SftpFile
Sftp file source connector
Support Those Engines
Spark
Flink
SeaTunnel Zeta
Key Features
- batch
- stream
- exactly-once
- column projection
- parallelism
- support user-defined split
- file format type
- text
- csv
- json
- excel
- xml
- binary
Description
Read data from sftp file server.
Supported DataSource Info
In order to use the SftpFile connector, the following dependencies are required. They can be downloaded via install-plugin.sh or from the Maven central repository.
Datasource | Supported Versions | Dependency |
---|---|---|
SftpFile | universal | Download |
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 Sftp 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 SeaTunnel 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 |
---|---|---|---|---|
host | String | Yes | - | The target sftp host is required |
port | Int | Yes | - | The target sftp port is required |
user | String | Yes | - | The target sftp username is required |
password | String | Yes | - | The target sftp password is required |
path | String | Yes | - | The source file path. |
file_format_type | String | Yes | - | Please check #file_format_type below |
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 |
read_columns | list | no | - | The read column list of the data source, user can use it to implement field projection. |
sheet_name | String | No | - | Reader the sheet of the workbook,Only used when file_format is excel. |
xml_row_tag | string | no | - | Specifies the tag name of the data rows within the XML file, only used when file_format is xml. |
xml_use_attr_format | boolean | no | - | Specifies whether to process data using the tag attribute format, only used when file_format is xml. |
schema | Config | No | - | Please check #schema below |
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: lzo snappy lz4 zlib None - parquet: lzo snappy lz4 gzip brotli zstd None Tips: excel type does Not support any compression format |
encoding | string | no | UTF-8 | |
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
xml
binary
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 |
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.
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.
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)
.
schema [config]
fields [Config]
The schema of upstream data.
How to Create a Sftp Data Synchronization Jobs
The following example demonstrates how to create a data synchronization job that reads data from sftp and prints it on the local client:
# Set the basic configuration of the task to be performed
env {
parallelism = 1
job.mode = "BATCH"
}
# Create a source to connect to sftp
source {
SftpFile {
host = "sftp"
port = 22
user = seatunnel
password = pass
path = "tmp/seatunnel/read/json"
file_format_type = "json"
result_table_name = "sftp"
schema = {
fields {
c_map = "map<string, string>"
c_array = "array<int>"
c_string = string
c_boolean = boolean
c_tinyint = tinyint
c_smallint = smallint
c_int = int
c_bigint = bigint
c_float = float
c_double = double
c_bytes = bytes
c_date = date
c_decimal = "decimal(38, 18)"
c_timestamp = timestamp
c_row = {
C_MAP = "map<string, string>"
C_ARRAY = "array<int>"
C_STRING = string
C_BOOLEAN = boolean
C_TINYINT = tinyint
C_SMALLINT = smallint
C_INT = int
C_BIGINT = bigint
C_FLOAT = float
C_DOUBLE = double
C_BYTES = bytes
C_DATE = date
C_DECIMAL = "decimal(38, 18)"
C_TIMESTAMP = timestamp
}
}
}
}
}
# Console printing of the read sftp data
sink {
Console {
parallelism = 1
}
}