A cloud-native distributed database optimization system with adaptive query processing

Abstract

A cloud-native distributed database optimization system with adaptive query processing, the system comprising the following: an adaptive query optimization module configured to dynamically adjust query execution plans at runtime based on real-time metrics, including query complexity, resource availability, and network conditions, to minimize latency and maximize throughput; a self-learning module for data partitioning and replication, configured to use machine learning algorithms to automatically partition and replicate data to ensure even distribution, minimize hotspots, and improve data locality; a scalable resource management module configured to dynamically allocate and release computing, storage, and network resources using predictive analytics to efficiently handle workload fluctuations; a proactive fault tolerance module integrated with a real-time monitoring and anomaly detection unit, configured to predict, mitigate, and recover from node or network failures with minimal downtime; and a cloud-native orchestration module configured for seamless integration into multi-cloud and hybrid environments, supporting automatic scaling, load balancing, and geographic data compliance.

Description

[0001] The present invention relates to the field of distributed databases in cloud-native environments and focuses on optimizing query processing to efficiently handle various workloads.

[0002] Distributed databases have become an essential backbone for modern applications, enabling the management of large and geographically distributed data. Despite their importance, traditional systems and methods have several critical limitations that impact their performance, scalability, and adaptability. Traditional distributed databases rely on static query execution plans determined in advance at compile time. These plans do not account for real-time fluctuations in workload, data distribution, network conditions, or resource availability. As a result, such systems are unable to dynamically adapt to changing conditions, leading to inefficiencies, increased latency, and suboptimal query performance.

[0003] Data partitioning and replication, critical to distributed databases, are often managed manually in traditional systems. This manual approach is time-consuming, prone to human error, and lacks sufficient sophistication to optimize data placement. Inadequate partitioning strategies can lead to data hotspots where certain nodes are overused while others are underutilized, impacting overall system performance. Furthermore, replication methods in legacy systems are often static, offering limited flexibility in balancing data consistency, availability, and latency.

[0004] Fault tolerance in traditional systems, while present, is often slow and inefficient. These systems rely on basic replication and recovery mechanisms that cannot effectively handle node failures or network partitions. Such limitations result in extended downtime and data unavailability during outages, posing a significant risk to mission-critical applications. Furthermore, these systems are unable to dynamically scale resources during peak loads or reduce resources during low-demand periods, leading to resource inefficiencies. Fixed resource allocation strategies result in overprovisioning, which drives up operating costs, or underprovisioning, which causes system bottlenecks and degraded user experience during high demand.

[0005] Another major disadvantage of traditional systems is the lack of real-time query adaptation. In modern applications, workloads are highly dynamic, and query patterns can change unpredictably. Traditional systems are unable to optimize query execution during runtime, making them unsuitable for use cases such as IoT systems, e-commerce platforms, and streaming analytics, where workloads and data flow are inconsistent.

[0006] To solve the problem, the present invention provides a cloud-native distributed database optimization system with adaptive query processing.

[0007] The cloud-native distributed database optimization system with adaptive query processing that dynamically optimizes query execution plans based on real-time metrics such as workload, network latency, and resource availability to minimize query response times

[0008] The cloud-native distributed database optimization system with adaptive query processing, which can ensure even data distribution across nodes, reduce hotspots, and improve data locality for faster query execution.

[0009] The cloud-native distributed database optimization system with adaptive query processing, capable of seamless horizontal and vertical scaling to efficiently handle dynamic workloads, and leveraging cloud-native infrastructure and container orchestration platforms.

[0010] The cloud-native distributed database optimization system with adaptive query processing that implements resource-aware optimization techniques to dynamically allocate and release resources such as CPU, memory, and bandwidth based on real-time requirements, thus reducing operating costs without compromising performance.

[0011] The cloud-native distributed database optimization system with adaptive query processing that introduces real-time workload monitoring and adaptive query adjustments to account for changing query patterns and ensure optimal performance under varying operating conditions.

[0012] The cloud-native distributed database optimization system with adaptive query processing that provides geographic redundancy, regulatory compliance, and improved fault tolerance across diverse infrastructures.

[0013] The cloud-native distributed database optimization system with adaptive query processing that uses local data placement and adaptive query optimization to significantly reduce query execution times, even for complex and distributed data sets.

[0014] The cloud-native distributed database optimization system with adaptive query processing that simplifies installation, upgrades, and maintenance while ensuring consistent performance across different environments.

[0015] The cloud-native distributed database optimization system with adaptive query processing that minimizes cloud infrastructure costs through intelligent scaling, efficient resource utilization, and proactive system optimizations tailored to workload requirements.

