This PR makes the following changes to the connection pool of JDBC Catalog
1. Set the maximum connection survival time, the default is 30 minutes
- Moreover, one-half of the maximum survival time is the recyclable time,
- One-tenth is the check interval for recycling connections
2. Keepalive only takes effect on the connection pool on BE, and will be activated based on one-fifth of the maximum survival time.
3. The maximum number of existing connections is changed from 100 to 10
4. Add the connection cache recycling thread on BE, and add a parameter to control the recycling time, the default is 28800 (8 hours)
5. Add CatalogID to the key of the connection pool cache to achieve better isolation, requires refresh catalog to take effect
6. Upgrade druid connection pool to version 1.2.20
7. Added JdbcResource's setting of default parameters when upgrading the FE version to avoid errors due to unset parameters.
This commit overhauls the JDBC connector logic within our project, transitioning from the previous mechanism of fetching data through JNI calls for individual ResultSet items to a more efficient and unified approach using the VectorTable data structure.
Add 2 metrics in jdbc scan node profile:
- `CallJniNextTime`: call get next from jdbc result set
- `ConvertBatchTime`: call convert jobject to columm block
Also fix a potential concurrency issue when init jdbc connection cache pool
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.
Co-authored-by: yiguolei <yiguolei@gmail.com>
Currently, exec node save exprcontext**, but the object is in object pool, the code is very unclear. we could just use exprcontext*.
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.
Issue Number: close #xxx
This pr fix two bugs:
_jdbc_scanner may be nullptr in vjdbc_connector.cpp, so we use another method to count jdbc statistic. close [Enhencement](jdbc scanner) add profile for jdbc scanner #15914
In the batch insertion scenario, oracle database does not support syntax insert into tables values (...),(...); , what it supports is:
insert all
into table(col1,col2) values(c1v1, c2v1)
into table(col1,col2) values(c1v2, c2v2)
SELECT 1 FROM DUAL;
The origin scan pools are in exec_env.
But after enable new_load_scan_node by default, the scan pool in exec_env is no longer used.
All scan task will be submitted to the scan pool in scanner_scheduler.
BTW, reorganize the scan pool into 3 kinds:
local scan pool
For olap scan node
remote scan pool
For file scan node
limited scan pool
For query which set cpu resource limit or with small limit clause
TODO:
Use bthread to unify all IO task.
Some trivial issues:
fix bug that the memtable flush size printed in log is not right
Add RuntimeProfile param in VScanner
In previous implementation, when doing list partition prune, we need to generation `rangeToId`
every time we doing prune.
But `rangeToId` is actually a static data that should be create-once-use-every-where.
So for hive partition, I created the `rangeToId` and all other necessary data structures for partition prunning
in partition cache, so that we can use it directly.
In my test, the cost of partition prune for 10000 partitions reduce from 8s -> 0.2s.
Aslo add "partition" info in explain string for hive table.
```
| 0:VEXTERNAL_FILE_SCAN_NODE |
| predicates: `nation` = '0024c95b' |
| inputSplitNum=1, totalFileSize=4750, scanRanges=1 |
| partition=1/10000 |
| numNodes=1 |
| limit: 10 |
```
Bug fix:
1. Fix bug that es scan node can not filter data
2. Fix bug that query es with predicate like `where substring(test2,2) = "ext2";` will fail at planner phase.
`Unexpected exception: org.apache.doris.analysis.FunctionCallExpr cannot be cast to org.apache.doris.analysis.SlotRef`
TODO:
1. Some problem when quering es version 8: ` Unexpected exception: Index: 0, Size: 0`, will be fixed later.
