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>
