Alert Rule Evaluation for Late Arriving Data
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Solution Overview
Problem
Conventional monitoring systems face challenges in accurately evaluating alert rules due to latent data ingestion, leading to false positives and false negatives, and existing solutions require costly and unmanageable latency statistics or increase data store load with retries.
Innovation Solution
A monitoring system that distinguishes between invariant and variant alert rules, using a machine learning model to classify queries and apply optimized evaluation methods to achieve a balance between alert latency, accuracy, and cost of goods sold (COGS), by modifying the scope of alert rule queries to cover previous time windows for invariant rules and re-executing variant rules at fixed intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the monitoring system uses conventional alert rule evaluation methods, then it can detect alerts based on query results, but latent data ingestion causes false positives and false negatives reducing alert accuracy
Solution Approach 1:
The system dynamically adjusts the evaluation approach based on query classification. Invariant rules use a static evaluation method where the query is executed once per time window, while variant rules use a dynamic re-execution method where the query is re-executed at fixed intervals after initial detection to verify alert validity as late-arriving data is incorporated.
Solution Approach 2:
The system performs preliminary classification of queries as increasing or non-increasing using a machine learning model before evaluation. This preliminary action enables the system to select the appropriate evaluation method in advance, preventing false positives and false negatives by matching the evaluation strategy to the query characteristics.
2Reliability
If the system re-executes queries frequently to handle late arriving data, then alert accuracy improves, but the number of queries executed against the data store increases raising COGS
Solution Approach 1:
The system applies partial re-execution only to variant rules that are likely to be affected by late-arriving data, rather than re-executing all queries. The machine learning model identifies which queries benefit from re-execution, performing the action partially and selectively to reduce unnecessary queries and lower COGS while maintaining alert accuracy for critical cases.
Solution Approach 2:
The system applies different evaluation strategies to different query types based on their local characteristics. Invariant rules receive a lightweight single evaluation, while variant rules receive targeted re-executions. This localized approach optimizes resource allocation by applying intensive processing only where needed rather than uniformly across all queries.
3Loss of time
If the system maintains latency statistics for all monitoring agents, then it can optimize alert timing, but the complexity and cost of maintaining these statistics becomes unmanageable
Solution Approach 1:
The system extracts and eliminates the need for maintaining comprehensive latency statistics by using machine learning to classify queries and their expected behavior regarding late-arriving data. Instead of tracking detailed timing information for all agents, the system uses the ML model to predict which queries will be affected by late data and applies appropriate evaluation methods, thereby removing the complexity of maintaining latency statistics while still optimizing alert timing.
4Reliability
If the system executes queries for multiple preceding time windows for invariant rules, then alert accuracy improves by capturing late arriving data, but the number of queries executed increases
Solution Approach 1:
The system segments alert rules into two distinct categories: invariant rules and variant rules. This segmentation enables differential treatment where invariant rules (which are insensitive to late-arriving data) use a simplified evaluation method executing the query once per time window, while variant rules use targeted re-execution. This segmentation reduces the total number of queries compared to evaluating all rules with the more conservative variant approach.
Data Source
AI summary
A monitoring system is configured to distinguish between two types of alert rules— namely, invariant alert rules and variant alert rules—and to apply a different method of alert rule evaluation to each, wherein each alert rule evaluation method deals with the issue of latent data ingestion in a different way. By tailoring the alert rule evaluation method to the type of alert rule being evaluated, the system can apply an optimized approach for each type of alert rule in terms of achieving a trade-off between alert latency, alert accuracy, and cost of goods sold. In an embodiment, the system utilizes a machine learning model to classify a query associated with an alert rule as either increasing or non-increasing. Then, based on the query classification and a condition associated with the alert rule, the system determines if the alert rule is invariant or variant.