* [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>
WITH t0 AS(
SELECT report.date1 AS date2 FROM(
SELECT DATE_FORMAT(date, '%Y%m%d') AS date1 FROM cir_1756_t1
) report GROUP BY report.date1
),
t3 AS(
SELECT date_format(date, '%Y%m%d') AS date3
FROM cir_1756_t2
)
SELECT row_number() OVER(ORDER BY date2)
FROM(
SELECT t0.date2 FROM t0 LEFT JOIN t3 ON t0.date2 = t3.date3
) tx;
The DATE_FORMAT(date, '%Y%m%d') was calculated in GROUP BY node, which is wrong. This expr should be calculated inside the subquery.
1. add support `CAST AS Struct` from Struct type;
2. fix crash while `CAST('{}' AS Struct)`;
3. `CAST('' AS complext_type)` should return NULL instead of empty object;
---------
Co-authored-by: cambyzju <zhuxiaoli01@baidu.com>
ECB algorithm, block_encryption_mode does not take effect, it only takes effect when init vector is provided.
Solved: 192/256 supports calculation without init vector
For other algorithms, an error should be reported when there is no init vector
Initialization Vector. The default value for the block_encryption_mode system variable is aes-128-ecb, or ECB mode, which does not require an initialization vector. The alternative permitted block encryption modes CBC, CFB1, CFB8, CFB128, and OFB all require an initialization vector.
Reference: https://dev.mysql.com/doc/refman/8.0/en/encryption-functions.html#function_aes-decrypt
Note: This fix does not support smooth upgrades. during upgrade process, query may report error: funciton not found
Result of select bitmap_to_string(bitmap_or(to_bitmap(1), null)) should be 1 instead of null.
This PR fix logic of bitmap_or and bitmap_or_count.
Other count related funcitons should also be checked and fix, they will be fixed in another PR.
bug: some chinese word not sort by pinyin in GBK coding
CREATE TABLE `test_convert` (
`a` varchar(100) NULL
) ENGINE=OLAP
DUPLICATE KEY(`a`)
DISTRIBUTED BY HASH(`a`) BUCKETS 3
PROPERTIES (
"replication_allocation" = "tag.location.default: 1"
);
insert into test_convert values("b"), ("a"), ("c"), ("睿"), ("多"), ("丝");
Query OK, 6 rows affected (0.03 sec)
{'label':'insert_ca73a6acc2194d5b_888218a3949355a6', 'status':'VISIBLE', 'txnId':'18068'}
mysql [test]>select * from test_convert;
+------+
| a |
+------+
| a |
| c |
| 丝 |
| b |
| 多 |
| 睿 |
+------+
6 rows in set (0.01 sec)
mysql [test]>select * from test_convert order by convert(a using gbk);
+------+
| a |
+------+
| a |
| b |
| c |
| 多 |
| 丝 |
| 睿 |
+------+
6 rows in set (0.01 sec)
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.
- change for Nereids
1. add a variable length parameter to the ctor of Count for a good error reporting of Count(a, b)
2. refactor StringRegexPredicate, let it inherit from ScalarFunction
3. remove useless class TypeCollection
4. use catalog.Type.Collection to check expression arguments type
5. change type coercion for TimestampArithmetic, divide, integral divide, comparison predicate, case when and in predicate. Let them same as legacy planner.
- change for legacy planner
1. change the common type of floating and Decimal from Decimal to Double
Introduced a new function non_nullable to BE, which can extract concrete data column from a nullable column. If the input argument is already not a nullable column, raise an error.
When the argument of truncate function is float type, it can match both truncate(DECIMALV3) and truncate(DOUBLE), if the match is truncate(DECIMALV3), the precision is lost when converting float to DECIMALV3(38, 0).
Here I modify it to match truncate(DOUBLE) for now, maybe we still need to solve the problem of losing precision when converting float to DECIMALV3.
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).
