Dynamic Parallel Apply Algorithm Selection for Database Replication

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional software replication deployment in database systems faces bottlenecks when data is replicated to target databases due to the need for manual configuration of parallel apply algorithms, which are sensitive to data changes and patterns, leading to inefficiencies and latency issues.

Innovation Solution

A computer-implemented method and system that analyzes source workload data and replication environment data using machine learning algorithms to predict and dynamically adjust optimal parallel apply algorithms, ensuring continuous and efficient data replication by adapting to changing data patterns and workloads.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual configuration of parallel apply algorithms is used, then system control is maintained, but replication efficiency decreases and latency increases

Engineering Contradiction:

Improvedata replication efficiencyVSAvoidreplication latency

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system automatically selects and configures parallel apply algorithms by analyzing data patterns and workload characteristics without manual intervention. The machine learning model autonomously determines optimal algorithm selection based on observed data, eliminating the need for manual configuration while improving replication efficiency and reducing latency.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system continuously monitors data patterns and replication performance, using this feedback to dynamically adjust algorithm selection. The machine learning model learns from observed data characteristics and replication outcomes, continuously optimizing the choice of parallel apply algorithms to maintain high efficiency and low latency.

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If fixed parallel apply algorithms are used, then system stability is maintained, but adaptability to changing data patterns decreases

Engineering Contradiction:

Improveadaptability to data changesVSAvoidalgorithm configuration stability

Core Design Contradiction:

Adaptability or versatilityVSStability of the object's composition

Solution Approach 1:

The system transitions from static, fixed algorithm configuration to dynamic algorithm selection. The machine learning model continuously analyzes changing data patterns and adjusts algorithm selection in real-time, enabling the system to adapt to evolving data characteristics while maintaining operational stability through automated consistency in selection criteria.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes the parameters of algorithm selection based on observed data characteristics. Instead of using fixed algorithms, the system adjusts which algorithm is applied based on data patterns, workload type, and performance metrics, allowing adaptability to changing conditions while maintaining stable performance through data-driven parameter optimization.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If multiple parallel apply algorithms are maintained, then algorithm versatility increases, but system complexity increases

Engineering Contradiction:

Improvealgorithm selection flexibilityVSAvoidsystem configuration complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The machine learning model serves as an intermediary between multiple available algorithms and the replication process. Instead of manually managing algorithm complexity, the system uses the ML model to automatically select and configure the appropriate algorithm based on data patterns, reducing configuration complexity while maintaining access to multiple algorithm options.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system autonomously manages the complexity of multiple algorithms through automated selection and configuration. The machine learning model independently determines which algorithm to apply and how to configure it, eliminating the need for manual management of algorithm diversity and reducing system configuration complexity while preserving algorithm versatility.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS11455574B2Dynamically predict optimal parallel apply algorithms

Publication Date: 2022.09.27 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A method, system, and computer program product to analyze data patterns in source workloads and predict the optimal parallel apply algorithms, where the method may include receiving source workload data and replication environment data, where the source workload data includes at least a stream of changes to a target DBMS. The method may also include analyzing characteristics of the source workload data and the replication environment data. The method may also include inputting, as input variables, the characteristics of the source workload data and the replication environment data into a machine learning algorithm. The method may also include obtaining, from the machine learning algorithm, an optimal parallel apply algorithm from a plurality of parallel apply algorithms. The method may also include applying the optimal parallel apply algorithm to the target database management system.