How to optimize the filtering of hot spot data to improve data synchronization efficiency?

Improve data synchronization efficiency: hot data filtering strategy

Efficient filtering of hotspot data is the key to data synchronization. The scenario discussed in this article is: the upstream system and the downstream system synchronize account flows through notification and timed polling. Due to frequent polling and no data updates for most accounts, the upstream system load is too high. Therefore, we need to optimize the strategy and only synchronize hotspot data.

The initial solution considers using Redis cache to maintain hotspot accounts, leverage zset or set storage, and set expiration time and LRU elimination strategy. However, a large number of accounts may cause bigkey problems and reduce cache utility.

The core of the problem is: how to identify the accounts that have changed since the last synchronization?

The best solution is: the upstream system provides an interface and returns the list of account IDs that have been changed within the specified time period according to update_time . Downstream systems only need to synchronize based on this list to avoid invalid queries and significantly improve efficiency. This will effectively solve the problem of excessive load on upstream systems and ensure the timeliness and accuracy of data synchronization.

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