[bugfix](docs)Fix some spelling mistakes in english doc (#31241)
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@ -125,8 +125,8 @@ message ColumnPB {
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required string type = 3; // Column type
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optional bool is_key = 4; // Whether column is a primary key column
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optional string aggregation = 5; // Aggregate type
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optional bool is_nullable = 6; // Whether column is allowed to assgin null
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optional bytes default_value = 7; // Defalut value
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optional bool is_nullable = 6; // Whether column is allowed to assign null
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optional bytes default_value = 7; // Default value
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optional int32 precision = 8; // Precision of column
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optional int32 frac = 9;
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optional int32 length = 10; // Length of column
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@ -30,7 +30,7 @@ Doris support to use [Clangd](https://clangd.llvm.org/) and [Clang-Tidy](https:/
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to diagnostic code. Clangd and Clang-Tidy already has in [LDB-toolchain](/docs/install/source-install/compilation-with-ldb-toolchain),also can install by self.
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### Clang-Tidy
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Clang-Tidy can do some diagnostic cofig, config file `.clang-tidy` is in Doris root path. Compared with vscode-cpptools, clangd can provide more powerful and accurate code jumping for vscode, and integrates the analysis and quick-fix functions of clang-tidy.
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Clang-Tidy can do some diagnostic config, config file `.clang-tidy` is in Doris root path. Compared with vscode-cpptools, clangd can provide more powerful and accurate code jumping for vscode, and integrates the analysis and quick-fix functions of clang-tidy.
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### Enable clangd on VSCODE
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@ -41,7 +41,7 @@ Commit is divided into ‘ title ’ and ‘ content ’ , the title should be l
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* fix: Bug fix
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* feature: New feature
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* feature-wip: Feature works-in-porgress.
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* feature-wip: Feature works-in-progress.
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* improvement: Optimization and improvement for the original feature.
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* style: Code style adjustment
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* typo: Code or Document correction
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@ -27,7 +27,7 @@ under the License.
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**Thrive together in [Apache Doris](https://github.com/apache/doris) community with users and developers from all around the world.**
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We have graduated from Apache incubator succesffully and become an Top-Level Project in June 2022.
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We have graduated from Apache incubator successfully and become an Top-Level Project in June 2022.
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<hr />
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@ -78,7 +78,7 @@ https://www.apache.org/dist/doris/1.xx/
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http://www.apache.org/dyn/closer.cgi/doris/1.xx/apache-doris-1.xx-src.tar.gz
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```
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Ssource package:
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Source package:
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```
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http://www.apache.org/dyn/closer.cgi/doris/1.xx/apache-doris-1.xx-src.tar.gz
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@ -129,7 +129,7 @@ curl -X POST "http://127.0.0.1:8030/api/debug_point/add/foo?execute=5"
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## Pass Custom Parameters
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When activating debug point, besides "timeout" and "execute" mentioned above, passing custom parameters is also allowed.<br/>
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A parameter is a key-value pair in the form of "key=value" in url path, after debug point name glued by charactor '?'.<br/>
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A parameter is a key-value pair in the form of "key=value" in url path, after debug point name glued by character '?'.<br/>
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See examples below.
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### API
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@ -296,8 +296,8 @@ and `disableDebugPointForAllFEs()` or `disableDebugPointForAllBEs()` needs to be
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### Concurrent Issue
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Enabled debug points affects FE or BE globally, which could cause other concurrent tests to fail unexpectly in your pull request. <br/>
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To avoid this, there's a convension that regression tests using debug points must be in directory regression-test/suites/fault_injection_p0, <br/>
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Enabled debug points affects FE or BE globally, which could cause other concurrent tests to fail unexpectedly in your pull request. <br/>
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To avoid this, there's a convention that regression tests using debug points must be in directory regression-test/suites/fault_injection_p0, <br/>
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and their group name must be "nonConcurrent", as these regression tests will be executed serially by pull request workflow.
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### Examples
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@ -32,7 +32,7 @@ under the License.
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## Description
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Used to obtain doris metrics infomation.
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Used to obtain doris metrics information.
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## Path parameters
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@ -122,7 +122,7 @@ Click the `+` button next to the plugin to add a Doris service:
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The meaning of some parameters of Config Properties is as follows:
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- `Username`/`Pasword`: the username and password of the Doris cluster. It is recommended to use the Admin user here.
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- `Username`/`Password`: the username and password of the Doris cluster. It is recommended to use the Admin user here.
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- `jdbc.driver_class`: Connect to the JDBC driver used by Doris. `com.mysql.cj.jdbc.Driver`
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- `jdbc.url`: JDBC url connection string of Doris cluster. `jdbc:mysql://172.21.0.101:9030?useSSL=false`
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- Additional parameters:
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@ -62,7 +62,7 @@ Add configuration items to BE's configuration file `be.conf`:
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be_node_role = computation
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```
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This defualt value of this is `mix`, and this is original BE node type. After setting to `computation`, the node is a computing node.
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This default value of this is `mix`, and this is original BE node type. After setting to `computation`, the node is a computing node.
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You can see the value of the'NodeRole 'field through the `show backends\G` command. If it is'mix ', it is a mixed node, and if it is'computation', it is a computing node
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@ -36,7 +36,7 @@ The Auto Partitioning feature supports automatic detection of whether the corres
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The auto partition function mainly solves the problem that the user expects to partition the table based on a certain column, but the data distribution of the column is scattered or unpredictable, so it is difficult to accurately create the required partitions when building or adjusting the structure of the table, or the number of partitions is so large that it is too cumbersome to create them manually.
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Take the time type partition column as an example, in the [Dynamic Partition](./dynamic-partition) function, we support the automatic creation of new partitions to accommodate real-time data at specific time periods. For real-time user behaviour logs and other scenarios, this feature basically meets the requirements. However, in more complex scenarios, such as dealing with non-real-time data, the partition column is independent of the current system time and contains a large number of discrete values. At this time to improve efficiency we want to partition the data based on this column, but the data may actually involve the partition can not be grasped in advance, or the expected number of required partitions is too large. In this case, dynamic partitioning or manually created partitions can not meet our needs, automatic partitioning function is very good to cover such needs.
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Take the time type partition column as an example, in the [Dynamic Partition](./dynamic-partition) function, we support the automatic creation of new partitions to accommodate real-time data at specific time periods. For real-time user behavior logs and other scenarios, this feature basically meets the requirements. However, in more complex scenarios, such as dealing with non-real-time data, the partition column is independent of the current system time and contains a large number of discrete values. At this time to improve efficiency we want to partition the data based on this column, but the data may actually involve the partition can not be grasped in advance, or the expected number of required partitions is too large. In this case, dynamic partitioning or manually created partitions can not meet our needs, automatic partitioning function is very good to cover such needs.
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Suppose our table DDL is as follows:
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