* [Optimize] optimize the speed of converting integer to string
* Use fmt and std::from_chars to make convert integer to string and convert string to integer more efficient
Co-authored-by: caiconghui <caiconghui@xiaomi.com>
In RuntimeFilter BloomFilter, decimal column will got a wrong hash value because violating aliasing rules
decimal12_t decimal = { 12, 12 };
murmurhash3(decimal) in bloom filter: 2167721464
expect: 4203026776
Use `commitAsync` to commit offset to kafka, instead of using `commitSync`, which may block for a long time.
Also assign a group.id to routine load if user not specified "property.group.id" property, so that all consumer of
this job will use same group.id instead of a random id for each consume task.
Fix#6316
If the size of memtable is greater than max segment size and the memtable will flush more than
one segment file. BE coredump will be triggered when flushing memtable.
when right table has null value in string column, runtime filter may coredump
```
select count(*) from baseall t1 join test t2 where t1.k7 = t2.k7;
```
Currently, the function lower()/upper() can only handle one char at a time.
A vectorized function has been implemented, it makes performance 2 times faster. Here is the performance test:
The length of char: 26, test 100 times
vectorized-function-cost: 99491 ns
normal-function-cost: 134766 ns
The length of char: 260, test 100 times
vectorized-function-cost: 179341 ns
normal-function-cost: 344995 ns
## Proposed changes
Add transaction for the operation of insert. It will cost less time than non-transaction(it will cost 1/1000 time) when you want to insert a amount of rows.
### Syntax
```
BEGIN [ WITH LABEL label];
INSERT INTO table_name ...
[COMMIT | ROLLBACK];
```
### Example
commit a transaction:
```
begin;
insert into Tbl values(11, 22, 33);
commit;
```
rollback a transaction:
```
begin;
insert into Tbl values(11, 22, 33);
rollback;
```
commit a transaction with label:
```
begin with label test_label;
insert into Tbl values(11, 22, 33);
commit;
```
### Description
```
begin: begin a transaction, the next insert will execute in the transaction until commit/rollback;
commit: commit the transaction, the data in the transaction will be inserted into the table;
rollback: abort the transaction, nothing will be inserted into the table;
```
### The main realization principle:
```
1. begin a transaction in the session. next sql is executed in the transaction;
2. insert sql will be parser and get the database name and table name, they will be used to select a be and create a pipe to accept data;
3. all inserted values will be sent to the be and write into the pipe;
4. a thread will get the data from the pipe, then write them to disk;
5. commit will complete this transaction and make these data visible;
6. rollback will abort this transaction
```
### Some restrictions on the use of update syntax.
1. Only ```insert``` can be called in a transaction.
2. If something error happened, ```commit``` will not succeed, it will ```rollback``` directly;
3. By default, if part of insert in the transaction is invalid, ```commit``` will only insert the other correct data into the table.
4. If you need ```commit``` return failed when any insert in the transaction is invalid, you need execute ```set enable_insert_strict = true``` before ```begin```.
At present, some constant expression calculations are implemented on the FE side,
but they are incomplete, and some expressions cannot be completely consistent with
the value calculated by BE (such as part of the time function)
Therefore, we provide a way to pass all the constants in SQL to BE for calculation,
and then begin to analyze and plan SQL. This method can also solve the problem that some
complex constant calculations issued by BI cannot be processed on the FE side.
Here through a session variable enable_fold_constant_by_be to control this function,
which is disabled by default.
* Revert "[Optimize] Put _Tuple_ptrs into mempool when RowBatch is initialized (#6036)"
This reverts commit f254870aeb18752a786586ef5d7ccf952b97f895.
* [BUG][Timeout][QueryLeak] Fixed memory not released in time, Fix Core dump in bloomfilter
* [Bug][RoutineLoad] Fix bug that routine load thread on BE may be blocked
This bug will cause the routine load job throw TOO MANY TASK error, and routine
load job is blocked.
* fix ut
Co-authored-by: chenmingyu <chenmingyu@baidu.com>
The log4j-config.xml will be generated at startup of FE and also when modifying FE config.
But in some deploy environment such as k8s, the conf dir is not writable.
So change the dir of log4j-config.xml to Config.custom_conf_dir.
Also fix some small bugs:
1. Typo "less then" -> "less than"
2. Duplicated `exec_mem_limit` showed in SHOW ROUTINE LOAD
3. Allow MAXVALUE in single partition column table.
4. Add IP info for "intolerate index channel failure" msg.
Change-Id: Ib4e1182084219c41eae44d3a28110c0315fdbd7d
Co-authored-by: chenmingyu <chenmingyu@baidu.com>
This is part of the array type support and has not been fully completed.
The following functions are implemented
1. fe array type support and implementation of array function, support array syntax analysis and planning
2. Support import array type data through insert into
3. Support select array type data
4. Only the array type is supported on the value lie of the duplicate table
this pr merge some code from #4655#4650#4644#4643#4623#2979
* [Bug] Filter out unavaiable backends when getting scan range location
In the previous implementation, we will eliminate non-surviving BEs in the Coordinator phase.
But for Spark or Flink Connector, there is no such logic, so when a BE node is down,
it will cause the problem of querying errors through the Connector.
* fix ut
* fix compiule
refactor runtime filter bloomfilter and eliminate some virtual function calls which obtained a performance improvement of about 5%
import block bloom filter, for avx version obtained 40% performance improvement
before: bloomfilter size:default, about 2000W item cost about 1s400ms
after: bloomfilter size:524288, about 2000W item cost about 400ms
1. support in/bloomfilter/minmax
2. support broadcast/shuffle/bucket shuffle/colocate join
3. opt memory use and cpu cache miss while build runtime filter
4. opt memory use in left semi join (works well on tpcds-95)
random_shuflle will generate same random sequence when call multiple times,
although we use twice random, but when there is no change in the size relationship
between the adjacent numbers, the result of the second shuffle will not change either
