Coherence within computer science may refer to a feature within Parallels Desktop for Mac, a media server, or Oracle Coherence. This blog post is an introduction to Oracle Coherence. Oracle Coherence is an in-memory data grid and distributed caching solution.
Coherence enables organizations to predictably scale mission-critical applications by providing fast access to frequently used data. It provides a robust data abstraction layer that brokers the supply and demand of data between applications and data sources. Coherence is composed of many individual nodes or JVMs which work together to provide highly reliable and high speed virtual caching. The complexity of the cluster is completely hidden from the user of the virtual cache. By automatically and dynamically partitioning data, Coherence ensures continuous data availability and transactional integrity, even in the event of a server failure.
Simply stated, Oracle Coherence is a peer-to-peer, high availability data grid that supports Extreme Scaling , Increased Performance and Improved Reliability for applications and middleware.
These factors are a crucial component to addressing the challenges faced by many applications today, which must grow and scale elastically, reduce back-end load on databases, applications and mainframes and operate in a vast landscape of the Cloud, Shared Services and custom applications all requiring low latency and reliable access to data.
Every piece of software needs to provide benefits or we would not use it. The benefits Coherence provides include high-profile items such as performance, reliability, scalability and availability. Coherence is middleware that uses its data grid capabilities to reliably manage data objects in memory across many servers. Coherence is a key component of Oracle's Cloud Application Foundation CAF , which provides the framework for building elastic, reliable and highly available cloud-based applications using Oracle Fusion Middleware.
Many Oracle Fusion Middleware products provide native integration with Coherence out of the box to provide linearly scalable, fault tolerant, in-memory data management. These include the following Oracle products:. If the cache were extremely critical, a higher backup count, such as two, could be used.
The backup count only affects the performance of cache modifications, such as those made by adding, changing or removing cache entries. Modifications to the cache are not considered complete until all backups have acknowledged receipt of the modification.
There is a slight performance penalty for cache modifications when using the distributed cache backups; however it guarantees that if a cluster node were to unexpectedly fail, that data consistency is maintained and no data is lost.
Failover of a distributed cache involves promoting backup data to be primary storage. When a cluster node fails, all remaining cluster nodes determine what data each holds in backup that the failed cluster node had primary responsible for when it died. Those data becomes the responsibility of whatever cluster node was the backup for the data:. Figure Failover in a Partitioned Cache Environment.
If there are multiple levels of backup, the first backup becomes responsible for the data; the second backup becomes the new first backup, and so on. Just as with the replicated cache service, lock information is also retained with server failure; the sole exception is when the locks for the failed cluster node are automatically released.
The distributed cache service also allows certain cluster nodes to be configured to store data, and others to be configured to not store data. The name of this setting is local storage enabled. Cluster nodes that are configured with the local storage enabled option provides the cache storage and the backup storage for the distributed cache. Regardless of this setting, all cluster nodes have the same exact view of the data, due to location transparency. The Java heap size of the cluster nodes that have turned off local storage enabled are not affected at all by the amount of data in the cache, because that data is cached on other cluster nodes.
This is particularly useful for application server processes running on older JVM versions with large Java heaps, because those processes often suffer from garbage collection pauses that grow exponentially with the size of the heap.
Coherence allows each cluster node to run any supported version of the JVM. That means that cluster nodes with local storage enabled turned on could be running a newer JVM version that supports larger heap sizes, or Coherence's off-heap storage using the Java NIO features. The local storage enabled option allows some cluster nodes to be used just for storing the cache data; such cluster nodes are called Coherence cache servers.
Cache servers are commonly used to scale up Coherence's distributed query functionality. A replicated cache is a clustered, fault tolerant cache where data is fully replicated to every member in the cluster.
This cache offers the fastest read performance with linear performance scalability for reads but poor scalability for writes as writes must be processed by every member in the cluster. Because data is replicated to all servers, adding servers does not increase aggregate cache capacity.
There are several challenges to building a reliable replicated cache. The first is how to get it to scale and perform well. Updates to the cache have to be sent to all cluster nodes, and all cluster nodes have to end up with the same data, even if multiple updates to the same piece of data occur at the same time.
Also, if a cluster node requests a lock, it should not have to get all cluster nodes to agree on the lock, otherwise it scales extremely poorly; yet with cluster node failure, all of the data and lock information must be kept safely.
Coherence handles all of these scenarios transparently, and provides the most scalable and highly available replicated cache implementation available for Java applications. Replicated caches have very high access speed. Since the data is replicated to each cluster node, it is available for use without any waiting. A near cache is a hybrid cache; it typically fronts a distributed cache or a remote cache with a local cache.
Near cache invalidates front cache entries, using a configured invalidation strategy, and provides excellent performance and synchronization. Near cache backed by a partitioned cache offers zero-millisecond local access for repeat data access, while enabling concurrency and ensuring coherency and fail over, effectively combining the best attributes of replicated and partitioned caches.
