TT concept check . . . requesting a example

I am still learning TT and specifically IMDB to increase the performance of the Oracle Database.
The three concepts that I have recently read about are:
*) DYNAMIC ASYNCHRONOUS WRITETHROUGH
*) Sliding Window Caching
*) Data-Aging Policy
I think that the above three concepts can be used to create a table in TT to support our need to cache the last 36 hours of transactions for our databases. Everything beyond the 36 hours can go to the Oracle database.
Does anyone have any code example to help me confirm or correct my understanding?
Also, how can we warm the cache or do the initial load of this type of sliding window?
I am downloading the Developer Day's VM now and what to be able to practice the concepts.

Yes, caching just the last 36 hours data (or indeed the last 'n' anything) is usually quite straightforward. Sliding Window is a generic term that just means you keep the 'last n something' data in the cache and that this window of data is updated in (near) real time.
For READONLY cache groups, sliding windows is usually accomplished using a WHERE clause in the cache group definition that references some timestamp field in each row that has the date/time that the row was inserted. The WHERE clause specifies to only cache rows where this timestamp is within the required time interval before now (SYSDATE).
For AWT cache groups one uses the aging featiure to manage the sliding windows. You would use aging based on a lifetime again based on a timestamp column in the data. The application must ensure that this timestamp is set properly (SYSDATE) on every row inserted and if rows are updated then the application needs to decide if that should also update the timestamp (depending on what this means to the application).
Note that the aging based on lifetime feature evaluates rows for aging out based on the defined unit of lifetime. So if you define the lifetime as 36 hours rows only become a candidate to be aged out when they are older, rounded up to hours, than the lifetime. So you may have data present that is up to 36 hours 59 minutes and 59 seconds old. Also, note that aging is by design a low priority background activity designed not to impact the application workload so under heavy load it may take aging a little while to remove 'expired' data. So, you need to understand that the cache will typically contain a little more data than that defined by the 'window'.
Chris

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