To support schema evolution, Iceberg add schema information to Parquet file metadata.
But for early iceberg version, it doesn't write any schema information to Parquet file.
This PR is to support read parquet without schema information.
External hms catalog table column names in doris are all in lower case,
while iceberg table or spark-sql created hive table may contain upper case column name,
which will cause empty query result. This pr is to fix this bug.
1. For parquet file, transfer all column names to lower case while parse parquet metadata.
2. For orc file, store the origin column names and lower case column names in two vectors, use the suitable names in different cases.
3. FE side, change the column name back to the origin column name in iceberg while doing convertToIcebergExpr.
This pr #15836 change the way to use parquet reader by first open() then init_reader().
But we forgot to call open() for iceberg delete file, which cause coredump.
Support iceberg schema evolution for parquet file format.
Iceberg use unique id for each column to support schema evolution.
To support this feature in Doris, FE side need to get the current column id for each column and send the ids to be side.
Be read column id from parquet key_value_metadata, set the changed column name in Block to match the name in parquet file before reading data. And set the name back after reading data.
The main purpose of this pr is to import `fileCache` for lakehouse reading remote files.
Use the local disk as the cache for reading remote file, so the next time this file is read,
the data can be obtained directly from the local disk.
In addition, this pr includes a few other minor changes
Import File Cache:
1. The imported `fileCache` is called `block_file_cache`, which uses lru replacement policy.
2. Implement a new FileRereader `CachedRemoteFilereader`, so that the logic of `file cache` is hidden under `CachedRemoteFilereader`.
Other changes:
1. Add a new interface `fs()` for `FileReader`.
2. `IOContext` adds some statistical information to count the situation of `FileCache`
Co-authored-by: Lightman <31928846+Lchangliang@users.noreply.github.com>
A deleted file may belong to multiple data files. Each data file will read a full amount of deleted files,
so a deleted file may be read repeatedly. The deleted files can be cached, and multiple data files
can reuse the first read content.
The performance is improved by 60% in the case of single thread, and by 30% in the case of multithreading.
**Optimize**
PR #14470 has used `Expr` to filter delete rows to match current data file,
but the rows in the delete file are [sorted by file_path then position](https://iceberg.apache.org/spec/#position-delete-files)
to optimize filtering rows while scanning, so this PR remove `Expr` and use binary search to filter delete rows.
In addition, delete files are likely to be encoded in dictionary, it's time-consuming to decode `file_path`
columns into `ColumnString`, so this PR use `ColumnDictionary` to read `file_path` column.
After testing, the performance of iceberg v2's MOR is improved by 30%+.
**Fix Bug**
Lazy-read-block may not have the filter column, if the whole group is filtered by `Expr`
and the batch_eof is generated from next batch.
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.
1. a icebergv2 delete file may cross many data paths, so the path of a file split is required as a predicate to filter rows of delete file
- create delete file structure to save predicate parameters
- create predicate for file path
2. add some log to print row range
3. fix bug when create file metadata
