I am using EhCache core 3.0. It internally uses BaseExpiry and Eh107Expiry class to check whether cache is expired or not. These classes implement Expiry interface. My query is, can we extend methods which are used to check whether cache is expired or not. I don't want to expire contents of the cache even if time is elapsed if my method is using some data from that cache.
Have a look at the dedicated section on Expiry in the documentation. It will help you understand what you can do and how to do it.
If that does not help you, please expand your question as suggested in comments.
If you add time-to-idle in xml or override getExpiryForAccess from Expiry interface,then your entries will not delete when you are accessing them.Below is the code to build Eh cache with custom Expire.This blog will help you for other properties with explanation.
CacheConfigurationBuilder<Integer,String> cacheConfigurationBuilder = CacheConfigurationBuilder.newCacheConfigurationBuilder();
cacheConfigurationBuilder.withExpiry(new Expiry() {
#Override
public Duration getExpiryForCreation(Object key, Object value) {
return new Duration(120, TimeUnit.SECONDS);
}
#Override
public Duration getExpiryForAccess(Object key, Object value) {
return new Duration(120, TimeUnit.SECONDS);
}
#Override
public Duration getExpiryForUpdate(Object key, Object oldValue, Object newValue) {
return null;
}
})
.usingEvictionPrioritizer(Eviction.Prioritizer.LFU)
.withResourcePools(ResourcePoolsBuilder.newResourcePoolsBuilder().heap(200, EntryUnit.ENTRIES))
// adding defaultSerializer config service to configuration
.add(new DefaultSerializerConfiguration(CompactJavaSerializer.class, SerializerConfiguration.Type.KEY))
.buildConfig(Integer.class, String.class);
I guess you can use an ehcache decorator and reimplement isExpiry to add your own conditions. Please refer to https://www.ehcache.org/documentation/2.8/apis/cache-decorators.html.
Related
Is there a way to populate a Map once from the DB (through Mongo repository) data and reuse it when required from multiple classes instead of hitting the Database through the repository.
As per your comment, what you are looking for is a Caching mechanism. Caches are components which allow data to live in memory, as opposed to files, databases or other mediums so as to allow for the fast retrieval of information (against a higher memory footprint).
There are probably various tutorials online, but usually caches all have the following behaviour:
1. They are key-value pair structures.
2. Each entity living in the cache also has a Time To Live, that is, how long will it considered to be valid.
You can implement this in the repository layer, so the cache mechanism will be transparent to the rest of your application (but you might want to consider exposing functionality that allows to clear/invalidate part or all the cache).
So basically, when a query comes to your repository layer, check in the cache. If it exists in there, check the time to live. If it is still valid, return that.
If the key does not exist or the TTL has expired, you add/overwrite the data in the cache. Keep in mind that when updating the data model yourself, you also invalidate the cache accordingly so that new/fresh data will be pulled from the DB on the next call.
You can declare the map field as public static and this would allow application wide access to hit via ClassLoadingData.mapField
I think a better solution, if I understood the problem would be a memoized function, that is a function storing the value of its call. Here is a sketch of how this could be done (note this does not handle possible synchronization problem in a multi threaded environment):
class ClassLoadingData {
private static Map<KeyType,ValueType> memoizedValues = new HashMap<>();
public Map<KeyType,ValueType> getMyData() {
if (memoizedData.isEmpty()) { // you can use more complex if to handle data refresh
populateData(memoizedData);
} else {
return memoizedData;
}
}
private void populateData() {
// do your query, and assign result to memoizedData
}
}
Premise: I suggest you to use an object-relational mapping tool like Hibernate on your java project to map the object-oriented
domain model to a relational database and let the tool handle the
cache mechanism implicitally. Hibernate specifically implements a multi-level
caching scheme ( take a look at the following link to get more
informations:
https://www.tutorialspoint.com/hibernate/hibernate_caching.htm )
Regardless my suggestion on premise you can also manually create a singleton class that will be used from every class in the project that goes to interact with the DB:
public class MongoDBConnector {
private static final Logger LOGGER = LoggerFactory.getLogger(MongoDBConnector.class);
private static MongoDBConnector instance;
//Cache period in seconds
public static int DB_ELEMENTS_CACHE_PERIOD = 30;
//Latest cache update time
private DateTime latestUpdateTime;
//The cache data layer from DB
private Map<KType,VType> elements;
private MongoDBConnector() {
}
public static synchronized MongoDBConnector getInstance() {
if (instance == null) {
instance = new MongoDBConnector();
}
return instance;
}
}
Here you can define then a load method that goes to update the map with values stored on the DB and also a write method that instead goes to write values on the DB with the following characteristics:
1- These methods should be synchronized in order to avoid issues if multiple calls are performed.
2- The load method should apply a cache period logic ( maybe with period configurable ) to avoid to load for each method call the data from the DB.
Example: Suppose your cache period is 30s. This means that if 10 read are performed from different points of the code within 30s you
will load data from DB only on the first call while others will read
from cached map improving the performance.
