alamb commented on code in PR #122:
URL: https://github.com/apache/datafusion-site/pull/122#discussion_r2639587916
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content/blog/2025-11-11-datafusion_case.md:
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+---
+layout: post
+title: Optimizing CASE Expression Evaluation
+date: 2025-11-11
+author: Pepijn Van Eeckhoudt
+categories: [features]
+---
+<!--
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+[TOC]
+
+<style>
+figure {
+ margin: 20px 0;
+}
+
+figure img {
+ display: block;
+ max-width: 80%;
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+}
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+}
+</style>
+
+# Optimizing CASE Expression Evaluation in DataFusion
+
+SQL's `CASE` expression is one of the few constructs the language provides to
perform conditional logic.
+Its deceptively simple syntax hides significant implementation complexity.
+Over the past few weeks, we've landed a series of improvements to DataFusion's
`CASE` expression evaluator that reduce both CPU time and memory allocations.
+This post walks through the original implementation, its performance
bottlenecks, and how we addressed them step by step.
+Finally we'll also take a look at some future improvements to `CASE` that are
in the works.
+
+## Background: CASE Expression Evaluation
+
+SQL supports two forms of CASE expressions:
+
+1. **Simple**: `CASE expr WHEN value1 THEN result1 WHEN value2 THEN result2
... END`
+2. **Searched**: `CASE WHEN condition1 THEN result1 WHEN condition2 THEN
result2 ... END`
+
+The simple form evaluates an expression once for each input row and then tests
that value against the constants (or expressions) in each `WHEN` clause using
equality comparisons.
+Think of it as a limited Rust `match` expression.
+
+Here's a simple example:
+
+```sql
+CASE status
+ WHEN 'pending' THEN 1
+ WHEN 'active' THEN 2
+ WHEN 'complete' THEN 3
+ ELSE 0
+END
+```
+
+In this `CASE` expression, `status` is evaluated once per row, and then its
value is tested for equality with the values `'pending'`, `'active'`, and
`'complete'` in that order.
+The `THEN` expression value for the first matching `WHEN` expression is
returned per row.
+
+The searched `CASE` form is a more flexible variant.
+It evaluates completely independent boolean expressions for each branch.
+
+This allows you to test different columns with different operators per branch
as can be seen in the following example:
+
+```sql
+CASE
+ WHEN age > 65 THEN 'senior'
+ WHEN childCount != 0 THEN 'parent'
+ WHEN age < 21 THEN 'minor'
+ ELSE 'adult'
+END
+```
+
+In both forms, branches are evaluated sequentially with short-circuit
semantics: for each row, once a `WHEN` condition matches, the corresponding
`THEN` expression is evaluated. Any further branches are not evaluated for that
row.
+This lazy evaluation model is critical for correctness.
+It let's you safely write `CASE` expressions like
+
+```sql
+CASE
+ WHEN denominator == 0 THEN NULL
+ ELSE nominator / denominator
+END
+```
+
+that are guaranteed to not trigger divide-by-zero errors.
+
+## `CASE` Evaluation in DataFusion 50.0.0
+
+For the rest of this post we'll be looking at 'searched case' evaluation.
+'Simple case' uses a distinct, but very similar implementation.
+The same set of improvements has been applied to both.
+
+The baseline implementation in DataFusion 50.0.0 evaluated `CASE` using a
straightforward approach:
+
+1. Start with an output array `out` with the same length as the input batch,
filled with nulls. Additionally, create a bit vector `remainder` with the same
length and each value set to `true`.
+2. For each `WHEN`/`THEN` branch:
+ - Evaluate the `WHEN` condition for remaining unmatched rows using
`PhysicalExpr::evaluate_selection`, passing in the input batch and the
`remainder` mask
+ - If any rows matched, evaluate the `THEN` expression for those rows using
`PhysicalExpr::evaluate_selection`
+ - Merge the results into the `out` using the `zip` kernel
+ - Update the `remainder` mask to exclude matched rows
+3. If there's an `ELSE` clause, evaluate it for any remaining unmatched rows
and merge using `zip`
+
+Here's a simplified version of the original loop:
+
+```rust
+let mut current_value = new_null_array(&return_type, batch.num_rows());
+let mut remainder = BooleanArray::from(vec![true; batch.num_rows()]);
+
+for (when_expr, then_expr) in &self.when_then_expr {
+ let when_value = when_expr.evaluate_selection(batch, &remainder)?
+ .into_array(batch.num_rows())?;
+ let when_value = and(&when_value, &remainder)?;
+
+ if when_value.true_count() == 0 {
+ continue;
+ }
+
+ let then_value = then_expr.evaluate_selection(batch, &when_value)?;
+ current_value = zip(&when_value, &then_value, ¤t_value)?;
+ remainder = and_not(&remainder, &when_value)?;
+}
+```
+
+While correct, this implementation had several performance issues mostly
related to the usage of `evaluate_selection`.
+To understand why, we need to dig a little deeper into the implementation of
that function.
+Here's a simplified version of it that captures the relevant parts:
+
+```rust
+pub trait PhysicalExpr {
+ fn evaluate_selection(
+ &self,
+ batch: &RecordBatch,
+ selection: &BooleanArray,
+ ) -> Result<ColumnarValue> {
+ let filtered_batch = filter_record_batch(batch, selection)?;
+ let filtered_result = self.evaluate(&filtered_batch)?;
+ scatter(selection, filtered_result)
+ }
+}
+```
+
+The `evaluate_selection` method first filters the input batch to only include
rows that match the `selection` mask.
