Fix threes bugs of timestampv2 precision:
1. Hive catalog doesn't set the precision of timestampv2, and can't get the precision from hive metastore, so set the largest precision for timestampv2;
2. Jdbc catalog use datetimev1 to parse timestamp, and convert to timestampv2, so the precision is lost.
3. TVF doesn't use the precision from meta data of file format.
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.
Fix dict cols not be converted back to string type in some cases, which includes introduced by #19039.
For dict cols, we will convert dict cols to int32 type firstly, then convert back to string type after read block.
The block will be reuse it, so it is necessary to convert it back.
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.
Using both `MergeRangeFileReader` and `BufferedStreamReader` simultaneously would waste a lot of memory,
so turn off prefetch data in `BufferedStreamReader` when using MergeRangeFileReader.
Issue Number: About #19038, we found in this case, l_orderkey has many nulls,
so we can filter it by null count statistics in the row group and page level,
then it can improve a lot of performance in this case.
We found qt_q11 in regression test test_external_catalog_hive is very slow.
The result is only one record, so other data should be filtered out in the parquet lazy read situation.
Then we found currently the parquet reader read many records because we can only skip parquet page. But in order to skip parquet page, currently we need to read page header, then it will caused prefetch data. Therefore, prefetch data in this case may be not good.
So there are two issues:
Skip whole row group in this case.
Prefetching data in this case may be not good, need to improve it.
This PR resolve issues 1.
Fix bug when reading array type in parquet file:
```
ERROR 1105 (HY000): errCode = 2, detailMessage = [INTERNAL_ERROR]Read parquet file xxx failed,
reason = [IO_ERROR]Decode too many values in current page
```
When reading normal columns, `ScalarColumnReader::_read_values` still calls `ColumnSelectVector::set_run_length_null_map` to initialize select vector, but `ScalarColumnReader::_read_nested_column` hasn't do this, making the number of values wrong.
The situation where this error occurs is particularly extreme: The column pages have remaining values to be read,
but all of them are null values at ancestor level, so there's no actual read operation, just skipping null values at ancestor level.
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).
Co-authored-by: ByteYue <[yj976240184@gmail.com](mailto:yj976240184@gmail.com)>
This PR is an optimization for https://github.com/apache/doris/pull/17478:
1. Change the buffer size of `LineReader` to 4MB to align with the size of prefetch buffer.
2. Lazily prefetch data in the first read to prevent wasted reading.
3. S3 block size is 32MB only, which is too small for a file split. Set 128MB as default file split size.
4. Add `_end_offset` for prefetch buffer to prevent wasted reading.
The query performance of reading data on object storage is improved by more than 3x+.
1. Fix value idx in bool rle decoder
2. Iceberg table support datetimev2(3). In the previous version, we converted hive timestamp to datetimev2(0) default.
1. Introduce hadoop libhdfs
2. For Linux-X86 platform, use the hadoop libhdfs
3. For other platform, use libhdfs3, because currently we don't have hadoop libhdfs binary for other platform
Co-authored-by: adonis0147 <adonis0147@gmail.com>
when loading big file with multi bytes line delimiter, some line record maybe incomplete because of _output_buf_limit, so this incomplete data will move to the beginning of the output buf and read more data into output buf. In this case, find line delimiter should start with no offset to avoid a bug that spilt two lines as one line.
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.
Problem:
1. FE will split the parquet file into split. So a file can have several splits.
2. BE will scan each split, read the footer of the parquet file.
3. If 2 splits belongs to a same parquet file, the footer of this file will be read twice.
This PR mainly changes:
1. Use kv cache to cache the footer of parquet file.
2. The kv cache is belong to a scan node, so all parquet reader belong to this scan node will share same kv cache.
3. In cache, the key is "meta_file_path", the value is parsed thrift footer.
The KV Cache is sharded into mutlti sub cache.
So that different file can use different sub cache, avoid blocking each other
In my test, a query with 26 splits can reduce the footer parse time from 4s -> 1s
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.
Before this PR when encountering null values with some columns which is specified as `NOT NULL`, null values will not be filtered,thi behavior does not match with the original load behavior.
Second column alignment logic has bug :
```
template <typename ColumnInserterFn>
void align_variant_by_name_and_type(ColumnObject& dst, const ColumnObject& src, size_t row_cnt,
ColumnInserterFn inserter) {
CHECK(dst.is_finalized() && src.is_finalized());
// Use rows() here instead of size(), since size() will check_consistency
// but we could not check_consistency since num_rows will be upgraded even
// if src and dst is empty, we just increase the num_rows of dst and fill
// num_rows of default values when meet new data
size_t num_rows = dst.rows();
```
1. introduce a new type `VARIANT` to encapsulate dynamic generated columns for hidding the detail of types and names of newly generated columns
2. introduce a new expression `SchemaChangeExpr` for doing schema change for extensibility
Support delta encoding and rle(bool) to read Glue data
add delta bit pack decoder,
add delta length byte array decoder,
add delta byte array decoder.
add rle bool decoder.
We find some data type is read with delta encoding on AWS Glue, so it should be supported.
The definition of delta encoding can refer to the delta encoding in parquet.
Fix three bugs:
1. `repeated_parent_def_level ` should be the definition of its repeated parent.
2. Failed to parse schema like `decimal(p, s)`
3. Fill wrong offsets for array type
remove duplicate type definition in function context
remove unused method in function context
not need stale state in vexpr context because vexpr is stateless and function context saves state and they are cloned.
remove useless slot_size in all tuple or slot descriptor.
remove doris_udf namespace, it is useless.
remove some unused macro definitions.
init v_conjuncts in vscanner, not need write the same code in every scanner.
using unique ptr to manage function context since it could only belong to a single expr context.
Issue Number: close #xxx
---------
Co-authored-by: yiguolei <yiguolei@gmail.com>
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.
