compute: intra-ts thinning for monotonic topk

src/compute/Cargo.toml

@@ -32,6 +32,7 @@ once_cell = "1.16.0"
 prometheus = { version = "0.13.3", default-features = false }
 scopeguard = "1.1.0"
 serde = { version = "1.0.152", features = ["derive"] }
+smallvec = { version = "1.10.0", features = ["serde", "union"] }
 timely = { git = "https://github.com/TimelyDataflow/timely-dataflow", default-features = false, features = ["bincode"] }
 tokio = { version = "1.23.0", features = ["fs", "rt", "sync", "net"] }
 tracing = "0.1.37"

src/compute/src/render/top_k.rs

@@ -11,6 +11,8 @@
 //!
 //! Consult [TopKPlan] documentation for details.
 
+use std::collections::HashMap;
+
 use differential_dataflow::hashable::Hashable;
 use differential_dataflow::lattice::Lattice;
 use differential_dataflow::operators::arrange::ArrangeBySelf;
@@ -19,6 +21,8 @@ use differential_dataflow::operators::Consolidate;
 use differential_dataflow::trace::implementations::ord::OrdValSpine;
 use differential_dataflow::AsCollection;
 use differential_dataflow::Collection;
+use timely::dataflow::channels::pact::Pipeline;
+use timely::dataflow::operators::Operator;
 use timely::dataflow::Scope;
 
