Encrypted Data Replication With Parallel Batch Processing
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Solution Overview
Problem
Existing data replication methods face challenges in achieving high throughput with robust security mechanisms, particularly for sensitive data, and are often limited by I/O waits, concurrency issues, and requirements for numerous network connections.
Innovation Solution
Implementing a flexible data replication framework that supports field-level encryption, dynamic encryption strategies, and adaptable partitioning, allowing for high-throughput data replication with secure handling of sensitive information, using a framework that is not tied to specific messaging services or streaming solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If field-level encryption and security mechanisms are implemented in data replication, then data security and compliance are improved, but processing overhead and latency increase
Solution Approach 1:
Encryption keys are pre-generated and pre-configured for each partition before data replication begins. The encryption framework is established in advance, allowing data to be encrypted and replicated in parallel without sequential key management overhead, thus maintaining security while reducing latency.
Solution Approach 2:
Data is divided into multiple partitions that can be encrypted and replicated simultaneously using different encryption keys. This segmentation enables parallel processing of encryption operations across multiple threads or processes, preventing encryption from becoming a sequential bottleneck and maintaining high throughput despite security requirements.
2Productivity
If high-throughput data replication is implemented to handle large volumes of events, then productivity and data availability are improved, but system complexity and resource requirements increase
Solution Approach 1:
The replication system divides data into partitions and uses multiple consumer threads to process different partitions in parallel. This segmentation transforms a single complex high-throughput requirement into multiple simpler parallel processes, each handling a subset of the data, thereby achieving high throughput without proportionally increasing overall system complexity.
Solution Approach 2:
The replication framework is designed to be service-agnostic and can work with multiple messaging services and streaming solutions through a unified interface. This universal design allows the system to achieve high throughput through parallel processing without requiring separate complex systems for different data sources or targets.
3Productivity
If parallel processing is used to improve throughput, then productivity is improved, but ensuring message order and data consistency becomes more difficult
Solution Approach 1:
Data is partitioned into separate segments that are processed in parallel by different consumer threads. Each partition maintains its own internal consistency and message ordering independently. This segmentation allows parallel processing to improve throughput while each partition's bounded scope makes consistency management tractable and predictable.
Solution Approach 2:
Each partition is assigned specific encryption keys and processing parameters tailored to its requirements. This local customization allows each parallel processing thread to operate with optimized settings for its specific data subset, ensuring data consistency and security requirements are met locally while enabling global parallel processing for high throughput.
Data Source
AI summary
Systems and methods are provided for securely replicating data, such as from an on-premises database to remote network-accessible storage, with high throughput and robust security mechanisms. Messages or events may be published via a first message queue service, such as an on-premises message queue service, that are then encrypted, optionally at the field level, before being passed to a managed message queue service. The encrypted messages may then be processed by a replication application configured to split batches of messages into sub-batches that are processed in parallel before being committed to a managed database at the batch level as individual batches complete processing.