Hazelcast
In-memory data grid and stream processing company
Hazelcast is a company that develops an in-memory data grid and stream processing platform used to store, distribute, and process data across a cluster of servers with very low latency. Its platform combines distributed caching, computing,…
Definition
Hazelcast is a company that develops an in-memory data grid and stream processing platform used to store, distribute, and process data across a cluster of servers with very low latency. Its platform combines distributed caching, computing, and real-time stream processing capabilities designed to sit alongside or in front of existing databases to accelerate data-intensive, high-concurrency applications that would otherwise be bottlenecked by disk-based reads and writes.
Overview
Hazelcast was created to give applications a way to hold and process frequently accessed data across a cluster's combined memory rather than relying solely on a central database that becomes a bottleneck under high concurrent load. The core idea behind an in-memory data grid is to partition data across many machines' RAM, so that as an application scales out, its effective memory capacity and processing throughput scale with it, rather than being capped by a single database server. Mechanically, the Hazelcast platform distributes data structures such as maps and queues across cluster nodes using automatic partitioning, replicating each partition to at least one backup node for fault tolerance without requiring a separate coordinator to manage every read and write. On top of this storage layer, Hazelcast added a stream processing engine, allowing continuous computations over data as it arrives rather than only querying data already at rest, positioning the platform to handle both traditional caching use cases and real-time event processing within the same cluster. Among in-memory platforms, Hazelcast is most directly compared to GridGain, both offering distributed in-memory data grids with SQL and compute capabilities, differing mainly in stream processing maturity, specific feature sets, and licensing approach. Compared to Redis, which is fundamentally a single-threaded key-value store with data structure commands, Hazelcast's data grid model is built around distributed data structures with cluster-wide computation baked in from the start, aiming at a different point on the spectrum between simple caching and full distributed compute. In practice, organizations use Hazelcast to cache frequently accessed data in front of slower backend databases, to build real-time fraud detection or monitoring pipelines that process streaming events as they arrive, and to share computed state across microservices that would otherwise each need to query a central database repeatedly. It is commonly embedded directly inside Java-based applications given its origins as a Java library, though it also supports clients in other languages. The trade-offs are typical of in-memory data grid technology broadly: keeping data resident in cluster memory is more expensive than disk-based storage at the same data volume, operating and tuning a distributed cluster requires more expertise than a single caching instance, and teams with simpler caching needs may find a lighter tool like Redis or Memcached sufficient without the operational overhead of a full data grid and stream processing platform. As with any distributed system, correctly reasoning about partition placement and backup counts becomes important once a cluster grows beyond a handful of nodes.
Key Features
- Automatic partitioning of distributed data structures across cluster nodes
- Built-in stream processing engine for continuous event computation
- Backup replication of partitions for fault tolerance
- Distributed maps, queues, and other collection-like data structures
- SQL querying support over distributed in-memory data
- Native Java library origins with multi-language client support
- Near-cache option for reducing repeated remote lookups
- Designed to scale memory and compute capacity with cluster size