Increasingly, companies are facing performance and scalability issues for their applications. Users want fast, real-time, and trusted data to make enhanced business decisions, while technology management wants to lower cost and improve operational efficiency.
Despite technical improvements over the years, such as processors or processor cores doubling and quadrupling, enterprises still face unpredictable workloads, increasing data volume and usage, and poorly designed applications. Performance will continue to top the list of data challenges as organizations look to manage growing data volumes, eradicate latency in response times, integrate fragmented data, and juggle data-platform complexity.
What’s an In-Memory Data Grid (IMDG)?
IMDGs are designed to provide high availability and scalability by distributing data across multiple machines. The rise of cloud, social media, and the Internet of Things (IoT) has created demand for applications that need to be extremely fast and capable of processing millions of transactions per second.
After testing, the Hazelcast IMDG was revealed to be the fastest open-source IMDG (Redis 3.2.8/Jedis Client 2.9.0 vs Hazelcast IMDG 3.8 Benchmark), and thus gives applications the capability to quickly process, store, and access data with the speed of RAM. To quickly summarize, Hazelcast shards and distributes data across a cluster of servers, its characteristics are as follows:
- The data is always stored in-memory (RAM) of the servers.
- Multiple copies are stored in multiple machines for automatic data recovery in case of single or multiple server failures.
- The data model is object-oriented and non-relational.
- Servers can be dynamically added or removed to increase the amount of CPU and RAM.
- The data can be persisted from Hazelcast to a relational or NoSQL database.
- A Java Map API accesses the distributed key-value store.