Coherence allows searching, aggregating, and sorting data, even including support for custom analytical functions. It parallelizes operations across the entire data grid, ensuring that server failures or slowdowns do not affect calculation results. Transactions - With Coherence, applications are able to manage transactional data in memory inside the data grid. The combination of scalability and performance see below which Coherence provides makes it optimal for extreme transaction processing workloads.
Its in-memory replication and guaranteed data consistency mean that it is suitable for managing transactions in memory until they are persisted to an external data source for archiving and reporting.
Events - Every transaction that occurs can potentially trigger many events. Each of these events may need to be processed in very short order often milliseconds. Coherence provides event-handling technologies capable of handling intense event rates, including server-side stream processing and interactive technologies such as continuous query for real-time desktop applications.
Benefits Every piece of software needs to provide benefits or we would not use it. Performance - Latency can cause big problems. Coherence helps to solve these by caching and processing data in real time. This has the nice side effect of dramatically increasing performance. In memory performance alleviates bottlenecks and reduces data contention, improving application responsiveness. Parallel query and computation also improves performance and scalability of real-time calculations.
Reliability - Coherence is built on a fault-tolerant mesh that provides data reliability and accuracy. Coherence provides data tolerance and continuous operation to support the data availability demands necessary in mission-critical environments.
This method accepts an arbitrary cache key and returns the appropriate value object. AbstractCacheStore or implement the interface com. CacheStore , which adds the following methods:. The method erase should remove the specified key from the external data source. The method store should update the specified item in the data source if it exists, or insert it if it does not presently exist. Coherence provides the ability to query cached data.
With partitioned caches, the queries are indexed and parallel, which means that adding servers to a partitioned cache not only increases throughput total queries per second but also reduces latency, with queries taking less user time. To query against a NamedCache, all objects should implement a common interface or base class.
Any field of an object can be queried; indexes are optional, and used to increase performance. With a replicated cache, queries are performed locally, and do not use indexes. To add an index to a NamedCache , you first need a value extractor which accepts as input a value object and returns an attribute of that object. Indexes can be added blindly duplicate indexes are ignored.
Indexes can be added at any time, before or after inserting data into the cache. It should be noted that queries apply only to cached data. For this reason, queries should not be used unless the entire data set has been loaded into the cache, unless additional support is added to manage partially loaded sets. Developers have the option of implementing additional custom filters for queries, thus taking advantage of query parallel behavior.
For particularly performance-sensitive queries, developers may implement index-aware filters, which can access Coherence's internal indexing structures. Coherence includes a built-in optimizer, and applies indexes in the optimal order.
Because of the focused nature of the queries, the optimizer is both effective and efficient. No maintenance is required. The Coherence invocation service can deploy computational agents to various nodes within the cluster. These agents can be either execute-style deploy and asynchronously listen or query-style deploy and synchronously listen.
The invocation service is accessed through the com. InvocationService interface and includes the following two methods:. An instance of the service can be retrieved from the com.
CacheFactory class. Coherence supports two event programming models that allow applications to receive and react to notifications of cluster operations. Applications observe events as logical concepts regardless of which computer caused the event. Events provide a common way of extending Coherence with application-specific logic. The event programming models are:. Live Events — The live event programming model uses user-defined event interceptors that are registered to receive different types of events.
Applications decide what action to take based on the event type. Many events that are available through the use of map events are also supported using live events. For details, see Chapter 27, "Using Live Events. Map Events — The map event programming model uses user-defined map listeners that are attached to the underlying map implementation. No cluster member is special, and any member can fail at any given time without significant impact on the cluster as a whole.
Coherence can be easily scaled out to increase both the storage and the processing capacity, and can be scaled in just as easily to reduce resource usage when the additional capacity is not needed, to the limit of the in-memory data set size.
Whenever you add or remove cluster members, Coherence will automatically rebalance the data by moving some of the partitions to new members or be restoring partitions from backup for the departed members. Coherence is aware of the network topology and always stores backups as far away as possible from the primary copy. You also have the option of persisting data to disk, either actively, as the changes happen, or on demand, by taking periodic snapshots of the cluster state.
You can use either local or shared disk, but we strongly recommend that you use SSD in either case if you care about peformance. When running inside of Kubernetes, both regular volumes and PVCs are fully supported. This allows you to quickly restore cluster state from disk in case of catastrophic failure, such as data center power loss, or to speed up whole-cluster upgrades if you can tolerate some downtime.
If you can't, you can still use persistence to create backups, and rolling upgrades to perform cluster upgrades while keeping the system fully operational. Map interface, so if you know how to use Java Map , you already know how to use Coherence for basic key-value operations.
The difference is that now you have a fault tolerant Map that can be scaled across many cluster members and optionally persisted to disk. Key-based operations in Coherence are very efficient. That means that any piece of data is only one direct network call away, regardless of the cluster size. Unlike some of the competitors, Coherence lets you work with classes and objects like you normally would, and converts them to binary data for network transfer and storage purposes when necessary, using one of the several supported serialization formats.
Being able to store and access data based on a key is great, but sometimes you need more than that. Coherence allows you to query on any data attribute present in your data model, and even allows you to define your own query filter if one of the built-in ones doesn't fit the bill. If you defined an index for a given query attribute, it will be used to optimize the query execution and avoid unnecessary deserialization.
Coherence executes queries in parallel, across the whole cluster, by default, allowing you to query across the whole data set at once without worrying about its physical distribution. However, it also allows you to execute a targeted query against a single cluster member in situations when you know that all possible results will be collocated based on data affinity configuration. This allows you to optimize queries even further, by limiting the amount of data that needs to be processed.
Sometimes you don't need the actual data objects that are stored within the data grid, but the derived, calculated result based on them. This is where Coherence aggragation features come in handy. Coherence aggregators are similar to Java Collectors -- they accumulate individual data objects into a partial result in parallel, across all cluster members, and then combine partial results from all the members into a final result that is ultimately returned to the caller.
Aggregations can be executed against the whole data set, or they can be limited to a subset of the data using a query or a key set.
They also tend to scale linearly, so you can typically improve aggregation performance by simply spreading the data and the processing load across more cluster members.
If there is one defining feature that makes Coherence stand out from many competing technologies, and allows you to write efficient distributed applications, this is it.
The problem with many applications that both hurts their performance and prevents them from scaling is that they simply move too much data over the network and require complicated distributed concurrency control. In particular, data clients provide:. Custom processing and aggregation of cluster side entries using the InvocableMap interface. Data clients cannot be notified of changes to data in a cluster. Further, data clients do not have the ability to use Near Caches or Continuous Query caches, as those capabilities also rely on the ability to receive notifications of data changes from the cluster.
For these capabilities, real-time clients must be used. Real Time Clients Extend-TCP provides the same capabilities associated with data clients; but, unlike data clients, a real-time client also supports:. Event Notifications — Using the standard Coherence event model, data changes that occur within the cluster are visible to the client application. Only events that a client application registers for are delivered over the wire.
This model results in efficient use of network bandwidth and client processing. Local Caches — While the client application can directly access the caches managed by the cluster, that may be inefficient depending on the network infrastructure.
For efficiency, a real-time client can use both Near Caching and Continuous Query Caching to maintain cache data locally. If the server to which the client application is attached happens to fail, the connection is automatically reestablished to another server, and any locally cached data is re-synchronized with the cluster.
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