Self-adaptive processing method and device of Kafka message queue system

Abstract

The invention discloses a self-adaptive processing method of a Kafka message queue system, which is characterized in that the message throughput is predicted based on an AI prediction method, and a resource scheduling instruction is generated according to a prediction result and a performance target to control the consumption concurrency; after local anomaly detection model training is carried out on each distributed node, model updating data are uploaded to a server side for safe aggregation, and global model updating parameters are generated and returned to optimize the detection capability of each node; carrying out parallel consumption processing on the message according to the instruction, carrying out classification retry and isolation on errors, and collecting processing performance indexes; and finally, the collected indexes are fed back to the resource scheduling and model updating step to form closed-loop control. According to the method, distributed collaborative anomaly detection of dynamic resource allocation and privacy security is realized, the throughput of the system is remarkably improved, the processing delay is reduced, and the reliability, the adaptability and the operation and maintenance automation level of the system are enhanced.

Description

Technical Field

[0001] This application relates to the fields of information technology and data processing technology, and more specifically, to an adaptive processing method and apparatus for a Kafka message queue system. Background Technology

[0002] With the rapid development of internet and IoT technologies, various real-time data pipelines, event stream processing, and log aggregation systems are widely used. Apache Kafka, as a high-throughput, horizontally scalable distributed message queue system, has become a core component of modern data architectures. When facing scenarios such as e-commerce promotions, financial transactions, and IoT device reporting, Kafka needs to handle massive and highly volatile message streams, placing increasingly stringent demands on system throughput, latency, and stability.

[0003] However, in actual large-scale deployments, traditional monitoring and scheduling methods based on static resource allocation and simple thresholds have significant limitations. Due to drastic traffic fluctuations, a fixed number of consumer instances often leads to resource over-provisioning or under-provisioning, making it difficult to simultaneously guarantee low latency and high throughput. Anomaly detection relies heavily on centralized collection and rule matching, which suffers from detection lag, weak generalization ability, and privacy leakage risks. In addition, system expansion and fault recovery are highly dependent on manual intervention, lacking proactive scheduling and closed-loop self-optimization capabilities, which seriously affects system reliability and operational efficiency.

[0004] Therefore, how to construct a Kafka message processing solution that can accurately predict traffic, dynamically schedule resources, achieve distributed collaborative anomaly detection while protecting data privacy, and form a complete autonomous closed loop has become a key technical problem that needs to be solved by those skilled in the art. Summary of the Invention

[0005] To address the existing technical problems, this application provides an adaptive processing method and apparatus for a Kafka message queue system.

[0006] In a first aspect, embodiments of this application provide an adaptive processing method for a Kafka message queue system, including:

[0007] The Kafka message throughput is predicted based on AI prediction methods. Resource scheduling instructions are generated based on the prediction results, combined with single-threaded processing capacity and latency threshold SLO. The prediction model or scheduling strategy is dynamically adjusted based on the performance indicators of monitoring feedback.

[0008] Local anomaly detection model training is performed on each distributed processing node, and the trained model update data is uploaded to the server. After the server performs secure aggregation on the model update data, global model update parameters are generated and sent back to each node to update the local model.

[0009] According to the resource scheduling instructions, Kafka messages are consumed in parallel. During the process, errors are classified, retried, and isolated, and processing performance indicators are collected for monitoring and feedback.

[0010] The resource scheduling instruction directly controls the concurrency of message consumption, the global model update parameters are used to improve the anomaly detection accuracy of each node, and the collected performance indicators are fed back to the resource scheduling and model update steps to form a closed-loop control.

[0011] Optionally, the AI ​​prediction method is a multi-model ensemble prediction method, which dynamically weights and integrates the outputs of multiple base prediction models, and adjusts the weights of each base prediction model online based on historical prediction errors.

[0012] Optionally, the base prediction model in the multi-model integrated prediction method includes at least an OLS linear regression model and a time-series prediction model.

[0013] Optionally, the specific process of the secure aggregation is as follows: on the server side, a weighted average algorithm is used to perform a collaborative average of the model update data from each node, and Gaussian noise that meets the differential privacy requirements is injected into the aggregation result to generate the privacy-preserving global model update parameters.

[0014] Optionally, the weights used in the collaborative averaging are determined based on the amount of data at each node or the quality of recent model updates.

[0015] Optionally, the classification, retrying, and isolation of errors includes: automatically retrying transient errors using an exponential backoff algorithm, and directly routing permanent errors to a dead-letter queue (DLQ) for isolation and recording based on error type labels.

[0016] Optionally, the parallel consumption processing adopts an asynchronous batch processing mechanism based on CompletableFuture, combined with zero-copy technology and memory pool management to reduce I / O and GC overhead.

[0017] Optionally, the method further includes: using a simplified silhouette coefficient to evaluate the quality of clustering results in the anomaly detection model, wherein the simplified silhouette coefficient is the ratio of inter-cluster center distance to global distortion, and automatically triggering model retraining when the score is below a threshold.

[0018] Optionally, the performance metrics include at least processing latency, batch size, error rate, consumption lag, and processing rate tagged with topic and group, and are collected uniformly through the Micrometer monitoring framework.

[0019] Optionally, the predictor, anomaly detector, and optimization strategy of the system are all designed as plug-ins, loaded through a microkernel architecture, and can be dynamically replaced after verifying the effectiveness of the new strategy through A / B testing or canary release.

[0020] Secondly, embodiments of this application provide an adaptive processing apparatus for a Kafka message queue system, comprising:

[0021] The prediction and scheduling module is configured to predict Kafka message throughput based on AI prediction methods, generate resource scheduling instructions based on the prediction results, combined with single-threaded processing capacity and latency threshold SLO, and dynamically adjust the prediction model or scheduling strategy based on performance indicators from monitoring feedback.

[0022] The collaborative learning module is configured to train a local anomaly detection model on each distributed processing node, upload the trained model update data to the server, and after the server performs secure aggregation on the model update data, generate global model update parameters and send them back to each node to update the local model.

[0023] The message processing module is configured to consume Kafka messages in parallel according to the resource scheduling instructions, classify and retry errors and isolate them during the process, and collect processing performance indicators.

[0024] The monitoring feedback module is configured to receive and analyze the performance indicators, and then generate feedback signals to the prediction scheduling module and the collaborative learning module.

[0025] The resource scheduling instruction generated by the predictive scheduling module is output to the message processing module, the global model update parameters generated by the collaborative learning module are output to each distributed node, the performance index output by the message processing module is input to the monitoring feedback module, and the feedback signal output by the monitoring feedback module is input to the predictive scheduling module and the collaborative learning module, thereby forming a closed-loop control system.

[0026] Optionally, the prediction scheduling module specifically includes a multi-model integration unit and a dynamic weight adjustment unit, used to perform dynamically weighted multi-model integration prediction.

