`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
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
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
we do not Implement any hash functions in array/map/struct column , so we use sql like this will make be core
select * from (
select
bdp.nc_num,
collect_list(distinct(bd.catalog_name)) as catalog_name,
material_qty
from
dataease.bu_delivery_product bdp
left join dataease.bu_trans_transfer btt on bdp.delivery_product_id = btt.delivery_product_id
left join dataease.bu_delivery bd on bdp.delivery_id = bd.delivery_id
where
bd.val_status in ('10', '20', '30', '90')
and bd.delivery_type in (0, 1, 2)
group by
nc_num,
material_qty
union
ALL
select
bdp.nc_num,
collect_list(distinct(bd.catalog_name)) as catalog_name,
material_qty
from
dataease.bu_trans_transfer btt
left join dataease.bu_delivery_product bdp on bdp.delivery_product_id = btt.delivery_product_id
left join dataease.bu_delivery bd on bdp.delivery_id = bd.delivery_id
where
bd.val_status in ('10', '20', '30', '90')
and bd.delivery_type in (0, 1, 2)
group by
nc_num,
material_qty
) aa;
core :
Test on SSB 100g:
select lo_suppkey, count(distinct lo_linenumber) from lineorder group by lo_suppkey;
exec time: 4.388s
create materialized view:
create materialized view customer_uv as select lo_suppkey, bitmap_union(to_bitmap(lo_linenumber)) from lineorder group by lo_suppkey;
select lo_suppkey, count(distinct lo_linenumber) from lineorder group by lo_suppkey;
exec time: 12.908s
test with the patch, exec time: 5.790s
* [Improve](performance) introduce SchemaCache to cache TabletSchame & Schema
1. When the system is under high-concurrency load with wide table point queries, the frequent memory allocation and deallocation of Schema become evident system bottlenecks. Additionally, the initialization of TabletSchema and Schema also becomes a CPU hotspot.Therefore, the introduction of a SchemaCache is implemented to cache these resources for reuse.
2. Make some variables wrapped with std::unique<unique_ptr>
Performance:
| 状态 | QPS | 平均响应时间 (avg) | P99 响应时间 |
|------------------|-----|------------------|-------------|
| 开启 SchemaCache | 501 | 20ms | 34ms |
| 关闭 SchemaCache | 321 | 31ms | 61ms |
* handle schema change with schema version
* remove useless header
* rebase
Refactoring the filtering conditions in the current ExecNode from an expression tree to an array can simplify the process of adding runtime filters. It eliminates the need for complex merge operations and removes the requirement for the frontend to combine expressions into a single entity.
By representing the filtering conditions as an array, each condition can be treated individually, making it easier to add runtime filters without the need for complex merging logic. The array can store the individual conditions, and the runtime filter logic can iterate through the array to apply the filters as needed.
This refactoring simplifies the codebase, improves readability, and reduces the complexity associated with handling filtering conditions and adding runtime filters. It separates the conditions into discrete entities, enabling more straightforward manipulation and management within the execution node.
/home/zcp/repo_center/doris_master/doris/be/src/olap/rowset/segment_v2/column_reader.cpp:895:21: runtime error: load of value 423208544, which is not a valid value for type 'doris::ReaderType'
/home/zcp/repo_center/doris_master/doris/be/src/vec/columns/column_decimal.cpp:260:33: runtime error: load of misaligned address 0x7fa3348b301c for type 'int64_t' (aka 'long'), which requires 8 byte alignment
/home/zcp/repo_center/doris_master/doris/be/src/olap/block_column_predicate.cpp:82:24: runtime error: variable length array bound evaluates to non-positive value 0
/home/zcp/repo_center/doris_master/doris/be/src/vec/columns/column_string.h:225:26: runtime error: null pointer passed as argument 2, which is declared to never be null
1. Get DataTypeSerde in advance to avoid get temporary DataTypeSerde iterate each column
2. Iterate the original row once is enoungh for deserializing by introducing a map for record the index of each column's unique id
To be more compatible with MySQL, rename JSONB type name and function name to JSON.
The old JSONB type name and jsonb_xx function can still be used for backward compatibility.
There is a function jsonb_extract remained since json_extract is used by json string function and more work need to change it. It will be changed further.
