docs/en/connector-v2/source/OssJindoFile.md

import ChangeLog from '../changelog/connector-file-oss-jindo.md';

# OssJindoFile

> OssJindo file source connector

## Support Those Engines

> Spark<br/>
> Flink<br/>
> SeaTunnel Zeta<br/>

## Key features

- [x] [batch](../../concept/connector-v2-features.md)
- [ ] [stream](../../concept/connector-v2-features.md)
- [x] [multimodal](../../concept/connector-v2-features.md#multimodal)

  Use binary file format to read and write files in any format, such as videos, pictures, etc. In short, any files can be synchronized to the target place.

- [x] [exactly-once](../../concept/connector-v2-features.md)

  Read all the data in a split in a pollNext call. What splits are read will be saved in snapshot.

- [ ] [column projection](../../concept/connector-v2-features.md)
- [x] [parallelism](../../concept/connector-v2-features.md)
- [ ] [support user-defined split](../../concept/connector-v2-features.md)
- [x] file format type
  - [x] text
  - [x] csv
  - [x] parquet
  - [x] orc
  - [x] json
  - [x] excel
  - [x] xml
  - [x] binary
  - [x] markdown

## Description

Read data from aliyun oss file system using jindo api.

:::tip

You need to download [jindosdk-4.6.1.tar.gz](https://jindodata-binary.oss-cn-shanghai.aliyuncs.com/release/4.6.1/jindosdk-4.6.1.tar.gz)
and then unzip it, copy jindo-sdk-4.6.1.jar and jindo-core-4.6.1.jar from lib to ${SEATUNNEL_HOME}/lib.

If you use spark/flink, In order to use this connector, You must ensure your spark/flink cluster already integrated hadoop. The tested hadoop version is 2.x.

If you use SeaTunnel Engine, It automatically integrated the hadoop jar when you download and install SeaTunnel Engine. You can check the jar package under ${SEATUNNEL_HOME}/lib to confirm this.

We made some trade-offs in order to support more file types, so we used the HDFS protocol for internal access to OSS and this connector need some hadoop dependencies.
It only supports hadoop version **2.9.X+**.

:::

## Options

| name                       | type    | required | default value               |
|----------------------------|---------|----------|-----------------------------|
| path                       | string  | yes      | -                           |
| file_format_type           | string  | yes      | -                           |
| bucket                     | string  | yes      | -                           |
| access_key                 | string  | yes      | -                           |
| access_secret              | string  | yes      | -                           |
| endpoint                   | string  | yes      | -                           |
| read_columns               | list    | no       | -                           |
| delimiter/field_delimiter  | string  | no       | \001 for text and , for csv |
| row_delimiter              | string  | no       | \n                          |
| parse_partition_from_path  | boolean | no       | true                        |
| date_format                | string  | no       | yyyy-MM-dd                  |
| datetime_format            | string  | no       | yyyy-MM-dd HH:mm:ss         |
| time_format                | string  | no       | HH:mm:ss                    |
| skip_header_row_number     | long    | no       | 0                           |
| schema                     | config  | no       | -                           |
| sheet_name                 | string  | no       | -                           |
| xml_row_tag                | string  | no       | -                           |
| xml_use_attr_format        | boolean | no       | -                           |
| csv_use_header_line        | boolean | no       | false                       |
| file_filter_pattern        | string  | no       |                             |
| compress_codec             | string  | no       | none                        |
| archive_compress_codec     | string  | no       | none                        |
| encoding                   | string  | no       | UTF-8                       |
| null_format                | string  | no       | -                           |
| common-options             |         | no       | -                           |
| file_filter_modified_start | string  | no       | -                           | 
| file_filter_modified_end   | string  | no       | -                           | 

### path [string]

The source file path.

### file_format_type [string]

File type, supported as the following file types:

`text` `csv` `parquet` `orc` `json` `excel` `xml` `binary` `markdown`

If you assign file type to `json`, you should also assign schema option to tell connector how to parse data to the row you want.

For example:

upstream data is the following:

```json

{"code":  200, "data":  "get success", "success":  true}

```

You can also save multiple pieces of data in one file and split them by newline:

```json lines

{"code":  200, "data":  "get success", "success":  true}
{"code":  300, "data":  "get failed", "success":  false}

```

you should assign schema as the following:

```hocon

schema {
    fields {
        code = int
        data = string
        success = boolean
    }
}

```

connector will generate data as the following:

| code |    data     | success |
|------|-------------|---------|
| 200  | get success | true    |

If you assign file type to `parquet` `orc`, schema option not required, connector can find the schema of upstream data automatically.

If you assign file type to `text` `csv`, you can choose to specify the schema information or not.

