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    Built-In Support for Metrics and Tracing

The best way to build modern stateful scalable cloud native


Welcome to Coherence Community Edition

Coherence CE (Community Edition) is a free and open source edition of Oracle Coherence, first and market-leading in-memory data grid. Since its initial release in 2001, it has been used by hundreds of customers across many industries to power some of the mission critical systems you use every day. Often immitated, but never duplicated, it is now available for everyone to use free of charge.

Coherence Features

Coherence is designed to run 24/7, all year long. Nothing makes us more proud than when the users tell us they haven't touched their production cluster in years.

Although, we would like them to upgrade and use the new features from time to time...

Clustering and Data Sharding

Coherence requires minimal configuration in order for the members to discover one another and form a cluster. This makes it just as easy to create a cluster of 500 members as it is to create a cluster of 5 members, both on the physical hardware and in virtualized environments.

Once the cluster is formed, Coherence will automatically shard the data across all the members and ensure that each piece of data has a backup copy as well. No cluster member is special, and any member can fail at any given time without significant impact on the cluster as a whole.

In other words, failure is expected and built into the architecture, not an exception.

Scalability and High Avalability

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. In the common case of a cluster spanning multiple availability domains/zones in the cloud region, this allows you to lose a whole availability domain without any data loss.

Disk-Based Persistence

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.

Key-Value Data Store

Coherence NamedCache API is an extension of java.util.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. Every cluster member is aware of the partition/shard distribution across the cluster, and the partition that a given key belongs to is determined using a consistent hashing algorithm. 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.

Parallel Queries

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.

Efficient Aggregation

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.

In-Place Processing

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. Read-modify-write is probably the most common data access pattern in vast majority of applications, even though it is very inefficient way of performing data modification.

Coherence addresses this problem by allowing you to send the data modification code into the grid and execute it where the data is, against one or more entries. This can not only significantly impact how much data needs to be moved over the wire, but it also takes care of cluster-wide concurrency control — each entry processor has the exclusive access to the entry it is processing for the duration of its execution.

Think of it as stored procedures, but using Java lambdas and rich server-side domain models.

Sophisticated Event Model

Coherence allows you to react to almost any event that happens within the cluster: members joining or leaving, services starting or shutting down, data partitions being transferred or restored from disk — you name it, it is probably there.

Of course, you can also observe data modification events, both on the server-side and using any of the supported clients. Whether it's entry inserts, updates and removals, or entry processor executions, it's all observable (and in many cases, vetoable).

Coherence events and interceptors also allow you to leverage Coherence data grid as the central integration hub for multiple back end systems, and provide what appears to be a single unified data store for your front end applications.

And many more...

Doesn't matter if you are a begginer or an advanced developer. Coherence is easy to use, distributed to its core, and has a number of features that will make development of modern cloud native applications a breeze.

  • Synchronous and Asynchronous API
  • Extensive Security Features
  • Support for CDI
  • Support for Eclipse MicroProfile
  • Support for OpenTracing
  • Support for OpenMetrics
  • Native Clients for Java, .NET and C++
  • JMX and REST-based Management
  • Data Affinity Support
  • Partition-Local Transactions
  • Built-In Durable Messaging
  • Custom Kubernetes Operator
  • Integration with Prometheus
  • Integration with Jaeger
  • Awesome Grafana Dashboards
  • Extensive Documentation
  • Realistic Sample Applications

Quick Start

Did we convince you to give Coherence a try?

The following sections will walk you through the implementation of a simple To Do List application using Coherence and Helidon, show you how to package it into a Docker image, run it, and prove that it works.

We could've done a simpler, "Hello World"-style app, but where is the fun in that?


You've just implemented your first stateful service using Coherence and Helidon

What are our users saying?

Stateless systems are extremely data hungry, and state management (at scale) is one of the biggest challenges we've faced. At our scale, no traditional data layer would've worked.

Coherence allows us to manage 9 terabytes of data in memory, in a format easily consumable by our services. This enables us to handle 1.3 billion calls, and produce 300 million events per day, across 5,600 microservices.

Arun Giri, Union Pacific Railroad