How does R handle distributed caching strategies in large-scale analytics systems?

R relies on external distributed caches like Redis or Spark caching rather than native distributed caching.

R itself is not a distributed system, so caching at scale is implemented via external systems. In Spark-based R workflows, data can be cached in memory across cluster nodes. In API-based architectures, Redis or Memcached is used to store intermediate results of expensive computations. This decouples compute-heavy R logic from stateful storage layers, improving scalability and reducing recomputation overhead.