One option, which may not be feasible depending on your use case, is to
simply replay *all* events from scratch, rebuilding current state on the
fly when needed. If you can configure enough retention on your Kafka
cluster, it's an option.
Another is to keep periodic snapshots so that you have a more recent
starting point. You start at the last snapshot, apply all events since the
snapshot and end up with current state.
This is how both Akka Persistence and Kafka's new log compaction are meant
to work.
On Wednesday, March 15, 2017 at 4:30:23 AM UTC-6, kant kodali wrote:
>
> if my current state A = {key1: val1, key2: val2, .......key50: val50} and
> the next state B = {key1: val1, key2: val2, .......key5: val5} then
> Persistence actor would store A-B = {key6: val6, key7: val7,
> .......key50: val50} ?
>
> On Tuesday, March 14, 2017 at 10:20:42 AM UTC-7, kant kodali wrote:
>>
>> Thanks for all the replies. When I first came across event sourcing it
>> clearly says one stores events and changes in the state but not the state
>> itself. so In my case, I get messages from Kafka and each message is say
>> JSON with 50 fields like this {key1: val1, key2: val2, .......key50: val50}
>> and every message will have all or subset of these keys. Now my goal is to
>> store these stream of messages as events in Cassandra and for me to store
>> the changes in state I always need to know the current state to see the
>> change in state caused by the next request but I wonder how this is done
>> typically with PersistenceActor? Any simplistic example would help a great
>> deal!
>>
>>
>>
>> On Sunday, March 12, 2017 at 7:09:06 AM UTC-7, kant kodali wrote:
>>>
>>> Hi,
>>>
>>> What does it mean when someone says we store the event itself rather
>>> than storing the state/data? such that the events can be replayed to store
>>> the state/data. Does it mean storing the functions/transformation itself
>>> (simply put the JVM byte code for the function itself) ?
>>>
>>> Thanks!
>>> kant
>>>
>>
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