Refactor the interface of create_file_reader
the file_size and mtime are merged into FileDescription, not in FileReaderOptions anymore.
Now the file handle cache can get correct file's modification time from FileDescription.
Add HdfsIO for hdfs file reader
pick from [Enhancement](multi-catalog) Add hdfs read statistics profile. #21442
Fix error for broker load with orc file when time_zone is CST of which message is "Failed to create orc row reader. reason = Can't open /usr/share/zoneinfo/CST"
Co-authored-by: caiconghui1 <caiconghui1@jd.com>
After supporting insert-only transactional hive full acid tables #19518, #19419, this PR support transactional hive full acid tables.
Support hive3 transactional hive full acid tables.
Hive2 transactional hive full acid tables need to run major compactions.
Fix some bugs of orc lazy materialization(#18615)
- Fix issue causing column size to continuously increase after `execute_conjuncts()` by calling `Block::erase_useless_column()`.
- Fix partition issues of orc lazy materialization.
- Fix lazy materialization will not be used when the predicate column is inconsistent with the orc file.
Refactoring the filtering conditions in the current ExecNode from an expression tree to an array can simplify the process of adding runtime filters. It eliminates the need for complex merge operations and removes the requirement for the frontend to combine expressions into a single entity.
By representing the filtering conditions as an array, each condition can be treated individually, making it easier to add runtime filters without the need for complex merging logic. The array can store the individual conditions, and the runtime filter logic can iterate through the array to apply the filters as needed.
This refactoring simplifies the codebase, improves readability, and reduces the complexity associated with handling filtering conditions and adding runtime filters. It separates the conditions into discrete entities, enabling more straightforward manipulation and management within the execution node.
#18976 introduced merge small IO facility to optimize performance, and used by parquet reader.
This PR support this facility in orc reader. Current ORC reader implementation need to reposition parent present stream when reading lazy columns in lazy materialization facility. So let it works by removing `DCHECK_GE(offset, cached_data.end_offset)`.
fix some wrong downcast founded by ubsan.
```cpp
doris/be/src/olap/bloom_filter_predicate.h:43:32: runtime error: downcast of address 0x7f8ec2b691a0 which does not point to an object of type 'doris::BloomFilterColumnPredicate<doris::TYPE_DATE>::SpecificFilter' (aka 'BloomFilterFunc<(doris::PrimitiveType)11U>')
0x7f8ec2b691a0: note: object is of type 'doris::BloomFilterFunc<(doris::PrimitiveType)12>'
e5 55 00 00 10 74 58 42 e5 55 00 00 00 00 10 00 8e 7f 00 00 20 07 6f cc 8e 7f 00 00 80 fe 68 cc
^~~~~~~~~~~~~~~~~~~~~~~
vptr for 'doris::BloomFilterFunc<(doris::PrimitiveType)12>'
```
1. TYPE_DATE/TYPE_DATETIME have same data format, so I change the cast about bloom filter to reinterpret cast.
```cpp
doris/be/src/vec/exec/format/orc/vorc_reader.h:281:17: runtime error: downcast of address 0x7f562f4c3180 which does not point to an object of type 'ColumnVector<int>'
0x7f562f4c3180: note: object is of type 'doris::vectorized::ColumnDecimal<doris::vectorized::Decimal<int> >'
74 65 00 00 20 91 70 f5 ca 55 00 00 02 00 00 00 00 00 00 00 f0 d4 4c 2f 56 7f 00 00 f0 d4 4c 2f
^~~~~~~~~~~~~~~~~~~~~~~
vptr for 'doris::vectorized::ColumnDecimal<doris::vectorized::Decimal<int> >'
```
2. doris use ColumnDecimal to store decimal elements.
Doris block does not support complex nested type now, but orc and parquet reader has generated complex nested column,
which makes the output of mysql client wrong and users confused.
Get the last modification time from file status, and use the combination of path and modification time to generate cache identifier.
When a file is changed, the modification time will be changed, so the former cache path will be invalid.
- Implements ORC lazy materialization, integrate with the implementation of https://github.com/apache/doris-thirdparty/pull/56 and https://github.com/apache/doris-thirdparty/pull/62.
