Service Mesh Ingestion Pipelines for Large Search Documents
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Solution Overview
Problem
Enterprise search engines face challenges with large document ingestion due to network timeouts, imbalanced ingestion load, memory issues, and lack of real-time observability, leading to inefficient and costly processing in cloud environments.
Innovation Solution
Implement a multi-pipeline architecture using Kubernetes microservices and a service mesh (e.g., Istio) to segregate documents by size ranges, with customized configurations for each bucket, and utilize an AI agent for dynamic optimization of ingestion processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If documents are ingested into the search engine without size-based segmentation, then the ingestion process is simple, but network timeouts and imbalanced load occur leading to poor reliability
Solution Approach 1:
The patent segments the document ingestion pipeline into multiple size-based buckets (e.g., small, medium, large documents) with dedicated processing channels for each. This segmentation prevents network timeouts and load imbalances by routing documents of different sizes through appropriately configured ingestion paths, thereby improving reliability without requiring complete system redesign.
Solution Approach 2:
The patent implements dynamic configuration of ingestion parameters based on document size buckets. Each bucket has customized timeout values, batch sizes, and worker thread counts that adapt to the specific characteristics of documents in that size range. This dynamic adjustment optimizes processing efficiency and prevents timeouts for large documents while maintaining simplicity for small documents.
2Productivity
If a single ingestion pipeline is used for all document sizes, then the system is easy to manage, but resource utilization is inefficient leading to increased costs
Solution Approach 1:
The patent divides the ingestion system into multiple parallel pipelines organized in a tree structure, where each node handles specific document size ranges. This segmentation enables efficient resource utilization by allocating appropriate computing and memory resources to each document size category, improving overall productivity while maintaining manageable complexity through modular design.
Solution Approach 2:
Each ingestion pipeline node is configured with local quality parameters optimized for its specific document size range. For example, large document buckets receive more memory allocation and longer timeout values, while small document buckets use faster processing configurations. This localized optimization improves productivity without requiring complete system-wide complexity.
3Difficulty of detecting and measuring
If real-time observability is not implemented, then the system is simpler to operate, but troubleshooting and optimization become difficult
Solution Approach 1:
The patent implements real-time observability with feedback loops that monitor ingestion metrics (throughput, latency, error rates) across all size-based buckets. This feedback mechanism enables automatic detection of performance issues and provides data for continuous optimization, improving monitoring capability while using standardized observability tools to manage infrastructure complexity.
Data Source
AI summary
In one embodiment, an illustrative method herein comprises: obtaining, by a process, a file having a given size; assigning, by the process, the file to a particular size-range bucket of a plurality of size-range buckets of a data ingestion pipeline, the plurality of size-range buckets having a corresponding size-based configuration; and forwarding, by the process, the file into a particular size-based service mesh ingress gateway of the data ingestion pipeline according to the particular size-range bucket to cause processing of the file within the data ingestion pipeline according to the corresponding size-based configuration of the particular size-range bucket.