I have a ignite near cache client with say size of 100(LRU). The cluster behind it has an LRU policy for on-heap with size 10K. Say any record got evicted from the near cache due to the small size of it UPDATE1(but is there in main cluster cache).
Will any subsequent get on near cache load the data from cluster cache?
The near cache and the server-side on-heap cache are just... caches. If the values get evicted then the next time you access them, they'll be brought into memory. Something else may be evicted to make room.
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When using BinaryObject for off-heap in-memory-only cache values, do we need to do anything to protect against the cache entry being evicted or expired while accessing fields via BinaryObject::field(String)?
For example, if the cache's data region has the default memory-size eviction (90% full?), or if the cache uses a creation expiry policy, and the region happens to evict entries or the cache expire entries while the code is making several calls to BinaryObject::field(String). Does Ignite automatically ensure that BinaryObject won't access invalid off-heap memory (throwing an exception perhaps), or can the developer use locking / transactions or a "touched" expiry to help prevent this?
Thanks!
BinaryObject instances returned from the Ignite API and accessed by the user code are copies. They do not reference Ignite storage memory directly.
You can work with BinaryObject even after the corresponding cache entry gets evicted.
When a cache object becomes eligible for eviction, it will be removed from memory.
Ignite has several Eviction policies:
Random-LRU
Random-2-LRU
Explained here
This means that if you recently used a cache value(or are using it at the moment that the eviction is taking place), either in BinaryObject representation or not, it will not be evicted. All Eviction algorithms use a LEAST RECENTLY USED algorithm.
Expiry policy on off-heap entries is not working as expected. I am seeing a linear increase in off-heap entries and after some time sudden dip in off-heap size. Is only marking of cache entry done after expiry time and collected later in bulk? I am not finding any documentation regarding the same.
We need to enable eager TTL in the cache configuration. It will create a thread in the background and removes the expired entries.
There are two modes of expiry policy in Ignite: eager and non-eager: https://www.gridgain.com/docs/latest/developers-guide/configuring-caches/expiry-policies#eager-ttl
In case of an eager TTL entries are expired periodically by a background thread. When a non-eager TTL is used, then entries are only expired upon access. So, it's possible for expired entries to be stored in off-heap for some time until they get read.
Also keep in mind that JVM can allocate more memory than is immediately required. The amount of memory allocated by the Java process is not directly related to the amount of data stored in Ignite.
I am experiencing a very strange case happen in production with redis, a large amount of redis keys are evicted unexpectedly even though memory not reach max configuration.
Current redis setting is max mem = 7GB, volatile-ttl.
Most of the keys are set a TTL when store to Redis.
Below graph showing a large drop in redis key eventhough memory at the time was only 3.5GB (<< 7GB)
According to my understanding, Redis would evict keys only when memory reach max-mem. And even when it does, it will only drop keys gradually according to the need for inserting new keys.
Thank you very much!
I have a redis cache in Azure with maxmemory policy set as Volatile-LRU. When writing to Redis, I am not adding an expiry time for the key. In this case, what will happen when the cache memory get filled?
Under the volatile-lru policy, redis will never evict a key without a expiry. If all of memory is used up by keys that do not have expiry set then the next time you use a command that requires allocating more memory than is available, say SET, the command will fail and you will get this error message:
OOM command not allowed when used memory > 'maxmemory'
You will still be able to use commands that don't allocate memory, like GET. If you get your database into this state, you can use the EXPIRE command to set and expiry time on keys after the fact.
I am new to redis, So please bear with me. Lets say I have configured a redis to have a maxmemory of 50mb, and I set eviction policy to allkeys-lru. And then I keep inserting and querying data. When the process memory gets to 50mb it starts to evict least recently used items.
My questions is do the evicted items persist on disk or are they lost for ever ? I mean if I do a GET for an evicted key, what do I get. Does redis fetch it from disk ?
Evicted is gone. With redis, nothing is on disk that isn't also in memory. (Technically, there will still probably be traces of it for some time, but that's just implementation details. As far as the data model is concerned, it's been deleted, and a GET won't find it.)