[0027] Optionally, the secure aggregation unit in the collaborative learning module specifically includes a weighted average calculator and a differential noise injector.

[0028] Optionally, the message processing module specifically includes a batch parallel executor, an error classifier, and a retry and routing manager.

[0029] Optionally, the device adopts a microkernel architecture, and the prediction scheduling module, collaborative learning module, message processing module and monitoring feedback module are all implemented in a plug-in form and can be hot-swapped.

[0030] This invention provides an adaptive processing method for a Kafka message queue system. The scheme first predicts message throughput using an intelligent prediction method and generates resource scheduling instructions based on the prediction results and system performance targets to achieve dynamic resource allocation. Second, after local training on each distributed node, model update data is securely aggregated to the server, generating global parameters and sending them back to optimize the detection capabilities of each node. Subsequently, messages are processed in parallel according to the instructions, implementing error classification, retry, and isolation mechanisms, and collecting processing performance metrics. Finally, the scheduling and model update steps are driven by metric feedback, forming a closed-loop control. This method effectively improves system throughput and processing efficiency, enhances the accuracy and privacy security of anomaly detection, significantly reduces latency and operational burden, and achieves overall optimization of system reliability. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application or the background art will be described below.

[0032] Figure 1 A flowchart illustrating the adaptive processing of a Kafka message queue system provided in an embodiment of this application is shown.

[0033] Figure 2 The following is an execution flowchart of an adaptive processing device for a Kafka message queue system provided in an embodiment of this application. Detailed Implementation

[0034] In the description of the embodiments of the present invention, those skilled in the art should understand that the embodiments of the present invention can be implemented as methods, apparatuses, electronic devices, computer-readable storage media, and computer program products. Therefore, the embodiments of the present invention can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. Furthermore, in some embodiments, the embodiments of the present invention can also be implemented as a computer program product in one or more computer-readable storage media, which contain computer program code.

[0035] The aforementioned computer-readable storage medium may be any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, optical disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any combination thereof. In embodiments of the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0036] The computer program code contained in the aforementioned computer-readable storage medium may be transmitted using any suitable medium, including wireless, wire, optical fiber, radio frequency (RF), or any suitable combination thereof.

[0037] Computer program code for performing the operations of the embodiments of the present invention can be written in assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, integrated circuit configuration data, or in one or more programming languages ​​or a combination thereof. The programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The computer program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer or an external computer via any type of network, including a local area network (LAN) or a wide area network (WAN).

[0038] The embodiments of the present invention describe the provided methods, apparatus, and electronic devices through flowcharts and / or block diagrams.

[0039] It should be understood that each block of a flowchart and / or block diagram, as well as combinations of blocks in a flowchart and / or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine that, when executed by a computer or other programmable data processing apparatus, creates means for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0040] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to function in a particular manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction apparatus product that includes the functions / operations specified in the blocks of a flowchart and / or block diagram.

[0041] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer-implemented process, such that the instructions that execute on the computer or other programmable data processing apparatus provide a process for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0042] The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0043] Figure 1 A flowchart illustrating an adaptive processing method for a Kafka message queue system provided by an embodiment of the present invention is shown. Figure 1 As shown, the method includes the following steps:

[0044] The Kafka message throughput is predicted based on AI prediction methods. Resource scheduling instructions are generated based on the prediction results, combined with single-threaded processing capacity and latency threshold SLO. The prediction model or scheduling strategy is dynamically adjusted based on the performance indicators of monitoring feedback.

[0045] Local anomaly detection model training is performed on each distributed processing node, and the trained model update data is uploaded to the server. After the server performs secure aggregation on the model update data, global model update parameters are generated and sent back to each node to update the local model.

[0046] According to the resource scheduling instructions, Kafka messages are consumed in parallel. During the process, errors are classified, retried, and isolated, and processing performance indicators are collected for monitoring and feedback.

[0047] The resource scheduling instruction directly controls the concurrency of message consumption, the global model update parameters are used to improve the anomaly detection accuracy of each node, and the collected performance indicators are fed back to the resource scheduling and model update steps to form a closed-loop control.

[0048] This invention relates to an adaptive processing method for a Kafka message queue system. The technical solution achieves a comprehensive improvement in system performance and reliability through the coordinated operation of multiple steps. First, message throughput is predicted using AI prediction methods. This step utilizes machine learning or time-series analysis algorithms to learn from historical and real-time traffic data to estimate the message arrival rate over a future period. Based on the prediction results, combined with the processing capacity of a single consumer thread and a pre-set latency target (SLO), the ideal number of concurrent consumers required by the system is calculated, and corresponding resource scheduling instructions are generated. Optionally, this prediction process can employ a multi-model integration approach, dynamically weighting and fusing the outputs of different models to improve the robustness and accuracy of the prediction. The core purpose of this step is to achieve proactive resource allocation, avoiding system overload or resource idleness caused by traffic fluctuations, thereby effectively balancing the relationship between throughput and latency and improving resource utilization efficiency.

[0049] Subsequently, the solution trains the local anomaly detection model on each distributed processing node. Each node trains its model locally based only on the data it receives, and then uploads the updated model data, such as gradients or statistics, to the central server. On the server, these updated data from different nodes undergo a secure aggregation operation, for example, by fusing information from each node using a weighted average algorithm. Differential privacy noise is injected during the aggregation process to protect data privacy, ultimately generating a set of global model update parameters and sending them back to each node. The design principle of this step is to build a more powerful and general global anomaly detection model using federated learning or similar collaborative learning frameworks without centralizing the original data. Its beneficial effects are that it significantly improves the entire system's ability to perceive and judge abnormal behavior, while strictly ensuring the data privacy and security of each participating node, and meeting the compliance requirements of cross-security domain collaboration.

[0050] Then, the system performs parallel consumption of Kafka messages according to the aforementioned resource scheduling instructions. This process adjusts the processing concurrency by changing the size of the consumer group or the thread pool, and employs mechanisms such as batch processing and asynchronous I / O to improve throughput. During processing, potential errors are finely categorized and managed. Temporary, recoverable errors are retried using strategies such as exponential backoff, while deterministic, permanent failures are isolated and routed to a dead-letter queue (DLQ). Simultaneously, the system comprehensively collects key performance indicators such as processing latency, throughput, and error rate. The purpose of this step is to efficiently and reliably complete the message consumption business logic, ensure eventual consistency and availability of the system through a categorized error handling mechanism, and provide data for system monitoring and optimization.

