In-Memory Data Grid

In-Memory Data Grids (IMDGs) process complex data at high speeds, generally for large-scale implementations, with parallelized distributed processing. Utilizing a unified API, and collocating the applications and data in the same memory space, the IMDG minimizes latency and maximizes performance, distributing data and workloads across computers within a network.

GigaSpaces cloud-native IMDG utilizes Space-Based Architecture (SBA), in which the data grid topology is composed of Spaces clusters. Spaces are application clusters, each of which is comprised of a number of self-sufficient Processing Units (PUs). Each PU is responsible for processing the services and data that are sent to the space partition that it runs.

Co-locating data and applications within IMDGs enables enhanced scalability of both the data and applications, and reduces latency and bottlenecks.

Smart DIH’s hosting layer provides a high performance, highly available and resilient data store for the use of near real-time digital services: 

  • Balances between cost and performance
  • Enables business growth 
  • Handling varying levels of data consumption
  • Provides a unified and easy to understand data model for app developers 
  • Supports a mix of structured and semi-structured data, and data enrichment
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The GigaSpaces IMDG empowers real-time event-driven microservices and distributed applications at high scale:

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Optimizes hardware costs with multi tiered storage

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Supports structured, semi-structured and unstructured data from data streams, data lakes, data warehouses and legacy infrastructure

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Provides complete scalability of both data and application load with horizontal scaling

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Deployed on-premises, hybrid, and multi-cloud environments

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Enables business continuity for immediate recovery at no data loss

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Provides a fully transactional, ACID-compliant in-memory
data grid

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Offers auto-recovery and self-healing with multi-region support

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Supports synchronous and asynchronous persistence

Smart DIHs IMDG is a high-performance data store that serves digital services with instantaneous access to data: 

  • Distributed Processing with Partitioned Space:
    • Increases overall RAM capacity of a single space by engaging RAM of multiple nodes
    • Enables parallel processing of queries by distributing the workload over multiple machines 
  • Multi-indexing Support to accelerate read and take operations, with no downtime to add indexes via code.
  • Space-based Remoting enables running code close to the data and running code at the server side, avoiding unnecessary network latency and allowing simple client applications to utilize distribution processing and achieve high availability 
  • Broadcast Object Types overcome the high-performance cost of cross-partition joins by duplicating relevant object types across all partitions; used for broadcasting of frequent referenced lookup or dictionary tables 
  • Interacted through standard JDBC/ODBC as well as Rest API and Java interfaces

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