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[GitHub] [incubator-doris] smallhibiscus opened a new pull request, #8927: [Refactor][doc] add data model and index doc
smallhibiscus opened a new pull request, #8927:
URL: https://github.com/apache/incubator-doris/pull/8927
# Proposed changes
Issue Number: close #xxx
## Problem Summary:
add data-table/advance-usage\basic-usage\hit-the-rollup\data-partition en and zh-CN docs.
## Checklist(Required)
1. Does it affect the original behavior: (Yes/No/I Don't know)
2. Has unit tests been added: (Yes/No/No Need)
3. Has document been added or modified: (Yes/No/No Need)
4. Does it need to update dependencies: (Yes/No)
5. Are there any changes that cannot be rolled back: (Yes/No)
## Further comments
If this is a relatively large or complex change, kick off the discussion at [dev@doris.apache.org](mailto:dev@doris.apache.org) by explaining why you chose the solution you did and what alternatives you considered, etc...
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[GitHub] [incubator-doris] github-actions[bot] commented on pull request #8927: [Refactor][doc] add data-table doc
Posted by GitBox <gi...@apache.org>.
github-actions[bot] commented on PR #8927:
URL: https://github.com/apache/incubator-doris/pull/8927#issuecomment-1093769367
PR approved by anyone and no changes requested.
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[GitHub] [incubator-doris] EmmyMiao87 commented on a diff in pull request #8927: [Refactor][doc] add data-table doc
Posted by GitBox <gi...@apache.org>.
EmmyMiao87 commented on code in PR #8927:
URL: https://github.com/apache/incubator-doris/pull/8927#discussion_r846032038
##########
new-docs/en/data-table/hit-the-rollup.md:
##########
@@ -26,20 +26,164 @@ under the License.
# Rollup and query
+ROLLUP in multidimensional analysis means "scroll up", which means that data is aggregated further at a specified granularity.
+
+### Basic concepts
+
+In Doris, we make the table created by the user through the table building statement a Base table. Base table holds the basic data stored in the way specified by the user's table-building statement.
+
+On top of the Base table, we can create any number of ROLLUP tables. These ROLLUP data are generated based on the Base table and physically **stored independently**.
+
+The basic function of ROLLUP tables is to obtain coarser aggregated data on the basis of Base tables.
+
+Let's illustrate the ROLLUP tables and their roles in different data models with examples.
+
+#### ROLLUP in Aggregate Model and Uniq Model
+
+Because Uniq is only a special case of the Aggregate model, we do not distinguish it here.
+
+Example 1: Get the total consumption per user
+
+Following [Data Model Aggregate Model](data-model.html#Aggregate Model) in the **Aggregate Model** section, the Base table structure is as follows:
+
+| ColumnName | Type | AggregationType | Comment |
+| --------------- | ------------ | --------------- | -------------------------------------- |
+| user_id | LARGEINT | | user id |
Review Comment:
```suggestion
| user\_id | LARGEINT | | user id |
```
##########
new-docs/en/data-table/hit-the-rollup.md:
##########
@@ -26,20 +26,164 @@ under the License.
# Rollup and query
+ROLLUP in multidimensional analysis means "scroll up", which means that data is aggregated further at a specified granularity.
+
+### Basic concepts
+
+In Doris, we make the table created by the user through the table building statement a Base table. Base table holds the basic data stored in the way specified by the user's table-building statement.
+
+On top of the Base table, we can create any number of ROLLUP tables. These ROLLUP data are generated based on the Base table and physically **stored independently**.
+
+The basic function of ROLLUP tables is to obtain coarser aggregated data on the basis of Base tables.
+
+Let's illustrate the ROLLUP tables and their roles in different data models with examples.
+
+#### ROLLUP in Aggregate Model and Uniq Model
+
+Because Uniq is only a special case of the Aggregate model, we do not distinguish it here.
