viirya commented on code in PR #424:
URL: https://github.com/apache/datafusion-comet/pull/424#discussion_r1600515535
##########
core/src/execution/datafusion/spark_hash.rs:
##########
@@ -193,27 +241,67 @@ macro_rules! hash_array_decimal {
fn create_hashes_dictionary<K: ArrowDictionaryKeyType>(
array: &ArrayRef,
hashes_buffer: &mut [u32],
+ multi_col: bool,
) -> Result<()> {
let dict_array =
array.as_any().downcast_ref::<DictionaryArray<K>>().unwrap();
+ if multi_col {
+ // unpack the dictionary array as each row may have a different hash
input
+ let unpacked = take(dict_array.values().as_ref(), dict_array.keys(),
None)?;
+ create_hashes(&[unpacked], hashes_buffer)?;
+ } else {
+ // Hash each dictionary value once, and then use that computed
+ // hash for each key value to avoid a potentially expensive
+ // redundant hashing for large dictionary elements (e.g. strings)
+ let dict_values = Arc::clone(dict_array.values());
+ // same initial seed as Spark
+ let mut dict_hashes = vec![42; dict_values.len()];
+ create_hashes(&[dict_values], &mut dict_hashes)?;
Review Comment:
Hmm, then I think this approach is actually incorrect, even for first
column. We cannot pre-compute hashes for values. The key/value order is not the
same as value order.
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