The invention provides a performance optimization method and
system for cross-data-source
paging query, and relates to the technical field of
data processing, and the method comprises the steps: calculating a data distribution weight and a
data source routing table, determining an optimal
data access path, and splitting a
paging query request into sub-query tasks; dynamically adjusting the number of parallel query threads based on the load state and distributing sub-query tasks; establishing a local index and adaptively adjusting an index structure; dynamically adjusting the
connection number of the
connection pool and acquiring data in batches; performing feature analysis according to a query result, determining the size of a
data fragment, and selecting an optimal merging strategy to merge the data; constructing a
cache access mode graph, and aggregating and storing related data based on the access association degree; and optimizing a performance strategy set through
reinforcement learning, adjusting a cache updating interval, a
data access path and a
feature matrix, and finally obtaining a query result. According to the method, the cross-data-source
paging query efficiency can be effectively improved, the query
delay is reduced, and the overall performance of the
system is improved.