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`.
This function can be used to replace bitmap_union(to_bitmap(expr))， because bitmap_union(to_bitmap(expr)) need create many many small bitmaps firstly and then merge them into a single bitmap.
bitmap_agg will convert the column value into a bitmap directly. Its performance is better than bitmap_union(to_bitmap(expr)) . In our test , there is about 30% improvement.
1. make mv matched when preagg have value column predicate contained in mv
'where clause
2. fix `org.apache.doris.common.AnalysisException: errCode = 2, detailMessage = BITMAP_UNION need input a bitmap column, but input INVALID_TYPE`
3. make the error message more detailed when create mv stmt parse failed
This PR was originally #16940 , but it has not been updated for a long time due to the original author @Cai-Yao . At present, we will merge some of the code into the master first.
thanks @Cai-Yao @yiguolei
when pushing down constant conjunct into set operation node, we should assign the conjunct to agg node if there is one. This is consistant with pushing constant conjunct into inlineview.
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.
sort out the test cases of external table.
After modify, there are 2 directories:
1. `external_table_p0`: all p0 cases of external tables: hive, es, jdbc and tvf
2. `external_table_p2`: all p2 cases of external tables: hive, es, mysql, pg, iceberg and tvf
So that we can run it with one line command like:
```
sh run-regression-test.sh --run -d external_table_p0,external_table_p2
```
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.
If a column is defined as: col VARCHAR/CHAR NULL and no default value. Then we load json data which misses column col, the result queried is not correct:
+------+
| col |
+------+
| 1 |
+------+
But expect:
+------+
| col |
+------+
| NULL |
+------+
---------
Co-authored-by: duanxujian <duanxujian@jd.com>
return empty result instead of error for empty match query as follows:
`SELECT * FROM t WHERE msg MATCH ''`
`SELECT * FROM t WHERE msg MATCH 'stop_word'`
Fix error when reading empty map values in parquet. The `offsets.back()` doesn't not equal the number of elements in map's key column.
### How does this happen
Map in parquet is stored as repeated group, and `repeated_parent_def_level` is set incorrectly when parsing map node in parquet schema.
```
the map definition in parquet:
optional group <name> (MAP) {
repeated group map (MAP_KEY_VALUE) {
required <type> key;
optional <type> value;
}
}
```
### How to fix
Set the `repeated_parent_def_level` of key/value node as the definition level of map node.
`repeated_parent_def_level` is the definition level of the first ancestor node whose `repetition_type` equals `REPEATED`. Empty array/map values are not stored in doris column, so have to use `repeated_parent_def_level` to skip the empty or null values in ancestor node.
For instance, considering an array of strings with 3 rows like the following:
`null, [], [a, b, c]`
We can store four elements in data column: `null, a, b, c`
and the offsets column is: `1, 1, 4`
and the null map is: `1, 0, 0`
For the `i-th` row in array column: range from `offsets[i - 1]` until `offsets[i]` represents the elements in this row, so we can't store empty array/map values in doris data column. As a comparison, spark does not require `repeated_parent_def_level`, because the spark column stores empty array/map values , and use anther length column to indicate empty values. Please reference: https://github.com/apache/spark/blob/master/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/ParquetColumnVector.java
Furthermore, we can also avoid store null array/map values in doris data column. The same three rows as above, We can only store three elements in data column: `a, b, c`
and the offsets column is: `0, 0, 3`
and the null map is: `1, 0, 0`
join commute rule will swap the left and right child. This cause the change of logical properties. So we need recompute the logical properties in plan post process to get the correct result
Problem:
When create view with projection group_concat(xxx, xxx order by orderkey). It will failed during second parse of inline view
For example:
it works when doing
"SELECT id, group_concat(`name`, "," ORDER BY id) AS test_group_column FROM test GROUP BY id"
but when create view it does not work
"create view test_view as SELECT id, group_concat(`name`, "," ORDER BY id) AS test_group_column FROM test GROUP BY id"
Reason:
when creating view, we will doing parse again of view.toSql() to check whether it has some syntax error. And when doing toSql() to group_concat with order by, it add seperate ', ' between second parameter and order by. So when parsing again, it
would failed because it is different semantic with original statement.
group_concat(`name`, "," ORDER BY id) ==> group_concat(`name`, "," , ORDER BY id)
Solved:
Change toSql of group_concat and add order by statement analyze() of group_concat in Planner cause it would work if we get order by from view statement and do not analyze and binding slot reference to it
