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docs: move-from-reference-folder-to-openmldbsql-dml-folder #3573

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Docs: Move-from-reference-folder-to-openmldbsql-dql-folder
TanZiYen Oct 27, 2023
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Docs: Detele-reference-sql-dml-folder
TanZiYen Oct 27, 2023
c38e174
Update ALTER_STATEMENT.md typo
Elliezza Nov 29, 2023
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Update DELETE_STATEMENT.md update new changes
Elliezza Nov 29, 2023
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Update LOAD_DATA_STATEMENT.md new changes
Elliezza Nov 29, 2023
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Update LOAD_DATA_STATEMENT.md typo
Elliezza Nov 29, 2023
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88 changes: 44 additions & 44 deletions docs/en/reference/sql/dml/ALTER_STATEMENT.md → docs/en/openmldb_sql/dml/ALTER_STATEMENT.md
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# ALTER
OpenMLDB supports ALERT command to update table's attributes.
## Syntax
```
ALTER TABLE TableName (AlterAction) 'offline_path' FilePath
TableName ::=
Identifier ('.' Identifier)?
AlterAction:
'add' | 'drop'
FilePath
::= URI
URI
::= 'file://FilePathPattern'
|'hdfs://FilePathPattern'
|'hive://[db.]table'
|'FilePathPattern'
FilePathPattern
::= string_literal
```
**Description**
- `ALTER` only supports updateing offline table's symbolic paths currently.
## Examples
```SQL
-- Add one offline path
ALTER TABLE t1 ADD offline_path 'hdfs://foo/bar';
-- Drop one offline path
ALTER TABLE t1 DROP offline_path 'hdfs://foo/bar';
-- Add one offline path and drop anthor offline path
ALTER TABLE t1 ADD offline_path 'hdfs://foo/bar', DROP offline_path 'hdfs://foo/bar2';
```
# ALTER

OpenMLDB supports ALERT command to update table's attributes.

## Syntax

```
ALTER TABLE TableName (AlterAction) 'offline_path' FilePath

TableName ::=
Identifier ('.' Identifier)?

AlterAction:
'add' | 'drop'

FilePath
::= URI

URI
::= 'file://FilePathPattern'
|'hdfs://FilePathPattern'
|'hive://[db.]table'
|'FilePathPattern'

FilePathPattern
::= string_literal
```

**Description**

- `ALTER` only supports updating offline table's symbolic paths currently.

## Examples

```SQL
-- Add one offline path
ALTER TABLE t1 ADD offline_path 'hdfs://foo/bar';

-- Drop one offline path
ALTER TABLE t1 DROP offline_path 'hdfs://foo/bar';

-- Add one offline path and drop anthor offline path
ALTER TABLE t1 ADD offline_path 'hdfs://foo/bar', DROP offline_path 'hdfs://foo/bar2';
```
7 changes: 3 additions & 4 deletions .../en/reference/sql/dml/DELETE_STATEMENT.md → docs/en/openmldb_sql/dml/DELETE_STATEMENT.md
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Expand Up @@ -11,9 +11,8 @@ TableName ::=
```

**Description**

- `DELETE` statement will delete all data from the index of specific column value of online table.
- The filter columns sepcified by `WHERE` must be an index column. if it is a key column, only `=` can be used.
- `DELETE` statement will delete data fulfilling specific requirements in online table, not all data from the index. Only index related to where condition will be deleted. For more examples please check [function_boundary](../../quickstart/function_boundary.md#delete).
- The filter columns specified by `WHERE` must be an index column. if it is a key column, only `=` can be used.

## Examples

Expand All @@ -25,4 +24,4 @@ DELETE FROM t1 WHERE col1 = 'aaaa' and ts_col = 1687145994000;
DELETE FROM t1 WHERE col1 = 'aaaa' and ts_col > 1687059594000 and ts_col < 1687145994000;

DELETE FROM t1 WHERE ts_col > 1687059594000 and ts_col < 1687145994000;
```
```
0 .../en/reference/sql/dml/INSERT_STATEMENT.md → docs/en/openmldb_sql/dml/INSERT_STATEMENT.md
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31 changes: 17 additions & 14 deletions .../reference/sql/dml/LOAD_DATA_STATEMENT.md → ...n/openmldb_sql/dml/LOAD_DATA_STATEMENT.md
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# LOAD DATA INFILE

The `LOAD DATA INFILE` statement load data efficiently from a file to a table. `LOAD DATA INFILE` is complementary to `SELECT ... INTO OUTFILE`. To export data from a table to a file, use [SELECT...INTO OUTFILE](../dql/SELECT_INTO_STATEMENT.md).
The `LOAD DATA INFILE` statement load data efficiently from a file to a table. `LOAD DATA INFILE` is complementary to `SELECT INTO OUTFILE`. To export data from a table to a file, use [SELECT INTO OUTFILE](../dql/SELECT_INTO_STATEMENT.md). To import data into a table, please use `LOAD DATA INFILE`.

