Distributed Data Access System for Containerized Analytics Lineage
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Solution Overview
Problem
Containerized analytics applications face inefficiencies when moved between computing nodes or clusters, leading to repeated processing and high data transfer costs, as they often require re-execution of operations and transfer of raw data, limiting flexibility and increasing resource expenditure.
Innovation Solution
A distributed data access system compares lineages of analytics applications across different containers to determine reusable insights, allowing for the reuse of processed data and reducing the need for re-processing, thereby enhancing efficiency and flexibility by utilizing existing insights and lineages across multiple containers and clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If containerized analytics applications are moved between computing nodes or clusters, then flexibility and resource utilization are improved, but processing time increases due to repeated execution and data transfer costs increase
Solution Approach 1:
The system performs preliminary actions by pre-processing data and generating insights before the analytics application is moved to a new computing node. Lineage information and processed data are prepared in advance and stored, so when the container is relocated, the new node can immediately access and reuse these pre-computed insights rather than re-processing the entire dataset, thereby reducing processing time while maintaining flexibility
Solution Approach 2:
Instead of copying the entire analytics application and its data processing logic to every computing node, the system creates a centralized lineage database that stores metadata about processed data and generated insights. This allows multiple computing nodes to access the same lineage information without duplicating the heavy data processing operations, enabling flexible deployment while avoiding redundant computations
2Ease of operation
If raw data is transferred across clusters to enable analytics processing, then data accessibility is improved, but network traffic and data transfer costs increase
Solution Approach 1:
The system extracts only the essential lineage information and processed insights from the raw data, separating these from the bulk raw data. By taking out only the necessary metadata and processed results, the system enables data accessibility across clusters while minimizing the amount of data that needs to be transferred over the network, thereby reducing network traffic and associated costs
Solution Approach 2:
The lineage database serves as an intermediary between data storage and analytics processing. Instead of transferring raw data directly between clusters for processing, the system uses the lineage database as a mediator that stores processed insights and metadata. This intermediary layer allows computing nodes to access processed data without moving the entire raw dataset, reducing network traffic while maintaining data accessibility
3Reliability
If analytics applications re-execute operations after migration, then data processing completeness is improved, but computational resource usage increases
Solution Approach 1:
The system implements feedback mechanisms where the lineage database records the results of previous data processing operations. When an analytics application is migrated to a new computing node, the system queries the lineage database for feedback information about what has already been processed. This feedback allows the new node to understand which operations have been completed and which need to be re-executed, enabling selective re-processing only where necessary to maintain completeness while avoiding redundant computational resources
Solution Approach 2:
Instead of discarding all processing results when a container is migrated, the system recovers and preserves processed data and insights in the lineage database. The processed information is discarded from the moving container but recovered in the centralized lineage store, allowing the new computing node to access and reuse these results. This approach maintains data processing completeness while avoiding the need to re-compute everything, thereby reducing computational resource usage
Data Source
AI summary
In an example, a distributed data access system compares a first lineage of a first analytics application with a second lineage of a second analytics application. The first analytics application may be run in a first container hosted by a first computing node and the second analytics application may run in a second container hosted by a second computing node. Based on the comparison, the distributed data access system determines whether a first insight generated by the first analytics application can be used in a processing of the second analytics application.