In-Memory Distributed Database
What is Infinispan?
Infinispan is an open-source in-memory database that can hold nearly any type of data, from plain-text to structured objects. Retrieve your data with lightning-fast speeds with Infinispan's powerful full-text and vector search capabilities. Guarantee reliability and integrity by distributing your data across elastically scalable Infinispan clusters that offer high availability, fault tolerance, and the ability to replicate between multiple geographic sites. Connect to Infinispan using either a native, high-performance protocol or a Redis or Memcached client of your choice.
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Get Started NowFeatures
Interoperability
Access data across multiple protocols and programming languages.
Resilient and Fault Tolerant Data
Ensure data is always available to meet demanding workloads.
ACID Transactions
Guarantee that data is always valid and consistent.
Clustered Processing
Process data in real-time without burdening resources.
Queries
Perform simple, accurate, and fast searches across distributed data sets.
Boost Application Performance
Infinispan turbocharges applications by storing data closer to processing logic, which reduces latency and increases throughput.
Available as a Java library, you simply add Infinispan to your application dependencies and then you’re ready to store data in the same memory space as the executing code.
If you want to provision a data layer that is independent of your applications, you can use Infinispan Server for remote access to data with in-memory performance. Clients are a single network hop away from data through consistent hashing techniques and can make requests over HTTP or with a custom binary TCP protocol called Hot Rod.
Learn MoreAchieve High Availability and Elasticity
Infinispan provides trusted open-source technology to deliver scalability to meet workload demands and reduce resource utilization. At the same time, Infinispan distributes your data across clusters so no single point of failure causes data loss.
One popular use for Infinispan is as a shared store for stateful data, such as user HTTP sessions. Applications can stay lightweight and avoid heap usage by externalizing sessions to Infinispan clusters, which act as an independent data layer.
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Backup Across Data Centers
Infinispan clusters running in different geographical locations can form global clusters to back up your data across sites. If sites go offline clients can immediately switch to an available cluster, making sure data center faults do not cause service interruptions.
When using the Infinispan Operator with Kubernetes environments such as Red Hat OpenShift, cross-site replication capabilities make your data ready for hybrid and multi cloud deployments.
Infinispan also guarantees data consistency when using cross-site replication, even in cases where clients make concurrent writes at different locations that use asynchronous replication. So your data is always there and always accurate, no matter where you’re running.
Learn MoreFeatured Use Cases
Quarkus extensions
Spring and Spring-Boot
Redis Hot Replacement
Keycloak
Langchain
Langchain4j
Hibernate
Apache Camel
Micronaut
Wildfly
Vert-x
Kogito
Recent Blog Posts
November 29, 2024
By Tristan Tarrant
Infinispan issue tracking is now on GitHub issues. We have decided to do this in order to make it easier for the community to report issues and for developers to cross-reference them in pull requests, release notes and discussions. GitHub issues offer a streamlined int...
November 26, 2024
By William Burns
Infinispan 15.1 will be shipping a new default Hot Rod client implementation. This implementation completely overhauls the "pool" implementation and adds many internal code optimizations and reductions. An overview of the changes Remove ChannelPool implementation, repla...
November 25, 2024
By Fabio Massimo Ercoli
search indexing spatial queries geographical
The upcoming Infinispan 15.1 will support geographical queries. The feature allows users to perform queries based on geographical criteria. Spatial predicates can used in combination with other predicates to implement additional filtering. Moreover, spatial fields can be us...
