q1: "select * from ut_p temporary partitions(tp1) where val > 0"
in q1, temporary partition tp1 is scaned
q2: "select * from ut_p where val > 0"
in q2, temporary partition tp1 is not scaned.
1. max compute partition prune,
we just support filter mc partitions by '='，it can filter just one partition
to support multiple partition filter and range operator('>','<', '>='..), the partition prune should be supported.
2. add max compute row count cache and partitionValues cache
3. add max compute regression case
this change improves performance of tpch q20. on sf500, improved from 6.3sec to 1.1 sec
this change has no impaction on tpcds
when column stats is unknown,
the basic algorithm to estimate left semi join output row count is its left child output row count.
q1: "A left semi join B on A.x=B.x"
the output row is estimated as A.rowCount.
But the basic algorithm is not good to following pattern:
q2: "A left semi join filter(B) on A.x=B.x"
Because there is a filter on B, usually this left semi join also reduce the row count of A, and we estimate
the output of q2 as A.rowCount * Filter.rowCount/B.rowCount
Opt zstd block decompression by `ZSTD_decompressDCtx()` to replace streaming decompression.
It will improve performance but consume more memory.
Test result:
- env: 1 node(16 cores, 64G).
- parquet column: 100 million rows of char(255) column.
- result: 5.2 -> 4.6.
Fix null map issue in parquet reader which cause result incorrect such as `min()`, `max()`.
In order to share null map between parquet converted src column and dst column to avoid copying. It is very tricky that will call mutable function `doris_nullable_column->get_null_map_column_ptr()` which will set `_need_update_has_null = true`. Because some operations such as agg will call `has_null()` to set `_need_update_has_null = false`.
support view as a independent unit of leading hint
add random test check of leading hint query
add more test with data of leading hint query
add random test check of distribute hint
Fixed the problem of not being able to read parquet lz4 compressed format. By default, it is decompressed according to the Hadoop lz4 format. If it fails, it will fall back to the standard lz4 compression format.
Improve the performance under the tpch data set by reconstructing the join related code and the use of hash table
Co-authored-by: HappenLee <happenlee@hotmail.com>
Co-authored-by: BiteTheDDDDt <pxl290@qq.com>
Support where, group by, having, order by clause without from clause in query statement.
For example as following:
SELECT 1 AS a, COUNT(), SUM(2), AVG(1), RANK() OVER() AS w_rank
WHERE 1 = 1
GROUP BY a, w_rank
HAVING COUNT() IN (1, 2) AND w_rank = 1
ORDER BY a;
this will return result:
| a |count(*)|sum(2)|avg(1)|w_rank|
+----+--------+------+------+------+
| 1 | 1| 2| 1.0| 1|
For another example as following:
select 1 c1, 2 union (select "hell0", "") order by c1
the second column datatype will be varchar(65533), 65533 is the default varchar length.
this will return result:
|c1 | 2 |
+------+---+
|1 | 2 |
|hell0 | |
All cases' results are tested and passed with datetime/date v2
Cases about:
Calculation ( +, -
Kinds of predicates(<, >, =, <>, in, not in, is null, is not null)
Load test(from csv and select into)
Runtime filter
Delete conditions
Key columns(agg/duplicate/uniq model, distributed/partition, bitmap index...)
