// * A cursor always yields at least one update.
///
/// The second guarantee is enforced through the type system: Every method that steps a cursor
/// forward consumes `self` and returns an `Option<Cursor>` that's `None` if the operation stepped
/// over the last update.
///
/// A cursor holds on to `Rc<Chunk>`s, allowing multiple cursors to produce updates from the same
/// chunks concurrently. As soon as a cursor is done producing updates from a [`Chunk`] it drops
/// its reference. Once the last cursor is done with a [`Chunk`] its memory can be reclaimed.
#[derive(Clone, Debug)]
struct Cursor<D: Data> {
/// The chunks from which updates can still be produced.
chunks: VecDeque<Rc<Chunk<D>>>,
/// The current offset into `chunks.front()`.
chunk_offset: usize,
/// An optional limit for the number of updates the cursor will produce.
limit: Option<usize>,
/// An optional overwrite for the timestamp of produced updates.
overwrite_ts: Option<Timestamp>,
}
impl<D: Data> Cursor<D> {
/// Construct a cursor over a list of chunks.
///
/// Returns `None` if `chunks` is empty.
fn new(chunks: VecDeque<Rc<Chunk<D>>>) -> Option<Self> {
if chunks.is_empty() {
return None;
}
Some(Self {
chunks,
chunk_offset: 0,
limit: None,
overwrite_ts: None,
})
}
/// Set a limit for the number of updates this cursor will produce.
///
/// # Panics
///
/// Panics if there is already a limit lower than the new one.
fn set_limit(mut self, limit: usize) -> Option<Self> {
assert!(self.limit.is_none_or(|l| l >= limit));
if limit == 0 {
return None;
}
// Release chunks made unreachable by the limit.
let mut count = 0;
let mut idx = 0;
let mut offset = self.chunk_offset;
while idx < self.chunks.len() && count < limit {
let chunk = &self.chunks[idx];
count += chunk.len() - offset;
idx += 1;
offset = 0;
}
self.chunks.truncate(idx);
if count > limit {
self.limit = Some(limit);
}
Some(self)
}
/// Get a reference to the current update.
fn get(&self) -> (&D, Timestamp, Diff) {
let chunk = self.get_chunk();
let (d, t, r) = chunk.index(self.chunk_offset);
let t = self.overwrite_ts.unwrap_or(t);
(d, t, r)
}
/// Get a reference to the current chunk.
fn get_chunk(&self) -> &Chunk<D> {
&self.chunks[0]
}
/// Step to the next update.
///
/// Returns the stepped cursor, or `None` if the step was over the last update.
fn step(mut self) -> Option<Self> {
if self.chunk_offset == self.get_chunk().len() - 1 {
return self.skip_chunk().map(|(c, _)| c);
}
self.chunk_offset += 1;
if let Some(limit) = &mut self.limit {
*limit -= 1;
if *limit == 0 {
return None;
}
}
Some(self)
}
/// Skip the remainder of the current chunk.
///
/// Returns the forwarded cursor and the number of updates skipped, or `None` if no chunks are
/// left after the skip.
fn skip_chunk(mut self) -> Option<(Self, usize)> {
let chunk = self.chunks.pop_front().expect("cursor invariant");
if self.chunks.is_empty() {
return None;
}
let skipped = chunk.len() - self.chunk_offset;
self.chunk_offset = 0;
if let Some(limit) = &mut self.limit {
if skipped >= *limit {
return None;
}
*limit -= skipped;
}
Some((self, skipped))
}
/// Skip all updates with times <= the given time.
///
/// Returns the forwarded cursor and the number of updates skipped, or `None` if no updates are
/// left after the skip.
fn skip_time(mut self, time: Timestamp) -> Option<(Self, usize)> {
if self.overwrite_ts.is_some_and(|ts| ts <= time) {
return None;
} else if self.get().1 > time {
return Some((self, 0));
}
let mut skipped = 0;
let new_offset = loop {
let chunk = self.get_chunk();
if let Some(index) = chunk.find_time_greater_than(time) {
break index;
}
let (cursor, count) = self.skip_chunk()?;
self = cursor;
skipped += count;
};
skipped += new_offset - self.chunk_offset;
self.chunk_offset = new_offset;
Some((self, skipped))
}
/// Advance all updates in this cursor by the given `since_ts`.