[0051] Ultimately, all steps are organically integrated through a closed-loop control structure. The generated resource scheduling instructions directly affect the concurrency control at the consumer end, while the generated global model parameters are used to continuously optimize the local anomaly detection models of each node. The collected system performance indicators are fed back to the resource scheduling and model update module in real time, serving as input for dynamically adjusting prediction model parameters or updating scheduling strategies. The principle of this closed-loop automated control lies in building an autonomous system capable of self-awareness, self-decision-making, and self-optimization. Its greatest benefit is enabling the entire message processing system to continuously adapt to changes in external traffic and internal state fluctuations, significantly reducing reliance on manual operation and maintenance. This achieves the overall operational goals of high throughput, low latency, and high availability, while also possessing the ability for continuous evolution and optimization.

[0052] In some embodiments, the AI ​​prediction method is optionally a multi-model ensemble prediction method, which dynamically weights and integrates the outputs of multiple base prediction models and adjusts the weights of each base prediction model online based on historical prediction errors. The system uses a multi-model ensemble prediction method to estimate message throughput. This method involves integrating multiple different base prediction algorithms—for example, combining time series analysis and regression models—and leveraging the different data pattern capture capabilities of each algorithm to work collaboratively. The system fuses the outputs of each base model through a dynamic weighting mechanism. This weighting is not fixed but adjusted online based on the recent historical prediction errors of each model. This results in higher confidence levels being assigned to models with more recent accuracy, allowing the overall prediction result to adaptively reflect the changing trends of real traffic. The beneficial effect of this design is that it effectively overcomes the limitations or biases that may exist in a single model. By comprehensively considering multiple prediction perspectives, it significantly improves the robustness and accuracy of predictions under different traffic patterns, thus providing a more reliable basis for subsequent resource scheduling decisions. This enables the system to more flexibly and efficiently cope with complex and changing real-time loads, ultimately achieving the dual goals of optimizing resource utilization and ensuring service performance.

[0053] In some embodiments, optionally, the base prediction model in the multi-model integrated prediction method includes at least an OLS linear regression model and a time-series prediction model. Specifically, the multi-model integrated prediction method employed by the system incorporates an OLS linear regression model and a time-series prediction model as its core basic prediction components. The OLS linear regression model effectively captures relatively stable dependency patterns between variables by modeling the linear relationship between historical throughput and relevant system indicators (such as resource utilization, message backlog, etc.); while the time-series prediction model focuses on analyzing the patterns of message traffic itself changing over time, such as periodic fluctuations and trend changes, thereby inferring future traffic trends. In actual operation, the system executes the prediction processes of these two models in parallel and inputs their outputs into the integration layer for fusion. The advantage of this combined strategy is that it can simultaneously utilize the interpretability of linear regression and the sensitivity of time-series models to dynamic changes, enabling the overall prediction framework to adapt to relatively stable data states and effectively respond to complex traffic fluctuations with significant time correlations. The beneficial effects are that it significantly enhances the adaptability and accuracy of the prediction system under different business scenarios and traffic patterns, provides a more comprehensive and reliable basis for subsequent resource scheduling decisions, and thus improves the stability and efficiency of the entire message processing system when facing diverse loads.

[0054] In some embodiments, optionally, the secure aggregation process is as follows: On the server side, a weighted average algorithm is used to collaboratively average the model update data from each node, and Gaussian noise conforming to differential privacy requirements is injected into the aggregation result to generate privacy-preserving global model update parameters. The system generates global model updates through a secure aggregation process that integrates collaborative learning and privacy enhancement technologies. This process is executed on the server side. First, a weighted average algorithm is used to integrate the model update data from each distributed node; this weighting strategy can dynamically allocate weights based on the data contribution or model quality of each node, achieving efficient knowledge collaboration by calculating the weighted average. Subsequently, the system injects precisely calibrated Gaussian noise into the aggregation result. This operation conforms to the technical requirements of differential privacy—differential privacy is a privacy protection framework that prevents inference from individual data records through controlled noise injection. The noise intensity is rigorously designed to maximize the statistical utility of the aggregated data while providing strict privacy protection. Finally, the server generates a set of privacy-preserving model parameters containing global knowledge and distributes them to each node. This implementation process enables the system to effectively aggregate the local learning results of each node without sharing the original data. This significantly improves the accuracy and robustness of the global anomaly detection model and fundamentally eliminates the risks of original data leakage and member reasoning, providing a reliable technical foundation for collaborative modeling across security domains or organizations.

[0055] In some embodiments, the weights used in the collaborative averaging are optionally determined based on the amount of data at each node or the quality of recent model updates. The system employs a dynamic adaptive weight allocation strategy to complete the collaborative averaging process. The core of this strategy lies in differentiating the importance of each participating node's model update based on the actual contribution quality of each node. Specifically, the weights are determined primarily based on two types of indicators: first, the amount of data each node relies on for local training; nodes with larger data volumes tend to provide more statistically significant and stable updates, and therefore may be assigned higher weights; second, the quality of the node's recent model updates, such as by calculating the consistency between the current update and historical updates, or by evaluating its improvement relative to the global model. This mechanism helps identify and reward nodes that consistently provide high-value information. In practice, the server integrates these two types of indicators and assigns appropriate weight coefficients to the update data from different nodes using a preset weight calculation function, and then performs a weighted average calculation. The benefits of this approach are obvious: it not only enhances the robustness and accuracy of the global model after aggregation, as more reliable and higher-quality updates play a more important role in the fusion process, but also incentivizes nodes to participate in high-quality training and can, to some extent, suppress the negative impact of low-quality or malicious updates on the global model, thereby improving the efficiency and security of the collaborative learning process as a whole.

[0056] In some embodiments, optionally, the error classification, retrying, and isolation includes: automatically retrying transient errors using an exponential backoff algorithm, and directly routing permanent errors to a dead-letter queue (DLQ) for isolation and recording based on error type labels. The system employs a classification and handling mechanism for errors occurring during processing. The core of this mechanism is to distinguish between recoverable transient errors and unrecoverable permanent errors based on their nature, and to apply differentiated response strategies. For transient errors, the system uses an exponential backoff algorithm for automatic retries. The core principle of this algorithm is to exponentially extend the waiting time for the next retry after each failed retry, thereby avoiding increased load due to frequent retries during brief system anomalies. For faults identified as permanent errors, the system does not perform unnecessary retries, but instead routes them directly to an independent storage area—the dead-letter queue (DLQ)—for isolation and placement based on their error type labels, and simultaneously records detailed error context information. This classification and handling method enables the system to intelligently identify the nature of faults and respond in a way that balances efficiency and safety. The beneficial effects are a significant improvement in the system's self-healing capabilities and operational reliability: on the one hand, the adaptive retry strategy effectively overcomes the interference of temporary failures, ensuring business continuity; on the other hand, the dead-letter queue mechanism isolates and archives specific failure cases, preventing invalid messages from blocking the main process and providing developers with clear problem tracking and post-event analysis basis, thus forming a closed loop for processing and operation.

