Zabbix-based database capacity expansion and real-time monitoring operation and maintenance system and method

Abstract

The invention relates to the technical field of digitization, and discloses a Zabbix-based database capacity expansion and real-time monitoring operation and maintenance system and method, and the method comprises the steps: uniformly configuring monitoring items and labels in Zabbix, constructing a metric vector, and carrying out approximate aggregation and collection window control; carrying out seasonal decomposition and short-term prediction on the load based on multiplicative Holt-Winters, and generating near-future estimation of the key quantile; the normalized indexes are aggregated into capacity expansion confidence coefficients according to weights, and stable triggering and mild recovery are achieved in combination with trend terms and double-threshold gating; sLO is used as a first principle to calculate default risks, and resource requirements and the number of nodes are estimated in a vectorization mode. According to the method, jitter expansion is inhibited through capacity expansion triggering, scale optimization and migration arrangement, false alarm noise reduction and tail time delay risk are incorporated into objective function optimization, predictable, full-coverage and low-risk database elastic capacity expansion and monitoring operation and maintenance closed loop are realized, SLO standard reaching and system stability are remarkably improved, and the total cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of digitalization, and more specifically, to a database expansion and real-time monitoring and maintenance system and method based on Zabbix. Background Technology

[0002] With the widespread adoption of enterprise digital transformation and cloud-native architectures, enterprise-level monitoring and business databases are continuously growing in scale and complexity. Metrics are being collected more frequently, with more dimensions, larger data volumes, and more pronounced peaks, troughs, and seasonal characteristics. Zabbix, as a mainstream open-source monitoring platform, is widely used in enterprises for cross-host, cross-network, and cross-application metric collection, alerting, and visualization. However, as the scale of monitoring and the volume of business data expand simultaneously, the database, as the layer where monitoring data is stored and the supporting layer for business data, often becomes a bottleneck for system availability and performance.

[0003] Traditional expansion strategies often rely on manual assessment or static threshold triggering, lacking a closed-loop linkage between monitoring and expansion, resulting in inaccurate expansion timing, resource waste, and increased alarm noise. Summary of the Invention

[0004] This invention provides a database expansion and real-time monitoring and maintenance system and method based on Zabbix, which solves the technical problems of traditional expansion strategies in related technologies, which often rely on manual evaluation or static threshold triggering, lack closed-loop linkage between monitoring and expansion, have insufficient representativeness and do not cover heavy query / hotspot shards, have overly optimistic health assessments, and expose problems after scaling up.

[0005] This invention provides a method for database expansion and real-time monitoring and maintenance based on Zabbix, including the following steps: S100, Metrics Collection and Normalization: In Zabbix, monitor items and labels are uniformly configured, metric vectors are constructed, and approximate aggregation and collection window control are performed. S200, Load Forecasting and Seasonal Decomposition: Based on multiplicative Holt-Winters, seasonal decomposition and short-term forecasting of load are performed to generate near-future estimates of key quantiles; S300, multi-indicator confidence aggregation and trigger judgment: normalized indicators are aggregated into expanded confidence scores according to weights, and combined with trend items and dual threshold gating to achieve stable triggering and gentle recovery; S400, SLO Risk Assessment and Capacity Requirement Calculation: Calculate default risk based on SLO as the first principle, estimate resource requirements and number of nodes in a vectorized manner, and approximate the quantile latency compliance after capacity expansion. S500, multi-objective optimization decision-making and expansion scale determination: comprehensively considers latency default, cost, risk and change disturbances to perform integer optimization, generate expansion primitives and execution sequences, and ensure idempotency and concurrency safety; S600, Canary Access and Rolling Migration: Canary privilege escalation is controlled by representative gating and hotspot coverage constraints. After the health check is passed, the volume is rolled out to ensure a smooth switch between query and write operations. S700, Verification and Writeback: After the expansion is completed, SLO verification and topology writeback are performed to form an auditable closed loop; if the target is not met, the rollback is quickly reversed and the risk is reported.

