Unified Query Processing Across Streaming and Indexed Machine Data
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Solution Overview
Problem
Current tools lack the ability to efficiently search and analyze large sets of raw machine data from diverse sources, making it challenging to derive insights due to the complexity and volume of data generated in IT environments.
Innovation Solution
A data intake and query system that utilizes a flexible schema and late-binding schema to process and store machine data, allowing for real-time search and analysis through a graphical programming system that facilitates data processing pipelines and indexing, enabling unified processing of queries for both indexed and streaming data.
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 reduced because most generated data is discarded
Solution Approach 1:
The system performs preliminary indexing of all generated data without discarding any information. Indexes are created in advance that enable efficient retrieval while preserving the complete raw data set for later flexible analysis, thus achieving both retrieval efficiency and data flexibility.
Solution Approach 2:
The system creates index copies of the raw data that enable efficient searching and retrieval. These indexes are separate from the original data storage, allowing the complete raw data to be preserved while providing fast access paths through the index structures for specific data items.
2Adaptability or versatility
If massive quantities of raw data are stored for later retrieval, then data flexibility and completeness are improved, but search and analysis complexity increases
Solution Approach 1:
The system segments the large raw data set into organized index structures that divide the data into manageable, searchable units. This segmentation maintains data completeness while reducing search complexity by providing structured access paths through multiple index layers.
Solution Approach 2:
The system introduces index structures as intermediary layers between the raw data storage and the search/analysis operations. These indexes act as mediators that simplify complex searches by providing pre-organized access paths without requiring direct scanning of the entire raw data set.
3Measurement precision
If separate tools are used to search different data systems, then specialized search capabilities are improved, but unified analysis capability deteriorates because results must be collected piecemeal
Solution Approach 1:
The system implements a universal search interface that can query across multiple diverse data systems through a single unified tool. This multi-functional search capability maintains specialized search precision for different data types while eliminating the need for separate tools by providing a common access point that handles all data systems uniformly.
Data Source
AI summary
Systems and methods are described for unified processing of indexed and streaming data. A system enables users to query indexed data or specify processing pipelines to be applied to streaming data. In some instances, a user may specify a query intended to be run against indexed data, but may specify criteria that includes not-yet-indexed data (e.g., a future time frame). The system may convert the query into a data processing pipeline applied to not-yet-indexed data, thus increasing the efficiency of the system. Similarly, in some instances, a user may specify a data processing pipeline to be applied to a data stream, but specify criteria including data items outside the data stream. For example, a user may wish to apply the pipeline retroactively, to data items that have already exited the data stream. The system can convert the pipeline into a query against indexed data to satisfy the users processing requirements.