Because of the rollup has the same keys and the keys's order is same, BE will do linked schema change. The base tablet's segments will link to the new rollup tablet. But the unique id from the base tablet is starting from 0 and as the rollup tablet also. In this case, the unique id 4 in the base table is column 'city', but in the rollup tablet is 'cost'. It will decode the varcode page to bigint page so that be coredump. It needs to be rejected.
I think that if a rollup add by link schema change, it means this rollup is redundant. It brings no additional revenue and wastes storage space. So It needs to be rejected.
If block bytes are bigger than the corresponding block's rows, then the avg_size_per_row would be zero. Which would end up diving zero in the following logic.
Since Filesystem inherited std::enable_shared_from_this , it is dangerous to create native point of FileSystem.
To avoid this behavior, making the constructor of XxxFileSystem a private method and using the static method create(...) to get a new FileSystem object.
`date_time_v2` will check scale when constructed datatimev2:
```
LOG(FATAL) << fmt::format("Scale {} is out of bounds", scale);
```
This [PR](https://github.com/apache/doris/pull/15510) has fixed this issue, but parquet does not use constructor to create `TypeDescriptor`, leading the `scale = -1` when reading datetimev2 data.
The origin scan pools are in exec_env.
But after enable new_load_scan_node by default, the scan pool in exec_env is no longer used.
All scan task will be submitted to the scan pool in scanner_scheduler.
BTW, reorganize the scan pool into 3 kinds:
local scan pool
For olap scan node
remote scan pool
For file scan node
limited scan pool
For query which set cpu resource limit or with small limit clause
TODO:
Use bthread to unify all IO task.
Some trivial issues:
fix bug that the memtable flush size printed in log is not right
Add RuntimeProfile param in VScanner
Support new table value function `iceberg_meta("table" = "ctl.db.tbl", "query_type" = "snapshots")`
we can use the sql `select * from iceberg_meta("table" = "ctl.db.tbl", "query_type" = "snapshots")` to get snapshots info of a table. The other iceberg metadata will be supported later when needed.
One of the usage:
Before we use following sql to time travel:
`select * from ice_table FOR TIME AS OF "2022-10-10 11:11:11"`;
`select * from ice_table FOR VERSION AS OF "snapshot_id"`;
we can use the snapshots metadata to get the `committed time` or `snapshot_id`,
and then, we can use it as the time or version in time travel clause
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>
For outer join / right outer join / right semi join, when HashJoinNode::pull->process_data_in_hashtable outputs a block, it will output all rows of a key in the hash table into a block, and the output of a key is completed After that, it will check whether the block size exceeds the batch size, and if it exceeds, the output will be terminated.
If a key has 2000w+ rows, memory overflow will occur when the subsequent block operations on the 2000w+ rows are performed.
Tablet::version_for_delete_predicate should travel all rowset metas in tablet meta which complex is O(N), however we can directly judge whether this rowset is a delete rowset by RowsetMeta::has_delete_predicate which complex is O(1).
As we won't call Tablet::version_for_delete_predicate when pick input rowsets for compaction, we can reduce the critical area of Tablet::_meta_lock.
Now in ScannerContext::push_back_scanner_and_reschedule, _num_running_scanners-- is before _num_scheduling_ctx++.
InPipScannerContext::can_finish, we check _num_running_scanners == 0 && _num_scheduling_ctx == 0 without obtaining _transfer_lock.
In follow case, PipScannerContext::can_finish will return wrong result.
_num_running_scanners--
Check _num_running_scanners == 0 && _num_scheduling_ctx == 0` return true.
_num_scheduling_ctx++
So, we can set _num_running_scanners-- in the last of this func.
Describe your changes.
PipScannerContext::get_block_from_queue not block.
Set _num_running_scanners-- in the last of ScannerContext::push_back_scanner_and_reschedule.
* [refactor] delete non vec load from memtable
delete non vec load from memtable totally.
remove function keys_type() in memtable.
Co-authored-by: zhoubintao <1229701101@qq.com>
fix a dcheck error for vertical compaction on Merge-On-Write table。
When merge rowsets with empty segment, VerticalHeapMergeIterator::init
return ok directly and _record_rowids not set, dcheck failed when
_unique_key_next_block call current_block_row_locations。
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.
This pr mainly to optimize the histogram(👉🏻https://github.com/apache/doris/pull/14910) aggregation function. Including the following:
1. Support input parameters `sample_rate` and `max_bucket_num`
2. Add UT and regression test
3. Add documentation
4. Optimize function implementation logic
Parameter description：
- `sample_rate`：Optional. The proportion of sample data used to generate the histogram. The default is 0.2.
- `max_bucket_num`：Optional. Limit the number of histogram buckets. The default value is 128.
---
Example：
```
MySQL [test]> SELECT histogram(c_float) FROM histogram_test;
+-------------------------------------------------------------------------------------------------------------------------------------+
| histogram(`c_float`) |
+-------------------------------------------------------------------------------------------------------------------------------------+
| {"sample_rate":0.2,"max_bucket_num":128,"bucket_num":3,"buckets":[{"lower":"0.1","upper":"0.1","count":1,"pre_sum":0,"ndv":1},...]} |
+-------------------------------------------------------------------------------------------------------------------------------------+
MySQL [test]> SELECT histogram(c_string, 0.5, 2) FROM histogram_test;
+-------------------------------------------------------------------------------------------------------------------------------------+
| histogram(`c_string`) |
+-------------------------------------------------------------------------------------------------------------------------------------+
| {"sample_rate":0.5,"max_bucket_num":2,"bucket_num":2,"buckets":[{"lower":"str1","upper":"str7","count":4,"pre_sum":0,"ndv":3},...]} |
+-------------------------------------------------------------------------------------------------------------------------------------+
```
Query result description：
```
{
"sample_rate": 0.2,
"max_bucket_num": 128,
"bucket_num": 3,
"buckets": [
{
"lower": "0.1",
"upper": "0.2",
"count": 2,
"pre_sum": 0,
"ndv": 2
},
{
"lower": "0.8",
"upper": "0.9",
"count": 2,
"pre_sum": 2,
"ndv": 2
},
{
"lower": "1.0",
"upper": "1.0",
"count": 2,
"pre_sum": 4,
"ndv": 1
}
]
}
```
Field description：
- sample_rate：Rate of sampling
- max_bucket_num：Limit the maximum number of buckets
- bucket_num：The actual number of buckets
- buckets：All buckets
- lower：Upper bound of the bucket
- upper：Lower bound of the bucket
- count：The number of elements contained in the bucket
- pre_sum：The total number of elements in the front bucket
- ndv：The number of different values in the bucket
> Total number of histogram elements = number of elements in the last bucket(count) + total number of elements in the previous bucket(pre_sum).
