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Version: 2.3.8

Redshift

JDBC Redshift Source Connector

Description​

Read external data source data through JDBC.

Support those engines​

Spark
Flink
Seatunnel Zeta

  1. You need to ensure that the jdbc driver jar package has been placed in directory ${SEATUNNEL_HOME}/plugins/.

For SeaTunnel Zeta Engine​

  1. You need to ensure that the jdbc driver jar package has been placed in directory ${SEATUNNEL_HOME}/lib/.

Key features​

supports query SQL and can achieve projection effect.

Supported DataSource list​

datasourcesupported versionsdriverurlmaven
redshiftDifferent dependency version has different driver class.com.amazon.redshift.jdbc.Driverjdbc:redshift://localhost:5439/databaseDownload

Database dependency​

Please download the support list corresponding to 'Maven' and copy it to the '$SEATNUNNEL_HOME/plugins/jdbc/lib/' working directory
For example Redshift datasource: cp RedshiftJDBC42-xxx.jar $SEATNUNNEL_HOME/plugins/jdbc/lib/

Data Type Mapping​

Redshift Data typeSeatunnel Data type
SMALLINT
INT2
SHORT
INTEGER
INT
INT4
INT
BIGINT
INT8
OID
LONG
DECIMAL
NUMERIC
DECIMAL((Get the designated column's specified column size)+1,
(Gets the designated column's number of digits to right of the decimal point.)))
REAL
FLOAT4
FLOAT
DOUBLE_PRECISION
FLOAT8
FLOAT
DOUBLE
BOOLEAN
BOOL
BOOLEAN
CHAR
CHARACTER
NCHAR
BPCHAR
VARCHAR
CHARACTER_VARYING
NVARCHAR
TEXT
SUPER
STRING
VARBYTE
BINARY_VARYING
BYTES
TIME
TIME_WITH_TIME_ZONE
TIMETZ
LOCALTIME
TIMESTAMP
TIMESTAMP_WITH_OUT_TIME_ZONE
TIMESTAMPTZ
LOCALDATETIME

Example​

Simple:​

This example queries type_bin 'table' 16 data in your test "database" in single parallel and queries all of its fields. You can also specify which fields to query for final output to the console.

env {
parallelism = 2
job.mode = "BATCH"
}
source{
Jdbc {
url = "jdbc:redshift://localhost:5439/dev"
driver = "com.amazon.redshift.jdbc.Driver"
user = "root"
password = "123456"

table_path = "public.table2"
# Use query filetr rows & columns
query = "select id, name from public.table2 where id > 100"

#split.size = 8096
#split.even-distribution.factor.upper-bound = 100
#split.even-distribution.factor.lower-bound = 0.05
#split.sample-sharding.threshold = 1000
#split.inverse-sampling.rate = 1000
}
}

sink {
Console {}
}

Multiple table read:​

Configuring table_list will turn on auto split, you can configure `split.` to adjust the split strategy*

env {
job.mode = "BATCH"
parallelism = 2
}
source {
Jdbc {
url = "jdbc:redshift://localhost:5439/dev"
driver = "com.amazon.redshift.jdbc.Driver"
user = "root"
password = "123456"

table_list = [
{
table_path = "public.table1"
},
{
table_path = "public.table2"
# Use query filetr rows & columns
query = "select id, name from public.table2 where id > 100"
}
]
#split.size = 8096
#split.even-distribution.factor.upper-bound = 100
#split.even-distribution.factor.lower-bound = 0.05
#split.sample-sharding.threshold = 1000
#split.inverse-sampling.rate = 1000
}
}

sink {
Console {}
}