- Refactor code: Move `execute_conjuncts()` and `execute_conjuncts_and_filter_block()` in `parquet_group_reader `to `VExprContext`, used by parquet reader and orc reader.
- Add session variables `enable_parquet_lazy_materialization` and `enable_orc_lazy_materialization` to control whether enable lazy materialization.
- Modify `build.sh` to update apache-orc submodule or download package every time.
Add file cache metrics and management.
1. Get file cache metrics
> If the performance of file cache is not efficient, there are currently no metrics to investigate the cause. In practice, hit ratio, disk usage, and segments removed status are very important information.
API: `http://be_host:be_webserver_port/metrics`
File cache metrics for each base path start with `doris_be_file_cache_` prefix. `hits_ratio` is the hit ratio of the cache since BE startup; `removed_elements` is the num of removed segment files since BE startup; Every cache path has three queues: index, normal and disposable. The capacity ratio of the three queues is 1:17:2.
```
doris_be_file_cache_hits_ratio{path="/mnt/datadisk1/gaoxin/file_cache"} 0.500000
doris_be_file_cache_hits_ratio{path="/mnt/datadisk1/gaoxin/small_file_cache"} 0.500000
doris_be_file_cache_removed_elements{path="/mnt/datadisk1/gaoxin/file_cache"} 0
doris_be_file_cache_removed_elements{path="/mnt/datadisk1/gaoxin/small_file_cache"} 0
doris_be_file_cache_normal_queue_max_size{path="/mnt/datadisk1/gaoxin/file_cache"} 912680550400
doris_be_file_cache_normal_queue_max_size{path="/mnt/datadisk1/gaoxin/small_file_cache"} 8500000000
doris_be_file_cache_normal_queue_max_elements{path="/mnt/datadisk1/gaoxin/file_cache"} 217600
doris_be_file_cache_normal_queue_max_elements{path="/mnt/datadisk1/gaoxin/small_file_cache"} 102400
doris_be_file_cache_normal_queue_curr_size{path="/mnt/datadisk1/gaoxin/file_cache"} 14129846
doris_be_file_cache_normal_queue_curr_size{path="/mnt/datadisk1/gaoxin/small_file_cache"} 14874904
doris_be_file_cache_normal_queue_curr_elements{path="/mnt/datadisk1/gaoxin/file_cache"} 18
doris_be_file_cache_normal_queue_curr_elements{path="/mnt/datadisk1/gaoxin/small_file_cache"} 22
...
```
2. Release file cache
> Frequent segment files swapping can seriously affect the performance of file cache. Adding a deletion interface helps users clean up the file cache.
API: `http://be_host:be_webserver_port/api/file_cache?op=release&base_path=${file_cache_base_path}`
Return the number of released segment files. If `base_path` is not provide in url, all cache paths will be released.
It's thread-safe to call this api, so only the segment files not been read currently can be released.
```
{"released_elements":22}
```
3. Specify the base path to store cache data
> Currently, regression testing lacks test cases of file cache, which cannot guarantee the stability of file cache. This interface is generally used in regression testing scenarios. Different queries use different paths to verify different usage cases and performance.
User can set session variable `file_cache_base_path` to specify the base path to store cache data. `file_cache_base_path="random"` as default, means chosing a random path from cached paths to store cache data. If `file_cache_base_path` is not one of the base paths in BE configuration, a random path is used.
Add `MergeRangeFileReader` to merge small IO to optimize parquet&orc read performance.
`MergeRangeFileReader` is a FileReader that efficiently supports random access in format like parquet and orc.
In order to merge small IO in parquet and orc, the random access ranges should be generated when creating the
reader. The random access ranges is a list of ranges that order by offset.
The range in random access ranges should be reading sequentially, can be skipped, but can't be read repeatedly.
When calling read_at, if the start offset located in random access ranges, the slice size should not span two ranges.
For example, in parquet, the random access ranges is the column offsets in a row group.
When reading at offset, if [offset, offset + 8MB) contains many random access ranges,
the reader will read data in [offset, offset + 8MB) as a whole, and copy the data in random access ranges into small
buffers(name as box, default 1MB, 64MB in total). A box can be occupied by many ranges,
and use a reference counter to record how many ranges are cached in the box. If reference counter equals zero,
the box can be release or reused by other ranges. When there is no empty box for a new read operation,
the read operation will do directly.
