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S3File

S3 File Sink Connector

Support Those Engines

Spark
Flink
SeaTunnel Zeta

Key Features

By default, we use 2PC commit to ensure exactly-once

  • file format type
    • text
    • csv
    • parquet
    • orc
    • json
    • excel

Description

Output data to aws s3 file system.

Supported DataSource Info

DatasourceSupported Versions
S3current

Database 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 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.

Data Type Mapping

If write to csv, text file type, All column will be string.

Orc File Type

SeaTunnel Data typeOrc Data type
STRINGSTRING
BOOLEANBOOLEAN
TINYINTBYTE
SMALLINTSHORT
INTINT
BIGINTLONG
FLOATFLOAT
FLOATFLOAT
DOUBLEDOUBLE
DECIMALDECIMAL
BYTESBINARY
DATEDATE
TIME
TIMESTAMP
TIMESTAMP
ROWSTRUCT
NULLUNSUPPORTED DATA TYPE
ARRAYLIST
MapMap

Parquet File Type

SeaTunnel Data typeParquet Data type
STRINGSTRING
BOOLEANBOOLEAN
TINYINTINT_8
SMALLINTINT_16
INTINT32
BIGINTINT64
FLOATFLOAT
FLOATFLOAT
DOUBLEDOUBLE
DECIMALDECIMAL
BYTESBINARY
DATEDATE
TIME
TIMESTAMP
TIMESTAMP_MILLIS
ROWGroupType
NULLUNSUPPORTED DATA TYPE
ARRAYLIST
MapMap

Sink Options

nametyperequireddefault valueDescription
pathstringyes-
tmp_pathstringno/tmp/seatunnelThe result file will write to a tmp path first and then use mv to submit tmp dir to target dir. Need a S3 dir.
bucketstringyes-
fs.s3a.endpointstringyes-
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.
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
custom_filenamebooleannofalseWhether you need custom the filename
file_name_expressionstringno"${transactionId}"Only used when custom_filename is true
filename_time_formatstringno"yyyy.MM.dd"Only used when custom_filename is true
file_format_typestringno"csv"
field_delimiterstringno'\001'Only used when file_format is text
row_delimiterstringno"\n"Only used when file_format is text
have_partitionbooleannofalseWhether you need processing partitions.
partition_byarrayno-Only used when have_partition is true
partition_dir_expressionstringno"${k0}=${v0}/${k1}=${v1}/.../${kn}=${vn}/"Only used when have_partition is true
is_partition_field_write_in_filebooleannofalseOnly used when have_partition is true
sink_columnsarraynoWhen this parameter is empty, all fields are sink columns
is_enable_transactionbooleannotrue
batch_sizeintno1000000
compress_codecstringnonone
common-optionsobjectno-
max_rows_in_memoryintno-Only used when file_format is excel.
sheet_namestringnoSheet${Random number}Only used when file_format is excel.
hadoop_s3_propertiesmapnoIf you need to add a other option, you could add it here and refer to this link
schema_save_modeEnumnoCREATE_SCHEMA_WHEN_NOT_EXISTBefore turning on the synchronous task, do different treatment of the target path
data_save_modeEnumnoAPPEND_DATABefore opening the synchronous task, the data file in the target path is differently processed

path [string]

Store the path of the data file to support variable replacement. For example: path=/test/${database_name}/${schema_name}/${table_name}

hadoop_s3_properties [map]

If you need to add a other option, you could add it here and refer to this link

hadoop_s3_properties {
"fs.s3a.buffer.dir" = "/data/st_test/s3a"
"fs.s3a.fast.upload.buffer" = "disk"
}

custom_filename [boolean]

Whether custom the filename

file_name_expression [string]

Only used when custom_filename is true

file_name_expression describes the file expression which will be created into the path. We can add the variable ${now} or ${uuid} in the file_name_expression, like test_${uuid}_${now}, ${now} represents the current time, and its format can be defined by specifying the option filename_time_format.

Please note that, If is_enable_transaction is true, we will auto add ${transactionId}_ in the head of the file.

filename_time_format [string]

Only used when custom_filename is true

When the format in the file_name_expression parameter is xxxx-${now} , filename_time_format can specify the time format of the path, and the default value is yyyy.MM.dd . The commonly used time formats are listed as follows:

SymbolDescription
yYear
MMonth
dDay of month
HHour in day (0-23)
mMinute in hour
sSecond in minute

file_format_type [string]

We supported as the following file types:

text json csv orc parquet excel

Please note that, The final file name will end with the file_format_type's suffix, the suffix of the text file is txt.

field_delimiter [string]

The separator between columns in a row of data. Only needed by text file format.

row_delimiter [string]

The separator between rows in a file. Only needed by text file format.

have_partition [boolean]

Whether you need processing partitions.

partition_by [array]

Only used when have_partition is true.

