Query Processing Engine Selectively Assigning Join Operations
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Solution Overview
Problem
Existing data processing systems face inefficiencies in distributing workload across different locations due to varying processing capacities and limitations, such as limited communication bandwidth and memory constraints, which can impact the performance of join operations in query processing.
Innovation Solution
Implementing a query processing engine that selectively assigns join operations to remote query processing engines based on processing constraints, such as memory utilization, to optimize the execution of aggregation queries by determining which operations can be efficiently executed remotely or locally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If join operations are executed remotely to distribute workload, then processing throughput is improved, but memory constraints and communication bandwidth limitations worsen performance
Solution Approach 1:
The patent segments the join operation into two distinct phases: a semi-join phase executed remotely that filters data based on join conditions, and a final join phase executed locally that combines results. This segmentation allows the remote system to process only relevant data subsets, reducing memory requirements while maintaining processing throughput benefits.
Solution Approach 2:
The patent applies partial action by executing only the filtering portion of the join operation remotely (semi-join), rather than completing the entire join locally. This partial remote execution reduces the data volume that needs to be transferred and processed in memory, addressing memory constraints while still leveraging remote processing capabilities.
2Reliability
If join operations are executed locally to ensure sufficient processing capacity, then processing reliability is improved, but communication bandwidth consumption and remote resource utilization worsen
Solution Approach 1:
The join operation is segmented into remote semi-join execution and local final join execution. The remote semi-join filters data to produce smaller result sets, reducing the communication bandwidth required to transfer data between remote and local systems while maintaining processing reliability through local completion of the join operation.
Solution Approach 2:
The patent extracts the filtering function from the complete join operation and executes it remotely as a semi-join. This extraction removes the computationally intensive filtering portion from the local system, reducing local processing load and communication bandwidth requirements while maintaining result accuracy through subsequent local join execution.
3Productivity
If all join operations are executed remotely to maximize distributed processing, then resource utilization is improved, but complexity of managing remote processing constraints worsens
Solution Approach 1:
The patent implements dynamic constraint evaluation that adapts to remote system conditions. The query processing engine evaluates memory availability, communication bandwidth, and other constraints at runtime, dynamically determining whether to execute semi-joins remotely or locally. This dynamic approach maximizes resource utilization while managing complexity through adaptive decision-making rather than rigid rules.
Solution Approach 2:
The system incorporates feedback mechanisms where the query processing engine receives information about remote system constraints and adjusts its execution plan accordingly. This feedback loop allows the system to optimize resource utilization by routing operations based on actual remote system capacity, while managing complexity through automated adaptation rather than manual configuration.
Data Source
AI summary
Join operations may be selectively assigned from local data processing to remote data processing. Aggregation queries directed to distributed data sets may have query plans generated that include join operations. Some join operations may be executed locally while others may be assigned for remote execution at a remote data processing engine. Join operations may be identified and evaluated for remote execution according to a processing constraint for the remote data processing engine. For those join operations that satisfy the remote data processing engine, the query plan may be modified to assign the join operation for remote execution. A result for the aggregation query may be generated according to the modified query plan and the result sent in response to the aggregation query.