Comments (4)
So why wasn't a method considered to allow setting expiration time for each cache key?
While it is technically feasible to implement such a feature, it could introduce complexity to the cache system. The bigcache library is based on a circular buffer (i.e., queue.BytesQueue) and does not have a defragmentation process. If we allow every key to be removed at different times, it could result in a significant amount of unused memory. On the other hand, we do allow the removal of a single key, but it is more like marking it as obsolete rather than actually deleting it.
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So why wasn't a method considered to allow setting expiration time for each cache key?
While it is technically feasible to implement such a feature, it could introduce complexity to the cache system. The bigcache library is based on a circular buffer (i.e., queue.BytesQueue) and does not have a defragmentation process. If we allow every key to be removed at different times, it could result in a significant amount of unused memory. On the other hand, we do allow the removal of a single key, but it is more like marking it as obsolete rather than actually deleting it.
Regarding the problem of memory waste, can we consider designing a compression algorithm? That is, when operating the bytesQueue, record the number of expired keys, and when the number of expired keys reaches a certain threshold, start a compression algorithm to asynchronously create a new, expanded bytesQueue. Then move the data from the old bytesQueue to the new one. During this process, read and write requests are synchronized between the two bytesQueues.
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In my opinion, we need to carefully consider the trade-off between complexity and usability when evaluating changes to our system. While adding new features can improve functionality, it can also introduce greater maintenance complexity. It's worth exploring other solutions available in the market for in-memory caching, as they may offer better performance or better align with specific requirements. For instance, the Bigcache solution is particularly well-suited for static maps that are not frequently modified after the application startup.
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So why wasn't a method considered to allow setting expiration time for each cache key?
While it is technically feasible to implement such a feature, it could introduce complexity to the cache system. The bigcache library is based on a circular buffer (i.e., queue.BytesQueue) and does not have a defragmentation process. If we allow every key to be removed at different times, it could result in a significant amount of unused memory. On the other hand, we do allow the removal of a single key, but it is more like marking it as obsolete rather than actually deleting it.
Regarding the problem of memory waste, can we consider designing a compression algorithm? That is, when operating the bytesQueue, record the number of expired keys, and when the number of expired keys reaches a certain threshold, start a compression algorithm to asynchronously create a new, expanded bytesQueue. Then move the data from the old bytesQueue to the new one. During this process, read and write requests are synchronized between the two bytesQueues.
However, this approach does seem to introduce some complexity to the entire system :(
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Related Issues (20)
- LifeWindow Not valid HOT 1
- Error logs about bytes allocation? HOT 1
- why did my key expired HOT 7
- resetKeyFromEntry HOT 1
- Excessive memory allocation HOT 1
- The index(int) of entries(queue.BytesQueue) overflows the hashmap's uint32 value HOT 4
- Memory so high, and when clean not reduce size HOT 5
- Benchmark is not fair, So the speed can't be trusted HOT 4
- Whether it is a bug ? HOT 1
- How does bigcache handle hash collisions HOT 1
- BigCache is logging allocations it should not HOT 4
- Support for the map? HOT 1
- compile a 32-bit LSB executable on arm and Execution error-> panic: unaligned 64-bit atomic operation HOT 4
- Is there a memory leak in bigcache? HOT 4
- Add multi-level cache design HOT 2
- Why do we need to configure both lifeWindow and cleanWindow? HOT 2
- GRPC server support HOT 4
- feature: Support uint64 keys without converting to string then back again HOT 6
- Proposal to Integrate SIEVE Eviction Algorithm
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