///
/// Returns a list of cursors, each of which yields ordered and consolidated updates that have
/// been advanced by `since_ts`.
fn advance_by(mut self, since_ts: Timestamp) -> Vec<Self> {
// If the cursor has an `overwrite_ts`, all its updates are at the same time already. We
// only need to advance the `overwrite_ts` by the `since_ts`.
if let Some(ts) = self.overwrite_ts {
if ts < since_ts {
self.overwrite_ts = Some(since_ts);
}
return vec![self];
}
// Otherwise we need to split the cursor so that each new cursor only yields runs of
// updates that are correctly (time, data)-ordered when advanced by `since_ts`. We achieve
// this by splitting the cursor at each time <= `since_ts`.
let mut splits = Vec::new();
let mut remaining = Some(self);
while let Some(cursor) = remaining.take() {
let (_, time, _) = cursor.get();
if time >= since_ts {
splits.push(cursor);
break;
}
let mut current = cursor.clone();
if let Some((cursor, skipped)) = cursor.skip_time(time) {
remaining = Some(cursor);
current = current.set_limit(skipped).expect("skipped at least 1");
}
current.overwrite_ts = Some(since_ts);
splits.push(current);
}
splits
}
/// Split the cursor at the given time.
///
/// Returns two cursors, the first yielding all updates at times < `time`, the second yielding
/// all updates at times >= `time`. Both can be `None` if they would be empty.
fn split_at_time(self, time: Timestamp) -> (Option<Self>, Option<Self>) {
let Some(skip_ts) = time.step_back() else {
return (None, Some(self));
};
let before = self.clone();
match self.skip_time(skip_ts) {
Some((beyond, skipped)) => (before.set_limit(skipped), Some(beyond)),
None => (Some(before), None),
}
}
/// Attempt to unwrap the cursor into a [`Chain`].
///
/// This operation efficiently reuses chunks by directly inserting them into the output chain
/// where possible.
///
/// An unwrap is only successful if the cursor's `limit` and `overwrite_ts` are both `None` and
/// the cursor has unique references to its chunks. If the unwrap fails, this method returns an
/// `Err` containing the cursor in an unchanged state, allowing the caller to convert it into a
/// chain by copying chunks rather than reusing them.
fn try_unwrap(self) -> Result<Chain<D>, (&'static str, Self)> {
if self.limit.is_some() {
return Err(("cursor with limit", self));
}
if self.overwrite_ts.is_some() {
return Err(("cursor with overwrite_ts", self));
}
if self.chunks.iter().any(|c| Rc::strong_count(c) != 1) {
return Err(("cursor on shared chunks", self));
}
let mut chain = Chain::default();
let mut remaining = Some(self);
// We might be partway through the first chunk, in which case we can't reuse it but need to
// allocate a new one to contain only the updates the cursor can still yield.
while let Some(cursor) = remaining.take() {
if cursor.chunk_offset == 0 {
remaining = Some(cursor);
break;
}
let update = cursor.get();
chain.push(update);
remaining = cursor.step();
}
if let Some(cursor) = remaining {
for chunk in cursor.chunks {
let chunk = Rc::into_inner(chunk).expect("checked above");
chain.push_chunk(chunk);
}
}
Ok(chain)
}
}
impl<D: Data> From<Cursor<D>> for Chain<D> {
fn from(cursor: Cursor<D>) -> Self {
match cursor.try_unwrap() {
Ok(chain) => chain,
Err((_, cursor)) => {
let mut chain = Chain::default();
chain.push_cursor(cursor);
chain
}
}
}
}
/// A non-empty chunk of updates, backed by a columnation region.
///
/// All updates in a chunk are sorted by (time, data) and consolidated.
