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>
Problem:
When window function in aggregation function, executor would report an error like: Required field 'node_type' was not present!
Example:
SELECT SUM(MAX(c1) OVER (PARTITION BY c2, c3)) FROM test_window_in_agg;
Reason:
When analyze aggregate, analytic expr (window function carrior when analyze) transfered to slot and loss message. So when
serialize to thrift package, TExpr can not determine node_type of analytic expr.
Solved:
We do not support aggregate(window function) yet. So we report an error when analyze.
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`
Problem:
When create view with join in table partitions, an error would rise like "Unknown column"
Example:
CREATE VIEW my_view AS SELECT t1.* FROM t1 PARTITION(p1) JOIN t2 PARTITION(p2) ON t1.k1 = t2.k1;
select * from my_view ==> errCode = 2, detailMessage = Unknown column 'k1' in 't2'
Reason:
When create view, we do tosql first in order to persistent view sql. And when doing tosql of table reference, partition key
word was removed to keep neat of sql string. But here when we remove partition keyword it would regarded as an alias.
So "PARTITION" keyword can not be removed.
Solved:
Add “PARTITION” keyword back to tosql string.
