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.
Add scanner isolation class loader to make each plugin non-conflicting.
The BE will get scanner classes by JNI call and use JniClassLoader load them.
In the last version，we always get canner classes from the system class path by default,
so it cannot isolate the classes for each scanner
Use spark-bundle to read hudi data instead of using hive-bundle to read hudi data.
**Advantage** for using spark-bundle to read hudi data:
1. The performance of spark-bundle is more than twice that of hive-bundle
2. spark-bundle using `UnsafeRow` can reduce data copying and GC time of the jvm
3. spark-bundle support `Time Travel`, `Incremental Read`, and `Schema Change`, these functions can be quickly ported to Doris
**Disadvantage** for using spark-bundle to read hudi data:
1. More dependencies make hudi-dependency.jar very cumbersome(from 138M -> 300M)
2. spark-bundle only provides `RDD` interface and cannot be used directly
Add JNI metrics, for example:
```
- HudiJniScanner: 0ns
- FillBlockTime: 31.29ms
- GetRecordReaderTime: 1m5s
- JavaScanTime: 35s991ms
- OpenScannerTime: 1m6s
```
Add three common performance metrics for JNI scanner:
1. `OpenScannerTime`: Time to init and open JNI scanner
2. `JavaScanTime`: Time to scan data and insert into vector table in java side
3. `FillBlockTime`: Time to convert java vector table to c++ block
And support user defined metrics in java side, for example: `OpenScannerTime` is a long time for the open process, we want to determine which sub-process takes too much time, so we add `GetRecordReaderTime` in java side.
The user defined metrics in java side can be attached to BE profile automatically.
Two optimizations:
1. Insert string bytes directly to remove decoding&encoding process.
2. Use native reader to read the hudi base file if it has no log file. Use `explain` to show how many splits are read natively.
The java-udf module has become increasingly large and difficult to manage, making it inconvenient to package and use as needed. It needs to be split into multiple sub-modules, such as : java-commom、java-udf、jdbc-scanner、hudi-scanner、 paimon-scanner.
Co-authored-by: lexluo <lexluo@tencent.com>
