An efficient database monitoring system based on netty

Abstract

The present invention discloses a high-efficiency database monitoring system based on Netty in the field of data communication technology, comprising: a data acquisition unit, which is used to establish a long connection with a database by using a Netty framework, and collect database operation status information in real time by monitoring data packets of a communication protocol; a data processing unit, which is used to perform hierarchical processing on the collected data packets based on a Pipeline mechanism of Netty; a message queue unit, which is used to push processed monitoring data to a message queue; a storage and analysis unit, which is used to store the monitoring data in a distributed storage system; an alarm and notification unit, which is used to trigger an alarm according to a predefined monitoring rule and supports a variety of alarm notification methods; and a modern high-performance network programming framework (Netty) and a database operation protocol parsing technology are combined to realize real-time monitoring and dynamic management of the database operation status.

Description

Technical Field

[0001] The present invention relates to the technical field of data communication, and in particular to a high-efficiency database monitoring system based on Netty. Background Art

[0002] In the modern information age, databases, as core systems for data storage and management, are widely used in various industries such as finance, e-commerce, medical care, and education. With the rapid growth of business needs and the explosive increase in data volume, database operations face the following challenges:

[0003] Necessity of database operation status monitoring: As a key infrastructure, the operation status of the database directly affects the performance and stability of the entire system. However, due to the complex internal structure of the database and the increasing external pressure, the following problems often occur during operation:

[0004] Performance bottlenecks: including slow queries, deadlocks, transaction blocking, etc., which can significantly reduce the system's response speed. Resource competition: too many concurrent connections may lead to the exhaustion of CPU, memory or I / O resources. Errors and exceptions: such as connection timeouts, permission errors and data inconsistencies, which seriously affect the system's availability and data integrity. Therefore, timely acquisition and analysis of database operation status data is the key to discovering and solving the above problems.

[0005] The existing database monitoring technology mainly adopts two methods:

[0006] Periodic polling method: collect key indicator data by periodically sending query requests to the database.

[0007] Disadvantages: There are sampling blind spots, making it difficult to capture sudden exceptions in a timely manner; too high a polling frequency will increase the load on the database.

[0008] Log file analysis: Obtain monitoring information by analyzing the operation log files generated by the database (such as slow query logs and error logs).

[0009] Disadvantages: Log files are usually generated offline and have poor real-time performance. In addition, the log volume is large and the structure is complex, resulting in high parsing costs.

[0010] These traditional methods are unable to meet the data update requirements in seconds or even milliseconds in high-concurrency scenarios, are prone to missing sudden exceptions, consume a lot of system resources during polling or log parsing, and may affect the normal operation of the database. When the database scale expands or the number of concurrent requests increases, the performance bottleneck problem of the monitoring system is particularly prominent. Summary of the invention

[0011] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of the present invention to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0012] Therefore, the purpose of the present invention is to provide an efficient database monitoring system based on an asynchronous event-driven framework (netty). This technology combines a modern high-performance network programming framework (netty) with database operation protocol parsing technology to achieve real-time monitoring and dynamic management of the database operation status, aiming to solve the performance bottleneck and lack of real-time performance of existing monitoring systems in a high-concurrency environment.

[0013] To solve the above technical problems, the present invention provides an efficient database monitoring system based on Netty, which adopts the following technical scheme: comprising: a data acquisition unit, which is used to establish a long connection with the database using the Netty framework, and collect database operation status information in real time by monitoring the data packets of the communication protocol; a data processing unit, which is used to perform hierarchical processing on the collected data packets based on the Pipeline mechanism of Netty; a message queue unit, which is used to push the processed monitoring data to the message queue; a storage and analysis unit, which is used to store the monitoring data in a distributed storage system; an alarm and notification unit, which is used to trigger alarms according to predefined monitoring rules and support multiple alarm notification methods.

