Data Consumption System Latency Reduction via Partitioned Processing
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Solution Overview
Problem
Marketing campaign servers face performance issues due to the sheer volume of data processing, leading to bottlenecks and errors that impede effective campaign delivery, particularly in retrieving target population information and handling errors during offer preparation.
Innovation Solution
A data consumption system is introduced, featuring a consumer component that receives customer information from multiple partitions, a timeline component that detects errors and initiates retry attempts, and a channel adapter for parallel processing across different channels, ensuring that errors in one partition do not impact others and permanently prohibiting failed offer generation to prevent redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data processing volume increases to support growing marketing campaigns, then campaign sophistication and target population coverage improve, but system resources are strained causing processing bottlenecks and errors
Solution Approach 1:
The system segments customer information into multiple partitions (e.g., partition 1, partition 2, partition 3) that can be processed independently by different consumer components. This segmentation allows the system to handle large volumes of data without overwhelming single processing units, thereby maintaining reliability while supporting campaign growth.
Solution Approach 2:
The system transitions from sequential processing to parallel processing across multiple dimensions - multiple consumer components process different partitions simultaneously, and multiple offers can be prepared in parallel. This dimensional change increases throughput without proportionally increasing resource strain, maintaining reliability at scale.
2Reliability
If error detection and retry mechanisms are implemented, then offer preparation reliability improves, but processing time increases due to retry attempts
Solution Approach 1:
The timeline component proactively monitors offer preparation status and preemptively initiates retry attempts when errors are detected, rather than waiting for complete failure. This preliminary action ensures reliability while minimizing time loss by acting early in the error detection process.
Solution Approach 2:
The system implements a feedback mechanism where the timeline component continuously monitors offer preparation status, detects errors, and triggers retry logic. This closed-loop feedback ensures reliable offer delivery while optimizing retry timing to minimize processing delays.
3Speed
If customer information is processed in parallel across multiple partitions, then processing speed improves, but error isolation becomes challenging
Solution Approach 1:
Each partition is processed by dedicated consumer components, creating natural error isolation boundaries. When an error occurs in one partition, it remains confined to that specific partition's processing stream, simplifying error isolation and debugging while maintaining parallel processing speed benefits.
Solution Approach 2:
The timeline component acts as an intermediary that coordinates error handling across parallel partitions. It receives error notifications from different consumer components, manages retry logic centrally, and ensures that errors in one partition do not propagate to others, reducing overall system complexity.
Data Source
AI summary
A campaign server is disclosed which includes a multi-level queue architecture that isolates campaign processing from the delays and inconsistencies of target population information retrieval and a data streaming service system with latency and error remediation measures are disclosed, where the campaign server and the data consumption system can be part of a consolidated system. The data consumption system may include various components to process one or more offers to one or more customers and component configured to detect an error associated the preparation of the errors and/or ii) initiate a retry attempt to process the one or more offers, and and/or storing an instruction in a storage component of the streaming data system to permanently prohibit an attempt to generate the one or more offers at a subsequent time if the retry attempt expires without processing the one or more offers.