wangbo commented on code in PR #10506:
URL: https://github.com/apache/doris/pull/10506#discussion_r910893618
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be/src/olap/rowset/segment_v2/segment_iterator.cpp:
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@@ -699,28 +699,12 @@ void SegmentIterator::_vec_init_lazy_materialization() {
// Step 2: check non-predicate read costs to determine whether need lazy
materialization
// fill _non_predicate_columns.
- // note(wb) For block schema, query layer and storage layer may have some
diff
- // query layer block schema not contains delete column, but storage
layer appends delete column to end of block schema
- // When output block to query layer, delete column can be skipped.
- // _schema.column_ids() stands for storage layer block schema, so it
contains delete columnid
- // we just regard delete column as common pred column here.
+ // After some optimization, we suppose lazy materialization is better
performance.
if (_schema.column_ids().size() > pred_column_ids.size()) {
for (auto cid : _schema.column_ids()) {
if (!_is_pred_column[cid]) {
_non_predicate_columns.push_back(cid);
- FieldType type = _schema.column(cid)->type();
-
- // todo(wb) maybe we can make read char type faster
- // todo(wb) support map/array type
- // todo(wb) consider multiple integer columns cost, such as
1000 columns, maybe lazy materialization faster
- if (!_lazy_materialization_read &&
- (_is_need_vec_eval ||
- _is_need_short_eval) && // only when pred exists, we need
to consider lazy materialization
- (type == OLAP_FIELD_TYPE_HLL || type ==
OLAP_FIELD_TYPE_OBJECT ||
- type == OLAP_FIELD_TYPE_VARCHAR || type ==
OLAP_FIELD_TYPE_CHAR ||
- type == OLAP_FIELD_TYPE_STRING || type ==
OLAP_FIELD_TYPE_BOOL ||
- type == OLAP_FIELD_TYPE_DATE || type ==
OLAP_FIELD_TYPE_DATETIME ||
- type == OLAP_FIELD_TYPE_DECIMAL)) {
+ if (_is_need_vec_eval || _is_need_short_eval) {
Review Comment:
This check can be moved out of the foreach.
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