Stream Data Operator Graph Execution Order Control
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Solution Overview
Problem
Current stream data processing methods face challenges in achieving low latency and efficient execution control, particularly in handling recursive queries and dealing with the limitations of heartbeat tuples, which result in processing delays and overheads.
Innovation Solution
The proposed method involves forming a single operator graph, defining an operator execution order, and using ignition operators to control the execution of operators in a loop, eliminating the need for heartbeat tuples by ensuring all operators are executed at their designated times, thus avoiding latency and enabling recursive query execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If heartbeat tuples are used to control operator execution in stream data processing, then operator execution timing can be synchronized, but processing latency increases and overhead is introduced
Solution Approach 1:
The patent extracts and eliminates the heartbeat tuple mechanism from the stream data processing system. By removing this intermediary synchronization mechanism, the system achieves operator execution timing control directly through the natural flow of stream data and operator dependencies, thereby eliminating the latency and overhead associated with heartbeat tuples while maintaining execution synchronization.
Solution Approach 2:
The patent enables operators to self-regulate their execution timing based on their own data dependencies and the natural arrival of stream data. Each operator determines when it should execute based on its input requirements and output deadlines, without needing external heartbeat signals. This self-service approach eliminates the synchronization overhead while maintaining proper execution ordering.
2Adaptability or versatility
If traditional stream data processing methods are used, then processing can be performed, but recursive queries cannot be executed and latency cannot be reduced
Solution Approach 1:
The patent introduces dynamic operator execution ordering that adapts to the specific needs of each query type. For recursive queries, the system dynamically determines execution order based on data flow dependencies and recursion depth requirements. This dynamic approach enables recursive query execution while maintaining low latency by executing operators as soon as their inputs are ready, rather than following a fixed execution schedule.
Solution Approach 2:
The patent performs preliminary analysis of operator dependencies and data flow patterns to pre-determine optimal execution orders for different query types. For recursive queries, the system pre-identifies execution paths and prepares operator chains in advance, enabling immediate execution when data arrives without waiting for heartbeat synchronization signals.
Data Source
AI summary
A network interface receives stream data and a processor processes the stream data. The processor: constructs an operator graph comprising information regarding a plurality of operators for processing the stream data based on a query; assigns an operator execution order to the plurality of operators based on an inter-operator input and output relation, with respect to all of the operators on the operator graph; extracts a set of the operators that maintains the stream data assigned with a time stamp, a time of which is earlier than or equal to a reference time, in an input queue; and executes processing of the stream data assigned with the time stamp, having the earliest time, in the operator assigned a minimum operator graph execution order among the operators and maintaining the stream data assigned with the time stamp having the earliest time, among the extracted set of the operators.