CDC Streaming Logs for Database Race Condition Mitigation
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Solution Overview
Problem
Existing data replication systems struggle to handle large data sets effectively, leading to data race conditions that result in unpredictable and erroneous behavior due to concurrent updates by multiple processes without proper synchronization, causing incorrect data insertion and potential data corruption.
Innovation Solution
A Change Data Capture (CDC) mechanism using an open-source distributed platform that captures and records data modifications in real-time or near-real-time, employing a Write-Ahead Logging strategy and synchronization mechanisms to ensure data integrity and consistency across systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple processes update the database simultaneously without proper synchronization, then productivity is improved through parallel processing, but data reliability deteriorates due to data race conditions and corruption
Solution Approach 1:
The system performs preliminary actions by capturing and logging data changes in a streaming job before they are fully committed to the database. Write-Ahead Logging ensures that change events are recorded in advance, creating a synchronized snapshot of modifications that can be processed later without causing data race conditions during concurrent access
Solution Approach 2:
The patent introduces an intermediary mechanism in the form of a streaming job that acts as a mediator between multiple data sources and the database. This intermediary captures change events, generates alarms, and processes updates in a controlled manner, preventing direct concurrent access conflicts while maintaining productivity
2Reliability
If traditional data replication systems are used to handle large data sets, then data availability is improved, but device complexity increases and scalability is limited
Solution Approach 1:
The system segments the data replication process into independent streaming jobs that can be executed in parallel. Each streaming job handles specific data sources or change types independently, reducing overall system complexity while improving scalability for large data sets through modular architecture
Solution Approach 2:
The streaming job mechanism serves multiple functions simultaneously: it captures data changes, generates alarms, logs events, and processes updates. This multi-functional approach reduces device complexity by consolidating what would otherwise require separate systems into a single universal platform
3Productivity
If concurrent access to shared data is allowed without synchronization, then productivity is improved through parallel operations, but measurement precision deteriorates due to unpredictable interleaving of operations
Solution Approach 1:
The system records the precise timing and sequence of data changes in advance through Write-Ahead Logging and alarm generation. By capturing change events with timestamps before execution, the system maintains measurement precision of operation timing while allowing concurrent access to proceed in parallel
Data Source
AI summary
The present disclosure relates to a method for mitigating data race conditions in a database (218). The method comprising requesting, by two or more data sources, one or more changes to be performed on a data stored in the database (218). The method comprising capturing the requested one or more changes in a streaming job to generate one or more alarms related to the one or more changes. The method comprising generating one or more logs related to the captured one or more changes. The method comprising determining a type of operation to be performed on the stored data based on the one or more generated logs and the one or more generated alarms. The method comprising responsive to determining, performing the type of operation on the data stored in the database (218).