[0016] In one embodiment, a cloud-native distributed database optimization system with adaptive query processing is provided. The cloud-native distributed database optimization system with adaptive query processing addresses the challenges of traditional distributed databases, including static query execution, manual data management, and inefficient resource utilization. Traditional systems struggle with high latency, limited scalability, and insufficient fault tolerance, making them unsuitable for modern, dynamic workloads. The system introduces adaptive query processing to optimize execution plans in real time based on workload, network conditions, and resource availability. Automated data partitioning and replication ensure balanced data distribution, reduce hotspots, and improve query efficiency.Its scalable design leverages cloud-native technologies and enables seamless horizontal and vertical scaling to effectively handle workload fluctuations. Dynamic resource management ensures efficient allocation of resources such as CPU, memory, and bandwidth, reducing costs while maintaining high performance. Robust fault tolerance mechanisms, including automatic failover and recovery, increase system availability in the event of outages. The system also supports multi-cloud and hybrid deployments and provides geographic redundancy and compliance with regional regulations.

[0017] The invention is explained again below with reference to the figure. It shows: Fig. : shows a cloud-native distributed database optimization system with adaptive query processing.

[0018] Fig.shows a cloud-native distributed database optimization system with adaptive query processing. The cloud-native distributed database optimization system with adaptive query processing includes an adaptive query optimization module, a self-learning data partitioning and replication module, a scalable resource management module, a proactive fault tolerance module, and a cloud-native orchestration module. The adaptive query optimization module is configured to dynamically adjust query execution plans at runtime based on real-time metrics, including query complexity, resource availability, and network conditions, to minimize latency and maximize throughput.The self-learning data partitioning and replication module is configured to use machine learning algorithms to automatically partition and replicate data to ensure even distribution, minimize hotspots, and improve data locality. The scalable resource management module is configured to dynamically allocate and release compute, storage, and network resources using predictive analytics to efficiently manage workload fluctuations. The proactive fault tolerance module is integrated with a real-time monitoring and anomaly detection unit configured to predict, mitigate, and recover from node or network failures with minimal downtime.The cloud-native orchestration module is configured for seamless integration into multi-cloud and hybrid environments and supports automatic scaling, load balancing, and geographic data conformance. The adaptive query optimization module leverages a combination of rule-based logic and machine learning models to predict and implement the most efficient query execution paths in distributed environments. The data partitioning module uses dynamic range partitioning to optimize performance for analytical queries with large datasets. The replication module dynamically adjusts replication factors based on access frequency and fault tolerance requirements. The fault tolerance module includes an automatic failover mechanism that redirects workloads to healthy nodes without interrupting ongoing operations.The orchestration layer supports container deployment and auto-scaling via Kubernetes or equivalent platforms. The query latency optimization module prioritizes execution paths for latency-critical queries based on data proximity and network conditions. The resource management module includes workload prediction models to anticipate and proactively allocate resources during peak traffic periods.

[0019] The system enables regulatory compliance by dynamically segmenting data into compliant geographic zones based on regional data protection laws. The real-time performance dashboard, which visualizes query execution metrics, system utilization, and fault tolerance status, aids proactive decision-making. List of reference symbols 100 systems

Claims

[1] A cloud-native distributed database optimization system with adaptive query processing, the system comprising: an adaptive query optimization engine configured to dynamically adjust query execution plans at runtime based on real-time metrics, including query complexity, resource availability, and network conditions, to minimize latency and maximize throughput; a self-learning data partitioning and replication module configured to use machine learning algorithms to automatically partition and replicate data to ensure even distribution, minimize hotspots, and improve data locality; a scalable resource management module configured to dynamically allocate and release compute, storage, and network resources using predictive analytics to efficiently manage workload fluctuations; a proactive fault tolerance module integrated with a real-time monitoring and anomaly detection unit and configured to predict, mitigate, and recover from node or network failures with minimal downtime; and a cloud-native orchestration engine configured for seamless integration into multi-cloud and hybrid environments, supporting automatic scaling, load balancing, and geographic data conformance. [2] The system of claim 1, wherein the adaptive query optimization module uses a combination of rule-based logic and machine learning models to predict and implement the most efficient query execution paths in distributed environments. [3] The system of claim 1, wherein the data partitioning module uses dynamic range partitioning to optimize performance for analytical queries with large data sets. [4] The system of claim 1, wherein the replication module dynamically adjusts the replication factors based on access frequency and fault tolerance requirements. [5] The system of claim 1, wherein the fault tolerance module includes an automatic failover mechanism that redirects workloads to healthy nodes without interrupting ongoing operations. [6] The system of claim 1, wherein the orchestration layer supports containerized deployment and auto-scaling via Kubernetes or equivalent platforms. [7] The system of claim 1, wherein the query latency optimization module prioritizes execution paths for latency-critical queries based on data proximity and network conditions. [8] The system of claim 1, wherein the resource management module incorporates workload prediction models to anticipate and preemptively allocate resources during periods of high traffic. [9] The system of claim 1, wherein the system enables compliance by dynamically segmenting data into compliant geographic zones based on regional data protection laws. [10] The system of claim 1, wherein the real-time performance dashboard visualizes query execution metrics, system load, and fault tolerance status to support proactive decision making.

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