+
+Example 1: Get the total consumption per user
+
+Following [Data Model Aggregate Model](data-model.html#Aggregate Model) in the **Aggregate Model** section, the Base table structure is as follows:
+
+| ColumnName | Type | AggregationType | Comment |
+| --------------- | ------------ | --------------- | -------------------------------------- |
+| user_id | LARGEINT | | user id |
+| date | DATE | | date of data filling |
+| Time stamp | DATETIME | | Data filling time, accurate to seconds |
+| City | VARCHAR (20) | | User City |
+| age | SMALLINT | | User age |
+| sex | TINYINT | | User gender |
+| Last_visit_date | DATETIME | REPLACE | Last user access time |
+| Cost | BIGINT | SUM | Total User Consumption |
+| max dwell time | INT | MAX | Maximum user residence time |
+| min dwell time | INT | MIN | User minimum residence time |
+
+The data stored are as follows:
+
+| user_id | date | timestamp | city | age | sex | last\_visit\_date | cost | max\_dwell\_time | min\_dwell\_time |
Review Comment:
```suggestion
| user\_id | date | timestamp | city | age | sex | last\_visit\_date | cost | max\_dwell\_time | min\_dwell\_time |
```
##########
new-docs/en/data-table/hit-the-rollup.md:
##########
@@ -26,20 +26,164 @@ under the License.
# Rollup and query
+ROLLUP in multidimensional analysis means "scroll up", which means that data is aggregated further at a specified granularity.
+
+### Basic concepts
+
+In Doris, we make the table created by the user through the table building statement a Base table. Base table holds the basic data stored in the way specified by the user's table-building statement.
+
+On top of the Base table, we can create any number of ROLLUP tables. These ROLLUP data are generated based on the Base table and physically **stored independently**.
+
+The basic function of ROLLUP tables is to obtain coarser aggregated data on the basis of Base tables.
+
+Let's illustrate the ROLLUP tables and their roles in different data models with examples.
+
+#### ROLLUP in Aggregate Model and Uniq Model
+
+Because Uniq is only a special case of the Aggregate model, we do not distinguish it here.
+
+Example 1: Get the total consumption per user
+
+Following [Data Model Aggregate Model](data-model.html#Aggregate Model) in the **Aggregate Model** section, the Base table structure is as follows:
+
+| ColumnName | Type | AggregationType | Comment |
+| --------------- | ------------ | --------------- | -------------------------------------- |
+| user_id | LARGEINT | | user id |
+| date | DATE | | date of data filling |
+| Time stamp | DATETIME | | Data filling time, accurate to seconds |
+| City | VARCHAR (20) | | User City |
+| age | SMALLINT | | User age |
+| sex | TINYINT | | User gender |
+| Last_visit_date | DATETIME | REPLACE | Last user access time |
+| Cost | BIGINT | SUM | Total User Consumption |
+| max dwell time | INT | MAX | Maximum user residence time |
+| min dwell time | INT | MIN | User minimum residence time |
+
+The data stored are as follows:
+
+| user_id | date | timestamp | city | age | sex | last\_visit\_date | cost | max\_dwell\_time | min\_dwell\_time |
+| ------- | ---------- | ------------------- | --------- | ---- | ---- | ------------------- | ---- | ---------------- | ---------------- |
+| 10000 | 2017-10-01 | 2017-10-01 08:00:05 | Beijing | 20 | 0 | 2017-10-01 06:00 | 20 | 10 | 10 |
+| 10000 | 2017-10-01 | 2017-10-01 09:00:05 | Beijing | 20 | 0 | 2017-10-01 07:00 | 15 | 2 | 2 |
+| 10001 | 2017-10-01 | 2017-10-01 18:12:10 | Beijing | 30 | 1 | 2017-10-01 17:05:45 | 2 | 22 | 22 |
+| 10002 | 2017-10-02 | 2017-10-02 13:10:00 | Shanghai | 20 | 1 | 2017-10-02 12:59:12 | 200 | 5 | 5 |
+| 10003 | 2017-10-02 | 2017-10-02 13:15:00 | Guangzhou | 32 | 0 | 2017-10-02 11:20:00 | 30 | 11 | 11 |
+| 10004 | 2017-10-01 | 2017-10-01 12:12:48 | Shenzhen | 35 | 0 | 2017-10-01 10:00:15 | 100 | 3 | 3 |
+| 10004 | 2017-10-03 | 2017-10-03 12:38:20 | Shenzhen | 35 | 0 | 2017-10-03 10:20:22 | 11 | 6 | 6 |
+
+On this basis, we create a ROLLUP:
+
+| ColumnName |
+| ---------- |
+| user_id |
+| cost |
+
+The ROLLUP contains only two columns: user_id and cost. After the creation, the data stored in the ROLLUP is as follows:
Review Comment:
```suggestion
The ROLLUP contains only two columns: user\_id and cost. After the creation, the data stored in the ROLLUP is as follows:
```
##########
new-docs/en/data-table/hit-the-rollup.md:
##########
@@ -26,20 +26,164 @@ under the License.
