Dataflow Graph Expectations for ETL Query Dependency Validation
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Solution Overview
Problem
Data engineers face challenges in orchestrating, validating, and tuning declarative queries in ETL pipelines due to the need to manually reason about and specify the order of operations, which can lead to inefficiencies and resource wastage.
Innovation Solution
A system for dataflow graph processing that automatically generates a DAG based on query dependencies, allowing for in-memory graph generation and verification without materializing tables, and includes a dataflow graph analyzer and tester to identify errors and enforce expectations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If declarative queries are used in ETL pipelines, then ease of operation is improved, but device complexity increases due to the need to manually orchestrate and validate multiple disparate queries
Solution Approach 1:
The patent introduces a dataflow graph as an intermediary representation that automatically captures and manages the orchestration logic between declarative queries. The graph analyzer and tester act as mediators that automatically validate query dependencies and detect errors, eliminating the need for manual orchestration while preserving the simplicity of declarative query writing.
Solution Approach 2:
The system enables self-service by automatically generating dataflow graphs from declarative queries, performing dependency analysis, and detecting errors without requiring manual intervention. The dataflow graph structure self-organizes the query execution plan, and the automated tester self-validates the correctness of query compositions.
2Reliability
If manual orchestration of queries is performed, then reliability can be maintained through validation, but loss of time increases due to the effort required to reason about and tune queries
Solution Approach 1:
The patent performs preliminary action by automatically analyzing query dependencies and generating dataflow graphs before query execution. The graph tester预先 detects potential errors and validates the query composition, ensuring reliability is maintained while eliminating the time-consuming manual reasoning and tuning processes.
3Productivity
If in-memory graph generation is used, then productivity is improved by reducing materialization time, but use of energy increases due to in-memory processing requirements
Solution Approach 1:
The patent changes the parameter of data representation from materialized tables to in-memory graph structures. This parameter change enables faster dependency analysis and error detection by working with lightweight graph representations rather than heavy table materializations, improving productivity while the energy consumption is managed through efficient graph traversal algorithms.
Data Source
AI summary
A system for dataflow graph processing comprises a communication interface and a processor. The communication interface is configured receive an indication to generate a dataflow graph, wherein the indication includes a set of queries. The processor is coupled to the communication interface and is configured to: determine dependencies of each query in the set of queries on another query; determine a DAG of nodes based at least in part on the dependencies; insert a node in the DAG of nodes to generate an updated DAG to enforce an expectation; determine a dataflow graph based on the updated DAG; and provide the dataflow graph.