view: revenue_pdt {
  label: "revenue_pdt view label"
#   # Or, you could make this view a derived table, like this:
  derived_table: {
    sql: SELECT
        'foo' as foo,
        segment,
        segment segment2,
        format_timestamp('%F %T', current_timestamp(), 'America/Los_Angeles') pdt_time_bis
      FROM gcc_internal.revenue
      ;;
  sql_trigger_value: SELECT concat('pdt_trigger hour is', EXTRACT(HOUR FROM CURRENT_TIMESTAMP()));;
    publish_as_db_view: yes
  }
#
#   # Define your dimensions and measures here, like this:
  dimension: foo {
    description: "foo dimension description"
    type: string
    sql: ${TABLE}.foo ;;
  }

  dimension: segment {
    type: string
    sql: ${TABLE}.segment ;;
  }

  dimension: segment2 {
    type: string
    sql: ${TABLE}.segment2 ;;
  }

  dimension: pdt_time {
    type: string
    sql: ${TABLE}.pdt_time_bis ;;

  }
#
#   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} ;;
#   }
}