The performance of ClickBench Q30 is affected by batch_size:
| batch_size | 1024 | 4096 | 20480 |
| -- | -- | -- | -- |
| Q30 query time | 2.27 | 1.08 | 0.62 |
Because aggregation operator will create a new result block for each batch block, and Q30 has 90 columns, which is time-consuming. Larger batch_size will decrease the number of aggregation blocks, so the larger batch_size will improve performance.
Doris internal reader will read at least 4064 rows even if batch_size < 4064, so this PR keep the process of reading external table the same as internal table.
Fix three bugs when read iceberg v2 tables:
1. The `delete position` in `delete file` represents the position of delete row in the entire file, but the `read range` in
`RowGroupReader` represents the position in current row group. Therefore, we need to subtract the position of first
row of current row group from `delete position`.
2. When only reading the partition columns, `RowGroupReader` skips processing the `delete position`.
3. If the `delete position` has delete all rows in a row group, the `read range` is empty, but we read the whole row
group in such case.
Optimize four performance issues:
1. We change `delete position` to `delete range`, and then merge `delete range` and `read range` into the final read
ranges. This process is too tedious and time-consuming. . we can merge `delete position` and `read range` directly.
2. `delete position` is ordered in a `delete file`, so we can use merge-sort, instead of ordered-set.
3. Initialize `RowGroupReader` when reading, instead of initialize all row groups when opening a `ParquetReader`, to
save memory usage, and the same as `IcebergReader`.
4. Change the recursive call of `_do_lazy_read` to loop logic.
PR https://github.com/apache/doris/pull/13917 has supported lazy read for non-predicate columns in ParquetReader,
but can't trigger lazy read when predicate columns are partition or missing columns.
This PR support such case, and fill partition and missing columns in `FileReader`.
This pr does three things：
1. Modified the framework of table-valued-function(tvf).
2. be support `fetch_table_schema` rpc.
3. Implemented `S3(path, AK, SK, format)` table-valued-function.
1. Fix issue #13115
2. Modify the method of `get_next_block` or `GenericReader`, to return "read_rows" explicitly.
Some columns in block may not be filled in reader, if the first column is not filled, use `block->rows()` can not return real row numbers.
3. Add more checks for broker load test cases.
Get schema from parquet reader.
The new VFileScanner need to get file schema (column name to type map) from parquet file while processing load job,
this pr is to set the type information for parquet columns.
Refactor of scanners. Support broker load.
This pr is part of the refactor scanner tasks. It provide support for borker load using new VFileScanner.
Work still in progress.
Currently, Doris has a variety of readers for different file formats,
such as parquet reader, orc reader, csv reader, json reader and so on.
The interfaces of these readers are not unified, which makes it impossible to call them through a unified method.
In this PR, I added a `GenericReader` interface class, and other Readers will implement this interface class
to use the `get_next_block()` method.
This PR currently only modifies `arrow_reader` and `parquet reader`.
Other readers will be modified one by one in subsequent PRs.
