Secure Dataframe Service for Query Execution Without Cluster Setup
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Solution Overview
Problem
Existing big data systems require developers to invest significant resources in setting up infrastructure and managing compute clusters for data analysis, diverting attention from the actual business logic and increasing complexity and cost.
Innovation Solution
A dataframe as a service system that abstracts infrastructure management, allowing users to input hints and business logic to execute queries, determining data sources, allocating resources, and enforcing security policies, thereby simplifying data analysis and reducing organizational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If developers set up infrastructure and manage compute clusters for data analysis, then data processing capability is improved, but device complexity and time investment increase
Solution Approach 1:
The patent introduces a dataframe service as an intermediary layer between users and the underlying compute clusters. This service automatically manages infrastructure provisioning, resource allocation, and security policies, allowing users to simply submit queries without directly managing complex compute resources. The intermediary handles the complexity of infrastructure management while providing simplified access to data processing capabilities.
Solution Approach 2:
The dataframe service implements self-service by automatically determining data sources, allocating compute resources, and enforcing security policies based on user queries. The system provisions and configures compute clusters autonomously without requiring manual infrastructure setup, and automatically manages the entire lifecycle of compute resources based on query requirements and termination policies.
2Measurement precision
If developers configure resources and query data sources before running evaluations, then data analysis accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The dataframe service performs preliminary actions by pre-configuring compute resources, establishing secure connections to data sources, and pre-establishing security policies before users submit queries. The system automatically determines appropriate data sources and configures necessary infrastructure in advance, so users only need to provide their evaluation queries without manual configuration.
Solution Approach 2:
The service acts as an intermediary that handles the complex configuration tasks between the user's simple query and the underlying data sources. It manages connection setup, resource allocation, and security authentication automatically, allowing users to focus solely on their analysis logic while the intermediary handles all operational complexity.
3Reliability
If extensive applications are used to configure resources and query data sources, then data processing reliability is improved, but ease of operation and development time worsen
Solution Approach 1:
The dataframe service implements self-service by automatically provisioning, configuring, and managing compute resources based on query requirements. It autonomously determines appropriate data sources, allocates necessary compute power, and establishes secure connections without requiring developers to write extensive configuration applications, thereby reducing time investment while maintaining reliable processing.
Solution Approach 2:
The system dynamically adjusts resource allocation parameters and configuration settings based on the specific query requirements and data source characteristics. It automatically modifies compute resource parameters, connection settings, and security policies to match the exact needs of each evaluation, eliminating the need for extensive manual configuration while ensuring reliable and optimized processing.
Data Source
AI summary
The present application discloses a method, system, and computer system for providing a dataframe as a service. The method includes (a) receiving, from a client system, one or more hints identifying parameters for source data; (b) executing a plan for a source dataframe including determining whether accessing the source data for the source dataframe is permitted based at least in part on one or more security policies, wherein the plan is based at least in part on the one or more hints; (c) receiving from the client system a business logic pertaining to a transformation to be applied to the source dataframe; and (d) providing to the client system information pertaining to an execution result obtained based at least in part on the business logic.