Query Coordinator for Distributed Data Search
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Solution Overview
Problem
Current data intake and query systems face challenges in seamlessly searching and analyzing diverse data types from various data sources, including external systems, due to limited scope and unidirectional processing flows, which restricts the ability to route data to different destinations and integrate insights from multiple data systems.
Innovation Solution
A data intake and query system that extends search and analytics capabilities by employing a search process master and query coordinators, coupled with a scalable network of distributed nodes, allowing for the collection and processing of data from diverse data systems and presenting harmonized search results, thereby enabling analysis across multiple data sources including MySQL, PostgreSQL, NoSQL databases, and cloud storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in separate data systems with unidirectional processing flows, then data storage capacity is improved, but the ability to search and analyze data across multiple data sources deteriorates
Solution Approach 1:
The query coordinator enables a single query to operate across multiple diverse data sources (relational databases, NoSQL databases, cloud storage, data lakes) by translating the query into data source-specific queries and coordinating their execution, making the search system universal rather than source-specific
Solution Approach 2:
The query coordinator acts as an intermediary between the user's search query and multiple heterogeneous data sources, translating the query into appropriate formats for each data source type and coordinating the aggregation of results, thereby enabling cross-source search without requiring changes to the underlying data systems
2Productivity
If distributed processing is implemented across multiple nodes, then processing capacity is improved, but task distribution and coordination complexity increases
Solution Approach 1:
The system segments the overall query processing task into multiple sub-tasks distributed across different worker nodes, with each node processing a specific portion of the data or a specific data source, thereby parallelizing processing while maintaining manageable task units
Solution Approach 2:
The query coordinator serves as a central intermediary that manages task distribution to worker nodes, monitors their execution status, and aggregates results, thereby coordinating distributed processing without requiring complex peer-to-peer communication between nodes
3Loss of information
If diverse data types from multiple external systems are integrated, then analytical insight quality is improved, but system integration complexity increases
Solution Approach 1:
The query coordinator implements a universal query translation mechanism that handles multiple data source types (SQL databases, NoSQL databases, cloud storage, data lakes) through a common interface, enabling integration of diverse data types without requiring separate integration logic for each source
Solution Approach 2:
The system dynamically adjusts query parameters and translation strategies based on the specific data source type being accessed, transforming the universal query into appropriately formatted queries for each data source while maintaining consistent coordination logic
Data Source
AI summary
Systems and methods are disclosed for processing data chunks from different data sources at an execution node in a distributed execution environment. The execution node receives data chunks from different sources and combines data from groups of data chunks into partitions based on an associated data source. The execution node executes the partitions using one or more processors.