Deterministic CDC Merge via Entry Consolidation
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Solution Overview
Problem
Change data capture (CDC) systems face non-deterministic behavior when processing CDC datasets with multiple entries for the same record, leading to unpredictable results due to the lack of guaranteed order in bulk SQL operations like MERGE, which can occur in databases not optimized for individual SQL operations.
Innovation Solution
A deterministic CDC system processes CDC datasets to produce a consolidated dataset with unique primary keys, resolving operations into single entries to ensure deterministic results by iteratively handling entries with the same primary key, thereby eliminating non-deterministic behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bulk SQL operations like MERGE are used to process CDC datasets, then processing efficiency is improved, but deterministic behavior is lost when multiple entries with the same primary key exist
Solution Approach 1:
The system performs preliminary actions by sorting the CDC dataset by primary key before executing the bulk MERGE operation. This pre-processing step ensures that when multiple entries with the same primary key exist, they are processed in a consistent order, eliminating non-deterministic behavior while maintaining the efficiency benefits of bulk operations.
2Reliability
If individual SQL operations are applied to each CDC entry, then deterministic behavior is maintained, but processing performance deteriorates for large datasets
Solution Approach 1:
The system merges multiple CDC entries with the same primary key into a single consolidated entry before execution. By combining redundant operations (e.g., multiple UPDATEs or DELETEs for the same record) into one operation, the system achieves both the performance benefits of bulk operations and the determinism of individual operations, as the consolidation logic ensures consistent results regardless of input order.
3Productivity
If CDC datasets with duplicate primary keys are processed without consolidation, then processing speed is maintained, but data integrity is compromised due to non-deterministic results
Solution Approach 1:
The system performs preliminary consolidation of CDC entries by grouping them by primary key and resolving conflicts according to defined rules (e.g., applying INSERT before UPDATE, UPDATE before DELETE). This pre-processing step ensures data integrity is maintained while allowing subsequent bulk operations to proceed at high speed without reprocessing individual entries.
Data Source
AI summary
Systems and methods herein describe accessing an original change data capture (CDC) dataset comprising information describing changes to a source database, the original CDC dataset comprising a plurality of entries; identifying a first entry of the plurality of entries comprising a primary-key, a first operation and entry data; identifying a set of entries in the plurality of entries that includes the primary-key; comparing the first operation of the first entry with a second operation of a second entry in the set of entries; updating the first operation and the entry data based on the comparison; generating a new entry based on the updating of the first operation and the entry data; storing the new entry in a consolidated CDC dataset; and applying the consolidated CDC dataset to a target database.