[0006] Furthermore, the steps for indicator collection and normalization are as follows: S110, Monitoring Template and Monitoring Item Configuration: Create / bind ZabbixTemplate for database, system, network and application respectively; enable key monitoring items, set collection period and heartbeat; enable differentiated items for different instance roles; Key monitoring items include connection count, active transactions, buffer hits, IO, replication latency, and P95 / P99 latency; instance roles include primary router and backup router. S120, Tag Semantic Alignment and Topology Synchronization: Unified tag keys; Timely synchronization of instance-shard-tenant-region mapping from topology source; Monitoring data is aggregated according to the "metric-role-topology" dimension; Tag keys include role, shard, partition, tenant, region, and env; topology sources include orchestration platform / CMDB; S130, Acquisition Window and Back Pressure Control: Set active acquisition for high-frequency indicators and passive acquisition for low-frequency indicators; Set adaptive acquisition window and back pressure threshold according to database load; S140, Indicator Normalization and Measurement Vector Construction: Denoise, standardize or Min-Max normalize the original indicators, and construct the measurement vector; S150, Approximate Aggregation and Storage Optimization: Enables approximate structures such as bucketing, TDigest, and Count-MinSketch for high cardinality / high throughput metrics, retaining quantile information to support alarms and decisions, while reducing storage and computing costs; S160, Quality and Integrity Verification: Interpolate or label data gaps and anomalies; set data freshness alarms for key indicators; trigger degradation and retry mechanisms when the Agent or Proxy is unreachable.

[0007] Furthermore, the steps for load forecasting and seasonal decomposition are as follows: S210, Data Preprocessing and Baseline Selection: Define the training window, eliminate the abnormal effects of holidays and activity peaks or model them separately; select stable periods as historical baselines; S220, Model Training and Parameter Optimization: Train Holt–Winters and optimize parameters using grid search / Bayesian; validate multi-period seasonality, and model different scenarios if necessary; S230, Quantile Forecasting and Uncertainty Assessment: Estimating quantiles and interval confidence based on point forecasting; using more conservative quantile thresholds for key business windows; S240, Drift Detection and Rollback Strategy: Monitors prediction error and distribution drift; when drift exceeds the threshold, switches to robust statistics or historical maximum window to avoid misleading expansion.

[0008] Furthermore, in model training and parameter optimization, the multiplicative Holt-Winters model is as follows: ; ; ; ; in, The length of the seasonal cycle; To predict the step size, ; Let t be the horizontal component; The trend component at time t; Let t be the seasonal component. These are the smoothing coefficients for horizontal, trend, and seasonality, respectively; the larger the coefficient, the higher the weight of the latest observation. A balance between response speed and stability is achieved through validation set grid search or Bayesian optimization. The horizontal component from the previous moment; This represents the trend component from the previous moment; This represents the seasonal component corresponding to the previous cycle. For the seasonal component corresponding to the seasonal index of the prediction time, These are the results of multiplicative Holt-Winters predictions; In quantile forecasting and uncertainty assessment, quantiles and intervals are defined as follows: ; in, For confidence level, To predict window offset, , The maximum allowable forward forecast span for the business. To predict the load metric within the forecast window, For future quantile estimation, Let be the confidence interval. The confidence bias is calculated using the empirical CDF of historical prediction errors at confidence levels. To obtain.

[0009] Furthermore, the steps for multi-indicator confidence aggregation and trigger judgment are as follows: S310, weight configuration and indicator selection: set weights for CPU, memory, IO, connection, lock wait, replication latency, and P95 / P99 latency; adjust according to the scenario to avoid single indicator dominance; S320, Confidence Calculation and Trend Detection: Calculates the expanded confidence score and its time derivative after multi-indicator aggregation, and smooths instantaneous spikes; sets cooling time and minimum hold duration for jitter scenarios; S330, Trigger and Recycling Gating: Configure upper and lower thresholds and trend thresholds for gating; when the expansion trigger condition is met, enter the policy engine; when the recycling condition is met, perform gentle recycling and record audits.