## Effects
The runtime of ClickBench reduces from 102s to 77s, and the runtime of Query 24 reduces from 24.74s to 9.45s.
The profile of Query 24:
```
VFILE_SCAN_NODE (id=0):(Active: 8s344ms, % non-child: 83.06%)
- FileReadBytes: 534.46 MB
- FileReadCalls: 1.031K (1031)
- FileReadTime: 28s801ms
- GetNextTime: 8s304ms
- MaxScannerThreadNum: 12
- MergedSmallIO: 0ns
- CopyTime: 157.774ms
- MergedBytes: 549.91 MB
- MergedIO: 94
- ReadTime: 28s642ms
- RequestBytes: 507.96 MB
- RequestIO: 1.001K (1001)
- NumScanners: 18
```
1001 request IOs has been merged into 94 IOs.
## Remaining problems
1. Add p2 regression test in nest PR
2. Profiles are scattered in various codes and will be refactored in the next PR
3. Support ORC reader
Fix decimal v3 precision loss issues in the multi-catalog module.
Now it will use decimal v3 to represent decimal type in the multi-catalog module.
Regression Test: `test_load_with_decimal.groovy`
Currently, there are some useless includes in the codebase. We can use a tool named include-what-you-use to optimize these includes. By using a strict include-what-you-use policy, we can get lots of benefits from it.
Fix tow bugs:
1. Enabling file caching requires both `FE session` and `BE` configurations(enable_file_cache=true) to be enabled.
2. `ParquetReader` has not used `IOContext` previously, but `CachedRemoteFileReader::read_at` needs `IOContext` after PR(#17586).
PR(#17330) has changed the column type of kay and value from array to normal column, but orc&parquet reader still cast to array column, resulting in cast error.
See #17764 for details
I have tested:
- Unit test for local/s3/hdfs/broker file system: be/test/io/fs/file_system_test.cpp
- Outfile to local/s3/hdfs/broker.
- Load from local/s3/hdfs/broker.
- Query file on local/s3/hdfs/broker file system, with table value function and catalog.
- Backup/Restore with local/s3/hdfs/broker file system
Not test:
- cold & host data separation case.
fix heap-use-after-free
The OrcReader has a internal FileInputStream, If the file is empty, the memory of FileInputStream will leak.
Besides, there is a Statistics instance in FileInputStream. FileInputStream maybe delete if the orc reader
is inited failed, but Statistics maybe used when orc reader is closed, causing heap-use-after-free error.
Potential memory leak
When init file scanner in file scan node, the file scanner prepare failed, the memory of file scanner will leak.
A const reference member variables as class member stores a temporary object, which cannot be got after the temporary object being destroyed, cause be core dump while enable debug level log
_broker_addr has been destroyed in BrokerFileReader
Support parsing map&struct type in parquet&orc reader.
## Remaining Problems
1. Doris use array type to build the key and value column of a `map`, but doesn't fill the offsets in value column, so the offsets in value column is wasted.
2. Parquet support reading only key or value column in `map`, this PR hasn't supported yet.
3. Parquet support reading partial columns in `struct`, this PR hasn't supported yet.
Orc doesn't fill null values in new batch, but the former batch has been release.
Other types like int/long/timestamp... are flat types without pointer in them,
so other types do not need to be handled separately like string.
In previous implementation, when querying tvf, FE will get schema from BE.
And BE will try to open the first file to get its schema info, but for orc or parquet format,
if the file is empty, it will return error.
But even for an empty file, we can still get schema info from file's footer.
So we should handle the empty file to get schema info correctly.
Also modify the catalog doc to add some FAQ.
Set column names from path to lower case in case-insensitive case.
This is for Iceberg columns from path. Iceberg columns are case sensitive,
which may cause error for table with partitions.
Hive 1.x may write orc file with internal column name (_col0, _col1, _col2...).
This will cause query result be NULL because column name in orc file doesn't match
with column name in Doris table schema. This pr is to support query Hive orc files with internal column names.
For now, we haven't see any problem in Parquet file, will send new pr to fix parquet if any problem show up in the future.
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.
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.
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>
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.