[0014] Optionally, the data collection unit includes a long connection establishment module, which is used to use the netty framework to establish a TCP protocol long connection with the target database, and monitor the database communication traffic in real time through a non-blocking I / O model; a netty configuration module, which is used to use EventLoopGroup to manage the connection thread pool, the main thread group is responsible for event reception, and the working thread group performs asynchronous processing; it is also used to configure a heartbeat detection mechanism to detect the survival status of the connection; a custom protocol processor, which is used to adapt the communication protocol of the database, including: MySQL protocol, PostgreSQL protocol.

[0015] Optionally, the real-time collection of database operation status information includes query and transaction logs: recording the execution time, statement content and status of each query; system performance indicators: number of connections, CPU usage, memory consumption, lock waiting time; exception information: timeout, deadlock, permission error.

[0016] Optionally, the data processing unit includes a protocol parsing layer: decoding received data packets and extracting meaningful information, including SQL statements and status identifiers; a data filtering layer: filtering redundant data according to predefined rules and retaining only key monitoring indicators; an indicator calculation layer: aggregating and counting data, including slow query statistics and average response time calculation.

[0017] Optionally, a compression algorithm is also included, which compresses the monitoring data processed by the data processing unit.

[0018] Optionally, the message queue unit uses Kafka, RabbitMQ or Redis as a data transfer layer to achieve asynchronous data transmission and load balancing.

[0019] Optionally, the distributed storage system uses Elasticsearch, IotDB or ClickHouse.

[0020] Optionally, the storage and analysis unit includes a storage module, which is used to store the monitoring data in slices by time or instance, and to index commonly used query fields; an analysis and query module, which is used to use the Apache Flink streaming processing framework to perform real-time analysis on the monitoring data, generate key performance indicators KPIs, provide a Restful API interface, and support fast query of the monitoring data; a data visualization module, which is used to display a trend chart of the database operation status, including slow query statistics and distribution and database abnormality alarm records.

[0021] Optionally, the multiple alarm notification methods include email, SMS, DingTalk or Slack.

[0022] Optionally, it also includes a deployment and expansion unit, which includes: a single-node deployment module: used to run the data acquisition unit, data processing unit, message queue unit, storage and analysis unit and alarm and notification unit on a single server; a distributed deployment module: used to deploy the data acquisition unit, data processing unit, message queue unit, storage and analysis unit and alarm and notification unit in a distributed manner to support large-scale monitoring; a horizontal expansion module: used to adapt to the dynamic growth of the monitoring scale by increasing the acquisition nodes of the data acquisition unit and the storage nodes of the storage and analysis unit; a load balancing module: used to introduce a load balancer between the data acquisition unit and the storage and analysis unit to improve the concurrent processing capability of the system.

[0023] In summary, the present invention includes at least one of the following beneficial effects:

[0024] 1) High concurrent processing capability: Through non-blocking I / O and multiplexing technology, it can handle a large number of connection requests at the same time. Low latency: The event-driven model can trigger the corresponding processing logic at the moment the event occurs, achieving millisecond-level response. High resource efficiency: Through reasonable thread pool management and task distribution mechanism, the consumption of CPU and memory is significantly reduced. As a high-performance network programming framework, netty is an ideal choice for building asynchronous event-driven systems. It has the following characteristics: Efficient I / O model: Based on the Reactor thread model, it can efficiently handle network communications. Flexible Pipeline mechanism: Supports hierarchical processing of data streams, which is easy to expand and customize. Rich protocol support: Built-in support for multiple network protocols (such as TCP, HTTP), and allows users to customize protocols. By introducing netty into the field of database monitoring, real-time capture and processing of database operation status can be achieved, thereby making up for the shortcomings of traditional monitoring technology. Multi-instance monitoring: It is necessary to monitor multiple database instances at the same time and provide a global view. High throughput and high availability: The monitoring system itself must have high concurrent processing capabilities and cannot become the bottleneck of the system. Data storage and analysis: The amount of monitoring data is huge, and distributed storage and analysis technology is required to meet real-time and scalability requirements.

