Current situation of Doris is that the cluster is balanced, but the disks of a backend may be unbalanced.
for example, backend A have two disks: disk1 and disk2, disk1's usage is 98%, but disk2's usage is only 40%.
disk1 is unable to take more data, therefore only one disk of backend A can take new data,
the available write throughput of backend A is only half of its ability, and we can not resolve this through load or
partition rebalance now.
So we introduce disk rebalancer, disk rebalancer is different from other rebalancer(load or partition)
which take care of cluster-wide data balancing. it takes care about backend-wide data balancing.
[For more details see #8550](https://github.com/apache/incubator-doris/issues/8550)
1. add a config string_type_soft_limit to soft limit max length of string type
2. disable using String type in Key column, partition column and
distribution column
3. remove String type alias BLOB for futrue use
Add a new column-type to speed up the approximation of quantiles.
1. The new column-type is named `quantile_state` with fixed aggregation function `quantile_union`, which stores the intermediate results of pre-aggregated approximation calculations for quantiles.
2. support pre-aggregation of new column-type and quantile_state related functions.
1. Avoid print large string in error log
If user load a unqualified large string, the all string will be saved in error log,
so the error log is too big that can not be shown be using `show load warnings on "url"`.
Err: `Got packet bigger than 'max_allowed_packet' bytes`
2. Remove duplicate help doc
Do not allow doc with same title, or error thrown when starting FE:
`java.lang.IllegalArgumentException: Multiple entries with same key:`
```
SET PROPERTY FOR 'jack' 'exec_mem_limit' = '2147483648';
SET PROPERTY FOR 'jack' 'load_mem_limit' = '2147483648';
```
The user level property will overwrite the value in session variables.
1. support SHOW LAST INSERT
In the current implementation, the insert operation returns a json string to describe the result information
of the insert. But this information is in the session track field of the mysql protocol,
and it is difficult to obtain programmatically.
Therefore, I provide a new syntax `show last insert` to explicitly obtain the result of the latest insert operation,
and return a normal query result set to facilitate the user to obtain the result information of the insert.
2. the `ReturnRows` field in fe.audit.log of insert operation will be set to the loaded row num of the insert.
3. Fix a bug described in #8354
SQL to reproduce:
```
SELECT * FROM table WHERE where FROM_UNIXTIME(d_datekey,'%Y-%m-%d %H:%i:%s') != '1970-08-20 00:11:43';
org.apache.doris.common.AnalysisException: errCode = 2, detailMessage = Unexpected exception: Illegal pattern character 'i'
at org.apache.doris.qe.StmtExecutor.analyze(StmtExecutor.java:584) ~[palo-fe.jar:3.4.0]
at org.apache.doris.qe.StmtExecutor.execute(StmtExecutor.java:345) ~[palo-fe.jar:3.4.0]
at org.apache.doris.qe.StmtExecutor.execute(StmtExecutor.java:318) ~[palo-fe.jar:3.4.0]
at org.apache.doris.qe.ConnectProcessor.handleQuery(ConnectProcessor.java:221) ~[palo-fe.jar:3.4.0]
at org.apache.doris.qe.ConnectProcessor.dispatch(ConnectProcessor.java:361) ~[palo-fe.jar:3.4.0]
at org.apache.doris.qe.ConnectProcessor.processOnce(ConnectProcessor.java:562) ~[palo-fe.jar:3.4.0]
at org.apache.doris.mysql.nio.ReadListener.lambda$handleEvent$0(ReadListener.java:50) ~[palo-fe.jar:3.4.0]
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) [?:?]
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) [?:?]
at java.lang.Thread.run(Thread.java:835) [?:?]
```
Describe the overview of changes.
Just support:
yyyy-MM-dd HH:mm:ss
yyyy-MM-dd
yyyyMMdd
1. en: add convert_tz.md
2. en: fix curdate.md, the fix is to modify the title
3. en: add curtime.md
4. en: fix str_to_date.md, the fix is to modify the title
5. zh-CN: fix convert_tz.md, the fix is to modify the description
6. zh-CN: fix curdate.md, the fix is to modify the title
In some scenarios, users cannot find a suitable hash key to avoid data skew, so we need to provide an additional data distribution for olap table to avoid data skew
example:
CREATE TABLE random_table
(
siteid INT DEFAULT '10',
citycode SMALLINT,
username VARCHAR(32) DEFAULT '',
pv BIGINT SUM DEFAULT '0'
)
AGGREGATE KEY(siteid, citycode, username)
DISTRIBUTED BY random BUCKETS 10
PROPERTIES("replication_num" = "1");
Co-authored-by: caiconghui1 <caiconghui1@jd.com>
1. move `group_concat` from string-functions to aggregate-functions.
2. add `json_array`/`json_object`/`json_quote` to sidebar file.
3. move `json_array`/`json_object`/`json_quote`/`get_json_double`/`get_json_int`/`get_json_string` to json-functions.
4. change `group_concat` document to uppercase
1. Add `iceberg_table_creation_strict_mode` in `fe.conf` to control iceberg external table creation, when data type is not supported in Doris.
2. Add `REFRESH` syntax to synchronize the Iceberg table and database.
3. Support create Iceberg external table with specific column definitions.
* [improvement](show) Support that user can use show data skew statement instead of admin
This PR mainly do two things:
1. Support that user can use show data skew statement instead of admin
2. Fix fe ut failed caused by pr [improvement](rewrite) Make RewriteDateLiteralRule to be compatible with mysql #7876 and pr [feature-wip](iceberg) Step1: Support create Iceberg external table #7391
Co-authored-by: caiconghui1 <caiconghui1@jd.com>
Close related #7389
Support create Iceberg external table in Doris.
This is the first step to support Iceberg external table.
