blob: ebc730a8c4a220b5ff0a22112134389c33a7e724 [file] [log] [blame]
view: revenue_view2 {
label: "revenue view label"
# # You can specify the table name if it's different from the view name:
sql_table_name: gcc_internal.revenue ;;
#
# # Define your dimensions and measures here, like this:
dimension: usage_date {
description: "usage date"
type: date
sql: ${TABLE}.usage_date ;;
}
dimension: billing_account_id {
description: "this is the billing account id"
type: string
sql: ${TABLE}.billing_account_id ;;
}
#
# dimension: lifetime_orders {
# description: "The total number of orders for each user"
# type: number
# sql: ${TABLE}.lifetime_orders ;;
# }
#
# dimension_group: most_recent_purchase {
# description: "The date when each user last ordered"
# type: time
# timeframes: [date, week, month, year]
# sql: ${TABLE}.most_recent_purchase_at ;;
# }
#
# measure: total_lifetime_orders {
# description: "Use this for counting lifetime orders across many users"
# type: sum
# sql: ${lifetime_orders} ;;
# }
}
# view: revenue_view {
# # Or, you could make this view a derived table, like this:
# derived_table: {
# sql: SELECT
# user_id as user_id
# , COUNT(*) as lifetime_orders
# , MAX(orders.created_at) as most_recent_purchase_at
# FROM orders
# GROUP BY user_id
# ;;
# }
#
# # Define your dimensions and measures here, like this:
# dimension: user_id {
# description: "Unique ID for each user that has ordered"
# type: number
# sql: ${TABLE}.user_id ;;
# }
#
# dimension: lifetime_orders {
# description: "The total number of orders for each user"
# type: number
# sql: ${TABLE}.lifetime_orders ;;
# }
#
# dimension_group: most_recent_purchase {
# description: "The date when each user last ordered"
# type: time
# timeframes: [date, week, month, year]
# sql: ${TABLE}.most_recent_purchase_at ;;
# }
#
# measure: total_lifetime_orders {
# description: "Use this for counting lifetime orders across many users"
# type: sum
# sql: ${lifetime_orders} ;;
# }
# }