[0025] 2) Improve operation and maintenance efficiency: Through real-time monitoring and alarms, the time for discovering and solving problems can be significantly shortened, and the efficiency of database management and operation and maintenance can be improved. Reduce operation and maintenance costs: The system has a high degree of automation, which reduces manual intervention and repetitive work, thereby reducing overall operation and maintenance costs. Improve database stability: Through continuous monitoring and optimization, system downtime can be reduced to ensure the stable operation of business systems. Wide range of applicable scenarios: Applicable to the monitoring of single databases, distributed databases, and multi-instance databases, covering full-scenario applications from small business systems to enterprise-level database clusters. It is particularly suitable for industries with high real-time requirements, such as financial transactions, online e-commerce, logistics tracking, and medical systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 This is a system architecture diagram of the present invention; DETAILED DESCRIPTION

[0028] The following will combine the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all the embodiments. All other embodiments obtained by ordinary technicians in this field without creative work based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0029] In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "top / bottom" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0030] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "provided with", "mounted / connected", "connected", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0031] Reference Figure 1 The present invention discloses an efficient database monitoring system based on Netty, which realizes real-time monitoring and dynamic management of database operation status through an asynchronous event-driven model. The core of the present invention is to combine high-performance network programming framework, database protocol parsing technology and distributed data processing architecture to meet the requirements of real-time, high concurrency and scalability. The present invention adopts modular design and mainly includes the following functional units:

[0032] The data collection unit establishes a long connection with the database and collects database operation status information in real time by monitoring the data packets in the communication protocol (the information includes query and transaction logs: recording the execution time, statement content and status of each query; system performance indicators: number of connections, CPU usage, memory consumption, lock waiting time; exception information: timeout, deadlock, permission error.). It supports communication protocol analysis of multiple mainstream databases (such as MySQL's Binlog, PostgreSQL's WAL log, Oracle's REDO log, etc.).

[0033] The data processing unit, based on Netty's Pipeline mechanism, processes the collected raw data in layers, including protocol parsing, data filtering, and indicator calculation. Stream processing: Stream processing technology is used during the parsing process to avoid loading large amounts of data at one time and reduce memory usage.

[0034] Message queue unit: The processed monitoring data is pushed to the message queue (such as Kafka, RabbitMQ, Redis) to achieve asynchronous data transmission and load balancing. The message queue provides persistence and breakpoint resuming functions to ensure reliable data delivery.

[0035] The message queue unit uses Kafka, RabbitMQ or Redis as the data transfer layer, supports high throughput and asynchronous processing, and ensures reliable transmission of monitoring data.

[0036] The storage and analysis unit stores monitoring data in distributed storage systems (such as Elasticsearch, IotDB, ClickHouse), supports real-time query and complex analysis of large-scale data, and displays trend charts, alarm information, and key performance indicators (KPIs) of the database operation status.

[0037] Alarm and notification unit, the system triggers alarms based on predefined monitoring rules (such as slow query exceeding threshold, resource usage is too high, etc.). Provides a flexible rule engine, users can customize alarm conditions: Static rules: such as slow query time threshold, connection number threshold. Dynamic rules: calculate dynamic baselines based on historical data (such as abnormal fluctuation detection).

[0038] It supports multiple alarm notification methods, including email, SMS, and real-time message push (such as DingTalk and Slack). It also supports multi-level alarm strategies: minor alarms only send notifications, and serious alarms trigger automated processing (such as suspending certain database operations).

[0039] Automated processing: Trigger automated scripts to perform recovery operations (such as restarting database services).

[0040] In detail, in this embodiment, the data acquisition unit includes a long connection establishment module, which is used to use the netty framework to establish a TCP protocol long connection with the target database, and monitor the database communication traffic in real time through a non-blocking I / O model; a netty configuration module, which is used to use EventLoopGroup to manage the connection thread pool, the main thread group is responsible for event reception, and the working thread group performs asynchronous processing; it is also used to configure a heartbeat detection mechanism to detect the survival status of the connection and ensure the stability of the monitoring process; a custom protocol processor is used to adapt the communication protocol of the database, including: MySQL protocol, PostgreSQL protocol.

