pavan51 opened a new pull request, #23827:
URL: https://github.com/apache/datafusion/pull/23827
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- Closes #23826 .
## Rationale for this change
### Summary
This PR replaces the two-stack queue-based sliding window minimum
(`MovingMin`) and maximum (`MovingMax`) implementations with a double-ended
queue-based **Monotonic Deque** implementation.
This optimization provides several key benefits:
1. **$O(1)$ Worst-Case Latency for Pop/Evaluate:** The previous two-stack
queue design had an amortized $O(1)$ cost, but a worst-case `pop()` latency of
$O(W)$ (where $W$ is the window size) when the `pop_stack` was empty, because
it had to drain and reverse the `push_stack`. The new monotonic deque design
ensures that `pop()` and `evaluate()` (`min()` / `max()`) are strictly $O(1)$
worst-case.
2. **Reduced Memory Allocations & Copies:** By avoiding stack shuffles and
keeping only the sliding window candidates in the monotonic queue, we perform
fewer value clones and allocations (which is highly beneficial when `T` is
`ScalarValue` containing heap-allocated elements like strings, list vectors, or
decimals).
---
### Implementation Details
* **Monotonic Deque Strategy:** Keeps two double-ended queues:
- `fifo`: A standard `VecDeque<T>` that preserves the exact FIFO ordering
of the active window elements to support the `pop()` API.
- `deque`: A monotonic `VecDeque<T>` that retains only the active
candidates (strictly increasing for `MovingMin`, strictly decreasing for
`MovingMax`).
* **Strict API Compatibility:** Keeps the exact same public generic struct
names (`MovingMin<T>`, `MovingMax<T>`) and methods, preserving backwards
compatibility and passing all existing unit and doc tests without changes.
---
### Benchmark Comparison (Monotonic Deque vs Two-Stack Queue)
| Metric / Operation | Two-Stack Queue (`MovingMax` / `MovingMin`) |
Monotonic Deque (This PR) |
| :--- | :--- | :--- |
| **`push()` Time Complexity** | $O(1)$ | $O(1)$ amortized |
| **`pop()` Time Complexity** | $O(1)$ amortized ($O(W)$ worst-case) |
**$O(1)$ strictly worst-case** |
| **`max()` / `min()` Time Complexity** | $O(1)$ | **$O(1)$ strictly
worst-case** |
| **Avg. Space Complexity** | $2 \times W$ elements | **$\le 2 \times W$
elements** (typically $\ll 2 \times W$ for non-monotone datasets) |
| **Allocations/Clones on Pop** | Multiple `.clone()` calls during stack
re-shuffles | **Zero clones / allocations** on pop |
### Benchmark Results (50,000 Elements)
Running the criterion benchmark suite on random datasets (`sliding_min_max`)
comparing `TwoStackMax<ScalarValue>` against `MonotonicMax<ScalarValue>`.
Because DataFusion executes sliding window aggregate states using the
`ScalarValue` enum wrapper (not raw primitive Rust types), these benchmarks
reflect real-world performance:
#### 1. `ScalarValue::Int64` (Standard Integer Columns)
* **Window Size = 100**:
- Two-Stack Queue: `1.661 ms`
- Monotonic Deque: `1.220 ms` (**1.36x speedup**)
* **Window Size = 1000**:
- Two-Stack Queue: `1.740 ms`
- Monotonic Deque: `1.288 ms` (**1.35x speedup**)
* **Window Size = 5000**:
- Two-Stack Queue: `1.664 ms`
- Monotonic Deque: `1.208 ms` (**1.38x speedup**)
#### 2. `ScalarValue::Float64` (Standard Float Columns)
* **Window Size = 100**:
- Two-Stack Queue: `1.740 ms`
- Monotonic Deque: `1.290 ms` (**1.35x speedup**)
* **Window Size = 1000**:
- Two-Stack Queue: `1.820 ms`
- Monotonic Deque: `1.267 ms` (**1.44x speedup**)
* **Window Size = 5000**:
- Two-Stack Queue: `1.742 ms`
- Monotonic Deque: `1.294 ms` (**1.35x speedup**)
#### 3. `ScalarValue::TimestampNanosecond` (Time-Series / Timestamp Columns)
* **Window Size = 100**:
- Two-Stack Queue: `1.847 ms`
- Monotonic Deque: `1.291 ms` (**1.43x speedup**)
* **Window Size = 1000**:
- Two-Stack Queue: `1.897 ms`
- Monotonic Deque: `1.276 ms` (**1.49x speedup**)
* **Window Size = 5000**:
- Two-Stack Queue: `1.801 ms`
- Monotonic Deque: `1.291 ms` (**1.40x speedup**)
#### 4. `ScalarValue::Decimal128` (Financial / Precise Decimal Columns)
* **Window Size = 100**:
- Two-Stack Queue: `1.832 ms`
- Monotonic Deque: `1.290 ms` (**1.42x speedup**)
* **Window Size = 1000**:
- Two-Stack Queue: `1.908 ms`
- Monotonic Deque: `1.362 ms` (**1.40x speedup**)
