Relational Database Search Indexing With Delta-Based Sync
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Solution Overview
Problem
Existing systems face challenges in providing timely search capabilities for large, frequently changing datasets in relational databases, struggling with data processing times and near real-time synchronization, especially with complex data structures, and requiring extensive code changes for schema modifications.
Innovation Solution
A system that integrates chunked data queries, bulk search engine indexing, and asynchronous data synchronization, using SQL NTILE queries, bulk API loading, and delta metadata to track database changes, allowing for parallel processing and adaptive indexing without code modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing methods are used to index large datasets, then data can be indexed, but the processing time becomes excessively long and search capabilities are delayed
Solution Approach 1:
The patent divides large datasets into smaller chunks or batches for processing. The indexing system processes data in manageable segments rather than attempting to index the entire dataset at once, which significantly reduces processing time and enables faster search capability deployment.
Solution Approach 2:
The system performs preliminary data preparation and chunking before the actual indexing process. By pre-processing data into ready-to-index segments, the system optimizes the subsequent indexing operation and reduces overall processing time.
2Reliability
If traditional synchronization methods are used to keep search engines updated with database changes, then data consistency can be maintained, but performance degrades with complex and highly structured data
Solution Approach 1:
The synchronization process is segmented into discrete change detection and application steps. Rather than synchronizing entire datasets, the system identifies and processes only the specific data changes that occurred, dramatically improving synchronization performance while maintaining consistency.
Solution Approach 2:
The patent introduces an intermediary change detection layer that sits between the database and search engine. This intermediary captures data changes as they occur and facilitates efficient transfer of only the necessary change information, preserving data consistency while optimizing performance.
3Stability of the object's composition
If the system uses fixed code structures to handle database schemas, then code stability is maintained, but adding new fields requires extensive code modifications
Solution Approach 1:
The system employs a universal data access layer that can handle multiple database schemas and field types through a common interface. This universal approach allows the system to adapt to schema changes and add new fields without requiring modifications to the core code structure.
Solution Approach 2:
The patent implements dynamic schema handling where the system can adapt its data access patterns based on the actual database schema. This dynamic approach allows new fields to be incorporated automatically without code changes, while maintaining stability in the core system architecture.
Data Source
AI summary
A system for real-time indexing and synchronization of relational databases with a search engine cluster. The system can retrieve datasets using NTILE queries, serialize the data, and load the data into the search engine index via a bulk API. Changes in the relational database can be captured using triggers and stored in a delta metadata table with table identifier, record identifier and type of change. Periodic scans of the delta metadata table can identify the changed records, which can then be retrieved and used to update the search engine index. The system can support parallel processing of data chunks and dynamic adaptation to new database fields without code changes. Users can search the indexed data through an interface that allows attribute-based filtering and hierarchical display of search results.