This short video explains why companies use Hazelcast for business-critical applications based on ultra-fast in-memory and/or stream processing technologies.
Stream processing is a hot topic right now, especially for any organization looking to provide insights faster. But what does it mean for users of Java applications, microservices, and in-memory computing? In this webinar, we will cover the evolution of stream processing and in-memory related to big data technologies and why it is the logical next step for in-memory processing projects.
Now, deploying Hazelcast-powered applications in a cloud-native way becomes even easier with the introduction of Hazelcast Cloud Enterprise, a fully-managed service built on the Enterprise edition of Hazelcast IMDG. Can't attend the live times? You should still register! We'll be sending out the recording after the webinar to all registrants.
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Overview
Caching puts actively used data in memory, where it can be accessed significantly more quickly. While this sounds simple, it can become very complex, as real-world systems are wildly diverse and constantly changing. Through meticulous engineering, deep caching expertise, and a focused commitment to customer needs, Hazelcast handles that diversity with a robust in-memory computing platform, delivering the benefits of distributed caching where high-speed, high-volume systems need it most.
Hazelcast’s relentless pursuit of speed has made our in-memory data store the fastest distributed cache available. As a fully in-memory data store, Hazelcast can transform and ingest data at blinding speeds, often shrinking milliseconds into microseconds. Because Hazelcast is built from the ground up as a distributed technology, it leverages the power of distributed processing while effectively eliminating the impact of network latency.
Hazelcast in-memory caching is designed to scale quickly and smoothly. And nodes cluster automatically, without production interruptions. A multitude of scaling options are offered, including High-Density Memory Store for large-scale enterprise use cases. Naturally, the Hazelcast Platform is cloud-ready, and elastic scaling makes it a perfect fit.
Hazelcast offers a wealth of development, deployment, and operational choices. Customers can take advantage of the powerful and easy-to-use JCache standard API through client-server or cluster-only architectures. Our platform also integrates seamlessly with popular frameworks and libraries like Spring, Memcached, and Hibernate.
The Hazelcast Platform puts the power of cache management within reach and keeps the total cost of ownership low by focusing on ease of development, implementation, and operation. The platform is surprisingly simple to set up and configure, offering remarkable resilience and high availability.
Speed in information processing is great, regardless of the industry or application. Speed at scale is a whole different level of opportunity. Speed at scale while maintaining stability? That’s what you need if you’re going to drive transformative change in your organization, and keep happy customers coming back.
The Hazelcast Platform delivers world-class, in-memory caching solutions, based on a distributed architecture that is wildly fast and seamlessly scalable.
Features
Cache management enables the monitoring and management of members running Hazelcast. In addition to monitoring the overall state of clusters, The Hazelcast Management Center can also analyze and browse data structures in detail, update map configurations, and take thread dump from members.
Hazelcast Enterprise scales up data sizes using High-Density Memory Store, a way for Java applications to access multiple terabytes of memory per cluster member without long and unpredictable garbage collection pauses. This memory store provides the benefits of “off-heap” memory using a variety of proprietary high-performance memory management techniques.
Allows multiple Hazelcast clusters to be kept in sync by replicating their state over WAN environments such as the Internet. Hazelcast supports both Active-Passive and Active-Active modes for most common scenarios of WAN Replication.
A high-performance option included in Hazelcast Enterprise in addition to the basic reliability options (write-through and write-behind). This persistence feature of Hot Restart Store provides fast cluster restarts by storing the states of the cluster members on disk in a format specially designed for restart performance and to work in concert with SSDs.
A rich set of JAAS-based security features that authenticate cluster members and clients in order to perform authorization checks on client operations. Socket Interceptor, Security Interceptor, Encryption, SSL, Credentials and ClusterLoginModule, Cluster Member Security, and Native Client Security are the main components of the security suite.
Allows an upgrade to a running Hazelcast cluster without service disruption, data loss, or interruption to execution. Hazelcast members and clients can be upgraded in a running cluster without interrupting operations.
Certified native images enable Hazelcast Enterprise to run in leading enterprise PaaS cloud container environments including Pivotal Cloud Foundry and Red Hat OpenShift Container Platform.
Hazelcast enables caching when connected to a persistent data store such as a relational database. The most common access patterns are read-through, write-through, and write-behind.
In a read-through pattern, applications request data directly from the caching system, and if the data exists in the cache, that data is returned to the application. If the data does not exist (a “cache miss”), then the system retrieves the data from the underlying backend store. The system then loads that data into the cache and returns the data to the backend store. Hazelcast handles the entire process so the application does not need to coordinate reads from the underlying store upon cache misses. To establish the link between Hazelcast and the backend store, application developers write pluggable query code that is executed by Hazelcast in the event of a cache miss.
In a write-through pattern, applications can directly update data in the cache, and whenever that is done, the updated data is synchronously and automatically persisted to the backend data store. This pattern is about ensuring the cache and backend store are synchronized and is not intended to address performance (performance is addressed with the write-behind cache pattern, described below) since the backend store is still the bottleneck in an update process.
In a write-behind pattern, applications can update data in the cache similarly to the write-through cache pattern, except the automatic updates to the backend store are asynchronous. This means this pattern offers a performance advantage since updates to the data do not have to wait for writes to the backend store. Updates to data in the cache are acknowledged quickly since only in-memory data is updated, and Hazelcast will later push the data updates to the backend store.
Near cache is highly recommended for data structures that are mostly read. When an object is fetched from a remote Hazelcast member, it is inserted into the local cache, so subsequent requests are handled by a local member retrieving from the local cache. Near cache gives Hazelcast users a huge advantage over NoSQL solutions like Redis, Cassandra, MongoDB, etc., which do not have near caching capabilities. In benchmark comparisons, Hazelcast is already 56% faster than Redis without using near cache. Enabling near cache makes Hazelcast 5 times faster than Redis. If Hazelcast near cache features in client-server deployments are being used, microsecond response times can be achieved with the right serialization.
Use Cases
Organizations rely on database caching to predictably scale mission-critical applications by providing in-memory access to frequently used data. As customer data grows exponentially, organizations of all sizes are turning to in-memory solutions to scale applications to meet service level agreements, offload over-burdened shared data services, and provide availability guarantees.
Hazelcast provides a cache-as-a-service for scalable, reliable, and fast caching. Applications can use Hazelcast as side-cache to their database, or place the database in-line behind the caching service.
Hazelcast enables organizations looking for a distributed Java cache to seamlessly integrate with JCache. These operations can help to scale out applications and manage their high-speed access to their frequently used data. Hazelcast smoothly achieves its caching potential with a 100 percent compliant implementation that transparently registers with the JCache subsystem.
Resources
This white paper provides a general overview of different strategies for application caching. It explains the advantages and disadvantages, as well as when to apply the appropriate strategy. Additionally, the paper gives a short introduction to JCache, the standard Java Caching API, as well as insight into the characteristics of the Hazelcast IMDG® JCache implementation and how it helps integrate different caching strategies into your application landscape.
If you need your applications to run faster, one way to speed them up is to use a data cache. In this white paper, we will discuss how Hazelcast offers a set of proven capabilities, beyond what other technologies provide, that make it worth exploring for your caching needs.
Are you a developer, software engineer or architect looking to apply in-memory technologies to your current architecture? Are you looking to deliver ultra-fast response times, better performance, scalability and availability? Are you seeking new tools and techniques to manage and scale data and processing through an in-memory-first and caching-first architecture?
Whether you're interested in learning the basics of in-memory systems, or you're looking for advanced, real-world production examples and best practices, we've got you covered.
