Query Acceleration Data Store for Multi-Source Analytics
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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 derive 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, extending search and analytics capabilities beyond internal data stores to include external data systems, common storage, and ingested data buffers, enabling scalable analytics across multiple data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is pre-processed and only specified data items are extracted and stored, then retrieval efficiency is improved, but data flexibility and analysis scope are reduced
Solution Approach 1:
The system performs preliminary indexing of specified data items during data ingestion to enable efficient retrieval, while simultaneously preserving the complete raw data in its original form. This dual approach allows both fast access to indexed fields and full flexibility for analyzing any aspect of the raw data later.
Solution Approach 2:
The system applies different processing qualities to different portions of the data: specified data items are extracted and indexed with high structure for efficient querying, while the remaining raw data is preserved with minimal processing to maintain its original flexibility and analysis potential.
2Adaptability or versatility
If massive quantities of raw data are stored for later retrieval, then data flexibility and analysis scope are improved, but storage requirements and system complexity increase
Solution Approach 1:
The system segments data storage into two distinct components: an indexed data structure for efficient retrieval of specified items, and a raw data storage component for preserving complete data flexibility. This segmentation allows the system to handle massive quantities of raw data without proportionally increasing complexity, as each segment serves a specific purpose.
3Adaptability or versatility
If tools search data systems separately and collect results over a network, then data source coverage is improved, but search time and operational complexity increase
Solution Approach 1:
The system merges multiple data sources including external data systems, common storage, and ingested data buffers into a unified queryable interface. The search process master and query coordinators orchestrate searches across all these sources simultaneously, combining results into a single comprehensive response, thereby maintaining broad data source coverage while eliminating the time loss associated with separate searches.
Solution Approach 2:
The system provides a universal search interface that can query across diverse data sources with a single operation. The query coordinators and distributed nodes are designed to handle multiple data source types through a common protocol, enabling the system to perform multi-source searches as efficiently as single-source searches.
Data Source
AI summary
Systems and methods for a data index and query system that utilize a query acceleration data store. An example method includes receiving a query identifying a set of data to be processed and a manner of processing the set of data. A query processing scheme for obtaining and processing the set of data is defined. First partial results of the query stored in a data store are identified, with the first partial results corresponding to a first portion of the set of data. One or more partitions are dynamically allocated to obtain a second portion of the set of data from different data sources. The second portion of the set of data is processed to obtain second partial results. The first partial results and second partial results are combined. The query is executed based on the query processing scheme.