Microsoft Technology Licensing, LLC

Snowflake Inc.

Walter Zhen Cai

Philip Alan Bernstein

Sangyong Hwang

Wentao Wu

Li Yan

Sourav Sikdar

Wesley A Leung

The subject technology receives a query. The subject technology generates a set of query blocks based on parsing the query. The subject technology stores query block metadata for each query block from the set of query blocks. The subject technology restores a set of logical query block boundaries. The subject technology performs a hash-based query block matching. The subject technology generates, after performing the hash-based query block matching, a final query plan. In an example, if a (same) query is executed again the same logical query blocks would produce the same metadata (e.g., identifier, name and hash). This metadata can be used to match logical query blocks between multiple executions of the same query.

Query block boundary identification

This application describes a data stream processing system for receiving and processing multiple data streams using a stream join model and based on the stream join model having a threshold function for which an output of the stream join model crosses the threshold two or less times (e.g., where the threshold function is a convex function, linear function, monotonic function, or other function having a similar property). The data stream processing system may generate filtered data streams using a number of techniques and algorithms without risk of false negatives and mis2169sing instances where an output of a stream join exceeds or violates a threshold condition. The data stream processing system can significantly reduce processing expense, particularly in cases where one or more devices have limited memory and where caching tuples from incoming data streams consumes significant processing resources.

Optimizing input streams for a stream join model having a threshold function

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Seattle, WA, United States of America

Walter Zhen Cai