[0057] In some embodiments, the parallel consumption processing optionally employs an asynchronous batch processing mechanism based on CompletableFuture, combined with zero-copy technology and memory pool management to reduce I / O and GC overhead. The system adopts a high-performance parallel message consumption processing mechanism, the core of which relies on a combination of modern asynchronous programming paradigms and underlying I / O optimization techniques. Specifically, the system utilizes the concurrent tool CompletableFuture to construct asynchronous batch processing flows. This tool allows large-scale message consumption tasks to be decomposed into multiple independent asynchronous subtasks for parallel execution, thereby significantly improving CPU utilization and overall processing throughput. Simultaneously, the system introduces zero-copy technology, which avoids redundant data copying between the operating system kernel space and the user application space, enabling more efficient direct data transfer between the network interface or disk and the application, significantly reducing performance losses caused by traditional I / O paths. Furthermore, the system implements a refined memory pool management strategy, replacing frequent heap memory creation and destruction by pre-allocating and uniformly reusing memory blocks. This effectively mitigates performance fluctuations and pause times caused by Java Virtual Machine garbage collection (GC) activities. The beneficial effect of this combined mechanism lies in its synergistic effect across three levels: programming model, data path, and memory management, which together construct an efficient and stable message processing runtime environment. It not only makes full use of multi-core computing resources to accelerate message processing speed, but also reduces the overall system load and response latency by cutting unnecessary data copying and memory allocation overhead. Thus, it can continuously provide high throughput and low latency service capabilities when facing massive message streams, while ensuring the stability and resource efficiency of the processing process.

[0058] Optionally, in some embodiments, the method further includes: evaluating the quality of clustering results in the anomaly detection model using a simplified silhouette coefficient, wherein the simplified silhouette coefficient is the ratio of inter-cluster center distance to global distortion, and automatically triggering model retraining when the score is below a threshold. The system introduces an automated evaluation mechanism based on a simplified silhouette coefficient to monitor the quality of clustering results in the anomaly detection model. The simplified silhouette coefficient is calculated using the ratio of inter-cluster center distance to global distortion as an evaluation metric, where inter-cluster center distance reflects the degree of separation between different categories, while global distortion reflects the average deviation of all data points in the clustering results from their respective cluster centers. When the system detects that the coefficient value is consistently below a preset threshold, it automatically triggers the model retraining process. The advantage of this approach is that it can promptly identify clustering performance degradation caused by changes in data distribution or model degradation, and ensure that anomaly detection performance remains at an ideal level through an active retraining mechanism. Its beneficial effects are not only reflected in improving the model's adaptive learning ability but also significantly reducing reliance on manual intervention, providing an important guarantee for the stable operation of the system in dynamic environments.

[0059] In some embodiments, the performance metrics optionally include at least processing latency, batch size, error rate, consumption lag, and processing rate tagged with topic and group, and are collected uniformly through the Micrometer monitoring framework. The system integrates the Micrometer monitoring framework to build a unified metric collection system. This framework, as a widely used measurement tool in modern distributed systems, can collect and expose various performance data of the system in a standardized manner. Specific performance metrics collected include, but are not limited to, multi-dimensional data such as processing latency, batch size, error frequency, message consumption lag, and system processing rate tagged with topic and group. These tagging information provides the necessary contextual dimensions for subsequent metric aggregation and analysis, enabling monitoring data to be subdivided and correlated according to business logic units. In the implementation process, the system embeds metric collection code at key points in the processing flow, automatically capturing various metrics and attaching corresponding metadata tags. Micrometer is responsible for metric aggregation, temporary storage, and external output. The beneficial effect of this approach is that it provides a solid data foundation for enterprise-level observability: it not only enables fine-grained real-time monitoring of system operation status, allowing maintenance personnel to quickly locate performance bottlenecks in specific topics or consumer groups, but also reduces the monitoring coupling between system components through standardized data collection methods, providing reliable data support for subsequent performance trend analysis, dynamic resource scheduling, and automated decision-making, thereby significantly improving the efficiency and accuracy of system operation and maintenance.

[0060] In some embodiments, optionally, the predictor, anomaly determiner, and optimization strategies of the system all adopt a plug-in design, are loaded through a microkernel architecture, and support dynamic replacement after verifying the effectiveness of new strategies through A / B testing or canary release. The system manages its core functional components, including the predictor, anomaly determiner, and various optimization strategies, using a combination of plug-in design and microkernel architecture. This implementation builds the core framework based on a microkernel architecture, where the microkernel, as a lightweight core system, is mainly responsible for plug-in lifecycle management, service communication, and basic resource scheduling, while the specific functionalities such as prediction algorithms, anomaly determination logic, and optimization strategies are encapsulated as independent plug-in modules. These plug-ins are developed according to predefined interface specifications and dynamically loaded into the runtime environment through configuration files or service registration mechanisms. The system further introduces A / B testing and canary release mechanisms. When a new strategy needs to be deployed, it can first be deployed as a plug-in in a specific scope or traffic ratio. By continuously collecting performance indicators and comparing them with the baseline version, its effectiveness is verified, and then the deployment scope is gradually expanded until the old version is completely replaced. The advantage of this architecture design lies in its greatly enhanced scalability and maintainability. It not only allows for hot updates of functional modules without restarting core services, but also effectively reduces the risk of launching new features through a scientific verification mechanism. At the same time, it provides a smooth transition path for technological evolution, enabling the system to continuously adapt to changes in business needs and technological development.

[0061] In some embodiments, such as Figure 2 As shown in the figure, this application provides an adaptive processing device for a Kafka message queue system, including:

[0062] The prediction and scheduling module is configured to predict Kafka message throughput based on AI prediction methods, generate resource scheduling instructions based on the prediction results, combined with single-threaded processing capacity and latency threshold SLO, and dynamically adjust the prediction model or scheduling strategy based on performance indicators from monitoring feedback.

[0063] The collaborative learning module is configured to train a local anomaly detection model on each distributed processing node, upload the trained model update data to the server, and after the server performs secure aggregation on the model update data, generate global model update parameters and send them back to each node to update the local model.

[0064] The message processing module is configured to consume Kafka messages in parallel according to the resource scheduling instructions, classify and retry errors and isolate them during the process, and collect processing performance indicators.

[0065] The monitoring feedback module is configured to receive and analyze the performance indicators, and then generate feedback signals to the prediction scheduling module and the collaborative learning module.

[0066] The resource scheduling instruction generated by the predictive scheduling module is output to the message processing module, the global model update parameters generated by the collaborative learning module are output to each distributed node, the performance index output by the message processing module is input to the monitoring feedback module, and the feedback signal output by the monitoring feedback module is input to the predictive scheduling module and the collaborative learning module, thereby forming a closed-loop control system.