Partition data based on selected fields.

partition_dir_expression [string]

Only used when have_partition is true.

If the partition_by is specified, we will generate the corresponding partition directory based on the partition information, and the final file will be placed in the partition directory.

Default partition_dir_expression is ${k0}=${v0}/${k1}=${v1}/.../${kn}=${vn}/. k0 is the first partition field and v0 is the value of the first partition field.

is_partition_field_write_in_file [boolean]

Only used when have_partition is true.

If is_partition_field_write_in_file is true, the partition field and the value of it will be write into data file.

For example, if you want to write a Hive Data File, Its value should be false.

sink_columns [array]

Which columns need be written to file, default value is all the columns get from Transform or Source. The order of the fields determines the order in which the file is actually written.

is_enable_transaction [boolean]

If is_enable_transaction is true, we will ensure that data will not be lost or duplicated when it is written to the target directory.

Please note that, If is_enable_transaction is true, we will auto add ${transactionId}_ in the head of the file.

Only support true now.

batch_size [int]

The maximum number of rows in a file. For SeaTunnel Engine, the number of lines in the file is determined by batch_size and checkpoint.interval jointly decide. If the value of checkpoint.interval is large enough, sink writer will write rows in a file until the rows in the file larger than batch_size. If checkpoint.interval is small, the sink writer will create a new file when a new checkpoint trigger.

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: lzo snappy lz4 zlib none
  • parquet: lzo snappy lz4 gzip brotli zstd none

Tips: excel type does not support any compression format

common options

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

max_rows_in_memory [int]

When File Format is Excel,The maximum number of data items that can be cached in the memory.

sheet_name [string]

Writer the sheet of the workbook

schema_save_mode[Enum]

Before turning on the synchronous task, do different treatment of the target path.
Option introduction:
RECREATE_SCHEMA :Will be created when the path does not exist. If the path already exists, delete the path and recreate it.
CREATE_SCHEMA_WHEN_NOT_EXIST :Will Created when the path does not exist, use the path when the path is existed.
ERROR_WHEN_SCHEMA_NOT_EXIST :Error will be reported when the path does not exist

data_save_mode[Enum]

Before opening the synchronous task, the data file in the target path is differently processed. Option introduction:
DROP_DATA: use the path but delete data files in the path. APPEND_DATA:use the path, and add new files in the path for write data.
ERROR_WHEN_DATA_EXISTS:When there are some data files in the path, an error will is reported.

Example

Simple:

This example defines a SeaTunnel synchronization task that automatically generates data through FakeSource and sends it to S3File Sink. FakeSource generates a total of 16 rows of data (row.num=16), with each row having two fields, name (string type) and age (int type). The final target s3 dir will also create a file and all of the data in write in it. Before run this job, you need create s3 path: /seatunnel/text. And if you have not yet installed and deployed SeaTunnel, you need to follow the instructions in Install SeaTunnel to install and deploy SeaTunnel. And then follow the instructions in Quick Start With SeaTunnel Engine to run this job.

# Defining the runtime environment
env {
parallelism = 1
job.mode = "BATCH"
}

source {
# This is a example source plugin **only for test and demonstrate the feature source plugin**
FakeSource {
parallelism = 1
result_table_name = "fake"
row.num = 16
schema = {
fields {
c_map = "map<string, array<int>>"
c_array = "array<int>"
name = string
c_boolean = boolean
age = tinyint
c_smallint = smallint
c_int = int
c_bigint = bigint
c_float = float
c_double = double
c_decimal = "decimal(16, 1)"
c_null = "null"
c_bytes = bytes
c_date = date
c_timestamp = timestamp
}
}
}
# If you would like to get more information about how to configure seatunnel and see full list of source plugins,
# please go to https://seatunnel.apache.org/docs/category/source-v2
}

