# View: dt_tiktok_song_tickets_by_week

**View Name:** dt_tiktok_song_tickets_by_week
**Table Source:** `INTELLIGENCE.DBT_PROD.DT_TIKTOK_SONG_TICKETS_BY_WEEK`
**File Path:** `dt_tiktok_song_tickets_by_week.view.lkml`

## Overview

- **File Size:** 1404 bytes
- **Lines of Code:** 41
- **Dimensions:** 1
- **Measures:** 1
- **Dimension Groups:** 0
- **Filters:** 0

## Comments & Notes

- derived_table: {
- sql: WITH dt_tiktok_top_song_by_day AS (WITH TIKTOK_TOP_SONG_BY_DAY AS (SELECT
- TO_CHAR(TO_DATE(D.DISPLAYDATE), 'YYYY-MM-DD') AS DOWNLOAD_DATE,

## Dimensions

| Name | Type |
|------|------|
| `filedate` | date |

## Measures

| Name | Type |
|------|------|
| `tickets` | sum |

## Derived Table

```sql
#     sql: WITH dt_tiktok_top_song_by_day AS (WITH TIKTOK_TOP_SONG_BY_DAY AS (SELECT
#   TO_CHAR(TO_DATE(D.DISPLAYDATE), 'YYYY-MM-DD') AS DOWNLOAD_DATE,
#   TIKTOK_TOP_SONG.ROW_ID AS ROW_ID
# FROM FACTS.PROD.STAGING_RAW_TIKTOK_TRENDS_TOPSONG AS TIKTOK_TOP_SONG
# LEFT JOIN FACTS.PROD.DIM_DAY D
# ON D.DISPLAYDATE = TIKTOK_TOP_SONG.DOWNLOAD_DATE
# ORDER BY 1)
# SELECT
# (TO_CHAR(TO_DATE(TIKTOK_TOP_SONG_BY_DAY.DOWNLOAD_DATE), 'YYYY-MM-DD')) AS filedate,
#     COUNT(TIKTOK_TOP_SONG_BY_DAY.ROW_ID) AS total_rows
# FROM TIKTOK_TOP_SONG_BY_DAY
# GROUP BY
#     filedate
# ORDER BY
#     1 DESC
# FETCH NEXT 500 ROWS ONLY)
# SELECT
#     (TO_CHAR(TO_DATE(dt_tiktok_top_song_by_day.filedate), 'YYYY-MM-DD')) AS filedate,
#     COALESCE(SUM(dt_tiktok_top_song_by_day.total_rows ), 0) AS totaltickets
# FROM dt_tiktok_top_song_by_day
# WHERE ((( dt_tiktok_top_song_by_day.filedate ) >= ((DATEADD('day', -29, CURRENT_DATE()))) AND ( dt_tiktok_top_song_by_day.filedate ) < ((DATEADD('day', 30, DATEADD('day', -29, CURRENT_DATE()))))))
# GROUP BY
#     (TO_DATE(dt_tiktok_top_song_by_day.filedate))
# ORDER BY
# 1 DESC ;;
#
```