Search Node Memory Bucket Copying for Query Efficiency
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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, as they often pre-process data, discarding unprocessed data and limiting analysis to pre-specified subsets, which restricts flexibility and insights.
Innovation Solution
A data intake and query system that utilizes a flexible schema to store and process raw machine data, allowing for late-binding schema application during search time, enabling field-searchable events and efficient query execution across a distributed environment with containerized indexing and search nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tools pre-process data based on anticipated analysis needs, then retrieval and analysis efficiency is improved, but data flexibility and completeness are reduced due to discarding unprocessed data
Solution Approach 1:
The patent segments data into multiple storage tiers: hot data in memory for fast access, warm data on fast storage, and cold data on slow storage. This segmentation allows the system to maintain both efficiency (through fast access to frequently queried data) and flexibility (through preserved access to all raw data in cold storage).
Solution Approach 2:
The patent adds a temporal dimension to data storage by implementing time-based data lifecycle management. Data transitions through storage tiers over time based on access patterns, enabling the system to optimize for both current query performance and historical data preservation without sacrificing either efficiency or flexibility.
2Adaptability or versatility
If all raw data is stored for later retrieval, then data flexibility and analysis completeness are improved, but storage costs and data management complexity increase
Solution Approach 1:
The patent performs preliminary actions by automatically classifying and distributing data to appropriate storage tiers based on predicted access patterns and data characteristics. This preliminary organization reduces the complexity of managing all raw data uniformly, as the system proactively prepares data for future retrieval needs rather than reacting to each query individually.
Solution Approach 2:
The system implements self-service through automated data lifecycle management that uses access patterns and metadata to autonomously move data between storage tiers. This self-service mechanism reduces management complexity by eliminating the need for manual intervention in data placement and retrieval operations.
3Speed
If data is copied from remote storage to memory for query execution, then query speed is improved, but memory usage and data transfer time increase
Solution Approach 1:
The patent applies partial action by copying only the specific data subsets required for current query execution from remote storage to memory, rather than pre-loading all possible data. This approach optimizes query speed for active operations while minimizing unnecessary data transfer time and memory usage.
Solution Approach 2:
The system dynamically adjusts data placement between storage and memory based on real-time query patterns and system state. Frequently accessed data is automatically promoted to memory for fast query execution, while less accessed data remains in remote storage, creating a dynamic balance between query speed and data transfer overhead.
Data Source
AI summary
Systems and methods are disclosed for processing and executing queries in a data intake and query system. The data intake and query system receives a query identifying a set of data to be processed and a manner of processing the set of data. The data intake and query system identifies buckets that are to be searched and stores a copy of buckets in memory associated with one or more search nodes. A search node performs a search on buckets residing in its memory.