Note: The greater is the cache period the more is the performance of your code but if the DB is managed you'll create inconsistency
with cache if an insertion is performed externally ( from another tool
or manually ). So choose the best value for you.
public synchronized Map<KType, VType> getElements() throws ConnectorException {
final DateTime currentTime = new DateTime();
if (latestUpdateTime == null || (Seconds.secondsBetween(latestUpdateTime, currentTime).getSeconds() > DB_ELEMENTS_CACHE_PERIOD)) {
LOGGER.debug("Cache is expired. Reading values from DB");
//Read from DB and update cache
//....
sampleTime = currentTime;
}
return elements;
}
3- The store method should automatically update the cache if insert is performed correctly regardless the cache period is expired:
public synchronized void storeElement(final VType object) throws ConnectorException {
//Insert object on DB ( throws a ConnectorException if insert fails )
//...
//Update cache regardless the cache period
loadElementsIgnoreCachePeriod();
}
Then you can get elements from every point in your code as follow:
Map<KType,VType> liveElements = MongoDBConnector.getElements();
I'm trying to write a AsyncLoadingCache that accepts a CacheWriter and I'm getting an IllegalStateException.
Here's my code:
CacheWriter<String, UUID> cacheWriter = new CacheWriter<String, UUID>() {
#Override
public void write(String key, UUID value) {
}
#Override
public void delete(String key, UUID value, RemovalCause cause) {
}
};
AsyncLoadingCache<String, UUID> asyncCache = Caffeine.newBuilder()
.expireAfterWrite(60, TimeUnit.SECONDS)
.writer(cacheWriter)
.maximumSize(100L)
.buildAsync((String s) -> { /* <== line 41, exception occurs here */
return UUID.randomUUID();
});
And I'm getting this trace
Exception in thread "main" java.lang.IllegalStateException
at com.github.benmanes.caffeine.cache.Caffeine.requireState(Caffeine.java:174)
at com.github.benmanes.caffeine.cache.Caffeine.buildAsync(Caffeine.java:854)
at com.mycompany.caffeinetest.Main.main(Main.java:41)
If I'll change the cache to a LoadingCache or remove .writer(cacheWriter) the code will run properly. What am I doing wrong? it seems I'm providing the right types to both objects.
Unfortunately these two features are incompatible. While the documentation states this, I have updated the exception to communicate this better. In Caffeine.writer it states,
This feature cannot be used in conjunction with {#link #weakKeys()} or {#link #buildAsync}.
A CacheWriter is a synchronous interceptor for a mutation of an entry. For example, it might be used to evict into a disk cache as a secondary layer, whereas a RemovalListener is asynchronous and using it would leave a race where the entry is not present in either caches. The mechanism is to use ConcurrentHashMap's compute methods to perform the write or removal, and call into the CacheWriter within that block.
In AsyncLoadingCache, the value materializes later when the CompletableFuture is successful, or is automatically removed if null or an error. When the entry is modified within the hash table, this future may be in-flight. This would mean that the CacheWriter would often be called without the materialized value and likely cannot do very intelligent things.
From an API perspective, unfortunately telescoping builders (which use the type system to disallow incompatible chains) become more confusing than using runtime exceptions. Sorry for not making the error clear, which should now be fixed.
I have something like this:
private final Cache<Long, BlockingDeque<Peer>> peers = CacheBuilder.newBuilder()
.expireAfterAccess(10, TimeUnit.MINUTES)
.build();
public class Peer {
public void hanleRequest(String request) { ... }
//....
}
Cache provides only two policies: expiredAfterWrite and expireAfterAccess. Either the first nor the second is suitable for me.
I want BlockingDeque<Peer> entity expires in 10 minutes after last invocation of Peer#handleRequest() method on one of Peer objects that belongs to that BlockingDeque. Means Peer#handleRequest() resets the expiration counter.
I want Any of other methods of Peer object doesn't reset counter.
I want peers.get(key) also doesn't reset counter.
Example
peers.getIfPresent(key); // doesn't reset counter
peers.getIfPresent(key).add(new Peer()); // doesn't reset counter
peers.getIfPresent(key).remove(peer); //doesn't reset counter
peers.getIfPresent(key).handleRequest(request); // RESET counter!
Questions
Is that possible with help of Guava Cache, ExpiringMap, MapMaker or any other Guava map?
If asnwer to the first question is NO. Can I just customize one of the Guava elements to have no need to implement all from scratch?
If answer to the second question is NO. What is the better way to implement that by my own? At the moment I suppose it'll be ConcurrentHashMap with daemon thread in addition. That thread will be iterate throught the whole map each 5-15 seconds and check if any entity is expired
Updated: Is that a good solution? As I suppose, handleRequest is a operation which will be performed on each user request, so it performance stays on the first place. Approximate BlockingDeque objects in peers cache is near 10, approximate number of Peer object in one deque is 2.
private final Cache<Long, BlockingDeque<Peer>> peers = CacheBuilder.newBuilder()
.expireAfterWrite(10, TimeUnit.MINUTES) //CHANGE TO WRITE POLICY
.build();
public class Peer {
public void hanleRequest(String request) {
BlockingDeque<Peer> deque = peers.getIfPresent(key);
peers.invalidate(key);
peers.put(key, deque);
//...
