Dynamic Partition Allocation for Multi-Source Data Query Coordination
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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 enterprise systems and open source technologies, due to limited scope and unidirectional processing flows, which restricts the ability to obtain comprehensive insights from large datasets.
Innovation Solution
A data intake and query system that employs a search process master and query coordinators combined with a scalable network of distributed nodes to collect and process data from diverse data systems, enabling search and analytics operations across internal and external data sources, including MySQL, PostgreSQL, NoSQL data stores, cloud storage, and ingested data buffers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is pre-processed to extract specified data items for efficient retrieval, then retrieval efficiency is improved, but data flexibility and completeness are worsened because most raw data is discarded
Solution Approach 1:
The system performs preliminary indexing of raw data at ingestion time, creating searchable indexes without discarding the original data. This allows efficient retrieval through pre-established indexes while preserving the complete raw data for flexible future analysis, resolving the contradiction between retrieval efficiency and data flexibility
Solution Approach 2:
The data storage system serves multiple functions simultaneously: it stores both processed indexed data for efficient retrieval and complete raw data for flexible analysis. This multi-functionality allows the system to support both fast querying and comprehensive data exploration without sacrificing either capability
2Adaptability or versatility
If tools search data systems separately and collect results over a network, then data source coverage is improved, but analysis complexity and time consumption are worsened
Solution Approach 1:
The system merges multiple data sources into a unified search interface, allowing users to query across diverse data systems simultaneously through a single tool. This consolidation eliminates the need to separately search each data source and manually aggregate results, reducing analysis time while maintaining comprehensive data source coverage
Solution Approach 2:
The system introduces a centralized search service as an intermediary between users and multiple data sources. This mediator handles query routing, result aggregation, and coordination across different data systems, simplifying the search process and reducing the time required to analyze data from multiple sources
3Device complexity
If the system processes data unidirectionally from data sources to data stores, then processing simplicity is improved, but search capability scope is worsened
Solution Approach 1:
The system implements dynamic, bidirectional data processing flows that adapt to different operational modes. Data can flow from sources to stores for ingestion, and queries can flow back from stores to sources for searching. This dynamic flexibility allows the system to support both simple unidirectional processing and complex bidirectional searching as needed
Data Source
AI summary
Systems and methods are disclosed for processing queries against one or more dataset sources utilizing dynamically allocated partitions operating on one or more worker nodes. The results of the processing are stored in a dataset destination. The queries can identify data in the one or more dataset sources for processing and a manner for processing the data. In addition, the queries can identify the dataset destination for storing results of the query. To process the query, a query coordinator can dynamically allocate partitions operating on worker nodes to retrieve data for processing, process the data, and communicate the data to the dataset sources. In addition, the query coordinator can dynamically allocate partitions based on an identification of the dataset destination.