[0041] High-performance data collection: Asynchronous and non-blocking data collection is implemented based on Netty to reduce the impact on database performance. Real-time enhancement: Long connection and event-driven mechanism are used to achieve millisecond-level delay monitoring. Multi-protocol support: Supports the parsing and expansion of multiple database protocols, with good versatility and compatibility.

[0042] In detail, in this embodiment, Netty's Pipeline is responsible for processing the received data stream layer by layer, and each layer implements a specific function: the data processing unit includes

[0043] Protocol parsing layer: decodes received data packets and extracts meaningful information, including SQL statements and status identifiers;

[0044] During the data analysis process, the system quickly identifies abnormal patterns through the rule engine: Slow query detection: If the execution time of a single query exceeds the preset threshold, it is marked as a slow query. Deadlock detection: Capture the situation where the lock wait is too long in the transaction and trigger an alarm. Connection abnormality: When the number of database connections approaches the upper limit, early warning is issued. Data filtering layer: Filter redundant data according to predefined rules and retain only key monitoring indicators; indicator calculation layer: Aggregate and count the data, including slow query statistics and average response time calculation.

[0045] It also includes a compression algorithm (such as Gzip), which compresses the monitoring data processed by the data processing unit to reduce the network transmission load.

[0046] Data cleaning: Eliminate redundant data and extract key indicator information (such as query statements, lock waiting time, etc.) Anomaly detection: Combined with the rule engine, analyze the collected data and quickly identify abnormal operations (such as slow queries and deadlocks). Data compression: Efficiently compress the transmitted monitoring data to reduce network bandwidth consumption.

[0047] In detail, in this embodiment, the distributed storage system uses Elasticsearch, IotDB or ClickHouse. The storage and analysis unit includes a storage module, which is used to store monitoring data in slices by time or instance, improve query performance, and index commonly used query fields, such as database instance ID, timestamp, indicator type, etc.; an analysis query module, which is used to use a streaming processing framework (such as Apache Flink) to perform real-time analysis on monitoring data, generate key performance indicators KPIs, provide a Restful API interface, and support fast query of monitoring data; a data visualization module, which is used to display a trend chart of the database operation status, including slow query statistics and distribution and database abnormality alarm records.

[0048] In detail, in this embodiment, a deployment and expansion unit is also included, and the deployment and expansion unit includes: a single-node deployment module: used to run the data acquisition unit, the data processing unit, the message queue unit, the storage and analysis unit, and the alarm and notification unit on a single server;

[0049] Distributed deployment module: used for distributed deployment of data collection units, data processing units, message queue units, storage and analysis units, and alarm and notification units to support large-scale monitoring;

[0050] Horizontal expansion module: used to adapt to the dynamic growth of monitoring scale by adding collection nodes of data collection unit and storage nodes of storage and analysis unit;

[0051] Load balancing module: used to introduce a load balancer between the data acquisition unit and the storage and analysis unit to improve the system's concurrent processing capabilities.

[0052] Example

[0053] Example 1: Slow query monitoring

[0054] The system detects that the execution time of a certain SQL query exceeds a threshold (such as 3 seconds).

[0055] The data processing unit marks the query as a slow query and records its statement, execution time, and trigger time.

[0056] The alarm and notification unit triggers a notification and sends the slow query details to the database administrator via email.

[0057] Example 2: Deadlock Detection and Recovery

[0058] The system detects that the lock waiting time of a transaction exceeds a threshold (such as 10 seconds).

[0059] The data processing unit determines that a deadlock may occur, and the alarm and notification unit immediately triggers an alarm.

[0060] The automated script terminates related transactions, releases lock resources, and restores the normal operation of the database.

[0061] Example 3: Distributed database monitoring

[0062] The system monitors 10 distributed database instances simultaneously and collects performance data (such as number of connections and latency) of each instance.

[0063] The data is stored in Elasticsearch in shards for real-time query.

[0064] The panel displays the health status and performance indicators of each instance, helping operation and maintenance personnel to understand the overall status.

[0065] The present invention can be widely used in computer network communications, database management, distributed systems and big data fields.