///
/// We would like all chunks to have the same fixed size, to make it easy for the allocator to
/// re-use chunk allocations. Unfortunately, the current `TimelyStack`/`ChunkedStack` API doesn't
/// provide a convenient way to pre-size regions, so chunks are currently only fixed-size in
/// spirit.
struct Chunk<D: Data> {
/// The contained updates.
data: TimelyStack<(D, Timestamp, Diff)>,
/// Cached value of the current chunk size, for efficient updating of metrics.
cached_size: Option<LengthAndCapacity>,
}
impl<D: Data> Default for Chunk<D> {
fn default() -> Self {
let mut data = TimelyStack::default();
data.ensure_capacity(&mut None);
Self {
data,
cached_size: None,
}
}
}
impl<D: Data> fmt::Debug for Chunk<D> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
write!(f, "Chunk(<{}>)", self.len())
}
}
impl<D: Data> Chunk<D> {
/// Create a new chunk containing a single update.
fn from_update<DT: Borrow<D>>(update: (DT, Timestamp, Diff)) -> Self {
let (d, t, r) = update;
let mut chunk = Self::default();
chunk.data.copy_destructured(d.borrow(), &t, &r);
chunk
}
/// Return the number of updates in the chunk.
fn len(&self) -> usize {
Container::len(&self.data)
}
/// Return the (local) capacity of the chunk.
fn capacity(&self) -> usize {
self.data.capacity()
}
/// Return whether the chunk is at capacity.
fn at_capacity(&self) -> bool {
self.data.at_capacity()
}
/// Return the update at the given index.
///
/// # Panics
///
/// Panics if the given index is not populated.
fn index(&self, idx: usize) -> (&D, Timestamp, Diff) {
let (d, t, r) = self.data.index(idx);
(d, *t, *r)
}
/// Return the first update in the chunk.
fn first(&self) -> (&D, Timestamp, Diff) {
self.index(0)
}
/// Return the last update in the chunk.
fn last(&self) -> (&D, Timestamp, Diff) {
self.index(self.len() - 1)
}
/// Push an update onto the chunk.
fn push<DT: Borrow<D>>(&mut self, update: (DT, Timestamp, Diff)) {
let (d, t, r) = update;
self.data.copy_destructured(d.borrow(), &t, &r);
self.invalidate_cached_size();
}
/// Return the index of the first update at a time greater than `time`, or `None` if no such
/// update exists.
fn find_time_greater_than(&self, time: Timestamp) -> Option<usize> {
if self.last().1 <= time {
return None;
}
let mut lower = 0;
let mut upper = self.len();
while lower < upper {
let idx = (lower + upper) / 2;
if self.index(idx).1 > time {
upper = idx;
} else {
lower = idx + 1;
}
}
Some(lower)
}
/// Return the size of the chunk, for use in metrics.
fn get_size(&mut self) -> LengthAndCapacity {
if self.cached_size.is_none() {
let length = Container::len(&self.data);
let mut capacity = 0;
self.data.heap_size(|_, cap| capacity += cap);
self.cached_size = Some(LengthAndCapacity { length, capacity });
}
self.cached_size.unwrap()
}
/// Invalidate the cached chunk size.
///
/// This method must be called every time the size of the chunk changed.
fn invalidate_cached_size(&mut self) {
self.cached_size = None;
}
}
/// A buffer for staging updates before they are inserted into the sorted chains.
#[derive(Debug)]
struct Stage<D> {
/// The contained updates.
///
/// This vector has a fixed capacity equal to the [`Chunk`] capacity.
data: Vec<(D, Timestamp, Diff)>,
}
impl<D: Data> Default for Stage<D> {
fn default() -> Self {
// Make sure that the `Stage` has the same capacity as a `Chunk`.
let chunk = Chunk::<D>::default();
let data = Vec::with_capacity(chunk.capacity());
Self { data }
}
}
impl<D: Data> Stage<D> {
fn is_empty(&self) -> bool {
self.data.is_empty()
}
/// Insert a batch of updates, possibly producing a ready [`Chain`].
fn insert(&mut self, updates: &mut Vec<(D, Timestamp, Diff)>) -> Option<Chain<D>> {
if updates.is_empty() {
return None;
}
// Determine how many chunks we can fill with the available updates.
let update_count = self.data.len() + updates.len();
let chunk_size = self.data.capacity();
let chunk_count = update_count / chunk_size;
let mut new_updates = updates.drain(..);
// If we have enough shipable updates, collect them, consolidate, and build a chain.
let maybe_chain = if chunk_count > 0 {
let ship_count = chunk_count * chunk_size;
let mut buffer = Vec::with_capacity(ship_count);
buffer.append(&mut self.data);
while buffer.len() < ship_count {
let update = new_updates.next().unwrap();
buffer.push(update);
}
consolidate(&mut buffer);
let mut chain = Chain::default();
chain.extend(buffer);
Some(chain)
} else {
None
};
// Stage the remaining updates.