# Rollup and query
+ROLLUP in multidimensional analysis means "scroll up", which means that data is aggregated further at a specified granularity.
+
+### Basic concepts
+
+In Doris, we make the table created by the user through the table building statement a Base table. Base table holds the basic data stored in the way specified by the user's table-building statement.
+
+On top of the Base table, we can create any number of ROLLUP tables. These ROLLUP data are generated based on the Base table and physically **stored independently**.
+
+The basic function of ROLLUP tables is to obtain coarser aggregated data on the basis of Base tables.
+
+Let's illustrate the ROLLUP tables and their roles in different data models with examples.
+
+#### ROLLUP in Aggregate Model and Uniq Model
+
+Because Uniq is only a special case of the Aggregate model, we do not distinguish it here.
+
+Example 1: Get the total consumption per user
+
+Following [Data Model Aggregate Model](data-model.html#Aggregate Model) in the **Aggregate Model** section, the Base table structure is as follows:
+
+| ColumnName | Type | AggregationType | Comment |
+| --------------- | ------------ | --------------- | -------------------------------------- |
+| user_id | LARGEINT | | user id |
+| date | DATE | | date of data filling |
+| Time stamp | DATETIME | | Data filling time, accurate to seconds |
+| City | VARCHAR (20) | | User City |
+| age | SMALLINT | | User age |
+| sex | TINYINT | | User gender |
+| Last_visit_date | DATETIME | REPLACE | Last user access time |
+| Cost | BIGINT | SUM | Total User Consumption |
+| max dwell time | INT | MAX | Maximum user residence time |
+| min dwell time | INT | MIN | User minimum residence time |
+
+The data stored are as follows:
+
+| user_id | date | timestamp | city | age | sex | last\_visit\_date | cost | max\_dwell\_time | min\_dwell\_time |
+| ------- | ---------- | ------------------- | --------- | ---- | ---- | ------------------- | ---- | ---------------- | ---------------- |
+| 10000 | 2017-10-01 | 2017-10-01 08:00:05 | Beijing | 20 | 0 | 2017-10-01 06:00 | 20 | 10 | 10 |
+| 10000 | 2017-10-01 | 2017-10-01 09:00:05 | Beijing | 20 | 0 | 2017-10-01 07:00 | 15 | 2 | 2 |
+| 10001 | 2017-10-01 | 2017-10-01 18:12:10 | Beijing | 30 | 1 | 2017-10-01 17:05:45 | 2 | 22 | 22 |
+| 10002 | 2017-10-02 | 2017-10-02 13:10:00 | Shanghai | 20 | 1 | 2017-10-02 12:59:12 | 200 | 5 | 5 |
+| 10003 | 2017-10-02 | 2017-10-02 13:15:00 | Guangzhou | 32 | 0 | 2017-10-02 11:20:00 | 30 | 11 | 11 |
+| 10004 | 2017-10-01 | 2017-10-01 12:12:48 | Shenzhen | 35 | 0 | 2017-10-01 10:00:15 | 100 | 3 | 3 |
+| 10004 | 2017-10-03 | 2017-10-03 12:38:20 | Shenzhen | 35 | 0 | 2017-10-03 10:20:22 | 11 | 6 | 6 |
+
+On this basis, we create a ROLLUP:
+
+| ColumnName |
+| ---------- |
+| user_id |
+| cost |
+
+The ROLLUP contains only two columns: user_id and cost. After the creation, the data stored in the ROLLUP is as follows:
+
+| user\_id | cost |
+| -------- | ---- |
+| 10000 | 35 |
+| 10001 | 2 |
+| 10002 | 200 |
+| 10003 | 30 |
+| 10004 | 111 |
+
+As you can see, ROLLUP retains only the results of SUM on the cost column for each user_id. So when we do the following query:
+
+`SELECT user_id, sum(cost) FROM table GROUP BY user_id;`
+
+Doris automatically hits the ROLLUP table, thus completing the aggregated query by scanning only a very small amount of data.