 use mz_compute_client::plan::top_k::{
@@ -56,6 +60,33 @@ where
                     arity,
                     limit,
                 }) => {
+                    let mut datum_vec = mz_repr::DatumVec::new();
+                    let collection = ok_input.map(move |row| {
+                        let group_row = {
+                            let datums = datum_vec.borrow_with(&row);
+                            let iterator = group_key.iter().map(|i| datums[*i]);
+                            let total_size = mz_repr::datums_size(iterator.clone());
+                            let mut group_row = Row::with_capacity(total_size);
+                            group_row.packer().extend(iterator);
+                            group_row
+                        };
+                        (group_row, row)
+                    });
+
+                    // For monotonic inputs, we are able to thin the input relation in two stages:
+                    // 1. First, we can do an intra-timestamp thinning which has the advantage of
+                    //    being computed in a streaming fashion, even for the initial snapshot.
+                    // 2. Then, we can do inter-timestamp thinning by feeding back negations for
+                    //    any records that have been invalidated.
+                    let collection = if let Some(limit) = limit {
+                        render_intra_ts_thinning(collection, order_key.clone(), limit)
+                    } else {
+                        collection
+                    };
+
+                    let collection =
+                        collection.map(|(group_row, row)| ((group_row, row.hashed()), row));
+
                     // For monotonic inputs, we are able to retract inputs that can no longer be produced
                     // as outputs. Any inputs beyond `offset + limit` will never again be produced as
                     // outputs, and can be removed. The simplest form of this is when `offset == 0` and
@@ -65,17 +96,24 @@ where
                     // of `offset` and `limit`, discarding only the records not produced in the intermediate
                     // stage.
                     use differential_dataflow::operators::iterate::Variable;
-                    let delay = std::time::Duration::from_nanos(10_000_000_000);
+                    let delay = std::time::Duration::from_secs(10);
                     let retractions = Variable::new(
                         &mut ok_input.scope(),
                         <G::Timestamp as crate::render::RenderTimestamp>::system_delay(
                             delay.try_into().expect("must fit"),
                         ),
                     );
-                    let thinned = ok_input.concat(&retractions.negate());
-                    let result = build_topk(thinned, group_key, order_key, 0, limit, arity);
-                    retractions.set(&ok_input.concat(&result.negate()));
-                    result
+                    let thinned = collection.concat(&retractions.negate());
+
+                    // As an additional optimization, we can skip creating the full topk hierachy
+                    // here since we now have an upper bound on the number records due to the
+                    // intra-ts thinning. The maximum number of records per timestamp is
+                    // (num_workers * limit), which we expect to be a small number and so we render
+                    // a single topk stage.
+                    let result = build_topk_stage(thinned, order_key, 1u64, 0, limit, arity);
+                    retractions.set(&collection.concat(&result.negate()));
+
+                    result.map(|((_key, _hash), row)| row)
                 }
                 TopKPlan::Basic(BasicTopKPlan {
                     group_key,
@@ -317,6 +355,154 @@ where
             // TODO(#7331): Here we discard the arranged output.
             result.as_collection(|_k, v| v.clone())
         }
+
+        fn render_intra_ts_thinning<G>(
+            collection: Collection<G, (Row, Row), Diff>,
+            order_key: Vec<mz_expr::ColumnOrder>,
+            limit: usize,
+        ) -> Collection<G, (Row, Row), Diff>
+        where
+            G: Scope,
+            G::Timestamp: Lattice,
+        {
+            let mut aggregates = HashMap::new();
+            let mut vector = Vec::new();
+            collection
+                .inner
+                .unary_notify(
+                    Pipeline,
+                    "TopKIntraTimeThinning",
+                    [],
+                    move |input, output, notificator| {
+                        while let Some((time, data)) = input.next() {
+                            data.swap(&mut vector);
+                            let agg_time = aggregates
+                                .entry(time.time().clone())
+                                .or_insert_with(HashMap::new);
+                            for ((grp_row, row), record_time, diff) in vector.drain(..) {
+                                let monoid = monoids::Top1Monoid {
+                                    row,
+                                    order_key: order_key.clone(),
+                                };
+                                let topk = agg_time.entry((grp_row, record_time)).or_insert_with(
+                                    move || {
+                                        topk_agg::TopKBatch::new(
+                                            limit.try_into().expect("must fit"),
+                                        )
+                                    },
+                                );
+                                topk.update(monoid, diff);
+                            }
+                            notificator.notify_at(time.retain());
+                        }
+
+                        notificator.for_each(|time, _, _| {
+                            if let Some(aggs) = aggregates.remove(time.time()) {
+                                let mut session = output.session(&time);
+                                for ((grp_row, record_time), topk) in aggs {
+                                    session.give_iterator(topk.into_iter().map(|(monoid, diff)| {
+                                        ((grp_row.clone(), monoid.row), record_time.clone(), diff)
+                                    }))
+                                }
+                            }
+                        });
+                    },
+                )
+                .as_collection()
+        }
+    }
+}
+
+/// Types for in-place intra-ts aggregation of monotonic streams.
+pub mod topk_agg {
+    use differential_dataflow::consolidation;
+    use smallvec::SmallVec;
+
+    // TODO: This struct looks a lot like ChangeBatch and indeed its code is a modified version of
+    // that. It would be nice to find a way to reuse some or all of the code from there.
+    //
+    // Additionally, because we're calling into DD's consolidate method we are forced to work with
+    // the `Ord` trait which for the usage we do above means that we need to clone the `order_key`
+    // for each record. It would be nice to also remove the need for cloning that piece of data
+    pub struct TopKBatch<T> {
+        updates: SmallVec<[(T, i64); 16]>,
+        clean: usize,
+        limit: i64,
+    }
+
+    impl<T: Ord> TopKBatch<T> {
+        pub fn new(limit: i64) -> Self {
+            Self {
+                updates: SmallVec::new(),
+                clean: 0,
+                limit,
+            }
+        }
+
+        /// Adds a new update, for `item` with `value`.
+        ///
+        /// This could be optimized to perform compaction when the number of "dirty" elements exceeds
+        /// half the length of the list, which would keep the total footprint within reasonable bounds
+        /// even under an arbitrary number of updates. This has a cost, and it isn't clear whether it
+        /// is worth paying without some experimentation.
+        #[inline]
+        pub fn update(&mut self, item: T, value: i64) {
+            self.updates.push((item, value));
+            self.maintain_bounds();
+        }
+
+        /// Compact the internal representation.
+        ///
+        /// This method sort `self.updates` and consolidates elements with equal item, discarding
+        /// any whose accumulation is zero. It is optimized to only do this if the number of dirty
+        /// elements is non-zero.
+        #[inline]
+        pub fn compact(&mut self) {
+            if self.clean < self.updates.len() && self.updates.len() > 1 {
+                let len = consolidation::consolidate_slice(&mut self.updates);
+                self.updates.truncate(len);
+
+                // We can now retain only the first K records and throw away everything else
+                let mut limit = self.limit;
+                self.updates.retain(|x| {
+                    if limit > 0 {
+                        limit -= x.1;
+                        true
+                    } else {
+                        false
+                    }
+                });
+                // By the end of the loop above `limit` will be less than or equal to zero. The
+                // case where it goes negative is when the last record we retained had more copies
+                // than necessary. For this reason we need to do one final adjustment of the diff
+                // field of the last record so that the total sum of the diffs in the batch is K.
+                if let Some(item) = self.updates.last_mut() {
+                    // We are subtracting the limit *negated*, therefore we are subtracting a value
+                    // that is *greater* than or equal to zero, which represents the excess.
+                    item.1 -= -limit;
+                }
+            }
+            self.clean = self.updates.len();
+        }
+
+        /// Maintain the bounds of pending (non-compacted) updates versus clean (compacted) data.
+        /// This function tries to minimize work by only compacting if enough work has accumulated.
+        fn maintain_bounds(&mut self) {
+            // if we have more than 32 elements and at least half of them are not clean, compact
+            if self.updates.len() > 32 && self.updates.len() >> 1 >= self.clean {
+                self.compact()
+            }
+        }
+    }
+
+    impl<T: Ord> IntoIterator for TopKBatch<T> {
+        type Item = (T, i64);
+        type IntoIter = smallvec::IntoIter<[(T, i64); 16]>;
+
+        fn into_iter(mut self) -> Self::IntoIter {
+            self.compact();
+            self.updates.into_iter()
+        }
     }
 }
 

