coderfender commented on code in PR #21575:
URL: https://github.com/apache/datafusion/pull/21575#discussion_r3077314158
##########
datafusion/functions-aggregate/benches/count_distinct.rs:
##########
@@ -150,5 +174,101 @@ fn count_distinct_benchmark(c: &mut Criterion) {
});
}
-criterion_group!(benches, count_distinct_benchmark);
+/// Create group indices with uniform distribution
+fn create_uniform_groups(num_groups: usize) -> Vec<usize> {
+ let mut rng = StdRng::seed_from_u64(42);
+ (0..BATCH_SIZE)
+ .map(|_| rng.random_range(0..num_groups))
+ .collect()
+}
+
+/// Create group indices with skewed distribution (80% in 20% of groups)
+fn create_skewed_groups(num_groups: usize) -> Vec<usize> {
+ let mut rng = StdRng::seed_from_u64(42);
+ let hot_groups = (num_groups / 5).max(1);
+ (0..BATCH_SIZE)
+ .map(|_| {
+ if rng.random_range(0..100) < 80 {
+ rng.random_range(0..hot_groups)
+ } else {
+ rng.random_range(0..num_groups)
+ }
+ })
+ .collect()
+}
+
+fn count_distinct_groups_benchmark(c: &mut Criterion) {
+ let count_fn = Count::new();
+
+ // bench different scenarios
+ let scenarios = [
+ // (name, num_groups, distinct_pct, group_fn)
+ ("sparse_uniform", 10, 80, "uniform"),
+ ("moderate_uniform", 100, 80, "uniform"),
+ ("dense_uniform", 1000, 80, "uniform"),
+ ("sparse_skewed", 10, 80, "skewed"),
+ ("dense_skewed", 1000, 80, "skewed"),
+ ("sparse_high_cardinality", 10, 99, "uniform"),
+ ("dense_low_cardinality", 1000, 20, "uniform"),
+ ];
+
+ for (name, num_groups, distinct_pct, group_type) in scenarios {
+ let n_distinct = BATCH_SIZE * distinct_pct / 100;
+ let values = Arc::new(create_i64_array(n_distinct)) as ArrayRef;
+ let group_indices = if group_type == "uniform" {
+ create_uniform_groups(num_groups)
+ } else {
+ create_skewed_groups(num_groups)
+ };
+
+ let (_schema, args) = prepare_args(DataType::Int64);
+
+ if count_fn.groups_accumulator_supported(args.clone()) {
Review Comment:
Sure what do you think is the better approach here?
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