[0066] 1. Computer network communication field

[0067] The present invention adopts netty as the underlying communication framework, and utilizes its technical characteristics such as non-blocking I / O, multiplexing, and event-driven model to realize efficient collection and transmission of database monitoring data. As a high-performance network library, netty is widely used in high-concurrency, high-throughput distributed systems and is one of the representatives of modern network programming technology. Through this framework, the present invention can establish efficient long connections with a variety of database systems and realize efficient processing of real-time data streams.

[0068] 2. Database management field

[0069] The field of database management involves core tasks such as monitoring the operation status of the database, performance optimization, and anomaly detection. Based on the in-depth analysis of the communication protocols of mainstream databases (such as MySQL, PostgreSQL, Oracle, etc.), the present invention obtains database operation logs, performance indicators, and error information, and monitors key indicators (such as slow queries, lock waits, number of connections, etc.) in real time. On this basis, by analyzing the monitoring data, decision support can be provided to users, such as performance tuning suggestions, resource allocation optimization solutions, etc.

[0070] 3. Distributed systems and big data

[0071] Since the amount of monitoring data grows rapidly with the increase of database scale and concurrent requests, the present invention combines distributed systems and streaming processing technology, adopts message queues and distributed storage architecture, and ensures the stability and scalability of the system in high concurrency and big data scenarios. This architectural design enables the system to not only monitor a single database, but also support centralized monitoring and management of multiple distributed database instances.

[0072] Specifically, it includes at least one of the following beneficial effects:

[0073] 1. High real-time performance

[0074] Millisecond response: Based on the asynchronous event-driven model of Netty, it monitors database operation logs, performance indicators and error information in real time through long connections, realizes millisecond-level data collection and processing, and greatly improves the response speed of the monitoring system.

[0075] Timely alerts: The built-in rule engine can trigger alerts the moment an anomaly occurs, ensuring that problems can be discovered and responded to quickly, avoiding the expansion of problems caused by delays.

[0076] 2. Low system overhead

[0077] Less resource usage: Compared with the traditional polling method, the asynchronous non-blocking model significantly reduces the usage of CPU, memory, and I / O resources, especially in high-concurrency scenarios.

[0078] Low impact on the database: The monitoring system avoids additional load on the database performance by listening to database protocol communications rather than directly querying the database.

[0079] 3. High concurrent processing capability

[0080] For large-scale scenarios: Thanks to Netty's high concurrent processing capabilities, the system can monitor hundreds or even thousands of database instances at the same time, and is suitable for complex enterprise-level database environments.

[0081] Strong scalability: Through distributed deployment and load balancing mechanism, monitoring nodes can be dynamically added in high-load scenarios to ensure stable operation of the system.

[0082] 4. Compatibility and adaptability

[0083] Multiple database support: The system supports protocol parsing of mainstream databases (such as MySQL, PostgreSQL, Oracle, etc.) and can be expanded to other types of databases to meet the needs of different business scenarios.

[0084] Flexible rule configuration: Users can customize monitoring rules and indicator thresholds according to business needs, and the system can flexibly adapt to various application scenarios.

[0085] 5. Strong data processing capabilities

[0086] Anomaly detection: By analyzing monitoring data in real time, the system can quickly detect and locate anomalies, such as slow queries, deadlocks, connection timeouts, and other issues, providing strong support for problem diagnosis and resolution.

[0087] Data integrity assurance: Asynchronous transmission and breakpoint-resume transmission are achieved through message queues to avoid data loss and ensure monitoring data integrity even in the event of network interruption or system anomaly.

[0088] 6. Reliability and scalability of distributed architecture

[0089] Distributed storage: Use distributed databases (such as Elasticsearch) to store and manage monitoring data, support real-time queries and large-scale analysis, and ensure high data availability.

[0090] High scalability: The monitoring system can be horizontally expanded by adding nodes to adapt to the growth of business scale.

[0091] 7. Intelligent operation and maintenance support

[0092] Visual display: The system provides a user-friendly data visualization interface to help users intuitively understand the operating status of the database through trend charts, alarm information and key performance indicators (KPIs).