+
+2. Example 2: Get the total consumption, the longest and shortest page residence time of users of different ages in different cities
+
+Follow example 1. Based on the Base table, we create a ROLLUP:
+
+| ColumnName | Type | AggregationType | Comment |
+| -------------- | ------------ | --------------- | --------------------------- |
+| City | VARCHAR (20) | | User City |
+| age | SMALLINT | | User age |
+| Cost | BIGINT | SUM | Total User Consumption |
+| max dwell time | INT | MAX | Maximum user residence time |
+| min dwell time | INT | MIN | User minimum residence time |
+
+After the creation, the data stored in the ROLLUP is as follows:
+
+| city | age | cost | max\_dwell\_time | min\_dwell\_time |
+| --------- | ---- | ---- | ---------------- | ---------------- |
+| Beijing | 20 | 35 | 10 | 2 |
+| Beijing | 30 | 2 | 22 | 22 |
+| Shanghai | 20 | 200 | 5 | 5 |
+| Guangzhou | 32 | 30 | 11 | 11 |
+| Shenzhen | 35 | 111 | 6 | 3 |
+
+When we do the following queries:
+
+```sql
+mysql> SELECT city, age, sum(cost), max(max_dwell_time), min(min_dwell_time) FROM table GROUP BY city, age;
+mysql> SELECT city, sum(cost), max(max_dwell_time), min(min_dwell_time) FROM table GROUP BY city;
+mysql> SELECT city, age, sum(cost), min(min_dwell_time) FROM table GROUP BY city, age;
+```
+
+Doris automatically hits the ROLLUP table.
+
+#### ROLLUP in Duplicate Model
+
+Because the Duplicate model has no aggregate semantics. So the ROLLLUP in this model has lost the meaning of "scroll up". It's just to adjust the column order to hit the prefix index. In the next section, we will introduce prefix index in [data model prefix index](data-model.html#prefix index), and how to use ROLLUP to change prefix index in order to achieve better query efficiency.
+
+## ROLLUP adjusts prefix index
+
+Because column order is specified when a table is built, there is only one prefix index for a table. This may be inefficient for queries that use other columns that cannot hit prefix indexes as conditions. Therefore, we can manually adjust the order of columns by creating ROLLUP. Examples are given.
+
+The structure of the Base table is as follows:
+
+| ColumnName | Type |
+| ---------------- | ------------ |
+| user\_id | BIGINT |
+| age | INT |
+| message | VARCHAR(100) |
+| max\_dwell\_time | DATETIME |
+| min\_dwell\_time | DATETIME |
+
+On this basis, we can create a ROLLUP table:
+
+| ColumnName | Type |
+| ---------------- | ------------ |
+| age | INT |
+| user\_id | BIGINT |
+| message | VARCHAR(100) |
+| max\_dwell\_time | DATETIME |
+| min\_dwell\_time | DATETIME |
+
+As you can see, the columns of ROLLUP and Base tables are exactly the same, just changing the order of user_id and age. So when we do the following query:
Review Comment:
```suggestion
As you can see, the columns of ROLLUP and Base tables are exactly the same, just changing the order of user\_id and age. So when we do the following query:
```
##########
new-docs/en/data-table/hit-the-rollup.md:
##########
@@ -26,20 +26,164 @@ under the License.
# Rollup and query
+ROLLUP in multidimensional analysis means "scroll up", which means that data is aggregated further at a specified granularity.
+
+### Basic concepts
+
+In Doris, we make the table created by the user through the table building statement a Base table. Base table holds the basic data stored in the way specified by the user's table-building statement.
+
+On top of the Base table, we can create any number of ROLLUP tables. These ROLLUP data are generated based on the Base table and physically **stored independently**.
+
+The basic function of ROLLUP tables is to obtain coarser aggregated data on the basis of Base tables.
+
+Let's illustrate the ROLLUP tables and their roles in different data models with examples.
+
+#### ROLLUP in Aggregate Model and Uniq Model
+
+Because Uniq is only a special case of the Aggregate model, we do not distinguish it here.