[0067] The data execution process of this device begins with the prediction and scheduling module. This module continuously receives historical throughput and system runtime metrics as input, and uses built-in AI prediction methods (such as multi-model ensemble algorithms) to estimate future message throughput. It then combines single-threaded processing capabilities with Delay Service Level Agreement (SLO) targets to generate resource scheduling instructions. These instructions are transmitted in real-time to the message processing module, driving it to consume the raw message stream from the Kafka cluster in parallel at dynamically adjusted concurrency. During processing, the message processing module performs batch consumption, error classification (e.g., distinguishing between transient and permanent errors), and retry isolation, while simultaneously collecting detailed performance metrics such as processing latency, error rate, and consumption lag. These metrics are received in real-time and deeply analyzed by the monitoring and feedback module. If a metric deviates from a preset threshold, this module generates a corresponding feedback signal. This feedback signal simultaneously affects both the prediction and scheduling module and the collaborative learning module: on the one hand, it guides the dynamic optimization of the prediction model or scheduling strategy; on the other hand, it triggers adjustments to the collaborative learning process. In the collaborative learning module, each distributed processing node trains anomaly detection models based on local data, uploading only model update data (such as gradients or statistics) to the server. The server performs collaborative aggregation using a weighted average algorithm, injecting Gaussian noise that meets differential privacy requirements to ensure security, and generates global model update parameters before sending them back to each node, thereby continuously improving the accuracy of anomaly detection. The entire process, through the tight connection between the inputs and outputs of the modules, forms a closed-loop control system from data perception and intelligent decision-making to execution feedback, enabling the system to adapt to traffic fluctuations and abnormal states, significantly improving processing efficiency, reliability, and autonomy.

[0068] In some embodiments, the prediction scheduling module optionally includes a multi-model integration unit and a dynamic weight adjustment unit for performing dynamically weighted multi-model ensemble prediction. Through the collaborative work of its internal multi-model integration unit and dynamic weight adjustment unit, the prediction scheduling module implements a prediction mechanism with self-learning capabilities. The multi-model integration unit is responsible for running multiple different types of basic prediction algorithms in parallel, such as models that consider time-series trend analysis and regression models that capture factor correlations, thereby forming multi-faceted prediction results. Simultaneously, the dynamic weight adjustment unit continuously tracks the recent prediction accuracy of each basic model and dynamically assigns different confidence weights based on its performance, allowing models with more accurate recent predictions to have a greater influence on the final result. The core of this dynamically weighted integration method is to enable the system to automatically identify which model is more reliable under different data fluctuation patterns, rather than relying on a single model. The beneficial effect of this design is that it significantly enhances the system's adaptability to complex and variable traffic scenarios. It effectively integrates the advantages of multiple algorithms, avoids prediction failures caused by cognitive biases of a single model, and thus provides a more stable and reliable basis for downstream resource scheduling decisions. Ultimately, it ensures the foresight and accuracy of resource allocation and improves the resource utilization efficiency and stability of the entire system.

[0069] In some embodiments, the secure aggregation unit in the collaborative learning module optionally includes a weighted average calculator and a differential noise injector. The secure aggregation unit of the collaborative learning module, through the coordinated operation of its internally integrated weighted average calculator and differential noise injector, implements a model update fusion mechanism that balances utility and privacy. The weighted average calculator is responsible for integrating model update data (such as gradients or statistics) from various distributed nodes. Its calculation process assigns differentiated weight coefficients to the contributions of different nodes according to a preset weight allocation strategy (e.g., based on the amount of node data or the quality of historical updates), and then calculates its weighted average. This operation aims to more fully absorb the information value of high-quality updates, improving the knowledge density and generalization ability of the fused global model. Next, the differential noise injector injects rigorously calibrated Gaussian noise into the weighted average result. The intensity of this noise is precisely designed to conform to the strict mathematical definition of differential privacy—a privacy protection framework that prevents attackers from inferring any single training sample attribute from the output result through controlled noise interference. This noise injection is not a simple perturbation, but mathematically ensures that even with the complete aggregation result, it is impossible to trace back or identify any original data details of the participating nodes. The beneficial effect of this combined unit lies in achieving two key objectives simultaneously: firstly, it optimizes the accuracy and convergence efficiency of the global model through a weighted strategy; secondly, it provides provable and rigorous privacy and security guarantees for all participating nodes through the introduction of differential noise. This makes cross-organizational or cross-security domain collaborative modeling possible while protecting the data sovereignty of all parties, greatly expanding the application boundaries of collaborative learning technology.

[0070] In some embodiments, the message processing module optionally includes a batch parallel executor, an error classifier, and a retry and routing manager. In this system embodiment, the message processing module, through the precise collaboration of its three core components—the batch parallel executor, the error classifier, and the retry and routing manager—completes a reliable processing flow from consumption to delivery. The batch parallel executor, as the processing core, does not process messages one by one. Instead, it pulls a batch of messages from a Kafka topic, breaks them down into multiple subtasks, and distributes these subtasks to multiple consumer threads for simultaneous execution using modern asynchronous programming frameworks (such as CompletableFuture). This aims to parallelize a large processing task, thereby significantly improving the utilization of the central processing unit and the overall throughput. Meanwhile, the error classifier, as a diagnostic hub, monitors the processing status of each message in real time. It does not simply categorize all anomalies, but intelligently diagnoses and marks them based on the nature, frequency, and recoverability of the errors. For example, it identifies transient failures caused by network jitter as transient errors, while classifying unrecoverable problems such as message format errors as permanent errors. The retry and routing manager acts as the execution control unit. Based on the labels output by the error classifier, it implements differentiated strategies: for transient errors, it initiates an exponential backoff retry mechanism, automatically extending the waiting interval for the next attempt after each failed retry to avoid a "snowball" effect during brief service unavailability; for messages marked as permanent errors, retries are immediately stopped, and the messages are routed to a separate dead-letter queue for isolation, while detailed error context is recorded for subsequent manual investigation. This component-based design constructs a message processing pipeline integrating efficient parallel processing, intelligent error diagnosis, and differentiated fault handling. The resulting benefits include a comprehensive improvement in system robustness and operational efficiency: it not only meets the performance requirements of high throughput and low latency through parallel batch processing, but more importantly, through a refined error management mechanism, it enables the system to self-recover from common transient failures while ensuring that permanent failures can be effectively isolated and traced, thereby greatly enhancing service continuity and maintainability.

