This pr fixes two issues:
1. when using s3 TVF to query files in AVRO format, due to the change of `TFileType`, the originally queried `FILE_S3 ` becomes `FILE_LOCAL`, causing the query failed.
2. currently, both parameters `s3.virtual.key` and `s3.virtual.bucket` are removed. A new `S3Utils` in jni-avro to parse the bucket and key of s3.
The purpose of doing this operation is mainly to unify the parameters of s3.
Truncate char or varchar columns if size is smaller than file columns or not found in the file column schema by session var `truncate_char_or_varchar_columns`.
For load request, there are 2 tuples on scan node, input tuple and output tuple.
The input tuple is for reading file, and it will be converted to output tuple based on user specified column mappings.
And the broker load support different column mapping in different data description to same table(or partition).
So for each scanner, the output tuples are same but the input tuple can be different.
The previous implements save the input tuple in scan node level, causing different scanner using same input tuple,
which is incorrect.
This PR remove the input tuple from scan node and save them in each scanners.
Check whether there are complex types in parquet/orc reader in broker/stream load. Broker/stream load will cast any type as string type, and complex types will be casted wrong. This is a temporary method, and will be replaced by tvf.
Optimization "select count(*) from table" stmtement , push down "count" type to BE.
support file type : parquet ，orc in hive .
1. 4kfiles , 60kwline num
before: 1 min 37.70 sec
after: 50.18 sec
2. 50files , 60kwline num
before: 1.12 sec
after: 0.82 sec
### Issue
when partition has null partitions, it throws error
`Failed to fill partition column: t_int=null`
### Resolution
- Fix the following null partitions error in iceberg tables by replacing null partition to '\N'.
- Add regression test for hive null partition.
1.support filesystem metastore
2.support predicate and project when split
3.fix partition table query error
todo: Now you need to manually put paimon-s3-0.4.0-incubating.jar in be/lib/java_extensions when use s3 filesystem
doc pr: #21966
Co-authored-by: yiguolei <yiguolei@gmail.com>
If the scanner is failed during init or open, then not need update counters because the query is fail and the counter is useless.
And it may core during update counters. For example, update counters depend on scanner's tablet, but the tablet == null when init failed.
### 1
In previous implementation, for each FileSplit, there will be a `TFileScanRange`, and each `TFileScanRange`
contains a list of `TFileRangeDesc` and a `TFileScanRangeParams`.
So if there are thousands of FileSplit, there will be thousands of `TFileScanRange`, which cause the thrift
data send to BE too large, resulting in:
1. the rpc of sending fragment may fail due to timeout
2. FE will OOM
For a certain query request, the `TFileScanRangeParams` is the common part and is same of all `TFileScanRange`.
So I move this to the `TExecPlanFragmentParams`.
After that, for each FileSplit, there is only a list of `TFileRangeDesc`.
In my test, to query a hive table with 100000 partitions, the size of thrift data reduced from 151MB to 15MB,
and the above 2 issues are gone.
### 2
Support when setting `max_external_file_meta_cache_num` <=0, the file meta cache for parquet footer will
not be used.
Because I found that for some wide table, the footer is too large(1MB after compact, and much more after
deserialized to thrift), it will consuming too much memory of BE when there are many files.
This will be optimized later, here I just support to disable this cache.
* [Fix](rowset) When a rowset is cooled down, it is directly deleted. This can result in data query misses in the second phase of a two-phase query.
related pr #20732
There are two reasons for moving the logic of delayed deletion from the Tablet to the StorageEngine. The first reason is to consolidate the logic and unify the delayed operations. The second reason is that delayed garbage collection during queries can cause rowsets to remain in the "stale rowsets" state, preventing the timely deletion of rowset metadata, It may cause rowset metadata too large.
* not use unused rowsets
After the last time to call scan_task.scan_func()，the should be ended, this means PipelineFragmentContext could be released.
Then after PipelineFragmentContext is released, visiting its field such as query_ctx or _state may cause core dump.
But it can only explain core 2
void ScannerScheduler::_task_group_scanner_scan(ScannerScheduler* scheduler,
taskgroup::ScanTaskTaskGroupQueue* scan_queue) {
while (!_is_closed) {
taskgroup::ScanTask scan_task;
auto success = scan_queue->take(&scan_task);
if (success) {
int64_t time_spent = 0;
{
SCOPED_RAW_TIMER(&time_spent);
scan_task.scan_func();
}
scan_queue->update_statistics(scan_task, time_spent);
}
}
}
Use spark-bundle to read hudi data instead of using hive-bundle to read hudi data.
**Advantage** for using spark-bundle to read hudi data:
1. The performance of spark-bundle is more than twice that of hive-bundle
2. spark-bundle using `UnsafeRow` can reduce data copying and GC time of the jvm
3. spark-bundle support `Time Travel`, `Incremental Read`, and `Schema Change`, these functions can be quickly ported to Doris
**Disadvantage** for using spark-bundle to read hudi data:
1. More dependencies make hudi-dependency.jar very cumbersome(from 138M -> 300M)
2. spark-bundle only provides `RDD` interface and cannot be used directly
Co-authored-by: Jerry Hu <mrhhsg@gmail.com>
1. Filtering is done at the sending end rather than the receiving end
2. Projection is done at the sending end rather than the receiving end
3. Each sender can use different shuffle policies to send data
For pipeline, olap table sink close is divided into three stages, try_close() --> pending_finish() --> close()
only after all node channels are done or canceled, pending_finish() will return false, close() will start.
this will avoid block pipeline on close().
In close, check the index channel intolerable failure status after each node channel failure,
if intolerable failure is true, the close will be terminated in advance, and all node channels will be canceled to avoid meaningless blocking.
Two optimizations:
1. Insert string bytes directly to remove decoding&encoding process.
2. Use native reader to read the hudi base file if it has no log file. Use `explain` to show how many splits are read natively.
