Configurable Data Compression Pipelines Across Multiple Data Domains
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Solution Overview
Problem
Existing data compression algorithms are domain-specific, leading to sub-par performance when used across different data domains, and maintaining multiple algorithms is resource-intensive and challenging.
Innovation Solution
A configurable data compressor/decompressor system that uses a shared registry of compression/decompression operators to generate and apply recipes for various data domains through offline, online, or hybrid training, optimizing compression pipelines based on specific data types and constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If domain-specific compression algorithms are used for each data domain, then compression performance for that domain is improved, but device complexity and resource management costs increase
Solution Approach 1:
The patent implements a universal compression system that can handle multiple data domains (text, logs, metrics, traces, etc.) through a single configurable compressor. The system uses a shared operator registry containing domain-specific operators that can be dynamically selected and combined via recipes to achieve domain-optimized compression without maintaining separate compression algorithms for each domain.
Solution Approach 2:
The compression system is segmented into independent, reusable operators (e.g., parsing operators, filtering operators, encoding operators) that can be individually selected and combined. Each operator handles a specific transformation step, and these segmented operators are orchestrated through recipes to create domain-specific compression pipelines without requiring complete separate algorithms.
2Manufacturing precision
If multiple domain-specific compression algorithms are maintained, then compression performance across different domains is improved, but ease of operation and update management deteriorate
Solution Approach 1:
A single universal compressor provides domain-optimized compression for multiple data types through configurable recipes. The system automatically selects appropriate operators from the shared registry based on the data domain, eliminating the need for manual selection and management of multiple separate compression algorithms.
Solution Approach 2:
The compression system performs self-service by automatically detecting the data domain and selecting the appropriate recipe and operators from the registry. The system self-configures the compression pipeline based on the input data characteristics, eliminating manual intervention for algorithm selection and simplifying operation.
3Ease of operation
If a single compression algorithm is used across all domains, then ease of operation is improved, but compression performance for specific domains deteriorates
Solution Approach 1:
The compression system dynamically adapts its configuration based on the data domain being compressed. Instead of using a fixed single algorithm, the system dynamically selects and configures operators from the registry according to the specific requirements of each data type, achieving both ease of operation and domain-optimized performance.
Solution Approach 2:
The system changes its operational parameters (operator selection, pipeline configuration, compression settings) based on the data domain. By adjusting these parameters through recipe selection rather than changing the fundamental algorithm structure, the system maintains ease of operation while achieving domain-specific optimization.
4Manufacturing precision
If domain-specific compression algorithms are used, then compression performance is improved, but loss of time for determining which algorithm to use increases
Solution Approach 1:
Compression recipes are pre-configured and stored in the system, containing predetermined sequences of operators for different data domains. When compression is needed, the system simply selects the appropriate pre-configured recipe rather than determining the algorithm sequence in real-time, significantly reducing the time required for algorithm selection.
Solution Approach 2:
The recipe acts as an intermediary layer between the data input and the operator registry. The recipe translates high-level domain requirements into specific operator sequences, eliminating the need for real-time algorithm determination and reducing selection time while maintaining domain-specific optimization.
Data Source
AI summary
A data service implements a configurable data compressor/decompressor using a recipe generated for a particular data set type and using compression operators of a common registry (e.g., pantry) that are referenced by the recipe, wherein the recipe indicates at which nodes of a compression graph respective ones of the compression operators of the registry are to be implemented. The configurable data compressor/decompressor provides a customizable framework for compressing data sets of different types (e.g., belonging to different data domains) using a common compressor/decompressor implemented using a common set of compression operators.