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# System prepended metadata

title: Untitled

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#### Feature repository data storage:

1.

|etype| entity-id |   record-key | feature             | value | timestamp  |
|----| --------- | ----- | ------------------- | ----- | ---------- |
|warehouse| 10002     |  fdgshefd| count_sum           | 50    | 14/05/2023 |
|product| 40001     | fdgshefd | count_sum       | 50  | 14/05/2023 |

2. weekly_table

|entitytype |entity1* | entity2 |   record-key | feature             | value | timestamp  |
|---|----| --------- | ----- | ------------------- | ----- | ---------- |
|warehouse|10002| 40001     |  fdgshefd| count_sum           | 50    | 14/05/2023 |
|sku|10002| 40002     | fdgshefd | price_average       | 500  | 14/05/2023 |
|40002| 10002     | fdgshefd | price_average       | 500  | 14/05/2023 |

mapping in-memory
{"warehouse" : entity1,
 "sku" : entity2,
 "store" : entity1} 

customerXcellXgrid
feature_id- averag_rsrp
feature_value - 120
timestamp - hourly_timestamp

hourly

|partition-key |record_key | feature_key| feature_value | timestamp|
|----------|--------|--------|---------|---------------| 
|customer |
|cell | 
|grid |
|customerxcell
|customerxgrid
|cellxgrid
|customerxcellxgrid

get me average_rsrp for this cell 
get me average_rsrp for the grid


store x product
sale of each product evening sale in the store

s1xp1 - 50
s1xp2 - 100
s1Xp3 - 150

feature_by_month 
|partition-key |record_key | feature_key| feature_value | timestamp|
|----------|--------|--------|---------|---------------| 
|store | sha | sale | 50| 30/05/2023 |
|product | sha | sale | 50 | 30/05/2023 |
|store x product| sha| sale | 50 | 30/05/2023| 


fetch all records where cell=2

#### Different fetch queries available on feature repository:

1.	Fetch Latest(1) features( /feature/discount_perc, /feature/sales_price_average) for warehouse (10002).
2.	Fetch Latest features( discount_perc, sales_price_average) for warehouse (10002) and sku (40001).
3.	Fetch Latest N features( discount_perc, sales_price_average) for warehouse (10002).
4.	Fetch features( discount_perc, sales_price_average) for warehouse (10002) for date range (01/04/2023 - 30/04/2023)
5.	Fetch features( discount_perc) for warehouse (10002) where value > 40.
6.	Fetch with lag criteria.(weekly)(lag1-5) 0,1,2,3,4,5

7.	Any custom query in form of CQL (Cassandra Query Language).