For example, upstream data is the following:

```text

tyrantlucifer#26#male

```

If you do not assign data schema connector will treat the upstream data as the following:

|        content        |
|-----------------------|
| tyrantlucifer#26#male |

If you assign data schema, you should also assign the option `field_delimiter` too except CSV file type

you should assign schema and delimiter as the following:

```hocon

field_delimiter = "#"
schema {
    fields {
        name = string
        age = int
        gender = string 
    }
}

```

connector will generate data as the following:

|     name      | age | gender |
|---------------|-----|--------|
| tyrantlucifer | 26  | male   |

If you assign file type to `binary`, SeaTunnel can synchronize files in any format,
such as compressed packages, pictures, etc. In short, any files can be synchronized to the target place.
Under this requirement, you need to ensure that the source and sink use `binary` format for file synchronization
at the same time. You can find the specific usage in the example below.

If you assign file type to `markdown`, SeaTunnel can parse markdown files and extract structured data.
The markdown parser extracts various elements including headings, paragraphs, lists, code blocks, tables, and more.
Each element is converted to a row with the following schema:
- `element_id`: Unique identifier for the element
- `element_type`: Type of the element (Heading, Paragraph, ListItem, etc.)
- `heading_level`: Level of heading (1-6, null for non-heading elements)
- `text`: Text content of the element
- `page_number`: Page number (default: 1)
- `position_index`: Position index within the document
- `parent_id`: ID of the parent element
- `child_ids`: Comma-separated list of child element IDs

Note: Markdown format only supports reading, not writing.

### bucket [string]

The bucket address of oss file system, for example: `oss://tyrantlucifer-image-bed`

### access_key [string]

The access key of oss file system.

### access_secret [string]

The access secret of oss file system.

### endpoint [string]

The endpoint of oss file system.

### read_columns [list]

The read column list of the data source, user can use it to implement field projection.

### delimiter/field_delimiter [string]

**delimiter** parameter will deprecate after version 2.3.5, please use **field_delimiter** instead.

Only need to be configured when file_format is text.

Field delimiter, used to tell connector how to slice and dice fields.

default `\001`, the same as hive's default delimiter

### row_delimiter [string]

Only need to be configured when file_format is text

Row delimiter, used to tell connector how to slice and dice rows

default `\n`

### parse_partition_from_path [boolean]

Control whether parse the partition keys and values from file path

For example if you read a file from path `oss://hadoop-cluster/tmp/seatunnel/parquet/name=tyrantlucifer/age=26`

Every record data from file will be added these two fields:

|     name      | age |
|---------------|-----|
| tyrantlucifer | 26  |

Tips: **Do not define partition fields in schema option**

### date_format [string]

Date type format, used to tell connector how to convert string to date, supported as the following formats:

`yyyy-MM-dd` `yyyy.MM.dd` `yyyy/MM/dd`

default `yyyy-MM-dd`

### datetime_format [string]

Datetime type format, used to tell connector how to convert string to datetime, supported as the following formats:

`yyyy-MM-dd HH:mm:ss` `yyyy.MM.dd HH:mm:ss` `yyyy/MM/dd HH:mm:ss` `yyyyMMddHHmmss`

default `yyyy-MM-dd HH:mm:ss`

### time_format [string]

Time type format, used to tell connector how to convert string to time, supported as the following formats:

`HH:mm:ss` `HH:mm:ss.SSS`

default `HH:mm:ss`

### skip_header_row_number [long]

Skip the first few lines, but only for the txt and csv.

For example, set like following:

`skip_header_row_number = 2`

then SeaTunnel will skip the first 2 lines from source files

### schema [config]

Only need to be configured when the file_format_type are text, json, excel, xml or csv ( Or other format we can't read the schema from metadata).

#### fields [Config]

The schema of upstream data.

### sheet_name [string]

Only need to be configured when file_format is excel.

Reader the sheet of the workbook.

### file_filter_pattern [string]

Filter pattern, which used for filtering files.

The pattern follows standard regular expressions. For details, please refer to https://en.wikipedia.org/wiki/Regular_expression.
There are some examples.

File Structure Example:
```
/data/seatunnel/20241001/report.txt
/data/seatunnel/20241007/abch202410.csv
/data/seatunnel/20241002/abcg202410.csv
/data/seatunnel/20241005/old_data.csv
/data/seatunnel/20241012/logo.png
```
Matching Rules Example:

