Doris updated array type output format, using double quote for Strings.
Before, it was using single quote. So we need to update the case out file using double quote.
before
mysql [test]>select cast(1 as DECIMALV3(16, 2)) / cast(3 as DECIMALV3(16, 2));
+-----------------------------------------------------------+
| CAST(1 AS DECIMALV3(16, 2)) / CAST(3 AS DECIMALV3(16, 2)) |
+-----------------------------------------------------------+
| 0.00 |
+-----------------------------------------------------------+
mysql [test]>select * from divtest;
+------+------+
| id | val |
+------+------+
| 3 | 5.00 |
| 2 | 4.00 |
| 1 | 3.00 |
+------+------+
mysql [test]>select cast(1 as decimalv3(16,2)) / val from divtest;
+-------------------------------------+
| CAST(1 AS DECIMALV3(16, 2)) / `val` |
+-------------------------------------+
| 0 |
| 0 |
| 0 |
+-------------------------------------+
after
mysql [test]>select cast(1 as DECIMALV3(16, 2)) / cast(3 as DECIMALV3(16, 2));
+-----------------------------------------------------------+
| CAST(1 AS DECIMALV3(16, 2)) / CAST(3 AS DECIMALV3(16, 2)) |
+-----------------------------------------------------------+
| 0.33 |
+-----------------------------------------------------------+
mysql [test]>select cast(1 as decimalv3(16,2)) / val from divtest;
+-------------------------------------+
| CAST(1 AS DECIMALV3(16, 2)) / `val` |
+-------------------------------------+
| 0.250000 |
| 0.200000 |
| 0.333333 |
+-------------------------------------+
This is because in the previous code, the constant 1.000 would be transformed into 1.
remove "ReduceType
recurrent:
./run-regression-test.sh --run -suiteParallel 1 -actionParallel 1 -parallel 1 -d query_p0/sql_functions/window_functions
select /*+ SET_VAR(query_timeout = 600) */ subq_0.`c1` as c0 from (select ref_1.`s_name` as c0, ref_1.`s_suppkey` as c1, ref_1.`s_address` as c2, ref_1.`s_address` as c3 from regression_test_query_p0_sql_functions_window_functions.tpch_tiny_supplier as ref_1 where (ref_1.`s_name` is NULL) or (ref_1.`s_acctbal` is not NULL)) as subq_0 where (subq_0.`c3` is NULL) or (subq_0.`c2` is not NULL)
reason:
FunctionIsNull and FunctionIsNotNull execute returns a const column, but their VectorizedFnCall::is_constant returns false, which causes problems with const handling when VCompoundPred::execute.
This pr converts const column to full column in VCompoundPred execute. In the future, there will be a more thorough solution to such problems.
* [Improve](performance) introduce SchemaCache to cache TabletSchame & Schema
1. When the system is under high-concurrency load with wide table point queries, the frequent memory allocation and deallocation of Schema become evident system bottlenecks. Additionally, the initialization of TabletSchema and Schema also becomes a CPU hotspot.Therefore, the introduction of a SchemaCache is implemented to cache these resources for reuse.
2. Make some variables wrapped with std::unique<unique_ptr>
Performance:
| 状态 | QPS | 平均响应时间 (avg) | P99 响应时间 |
|------------------|-----|------------------|-------------|
| 开启 SchemaCache | 501 | 20ms | 34ms |
| 关闭 SchemaCache | 321 | 31ms | 61ms |
* handle schema change with schema version
* remove useless header
* rebase
1. Delete the stats for data size, since it would cost too much time but useless
2. Make task time out configurable since when it's common to analyze a quite huge table that the default 10 min is not suitable
This pr is to fix the bug introduced by PR #19909
The bug failed to set column unique id for iceberg table, which will cause the query result for iceberg table are all NULL.
```
mysql> select * from iceberg_partition_lower_case_parquet limit 1;
+------+------+------+---------+
| k1 | k2 | k3 | city |
+------+------+------+---------+
| NULL | NULL | NULL | Beijing |
+------+------+------+---------+
1 row in set (0.60 sec)
```
After fix:
```
mysql> select * from iceberg_partition_lower_case_parquet limit 1;
+------+------+------+---------+
| k1 | k2 | k3 | city |
+------+------+------+---------+
| 1 | k2_1 | k3_1 | Beijing |
+------+------+------+---------+
1 row in set (0.35 sec)
```
when olap table is dynamic partition enable, if drop and recover olap table, the table should be added to DynamicPartitionScheduler again
---------
Co-authored-by: caiconghui1 <caiconghui1@jd.com>
* [Fix](inverted index) fix memeory leak when inverted index writer do not finish correctly
* [Update](inverted index) use smart pointer to avoid memeory leak
* [Chore](format) code format
---------
Co-authored-by: airborne12 <airborne12@gmail.com>
before
mysql [(none)]>select cast("10:10:10" as time);
+-------------------------------+
| CAST('10:10:10' AS TIMEV2(0)) |
+-------------------------------+
| 00:00:00 |
+-------------------------------+
after
mysql [(none)]>select cast("10:10:10" as time);
+-------------------------------+
| CAST('10:10:10' AS TIMEV2(0)) |
+-------------------------------+
| 10:10:10 |
+-------------------------------+
In the past, we supported this syntax.
mysql [(none)]>select cast("2023:05:01 13:14:15" as time);
+------------------------------------------+
| CAST('2023:05:01 13:14:15' AS TIMEV2(0)) |
+------------------------------------------+
| 13:14:15 |
+------------------------------------------+
However, "10:10:10" is also a valid datetime.
mysql [(none)]>select cast("10:10:10" as datetime);
+-----------------------------------+
| CAST('10:10:10' AS DATETIMEV2(0)) |
+-----------------------------------+
| 2010-10-10 00:00:00 |
+-----------------------------------+
So here, the order of parsing has been adjusted.
Refactoring the filtering conditions in the current ExecNode from an expression tree to an array can simplify the process of adding runtime filters. It eliminates the need for complex merge operations and removes the requirement for the frontend to combine expressions into a single entity.
By representing the filtering conditions as an array, each condition can be treated individually, making it easier to add runtime filters without the need for complex merging logic. The array can store the individual conditions, and the runtime filter logic can iterate through the array to apply the filters as needed.
This refactoring simplifies the codebase, improves readability, and reduces the complexity associated with handling filtering conditions and adding runtime filters. It separates the conditions into discrete entities, enabling more straightforward manipulation and management within the execution node.
Increase the functionality of advanced materialized view
This feature already supported by legacy planner with PR #19650
This PR implement it in Nereids. This PR implement the features as below:
1. Support multiple columns in aggregate function. eg: select sum(c1 + c2) from t1;
2. Supports complex expressions. eg: select abs(c1), sum(abc(c1+1) + 1) from t1;
TODO:
1. Support adding where in materialized view
We encountered one confusing situation where buffered reader were trapped in one endless loop when calling readat. Then we found out that it was all due to the return data size is less than requested.
As the following picture shows, the actual data size is about 2M, and when we called readat it only retrieved about 1MB.
