Distributed Query Partition Tracking via Extraction and Intermediary
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Solution Overview
Problem
Current data intake and query systems face challenges in seamlessly searching and analyzing large sets of diverse data from various sources, including structured, semi-structured, and unstructured data, due to limited scope and unidirectional processing flows that restrict the ability to route data to different destinations, leading to isolated search and analytics capabilities.
Innovation Solution
A data intake and query system that extends search and analytics capabilities by employing a search process master and query coordinators combined with a scalable network of distributed nodes, enabling the collection and processing of data from diverse data systems and providing search results across internal and external data sources, including MySQL, PostgreSQL, NoSQL data stores, cloud storage, and Hadoop distributed file systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in a centralized data system for later retrieval and analysis, then data flexibility and analysis capability are improved, but data access time and system complexity increase
Solution Approach 1:
The patent segments the centralized data system into multiple distributed worker nodes, each capable of independent data storage and processing. This segmentation allows parallel data access across multiple nodes simultaneously, reducing overall access time while maintaining the flexibility of a distributed architecture that can handle diverse data types and analysis requirements.
Solution Approach 2:
The patent introduces a new dimension of data access by implementing a distributed network topology instead of traditional centralized access. Data can be retrieved through multiple paths and nodes simultaneously, adding spatial dimensionality to data access operations and significantly reducing access time while preserving analytical flexibility.
2Productivity
If a distributed network of worker nodes is used for data processing, then processing capacity and scalability are improved, but memory consumption for partition tracking increases
Solution Approach 1:
The patent extracts the partition tracking function from individual worker nodes and consolidates it into a dedicated coordination service. This extraction allows worker nodes to focus on data processing while the coordination service manages partition information centrally, significantly reducing the memory burden on each worker node while maintaining overall system processing capacity.
Solution Approach 2:
The patent introduces a coordination service as an intermediary between worker nodes and the partition management system. This intermediary handles partition tracking and assignment, allowing worker nodes to process data with minimal memory overhead for partition information while still achieving high distributed processing capacity through coordinated operation.
3Speed
If partition information is stored in memory for fast access during data processing, then processing speed is improved, but memory limits are exceeded when handling large datasets
Solution Approach 1:
The patent changes the storage parameter of partition information from volatile memory to persistent storage with on-demand loading. Partition metadata is stored on disk and loaded into memory only when needed for specific processing operations, allowing the system to handle large datasets exceeding available memory while maintaining fast processing speeds through efficient caching and memory-mapped file operations.
Data Source
AI summary
Systems and methods are described for distributed processing a query in a first query language utilizing a query execution engine intended for single-device execution. While distributed processing provides numerous benefits over single-device processing, distributed query execution engines can be significantly more difficult to develop that single-device engines. Embodiments of this disclosure enable the use of a single-device engine to support distributed processing, by dividing a query into multiple stages, each of which can be executed by multiple, concurrent executions of a single-device engine. Between stages, data can be shuffled between executions of the engine, such that individual executions of the engine are provided with a complete set of records needed to implement an individual stage. Because single-device engines can be significantly less difficult to develop, use of the techniques described herein can enable a distributed system to rapidly support multiple query languages.