}
//....
}
First a remark: The kind of question you ask smells like a XY problem, see:
https://meta.stackexchange.com/questions/66377/what-is-the-xy-problem
So maybe some background what you really want to achieve would be good.
Taking the question literally, I would do the following:
Use a second cache without expiration for the "don't reset counter" accesses. Add a removal listener to the peers cache, to remove the value from the second cache. Maybe just a HashMap is fine, too. The resource usage is actually controlled by the peers cache.
#cruftex's suggestion of using a second cache is fine.
Regarding your updated question, you don't need to invalidate before "updating" the value, just update it:
public class Peer {
public void handleRequest(String request) {
BlockingDeque<Peer> deque = peers.getIfPresent(key);
if (deque != null) {
peers.put(key, deque);
}
//...
}
//....
}
example from : SpringSource
#Cacheable(value = "vets")
public Collection<Vet> findVets() throws DataAccessException {
return vetRepository.findAll();
}
How does findVets() work exactly ?
For the first time, it takes the data from vetRepository and saves the result in cache. But what happens if a new vet is inserted in the database - does the cache update (out of the box behavior) ? If not, can we configure it to update ?
EDIT:
But what happens if the DB is updated from an external source (e.g. an application which uses the same DB) ?
#CachePut("vets")
public void save(Vet vet) {..}
You have to tell the cache that an object is stale. If data change without using your service methods then, of course, you would have a problem. You can, however, clear the whole cache with
#CacheEvict(value = "vets", allEntries = true)
public void clearCache() {..}
It depends on the Caching Provider though. If another app updates the database without notifying your app, but it uses the same cache it, then the other app would probably update the cache too.
It would not do it automatically and there is not way for the cache to know if the data has been externally introduced.
Check #CacheEvict which will help you invalidate the cache entry in case of any change to the underlying collections.
#CacheEvict(value = "vet", allEntries = true)
public void saveVet() {
// Intentionally blank
}
allEntries
Whether or not all the entries inside the cache(s) are removed or not.
By default, only the value under the associated key is removed. Note that specifying setting this parameter to true and specifying a key is not allowed.
you also can use #CachePut on the method which creates the new entry. The return type has to be the same as in your #Cachable method.
#CachePut(value = "vets")
public Collection<Vet> updateVets() throws DataAccessException {
return vetRepository.findAll();
}
In my opinion an exernal service has to call the same methods.
Two things I really like about Guava 11's CacheLoader (thanks, Google!) are loadAll(), which allows me to load multiple keys at once, and reload(), which allows me to reload a key asynchronously when it's "stale" but an old value exists. I'm curious as to how they play together, since reload() operates on but a single key.
Concretely, extending the example from CachesExplained:
LoadingCache<Key, Graph> graphs = CacheBuilder.newBuilder()
.maximumSize(1000)
.refreshAfterWrite(1, TimeUnit.MINUTES)
.build(
new CacheLoader<Key, Graph>() {
public Graph load(Key key) { // no checked exception
return getGraphFromDatabase(key);
}
public Map<Key, Graph> loadAll(Iterable<? extends K> keys) {
return getAllGraphsFromDatabase(keys);
}
public ListenableFuture<Graph> reload(final Key key, Graph prevGraph) {
if (neverNeedsRefresh(key)) {
return Futures.immediateFuture(prevGraph);
} else {
// asynchronous!
return ListenableFutureTask.create(new Callable<Graph>() {
public Graph call() {
return getGraphFromDatabase(key);
}
});
}
}
});
...where "getAllGraphsFromDatabase()" does an aggregate database query rather than length(keys) individual queries.
How do these two components of a LoadingCache play together? If some keys in my request to getAll() aren't present in the cache, they are loaded as a group with loadAll(), but if some need refreshing, do they get reloaded individually with load()? If so, are there plans to support a reloadAll()?
Here's how refreshing works.
Refreshing on a cache entry can be triggered in two ways:
Explicitly, with cache.refresh(key).
Implicitly, if the cache is configured with refreshAfterWrite and the entry is queried after the specified amount of time after it was written.
If an entry that is eligible for reload is queried, then the old value is returned, and a (possibly asynchronous) refresh is triggered. The cache will continue to return the old value for the key while the refresh is in progress. (So if some keys in a getAll request are eligible for refresh, their old values will be returned, but the values for those keys will be (possibly asynchronously) reloaded.)
The default implementation of CacheLoader.reload(key, oldValue) just returns Futures.immediateFuture(load(key)), which (synchronously) recomputes the value. More sophisticated, asynchronous implementations are recommended if you expect to be doing cache refreshes.
I don't think we're inclined to provide reloadAll at the moment. I suspect it's possible, but things are complicated enough as it is, and I think we're inclined to wait until we see specific demand for such a thing.