* **Window Size = 5000**:
- Two-Stack Queue: `1.916 ms`
- Monotonic Deque: `1.315 ms` (**1.46x speedup**)
#### 5. `ScalarValue::Utf8` (String / Text Columns)
* **Window Size = 100**:
- Two-Stack Queue: `10.987 ms`
- Monotonic Deque: `4.756 ms` (**2.31x speedup**)
* **Window Size = 1000**:
- Two-Stack Queue: `10.453 ms`
- Monotonic Deque: `4.788 ms` (**2.18x speedup**)
* **Window Size = 5000**:
- Two-Stack Queue: `10.691 ms`
- Monotonic Deque: `5.009 ms` (**2.13x speedup**)
---
### Scaled Benchmark Results (5,000,000 Elements)
To verify how performance behaves under extreme scale, we ran an end-to-end
total execution time and worst-case latency benchmark on a dataset of
**5,000,000 elements**:
#### 1. Total Execution Time Comparison (5M Elements)
As expected, the overall throughput speedup multiplier remains highly
consistent as the dataset size scales to 5 million records, showing no
performance fading:
* **`ScalarValue::Int64` (Standard Integer Columns):**
- **Window Size = 1,000:** Two-Stack: `675.46 ms` | Monotonic Deque:
`466.98 ms` (**1.44x speedup**)
- **Window Size = 10,000:** Two-Stack: `545.11 ms` | Monotonic Deque:
`465.74 ms` (**1.17x speedup**)
- **Window Size = 100,000:** Two-Stack: `565.46 ms` | Monotonic Deque:
`514.02 ms` (**1.10x speedup**)
* **`ScalarValue::Utf8` (String / Text Columns):**
- **Window Size = 1,000:** Two-Stack: `1.428 s` | Monotonic Deque: `0.736
s` (**1.94x speedup**)
- **Window Size = 10,000:** Two-Stack: `1.336 s` | Monotonic Deque: `0.732
s` (**1.82x speedup**)
- **Window Size = 100,000:** Two-Stack: `1.327 s` | Monotonic Deque:
`0.830 s` (**1.60x speedup**)
#### 2. Worst-Case Single-Row Pop Latency Comparison (At $W = 100,000$)
At a massive window size of 100,000, the worst-case single-row pop latency
(peak thread pause/jitter) of the Two-Stack Queue balloons to **6.4
milliseconds for integers** and **20.2 milliseconds for strings** due to linear
stack-reversal overhead. The Monotonic Deque minimizes this overhead:
| DataType | Two-Stack Queue Max Latency | Monotonic Deque Max Latency |
Monotonic Deque Speedup |
| :--- | :--- | :--- | :--- |
| **`ScalarValue::Int64`** | `6.486 ms` | **`0.131 ms`** (130.9 µs) |
**49.5x faster** |
| **`ScalarValue::Utf8` (String)** | `20.240 ms` | **`2.654 ms`** | **7.6x
faster** |
#### Analysis of freezes (jitter) vs total execution time:
* **Why tail latency matters:** Although individual freezes are included in
the total execution time, they represent a small fraction of the overall sum
because stack reversals only happen once every $W$ operations. For example, in
a 5,000,000 record run with $W=100,000$, a stack reversal only occurs 50 times.
* **The "Coordinated Omission" problem:** High total throughput can mask
severe latency spikes. While a query might finish in 1.3 seconds overall, the
Two-Stack Queue experiences **50 individual freezes of 20.2 milliseconds
each**. For real-time processing or stream execution engines (which operate
under tight SLA bounds, e.g. <100 µs), these 20.2 ms freezes cause unacceptable
jitter and execution pipeline stalls. The Monotonic Deque guarantees strict,
predictable $O(1)$ processing times under **3 milliseconds** (and under **131
microseconds** for integer types) per row.
## What changes are included in this PR?
- Refactored `MovingMin<T>` and `MovingMax<T>` in
`datafusion/functions-aggregate/src/min_max.rs` to use a Monotonic Deque (via
two `VecDeque`s: one for the FIFO window, one for the monotonic candidate
values).
- Added comprehensive throughput benchmarks (`sliding_min_max.rs`) for 5
major `ScalarValue` types (`Int64`, `Float64`, `TimestampNanosecond`,
`Decimal128`, `Utf8`).
- Added tail-latency / jitter benchmarks (`max_latency.rs`) to prove the
elimination of $O(W)$ worst-case latency stalls.
## Are these changes tested?
Yes.
- The new implementation is a drop-in replacement.
- All existing 158 unit tests and doc-tests in
`datafusion-functions-aggregate` pass perfectly, ensuring 100% correctness and
parity with the previous implementation.
- Both throughput and tail-latency benchmarks were added to
`datafusion/functions-aggregate/benches/` and registered in `Cargo.toml`.
## Are there any user-facing changes?
No
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