Fixed the problem of not being able to read parquet lz4 compressed format. By default, it is decompressed according to the Hadoop lz4 format. If it fails, it will fall back to the standard lz4 compression format.
- Fix complex type crash when using the dict filter facility in the parquet-reader by turning off the dict filter facility in this case.
- Add orc complex types regression test.
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.
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.
Two improvements:
1. Move the `Job_id` column for the return info of `Analyze table` command to the first column. To keep consistent with `show analyze`.
```
mysql> analyze table hive.tpch100.region;
+--------+--------------+-------------------------+------------+--------------------------------+
| Job_Id | Catalog_Name | DB_Name | Table_Name | Columns |
+--------+--------------+-------------------------+------------+--------------------------------+
| 14403 | hive | default_cluster:tpch100 | region | [r_regionkey,r_comment,r_name] |
+--------+--------------+-------------------------+------------+--------------------------------+
1 row in set (0.03 sec)
```
2. Add `analyze_timeout` session variable, to control `analyze table/database with sync` timeout.
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.
1. do not split compress data file
Some data file in hive is compressed with gzip, deflate, etc.
These kinds of file can not be splitted.
2. Support lz4 block codec
for hive scan node, use lz4 block codec instead of lz4 frame codec
4. Support snappy block codec
For hadoop snappy
5. Optimize the `count(*)` query of csv file
For query like `select count(*) from tbl`, only need to split the line, no need to split the column.
Need to pick to branch-2.0 after this PR: #22304
Fix incorrect result if null partition fields in orc file.
### Root Cause
Theoretically, the underlying file of the hive partition table should not contain partition fields. But we found that in some user scenarios, the partition field will exist in the underlying orc/parquet file and are null values. As a result, the pushed down partition field which are null values. filter incorrectly.
### Solution
we handle this case by only reading non-partition fields. The parquet reader is already handled this way, this PR handles the orc reader.
`ParquetReader` confuses logical/physical/slot id of columns. If only reading the scalar types, there's nothing wrong, but when reading complex types, `RowGroup` and `PageIndex` will get wrong statistics. Therefore, if the query contains complex types and pushed-down predicates, the probability of the result set is incorrect.
[Fix](orc-reader) Fix filling partition or missing column used incorrect row count.
`_row_reader->nextBatch` returns number of read rows. When orc lazy materialization is turned on, the number of read rows includes filtered rows, so caller must look at `numElements` in the row batch to determine how
many rows were not filtered which will to fill to the block.
In this case, filling partition or missing column used incorrect row count which will cause be crash by `filter.size() != offsets.size()` in filter column step.
When orc lazy materialization is turned off, add `_convert_dict_cols_to_string_cols(block, nullptr)` if `(block->rows() == 0)`.
sort out the test cases of external table.
After modify, there are 2 directories:
1. `external_table_p0`: all p0 cases of external tables: hive, es, jdbc and tvf
2. `external_table_p2`: all p2 cases of external tables: hive, es, mysql, pg, iceberg and tvf
So that we can run it with one line command like:
```
sh run-regression-test.sh --run -d external_table_p0,external_table_p2
```
