Prefix-Based Partitioned Data Storage Load Distribution
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Solution Overview
Problem
Current data storage systems in network computing environments face inefficiencies in dynamically managing storage resources based on request activity, leading to potential bottlenecks and reduced performance due to inadequate load distribution across partitions.
Innovation Solution
Implementing a data storage service that partitions data storage nodes based on request activity by tracking key prefixes, splitting partitions when a threshold is exceeded, and dynamically adjusting computing resources to distribute load effectively, using a modified hierarchical heavy hitters algorithm to track frequent prefixes and decay counters to manage memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data storage nodes are statically allocated without dynamic partitioning, then system simplicity is maintained, but load distribution efficiency deteriorates and performance bottlenecks occur
Solution Approach 1:
The patent implements dynamic partitioning of data storage nodes based on real-time request activity analysis. The system continuously monitors request patterns, identifies hot prefixes, and dynamically creates or merges partitions to optimize load distribution. This dynamic approach allows the system to adapt to changing data access patterns without manual intervention, resolving the contradiction between productivity improvement and complexity increase.
Solution Approach 2:
The patent segments the data storage space into multiple partitions based on key prefixes. By dividing the key space into manageable segments (partitions), the system can independently manage and optimize each partition's load. This segmentation enables parallel processing of requests across different partitions, improving overall productivity while maintaining manageable complexity through automated partition management.
2Productivity
If storage resources are fixed without dynamic adjustment, then system stability is maintained, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements a feedback mechanism that continuously monitors request activity, prefix frequencies, and storage node performance. Based on this feedback, the system automatically adjusts partition configurations, creates new partitions for high-demand prefixes, and merges low-activity partitions. This closed-loop feedback system optimizes resource utilization while maintaining stability through automated, controlled adjustments.
Solution Approach 2:
The patent dynamically changes storage system parameters such as partition count, partition assignment rules, and resource allocation based on monitored performance metrics. By adjusting these parameters in response to real-time data, the system optimizes resource utilization efficiency without compromising stability, as changes are made based on measured performance rather than fixed configurations.
3Loss of time
If all data is stored in a single partition, then system simplicity is maintained, but response time for frequently accessed data deteriorates
Solution Approach 1:
The patent applies local quality optimization by creating dedicated partitions for frequently accessed data prefixes. Instead of uniform treatment of all data, the system identifies hot prefixes through request pattern analysis and creates specialized partitions for these prefixes, allowing them to be accessed with optimized latency. This local optimization improves response time for critical data while the overall system maintains manageable complexity through automated identification and isolation of hot data.
Data Source
AI summary
A computing resource service provider may store user data in a distributed data storage system. The distributed data storage system may contain one or more storage partitions configured to store based at least in part on prefixes of keys contained in a key-value store, where the size of the keys may vary. The one or more storage partitions may track requests to access data where the requests include a key such that the data may be located by the service provider based at least in part on the key. When a request is received a counter associated with the prefix included in the request may be incremented, the counter may be configured to decay over time. If the counter exceeds a threshold the service provider may split the prefix associated with the counter and generate new partitions responsible for the split prefix.