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OssFile

Oss file source connector

Support Those Engines

Spark
Flink
SeaTunnel Zeta

Usage Dependency

  1. You must ensure your spark/flink cluster already integrated hadoop. The tested hadoop version is 2.x.
  2. You must ensure hadoop-aliyun-xx.jar, aliyun-sdk-oss-xx.jar and jdom-xx.jar in ${SEATUNNEL_HOME}/plugins/ dir and the version of hadoop-aliyun jar need equals your hadoop version which used in spark/flink and aliyun-sdk-oss-xx.jar and jdom-xx.jar version needs to be the version corresponding to the hadoop-aliyun version. Eg: hadoop-aliyun-3.1.4.jar dependency aliyun-sdk-oss-3.4.1.jar and jdom-1.1.jar.

For SeaTunnel Zeta Engine

  1. You must ensure seatunnel-hadoop3-3.1.4-uber.jar, aliyun-sdk-oss-3.4.1.jar, hadoop-aliyun-3.1.4.jar and jdom-1.1.jar in ${SEATUNNEL_HOME}/lib/ dir.

Key features

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

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 Oss 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 oss file system, for example: oss://seatunnel-test.
endpointstringyes-fs oss endpoint
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-
access_secretstringno-
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 oss://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_codecstringnononeWhich compress codec the files used.
file_filter_patternstringno*.txt means you only need read the files end with .txt
common-optionsconfigno-Source plugin common parameters, please refer to Source Common Options for details.

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.

file_filter_pattern [string]

Filter pattern, which used for filtering files.

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 {
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 {
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 {
}
}

Multiple Table

No need to config schema file type, eg: orc.

env {
parallelism = 1
spark.app.name = "SeaTunnel"
spark.executor.instances = 2
spark.executor.cores = 1
spark.executor.memory = "1g"
spark.master = local
job.mode = "BATCH"
}

source {
OssFile {
tables_configs = [
{
schema = {
table = "fake01"
}
bucket = "oss://whale-ops"
access_key = "xxxxxxxxxxxxxxxxxxx"
access_secret = "xxxxxxxxxxxxxxxxxxx"
endpoint = "https://oss-accelerate.aliyuncs.com"
path = "/test/seatunnel/read/orc"
file_format_type = "orc"
},
{
schema = {
table = "fake02"
}
bucket = "oss://whale-ops"
access_key = "xxxxxxxxxxxxxxxxxxx"
access_secret = "xxxxxxxxxxxxxxxxxxx"
endpoint = "https://oss-accelerate.aliyuncs.com"
path = "/test/seatunnel/read/orc"
file_format_type = "orc"
}
]
result_table_name = "fake"
}
}

sink {
Assert {
rules {
table-names = ["fake01", "fake02"]
}
}
}

Need config schema file type, eg: json


env {
execution.parallelism = 1
spark.app.name = "SeaTunnel"
spark.executor.instances = 2
spark.executor.cores = 1
spark.executor.memory = "1g"
spark.master = local
job.mode = "BATCH"
}

source {
OssFile {
tables_configs = [
{
bucket = "oss://whale-ops"
access_key = "xxxxxxxxxxxxxxxxxxx"
access_secret = "xxxxxxxxxxxxxxxxxxx"
endpoint = "https://oss-accelerate.aliyuncs.com"
path = "/test/seatunnel/read/json"
file_format_type = "json"
schema = {
table = "fake01"
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
}
}
}
},
{
bucket = "oss://whale-ops"
access_key = "xxxxxxxxxxxxxxxxxxx"
access_secret = "xxxxxxxxxxxxxxxxxxx"
endpoint = "https://oss-accelerate.aliyuncs.com"
path = "/test/seatunnel/read/json"
file_format_type = "json"
schema = {
table = "fake02"
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
}
}
}
}
]
result_table_name = "fake"
}
}

sink {
Assert {
rules {
table-names = ["fake01", "fake02"]
}
}
}

Changelog

2.2.0-beta 2022-09-26

  • Add OSS File Source Connector

2.3.0-beta 2022-10-20

  • [BugFix] Fix the bug of incorrect path in windows environment (2980)
  • [Improve] Support extract partition from SeaTunnelRow fields (3085)
  • [Improve] Support parse field from file path (2985)

Tips

1.SeaTunnel Deployment Document.