1. max compute partition prune,
we just support filter mc partitions by '='，it can filter just one partition
to support multiple partition filter and range operator('>','<', '>='..), the partition prune should be supported.
2. add max compute row count cache and partitionValues cache
3. add max compute regression case
Introduction to Main Classes：
- MTMVService：MTMV services for other modules to call
- MTMVHookService：All operations that affect the MTMV
- MTMVJobManager：All operations that affect the MTMV job
- MTMVCacheManager：All operations that affect the MTMV Cache
- MTMVTask&MTMVJob：Inherit from job framework
Fix two bugs:
1. Missing column is case sensitive, change the column name to lower case in FE for hive/iceberg/hudi
2. Iceberg use custom method to encode special characters in column name. Decode the column name to match the right column in parquet reader.
* [fix] scanner hangs due to negative num_running_scanners
Before the patch, num_running_scanners is increased after submitting,
then it may be decreased before increasing then negative values can
be seen by get_block_from_queue and a expected submit does not happend.
Co-authored-by: Mingyu Chen <morningman.cmy@gmail.com>
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.
`VFileScanner` will try to append late arrival runtime filters in each loop of `ScannerScheduler::_scanner_scan`. However, `VFileScanner::_get_next_reader` only generates the `_push_down_conjuncts` in the first loop, so the late arrival runtime filters are ignored.
1.Reconstruct the logic of decode to read parquet. The parquet reader first reads the data according to the parquet physical type, and then performs a type conversion.
2.Support hive alter table.
When enable shared scan, all scanners will be created by one instance. When the main instance reach eos and quit, all states of it will be released. But other instances are still possible to get block from those scanners. So we must assure scanners will not be dependent on any states of the main instance after it quit.
In previous, when using file scan node(eq, querying hive table), the max number of scanner for each scan node
will be the `doris_scanner_thread_pool_thread_num`(default is 48).
And if the query parallelism is N, the total number of scanner would be 48 * N, which is too many.
In this PR, I change the logic, the max number of scanner for each scan node
will be the `doris_scanner_thread_pool_thread_num / query parallelism`. So that the total number of scanners
will be up to `doris_scanner_thread_pool_thread_num`.
Reduce the number of scanner can significantly reduce the memory usage of query.
Add 3 counters for ExecNode:
ExecTime - Total execution time(excluding the execution time of children).
OutputBytes - The total number of bytes output to parent.
BlockCount - The total count of blocks output to parent.
For comparison predicate, two arguments must be cast to datetime and push down to storage if either one is date type. This PR disables predicate push-down for this case.
When executing broker load in ASAN mode, BE may crash with error:
```
F20231010 18:18:17.044978 185490 block.cpp:694] Check failed: d.column->use_count() == 1 (3 vs. 1)
*** Check failure stack trace: ***
@ 0x55e9d94c4e46 google::LogMessage::SendToLog()
@ 0x55e9d94c1410 google::LogMessage::Flush()
@ 0x55e9d94c5689 google::LogMessageFatal::~LogMessageFatal()
@ 0x55e9c509f80d doris::vectorized::Block::clear_column_data()
@ 0x55e9b6c170b3 doris::PlanFragmentExecutor::get_vectorized_internal()
@ 0x55e9b6c147e6 doris::PlanFragmentExecutor::open_vectorized_internal()
@ 0x55e9b6c12d9a doris::PlanFragmentExecutor::open()
@ 0x55e9b6c18426 doris::PlanFragmentExecutor::execute()
@ 0x55e9b6945cca doris::FragmentMgr::_exec_actual()
@ 0x55e9b696456c doris::FragmentMgr::exec_plan_fragment()::$_0::operator()()
```
It may happen when there is column maping like:
```
(k1,v2,v3,v4,v5,v6,v7,v8)
set (k2=v4,k3=v4,k4=v4)
```
in load stmt.
Case is covered by Baidu test cases
this pr is aim to update for filter_by_select logic and change delete limit
only support scala type in delete statement where condition
only support column nullable and predict column support filter_by_select logic, because we can not push down non-scala type to storage layer to pack in predict column but do filter logic