self.data.extend(new_updates);
maybe_chain
}
/// Flush all currently staged updates into a chain.
fn flush(&mut self) -> Option<Chain<D>> {
consolidate(&mut self.data);
if self.data.is_empty() {
return None;
}
let mut chain = Chain::default();
chain.extend(self.data.drain(..));
Some(chain)
}
/// Advance the times of staged updates by the given `since`.
fn advance_times(&mut self, since: &Antichain<Timestamp>) {
let Some(since_ts) = since.as_option() else {
// If the since is the empty frontier, discard all updates.
self.data.clear();
return;
};
for (_, time, _) in &mut self.data {
*time = std::cmp::max(*time, *since_ts);
}
}
/// Return the size of the stage, for use in metrics.
fn get_size(&self) -> LengthAndCapacity {
LengthAndCapacity {
length: self.data.len(),
capacity: self.data.capacity(),
}
}
}
/// Sort and consolidate the given list of updates.
///
/// This function is the same as [`differential_dataflow::consolidation::consolidate_updates`],
/// except that it sorts updates by (time, data) instead of (data, time).
fn consolidate<D: Data>(updates: &mut Vec<(D, Timestamp, Diff)>) {
if updates.len() <= 1 {
return;
}
let diff = |update: &(_, _, Diff)| update.2;
updates.sort_unstable_by(|(d1, t1, _), (d2, t2, _)| (t1, d1).cmp(&(t2, d2)));
let mut offset = 0;
let mut accum = diff(&updates[0]);
for idx in 1..updates.len() {
let this = &updates[idx];
let prev = &updates[idx - 1];
if this.0 == prev.0 && this.1 == prev.1 {
accum += diff(&updates[idx]);
} else {
if accum != 0 {
updates.swap(offset, idx - 1);
updates[offset].2 = accum;
offset += 1;
}
accum = diff(&updates[idx]);
}
}
if accum != 0 {
let len = updates.len();
updates.swap(offset, len - 1);
updates[offset].2 = accum;
offset += 1;
}
updates.truncate(offset);
}
/// Merge the given chains, advancing times by the given `since` in the process.
fn merge_chains<D: Data>(
chains: impl IntoIterator<Item = Chain<D>>,
since: &Antichain<Timestamp>,
) -> Chain<D> {
let Some(&since_ts) = since.as_option() else {
return Chain::default();
};
let mut to_merge = Vec::new();
for chain in chains {
if let Some(cursor) = chain.into_cursor() {
let mut runs = cursor.advance_by(since_ts);
to_merge.append(&mut runs);
}
}
merge_cursors(to_merge)
}
/// Merge the given chains, advancing times by the given `since` in the process, but only up to the
/// given `upper`.
///
/// Returns the merged chain and a list of non-empty remainders of the input chains.
fn merge_chains_up_to<D: Data>(
chains: Vec<Chain<D>>,
since: &Antichain<Timestamp>,
upper: &Antichain<Timestamp>,
) -> (Chain<D>, Vec<Chain<D>>) {
let Some(&since_ts) = since.as_option() else {
return (Chain::default(), Vec::new());
};
let Some(&upper_ts) = upper.as_option() else {
let merged = merge_chains(chains, since);
return (merged, Vec::new());
};
if since_ts >= upper_ts {
// After advancing by `since` there will be no updates before `upper`.
return (Chain::default(), chains);
}
let mut to_merge = Vec::new();
let mut to_keep = Vec::new();
for chain in chains {
if let Some(cursor) = chain.into_cursor() {
let mut runs = cursor.advance_by(since_ts);
if let Some(last) = runs.pop() {
let (before, beyond) = last.split_at_time(upper_ts);
before.map(|c| runs.push(c));
beyond.map(|c| to_keep.push(c));
}
to_merge.append(&mut runs);
}
}
let merged = merge_cursors(to_merge);
let remains = to_keep
.into_iter()
.map(|c| c.try_unwrap().expect("unwrapable"))
.collect();
(merged, remains)
}
/// Merge the given cursors into one chain.
fn merge_cursors<D: Data>(cursors: Vec<Cursor<D>>) -> Chain<D> {
match cursors.len() {
0 => Chain::default(),
1 => {
let [cur] = cursors.try_into().unwrap();
Chain::from(cur)
}
2 => {
let [a, b] = cursors.try_into().unwrap();
merge_2(a, b)
}
_ => merge_many(cursors),
}
}
/// Merge the given two cursors using a 2-way merge.
