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OssFile

Oss file source connector

Support Those Engines

Spark
Flink
SeaTunnel Zeta

Key features

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

Description

Read data from aliyun oss file system.

Supported DataSource Info

In order to use the OssFile connector, the following dependencies are required. They can be downloaded via install-plugin.sh or from the Maven central repository.

Datasource Supported Versions Dependency
OssFile universal Download

:::tip

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+.

:::

Data Type Mapping

The File does not have a specific type list, and we can indicate which SeaTunenl data type the corresponding data needs to be converted to by specifying the Schema in the config.

SeaTunnel Data type
STRING
SHORT
INT
BIGINT
BOOLEAN
DOUBLE
DECIMAL
FLOAT
DATE
TIME
TIMESTAMP
BYTES
ARRAY
MAP

Source Options

Name Type Required default value Description
path String Yes - The source file path.
file_format_type String Yes - Please check #file_format_type below
bucket String Yes - The bucket address of oss file system, for example: oss://tyrantlucifer-image-bed
endpoint String Yes - The endpoint of oss file system.
read_columns List No - The read column list of the data source, user can use it to implement field projection.
The file type supported column projection as the following shown:
- text
- json
- csv
- orc
- parquet
- excel
Tips: If the user wants to use this feature when reading text json csv files, the schema option must be configured
access_key String No - The access key of oss file system.
access_secret String No - The access secret of oss file system.
file_filter_pattern String No - Filter pattern, which used for filtering files.
delimiter/field_delimiter String No \001 delimiter parameter will deprecate after version 2.3.5, please use field_delimiter instead.
Field delimiter, used to tell connector how to slice and dice fields when reading text files.
Default \001, the same as hive's default delimiter
parse_partition_from_path Boolean No true 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 No yyyy-MM-dd 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 No yyyy-MM-dd HH:mm:ss 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 No HH:mm:ss 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 No 0 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
sheet_name String No - Reader the sheet of the workbook,Only used when file_format is excel.
schema Config No - Please check #schema below
file_filter_pattern string no - Filter pattern, which used for filtering files.
compress_codec string no none 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.
common-options No - Source plugin common parameters, please refer to Source Common Options for details.

file_format_type [string]

File type, supported as the following file types:

text csv parquet orc json excel

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:

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

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

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

you should assign schema as the following:

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:


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:

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

connector will generate data as the following:

name age gender
tyrantlucifer 26 male

schema [config]

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

fields [Config]

The schema of upstream data.

How to Create a Oss Data Synchronization Jobs

The following example demonstrates how to create a data synchronization job that reads data from Oss and prints it on the local client:

# Set the basic configuration of the task to be performed
env {
  execution.parallelism = 1
  job.mode = "BATCH"
}

# Create a source to connect to Oss
source {
  OssFile {
    path = "/seatunnel/orc"
    bucket = "oss://tyrantlucifer-image-bed"
    access_key = "xxxxxxxxxxxxxxxxx"
    access_secret = "xxxxxxxxxxxxxxxxxxxxxx"
    endpoint = "oss-cn-beijing.aliyuncs.com"
    file_format_type = "orc"
  }
}

# Console printing of the read Oss data
sink {
  Console {
  }
}
# Set the basic configuration of the task to be performed
env {
  execution.parallelism = 1
  job.mode = "BATCH"
}

# Create a source to connect to Oss
source {
  OssFile {
    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
      }
    }
  }
}

# Console printing of the read Oss data
sink {
  Console {
  }
}

Tips

1.SeaTunnel Deployment Document.