feat: caching optd stats, 12x speedup on TPC-H SF1 (#132)

**Summary**: Now caching the stat objects used by `OptCostModel`,
meaning we don't need to load data into DataFusion after doing it the
first time.

**Demo**:
12x speedup on TPC-H SF1 compared to not caching stats.

Caching everything _except_ optd stats takes 45.6s total.
![Screenshot 2024-03-23 at 16 59
04](https://github.com/cmu-db/optd/assets/20631215/4c199374-e2df-43fb-9eba-f348ea1e275a)

Caching everything, _including_ optd stats, takes 3.9s total.
![Screenshot 2024-03-23 at 16 57
45](https://github.com/cmu-db/optd/assets/20631215/4ef01ae9-c5a9-4fcd-bad9-c52d9a73c147)

**Details**:
* This caching is **disabled by default** to avoid accidentally using
stale stats. I added a CLI arg to enable it.
* The main challenge of this PR was making `PerTableStats` a
serializable object for `serde`.
* The serializability refactor will also help down the line when we want
to **put statistics in the catalog**, since that is fundamentally a
serialization problem too. Having `Box<dyn ...>` would make putting
stats in the catalog more difficult.
* This required a significant refactor of how the `MostCommonValues` and
`Distribution` traits are handled in `OptCostModel`. Instead of having
`Box<dyn ...>` values in `PerColumnStats` which store any object that
implements these traits, I made `PerColumnStats` a templated object.
* The one downside of this refactor is that we can no longer have a
database which uses _different_ data structures for `Distribution` (like
a t-digest for one column, a histogram for another, etc.). I didn't see
this as a big enough reason to not do the refactor because it seems like
a rare thing to do. Additionally, if we really needed to do this, we
could just make an enum that had both types.

Author: wangpatrick57

Cargo.lock

@@ -14,3 +14,4 @@ ordered-float = "4"
 tracing-subscriber = "0.3"
 pretty-xmlish = "0.1"
 itertools = "0.11"
+serde = {version = "1.0", features = ["derive", "rc"]}

optd-core/Cargo.toml

@@ -9,6 +9,7 @@ use std::{
 };
 
 use ordered_float::OrderedFloat;
+use serde::{Deserialize, Deserializer, Serialize, Serializer};
 
 use crate::{cascades::GroupId, cost::Cost};
 
@@ -27,6 +28,30 @@ pub trait RelNodeTyp:
 }
 
 #[derive(Clone, Debug, PartialEq, Eq, Hash)]
+pub struct SerializableOrderedF64(pub OrderedFloat<f64>);
+
+impl Serialize for SerializableOrderedF64 {
+    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
+    where
+        S: Serializer,
+    {
+        // Directly serialize the inner f64 value of the OrderedFloat
+        self.0 .0.serialize(serializer)
+    }
+}
+
+impl<'de> Deserialize<'de> for SerializableOrderedF64 {
+    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
+    where
+        D: Deserializer<'de>,
+    {
+        // Deserialize an f64 and wrap it in an OrderedFloat
+        let float = f64::deserialize(deserializer)?;
+        Ok(SerializableOrderedF64(OrderedFloat(float)))
+    }
+}
+
+#[derive(Clone, Debug, PartialEq, Eq, Hash, Serialize, Deserialize)]
 pub enum Value {
     UInt8(u8),
     UInt16(u16),
@@ -37,7 +62,7 @@ pub enum Value {
     Int32(i32),
     Int64(i64),
     Int128(i128),
-    Float(OrderedFloat<f64>),
+    Float(SerializableOrderedF64),
     String(Arc<str>),
     Bool(bool),
     Date32(i32),
@@ -57,7 +82,7 @@ impl std::fmt::Display for Value {
             Self::Int32(x) => write!(f, "{x}"),
             Self::Int64(x) => write!(f, "{x}"),
             Self::Int128(x) => write!(f, "{x}"),
-            Self::Float(x) => write!(f, "{x}"),
+            Self::Float(x) => write!(f, "{}", x.0),
             Self::String(x) => write!(f, "\"{x}\""),
             Self::Bool(x) => write!(f, "{x}"),
             Self::Date32(x) => write!(f, "{x}"),
@@ -133,7 +158,7 @@ impl Value {
 
     pub fn as_f64(&self) -> f64 {
         match self {
-            Value::Float(i) => **i,
+            Value::Float(i) => *i.0,
             _ => panic!("Value is not an f64"),
         }
     }

