How to filter and synchronize hotspot data to improve the efficiency of large-scale data synchronization?

Hotspot data optimization strategies in large-scale data synchronization

In large-scale data synchronization, efficient screening of hot data is crucial. Suppose there is an upstream system based on notification and timed polling mechanisms for synchronizing account flows. High-frequency batch polling leads to a large number of synchronous requests for unchanged accounts, which puts huge pressure on the upstream systems. Therefore, we need to optimize our strategy and only synchronize the changing hotspot data.

The initial solution considers using Redis cache, checking whether the cache exists when polling regularly, synchronizing if it exists, otherwise skipping. At the same time, set up a full synchronization timing task. Use Redis ZSet or Set to store your account, set the expiration time, and adopt the LRU memory elimination strategy with TTL. However, a large number of accounts may cause BigKey problems, and the LRU strategy effect is difficult to evaluate, and it is no different from full query when the cache is too large.

Better solutions should start from the perspective of downstream systems and focus on:

Which accounts have changed since the last sync?

Ideally, the upstream system should provide an interface to return a list of account IDs that have changed after a specified time point (e.g., query based on update_time ). Downstream systems only need to synchronize data based on this list to avoid invalid queries and significantly improve efficiency. This is more efficient than relying on caches, avoiding the complexity and potential problems of cache management.

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