Managed Search Indexing for Automated Data Discovery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Implementing full-text search functionality across multiple data sources is complex and resource-intensive, requiring users to manually configure search engines, set up replication groups, and maintain ETL jobs to keep search indexes up to date, especially as data volumes change.
Innovation Solution
A managed search system that automatically identifies and indexes data sources, uses an ETL service for full-text indexing, and scales indexes dynamically, allowing for real-time data indexing and reduced user maintenance through a data discovery service and index policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual configuration and maintenance of search engines is implemented, then search functionality can be provided across data sources, but user development effort and maintenance complexity increase significantly
Solution Approach 1:
The system enables self-service by allowing search functionality to be automatically configured and maintained without user intervention. The service provider automatically discovers data sources, configures appropriate search engines, sets up ETL jobs, and manages indexing based on policies, eliminating the need for users to manually perform these complex tasks
Solution Approach 2:
A managed search service acts as an intermediary between users and search engine infrastructure. This service abstracts the complexity of search engine configuration, replication group setup, and ETL job management, providing a simplified interface while handling the complex backend operations automatically
2Reliability
If search indexes are manually maintained through ETL jobs, then search results stay up to date, but resource consumption and maintenance effort increase
Solution Approach 1:
The system dynamically adjusts ETL job execution and indexing operations based on data source characteristics, update frequencies, and search requirements. Rather than using static, over-provisioned resources, the system adapts resource allocation to actual needs, running ETL jobs only when necessary to maintain index currency while optimizing resource usage
Solution Approach 2:
The managed search service changes operational parameters such as ETL job schedules, indexing strategies, and resource allocation based on detected data source properties and usage patterns. This allows the system to maintain reliable search indexes while consuming fewer resources by adjusting parameters to match actual requirements rather than using fixed, conservative settings
3Productivity
If search infrastructure is manually scaled, then search capacity matches data volume, but user maintenance burden increases
Solution Approach 1:
The system provides self-service scaling by automatically detecting data source growth and search workload changes, then dynamically provisioning additional search capacity and adjusting resource allocation without user intervention. This maintains high search productivity while eliminating the manual maintenance burden of scaling operations
Data Source
AI summary
A data discovery service provides an automated method of identifying and indexing data sources associated with a user's account without requiring additional configuration by the user. This enables search services to be provided across various different types of data sources a user may utilize. The data discovery service can generate a list of data sources associated with the user's account, the list may include access information for each data source. Each data source or type of data source may be associated with an index policy that identifies the types of data from a corresponding data source are to be indexed. An index may then be generated based on those policies for each data source associated with the account.