Production Database Testing via Data Silo and Context Filter
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Solution Overview
Problem
In on-demand systems, testing code in a development environment separate from production can be inefficient due to resource competition and data fidelity issues, while testing in the production environment risks affecting live data and performance. Existing methods either waste resources or pose operational burdens with data access failures and rollback challenges.
Innovation Solution
Creating a data silo within the production environment by inserting new data and injecting a test context filter into queries, allowing tests to operate solely on new data without affecting live production data, thus isolating test transactions and maintaining data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If code is tested in a separate development environment, then production environment is protected from test side-effects, but resource efficiency decreases and data fidelity is compromised
Solution Approach 1:
The patent segments the production database into isolated test contexts using unique identifiers. Test data is inserted with specific context IDs, and queries are filtered to operate only within those contexts. This creates logical segmentation within the physical production environment, allowing multiple concurrent test sequences to run independently without interfering with each other or with production operations.
Solution Approach 2:
The patent introduces a test context filter as an intermediary mechanism between test queries and production data. This filter acts as a mediator that allows test sequences to access the production database while preventing unauthorized access to production data by routing all test operations through context-specific filters that use unique test context identifiers.
2Reliability
If code is tested in a separate development environment, then production data integrity is maintained, but data fidelity between test and production decreases
Solution Approach 1:
The patent creates test data copies within the production database by inserting copies of production data structures with modified identifiers. These test copies maintain the same schema, relationships, and data characteristics as production data, allowing tests to run against realistic data while remaining distinguishable through context identifiers.
3Reliability
If tests are run against production data with rollback capability, then negative side-effects can be prevented, but operational burden increases and performance is affected
Solution Approach 1:
The patent performs preliminary action by inserting test data with unique context identifiers before running tests. This pre-establishes isolated test contexts that automatically prevent interference with production data, eliminating the need for post-test rollback operations. The isolation is built-in from the start rather than requiring corrective action afterward.
Data Source
AI summary
In accordance with disclosed embodiments, there are provided methods, systems, and apparatuses for creating a data silo and testing with a data silo including, for example, initiating a test sequence against a production database within a host organization, wherein the test sequence specifies: (a) new data for insertion into the production database during the test sequence and (b) one or more queries for execution against the production database during the test sequence. Such a method further includes inserting the new data into the production database; recording one or more row IDs corresponding to the inserted new data; injecting a test context filter into the one or more queries based on the one or more row IDs; and executing the one or more queries against the production database.