The objective of a near cache is to provide the best of both worlds between the extreme performance of replicated caches and the extreme scalability of distributed caches by providing fast read access to Most Recently Used MRU and Most Frequently Used MFU data.
The "front cache" provides local cache access. It is assumed to be inexpensive, in that it is fast, and is limited in terms of size. The "back cache" can be a centralized or multitiered cache that can load-on-demand in case of local cache misses. The "back cache" is assumed to be complete and correct in that it has much higher capacity, but more expensive in terms of access speed.
This design allows near caches to configure cache coherency, from the most basic expiry-based caches and invalidation-based caches, up to advanced caches that version data and provide guaranteed coherency. The result is a tunable balance between the preservation of local memory resources and the performance benefits of truly local caches. The typical deployment uses a local cache for the "front cache". A local cache is a reasonable choice because it is thread safe, highly concurrent, size-limited, auto-expiring, and stores the data in object form.
For the "back cache", a partitioned cache is used. The following figure illustrates the data flow in a near cache. If the client writes an object D into the grid, the object is placed in the local cache inside the local JVM and in the partitioned cache which is backing it including a backup copy. If the client requests the object, it can be obtained from the local, or "front cache", in object form with no latency.
Figure provides a conceptual view of a near cache during put operations. If the client requests an object that has been expired or invalidated from the "front cache", then Coherence automatically retrieves the object from the partitioned cache. The "front cache" stores the object before the object is delivered to the client. Figure provides a conceptual view of a near cache during get operations. While it is not a clustered service, the local cache implementation is often used in combination with various clustered cache services as part of a near cache.
In particular, the near cache implementation uses key objects to synchronize the local cache front map , and the key may also be cached in an internal map. Therefore, a key object passed to the get method is used as a lock. If the key object is modified while a thread holds the lock, then the thread may fail to unlock the key.
In addition, if there are other threads waiting for the locked key object to be freed, or if there are threads who attempt to acquire a lock to the modified key, then threads may hang and deadlocks can occur.
For details on the use of keys, see the java. Map API documentation. A local cache is completely contained within a particular cluster node. There are several attributes of the local cache that are particularly interesting:. The local cache implements the same standard collections interface that the clustered caches implement, meaning that there is no programming difference between using a local or a clustered cache. Just like the clustered caches, the local cache is tracking to the JCache API , which itself is based on the same standard collections API that the local cache is based on.
The local cache can be size-limited. The local cache can restrict the number of entries that it caches, and automatically evict entries when the cache becomes full. Furthermore, both the sizing of entries and the eviction policies can be customized. For example, the cache can be size-limited based on the memory used by the cached entries. This algorithm is the best general-purpose eviction algorithm because it works well for short duration and long duration caches, and it balances frequency versus recentness to avoid cache thrashing.
Create a plug-in to define the third-party caching system as an Oracle CEP caching system provider. Use the wlevs:caching-system element to declare a third-party implementation; use the class or provider attributes to specify additional information.
For simplicity, you can include the third-party implementation code inside the Oracle CEP application bundle itself to avoid having to import or export packages and managing the lifecycle of a separate bundle that contains the third-party implementation.
In this case the wlevs:caching-system element appears in the EPN assembly file as shown in the following example:. The class attribute specifies a Java class that must implement the com. CachingSystem interface. Sometimes, however, you might not be able, or want, to include the third-party caching implementation in the same bundle as the Oracle CEP application that is using it.
In this case, you must create a separate bundle whose Spring application context includes the wlevs:caching-system element, with the advertise attribute mandatory:. Alternatively, if you want to decouple the implementation bundle from the bundle that references it, or you are plugging in a caching implementation that supports multiple caching systems per Java process, you can specify a factory as a provider:.
The factory class the. CachingSystemFactory interface. This interface has a single method, create , that returns a com. CachingSystem instance. You must deploy this bundle alongside the application bundle so that the latter can start using it. Configure the third-party caching system and its caches by updating the third-party caching configuration file or files for the application. Optionally, override the default third-party cache configuration by updating the appropriate configuration file with one or more additional cache element child elements.
You can reference a cache from an Oracle CQL statement in much the same way you reference a channel; this feature enables you to enrich standard streaming data with data from a separate source.
Example shows a valid Oracle CQL query that joins trade events from a standard channel named S1 with stock symbol data from a cache named stockCache :. This guarantees that the query will execute against a snapshot of the cache. If you join against any other window type, then if the cache changes before the window expires, the query will be incorrect. If the cache changes before this window expires, the query will be incorrect. Consequently, this query will raise Oracle CEP server error " external relation must be joined with s[now] ".
This means that, continuing with Example , the query executes only when a channel pushes a trade event to the query; the stock symbol data in the cache never causes a query to execute, it is only pulled by the query when needed. Consider two streams S and C with schemas id , group , value where the cache key is id , group. The following query is invalid because although the WHERE clause specifies both key properties id and group , the query does not supply a value for one of the key properties id :.