optd-core/src/rel_node.rs

@@ -8,7 +8,7 @@ use optd_core::{
     rules::{Rule, RuleWrapper},
 };
 use optd_datafusion_repr::{
-    cost::{OptCostModel, PerTableStats},
+    cost::{base_cost::DataFusionPerTableStats, OptCostModel},
     plan_nodes::{
         BinOpExpr, BinOpType, ColumnRefExpr, ConstantExpr, JoinType, LogicalFilter, LogicalJoin,
         LogicalScan, OptRelNode, OptRelNodeTyp, PlanNode,
@@ -45,7 +45,7 @@ pub fn main() {
         Box::new(OptCostModel::new(
             [("t1", 1000), ("t2", 100), ("t3", 10000)]
                 .into_iter()
-                .map(|(x, y)| (x.to_string(), PerTableStats::new(y, vec![])))
+                .map(|(x, y)| (x.to_string(), DataFusionPerTableStats::new(y, vec![])))
                 .collect(),
         )),
         vec![],

optd-datafusion-repr/src/bin/test_optimize.rs

@@ -1,5 +1,5 @@
-mod adaptive_cost;
-mod base_cost;
+pub mod adaptive_cost;
+pub mod base_cost;
 mod stats;
 
 pub use adaptive_cost::{AdaptiveCostModel, RuntimeAdaptionStorage, DEFAULT_DECAY};

optd-datafusion-repr/src/cost.rs

@@ -10,9 +10,14 @@ use optd_core::{
     rel_node::{RelNode, Value},
 };
 
-use super::base_cost::BaseTableStats;
+use super::base_cost::{
+    BaseTableStats, DataFusionDistribution, DataFusionMostCommonValues, Distribution,
+    MostCommonValues,
+};
 
 pub type RuntimeAdaptionStorage = Arc<Mutex<RuntimeAdaptionStorageInner>>;
+pub type DataFusionAdaptiveCostModel =
+    AdaptiveCostModel<DataFusionMostCommonValues, DataFusionDistribution>;
 
 #[derive(Default, Debug)]
 pub struct RuntimeAdaptionStorageInner {
@@ -22,13 +27,13 @@ pub struct RuntimeAdaptionStorageInner {
 
 pub const DEFAULT_DECAY: usize = 50;
 
-pub struct AdaptiveCostModel {
+pub struct AdaptiveCostModel<M: MostCommonValues, D: Distribution> {
     runtime_row_cnt: RuntimeAdaptionStorage,
-    base_model: OptCostModel,
+    base_model: OptCostModel<M, D>,
     decay: usize,
 }
 
-impl CostModel<OptRelNodeTyp> for AdaptiveCostModel {
+impl<M: MostCommonValues, D: Distribution> CostModel<OptRelNodeTyp> for AdaptiveCostModel<M, D> {
     fn explain(&self, cost: &Cost) -> String {
         self.base_model.explain(cost)
     }
@@ -56,11 +61,11 @@ impl CostModel<OptRelNodeTyp> for AdaptiveCostModel {
             {
                 if *iter + self.decay >= guard.iter_cnt {
                     let runtime_row_cnt = (*runtime_row_cnt).max(1) as f64;
-                    return OptCostModel::cost(runtime_row_cnt, 0.0, runtime_row_cnt);
+                    return OptCostModel::<M, D>::cost(runtime_row_cnt, 0.0, runtime_row_cnt);
                 }
             }
         }
-        let (mut row_cnt, compute_cost, io_cost) = OptCostModel::cost_tuple(
+        let (mut row_cnt, compute_cost, io_cost) = OptCostModel::<M, D>::cost_tuple(
             &self
                 .base_model
                 .compute_cost(node, data, children, context.clone(), optimizer),
@@ -74,16 +79,16 @@ impl CostModel<OptRelNodeTyp> for AdaptiveCostModel {
                 }
             }
         }
-        OptCostModel::cost(row_cnt, compute_cost, io_cost)
+        OptCostModel::<M, D>::cost(row_cnt, compute_cost, io_cost)
     }
 
     fn compute_plan_node_cost(&self, node: &RelNode<OptRelNodeTyp>) -> Cost {
         self.base_model.compute_plan_node_cost(node)
     }
 }
 
-impl AdaptiveCostModel {
-    pub fn new(decay: usize, stats: BaseTableStats) -> Self {
+impl<M: MostCommonValues, D: Distribution> AdaptiveCostModel<M, D> {
+    pub fn new(decay: usize, stats: BaseTableStats<M, D>) -> Self {
         Self {
             runtime_row_cnt: RuntimeAdaptionStorage::default(),
             base_model: OptCostModel::new(stats),