Raw Data Size Allocation for Machine Data Storage
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Solution Overview
Problem
Modern data centers face challenges in managing and indexing large volumes of machine-generated data due to its unstructured nature, making it difficult to perform semantic indexing and searching operations effectively.
Innovation Solution
A system is implemented that allows users to purchase and manage data storage capacity based on the size of raw machine data, with indexing and processing operations such as metadata addition, compression, and replication affecting the storage footprint, while monitoring and managing storage consumption through graphical user interfaces and automated actions when thresholds are exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If storage capacity is allocated based on processed data size including metadata and replication, then actual storage footprint is covered, but user storage consumption appears inflated compared to raw data size
Solution Approach 1:
The patent segments storage capacity tracking into two distinct components: raw data size (original user data) and processed data size (including metadata, compression, and replication). This segmentation allows the system to separately track and report both measurements, providing users with transparent visibility into their actual storage consumption while maintaining accurate capacity allocation based on processed data size.
2Productivity
If monitoring tracks processed data size with metadata and replication factors, then storage capacity management is accurate, but user perception of storage consumption diverges from expectations
Solution Approach 1:
The patent implements a feedback mechanism that provides users with dual perspectives on storage consumption: processed data size for accurate capacity management and raw data size for user-friendly consumption tracking. The system continuously monitors both metrics and provides feedback through interfaces that show the relationship between raw data input and processed storage requirements, helping users understand their storage usage without causing confusion.
3Ease of operation
If storage allocation is based on raw data size only, then user consumption is clear and predictable, but actual storage capacity requirements may not be met
Solution Approach 1:
The patent adds another dimension to storage tracking by maintaining parallel records of both raw data size and processed data size. This dimensional approach allows the system to use raw data size for user-facing consumption metrics (providing predictability) while simultaneously using processed data size for internal capacity management (ensuring sufficiency). The system reconciles these two dimensions through configured factors that account for metadata overhead and replication requirements.
Data Source
AI summary
Provided are systems and methods for managing storage of machine data. In one embodiment, a method can be provided. The method can include receiving, from one or more data sources, raw machine data; processing the raw machine data to generate processed machine data; storing the processed machine data in a data store; and determining an allocated data size associated with the processed machine data stored in the data store, wherein the allocated data size is the size of the raw machine data corresponding to the processed machine data stored in the data store.