How to build datasets based on shared semantics
lineitem
two fields: l_linestatus
(an attribute) and sum_qty
(a metric).
The sums up item quantity per their status delivery status.
The schema for it is -
hdapi
python moduleGROUP BY
in the metric definition).SET
SQL command.AUTO
/ FULL
/ INCREMENTAL
). Default is FULL
, if not provided.ON_CREATE
/ ON_SCHEDULE
). Default is ON_CREATE
, if not provided.example
that include:
lineitem.l_linestatus
and orders.o_orderdate
lineitem.sum_qty
and lineitem.revenue
lineitem.l_commitdate < date('1997-01-01')
lineitem.l_linestatus
unique valuesrevenue
aggregated named metric grouped by lineitem.l_linestatus
min_order_date
ad-hoc metric grouped by lineitem.l_linestatus
"orders.o_orderdate" > date('1997-01-01')
given in the querylineitem.l_commitdate < date('1997-01-01')
defined in the dynamic datasetname
: Name of the datasetowner
, description
, labels
, folder
, display_name
, hidden
(all optional): Metadatadomain
(optional): Domain in which context to runattributes
(optional): List of attribute expressions (can be named attributes or ad-hoc expressions)metrics
(optional): List of metric expressions (can be named metrics or ad-hoc expressions)filters
(optional): List of filter expressions (can be named boolean attributes or ad-hoc expressions)parameters
(optional): List of parameters and values (override workspace values, if exist)use_cache
(optional): Whether to use caches (for entities and/or
pre-aggregations). Default - yes.transform_sql
(optional): An additional SQL transformation that will be applied to the dataset.
A typical use case includes adding ORDER BY
or LIMIT
clauses.