Bucket Catalog Query Routing for Data Intake Systems
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Solution Overview
Problem
Current data systems face challenges in efficiently searching and analyzing large sets of diverse, rapidly growing machine data, particularly in providing user-friendly tools for visually identifying data subsets of interest within massive quantities of raw machine data.
Innovation Solution
A data intake and query system that utilizes a flexible schema for extracting information from events, allowing for late-binding schema application during search time, and employs containerized state-free indexing nodes and search nodes to facilitate efficient query execution and data retrieval across a shared storage system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is pre-processed and stored in pre-specified subsets, then query execution speed is improved, but data flexibility and analysis capability are worsened
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing bucket metadata (size, location, timestamp ranges) in a catalog before queries are executed. This allows the system to quickly identify relevant data buckets during query execution without pre-processing the actual data subsets, thus maintaining both query speed and data flexibility.
Solution Approach 2:
The patent introduces a bucket catalog as an intermediary layer between the query system and the raw machine data. The catalog stores metadata about data buckets without containing the actual data, enabling efficient query routing while preserving access to all raw data for flexible analysis when needed.
2Adaptability or versatility
If all raw machine data is stored for later retrieval, then data flexibility is improved, but search and analysis efficiency are worsened
Solution Approach 1:
The patent segments raw machine data into time-based buckets with metadata stored in a catalog. This segmentation allows the system to store all raw data for flexibility while enabling efficient search by quickly identifying and retrieving only relevant buckets based on query parameters like time ranges and data types.
Solution Approach 2:
The bucket catalog serves as an intermediary that indexes segmented data buckets by metadata (time ranges, data types, sizes). This allows efficient search through the catalog rather than scanning all raw data, while still maintaining access to complete raw data sets for comprehensive analysis.
3Productivity
If pre-processing is applied to reduce data volume, then processing efficiency is improved, but data completeness and insight quality are worsened
Solution Approach 1:
The patent extracts only the essential metadata (bucket size, location, timestamp ranges, data types) from the raw machine data and stores it in a catalog, while leaving the complete raw data intact in time-based buckets. This extraction enables efficient processing for query routing without losing any information from the original data sets.
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 uses a search node catalog to identify search nodes that are available to execute the query and uses a bucket catalog to identify buckets to be searched. The data intake and query system executes the query using the identified bucket and search nodes.