+It then calls the regular `evaluate` method using the filtered batch as input.
+Finally, to return a result array with the same number of rows as `batch`, the
`scatter` function is called.
+This function produces a new array padded with `null` values for any rows that
didn't match the `selection` mask.
+
+So how does the simple evaluation strategy and use of `evaluate_selection`
cause performance overhead?
+
+### Problem 1: No Early Exit
+
+The case evaluation loop always iterated through all branches, even when every
row had already been matched.
+In queries where early branches match many rows, this meant unnecessary work
was done for remaining rows.
+
+### Problem 2: Repeated Filtering, Scattering, and Merging
+
+Each iteration performed operations that are very well-optimized but still not
cost free to execute:
+- **Filtering**: `PhysicalExpr::evaluate_selection` filters the entire
`RecordBatch` (all columns) for each branch. For the `WHEN` expression, this
was done even if the selection mask was entirely empty.
+- **Scattering**: `PhysicalExpr::evaluate_selection` scatters the filtered
result back to the original `RecordBatch` length.
+- **Merging**: The `zip` kernel is called once per branch to merge partial
results into the output array
+
+Each of these steps allocates new arrays and shuffles a lot of data around.
+
+### Problem 3: Filtering Unused Columns
+
+The `PhysicalExpr::evaluate_selection` method filters the entire record batch,
including columns that the current branch's `WHEN` and `THEN` expressions don't
reference.
+For wide tables (many columns) with narrow expressions (few column
references), this is wasteful.
+
+Suppose we have a table with 26 columns named `a` through `z`.
+For a simple CASE expression like:
+
+```sql
+CASE
+ WHEN a > 1000 THEN 'large'
+ WHEN a >= 0 THEN 'positive'
+ ELSE 'negative'
+END
+```
+
+the implementation would filter all columns 26 columns even though only a
single column is needed for the entire `CASE` expression evaluation.
+Again this involves a non-negligible amount of allocation and data copying.
+
+## Performance Improvements
+
+### Optimization 1: Short-Circuit Early Exit (commit 7c215ed)
+
+The first optimization added early exit logic to the evaluation loop:
+
+```rust
+let mut remainder_count = batch.num_rows();
+
+for (when_expr, then_expr) in &self.when_then_expr {
+ if remainder_count == 0 {
+ break; // All rows matched, exit early
+ }
+
+ // ... evaluate branch ...
+
+ let when_match_count = when_value.true_count();
+ remainder_count -= when_match_count;
+}
+```
+
+Additionally, we avoid evaluating the `ELSE` clause when no rows remain:
+
+```rust
+if let Some(else_expr) = &self.else_expr {
+ remainder = or(&base_nulls, &remainder)?;
+ if remainder.true_count() > 0 {
+ // ... evaluate else ...
+ }
+}
+```
+
+**Impact**: For queries where early branches match all rows, this eliminates
unnecessary branch evaluations and `ELSE` clause processing.
+
+### Optimization 2: Optimized Result Merging (commit e9431fc)
+
+The second optimization fundamentally restructured how partial results are
merged.
+Instead of using `zip()` after each branch to merge results into an output
array, we now:
+
+1. Maintain the subset of rows still needing evaluation across loop iterations
+2. Filter the batch progressively as rows are matched
+3. Build an index structure that tracks which branch produced each row's result
+4. Perform a single merge operation at the end
+
+The key insight is that we can defer all merging until the end of the
evaluation loop by tracking result provenance.
+When a branch matches a number of rows, instead of immediately merging with
`zip()`, we:
+1. Store the partial result array
+2. Mark the cells corresponding to each row in an indices array as needing to
take one value from the partial result array
+
+In the example below, three `WHEN/THEN` branches produced results.
+The first branch produced the result `A` for 2, the second produced `B` for
row 1, and the third produced `C` and `D` for rows 4 and 5.
+The final result array is obtained by running the arrays through the merge
operation.
+
+```aiignore
+┌───────────┐ ┌─────────┐ ┌─────────┐
+│┌─────────┐│ │ None │ │ NULL │
+││ A ││ ├─────────┤ ├─────────┤
+│└─────────┘│ │ 1 │ │ B │
+│┌─────────┐│ ├─────────┤ ├─────────┤
+││ B ││ │ 0 │ merge_n(values, indices) │ A │
+│└─────────┘│ ├─────────┤ ─────────────────────────▶ ├─────────┤
+│┌─────────┐│ │ None │ │ NULL │
+││ C ││ ├─────────┤ ├─────────┤
+│├─────────┤│ │ 2 │ │ C │
+││ D ││ ├─────────┤ ├─────────┤
+│└─────────┘│ │ 2 │ │ D │
+└───────────┘ └─────────┘ └─────────┘
+ arrays indices result
+```
+
+The main benefits of this merge operation are that the `scatter` step is
eleminated enitrely, and instead of requiring
Review Comment:
```suggestion
The main benefits of this merge operation are that the `scatter` step is
eliminated entirely, and instead of requiring
```
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