### Create Iceberg external table
This pr describes two ways to create Iceberg external tables. Both ways do not require explicitly specifying column definitions, Doris automatically converts them based on Iceberg's column definitions.
1. Create an Iceberg external table directly
```sql
CREATE [EXTERNAL] TABLE table_name
ENGINE = ICEBERG
[COMMENT "comment"]
PROPERTIES (
"iceberg.database" = "iceberg_db_name",
"iceberg.table" = "icberg_table_name",
"iceberg.hive.metastore.uris" = "thrift://192.168.0.1:9083",
"iceberg.catalog.type" = "HIVE_CATALOG"
);
```
2. Create an Iceberg database and automatically create all the tables under that db.
```sql
CREATE DATABASE db_name
[COMMENT "comment"]
PROPERTIES (
"iceberg.database" = "iceberg_db_name",
"iceberg.hive.metastore.uris" = "thrift://192.168.0.1:9083",
"iceberg.catalog.type" = "HIVE_CATALOG"
);
```
### Show table creation
1. For individual tables you can view them with `help show create table`.
```sql
mysql> show create table iceberg_db.logs_1;
+--------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Table | Create Table |
+--------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| logs_1 | CREATE TABLE `logs_1` (
`level` varchar(-1) NOT NULL COMMENT "null",
`event_time` datetime NOT NULL COMMENT "null",
`message` varchar(-1) NOT NULL COMMENT "null"
) ENGINE=ICEBERG
COMMENT "ICEBERG"
PROPERTIES (
"iceberg.database" = "doris",
"iceberg.table" = "logs_1",
"iceberg.hive.metastore.uris" = "thrift://10.10.10.10:9087",
"iceberg.catalog.type" = "HIVE_CATALOG"
) |
+--------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
```
2. For Iceberg database, you can view it with `help show table creation`.
```sql
mysql> show table creation from iceberg_db;
+--------+---------+---------------------+---------------------------------------------------------+
| Table | Status | Create Time | Error Msg |
+--------+---------+---------------------+---------------------------------------------------------+
| logs | fail | 2021-12-14 13:50:10 | Cannot convert unknown type to Doris type: list<string> |
| logs_1 | success | 2021-12-14 13:50:10 | |
+--------+---------+---------------------+---------------------------------------------------------+
2 rows in set (0.00 sec)
```
This is a new syntax.
Show table creation records in Iceberg database:
Syntax:
```sql
SHOW TABLE CREATION [FROM db] [LIKE mask]
```
# Proposed changes
Issue Number: close#6238
Co-authored-by: HappenLee <happenlee@hotmail.com>
Co-authored-by: stdpain <34912776+stdpain@users.noreply.github.com>
Co-authored-by: Zhengguo Yang <yangzhgg@gmail.com>
Co-authored-by: wangbo <506340561@qq.com>
Co-authored-by: emmymiao87 <522274284@qq.com>
Co-authored-by: Pxl <952130278@qq.com>
Co-authored-by: zhangstar333 <87313068+zhangstar333@users.noreply.github.com>
Co-authored-by: thinker <zchw100@qq.com>
Co-authored-by: Zeno Yang <1521564989@qq.com>
Co-authored-by: Wang Shuo <wangshuo128@gmail.com>
Co-authored-by: zhoubintao <35688959+zbtzbtzbt@users.noreply.github.com>
Co-authored-by: Gabriel <gabrielleebuaa@gmail.com>
Co-authored-by: xinghuayu007 <1450306854@qq.com>
Co-authored-by: weizuo93 <weizuo@apache.org>
Co-authored-by: yiguolei <guoleiyi@tencent.com>
Co-authored-by: anneji-dev <85534151+anneji-dev@users.noreply.github.com>
Co-authored-by: awakeljw <993007281@qq.com>
Co-authored-by: taberylyang <95272637+taberylyang@users.noreply.github.com>
Co-authored-by: Cui Kaifeng <48012748+azurenake@users.noreply.github.com>
## Problem Summary:
### 1. Some code from clickhouse
**ClickHouse is an excellent implementation of the vectorized execution engine database,
so here we have referenced and learned a lot from its excellent implementation in terms of
data structure and function implementation.
We are based on ClickHouse v19.16.2.2 and would like to thank the ClickHouse community and developers.**
The following comment has been added to the code from Clickhouse, eg:
// This file is copied from
// https://github.com/ClickHouse/ClickHouse/blob/master/src/Interpreters/AggregationCommon.h
// and modified by Doris
### 2. Support exec node and query:
* vaggregation_node
* vanalytic_eval_node
* vassert_num_rows_node
* vblocking_join_node
* vcross_join_node
* vempty_set_node
* ves_http_scan_node
* vexcept_node
* vexchange_node
* vintersect_node
* vmysql_scan_node
* vodbc_scan_node
* volap_scan_node
* vrepeat_node
* vschema_scan_node
* vselect_node
* vset_operation_node
* vsort_node
* vunion_node
* vhash_join_node
You can run exec engine of SSB/TPCH and 70% TPCDS stand query test set.
### 3. Data Model
Vec Exec Engine Support **Dup/Agg/Unq** table, Support Block Reader Vectorized.
Segment Vec is working in process.
### 4. How to use
1. Set the environment variable `set enable_vectorized_engine = true; `(required)
2. Set the environment variable `set batch_size = 4096; ` (recommended)
### 5. Some diff from origin exec engine
https://github.com/doris-vectorized/doris-vectorized/issues/294
## Checklist(Required)
1. Does it affect the original behavior: (No)
2. Has unit tests been added: (Yes)
3. Has document been added or modified: (No)
4. Does it need to update dependencies: (No)
5. Are there any changes that cannot be rolled back: (Yes)