```{note}
The filepath of INFILE can be a single file name or a directory. `*` can also be used. If multiple file format exists in the directory, only FORMAT specified in LoadDataInfileOptionsList will be chosen. The format is the same as `DataFrameReader.read.load(String)`. You can read the directory with spark-shell to confirm if the read is successful.
```

## Syntax

Expand Down Expand Up @@ -43,18 +47,17 @@ Supports loading data from Hive, but needs extra settings, see [Hive Support](#H

The following table introduces the parameters of `LOAD DATA INFILE`.

| Parameter | Type | Default Value | Note |
| ----------- | ------- | ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| delimiter | String | , | It defines the column separator, the default value is `,`. |
| header | Boolean | true | It indicates that whether the table to import has a header. If the value is `true`, the table has a header. |
| null_value | String | null | It defines the string that will be used to replace the `NULL` value when loading data. |
| format | String | csv | It defines the format of the input file.<br />`csv` is the default format. <br />`parquet` format is supported in the cluster version. |
| quote | String | " | It defines the string surrounding the input data. The string length should be <= 1. <br />load_mode='cluster': default is `"`, the content surrounded by a pair of the quote characters will be parsed as a whole. For example, if the surrounding string is `"#"` then the original data like `1, 1.0, #This is a string, with comma#, normal_string` will be converted to four fields. The first field is an integer 1, the second is a float 1.0, the third field is a string "This is a string, with comma" and the 4th is "normal_string" even it's no quote. <br /> load_mode='local': default is `\0`, which means that the string surrounding the input data is empty. |
| mode | String | "error_if_exists" | It defines the input mode.<br />`error_if_exists` is the default mode which indicates that an error will be thrown out if the offline table already has data. This input mode is only supported by the offline execution mode.<br />`overwrite` indicates that if the file already exists, the data will overwrite the contents of the original file. This input mode is only supported by the offline execution mode.<br />`append` indicates that if the table already exists, the data will be appended to the original table. Both offline and online execution modes support this input mode. |
| deep_copy | Boolean | true | It defines whether `deep_copy` is used. Only offline load supports `deep_copy=false`, you can specify the `INFILE` path as the offline storage address of the table to avoid hard copy. |
| load_mode | String | cluster | `load_mode='local'` only supports loading the `csv` local files into the `online` storage; It loads the data synchronously by the client process. <br /> `load_mode='cluster'` only supports the cluster version. It loads the data via Spark synchronously or asynchronously. |
| thread | Integer | 1 | It only works for data loading locally, i.e., `load_mode='local'` or in the standalone version; It defines the number of threads used for data loading. The max value is `50`. |
| writer_type | String | single | The writer type for online loading in cluster mode, `single` or `batch`, default is `single`。`single` means write to cluster when reading, cost less memory. `batch` will read the whole rdd partition, check all data is right to pack, then write to cluster, it needs more memory. In some cases, `batch` is better to get the unwritten data, and retry the unwritten part |
| Parameter | Type | Default Value | Note |
| ---------- | ------- | ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| delimiter | String | , | It defines the column separator, the default value is `,`. |
| header | Boolean | true | It indicates that whether the table to import has a header. If the value is `true`, the table has a header. |
| null_value | String | null | It defines the string that will be used to replace the `NULL` value when loading data. |
| format | String | csv | It defines the format of the input file.<br />`csv` is the default format. <br />`parquet` format is supported in the cluster version. |
| quote | String | " | It defines the string surrounding the input data. The string length should be <= 1. <br />load_mode='cluster': default is `"`, the content surrounded by a pair of the quote characters will be parsed as a whole. For example, if the surrounding string is `"#"` then the original data like `1, 1.0, #This is a string, with comma#, normal_string` will be converted to four fields. The first field is an integer 1, the second is a float 1.0, the third field is a string "This is a string, with comma" and the 4th is "normal_string" even it's no quote. <br /> load_mode='local': default is `\0`, which means that the string surrounding the input data is empty. |
| mode | String | "error_if_exists" | It defines the input mode.<br />`error_if_exists` is the default mode which indicates that an error will be thrown out if the offline table already has data. This input mode is only supported by the offline execution mode.<br />`overwrite` indicates that if the file already exists, the data will overwrite the contents of the original file. This input mode is only supported by the offline execution mode.<br />`append` indicates that if the table already exists, the data will be appended to the original table. Both offline and online execution modes support this input mode. |
| deep_copy | Boolean | true | It defines whether `deep_copy` is used. Only offline load supports `deep_copy=false`, you can specify the `INFILE` path as the offline storage address of the table to avoid hard copy. |
| load_mode | String | cluster | `load_mode='local'` only supports loading the `csv` local files into the `online` storage; It loads the data synchronously by the client process. <br /> `load_mode='cluster'` only supports the cluster version. It loads the data via Spark synchronously or asynchronously. |
| thread | Integer | 1 | It only works for data loading locally, i.e., `load_mode='local'` or in the standalone version; It defines the number of threads used for data loading. The max value is `50`. |

```{note}
- In the cluster version, the specified execution mode (defined by `execute_mode`) determines whether to import data to online or offline storage when the `LOAD DATA INFILE` statement is executed. For the standalone version, there is no difference in storage mode and the `deep_copy` option is not supported.
Expand All @@ -76,7 +79,7 @@ Therefore, you are suggested to use absolute paths. In the stand-alone version,
## SQL Statement Template

```sql
LOAD DATA INFILE 'file_name' INTO TABLE 'table_name' OPTIONS (key = value, ...);
LOAD DATA INFILE 'file_path' INTO TABLE 'table_name' OPTIONS (key = value, ...);
```

### Example
Expand Down
0 docs/en/reference/sql/dml/index.rst → docs/en/openmldb_sql/dml/index.rst
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