[0010] Furthermore, the steps for SLO risk assessment and capacity requirement calculation are as follows: S410, SLO Default Risk Measurement: Calculates the intensity of default risk; increases expansion priority when risk rises and the trend is upward. S420, Demand Vector and Node Count Estimation: Form a demand vector and calculate the required number of nodes by combining the available resources of a single node and the safety factor; S430, Quantile Delay Approximation and Parameter Calibration: Use quantile delay estimation to assess compliance; calibrate the single-node effective processing capacity factor and fixed overhead to reflect the true topology and routing costs.

[0011] Furthermore, the steps for multi-objective optimization decision-making and determining the expansion scale are as follows: S510, Objective Function and Constraint Definition: Set the objective function, number of constraint nodes, maintenance window, freeze strategy and quota limit; S520, Solution and Selection: Use a planning algorithm to minimize the objective function with the optimal number of nodes; hierarchical decision-making may be necessary. The hierarchical decision-making process prioritizes replicating before sharding. Planning algorithms include discrete grid search, branch and bound, and heuristic search; S530, Expansion Primitive Generation and Orchestration: Generates primitives for "adding replicas / expanding shards / expanding partitions / increasing quotas / adjusting routes / adding proxies"; calculates idempotent keys, prerequisite dependencies, rollback sequences, and concurrency limits for each primitive; The primitives are calculated as follows: Idempotent keys: ensure that repeated commits only take effect once; Prerequisites: such as image preheating, configuration center version number checking; Rollback sequence: Rollback in reverse order in case of failure; Concurrency limiting: The number of primitives executed simultaneously is controlled by a priority queue to prevent cascading failures.

[0012] Furthermore, the steps for canary access and rolling migration are as follows: S610, Canary Plan and Sampling Design: Define initial weights and weighting step size; Sampling by query category / tenant / shard to construct canary traffic baskets and monitoring metric sets; S620, Representative Gating and Hotspot Coverage: Calculate representativeness score and hotspot coverage rate, and gating; when the target is not met, inject hotspots and re-query shadow reads / replays to supplement representativeness; S630, Privilege Escalation and Health Check: Performs weight updates; performs phased gating of latency, error rate, replication consistency, and snapshot integrity; implements progressive flow switching and connection pool emptying in the query layer, and implements queue buffering and backpressure protection in the write layer; S640, Anomalies and Rollbacks: If a health indicator exceeds the threshold or consistency is abnormal, immediately demote the status and roll back to the previous stable version; record the event and context for subsequent policy adjustments.

[0013] Furthermore, the verification and write-back steps are as follows: S710, SLO and Metrics Verification: Verify whether P95 / P99 and availability meet the standards; perform end-to-end verification of the critical path to confirm that there is no performance rollback and data inconsistency; S720, Topology and Quota Write-back: Writes replica / shard / partition / quota / routing changes to the orchestration platform or configuration center; updates monitoring tags and template bindings to ensure subsequent data and visual consistency. Figure 1 To; S730, Audit and Knowledge Update: Record audit logs for strategy evaluation, execution trajectory, health checks and rollback operations; update dynamic thresholds, weights, baselines and seasonality models; incorporate abnormal cases into the risk library and rollback scripts; S740, Rollback and Alarm: When verification fails or an unacceptable risk occurs, rollback is performed according to a predefined script and a high-priority alarm is triggered; the impact and recovery time are assessed and recorded.