[0093] Predictive maintenance: Combines machine learning algorithms to analyze historical data, provide performance tuning suggestions and predictive maintenance support, and help users prevent possible problems in advance.

[0094] Automated operation and maintenance: The system supports integration with automated operation and maintenance tools (such as Ansible and Kubernetes) to trigger automatic recovery operations based on alarms, such as restarting database services and adjusting resource configurations.

[0095] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An efficient database monitoring system based on Netty, characterized by: include: The data collection unit is used to establish a long connection with the database using the netty framework, and collect database operation status information in real time by monitoring the data packets of the communication protocol; The data processing unit is used to process the collected data packets in layers based on the Pipeline mechanism of Netty; The message queue unit is used to push the processed monitoring data to the message queue; A storage and analysis unit, used for storing monitoring data in a distributed storage system; The alarm and notification unit is used to trigger alarms according to predefined monitoring rules and supports multiple alarm notification methods.

2. According to the netty-based efficient database monitoring system of claim 1, it is characterized by: The data acquisition unit comprises The long connection establishment module is used to establish a TCP protocol long connection with the target database using the netty framework, and monitor the database communication traffic in real time through the non-blocking I / O model; The netty configuration module is used to use EventLoopGroup to manage the connection thread pool. The main thread group is responsible for event reception, and the worker thread group performs asynchronous processing; It is also used to configure the heartbeat detection mechanism to detect the survival status of the connection; Custom protocol processor, used to adapt the database communication protocol, including: MySQL protocol, PostgreSQL protocol.

3. According to the netty-based efficient database monitoring system of claim 1, it is characterized in that: The real-time acquisition of database operation status information includes Query and transaction logs: record the execution time, statement content and status of each query; System performance indicators: number of connections, CPU usage, memory consumption, lock waiting time; Exception information: timeout, deadlock, permission error.

4. According to the netty-based efficient database monitoring system of claim 1, it is characterized in that: The data processing unit comprises Protocol parsing layer: decodes received data packets and extracts meaningful information, including SQL statements and status identifiers; Data filtering layer: Filter redundant data according to predefined rules and retain only key monitoring indicators; Indicator calculation layer: aggregates and counts data, including slow query statistics and average response time calculation.

5. According to claim 4, a netty-based efficient database monitoring system is characterized in that: It also includes a compression algorithm, which compresses the monitoring data processed by the data processing unit.

6. According to the netty-based efficient database monitoring system of claim 1, it is characterized in that: The message queue unit uses Kafka, RabbitMQ or Redis as a data transfer layer to achieve asynchronous data transmission and load balancing.

7. According to the netty-based efficient database monitoring system of claim 1, it is characterized by: The distributed storage system adopts Elasticsearch, IotDB or ClickHouse.

8. According to claim 7, a netty-based efficient database monitoring system is characterized in that: The storage and analysis unit comprises The storage module is used to store monitoring data in segments by time or instance and to index commonly used query fields. The analysis and query module uses the Apache Flink streaming processing framework to perform real-time analysis on monitoring data, generate key performance indicators (KPIs), and provide a RESTful API interface to support fast query of monitoring data. The data visualization module is used to display trend charts of the database operation status, including slow query statistics and distribution and database abnormality alarm records.

9. According to the netty-based efficient database monitoring system of claim 1, it is characterized by: The multiple alarm notification methods include email, SMS, DingTalk or Slack.

10. An efficient database monitoring system based on Netty according to any one of claims 1 to 9, characterized in that: Also included is a deployment and expansion unit, the deployment and expansion unit comprising: Single-node deployment module: used to run the data collection unit, data processing unit, message queue unit, storage and analysis unit, and alarm and notification unit on a single server; Distributed deployment module: used for distributed deployment of data collection units, data processing units, message queue units, storage and analysis units, and alarm and notification units to support large-scale monitoring; Horizontal expansion module: used to adapt to the dynamic growth of monitoring scale by adding collection nodes of data collection unit and storage nodes of storage and analysis unit; Load balancing module: used to introduce a load balancer between the data acquisition unit and the storage and analysis unit to improve the system's concurrent processing capabilities.

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