Amazon Redshift

The following job options are used when writing to Amazon Redshift tables. Upsolver will create missing tables in Redshift.

Job options

[ AGGREGATION_PARALLELISM = <integer> ]
[ COMMENT = '<comment>' ]
[ COMPUTE_CLUSTER = <cluster_identifier> ]
[ END_AT = { NOW | timestamp } ]
[ FAIL_ON_WRITE_ERROR = { TRUE | FALSE } ]
[ RUN_INTERVAL = <integer> { MINUTE[S] | HOUR[S] | DAY[S] } ]
[ RUN_PARALLELISM = <integer> ]
[ SKIP_FAILED_FILES = { TRUE | FALSE } ]
[ START_FROM = { NOW | BEGINNING | timestamp } ]
[ TRUNCATE_COLUMNS = { TRUE | FALSE } ]

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Amazon Redshift job options:

• AGGREGATION_PARALLELISM

• FAIL_ON_WRITE_ERROR

• SKIP_FAILED_FILES

• TRUNCATE_COLUMNS

• ADD_MISSING_COLUMNS

• ON_COLUMN_TYPE_MISMATCH

General job options:

• COMMENT

• COMPUTE_CLUSTER

• END_AT

• RUN_INTERVAL

• RUN_PARALLELISM

• START_FROM

AGGREGATION_PARALLELISM — editable

Type: integer

Default: 1

(Optional) Only supported when the query contains aggregations. Formally known as "output sharding."

FAIL_ON_WRITE_ERROR

Type: Boolean

Default: false

(Optional) When true, the job will fail when an on-write error occurs.

SKIP_FAILED_FILES

Type: Boolean

Default: true

(Optional) When true, the job will skip any files in which the job is unsuccessful and continue with the rest of the files.

TRUNCATE_COLUMNS

Type: Boolean

Default: false

(Optional) When true, data is truncated when inserting into an existing table containing VARCHAR or CHARACTER VARYING columns smaller than the incoming data size, to prevent the insert from failing.

When false, the insert fails if the incoming data is larger than the target column and SKIP_FAILED_FILES is also false.

ADD_MISSING_COLUMNS

Type: Boolean

Default: false

(Optional) When true, columns that don't exist in the target table are added automatically when encountered.

When false, you cannot do SELECT * within the SELECT statement of your transformation job.

ON_COLUMN_TYPE_MISMATCH

Type: String

Default: None

Possible values: Add Column ,None

This option is applicable only if ADD_MISSING_COLUMNS=true. It determines how to handle cases where the datatype of the source data does not match the datatype of the corresponding column in the target table.

If set to Add Column, Upsolver will attempt to cast the incoming data to the original column's datatype. If the cast fails, a new column with the format <originalColumnName>_newDataType will be created, and the mismatched data will be written to this new column. For example, if the CLIENT_ID column is a number in the target table and a VARCHAR arrives in that column, a new column called CLIENT_ID_VARCHAR will be added for the string data. The original column will continue to be populated if the data can be cast successfully.

If set to None, no new columns will be added, and only valid casts will be written to the original column.