Data Sharding and Columnar Compression for Rapid Anomaly Analysis
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Solution Overview
Problem
Existing data analytics methods struggle with slow query performance due to the infeasibility of generating data aggregates and indices across all dimensions in large datasets, leading to inefficient data analysis.
Innovation Solution
A method involving data structuring, compression, and storage techniques that enable efficient sampling and querying of large datasets in a distributed computing environment, allowing for rapid data analysis by structuring data into shards, using columnar compression, and performing multi-pass querying.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data aggregates and indices are generated across all dimensions, then query performance is improved, but it becomes infeasible for large datasets
Solution Approach 1:
The patent segments the large dataset into multiple partitions or shards, each with its own manageable set of aggregates and indices. This allows query processing to be distributed across segments, improving performance without requiring full-dimensional aggregation across the entire dataset. The segmentation enables scalable handling of large data volumes while maintaining query efficiency.
Solution Approach 2:
The patent implements partial aggregation by generating data aggregates and indices only for specific dimensions or subsets of dimensions that are most frequently queried. Rather than creating complete multi-dimensional aggregates across all dimensions, the system selectively creates aggregates for relevant dimensions, reducing storage requirements and computational overhead while still achieving acceptable query performance for common query patterns.
2Quantity of substance
If data is stored in traditional formats, then storage capacity is maximized, but data analysis speed decreases
Solution Approach 1:
The patent changes the storage parameters by organizing data into columnar formats rather than traditional row-based formats. This parameter change enables efficient compression of data while maintaining or improving query performance. Columnar storage allows for better data locality, reduced I/O operations, and optimized compression ratios, thereby increasing both storage capacity and analysis speed simultaneously.
3Measurement precision
If entire datasets are searched, then data analysis accuracy is maximized, but time consumption increases
Solution Approach 1:
The patent performs preliminary actions by pre-computing and storing data aggregates, summaries, and indices before queries are executed. These pre-computed structures enable the system to quickly filter and identify relevant data without scanning entire datasets. The preliminary aggregation preserves analytical accuracy for common query patterns while dramatically reducing query execution time through efficient data retrieval.
Data Source
AI summary
A method for enhancing rapid data analysis includes receiving a set of data; storing the set of data in a first set of data shards sharded by a first field; and identifying anomalous data from the set of data by monitoring a range of shard indices associated with a first shard of the first set of data shards, detecting that the range of shard indices is smaller than an expected range by a threshold value, and identifying data of the first shard as anomalous data.