S3File

S3 file source connector

Description

Read data from aws s3 file system.

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.

To use this connector you need put hadoop-aws-3.1.4.jar and aws-java-sdk-bundle-1.11.271.jar in ${SEATUNNEL_HOME}/lib dir.

Key features

• batch
• stream
• exactly-once

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

• column projection
• parallelism
• support user-defined split
• file format type
  • text
  • csv
  • parquet
  • orc
  • json
  • excel

Options

name	type	required	default value
path	string	yes	-
file_format_type	string	yes	-
bucket	string	yes	-
fs.s3a.endpoint	string	yes	-
fs.s3a.aws.credentials.provider	string	yes	com.amazonaws.auth.InstanceProfileCredentialsProvider
read_columns	list	no	-
access_key	string	no	-
access_secret	string	no	-
hadoop_s3_properties	map	no	-
delimiter	string	no	\001
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	-
common-options		no	-
sheet_name	string	no	-

path [string]

The source file path.

fs.s3a.endpoint [string]

fs s3a endpoint

fs.s3a.aws.credentials.provider [string]

The way to authenticate s3a. We only support org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider and com.amazonaws.auth.InstanceProfileCredentialsProvider now.

More information about the credential provider you can see Hadoop AWS Document

delimiter [string]

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]

Control whether parse the partition keys and values from file path

For example if you read a file from path s3n://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

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 delimiter too except CSV file type

you should assign schema and delimiter as the following:

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

connector will generate data as the following:

name	age	gender
tyrantlucifer	26	male

bucket [string]

The bucket address of s3 file system, for example: s3n://seatunnel-test, if you use s3a protocol, this parameter should be s3a://seatunnel-test.

access_key [string]

The access key of s3 file system. If this parameter is not set, please confirm that the credential provider chain can be authenticated correctly, you could check this hadoop-aws

access_secret [string]

The access secret of s3 file system. If this parameter is not set, please confirm that the credential provider chain can be authenticated correctly, you could check this hadoop-aws

hadoop_s3_properties [map]

If you need to add a other option, you could add it here and refer to this hadoop-aws

hadoop_s3_properties {
      "xxx" = "xxx"
   }

schema [config]

fields [Config]

The schema of upstream data.

read_columns [list]

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

common options

Source plugin common parameters, please refer to Source Common Options for details.

sheet_name [string]

Reader the sheet of the workbook,Only used when file_format is excel.

Example

  S3File {
    path = "/seatunnel/text"
    fs.s3a.endpoint="s3.cn-north-1.amazonaws.com.cn"
    fs.s3a.aws.credentials.provider = "org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider"
    access_key = "xxxxxxxxxxxxxxxxx"
    secret_key = "xxxxxxxxxxxxxxxxx"
    bucket = "s3a://seatunnel-test"
    file_format_type = "orc"
  }

  S3File {
    path = "/seatunnel/json"
    bucket = "s3a://seatunnel-test"
    fs.s3a.endpoint="s3.cn-north-1.amazonaws.com.cn"
    fs.s3a.aws.credentials.provider="com.amazonaws.auth.InstanceProfileCredentialsProvider"
    file_format_type = "json"
    schema {
      fields {
        id = int 
        name = string
      }
    }
  }

Changelog

2.3.0-beta 2022-10-20

• Add S3File Source Connector

Next version

• [Feature] Support S3A protocol (3632)
  • Allow user to add additional hadoop-s3 parameters
  • Allow the use of the s3a protocol
  • Decouple hadoop-aws dependencies
• [Feature]Set S3 AK to optional (3688)