///
/// This function is a specialization of `merge_many` that avoids the overhead of a binary heap.
fn merge_2<D: Data>(cursor1: Cursor<D>, cursor2: Cursor<D>) -> Chain<D> {
let mut rest1 = Some(cursor1);
let mut rest2 = Some(cursor2);
let mut merged = Chain::default();
loop {
match (rest1, rest2) {
(Some(c1), Some(c2)) => {
let (d1, t1, r1) = c1.get();
let (d2, t2, r2) = c2.get();
match (t1, d1).cmp(&(t2, d2)) {
Ordering::Less => {
merged.push((d1, t1, r1));
rest1 = c1.step();
rest2 = Some(c2);
}
Ordering::Greater => {
merged.push((d2, t2, r2));
rest1 = Some(c1);
rest2 = c2.step();
}
Ordering::Equal => {
let r = r1 + r2;
if r != 0 {
merged.push((d1, t1, r));
}
rest1 = c1.step();
rest2 = c2.step();
}
}
}
(Some(c), None) | (None, Some(c)) => {
merged.push_cursor(c);
break;
}
(None, None) => break,
}
}
merged
}
/// Merge the given cursors using a k-way merge with a binary heap.
fn merge_many<D: Data>(cursors: Vec<Cursor<D>>) -> Chain<D> {
let mut heap = MergeHeap::from_iter(cursors);
let mut merged = Chain::default();
while let Some(cursor1) = heap.pop() {
let (data, time, mut diff) = cursor1.get();
while let Some((cursor2, r)) = heap.pop_equal(data, time) {
diff += r;
if let Some(cursor2) = cursor2.step() {
heap.push(cursor2);
}
}
if diff != 0 {
merged.push((data, time, diff));
}
if let Some(cursor1) = cursor1.step() {
heap.push(cursor1);
}
}
merged
}
/// A binary heap specialized for merging [`Cursor`]s.
struct MergeHeap<D: Data>(BinaryHeap<MergeCursor<D>>);
impl<D: Data> FromIterator<Cursor<D>> for MergeHeap<D> {
fn from_iter<I: IntoIterator<Item = Cursor<D>>>(cursors: I) -> Self {
let inner = cursors.into_iter().map(MergeCursor).collect();
Self(inner)
}
}
impl<D: Data> MergeHeap<D> {
/// Pop the next cursor (the one yielding the least update) from the heap.
fn pop(&mut self) -> Option<Cursor<D>> {
self.0.pop().map(|MergeCursor(c)| c)
}
/// Pop the next cursor from the heap, provided the data and time of its current update are
/// equal to the given values.
///
/// Returns both the cursor and the diff corresponding to `data` and `time`.
fn pop_equal(&mut self, data: &D, time: Timestamp) -> Option<(Cursor<D>, Diff)> {
let MergeCursor(cursor) = self.0.peek()?;
let (d, t, r) = cursor.get();
if d == data && t == time {
let cursor = self.pop().expect("checked above");
Some((cursor, r))
} else {
None
}
}
/// Push a cursor onto the heap.
fn push(&mut self, cursor: Cursor<D>) {
self.0.push(MergeCursor(cursor));
}
}
/// A wrapper for [`Cursor`]s on a [`MergeHeap`].
///
/// Implements the cursor ordering required for merging cursors.
struct MergeCursor<D: Data>(Cursor<D>);
impl<D: Data> PartialEq for MergeCursor<D> {
fn eq(&self, other: &Self) -> bool {
self.cmp(other).is_eq()
}
}
impl<D: Data> Eq for MergeCursor<D> {}
impl<D: Data> PartialOrd for MergeCursor<D> {
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
Some(self.cmp(other))
}
}
impl<D: Data> Ord for MergeCursor<D> {
fn cmp(&self, other: &Self) -> Ordering {
let (d1, t1, _) = self.0.get();
let (d2, t2, _) = other.0.get();
(t1, d1).cmp(&(t2, d2)).reverse()
}
}