In-Memory Cache - VoltDB
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In-Memory Cache

Boost performance & scale old applications simply and cost effectively

With easy access to information, your customers now have more choices than ever. Seconds or even double digit millisecond latency is just not acceptable. The success of your business depends on fixing your tired old application. While the application may have worked fine a few years ago, as data volumes grew so did the latency in the backend database. Rearchitecting the application is just not an option as it is extremely cost prohibitive and can take years to complete. Rest easy, there is a simple solution to your problem.

A lot of organizations use VoltDB as an in-memory cache to vastly improve the speed and scale their old applications. VoltDB is easy to add to your existing infrastructure either in the cloud or on-premise. Scaling the legacy database is an extremely expensive proposition, requiring additional licenses of an already expensive and outdated solution. VoltDB has a scale-out architecture as opposed to scale-up architectures of legacy systems. You can scale-out by adding VoltDB instances running on inexpensive commodity servers. Best-of-all, you can turbo charge your old application in just a few hours without any dreaded downtime.

VoltDB Data Architecture: In-Memory Cache

VoltDB can do much more than offloading low latency workloads from your backend database. In addition to sub-millisecond performance and high throughput, you will also need built-in logic, along with other advanced transactional and analytical features that most caching engines lack. With VoltDB you get a proven next generation in-memory operational database that can process billions of transactions/minute, scale horizontally, and take complex decisions referencing thousands of rules in real-time. VoltDB offers:

  • Blazing fast responses at scale with an in-memory, distributed architecture.
  • A future proof investment – full-featured relational database with transactional analytics built-in for a multitude of fast data use cases.
  • A complete plug-and-play solution – you can implement in production in just a few hours. Includes native connectors to ingest streaming data.
  • Freedom from data schema limits that non-relational databases inherently possess.
  • Per-machine efficiency to do more operational work for the same amount of computing resources. This leads to a considerably lower hardware footprint, and easier to manage smaller clusters.
  • The ability to apply business logic in-cache for decision making.
  • Seamless deployment on large-scale distributed cloud environments.
  • Limitless scale by adding nodes, even while the database is running. VoltDB enables your transactional data processing to grow as your business grows.
  • Familiar, full-featured ANSI SQL enabling you to leverage the most widely used and comprehensive database language in the world.
  • Designed to deliver high performance on commodity hardware at a much lower total cost of ownership than legacy databases.
  • Guaranteed ACID compliance.
  • Database replication (active-passive and active-active) – Constantly updated database copies ensure that your data is always safe, VoltDB can read/write cache with immediate consistency across replicas.
  • Active-active high availability ensures that you continue smooth operations even when faced with hardware, software, or network failure. While most NoSQL solutions such as Redis have a Master/Slave node architecture, and as a result a single point of failure.

In-Memory Cache Use Cases

eCommerce Session Management

Websites rely on cookies to track and analyze data about the users navigation patterns, site visits, individual page views, transactions processed on website, social interactions and more. This data is used to manage user sessions in real-time.

Telecommunications Mobility Management

In the hyper competitive telco space, providing a high Quality of Service (QoS) is essential to retain customers. Managing customer mobility to deliver a superior QoS is a challenge; as cell phone calls can easily be dropped while transferring from one cell tower to another. Poor audio and data quality can also be a factor in QoS, due to areas with bad cell coverage. Data collected and aggregated from various sources is analyzed to fine tune and improve QoS.