Eviction policies seem to be removed in EhCache3. There is an EvictionAdvisor interface that can be implemented, but what would be the default behaviour?
I am using EhCache3 with SpringBoot.
The exact eviction algorithm depends on the tier configuration. As such Ehcache does not detail it explicitly to enable tweaking it in the future.
Eviction advisors are an opt-in way of saying that some elements should really remain in the cache over others. It effectively means they are not considered by the eviction algorithm unless no eviction candidates can be found.
Note that it is an advanced feature that can have quite severe performance effect if you end up advising against eviction a large portion of the cache.
And as said in another answer, there is no default - that is by default all entries are treated equal and subject to the internal eviction algorithm
The default is NO_ADVICE
From the javadoc :
Returns an {#link EvictionAdvisor} where no mappings are advised
against eviction.
Related
In version 3.8, how can I configure an EhCache cache to use an LRU eviction strategy?
I've looked at the EvictionAdvisor, but it only seems to get called for the most recently inserted item. So I can in essence say "yes" or "no" on evicting the most recently added item. But it is not useful in identifying other items that should be evicted.
I seem to recall that in EhCache 2.8 (it's been awhile), I could provide information in the ehcache.xml configuration file to specify that the cache use an LRU eviction strategy.
int these two documentation mentioned that ehcache is using LRU as default eviction strategy :
A cache eviction algorithm is a way of deciding which element to evict when the cache is full. In Ehcache, the MemoryStore may be limited in size (see How to Size Caches for more information). When the store gets full, elements are evicted. The eviction algorithms in Ehcache determine which elements are evicted. The default is LRU.
https://www.ehcache.org/documentation/2.8/apis/cache-eviction-algorithms.html
Ehcache uses Last Recently Used (LRU) as the default eviction strategy for the memory stores. The eviction strategy determines which cache entry is to be evicted when the cache is full.
https://springframework.guru/using-ehcache-3-in-spring-boot/
I have read in the redis documentation that caching eviction policy for CRDB should be set to No Eviction .
"Note: Geo-Distributed CRDBs always operate in noeviction mode."
https://docs.redislabs.com/latest/rs/administering/database-operations/eviction-policy/
Reasoning for that is the garbage collection might cause inconsistencies as both the data center will have bidirectional synch.
I am not getting this point, can someone explain by giving a real world problem that might occur if suppose we have cache eviction policy LRU .
I got to know after doing some research that it is often a trouble to handle eviction when we have active replication. For example if one of the master runs out of memory and cache is trying to evict the keys to make some room for latest data, what might happen is - it will delete those keys from the other master even if there are no memory issues there. So until and unless there is really a good way to handle this ,eviction is not supported.
I have set up a Spring cache manager backed by a ConcurrentMapCache for my application.
I am seeking for ways to monitor the cache and especially make sure the data in cache fits in memory. I considered using jvisualvm for that purpose but there might be other ways... If so what are they?
So my question is basically twofold:
What is the best way to monitor a cache backed by a ConcurrentMapCache?
What are the general guidelines for setting the time to live and cache size values of a cache?
It looks like you are searching for cache related features that can't and won't be available with a "simple" map implementation provided by the JVM.
There are many cache providers out there that provides what you want, that is monitoring, limiting the size of the cache and providing a TTL contract for the cache elements. I would encourage you to look around and switch your CacheManager implementation which will have zero impact on your code since you're using the abstraction.
I am using ehcache with terracotta in my application. My response time increased by 700 folds when i am using ehcache with terracotta. I think the terracotta is taking time in measuring the size of objects as it giving me warning:
net.sf.ehcache.pool.sizeof.ObjectGraphWalker checkMaxDepth
WARNING:
The configured limit of 1,000 object references was reached while
attempting to calculate the size of the object graph. Severe
performance degradation could occur if the sizing operation continues.
This can be avoided by setting the CacheManger or Cache
elements maxDepthExceededBehavior to "abort" or adding stop points
with #IgnoreSizeOf annotations. If performance degradation is NOT an
issue at the configured limit, raise the limit value using the
CacheManager or Cache elements maxDepth attribute. For
more information, see the Ehcache configuration documentation.
When i used #IgnoreSizeOf annotation on my class, the response time reduced to a lot . My question is does using #IgnoreSizeOf annotation has any disadvantages. For what it is being used and how it is reducing the response time of my application Please help.
Thanks in advance.
This annotation isn't related to Terracotta clustering. I guess you posted that other question on this subject.
The #IgnoreSizeOf annotation will have the sizeOfEngine, that measures the memory footprint of entries in your caches, ignore instances of annotated classes (or entire packages) or subgraphs (annotated fields) of your cached entries.
So if an object graph that you cache has a "shared" subgraph, you'd annotate the field where that graph starts with the annotation. If you ignore everything then nothing will be sized and the maxBytesLocalHeap setting has no semantic (you'll eventually suffer from OOME).
You need to understand the object graphs you're caching in order to use the annotation properly. See http://ehcache.org/documentation/configuration/cache-size#built-in-sizing-computation-and-enforcement for more details.
Now, to the performance issue you're seeing, you might want to test with and without the maxBytesLocalHeap setting and with and without clustering to try to pin point your problem. But I suspect you might be caching more than you expect, resulting in big men footprint, as well as overhead clustering the data...
What is the best mechanism to understand how caching frameworks/ caching algorithms works , is there any book which covers following topics in details.
cache hits
cache miss
LFU
LRU
LRU2
Two Queues
ARC
MRU
FIFO
Second Chance
Distributed caching