Real-time Log Sampling and Streaming for Latency Reduction
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Solution Overview
Problem
Real-time log analysis systems face high latency in processing and publishing metrics from large volumes of log data, leading to delayed investigation and resolution of issues, which can result in service unavailability and revenue loss, especially during sudden increases in log volume due to events like denial-of-service attacks.
Innovation Solution
Implementing smart sampling and streaming of log data to reduce the volume of log messages processed, allowing for near-real-time availability of estimated metrics and representative data, thereby reducing latency to seconds instead of minutes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the RTLA system processes and analyzes all collected log data to ensure complete and accurate metrics, then measurement precision is improved, but processing time and latency increase significantly
Solution Approach 1:
The patent extracts only the essential information from log data through sampling techniques. Instead of processing every log entry, the system samples a representative subset that captures the key metrics needed for monitoring, thereby reducing processing time while maintaining measurement accuracy.
Solution Approach 2:
The system dynamically adjusts sampling rates and processing parameters based on log volume and system conditions. During normal operation, higher sampling rates maintain precision, while during high-volume periods, the system reduces sampling intensity to reduce latency, accepting approximate metrics temporarily.
2Measurement precision
If the RTLA system collects and processes all log data from tens of thousands of hosts to ensure comprehensive monitoring, then measurement precision is improved, but device complexity and processing load increase
Solution Approach 1:
The patent implements selective sampling that extracts only the necessary log entries for monitoring purposes. By filtering and sampling rather than processing all data, the system maintains monitoring accuracy while significantly reducing the complexity of data processing operations.
Solution Approach 2:
The system segments the log processing workload by dividing data into samples and batches. Instead of handling the complete log stream as a single complex operation, the system processes discrete sampled portions independently, reducing overall system complexity.
3Quantity of substance
If the RTLA system processes high volumes of log data to capture all events and errors, then completeness of data is improved, but processing speed and productivity decrease
Solution Approach 1:
The patent extracts representative samples from large volumes of log data rather than processing every entry. This extraction approach maintains sufficient data quantity for meaningful analysis while dramatically improving processing speed by working with a subset of the total data.
Solution Approach 2:
The system applies partial processing by handling only the necessary portion of data through sampling. Instead of processing all log entries, it processes a strategically selected partial set that provides adequate information for monitoring and alerting functions.
4Measurement precision
If the RTLA system waits for complete log data accumulation before publishing metrics to ensure accuracy, then measurement precision is improved, but response time to potential problems increases
Solution Approach 1:
The patent implements preliminary sampling and metric estimation before complete data accumulation. By continuously sampling log data and publishing estimated metrics in near-real-time, the system enables early detection of problems while the full dataset is still being collected, allowing for preliminary alerts and responses.
Solution Approach 2:
The system uses feedback mechanisms where sampled data immediately triggers metric publishing and alerting, which then feeds back into continuous monitoring. This feedback loop enables rapid response to anomalies while maintaining ongoing refinement of metrics as more data becomes available.
Data Source
AI summary
Technologies are described herein for incorporating streaming and/or sampling in real-time log analysis. Representative samples of log data are extracted from the log files on a number of monitored hosts and streamed in real-time to log processors for processing. The log processors accumulate and process the representative samples of log data, and track a data completeness value representing an indication of a proportion of total log data represented by the representative samples received. The representative samples of log data are merged and collated. Estimated metrics are calculated from the merged and collated representative samples and the data completeness, and the estimated metrics are published to consumers in near real-time.