GridGain vs Redis: The Advantages of In-Memory Computing Over Caching

Learn why HTAP applications outgrow Redis caching and how GridGain adds SQL, ACID transactions, MPP, and real-time analytics.

  • Overcome Redis cache-aside data freshness and update complexity
  • Accelerate SQL applications with ANSI-99 SQL and distributed JOINs
  • Run HTAP with collocated processing to reduce network bottlenecks
  • Enable streaming analytics and continuous queries at scale
  • Add machine learning and deep learning with MPP-style execution
  • Use native persistence for durable, scalable in-memory database workloads

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About this white paper

Modern digital experiences require real-time responsiveness across transactions and analytics (HTAP). The white paper explains why distributed caching solutions like Redis can help offload databases, but often fall short when applications need analytics, decision automation, and compute at scale.

It walks through four Redis limitations—data updates, lack of SQL, HTAP scaling bottlenecks, and limited support for streaming + ML/DL—and contrasts them with GridGain’s approach: read-through/write-through, ANSI-99 SQL, distributed ACID transactions, and MPP “collocated processing” that sends code to the data to avoid moving big datasets over the network.

A real-world example (HomeAway) illustrates why moving data to the application can break down at peak loads, and why collocating compute with in-memory data can eliminate network bottlenecks for real-time decisions.

Distributed-caching products such as Redis… do not support SQL, the most common analytics protocol.

GridGain vs Redis: The Advantages of In-Memory Computing Over Caching

Learn why Redis falls short for real-time analytics and HTAP—and how the GridGain in-memory computing platform delivers true speed and personalization.

Get the full white paper