`ParquetReader` confuses logical/physical/slot id of columns. If only reading the scalar types, there's nothing wrong, but when reading complex types, `RowGroup` and `PageIndex` will get wrong statistics. Therefore, if the query contains complex types and pushed-down predicates, the probability of the result set is incorrect.
Now we can not support nested type array/map
so this pr aim to:
1. add format option for string convert defined datatype to keep with origin from_string
2. support array map can nested array and map
1. the real value of BE config `file_cache_max_file_reader_cache_size` will be the 1/3 of process's max open file number.
2. use thread pool to create or init the file cache concurrently.
To solve the issue that when there are lots of files in file cache dir, the starting time of BE will be very slow because
it will traverse all file cache dirs sequentially.
Previously, delete statement with conditions on value columns are only supported on duplicate tables. After we introduce delete sign mechanism to do batch delete, a delete statement with conditions on value columns on unique tables will be transformed into the corresponding insert into ..., __DELETE_SIGN__ select ... statement. However, for unique table with merge-on-write enabled, the overhead of inserting these data can be eliminated. So this PR add the ability to allow delete predicate on value columns for merge-on-write unique tables.
count_by_enum(expr1, expr2, ... , exprN);
Treats the data in a column as an enumeration and counts the number of values in each enumeration. Returns the number of enumerated values for each column, and the number of non-null values versus the number of null values.
FunctionStringConcat::execute_impl resized with size that include string null terminator, which causes ColumnString::chars.size() does not match with ColumnString::offsets.back, this will cause problems for some string functions, e.g. like and regexp.
Now we make wrong for decimal parse from string
if given string precision is bigger than defined decimal precision, we will return a overflow error, but only digit part is bigger than typed digit length , we should return overflow error when we traverse given string to decimal value
Use weak_ptr to cache the file handle of file segment. The max cached number of file handles can be configured by `file_cache_max_file_reader_cache_size`, default `1000000`.
Users can inspect the number of cached file handles by request BE metrics: `http://be_host:be_webserver_port/metrics`:
```
# TYPE doris_be_file_cache_segment_reader_cache_size gauge
doris_be_file_cache_segment_reader_cache_size{path="/mnt/datadisk1/gaoxin/file_cache"} 2500
```
arrow is not support key column has null element , but doris default map key column is nullable , so need to deal with if doris map row if key column has null element , we put null to arrow
Issue Number: close #xxx
when cal array hash, elem size is not need to seed hash
hash = HashUtil::zlib_crc_hash(reinterpret_cast<const char*>(&elem_size),
sizeof(elem_size), hash);
but we need to be care [[], [1]] vs [[1], []], when array nested array , and nested array is empty, we should make hash seed to
make difference
2. use range for one hash value to avoid virtual function call in loop.
which double the performance. I make it in ut
column: array[int64]
50 rows , and single array has 10w elements
Here we will calculate all the rowsets delete bitmaps which are committed but not published to reduce the calculation pressure of publish phase.
Step1: collect this tablet's all committed rowsets' delete bitmaps.
Step2: calculate all rowsets' delete bitmaps which are published during compaction.
Step3: write back updated delete bitmap and tablet info.
Fix problem:
For the same column, there are concurrent drop index request and build index request, if build index obtain lock before drop index, build a new index file, but when drop index request execute, link file not contains all index files for the column, that lead to new index file is missed.
Based on the above questions, use index id instead of column unique id to determine whether a hard link is required when do build index
