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.
* be scanner
- Upgrade avro to 1.11.2
fe
- Upgrade quartz to 2.5.0-rc1
- Upgrade maxcompute to 0.45-2-publish
- Binding avro-ipc to 1.11.2
* Binding hbase version to 2.5.5
binding nimbusds version to 9.35
Fix three bugs:
1. Hudi slice maybe has log files only, so `new Path(filePath)` will throw errors.
2. Hive column names are lowercase only, so match column names in ignore-case-mode.
3. Compatible with [Spark Datasource Configs](https://hudi.apache.org/docs/configurations/#Read-Options), so users can add `hoodie.datasource.merge.type=skip_merge` in catalog properties to skip merge logs files.
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
1. Reduce the number of threads reading avro logs and keep the readers in a fixed thread pool.
2. Regularly cleaning the cached resolvers in the thread local map by reflection.
Sometimes, the partitions of a hive table may on different storage, eg, some is on HDFS, others on object storage(cos, etc).
This PR mainly changes:
1. Fix the bug of accessing files via cosn.
2. Add a new field `fs_name` in TFileRangeDesc
This is because, when accessing a file, the BE will get a hdfs client from hdfs client cache, and different file in one query
request may have different fs name, eg, some of are `hdfs://`, some of are `cosn://`, so we need to specify fs name
for each file, otherwise, it may return error:
`reason: IllegalArgumentException: Wrong FS: cosn://doris-build-1308700295/xxxx, expected: hdfs://[172.xxxx:4007](http://172.xxxxx:4007/)`
The avro-scanner-jar package is reduced from 204M to 160M.
Hadoop-related dependencies in the original avro pom are directly packaged into a jar package, resulting in a jar volume of 200M. Now since there is already a hadoop jar package environment in be lib, it can be directly referenced.
This pr fixes two issues:
1. when using s3 TVF to query files in AVRO format, due to the change of `TFileType`, the originally queried `FILE_S3 ` becomes `FILE_LOCAL`, causing the query failed.
2. currently, both parameters `s3.virtual.key` and `s3.virtual.bucket` are removed. A new `S3Utils` in jni-avro to parse the bucket and key of s3.
The purpose of doing this operation is mainly to unify the parameters of s3.
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
1. If only read the partition columns, the `JniConnector` will produce empty required fields, so `HudiJniScanner` should read the "_hoodie_record_key" field at least to know how many rows in current hoodie split. Even if the `JniConnector` doesn't read this field, the call of `releaseTable` in `JniConnector` will reclaim the resource.
2. To prevent BE failure and exit, `JniConnector` should call release methods after `HudiJniScanner` is initialized. It should be noted that `VectorTable` is created lazily in `JniScanner`, so we don't need to reclaim the resource when `HudiJniScanner` is failed to initialize.
## Remaining works
Other jni readers like `paimon` and `maxcompute` may encounter the same problems, the jni reader need to handle this abnormal situation on its own, and currently this fix can only ensure that BE will not exit.
Upgrade hudi version from 0.13.0 to 0.13.1, and keep the hudi version of jni scanner the same as that of FE.
This may fix the bug of the table schema is not same as parquet schema.
First of all, mysql does not have a boolean type, its boolean type is actually tinyint(1), in the previous logic, We force tinyint(1) to be a boolean by passing tinyInt1isBit=true, which causes an error if tinyint(1) is not a 0 or 1, Therefore, we need to match tinyint(1) according to tinyint instead of boolean, and this change will not affect the correctness of where k = 1 or where k = true queries
