ixu ubsan errors:
doris/be/src/util/string_parser.hpp:275:58: runtime error: signed integer overflow: 2147483647 + 1 cannot be represented in type 'int'
doris/be/src/vec/functions/functions_comparison.h:214:51: runtime error: addition of unsigned offset to 0x7fea6c6b7010 overflowed to 0x7fea6c6b700c
doris/be/src/vec/functions/multiply.cpp:67:50: runtime error: signed integer overflow: 1295699415680000000 * 0x0000000000015401d0a4cd4890a77700 cannot be represented in type '__int128
doris/be/src/vec/aggregate_functions/aggregate_function_percentile_approx.h:445:73: runtime error: addition of unsigned offset to 0x7feca3343d10 overflowed to 0x7feca3343d08
doris/be/src/exec/schema_scanner/schema_tables_scanner.cpp:330:24: run
1. fix function define of `Retention` inconsist, this function return tinyint on `FE` and return uint8 on `BE`
2. make assert_cast support cast to derived
3. change some static cast to assert cast
4. support sum(bool)/avg(bool)
Currently, there are some useless includes in the codebase. We can use a tool named include-what-you-use to optimize these includes. By using a strict include-what-you-use policy, we can get lots of benefits from it.
rpc common is duplicate, all its method is included in function rpc. So that I remove it.
get_field_type is never used, remove it.
---------
Co-authored-by: yiguolei <yiguolei@gmail.com>
* [Feature](vectorized)(quantile_state): support vectorized quantile state functions
1. now quantile column only support not nullable
2. add up some regression test cases
3. set default enable_quantile_state_type = true
---------
Co-authored-by: spaces-x <weixiang06@meituan.com>
1. change PipelineTaskState to enum class
2. remove some row-based code on FoldConstantExecutor::_get_result
3. reduce memcpy on minmax runtime filter function(Now we can guarantee that the input data is aligned)
4. add Wunused-template check, and remove some unused function, change some static function to inline function.
Enhance aggregate function `collect_set` and `collect_list` to support optional `max_size` param,
which enables to limit the number of elements in result array.
1. Fixed a problem with histogram statistics collection parameters.
2. Solved the problem that it takes a long time to collect histogram statistics.
TODO: Optimize histogram statistics sampling method and make the sampling parameters effective.
The problem is that the histogram function works as expected in the single-node test, but doesn't work in the multi-node test. In addition, the performance of the current support sampling to collect histogram is low, resulting in a large time consumption when collecting histogram information.
Fixed the parameter issue and temporarily removed support for sampling to speed up the collection of histogram statistics.
Will next support sampling to collect histogram information.
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).
