Datastore-Aware Stream Computing for Database Bottleneck Resolution
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Solution Overview
Problem
Stream computing applications face performance issues due to bottlenecks in data output to single databases, leading to increased latency, reduced throughput, and potential data loss, especially when dealing with large volumes of unstructured and continuous data from intelligent devices and sensors.
Innovation Solution
A Datastore-Aware Stream Application (DASA) monitors the performance of multiple databases and dynamically adjusts output by allocating or deallocating resources, including additional databases and computing resources, to maintain efficient data processing and prevent slowdowns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If stream computing applications output data to a single database, then the system structure is simple, but performance bottlenecks occur leading to increased latency and reduced throughput
Solution Approach 1:
The patent segments the single database output into multiple database outputs. The stream computing application is configured to output processed data to multiple databases simultaneously, dividing the data flow into parallel channels. This segmentation eliminates the bottleneck of a single database while maintaining manageable system complexity through structured multi-database architecture.
Solution Approach 2:
The patent transitions from a single-dimension (single database) output structure to a multi-dimensional (multiple databases) output structure. By adding the dimension of multiple database destinations, the system increases data throughput capacity without proportionally increasing complexity, as each database operates independently in its own dimension of data handling.
2Loss of time
If stream computing applications process large volumes of continuous data in real-time, then analytics timeliness is improved, but performance bottlenecks and data loss occur
Solution Approach 1:
The patent implements feedback mechanisms where the stream computing application monitors the performance and status of multiple databases in real-time. Based on this feedback, the system dynamically adjusts data routing, load balancing, and resource allocation to prevent bottlenecks before they cause data loss, while maintaining low latency through continuous optimization of the data flow paths.
Solution Approach 2:
The patent prepares multiple database destinations in advance as buffer pathways. When processing large volumes of continuous data, the system has pre-configured alternative routes and additional database capacity ready to absorb sudden data spikes, preventing data loss and overflow errors before they occur during high-volume processing events.
3Productivity
If additional databases are allocated to handle data output, then throughput is increased, but system complexity and resource management difficulty increase
Solution Approach 1:
The patent creates a universal data output framework that can adapt to multiple database types and configurations. The stream computing application uses a standardized interface and configuration system that works across different database platforms, allowing the system to leverage multiple databases for increased throughput while managing complexity through a unified, multi-functional output architecture that handles various database protocols and formats consistently.
Data Source
AI summary
A stream of tuples is processed by a stream application. The stream application includes a plurality of processing elements that operate on one or more compute nodes, each processing element includes one or more stream operators. One or more databases that are capable of communicating with the stream application are monitored during the processing of the stream of tuples. A potential performance condition of a first database of the one or more databases is detected based on the monitoring. An output adjustment is performed, in response to the potential performance condition.