**Example 1**: *Match all .txt files*，Regular Expression:
```
/data/seatunnel/20241001/.*\.txt
```
The result of this example matching is:
```
/data/seatunnel/20241001/report.txt
```
**Example 2**: *Match all file starting with abc*，Regular Expression:
```
/data/seatunnel/20241002/abc.*
```
The result of this example matching is:
```
/data/seatunnel/20241007/abch202410.csv
/data/seatunnel/20241002/abcg202410.csv
```
**Example 3**: *Match all file starting with abc，And the fourth character is either h or g*, the Regular Expression:
```
/data/seatunnel/20241007/abc[h,g].*
```
The result of this example matching is:
```
/data/seatunnel/20241007/abch202410.csv
```
**Example 4**: *Match third level folders starting with 202410 and files ending with .csv*, the Regular Expression:
```
/data/seatunnel/202410\d*/.*\.csv
```
The result of this example matching is:
```
/data/seatunnel/20241007/abch202410.csv
/data/seatunnel/20241002/abcg202410.csv
/data/seatunnel/20241005/old_data.csv
```

### filename_extension [string]

Filter filename extension, which used for filtering files with specific extension. Example: `csv` `.txt` `json` `.xml`.

### compress_codec [string]

The compress codec of files and the details that supported as the following shown:

- txt: `lzo` `none`
- json: `lzo` `none`
- csv: `lzo` `none`
- orc/parquet:  
  automatically recognizes the compression type, no additional settings required.

### archive_compress_codec [string]

The compress codec of archive files and the details that supported as the following shown:

| archive_compress_codec | file_format        | archive_compress_suffix |
|------------------------|--------------------|-------------------------|
| ZIP                    | txt,json,excel,xml | .zip                    |
| TAR                    | txt,json,excel,xml | .tar                    |
| TAR_GZ                 | txt,json,excel,xml | .tar.gz                 |
| GZ                     | txt,json,excel,xml | .gz                     |
| NONE                   | all                | .*                      |

Note: gz compressed excel file needs to compress the original file or specify the file suffix, such as e2e.xls ->e2e_test.xls.gz

### encoding [string]

Only used when file_format_type is json,text,csv,xml.
The encoding of the file to read. This param will be parsed by `Charset.forName(encoding)`.

### null_format [string]

Only used when file_format_type is text.
null_format to define which strings can be represented as null.

e.g: `\N`

### file_filter_modified_start [string]

File modification time filter. The connector will filter some files base on the last modification start time (include start time). The default data format is `yyyy-MM-dd HH:mm:ss`.

### file_filter_modified_end [string]

File modification time filter. The connector will filter some files base on the last modification end time (not include end time). The default data format is `yyyy-MM-dd HH:mm:ss`.

### common options

Source plugin common parameters, please refer to [Source Common Options](../source-common-options.md) for details.

## Example

```hocon

OssJindoFile {
    path = "/seatunnel/orc"
    bucket = "oss://tyrantlucifer-image-bed"
    access_key = "xxxxxxxxxxxxxxxxx"
    access_secret = "xxxxxxxxxxxxxxxxxxxxxx"
    endpoint = "oss-cn-beijing.aliyuncs.com"
    file_format_type = "orc"
  }

```

```hocon

OssJindoFile {
    path = "/seatunnel/json"
    bucket = "oss://tyrantlucifer-image-bed"
    access_key = "xxxxxxxxxxxxxxxxx"
    access_secret = "xxxxxxxxxxxxxxxxxxxxxx"
    endpoint = "oss-cn-beijing.aliyuncs.com"
    file_format_type = "json"
    schema {
      fields {
        id = int 
        name = string
      }
    }
  }

```

### Transfer Binary File

```hocon

env {
  parallelism = 1
  job.mode = "BATCH"
}

source {
  OssJindoFile {
    bucket = "oss://tyrantlucifer-image-bed"
    access_key = "xxxxxxxxxxxxxxxxx"
    access_secret = "xxxxxxxxxxxxxxxxxxxxxx"
    endpoint = "oss-cn-beijing.aliyuncs.com"
    path = "/seatunnel/read/binary/"
    file_format_type = "binary"
  }
}
sink {
  // you can transfer local file to s3/hdfs/oss etc.
  OssJindoFile {
    bucket = "oss://tyrantlucifer-image-bed"
    access_key = "xxxxxxxxxxxxxxxxx"
    access_secret = "xxxxxxxxxxxxxxxxxxxxxx"
    endpoint = "oss-cn-beijing.aliyuncs.com"
    path = "/seatunnel/read/binary2/"
    file_format_type = "binary"
  }
}

```

### Filter File

```hocon
env {
  parallelism = 1
  job.mode = "BATCH"
}

source {
  OssJindoFile {
    bucket = "oss://tyrantlucifer-image-bed"
    access_key = "xxxxxxxxxxxxxxxxx"
    access_secret = "xxxxxxxxxxxxxxxxxxxxxx"
    endpoint = "oss-cn-beijing.aliyuncs.com"
    path = "/seatunnel/read/binary/"
    file_format_type = "binary"
    // file example abcD2024.csv
    file_filter_pattern = "abc[DX]*.*"
  }
}

sink {
  Console {
  }
}
```

## Changelog

<ChangeLog />