If the cache is a processor source, you connect the cache directly to the processor on the EPN as Figure shows. If the cache is a processor sink, you connect the processor to the cache using a channel as Figure shows. This procedure assumes that you have already configured the caching system and caches. For more information, see:.
If you have not already done so, create the event type that corresponds to the cache data and register it in the event repository. In the EPN assembly file, update the configuration of the cache to declare the event type of its values; use the value-type attribute of the wlevs:cache element. The value-type attribute specifies the type for the values contained in the cache. This must be a valid type name in the event type repository. This attribute is required only if the cache is referenced in an Oracle CQL query.
This is because the query processor needs to know the type of events in the cache. If the cache is a processor source: you connect the cache directly to the processor on the EPN as Figure shows. Update the wlevs:processor element a wlevs:cache-source child element that references the cache.
In the example, the processor will have data pushed to it from the S1 channel as usual; however, the Oracle CQL queries that execute in the processor can also pull data from the cache-id cache.
When the query processor matches an event type in the FROM clause to an event type supplied by a cache, such as CompanyEvent , the processor pulls instances of that event type from the cache. If the cache is a processor sink: you must connect the processor to the cache using a channel on the EPN that is, there must be a channel between the processor and the cache sink as Figure shows.
You can reference a cache from an EPL statement in much the same way you reference a channel; this feature enables you to enrich standard streaming data with data from a separate source. For example, the following EPL query joins trade events from a standard channel with company data from a cache:. In the example, both TradeEvent and Company are event types registered in the repository, but they have been configured in such a way that TradeEvents come from a standard stream of events but Company maps to a cache in the event processing network.
This configuration happens outside of the EPL query, which means that the source of the data is transparent in the query itself. This means that, continuing with the preceding sample, the query executes only when a channel pushes a trade event to the query; the company data in the cache never causes a query to execute, it is only pulled by the query when needed.
Using multiple cache sources and parameterized SQL queries is supported. If you have not already done so, create the event type that corresponds to the cache data, such as Company in the preceding example, and registered it in the event repository.
Specify the key properties for the data in the cache. There are a variety of ways to do this; see. In the EPN assembly file, update the configuration of the cache in the EPN assembly file to declare the event type of its values; use the value-type attribute of the wlevs:cache element.
This attribute is required only if the cache is referenced in an EPL query. In the EPN assembly file, update the configuration of the processor that executes the EPL query that references a cache, adding a wlevs:cache-source child element that references the cache.
In the example, the processor will have data pushed to it from the stream-id channel as usual; however, the EPL queries that execute in the processor can also pull data from the cache-id cache. When the query processor matches an event type in the FROM clause to an event type supplied by a cache, such as Company , the processor pulls instances of that event type from the cache. An adapter can also be injected with a cache using the standard Spring mechanism for referencing another bean.
A cache bean implements the java. Map interface which is what the adapter uses to access the injected cache. First, the configuration of the adapter in the EPN assembly file must be updated with a wlevs:instance-property child element, as shown in the following example:. In the example, the ref attribute of wlevs:instance-property references the id value of the wlevs:cache element.
Oracle CEP automatically injects the cache, implemented as a java. Map , into the adapter. In the adapter Java source, add a setMap Map method with the code that implements whatever you want the adapter to do with the cache:. A business POJO, configured as a standard Spring bean in the EPN assembly file, can be injected with a cache using the standard Spring mechanism for referencing another bean. In this way the POJO can view and manipulate the cache.
Map interface which is what the business POJO uses to access the injected cache. A cache bean can also implement a vendor-specific sub-interface of java. Map , but for portability it is recommended that you implement Map.
In the example, the ref attribute of the property element references the id value of the wlevs:cache element. Map , into the business POJO bean. In addition to standard event streams, Oracle CQL rules can also invoke the member methods of a user-defined function. These user-defined functions are implemented as standard Java classes and are declared in the component configuration file of the Oracle CQL processor, as shown in the following example:.
The processor in which the relevant Oracle CQL rule runs must then be injected with the user-defined function using the wlevs:function child element, referencing the Spring bean with the ref attribute:.
The following Oracle CQL rule, assumed to be configured for the tradeProcessor processor, shows how to invoke the existsOrder method of the orderFunction user-defined function:. You can also configure the user-defined function to access a cache by injecting the function with a cache using the standard Spring mechanism for referencing another bean.
Map interface which is what the user-defined function uses to access the injected cache. First, the configuration of the user-defined function in the EPN assembly file must be updated with a wlevs:property child element, as shown in the following example:.
In the example, the ref attribute of the wlevs:property element references the id value of the wlevs:cache element. Map , into the user-defined function. In the user-defined function's Java source, add a setMap Map method with the code that implements whatever you want the function to do with the cache:.
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