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.
Dynamic mode used in array type when serialize it to mysql row buffer using dynamic mode, when combine binary row format with dynamic mode,something goes wrong, and lead to invalid binary row format.
* [fix](segment_iter) do not init segment_iterator twice
SegmentIterator::init is called by Segment::new_iterator and
BetaRowsetReader::get_segment_iterators twice.
This reverts commit 296b0c92f702675b92eee3c8af219f3862802fb2.
we can use drop table force stmt to fast drop tablets, no need to check tablet dropped state in every report
Co-authored-by: caiconghui1 <caiconghui1@jd.com>
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.
1. Refactor file cache. Before refactor, the file cache config format is "[{"path":"/path/to/file_cache","normal":21474836480,"persistent":10737418240,"query_limit":10737418240}]" and now change to "[{"path":"/mnt/disk3/selectdb_cloud/file_cache","total_size":21474836480,"query_limit":10737418240}]". It will be simpler than before.
2. Support more strategy. Support file cache priority. The file cache will have three queue, name as 'index'/'normal'/'disposable'. We can avoid that the higher priority data is eliminate by the lower priority data.
int64_t months = _year * 12 + _month - 1 + sign * (12 * interval.year + interval.month);
_year = months / 12;
if (_year > 9999) {
return false;
}
_month = (months % 12) + 1;
if (_day > s_days_in_month[_month]) {
_day = s_days_in_month[_month];
if (_month == 2 && doris::is_leap(_year)) {
_day++;
}
}
The variable "months" may be negative. Taking modulus with it (_month) may also result in a negative value, which can cause an array access overflow.
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.
Fe will clear transaction info when transaction timeout, but calc delete bitmap
related logic in DeltaWriter::close_wait will continue. In set_txn_related_delete_bitmap,
we return directly in such case.
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.
TabletSink and LoadChannel in BE are M: N relationship,
Every once in a while LoadChannel will randomly return its own runtime profile to a TabletSink, so usually all LoadChannel runtime profiles are saved on each TabletSink, and the timeliness of the same LoadChannel profile saved on different TabletSinks is different, and each TabletSink will periodically send fe reports all the LoadChannel profiles saved by itself, and ensures to update the latest LoadChannel profile according to the timestamp.
The function compoundReader->openInput is called three times, and if any of these calls fail,
an error is logged, and the function returns early. If one or two of the calls succeed, but the others fail,
there might be a situation where the allocated memory for the IndexInput objects is not freed.
To fix this, you could use std::unique_ptr to manage the memory for IndexInput objects.
This would automatically clean up the memory when the function goes out of scope.
Formerly S3FileWriter has to write each buffer with 5MB or more then upload one part, after all these works are done it could then process the incoming data, it's blocking and inefficient. This pr brings one bufferpool where the data could write into memory buffer immediately if has free buffer and then it would be uploaded into the S3.
This pr doesn't provide the ability to elegantly support cases where there is no free buffer, i'll leave it as one future work.
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
