Cache Based Efficient Access Scheduling for Super Scaled Stream Processing Systems

The technology disclosed relates to discovering a previously unknown attribute of stream processing systems according to which client offsets or client subscription queries for a streaming data store rapidly converge to a dynamic tip of a data stream that includes the most recent messages or events. In particular, it relates to grouping clients into bins to reduce a number of queries to the streaming data store by several orders of magnitude when servicing tens, hundreds, thousands or millions of clients. The bin count is further reduced by coalescing bins that have overlapping offsets. It also relates to establishing separate caches only for the current tips of data streams and serving the bins from the caches instead of the backend data store using group queries. Further, the caches are periodically updated to include the most recent messages or events appended to the dynamic tips of the data streams.