* [improvement](jdbc catalog) Optimize the closing logic of Jdbc connection after abort
* [improvement](jdbc catalog) Optimize the closing logic of Jdbc connection after abort
* fix
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 PR proposes mapping external catalog JSON types to String instead of JsonB in Apache Doris. This change is motivated by the realization that JDBC retrieves JSON data as a String JSON string, regardless of its storage format (Json(String) or Json(Binary)). Mapping to String streamlines data retrieval, simplifies write-backs, and ensures compatibility with all JSON(String) and JSON(Binary) functions, despite potentially misleading displays of JSON data as Strings in Doris. This approach avoids the performance overhead and complexity of converting each row of data from JsonB to String, making the process more efficient and elegant.
About Upgrade
To ensure query compatibility with existing Catalogs in the upgraded version,we currently still retain the capability to query external JSON types as JSONB. However, once you upgrade to the new version and either refresh the Catalog or create a new one, all external JSON types will be treated as Strings. To ensure consistent behavior,and possible future removal of support for JSON as JSONB query code, it is highly recommended that you manually refresh your Catalog as soon as possible after upgrading to the new version.
Before this PR, Paimon has created the schema of `VectorTable` by accessing meta information. However, once the schema of `VectorTable` in java is not same as `Block` in c++, BE will crashed, and there is no good way to troubleshoot errors.
In the old caching logic, we only used jdbcurl, user, and password as cache keys. This may cause the old link to be still used when replacing the jar package, so we should concatenate all the parameters required for the connection pool as the key.
1. Remove `doris_max_remote_scanner_thread_pool_thread_num`, use `doris_scanner_thread_pool_thread_num` only.
2. Set the default value `doris_scanner_thread_pool_thread_num` as `std::max(48, CpuInfo::num_cores() * 4)`
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
1. Do not use FATAL log when jni encounter error, to avoid crash.
2. Fix NPE when closing PaimonReader, the reader may not be assigned if PaimonReader open failed.
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.
Follow https://github.com/apache/doris/pull/25302, and use the unified jni framework to refactor java udaf.
This PR has removed the old interfaces to run java udf/udaf. Thanks to the ease of use of the new framework, the core code for modifying UDAF does not exceed 100 lines, and the logic is similar to that of UDF.
Use the unified jni framework to refactor java udf.
The unified jni framework takes VectorTable as the container to transform data between c++ and java, and hide the details of data format conversion.
In addition, the unified framework supports complex and nested types.
The performance of basic types remains consistent, with a 30% improvement in string types and an order of magnitude improvement in complex types.
Support complex types in jni framework, and successfully run end-to-end on hudi.
### How to Use
Other scanners only need to implement three interfaces in `ColumnValue`:
```
// Get array elements and append into values
void unpackArray(List<ColumnValue> values);
// Get map key array&value array, and append into keys&values
void unpackMap(List<ColumnValue> keys, List<ColumnValue> values);
// Get the struct fields specified by `structFieldIndex`, and append into values
void unpackStruct(List<Integer> structFieldIndex, List<ColumnValue> values);
```
Developers can take `HudiColumnValue` as an example.
