Stage-Pipelined Database Queries for Streaming Throughput
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Solution Overview
Problem
Existing database query execution for streaming data in computing systems suffers from inefficiencies such as low cluster utilization, waiting for input/output resources, and straggler tasks, leading to poor performance and low throughput.
Innovation Solution
Implementing pipelined execution of database queries by scheduling stages of the query in parallel for different batches of streaming data, with stateless stages overlapping and stateful stages executed sequentially to maintain logical correctness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequential batch processing is used to ensure data correctness, then logical correctness is maintained, but resource utilization and throughput deteriorate to 3%-10%
Solution Approach 1:
The patent segments the query execution into multiple independent stages (e.g., scan, filter, aggregate, join) that can be executed in parallel across different batches. Each stage processes data independently and passes results to the next stage, enabling concurrent execution while maintaining correctness through stage dependency management.
Solution Approach 2:
The system performs preliminary actions by pre-processing and buffering data in intermediate storage between stages. This allows subsequent stages to execute without waiting for previous stages to complete entirely, enabling overlapping batch processing while ensuring data readiness and correctness.
2Productivity
If parallel batch execution is implemented to improve throughput, then resource utilization increases, but coordination overhead and scheduling complexity increase
Solution Approach 1:
By dividing the query into discrete, independent stages with well-defined interfaces and data contracts, the system simplifies parallel coordination. Each stage can be scheduled independently with clear input-output dependencies, reducing the complexity of managing parallel execution compared to coordinating fine-grained task synchronization.
Solution Approach 2:
The patent introduces intermediate storage and stage buffers as mediators between parallel execution stages. These intermediaries decouple the timing and coordination requirements of different stages, allowing parallel batches to execute independently while automatically managing data flow and synchronization without complex scheduling logic.
3Productivity
If waiting for I/O resources is avoided through overlapping execution, then resource utilization improves, but data consistency and correctness may deteriorate
Solution Approach 1:
The system performs preliminary data validation, filtering, and transformation in early stages before passing data to subsequent stages. This preliminary processing ensures data consistency and correctness is established early, allowing later stages to execute in parallel without compromising data integrity while improving overall throughput.
Solution Approach 2:
The patent implements feedback mechanisms where each stage monitors data quality, consistency constraints, and processing correctness. This feedback allows the system to detect and correct issues in parallel execution, ensuring data consistency is maintained even when batches overlap and I/O waiting is minimized.
Data Source
AI summary
A database system performs pipelined execution of queries that process batches of streaming data. The database system compiles a database query to generate an execution plan and determines a set of stages based on the execution plan. The database query processes streaming data comprising batches. A scheduler schedules pipelined execution stages of the database query. Accordingly, the database system performs execution of a particular stage processing a batch of the streaming data in parallel with subsequent stages of the database query processing previous batches of the streaming data. The system further maintains watermarks for different stages of the database query.