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This page covers managing the life of the data in each of BigMemory's data storage tiers, including the pinning features of Automatic Resource Control (ARC).
The following are options for data life within the BigMemory tiers:
BigMemory data entries expire based on parameters with configurable values. When eviction occurs, expired elements are the first to be removed. Having an effective expiration configuration is critical to optimizing the use of resources such as heap and maintaining overall performance.
To add expiration, edit the values of the following
<cache> attributes, and tune these values based on results of performance tests:
timeToIdleSeconds– The maximum number of seconds an element can exist in the BigMemory data store without being accessed. The element expires at this limit and will no longer be returned from BigMemory. The default value is 0, which means no TTI eviction takes place (infinite lifetime).
timeToLiveSeconds– The maximum number of seconds an element can exist in the the BigMemory data store regardless of use. The element expires at this limit and will no longer be returned from BigMemory. The default value is 0, which means no TTL eviction takes place (infinite lifetime).
eternal– If the cache's
eternalflag is set, it overrides any finite TTI/TTL values that have been set. Individual cache elements may also be set to eternal. Eternal elements and caches do not expire, however they may be evicted.
See How Configuration Affects Element Eviction for more information on how configuration can impact eviction.
Without pinning, expired cache entries can be flushed and eventually evicted, and even most non-expired elements can also be flushed and evicted as well, if resource limitations are reached. Pinning gives per-cache control over whether data can be evicted from BigMemory.
Data that should remain in memory can be pinned. You cannot pin individual entries, only an entire cache. There are two types of pinning, depending upon whether the pinning configuration should take precedence over resource constraints or the other way around. See the next sections for details.
Entire caches can be pinned using the
pinning element in the Ehcache configuration. This element has a required attribute (
store) to specify how the pinning will be accomplished.
store attribute can have either of the following values:
For example, the following cache is configured to pin its entries:
<cache name="Cache1" ... > <pinning store="inCache" /> </cache>
The following cache is configured to pin its entries to heap or off-heap only:
<cache name="Cache2" ... > <pinning store="localMemory" /> </cache>
The interaction of the pinning configuration with the cache sizing configuration depends upon which pinning option is used.
inCache pinning, the pinning setting takes priority over the configured cache size. Elements resident in a cache with this pinning option cannot be evicted if they haven't expired. This type of pinned cache is not eligible for eviction at all, and
maxEntriesInCache should not be configured for this cache.
|Potentially, pinned caches could grow to an unlimited size. Caches should never be pinned unless they are designed to hold a limited amount of data (such as reference data) or their usage and expiration characteristics are understood well enough to conclude that they cannot cause errors.|
localMemory pinning, the configured cache size takes priority over the pinning setting.
localMemory pinning should be used for optimization, to keep data in heap or off-heap memory, unless or until the tier becomes too full. If the number of entries surpasses the configured size, entries will be evicted. For example, in the following cache the
maxBytesLocalOffHeap settings override the pinning configuration:
<cache name="myCache" maxEntriesOnHeap="10000" maxBytesLocalOffHeap="8g" ... > <pinning store="localMemory" /> </cache>
Pinning achieved programmatically will not be persisted — after a restart the pinned entries are no longer pinned. Cache pinning in configuration is reinstated with the configuration file.
To unpin all of a cache's pinned entries, clear the cache. Specific entries can be removed from a cache using
Cache.remove(). To empty the cache,
Cache.removeAll(). If the cache itself is removed (
CacheManager.removeCache()), then any data still remaining in the cache is also removed locally. However, that remaining data is not removed from disk (if localRestartable).
The following example shows a cache with certain expiration settings:
<cache name="myCache" eternal="false" timeToIdleSeconds="3600" timeToLiveSeconds="0" memoryStoreEvictionPolicy="LFU"> </cache>
Note the following about the myCache configuration:
timeToIdleSeconds), that element is evicted.
timeToLiveSeconds). However, unexpired entries may still be flushed based on other limitations (see Sizing BigMemory Tiers).
Databases and other Systems of Record (SOR) that were not built to accommodate caching outside of the database do not normally come with any default mechanism for notifying external processes when data has been updated or modified.
When using BigMemory as a caching system, the following strategies can help to keep the data in the cache in sync:
Data Expiration: Use the eviction algorithms included with Ehcache, along with the
timetoLiveSeconds settings, to enforce a maximum time for elements to live in the cache (forcing a re-load from the database or SOR).
Message Bus: Use an application to make all updates to the database. When updates are made, post a message onto a message queue with a key to the item that was updated. All application instances can subscribe to the message bus and receive messages about data that is updated, and can synchronize their local copy of the data accordingly (for example by invalidating the cache entry for updated data)
Triggers: Using a database trigger can accomplish a similar task as the message bus approach. Use the database trigger to execute code that can publish a message to a message bus. The advantage to this approach is that updates to the database do not have to be made only through a special application. The downside is that not all database triggers support full execution environments and it is often unadvisable to execute heavy-weight processing such as publishing messages on a queue during a database trigger.
The Data Expiration method is the simplest and most straightforward. It gives you the most control over the data synchronization, and doesn't require cooperation from any external systems. You simply set a data expiration policy and let Ehcache expire data from the cache, thus allowing fresh reads to re-populate and re-synchronize the cache.
If you choose the Data Expiration method, you can read more about the cache configuration settings at cache eviction algorithms, and review the timeToIdle and timeToLive configuration settings. The most important consideration when using the expiration method is balancing data freshness with database load. The shorter you make the expiration settings - meaning the more "fresh" you try to make the data - the more load you will incur on the database.
Try out some numbers and see what kind of load your application generates. Even modestly short values such as 5 or 10 minutes will afford significant load reductions.