Streaming Log Sessionization System with Latency Reduction
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Solution Overview
Problem
Conventional logging systems experience high latency in summarizing log events from streaming sources, making the data stale and less relevant for enhancing user viewing experiences and content monetization, often taking over 24 hours to process.
Innovation Solution
A latency reduction system that includes monitoring, aggregation, analysis, and summarization components, along with a map-reduce component for parallel processing, a learning component for timely trending, and diagnostic and integration components to address network slowdowns and spamming, allowing for real-time monitoring and summarization of watch time event data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional logging systems summarize log events from streaming sources, then data processing is completed, but latency exceeds 24 hours making data stale
Solution Approach 1:
The system segments the log event processing into multiple components: monitoring component for real-time collection, aggregation component for data consolidation, analysis component for processing, and summarization component for final output. This segmentation enables each component to operate independently and efficiently, reducing overall processing time from 24+ hours to near real-time.
Solution Approach 2:
The system implements continuous monitoring and processing of log events from streaming sources. The monitoring component continuously collects events, the aggregation component continuously consolidates data, and the summarization component continuously generates playback records. This continuous operation eliminates the batch processing delays of conventional systems, maintaining data freshness while reducing latency.
2Loss of time
If real-time monitoring and summarization is implemented, then latency is reduced, but system complexity increases
Solution Approach 1:
Each component in the system performs multiple functions: the monitoring component collects and validates events, the aggregation component consolidates and partitions data, the analysis component processes and filters information, and the summarization component generates final records. This multi-functionality reduces the need for separate specialized components, managing system complexity while achieving real-time processing.
Solution Approach 2:
The system introduces intermediary components between data collection and final summarization: the aggregation component acts as a mediator that consolidates raw events into manageable datasets, and the analysis component serves as an intermediary that processes and validates data before summarization. These intermediaries simplify the overall architecture by breaking down the processing pipeline into manageable stages.
3Productivity
If frequent analytics are generated, then user experience is enhanced, but computational resources are consumed
Solution Approach 1:
The system generates analytics at appropriate frequencies rather than continuously. The summarization component creates playback records based on aggregated data thresholds and time windows, generating analytics only when necessary to maintain data freshness and relevance. This partial action approach reduces computational resource consumption while still providing frequent enough analytics to enhance user experience.
Solution Approach 2:
The system dynamically adjusts processing parameters such as aggregation time windows, data thresholds, and summarization frequencies based on workload conditions and data characteristics. By changing these parameters adaptively, the system optimizes the balance between analytics frequency and computational resource consumption, generating sufficient analytics for user experience without excessive resource expenditure.
Data Source
AI summary
Systems and methods for reducing latency times associated with sessionizing log events from streaming logs are disclosed herein. In an aspect, event data related to user consumption of media content items can be monitored. Further, respective event data based at least in part on user playback of the media content items can be analyzed. In another aspect, pending and finalized event data can be summarized into a finalized playback record. In yet another aspect, the size of a time window can be adjusted based in part on observed logsaver latency. Further, in an aspect, watch time data can be integrated into a de-spamming pipeline.