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 {
S3File {
bucket = "s3a://seatunnel-test"
tmp_path = "/tmp/seatunnel"
path="/seatunnel/text"
fs.s3a.endpoint="s3.cn-north-1.amazonaws.com.cn"
fs.s3a.aws.credentials.provider="com.amazonaws.auth.InstanceProfileCredentialsProvider"
file_format_type = "text"
field_delimiter = "\t"
row_delimiter = "\n"
have_partition = true
partition_by = ["age"]
partition_dir_expression = "${k0}=${v0}"
is_partition_field_write_in_file = true
custom_filename = true
file_name_expression = "${transactionId}_${now}"
filename_time_format = "yyyy.MM.dd"
sink_columns = ["name","age"]
is_enable_transaction=true
hadoop_s3_properties {
"fs.s3a.buffer.dir" = "/data/st_test/s3a"
"fs.s3a.fast.upload.buffer" = "disk"
}
}
# If you would like to get more information about how to configure seatunnel and see full list of sink plugins,
# please go to https://seatunnel.apache.org/docs/category/sink-v2
}

For text file format with have_partition and custom_filename and sink_columns and com.amazonaws.auth.InstanceProfileCredentialsProvider


S3File {
bucket = "s3a://seatunnel-test"
tmp_path = "/tmp/seatunnel"
path="/seatunnel/text"
fs.s3a.endpoint="s3.cn-north-1.amazonaws.com.cn"
fs.s3a.aws.credentials.provider="com.amazonaws.auth.InstanceProfileCredentialsProvider"
file_format_type = "text"
field_delimiter = "\t"
row_delimiter = "\n"
have_partition = true
partition_by = ["age"]
partition_dir_expression = "${k0}=${v0}"
is_partition_field_write_in_file = true
custom_filename = true
file_name_expression = "${transactionId}_${now}"
filename_time_format = "yyyy.MM.dd"
sink_columns = ["name","age"]
is_enable_transaction=true
hadoop_s3_properties {
"fs.s3a.buffer.dir" = "/data/st_test/s3a"
"fs.s3a.fast.upload.buffer" = "disk"
}
}

For parquet file format simple config with org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider


S3File {
bucket = "s3a://seatunnel-test"
tmp_path = "/tmp/seatunnel"
path="/seatunnel/parquet"
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"
file_format_type = "parquet"
hadoop_s3_properties {
"fs.s3a.buffer.dir" = "/data/st_test/s3a"
"fs.s3a.fast.upload.buffer" = "disk"
}
}

For orc file format simple config with org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider


S3File {
bucket = "s3a://seatunnel-test"
tmp_path = "/tmp/seatunnel"
path="/seatunnel/orc"
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"
file_format_type = "orc"
schema_save_mode = "CREATE_SCHEMA_WHEN_NOT_EXIST"
data_save_mode="APPEND_DATA"
}

Multi-table writing and saveMode

env {
"job.name"="SeaTunnel_job"
"job.mode"=STREAMING
}
source {
MySQL-CDC {

"connect.max-retries"=3
"connection.pool.size"=6
"startup.mode"=INITIAL
"exactly_once"="true"
"stop.mode"=NEVER
parallelism=1
"result_table_name"=Table11519548644512
"dag-parsing.mode"=MULTIPLEX
catalog {
factory=Mysql
}
database-names=[
"wls_t1"
]
table-names=[
"wls_t1.mysqlcdc_to_s3_t3",
"wls_t1.mysqlcdc_to_s3_t4",
"wls_t1.mysqlcdc_to_s3_t5",
"wls_t1.mysqlcdc_to_s3_t1",
"wls_t1.mysqlcdc_to_s3_t2"
]
password="xxxxxx"
username="xxxxxxxxxxxxx"
base-url="jdbc:mysql://localhost:3306/qa_source"
server-time-zone=UTC
}
}
transform {
}
sink {
S3File {
bucket = "s3a://seatunnel-test"
tmp_path = "/tmp/seatunnel"
path="/test/${table_name}"
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"
file_format_type = "orc"
schema_save_mode = "CREATE_SCHEMA_WHEN_NOT_EXIST"
data_save_mode="APPEND_DATA"
}
}

Changelog

2.3.0-beta 2022-10-20

  • Add S3File Sink Connector

2.3.0 2022-12-30

  • [BugFix] Fixed the following bugs that failed to write data to files (3258)
    • When field from upstream is null it will throw NullPointerException
    • Sink columns mapping failed
    • When restore writer from states getting transaction directly failed
  • [Feature] Support S3A protocol (3632)
    • Allow user to add additional hadoop-s3 parameters
    • Allow the use of the s3a protocol
    • Decouple hadoop-aws dependencies
  • [Improve] Support setting batch size for every file (3625)
  • [Feature]Set S3 AK to optional (3688)

Next version

  • [Improve] Support file compress (3899)