Distributed Metrics Manager In-Memory Aggregation Pipeline
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Solution Overview
Problem
Current performance monitoring tools in high-scale environments face scalability issues due to high I/O operations and latency when processing large volumes of metrics, limiting their ability to support real-time analytics.
Innovation Solution
A scalable distributed metrics manager processes metrics in a pipeline with multiple aggregation stages, maintaining results in memory for fast access and analysis, and persisting them to persistent storage, reducing I/O operations and enabling real-time analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If metrics are saved to a database for analysis, then metrics can be stored and processed, but the high number of I/O operations causes poor scalability and latency
Solution Approach 1:
The patent extracts the aggregation function from the database processing workflow and performs it in-memory before storage. This separates the high-frequency metric collection and aggregation operations from the lower-frequency database write operations, reducing I/O overhead and improving processing speed while maintaining the ability to handle large volumes of metrics
Solution Approach 2:
The system performs preliminary aggregation of metrics in-memory at the collector before writing to the database. By pre-processing and consolidating metrics data before storage, the system reduces the volume of I/O operations required and enables faster real-time analysis without compromising the quantity of metrics that can be processed
2Reliability
If metrics are stored in a database, then data can be persisted, but the latency and processing overhead prevent real-time analytics
Solution Approach 1:
The patent introduces an in-memory aggregation buffer as an intermediary between metric collection and database storage. This intermediary layer allows real-time analytics to operate on aggregated data in memory with low latency, while simultaneously maintaining reliable persistence by periodically flushing data to the database, thus resolving the conflict between data persistence and real-time processing speed
3Adaptability or versatility
If multiple applications generate metrics simultaneously, then comprehensive monitoring is achieved, but the system cannot scale to handle billions of metrics per hour
Solution Approach 1:
The patent segments the metrics processing system into independent collector components, each capable of aggregating metrics from multiple applications. This segmentation allows the system to scale horizontally by adding more collector instances, each handling a portion of the total metric volume, thereby maintaining the ability to monitor comprehensive multi-application environments while scaling processing capacity to handle billions of metrics per hour
Data Source
AI summary
Managing performance metrics includes: obtaining a plurality of performance metrics associated with a plurality of sources on a network; aggregating, at a first rate, the plurality of performance metrics associated with the plurality of sources to generate a plurality of first aggregated results; maintaining at least some of the plurality of first aggregated results in one or more memories; aggregating, at a second rate, the plurality of first aggregated results to generate a plurality of second aggregated results, the second rate being a lower rate than the first rate; and maintaining at least some of the plurality of second aggregated results in the one or more memories.