The document discusses using an "active cache" to reduce response times for web applications. An active cache stores the business logic methods and their arguments, allowing the cache to automatically update itself by re-running methods in the background. This is compared to a passive cache which only updates when explicitly called. Key challenges with active caching include defining cache keys, designing the updater and scheduler, and handling class definition changes that break deserialization.

1. Talk about ``Active’’ Cache
by qrtt1

2. Cache For Web Application
http request
Web Handler Layer

3. Cache For Web Application
http request
Web Handler Layer
Business Logic Layer
call some business logic

4. Cache For Web Application
http request
Web Handler Layer
Business Logic Layer
call some business logic
very very busy business

5. Cache For Web Application
http request
Web Handler Layer
Business Logic Layer
call some business logic
http response
compose the final output
very very busy business

6. Cache For Web Application
http request
Web Handler Layer
Business Logic Layer
call some business logic
http response
compose the final output
very very busy business
HOW LONG DOES
THE CLIENT WAIT ?

7. Cache For Web Application
http request
Web Handler Layer
Business Logic Layer
http response
Cache Layer
using cacheto reduce the response time

8. Cache For Web Application
http request
Web Handler Layer
Business Logic Layer
http response
Cache Layer
HIT CACHE

9. Cache For Web Application
http request
Web Handler Layer
Business Logic Layer
http response
Cache Layer
MISS CACHE
RUN IN THE BUSY WAY

10. Cache Layer
Cache is passive and only update by caller
if foo in cache:
return cache[foo]
data = business(xyz)
cache[foo] = data
return data

11. Cache Layer
Data in the cache is expired eventually.
if foo in cache:
return cache[foo]
else:
# users should wait the data available
do_something_with_expired..

12. ACTIVECache Layer
Data in the cache is updated eventually.
if foo in active_cache:
return active_cache[foo]
data = active_cache({business, xyz})
return data

13. The difference
Passive Cache
put the data into cache
update the data by caller
Active Cache
put the update-method into cache
update the data by itself

14. ACTIVE Cache Layer
POC in Java Web
Using AspectJ add the advice to Business Logic
foo(a, b, c, ...)
Business Logic Layer
waving the aop-advice learning how to invoke the business logic
by keep the information about {instance, method signature and arguments}
HIT CACHE forever
refresh cache data automatically

15. Active Cache Problem
How does the key to define ?
key(args of method) or ...
How to design the cache updater and
scheduler ?
How to migrate the broken deserialiazation
from the class definition change ?

16. Cache Key Building
methodA(userInfo, str, int, otherPojo)
what does a user
see ? it depends on
userInfo
how does data filter or
sort ?
key = signauure_ + args[0] + args[1] + args[...]
=> signature_UserInfo@a3a4a3a5_3_{a:3,b:3_}
wtf, the key is so bad and never hit cache

17. Cache Key Building
=> signature_UserInfo@a3a4a3a5_3_{a:3,b:3_}
sometimes toString() is a not good enough
treat userInfo as userInfo.getGroupId()
or convert to the better key format
=> signature_1234567890_3_{a:3,b:3_}

18. Cache Key Building
=> signature_OtherClass@a3a4a3a5_3_{a:3,b:3_}
sometimes OtherClass no available getters
the useful information is assigned by its constructor
using AOP & mix-in ActiveCacheToString interface
to provide alternative toString()

19. Should I update it
request from Http Client should not update
request from Scheduler should update
How to check the issue coming from ?
check the stacktrace having
javax.servlet.http.HttpServlet.service is
request from Http Client.

20. Handle the class definition change
class definition changes will break
everything when deserialization
should change the cache storage pool, too
DevOps should support to check the change
happening