+
+Example 1: Get the total consumption per user
+
+Following [Data Model Aggregate Model](data-model.html#Aggregate Model) in the **Aggregate Model** section, the Base table structure is as follows:
+
+| ColumnName | Type | AggregationType | Comment |
+| --------------- | ------------ | --------------- | -------------------------------------- |
+| user_id | LARGEINT | | user id |
+| date | DATE | | date of data filling |
+| Time stamp | DATETIME | | Data filling time, accurate to seconds |
+| City | VARCHAR (20) | | User City |
+| age | SMALLINT | | User age |
+| sex | TINYINT | | User gender |
+| Last_visit_date | DATETIME | REPLACE | Last user access time |
+| Cost | BIGINT | SUM | Total User Consumption |
+| max dwell time | INT | MAX | Maximum user residence time |
+| min dwell time | INT | MIN | User minimum residence time |
+
+The data stored are as follows:
+
+| user_id | date | timestamp | city | age | sex | last\_visit\_date | cost | max\_dwell\_time | min\_dwell\_time |
+| ------- | ---------- | ------------------- | --------- | ---- | ---- | ------------------- | ---- | ---------------- | ---------------- |
+| 10000 | 2017-10-01 | 2017-10-01 08:00:05 | Beijing | 20 | 0 | 2017-10-01 06:00 | 20 | 10 | 10 |
+| 10000 | 2017-10-01 | 2017-10-01 09:00:05 | Beijing | 20 | 0 | 2017-10-01 07:00 | 15 | 2 | 2 |
+| 10001 | 2017-10-01 | 2017-10-01 18:12:10 | Beijing | 30 | 1 | 2017-10-01 17:05:45 | 2 | 22 | 22 |
+| 10002 | 2017-10-02 | 2017-10-02 13:10:00 | Shanghai | 20 | 1 | 2017-10-02 12:59:12 | 200 | 5 | 5 |
+| 10003 | 2017-10-02 | 2017-10-02 13:15:00 | Guangzhou | 32 | 0 | 2017-10-02 11:20:00 | 30 | 11 | 11 |
+| 10004 | 2017-10-01 | 2017-10-01 12:12:48 | Shenzhen | 35 | 0 | 2017-10-01 10:00:15 | 100 | 3 | 3 |
+| 10004 | 2017-10-03 | 2017-10-03 12:38:20 | Shenzhen | 35 | 0 | 2017-10-03 10:20:22 | 11 | 6 | 6 |
+
+On this basis, we create a ROLLUP:
+
+| ColumnName |
+| ---------- |
+| user_id |
+| cost |
+
+The ROLLUP contains only two columns: user_id and cost. After the creation, the data stored in the ROLLUP is as follows:
+
+| user\_id | cost |
+| -------- | ---- |
+| 10000 | 35 |
+| 10001 | 2 |
+| 10002 | 200 |
+| 10003 | 30 |
+| 10004 | 111 |
+
+As you can see, ROLLUP retains only the results of SUM on the cost column for each user_id. So when we do the following query:
Review Comment:
```suggestion
As you can see, ROLLUP retains only the results of SUM on the cost column for each user\_id. So when we do the following query:
```
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[GitHub] [incubator-doris] morningman merged pull request #8927: [Refactor][doc] add data-table doc
Posted by GitBox <gi...@apache.org>.
morningman merged PR #8927:
URL: https://github.com/apache/incubator-doris/pull/8927
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[GitHub] [incubator-doris] github-actions[bot] commented on pull request #8927: [Refactor][doc] add data-table doc
Posted by GitBox <gi...@apache.org>.
github-actions[bot] commented on PR #8927:
URL: https://github.com/apache/incubator-doris/pull/8927#issuecomment-1093769359
PR approved by at least one committer and no changes requested.
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[GitHub] [incubator-doris] smallhibiscus commented on pull request #8927: [Refactor][doc] add data-table doc
Posted by GitBox <gi...@apache.org>.
smallhibiscus commented on PR #8927:
URL: https://github.com/apache/incubator-doris/pull/8927#issuecomment-1093618794
Thank you,I have modified these.
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[GitHub] [incubator-doris] EmmyMiao87 commented on a diff in pull request #8927: [Refactor][doc] add data-table doc
Posted by GitBox <gi...@apache.org>.
EmmyMiao87 commented on code in PR #8927:
URL: https://github.com/apache/incubator-doris/pull/8927#discussion_r846019588
##########
new-docs/en/data-table/data-partition.md:
##########
@@ -209,9 +212,46 @@ It is also possible to use only one layer of partitioning. When using a layer pa
> A new void appeared: [2017-01-01, 2017-02-01)
+
In summary, the deletion of a partition does not change the scope of an existing partition. There may be holes in deleting partitions. When a partition is added by the `VALUES LESS THAN` statement, the lower bound of the partition immediately follows the upper bound of the previous partition.
+
+ In addition to the single-column partitioning we have seen above, Range partition also supports **multi-column partitioning**, examples are as follows:
+
+ ~~~text
+ ```
Review Comment:
```suggestion
```text
```
##########
new-docs/en/data-table/hit-the-rollup.md:
##########
@@ -26,20 +26,164 @@ under the License.
# Rollup and query
+ROLLUP in multidimensional analysis means "scroll up", which means that data is aggregated further at a specified granularity.