[0071] In some embodiments, the device optionally adopts a microkernel architecture, where the prediction scheduling module, collaborative learning module, message processing module, and monitoring feedback module are all implemented in a plug-in format and are hot-swappable. In one implementation of the system, the foundation of the entire device is a microkernel architecture. This architecture encapsulates the system's core basic services, such as plug-in lifecycle management, inter-module communication coordination, and resource allocation, into a minimally sized kernel, called the microkernel. Specific business logic functions, including the four core capabilities of prediction scheduling, collaborative learning, message processing, and monitoring feedback, are all designed as independent plug-in modules conforming to predetermined interface specifications. These plug-in modules communicate with the microkernel through clearly defined interfaces, and are uniformly loaded, initialized, and maintained by the plug-in manager within the kernel. The ingenuity of this design lies in achieving "hot-swappable" characteristics, meaning that administrators or automated operation and maintenance systems can dynamically deploy a new version of a plug-in to the running system without stopping the entire message processing service. For example, replacing an old version with a new prediction algorithm plug-in, or temporarily loading a dedicated fault diagnosis plug-in, the entire process has minimal impact on the continuous and stable operation of the system. This microkernel-based plug-in implementation offers core advantages, providing the system with unprecedented scalability, maintainability, and technical agility. In terms of scalability, when introducing new prediction algorithms, anomaly detection models, or optimization strategies, developers only need to implement a new plug-in following the interface specifications, without modifying the core system code, greatly reducing the complexity and risk of feature expansion. In terms of maintainability, the highly decoupled functional modules mean that the failure or defect of a single plug-in can be isolated within a limited scope, preventing the entire system from crashing and significantly improving system reliability. Simultaneously, testing and debugging can be performed on specific plug-ins, improving operational efficiency. Most importantly, it supports smooth technology evolution and A / B testing, allowing new and old strategies to run and be compared safely in parallel in a production environment, laying a solid foundation for continuous autonomous system optimization and enabling it to flexibly adapt to future changing business needs and technological developments.

[0072] In the several embodiments provided in this application, it should be understood that the disclosed apparatus, electronic devices, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, or it may be an electrical, mechanical, or other form of connection.

[0073] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to solve the problems addressed by the embodiments of the present invention, depending on actual needs.

[0074] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0075] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a program product. This computer program product is stored in a storage medium and includes several instructions to cause a computer device (including: a personal computer, a server, a data center, or other network device) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media listed above that can store program code.

[0076] Example 1

[0077] This application provides an adaptive processing method for a Kafka message queue system. This method achieves accurate prediction, efficient processing, and continuous optimization of the message processing flow by constructing an intelligent closed-loop control system. The method will be described in detail below with reference to specific implementation details.

[0078] This system uses AI-based prediction methods to forecast Kafka message throughput. Based on the prediction results, combined with single-threaded processing capacity and the Service Level Agreement (SLO) threshold, resource scheduling instructions are generated. The prediction model or scheduling strategy is dynamically adjusted based on performance metrics from monitoring feedback. In this step, the system first collects historical message throughput data, current system load metrics, and preset SLO parameters as input. AI prediction methods can be implemented using various models. For example, time series analysis models (such as ARIMA and Prophet) can be used to capture the periodicity of traffic, while regression models can be combined to analyze the correlation between throughput and external factors (such as promotional activities and time-of-day characteristics). A preferred implementation uses multi-model ensemble prediction, where multiple base prediction models are run in parallel, and their results are fused through a dynamic weighting mechanism. For example, the system can simultaneously deploy linear regression (OLS) models and long short-term memory (LSTM) network models. Linear regression models excel at capturing relatively stable linear trends, while LSTM models have better fitting capabilities for complex nonlinear time-series patterns. The dynamic weighting unit adjusts the weights of each model online based on its recent prediction errors, with models that have made more accurate recent predictions receiving higher weights in the ensemble result. The predicted future throughput is compared with the processing capacity of a single consumer thread (e.g., messages per second) and combined with the maximum allowable latency specified in the SLO (e.g., the P99 latency threshold) to calculate the optimal number of consumer concurrency required to achieve the target latency. This calculation process must consider the system resource limits and use a limiting function to avoid drastic fluctuations in resource allocation. The final generated resource scheduling instruction includes specific concurrency adjustment values, such as adjusting the number of consumer instances for a topic from 15 to 22. Furthermore, the predictive scheduling module continuously receives feedback data from the monitoring system. When a persistently large prediction deviation is detected, it automatically triggers retraining of the prediction model or adjustment of the weighting strategy, enabling the prediction system to adapt to changes in business models. The beneficial effect of this step is that, through intelligent prediction and dynamic decision-making, proactive resource allocation is achieved, avoiding delayed scheduling based on instantaneous reactions, thereby significantly improving resource utilization while ensuring service quality.

[0079] Local anomaly detection model training is performed on each distributed processing node, and the updated model data is uploaded to the server. The server then securely aggregates the updated model data, generates global model update parameters, and sends them back to each node to update the local model. This step involves the specific implementation of a distributed collaborative learning framework. On each consumer service node, a lightweight anomaly detection model is deployed, such as an algorithm based on Isolation Forest or Local Outlier Factor (LOF). This model is continuously trained using the message characteristics consumed by the node (such as message size, processing time, source IP, etc.). To protect data privacy, nodes only complete model training locally and never transmit the original message data. Instead, they encrypt and upload the updated model parameters, such as gradients or changes in model weights, to the central server. The secure aggregation unit on the server is a core component, and its workflow includes two main operations: weighted averaging and differential privacy protection. The weighted average calculator receives updated data from all nodes and assigns weights to each node's update according to a preset strategy. Weighting can be determined based on the amount of data a node has (nodes with larger data volumes contribute more significantly and have higher weights) or the quality of their historical updates (nodes that provide high-quality updates recently receive higher weights). Subsequently, the differential noise injector injects precisely calibrated Gaussian or Laplace noise into the weighted average aggregation result, adhering to strict differential privacy requirements. The noise intensity (e.g., the noise standard deviation σ) is mathematically calculated to minimize its impact on model utility while providing strong privacy guarantees. The resulting noise-processed global model parameters are then fed back to all participating nodes. Each node uses these global parameters to update its local model, thereby gaining a more robust anomaly detection capability based on the experience of all node data and meeting privacy requirements. This step has two beneficial effects: firstly, it integrates distributed knowledge through the federated learning paradigm, significantly improving the accuracy and robustness of anomaly detection; secondly, it provides provable and rigorous privacy guarantees through differential privacy technology, making collaborative modeling possible in sensitive data scenarios.

