For outer join / right outer join / right semi join, when HashJoinNode::pull->process_data_in_hashtable outputs a block, it will output all rows of a key in the hash table into a block, and the output of a key is completed After that, it will check whether the block size exceeds the batch size, and if it exceeds, the output will be terminated.
If a key has 2000w+ rows, memory overflow will occur when the subsequent block operations on the 2000w+ rows are performed.
Tablet::version_for_delete_predicate should travel all rowset metas in tablet meta which complex is O(N), however we can directly judge whether this rowset is a delete rowset by RowsetMeta::has_delete_predicate which complex is O(1).
As we won't call Tablet::version_for_delete_predicate when pick input rowsets for compaction, we can reduce the critical area of Tablet::_meta_lock.
Now in ScannerContext::push_back_scanner_and_reschedule, _num_running_scanners-- is before _num_scheduling_ctx++.
InPipScannerContext::can_finish, we check _num_running_scanners == 0 && _num_scheduling_ctx == 0 without obtaining _transfer_lock.
In follow case, PipScannerContext::can_finish will return wrong result.
_num_running_scanners--
Check _num_running_scanners == 0 && _num_scheduling_ctx == 0` return true.
_num_scheduling_ctx++
So, we can set _num_running_scanners-- in the last of this func.
Describe your changes.
PipScannerContext::get_block_from_queue not block.
Set _num_running_scanners-- in the last of ScannerContext::push_back_scanner_and_reschedule.
* [refactor] delete non vec load from memtable
delete non vec load from memtable totally.
remove function keys_type() in memtable.
Co-authored-by: zhoubintao <1229701101@qq.com>
fix a dcheck error for vertical compaction on Merge-On-Write table。
When merge rowsets with empty segment, VerticalHeapMergeIterator::init
return ok directly and _record_rowids not set, dcheck failed when
_unique_key_next_block call current_block_row_locations。
A deleted file may belong to multiple data files. Each data file will read a full amount of deleted files,
so a deleted file may be read repeatedly. The deleted files can be cached, and multiple data files
can reuse the first read content.
The performance is improved by 60% in the case of single thread, and by 30% in the case of multithreading.
This pr mainly to optimize the histogram(👉🏻https://github.com/apache/doris/pull/14910) aggregation function. Including the following:
1. Support input parameters `sample_rate` and `max_bucket_num`
2. Add UT and regression test
3. Add documentation
4. Optimize function implementation logic
Parameter description：
- `sample_rate`：Optional. The proportion of sample data used to generate the histogram. The default is 0.2.
- `max_bucket_num`：Optional. Limit the number of histogram buckets. The default value is 128.
---
Example：
```
MySQL [test]> SELECT histogram(c_float) FROM histogram_test;
+-------------------------------------------------------------------------------------------------------------------------------------+
| histogram(`c_float`) |
+-------------------------------------------------------------------------------------------------------------------------------------+
| {"sample_rate":0.2,"max_bucket_num":128,"bucket_num":3,"buckets":[{"lower":"0.1","upper":"0.1","count":1,"pre_sum":0,"ndv":1},...]} |
+-------------------------------------------------------------------------------------------------------------------------------------+
MySQL [test]> SELECT histogram(c_string, 0.5, 2) FROM histogram_test;
+-------------------------------------------------------------------------------------------------------------------------------------+
| histogram(`c_string`) |
+-------------------------------------------------------------------------------------------------------------------------------------+
| {"sample_rate":0.5,"max_bucket_num":2,"bucket_num":2,"buckets":[{"lower":"str1","upper":"str7","count":4,"pre_sum":0,"ndv":3},...]} |
+-------------------------------------------------------------------------------------------------------------------------------------+
```
Query result description：
```
{
"sample_rate": 0.2,
"max_bucket_num": 128,
"bucket_num": 3,
"buckets": [
{
"lower": "0.1",
"upper": "0.2",
"count": 2,
"pre_sum": 0,
"ndv": 2
},
{
"lower": "0.8",
"upper": "0.9",
"count": 2,
"pre_sum": 2,
"ndv": 2
},
{
"lower": "1.0",
"upper": "1.0",
"count": 2,
"pre_sum": 4,
"ndv": 1
}
]
}
```
Field description：
- sample_rate：Rate of sampling
- max_bucket_num：Limit the maximum number of buckets
- bucket_num：The actual number of buckets
- buckets：All buckets
- lower：Upper bound of the bucket
- upper：Lower bound of the bucket
- count：The number of elements contained in the bucket
- pre_sum：The total number of elements in the front bucket
- ndv：The number of different values in the bucket
> Total number of histogram elements = number of elements in the last bucket(count) + total number of elements in the previous bucket(pre_sum).
According to the post https://developer.apple.com/forums/thread/676684, the executable whose size is bigger than 2G may fail to start. The size of the executable `doris_be_test` generated by run-be-ut.sh is 2.1G (> 2G) now and we can't run it on macOS (arm64).
We can separate the debug info from the executable `doris_be_test` to reduce the size. After that, we can run `doris_be_test` successfully.
1. the agg function without distinct keyword should be a "merge" funcion in threePhaseAggregateWithDistinct
2. use aggregateParam.aggMode.consumeAggregateBuffer instead of aggregateParam.aggPhase.isGlobal() to indicate if a agg function is a "merge" function
3. add an AvgDistinctToSumDivCount rule to support avg(distinct xxx) in some case
4. AggregateExpression's nullable method should call inner function's nullable method.
5. add a bind slot rule to bind pattern "logicalSort(logicalHaving(logicalProject()))"
6. don't remove project node in PhysicalPlanTranslator
7. add a cast to bigint expr when count( distinct datelike type )
8. fallback to old optimizer if bitmap runtime filter is enabled.
9. fix exchange node mem leak
* [feature-wip](inverted index)inverted index api: reader
* [feature-wip](inverted index) Fulltext query syntax with MATCH/MATCH_ALL/MATCH_ALL
* [feature-wip](inverted index) Adapt to index meta
* [enhance] add more metrics
* [enhance] add fulltext match query check for column type and index parser
* [feature-wip](inverted index) Support apply inverted index in compound predicate which except leaf node of and node