+
+### Basic concepts
+
+In Doris, we make the table created by the user through the table building statement a Base table. Base table holds the basic data stored in the way specified by the user's table-building statement.
+
+On top of the Base table, we can create any number of ROLLUP tables. These ROLLUP data are generated based on the Base table and physically **stored independently**.
+
+The basic function of ROLLUP tables is to obtain coarser aggregated data on the basis of Base tables.
+
+Let's illustrate the ROLLUP tables and their roles in different data models with examples.
+
+#### ROLLUP in Aggregate Model and Uniq Model
+
+Because Uniq is only a special case of the Aggregate model, we do not distinguish it here.
+
+Example 1: Get the total consumption per user
+
+Following [Data Model Aggregate Model](data-model.html#Aggregate Model) in the **Aggregate Model** section, the Base table structure is as follows:
+
+| ColumnName | Type | AggregationType | Comment |
+| --------------- | ------------ | --------------- | -------------------------------------- |
+| user_id | LARGEINT | | user id |
+| date | DATE | | date of data filling |
+| Time stamp | DATETIME | | Data filling time, accurate to seconds |
+| City | VARCHAR (20) | | User City |
+| age | SMALLINT | | User age |
+| sex | TINYINT | | User gender |
+| Last_visit_date | DATETIME | REPLACE | Last user access time |
+| Cost | BIGINT | SUM | Total User Consumption |
+| max dwell time | INT | MAX | Maximum user residence time |
+| min dwell time | INT | MIN | User minimum residence time |
+
+The data stored are as follows:
+
+| user_id | date | timestamp | city | age | sex | last\_visit\_date | cost | max\_dwell\_time | min\_dwell\_time |
+| ------- | ---------- | ------------------- | --------- | ---- | ---- | ------------------- | ---- | ---------------- | ---------------- |
+| 10000 | 2017-10-01 | 2017-10-01 08:00:05 | Beijing | 20 | 0 | 2017-10-01 06:00 | 20 | 10 | 10 |
+| 10000 | 2017-10-01 | 2017-10-01 09:00:05 | Beijing | 20 | 0 | 2017-10-01 07:00 | 15 | 2 | 2 |
+| 10001 | 2017-10-01 | 2017-10-01 18:12:10 | Beijing | 30 | 1 | 2017-10-01 17:05:45 | 2 | 22 | 22 |
+| 10002 | 2017-10-02 | 2017-10-02 13:10:00 | Shanghai | 20 | 1 | 2017-10-02 12:59:12 | 200 | 5 | 5 |
+| 10003 | 2017-10-02 | 2017-10-02 13:15:00 | Guangzhou | 32 | 0 | 2017-10-02 11:20:00 | 30 | 11 | 11 |
+| 10004 | 2017-10-01 | 2017-10-01 12:12:48 | Shenzhen | 35 | 0 | 2017-10-01 10:00:15 | 100 | 3 | 3 |
+| 10004 | 2017-10-03 | 2017-10-03 12:38:20 | Shenzhen | 35 | 0 | 2017-10-03 10:20:22 | 11 | 6 | 6 |
+
+On this basis, we create a ROLLUP:
+
+| ColumnName |
+| ---------- |
+| user_id |
Review Comment:
```suggestion
| user\_id |
```
##########
new-docs/en/data-table/data-partition.md:
##########
@@ -209,9 +212,46 @@ It is also possible to use only one layer of partitioning. When using a layer pa
> A new void appeared: [2017-01-01, 2017-02-01)
+
In summary, the deletion of a partition does not change the scope of an existing partition. There may be holes in deleting partitions. When a partition is added by the `VALUES LESS THAN` statement, the lower bound of the partition immediately follows the upper bound of the previous partition.
+
+ In addition to the single-column partitioning we have seen above, Range partition also supports **multi-column partitioning**, examples are as follows:
+
+ ~~~text
Review Comment:
Remove it
##########
new-docs/en/data-table/advance-usage.md:
##########
@@ -28,9 +28,9 @@ under the License.
Here we introduce some of Doris's advanced features.
-## Table 1 Structural Change
+## Table Structural Change
Review Comment:
```suggestion
## Table Structural Change
```
##########
new-docs/en/data-table/data-partition.md:
##########
@@ -244,7 +284,38 @@ It is also possible to use only one layer of partitioning. When using a layer pa
p_uk: ("London")
```
- You cannot add partitions with overlapping ranges.
+ List partition also supports **multi-column partition**, examples are as follows:
+
+ ~~~text
Review Comment:
same as above
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