[0014] This invention also proposes a database expansion and real-time monitoring and maintenance system based on Zabbix, including: Module 1: Complete template binding and enable key monitoring items in Zabbix, unify tag semantics and continuously synchronize with the topology, implement adaptive acquisition window and back pressure control without increasing database load, denoise and normalize the original indicators to construct metric vectors, enable the approximate structure of bucketing, TDigest, and Count-Min Sketch for aggregation and storage optimization, and verify data freshness and quality and perform necessary degradation processing. Module 2: This module preprocesses historical data and selects a stable baseline, trains and optimizes the multiplicative Holt-Winters model to generate point predictions, quantile predictions and uncertainty measures, continuously monitors prediction errors and distribution drift, and switches to robust statistics or historical maximum window when the drift exceeds the threshold, thereby ensuring the forward-looking and robust nature of expansion decisions. Module 3: This module configures the weights of various indicators according to the scenario, calculates the aggregate confidence and trend of multiple indicators, and combines uplink / downlink dual thresholds and cooling and minimum hold strategies to execute expansion triggering and gentle recycling gating to suppress jitter and false triggering and output corresponding audit records; Module 4: This module calculates default risk with SLO as the first principle, vectorizes the resource requirements and the number of nodes required, and combines a quantile delay approximation model to evaluate the compliance under the target scale. At the same time, it completes the calibration of single-node capability factor and fixed overhead parameters to match the real topology and routing cost. Module 5: This module constructs an objective function that integrates latency default, resource cost, change risk and disturbance. It is then solved by integer optimization in combination with the constraints of maintenance window, freeze strategy and quota limit. Based on this, it generates executable expansion primitives and orchestration sequences, with idempotent keys, pre-dependencies, rollback scripts and concurrency limits to ensure safe and auditable implementation. Module 6: This module formulates the canary plan and sampling strategy, controls the release pace based on representativeness and hot spot coverage gating, performs health checks on latency, error rate, replication consistency and snapshot integrity in stages, implements gradual flow switching and connection pool emptying, and enables queue buffering and back pressure protection on the write end. If an anomaly is encountered, it immediately demotes the priority and rolls back to the stable version. Module 7: After the expansion is completed, perform P95 / P99 and availability SLO verification, perform end-to-end checks on critical paths and confirm that there is no performance rollback or data inconsistency, write back changes to replicas, shards, partitions, quotas and routes to the configuration center and synchronize monitoring tags, fully record policy evaluation and execution trajectory to update thresholds, weights, baselines and seasonal models, and roll back according to the script and trigger high-priority alarms when necessary.

[0015] The beneficial effects of this invention are as follows: This invention constructs a strong closed loop of "monitoring-policy-execution-verification-writeback" using the Zabbix event as a link. It adopts multiplicative Holt-Winters preload prediction, SLO default risk measurement, and dual threshold and trend gating with multi-indicator confidence aggregation to replace the lag and instability of manual assessment and static thresholds, so as to complete capacity preparation and balancing before the load arrives. At the same time, representative gating and hot spot coverage (based on JSD distribution similarity and hot spot hit rate) are introduced during the ramp-up phase. This is combined with phased health checks, gradual flow switching and connection pool emptying, write-side queue buffering and back pressure protection, and idempotent rollback and full-link auditing to ensure that canary assessment is no longer "optimistically biased" and fully covers re-queries and hot spot sharding, so as to avoid problems being exposed after the go-live from the source.

[0016] This closed loop suppresses "jittering" through stable expansion triggering, scale optimization, and migration orchestration, incorporates false alarm noise reduction and tail delay risks into the objective function optimization, and improves the compliance rate and controllability of changes.

