Data Pruning via Sampling for Storage Optimization
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Solution Overview
Problem
Managing large volumes of data generated by social networks is challenging due to storage footprint and generation rate, making it difficult for operators to analyze user activity and system performance effectively, with existing systems experiencing high latency and inadequate responsiveness.
Innovation Solution
Implementing a data management system that prunes and retains data based on constraints such as age and storage space using sampling techniques, allowing for efficient data retention and removal, thereby optimizing storage and query performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all generated data is retained for analysis, then data completeness is improved, but storage footprint and query latency increase
Solution Approach 1:
The patent extracts and removes redundant or less important data elements from the complete dataset, retaining only the most valuable information for analysis. This extraction process reduces the overall data volume while preserving the essential insights needed for analytics, thereby decreasing query latency without completely sacrificing data completeness.
Solution Approach 2:
The system dynamically adjusts data retention parameters such as sampling rates, retention periods, and data priority weights based on analytical needs and storage constraints. By changing these parameters, the system optimizes the balance between maintaining sufficient data completeness and reducing query latency through selective data retention.
2Duration of action of stationary object
If data retention period is extended, then historical analysis capability is improved, but storage space consumption increases
Solution Approach 1:
The patent segments the retained data into different categories such as hot data (recently accessed), warm data (moderately accessed), and cold data (rarely accessed). Each segment is stored with different retention policies and storage priorities, allowing the system to extend the effective retention period for historical analysis while managing storage space consumption through differentiated retention strategies.
Solution Approach 2:
The system implements periodic data pruning and archival operations that review and adjust retained data at scheduled intervals. This periodic action allows the system to maintain extended data retention periods for historical analysis while periodically removing or archiving older data to control storage space consumption.
3Quantity of substance
If data pruning is performed aggressively, then storage efficiency is improved, but data quality and analytical accuracy deteriorate
Solution Approach 1:
The patent applies different pruning aggressiveness levels to different data segments based on their local quality requirements. Critical data segments with high analytical value undergo minimal or no pruning, while less critical segments can be pruned more aggressively. This local differentiation allows the system to improve overall storage efficiency while preserving data quality in segments where it matters most.
Solution Approach 2:
The system implements feedback mechanisms that monitor analytical accuracy and data quality metrics after pruning operations. Based on this feedback, the system adjusts future pruning aggressiveness to maintain an optimal balance between storage efficiency and data quality, preventing过度 pruning that would compromise analytical accuracy.
4Measurement precision
If sampling rate is increased for data retention, then data representativeness is improved, but remaining storage requirements increase
Solution Approach 1:
The patent applies partial sampling strategies where different sampling rates are used for different data types, time periods, or priority levels. Instead of uniformly increasing the sampling rate across all data, the system selectively applies higher sampling rates only to critical segments, thereby improving data representativeness where needed while controlling overall storage requirements through selective partial action.
Data Source
AI summary
Techniques provided herein allow for management of data. In various embodiments, systems and methods prune and retain data being managed by a data management system, where the managed data can include log data aggregated from one or more servers for analysis purposes. According to some embodiments, pruning can be triggered according to one or more constraints, such as the age of managed data (e.g., retain only 30 days of managed data) or the memory space required to store the managed data (e.g., retain only 100 GB worth of managed data). The constraints that trigger data pruning can be based on a data retention policy. When triggered, pruning can be performed on a fraction of the managed data stored based on the data retention policy (e.g., 3 days of full managed data, 27 days of pruned managed data). The pruning may be performed by sampling, at a desired rate, the managed data.