Quantized Time Range Indexing for Out-of-Order Log Data
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Solution Overview
Problem
In modern computer systems with numerous services, resources, and applications, managing and searching large volumes of log data becomes complex due to the sheer amount of data and potential delays in data ingestion, leading to inefficiencies in processing and storage, especially when log entries are not in chronological order.
Innovation Solution
The approach involves subdividing log data into equal size blocks based on timestamps, generating an index using encoded bits, and interweaving these bits to create a shorter index value that can be used for efficient querying and storage, allowing for quick retrieval of log data within specific time ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If log data is stored in large volumes without indexing, then storage capacity is maintained, but search efficiency deteriorates
Solution Approach 1:
The patent divides the large log data into equal-size blocks based on timestamp ranges. Each block is assigned a quantized index value derived from interweaving encoded bits of the start and end timestamps. This segmentation allows the system to maintain large volumes of log data while enabling efficient block-level search operations without processing the entire dataset.
2Adaptability or versatility
If log entries are collected and stored without chronological ordering, then data collection flexibility is improved, but search complexity increases
Solution Approach 1:
The patent transforms the search problem by changing the parameter representation. Instead of searching through unsorted log entries chronologically, the system encodes timestamps into quantized index values by interweaving their binary representations. This parameter transformation enables efficient range queries on unsorted data by comparing compact index values rather than full timestamps.
3Measurement precision
If detailed timestamp indexing is implemented, then search precision is improved, but computational resources required increase
Solution Approach 1:
The patent extracts only the essential timestamp information needed for range queries by encoding start and end timestamps into quantized index values. The interweaving process extracts the most significant bits of each timestamp and combines them, creating a compressed representation that preserves temporal ordering information while discarding less significant details. This extraction reduces computational overhead while maintaining sufficient precision for time-range searches.
Data Source
AI summary
A system receives a set of log data generated from one or more computing services. The system identifies a first timestamp and a second timestamp associated with a set of log data. The system generates an index by encoding the quantized first and second timestamps and identifies a prefix length between the first and second timestamps. The prefix length is then used as a basis to interweave the encoded bits associated with the first and second timestamps to generate an index value. The index value may then be used as a key in connection with the index to locate log data to satisfy a query request.