test/testdrive/top-k-monotonic.td

@@ -0,0 +1,53 @@
+# Copyright Materialize, Inc. and contributors. All rights reserved.
+#
+# Use of this software is governed by the Business Source License
+# included in the LICENSE file at the root of this repository.
+#
+# 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.
+
+# Test monotonicity analyses which derive from ENVELOPE NONE sources.
+# Note that these only test the implementation for monotonic sources,
+# they do not test that the analysis doesn't have false positives on
+# non-monotonic sources.
+
+$ set non-dbz-schema={
+    "type": "record",
+    "name": "cpx",
+    "fields": [
+      {"name": "a", "type": "long"},
+      {"name": "b", "type": "long"}
+    ]
+  }
+
+$ kafka-create-topic topic=non-dbz-data
+
+$ kafka-ingest format=avro topic=non-dbz-data schema=${non-dbz-schema} timestamp=1
+{"a": 1, "b": 1}
+{"a": 1, "b": 2}
+{"a": 1, "b": 3}
+{"a": 1, "b": 4}
+{"a": 1, "b": 5}
+{"a": 2, "b": 1000}
+{"a": 2, "b": 1001}
+{"a": 2, "b": 1002}
+{"a": 2, "b": 1003}
+{"a": 2, "b": 1004}
+
+> CREATE CONNECTION kafka_conn
+  TO KAFKA (BROKER '${testdrive.kafka-addr}');
+
+> CREATE SOURCE non_dbz_data
+  FROM KAFKA CONNECTION kafka_conn (TOPIC 'testdrive-non-dbz-data-${testdrive.seed}')
+  FORMAT AVRO USING SCHEMA '${non-dbz-schema}'
+  ENVELOPE NONE
+
+# Create a monotonic topk plan that has both a limit and a group to test that thinning works as expected
+> SELECT * FROM (SELECT DISTINCT a FROM non_dbz_data) grp, LATERAL (SELECT b FROM non_dbz_data WHERE a = grp.a ORDER BY b LIMIT 2);
+a b
+---------
+1 1
+1 2
+2 1000
+2 1001