[0017] In summary, this solution achieves a predictable, comprehensive, and low-risk closed loop for elastic database scaling and monitoring and maintenance, significantly improving SLO compliance and system stability while reducing overall costs. Attached Figure Description

[0018] Figure 1 This is a flowchart of the database expansion and real-time monitoring and maintenance method based on Zabbix of the present invention; Figure 2 This is a structural block diagram of the database expansion and real-time monitoring and maintenance system based on Zabbix of the present invention; Figure 3 This is a schematic diagram of the operation and maintenance architecture in an example of the present invention; Figure 4 This is a schematic diagram of the canary and rolling migration of the present invention; Figure 5 This is a schematic diagram illustrating the determination of the expansion scale and sequence before and after the changes in this invention; Figure 6 This is a schematic diagram of the closed-loop structure of the operation and maintenance method in an example of the present invention. Detailed Implementation

[0019] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0020] like Figures 1-6 As shown, the method for database scaling and real-time monitoring and maintenance based on Zabbix includes the following steps: S100, Metrics Collection and Normalization: In Zabbix, monitoring items and labels are uniformly configured, metric vectors are constructed, approximate aggregation and collection window control are performed to ensure both timeliness and low overhead.

[0021] It should be noted that Zabbix is ​​a web-based enterprise-grade distributed open-source monitoring solution, consisting of a Zabbix server and optional components such as Zabbix agent and proxy. It supports data collection via SNMP, Zabbix agent, ping, and port monitoring, and can run on operating system platforms such as Linux, Solaris, HP-UX, AIX, Free BSD, Open BSD, and OSX. In one embodiment of the present invention, the following steps are specifically included: S110, Monitoring Template and Monitoring Item Configuration: Create / bind ZabbixTemplate for database, system, network and application respectively; enable key monitoring items (connection count, active transactions, buffer hits, IO, replication latency, P95 / P99 latency, etc.), set collection period and heartbeat; enable differentiated items for different instance roles (primary / backup / router); S120, Tag Semantic Alignment and Topology Synchronization: Unified tag keys (role, shard, partition, tenant, region, env); Timely synchronization of instance-shard-tenant-region mapping from topology source (orchestration platform / CMDB) to ensure that monitoring data can be aggregated according to the "metric-role-topology" dimension; Tag mapping: in, Mapping the instance-shard-tenant-region topology; in, To standardize label keys, These are the original, labeled metrics. This is a set of labeled raw metrics. For tagging functions; Among them, role, shard, partition, tenant, region, and env represent role, shard, partition, tenant, region, and environment, respectively. Among them, the topology consistent view refers to a consistent instance-shard-tenant mapping view; S130, Acquisition Window and Backpressure Control: Set active acquisition (active agent) for high-frequency indicators and passive acquisition for low-frequency indicators; set adaptive acquisition window and backpressure threshold according to database load to avoid additional pressure on the database from monitoring acquisition; Sampling window update: ; Back pressure threshold: when At that time, downsampling / buffering is performed to ensure the database safety threshold; in, This is a measure of the current database load. To collect or process queue length, Adjust the step size for the sampling window. The target threshold for the load. These represent the lower and upper bounds of the sampling window, respectively. For back pressure threshold, For a moment The size of the sampling window; S140, Indicator Normalization and Metric Vector Construction: Denoise the original indicators, standardize them, or perform Min-Max normalization to construct metric vectors. ; Ensure that the timestamp matches the time zone, and record the source tag and instance identifier for subsequent policy queries; Normalization: ; ; in, For the first Minimum / maximum value of the indicator sliding window For the normalized first Items, The first indicator after normalization. For the normalized k-th indicator, These are the original indicators after window and back pressure control. Let be the metric vector at time t, whose elements are normalized monitoring metrics (CPU / memory / IO / connection / lock, etc.), typically mapped to [0, 1]. S150, Approximate Aggregation and Storage Optimization: Enables approximate structures such as bucketing, TDigest, and Count-MinSketch for high cardinality / high throughput metrics, retains quantile information to support alarms and decisions, and reduces storage and computing costs. Approximate quantiles: ; in, It is an approximate aggregated index (TDigest / Count-Min Sketch). For quantile levels, for Quantile estimation Estimate for Digest Quantile; S160, Quality and Integrity Verification: Interpolate or label data gaps and anomalies; set data freshness alarms for key indicators; trigger degradation and retry mechanisms when the Agent or Proxy is unreachable.

