Data Lake Tables

These job options are used when writing to data lake tables or Upsolver-managed tables.

Tables created within Upsolver using your metastore connection are considered Upsolver-managed tables. Note that these tables can still be queried externally. For example, you can create an AWS Glue Data Catalog table within Upsolver, and this table can be queried within Upsolver itself or when you go to your Athena console.

Job options

[ ADD_MISSING_COLUMNS = { TRUE | FALSE } ]
[ AGGREGATION_PARALLELISM = <integer> ]
[ COMMENT = '<comment>' ]
[ COMPUTE_CLUSTER = <cluster_identifier> ]
[ END_AT = { NOW | timestamp } ]
[ FLATTEN_PATHS = (<array_path> [, ...]) ]
[ RUN_INTERVAL = <integer> { MINUTE[S] | HOUR[S] | DAY[S] } ]
[ RUN_PARALLELISM = <integer> ]
[ START_FROM = { NOW | BEGINNING | timestamp } ]

Jump to

Data lake table options:

• ADD_MISSING_COLUMNS

• AGGREGATION_PARALLELISM

• FLATTEN_PATHS

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."

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.

FLATTEN_PATHS

Type: Array<String>

Default: ()

(Optional) Allows specifying arrays that will be used to flatten the output rows. Please see the Flattening Arrays guide for details and examples.