Hi, You know CacheStore API that is commonly used for read/write-through relationship of the in-memory data with the persistence storage.
There is also IgniteCache.loadCache method for hot-loading the cache on startup. Invocation of this method causes execution of CacheStore.loadCache on the all nodes storing the cache partitions. Because of none keys are passed to the CacheStore.loadCache methods, the underlying implementation is forced to read all the data from the persistence storage, but only part of the data will be stored on each node. So, the current implementation have two general drawbacks: 1. Persistence storage is forced to perform as many identical queries as many nodes on the cluster. Each query may involve much additional computation on the persistence storage server. 2. Network is forced to transfer much more data, so obviously the big disadvantage on large systems. The partition-aware data loading approach, described in https://apacheignite.readme.io/docs/data-loading#section-partition-aware-data-loading , is not a choice. It requires persistence of the volatile data depended on affinity function implementation and settings. I propose using something like IgniteDataStreamer inside IgniteCache.loadCache implementation. -- Thanks, Alexandr Kuramshin