[0080] According to the resource scheduling instructions, Kafka messages are consumed in parallel. During the process, errors are classified, retried, and isolated, and processing performance metrics are collected for monitoring and feedback. The message processing module is the execution layer of the solution. It receives resource scheduling instructions from the predictive scheduling module and dynamically adjusts the size of the consumer thread pool accordingly, for example, by increasing or decreasing the number of worker threads using Java's ExecutorService framework. In actual message consumption, the module employs an efficient parallel processing mechanism. A typical implementation uses CompletableFuture for asynchronous batch processing: after batching messages from Kafka partitions, a batch of messages is split into multiple subtasks, which are then executed asynchronously by CompletableFuture. Finally, methods such as allOf().join() are used to wait for all subtasks to complete, achieving batch merging. To improve I / O efficiency, zero-copy technology (such as FileChannel.transferTo in Java NIO) can be used to avoid unnecessary data copying between the kernel buffer and the user buffer. Simultaneously, message caching is managed through a pre-allocated memory pool (such as Netty's ByteBuf), reducing garbage collection (GC) pressure caused by frequent memory allocation and reclamation. Error handling is crucial in message processing. The system's built-in error classifier diagnoses anomaly types in real time, marking network timeouts and database temporary lock conflicts as transient errors, while marking message format errors and business logic validation failures as permanent errors. For transient errors, the retry manager uses an exponential backoff algorithm for automatic retries; after the first retry, it waits a short time, and if it fails again, the waiting time increases exponentially to avoid a cascading failure effect. For permanent errors, retries are stopped, and the routing manager immediately delivers them to a dedicated dead-letter queue (DLQ), recording detailed error context (such as message ID, error stack trace, and timestamp) for subsequent manual investigation. Throughout the process, the module integrates monitoring frameworks like Micrometer to collect detailed performance metrics with multi-dimensional tags (such as topic and consumer_group), including but not limited to average processing latency, messages processed per second (throughput), error rate, and consumer lag. These metrics form the data foundation for system status awareness and subsequent optimization. The beneficial effect of this step is that, through efficient parallel processing and intelligent error management, it not only significantly improves the speed and throughput of message processing, but also ensures the eventual consistency and availability of the system under certain abnormal conditions.

[0081] The resource scheduling instructions directly control the concurrency of message consumption, while the global model update parameters improve the anomaly detection accuracy of each node. The collected performance metrics are fed back to the resource scheduling and model update steps, forming a closed-loop control. Thus, these four core steps constitute a complete adaptive control loop. The resource scheduling instructions, as feedforward control signals, directly determine the concurrency capability of the message processing module. The global model update parameters, as increments in the knowledge base, continuously optimize the "immune system" (anomaly detection) of each node. The monitoring and feedback module, acting as the system's "sensory nerves," continuously analyzes the performance metrics generated in the processing stages. When metrics become abnormal (e.g., latency consistently exceeds the SLO threshold, or the anomaly detection model's evaluation metrics, such as the simplified silhouette coefficient, fall below the threshold), the monitoring module generates a feedback signal. This signal triggers the prediction scheduling module to re-evaluate or adjust its prediction model and strategy, and may also trigger the collaborative learning module to initiate a new round of model aggregation and retraining. This closed-loop design ensures that the entire system is no longer passively responding to faults, but actively perceiving, deciding, executing, and verifying, thereby achieving continuous self-optimization. The benefits of this closed-loop mechanism are fundamental. It endows the system with a high degree of autonomy and resilience, effectively copes with issues such as traffic fluctuations, local failures, and model degradation, significantly reduces operational complexity, and ensures long-term operational stability.

[0082] Optionally, the AI ​​prediction method is a multi-model ensemble prediction method, which dynamically weights and integrates the outputs of multiple base prediction models and adjusts the weights of each base prediction model online based on historical prediction errors. For example, the system can maintain a model pool containing the Holt-Winters model, which is sensitive to short-term fluctuations, and the SARIMAX model, which captures long-term trends. The dynamic weight adjustment unit periodically (e.g., every 5 minutes) calculates the mean absolute percentage error (MAPE) of each model over a past time window and recalculates the weights based on the inverse of the error or the softmax function; models with smaller errors have higher weights. This approach effectively utilizes the advantages of different models, avoids the failure of a single model in certain scenarios, and improves the overall robustness and accuracy of the prediction.

[0083] Optionally, the base prediction model in the multi-model ensemble prediction method includes at least an OLS linear regression model and a time-series prediction model. The OLS model can quickly fit the linear relationship between throughput and system metrics (such as CPU load and memory utilization), while the time-series prediction model (such as LSTM) focuses on uncovering the inherent patterns of traffic itself. Combining the two allows the prediction to consider both the influence of external resource conditions and the temporal regularity of traffic changes, resulting in more comprehensive and reliable prediction results.

[0084] Optionally, the specific process of secure aggregation is as follows: On the server side, a weighted averaging algorithm is used to collaboratively average the model update data from each node, and Gaussian noise conforming to differential privacy requirements is injected into the aggregation result to generate privacy-preserving global model update parameters. The server first verifies the node identity and the integrity of the update data, then calculates weights based on the amount of data from each node and performs a weighted average. Next, noise is sampled from a Gaussian distribution, with the noise's scale parameter ε strictly set according to the required privacy budget, and added to the aggregated model parameters. This process mathematically guarantees that attackers cannot infer the training data information of any individual node from the published global model, achieving strict privacy protection.

[0085] Optionally, the weights used in the collaborative averaging are determined based on the amount of data at each node or the quality of recent model updates. Assigning weights based on data volume is a simple and effective strategy, assuming that nodes with larger data volumes contribute more statistical information. Assigning weights based on update quality is more refined; for example, the consistency between the current node update and the previous round of global updates can be calculated, with higher consistency considered higher quality. This quality-oriented weight allocation helps suppress the influence of malicious nodes or low-quality updates, accelerating the convergence of the global model.

[0086] Optionally, the error classification, retry, and isolation includes: automatically retrying transient errors using an exponential backoff algorithm, and directly routing permanent errors to a dead-letter queue (DLQ) for isolation and recording based on error type labels. For example, in the event of a database connection timeout, the system will attempt to retry after 1 second, 2 seconds, and 4 seconds. If the message body does not conform to a predefined JSON schema, it is immediately delivered to a DLQ named after the current date, and error details are recorded. This classification and processing mechanism greatly improves the system's self-healing capability and the efficiency of fault management.

[0087] Optionally, the parallel consumption processing employs an asynchronous batch processing mechanism based on CompletableFuture, combined with zero-copy technology and memory pool management to reduce I / O and GC overhead. In actual coding, a batch of records can be obtained using the poll method of Kafka Consumer, and then mapped as asynchronous tasks for concurrent execution using the Stream API or CompletableFuture.supplyAsync. Simultaneously, zero-copy technologies such as MappedByteBuffer are used to accelerate log append operations, and pooling technologies (such as Apache Commons Pool) are used to manage resources such as database connections, thereby systematically reducing various overheads and improving throughput.

