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S3File

S3 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 aws s3 file system.

Supported DataSource Info

DatasourceSupported versions
S3current

Dependency

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 Zeta, It automatically integrated the hadoop jar when you download and install SeaTunnel Zeta. 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.

Data Type Mapping

Data type mapping is related to the type of file being read, We supported as the following file types:

text csv parquet orc json excel

JSON File Type

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:

codedatasuccess
200get successtrue

Text Or CSV File Type

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:

nameagegender
tyrantlucifer26male

Orc File Type

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

Orc Data typeSeaTunnel Data type
BOOLEANBOOLEAN
INTINT
BYTEBYTE
SHORTSHORT
LONGLONG
FLOATFLOAT
DOUBLEDOUBLE
BINARYBINARY
STRING
VARCHAR
CHAR
STRING
DATELOCAL_DATE_TYPE
TIMESTAMPLOCAL_DATE_TIME_TYPE
DECIMALDECIMAL
LIST(STRING)STRING_ARRAY_TYPE
LIST(BOOLEAN)BOOLEAN_ARRAY_TYPE
LIST(TINYINT)BYTE_ARRAY_TYPE
LIST(SMALLINT)SHORT_ARRAY_TYPE
LIST(INT)INT_ARRAY_TYPE
LIST(BIGINT)LONG_ARRAY_TYPE
LIST(FLOAT)FLOAT_ARRAY_TYPE
LIST(DOUBLE)DOUBLE_ARRAY_TYPE
Map<K,V>MapType, This type of K and V will transform to SeaTunnel type
STRUCTSeaTunnelRowType

Parquet File Type

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

Orc Data typeSeaTunnel Data type
INT_8BYTE
INT_16SHORT
DATEDATE
TIMESTAMP_MILLISTIMESTAMP
INT64LONG
INT96TIMESTAMP
BINARYBYTES
FLOATFLOAT
DOUBLEDOUBLE
BOOLEANBOOLEAN
FIXED_LEN_BYTE_ARRAYTIMESTAMP
DECIMAL
DECIMALDECIMAL
LIST(STRING)STRING_ARRAY_TYPE
LIST(BOOLEAN)BOOLEAN_ARRAY_TYPE
LIST(TINYINT)BYTE_ARRAY_TYPE
LIST(SMALLINT)SHORT_ARRAY_TYPE
LIST(INT)INT_ARRAY_TYPE
LIST(BIGINT)LONG_ARRAY_TYPE
LIST(FLOAT)FLOAT_ARRAY_TYPE
LIST(DOUBLE)DOUBLE_ARRAY_TYPE
Map<K,V>MapType, This type of K and V will transform to SeaTunnel type
STRUCTSeaTunnelRowType

Options

nametyperequireddefault valueDescription
pathstringyes-The s3 path that needs to be read can have sub paths, but the sub paths need to meet certain format requirements. Specific requirements can be referred to "parse_partition_from_path" option
file_format_typestringyes-File type, supported as the following file types: text csv parquet orc json excel
bucketstringyes-The bucket address of s3 file system, for example: s3n://seatunnel-test, if you use s3a protocol, this parameter should be s3a://seatunnel-test.
fs.s3a.endpointstringyes-fs s3a endpoint
fs.s3a.aws.credentials.providerstringyescom.amazonaws.auth.InstanceProfileCredentialsProviderThe 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
read_columnslistno-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 csv parquet orc json excel . If the user wants to use this feature when reading text json csv files, the "schema" option must be configured.
access_keystringno-Only used when fs.s3a.aws.credentials.provider = org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider
access_secretstringno-Only used when fs.s3a.aws.credentials.provider = org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider
hadoop_s3_propertiesmapno-If you need to add other option, you could add it here and refer to this link
delimiter/field_delimiterstringno\001Field 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_pathbooleannotrueControl 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="tyrantlucifer", age=16
date_formatstringnoyyyy-MM-ddDate 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_formatstringnoyyyy-MM-dd HH:mm:ssDatetime 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
time_formatstringnoHH:mm:ssTime type format, used to tell connector how to convert string to time, supported as the following formats:HH:mm:ss HH:mm:ss.SSS
skip_header_row_numberlongno0Skip 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
schemaconfigno-The schema of upstream data.
sheet_namestringno-Reader the sheet of the workbook,Only used when file_format is excel.
compress_codecstringnonone
common-optionsno-Source plugin common parameters, please refer to Source Common Options for details.

delimiter/field_delimiter [string]

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

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.

Example

  1. In this example, We read data from s3 path s3a://seatunnel-test/seatunnel/text and the file type is orc in this path. We use org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider to authentication so access_key and secret_key is required. All columns in the file will be read and send to sink.
# Defining the runtime environment
env {
parallelism = 1
job.mode = "BATCH"
}

source {
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"
}
}

transform {
# If you would like to get more information about how to configure seatunnel and see full list of transform plugins,
# please go to https://seatunnel.apache.org/docs/category/transform-v2
}

sink {
Console {}
}
  1. Use InstanceProfileCredentialsProvider to authentication The file type in S3 is json, so need config schema option.

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
}
}
}

  1. Use InstanceProfileCredentialsProvider to authentication The file type in S3 is json and has five fields (id, name, age, sex, type), so need config schema option. In this job, we only need send id and name column to mysql.
# Defining the runtime environment
env {
parallelism = 1
job.mode = "BATCH"
}

source {
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"
read_columns = ["id", "name"]
schema {
fields {
id = int
name = string
age = int
sex = int
type = string
}
}
}
}

transform {
# If you would like to get more information about how to configure seatunnel and see full list of transform plugins,
# please go to https://seatunnel.apache.org/docs/category/transform-v2
}

sink {
Console {}
}

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)