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
Optimization "select count(*) from table" stmtement , push down "count" type to BE.
support file type : parquet ，orc in hive .
1. 4kfiles , 60kwline num
before: 1 min 37.70 sec
after: 50.18 sec
2. 50files , 60kwline num
before: 1.12 sec
after: 0.82 sec
Enhance broadcast join cost calculation, by considering both the build side effort from building bigger hash table, and more probe side effort from bigger cost of ProbeWhenBuildSideOutput and ProbeWhenSearchHashTable, if parallel_fragment_exec_instance_num is more than 1.
Current solution gives a penalty factor on rightRowCount, and the factor is the total instance number to the power of 2.
Penalty on outputRows is not taken currently and will be refined in next generation cost model.
Also brings some update for shape checking:
update original control variable in shape file parallel_fragment_exec_instance_num to parallel_pipeline_task_num, if pipeline is enabled.
fix a be_number variable inactive issue.
consider sql:
```
SELECT *
FROM sub_query_correlated_subquery1 t1
WHERE coalesce(bitand(
cast(
(SELECT sum(k1)
FROM sub_query_correlated_subquery3 ) AS int),
cast(t1.k1 AS int)),
coalesce(t1.k1, t1.k2)) is NULL
ORDER BY t1.k1, t1.k2;
```
is Null conjunct is lost in SubqueryToApply rule. This pr fix it
Fix tow bugs:
1. Unexpected null values in array column. If 65535 consecutive values are not null in nullable array column, this error will be triggered. The reason is that the array parser did not handle boundary conditions.
2. The number of rows of key filed, and that of value field in map column are not equal. Similarly, the number of rows among fields in struct column are not the same. This would be triggered when the number of rows are not equal among parquet pages of different columns in a row group.
### Issue
when partition has null partitions, it throws error
`Failed to fill partition column: t_int=null`
### Resolution
- Fix the following null partitions error in iceberg tables by replacing null partition to '\N'.
- Add regression test for hive null partition.
Improve external table statistics collection, including log, observability and fix some bugs.
1. Add Running state for statistics job.
2. Add progress for show analyze job. (n/m tasks finished, n/m task failed and so on)
3. Add analyze time cost for show analyze task.
4. Make task failure message more clear.
5. Synchronize the job status updating code in updateTaskStatus.
6. Fix NPE in HMSAnalyzeTask. (Avoid refreshing statistics cache if the collection sql failed)
7. Return error message for with sync collection while timeout.
8. Log level improvement
9. Fix misuse of logCreateAnalysisJob for tasks.
problem:
1. create a iceberg_type catalog:
2. use iceberg catalog to specify verison
```
mysql> show catalog iceberg;
+----------------------+--------------------------+
| Key | Value |
+----------------------+--------------------------+
| type | iceberg |
| iceberg.catalog.type | hms |
| hive.metastore.uris | thrift://127.0.0.1:9083 |
| hadoop.username | hadoop |
| create_time | 2023-07-25 16:51:00.522 |
+----------------------+--------------------------+
5 rows in set (0.02 sec)
mysql> select * from iceberg.iceberg_db.tb1 FOR VERSION AS OF 8783036402036752909;
ERROR 5090 (42000): errCode = 2, detailMessage = Only iceberg/hudi external table supports time travel in current version
```
change:
Add `ICEBERG_EXTERNAL_TABLE` type for specify the version and time
Currently, the new optimizer don't consider anything about partial update.
This PR add the ability to convert a delete statement to a partial update insert statement
for merge-on-write unique table
First of all, mysql does not have a boolean type, its boolean type is actually tinyint(1), in the previous logic, We force tinyint(1) to be a boolean by passing tinyInt1isBit=true, which causes an error if tinyint(1) is not a 0 or 1, Therefore, we need to match tinyint(1) according to tinyint instead of boolean, and this change will not affect the correctness of where k = 1 or where k = true queries
In this PR, we introduce TOKENIZE function for inverted index, it is used as following:
```
SELECT TOKENIZE('I love my country', 'english');
```
It has two arguments, first is text which has to be tokenized, the second is parser type which can be **english**, **chinese** or **unicode**.
It also can be used with existing table, like this:
```
mysql> SELECT TOKENIZE(c,"chinese") FROM chinese_analyzer_test;
+---------------------------------------+
| tokenize(`c`, 'chinese') |
+---------------------------------------+
| ["来到", "北京", "清华大学"] |
| ["我爱你", "中国"] |
| ["人民", "得到", "更", "实惠"] |
+---------------------------------------+
```
the global limit will create a gather action, and all the data will be calculated in one instance. If we push down the global limit, the node run after the limit node will run slowly.
We fix it by push down only local limit.
a join plan tree before fixing:
```
LogicalLimit(global)
LogicalLimit(local)
Plan()
LogicalLimit(global)
LogicalLimit(local)
LogicalJoin
LogicalLimit(global)
LogicalLimit(local)
Plan()
LogicalLimit(global)
LogicalLimit(local)
Plan()
after fixing:
LogicalLimit(global)
LogicalLimit(local)
Plan()
LogicalLimit(local)
LogicalJoin
LogicalLimit(local)
Plan()
LogicalLimit(local)
Plan()
```