[0088] Optionally, the method further includes: evaluating the quality of clustering results in the anomaly detection model using a simplified silhouette coefficient, where the simplified silhouette coefficient is the ratio of inter-cluster center distance to global distortion; and automatically triggering model retraining when the score falls below a threshold. The silhouette coefficient is a traditional metric for evaluating clustering performance, but it is computationally expensive. The simplified silhouette coefficient approximates the average distance between each sample and other samples within the cluster using global distortion, resulting in higher computational efficiency. The monitoring module calculates this coefficient periodically (e.g., hourly), and when it falls below 0.5 (this value is an example), it sends a retraining signal to the collaborative learning module to ensure that the anomaly detection model does not fail due to changes in data distribution.

[0089] Optionally, the performance metrics include at least processing latency, batch size, error rate, consumption lag, and processing rate tagged with topic and group, and are collected uniformly through the Micrometer monitoring framework. Micrometer provides the ability to integrate with various monitoring backends (such as Prometheus and InfluxDB). At key points in the code, tracking points are placed using metric types such as Timer and Counter, and each metric is tagged with a topic and consumer group. This allows operations personnel to analyze the performance of different business lines and different consumer groups with fine granularity and quickly locate bottlenecks.

[0090] Optionally, the predictor, anomaly checker, and optimization strategy of the system all adopt a plug-in design, are loaded through a microkernel architecture, and support dynamic replacement after verifying the effectiveness of the new strategy through A / B testing or canary release. The core of the system is a lightweight microkernel responsible for loading, unloading, and lifecycle management of plug-ins. Each functional component is implemented as an independent JAR package, following a predefined SPI (Service Provider Interface). When the prediction algorithm needs to be upgraded, the old and new algorithm plug-ins can be deployed to different consumer groups for A / B testing. After comparing the effects by monitoring data, the configuration center can dynamically switch traffic to the better new plug-in, achieving a smooth upgrade and greatly improving the system's maintainability and technological evolution capabilities.

[0091] The above description is merely a specific implementation of the embodiments of the present invention, but the protection scope of the embodiments of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the embodiments of the present invention should be included within the protection scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention should be determined by the protection scope of the claims.

Claims

1. An adaptive processing method for a Kafka message queue system, characterized in that, The method includes: The Kafka message throughput is predicted based on AI prediction methods. Resource scheduling instructions are generated based on the prediction results, combined with single-threaded processing capacity and latency threshold SLO. The prediction model or scheduling strategy is dynamically adjusted based on the performance indicators of monitoring feedback. Local anomaly detection model training is performed on each distributed processing node, and the trained model update data is uploaded to the server. After the server performs secure aggregation on the model update data, global model update parameters are generated and sent back to each node to update the local model. According to the resource scheduling instructions, Kafka messages are consumed in parallel. During the process, errors are classified, retried, and isolated, and processing performance indicators are collected for monitoring and feedback. The resource scheduling instruction directly controls the concurrency of message consumption, the global model update parameters are used to improve the anomaly detection accuracy of each node, and the collected performance indicators are fed back to the resource scheduling and model update steps to form a closed-loop control.

2. The method according to claim 1, characterized in that, The AI ​​prediction method is a multi-model ensemble prediction method, which dynamically weights and integrates the outputs of multiple base prediction models, and adjusts the weights of each base prediction model online based on historical prediction errors.

3. The method according to claim 2, characterized in that, The base prediction model in the multi-model ensemble prediction method includes at least an OLS linear regression model and a time-series prediction model.

4. The method according to claim 1, characterized in that, The specific process of secure aggregation is as follows: on the server side, a weighted average algorithm is used to perform a collaborative average of the model update data from each node, and Gaussian noise that meets the differential privacy requirements is injected into the aggregation result to generate the privacy-preserving global model update parameters.

5. The method according to claim 4, characterized in that, The weights used in the collaborative averaging are determined based on the amount of data at each node or the quality of recent model updates.

6. The method according to claim 1, characterized in that, The classification, retrying, and isolation of errors include: using the exponential backoff algorithm to automatically retry transient errors, and directly routing permanent errors to a dead-letter queue (DLQ) for isolation and recording based on the error type label.

7. The method according to claim 1, characterized in that, The parallel consumption processing adopts an asynchronous batch processing mechanism based on CompletableFuture, combined with zero-copy technology and memory pool management to reduce I / O and GC overhead.

8. The method according to claim 1, characterized in that, The method further includes: using a simplified silhouette coefficient to evaluate the quality of clustering results in the anomaly detection model, wherein the simplified silhouette coefficient is the ratio of inter-cluster center distance to global distortion, and when the score is lower than a threshold, the model is automatically retrained.

9. The method according to claim 1, characterized in that, The performance metrics include at least processing latency, batch size, error rate, consumption lag, and processing rate tagged with topic and group, and are collected uniformly through the Micrometer monitoring framework.

10. The method according to claim 1, characterized in that, The predictor, anomaly detector, and optimization strategy of the system are all designed as plug-ins, loaded through a microkernel architecture, and can be dynamically replaced after verifying the effectiveness of the new strategy through A / B testing or canary release.

11. An adaptive processing apparatus for a Kafka message queue system for implementing the method of any one of claims 1-10, characterized in that, The device includes: The prediction and scheduling module is configured to predict Kafka message throughput based on AI prediction methods, generate resource scheduling instructions based on the prediction results, combined with single-threaded processing capacity and latency threshold SLO, and dynamically adjust the prediction model or scheduling strategy based on performance indicators from monitoring feedback. The collaborative learning module is configured to train a local anomaly detection model on each distributed processing node, upload the trained model update data to the server, and after the server performs secure aggregation on the model update data, generate global model update parameters and send them back to each node to update the local model. The message processing module is configured to consume Kafka messages in parallel according to the resource scheduling instructions, classify and retry errors and isolate them during the process, and collect processing performance indicators. The monitoring feedback module is configured to receive and analyze the performance indicators, and then generate feedback signals to the prediction scheduling module and the collaborative learning module. The resource scheduling instruction generated by the predictive scheduling module is output to the message processing module, the global model update parameters generated by the collaborative learning module are output to each distributed node, the performance index output by the message processing module is input to the monitoring feedback module, and the feedback signal output by the monitoring feedback module is input to the predictive scheduling module and the collaborative learning module, thereby forming a closed-loop control system.

12. The apparatus according to claim 11, characterized in that, The prediction scheduling module specifically includes a multi-model integration unit and a dynamic weight adjustment unit, which are used to perform dynamically weighted multi-model integration prediction.

13. The apparatus according to claim 11, characterized in that, The secure aggregation unit in the collaborative learning module specifically includes a weighted average calculator and a differential noise injector.

14. The apparatus according to claim 11, characterized in that, The message processing module specifically includes a batch parallel executor, an error classifier, and a retry and routing manager.

15. The apparatus according to claim 11, characterized in that, The device adopts a microkernel architecture, and the prediction scheduling module, collaborative learning module, message processing module and monitoring feedback module are all implemented in a plug-in form and can be hot-swapped.

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