[0022] Freshness determination: ; Anomaly detection: ; Gap interpolation: ; in, For freshness, These are the current and last collected timestamps, respectively. For data freshness threshold, These are the mean / standard deviation of the sliding window statistics, respectively. This is the threshold for anomaly detection fold. This is the value after gap interpolation. For quality reports, This is a downgrade event. This is the anomaly detection factor. For the first For the historical sequence of the index, interpolation methods can include linear, spline, or time series models. For interpolation functions, Measure the target load; S200, Load Forecasting and Seasonal Decomposition: Based on multiplicative Holt-Winters, seasonal decomposition and short-term forecasting of load are performed to generate near-future estimates of key quantiles, providing a preliminary basis for capacity expansion decisions.

[0023] In one embodiment of the present invention, the following steps are specifically included: S210, Data Preprocessing and Baseline Selection: Define the training window (last 7–30 days), remove the abnormal effects of holidays and activity peaks or model them separately; select stable periods as historical baselines to ensure convergence of seasonal terms; enter: Business calendar tags ,window Abnormal threshold .

[0024] Cleaning and baseline selection: Obtaining the cleaned training sequences Historical baseline window and baseline statistics .

[0025] Output: , , Enter S220 for model training and parameter optimization.

[0026] in, For historical measurement sequences, For business calendar tags, For the training window length, The threshold for abnormal removal. The cleaned training sequence, For historical baseline window, For baseline statistics, These represent the baseline statistical mean / standard deviation; S220, Model Training and Parameter Optimization: Training Holt–Winters ( And perform grid search / Bayesian optimization of parameters; verify multi-period seasonality (such as daily / weekly overlay), and model different scenarios if necessary; Model form (multiplicative Holt–Winters): ; ; ; ; in, The length of a seasonal cycle, such as a daily cycle. (5 min particle size), cycle time ; To predict the step size, ; Let t be the horizontal component; The trend component at time t; Let t be the seasonal component. These are the smoothing coefficients for horizontal, trend, and seasonality, respectively. The larger the coefficient, the higher the weight of the latest observation. A balance between response speed and stability is achieved through validation set grid search or Bayesian optimization. The default search step size is 0.05. The horizontal component from the previous moment; This represents the trend component from the previous moment; This represents the seasonal component corresponding to the previous cycle. For the seasonal component corresponding to the seasonal index of the prediction time, For multiplicative Holt-Winters prediction results, Select a load metric.

[0027] Training and Optimization: ; in, For the parameter search space, For smoothing coefficients, Select the load metric (such as QPS, write rate, etc.). For optimal parameters, To measure the training load; S230, Quantile Prediction and Uncertainty Assessment: Estimating quantiles based on point predictions Compared to interval confidence; more conservative quantile thresholds are used for key business windows (promotions, settlement); Quantiles and Intervals: ; in, For confidence level, To predict window offset, , The maximum allowable forward forecast span for the business. To predict the load metric within the forecast window, For future quantile estimation, Let be the confidence interval. The confidence bias is calculated using the empirical CDF of historical prediction errors at confidence levels. Seek; S240, Drift Detection and Rollback Strategy: Monitors prediction error and distribution drift; when drift exceeds the threshold, switches to robust statistics or historical maximum window to avoid misleading expansion. Prediction error: ; Drift detection quantity: KL divergence or PSI (Population Stability Index), denoted as threshold Default is 0.2; Rollback status: ; when When, set to And trigger adopting the past Prediction of the maximum window of the day. For drifting, To revert, This is normal.

[0028] in, To select a load metric, To predict load metrics, For business calendar tags, The drift threshold, To roll back the number of days, For drift detection quantity, In rollback state; S300, Multi-indicator Confidence Aggregation and Trigger Judgment: Aggregates normalized indicators by weight into expanded confidence levels. By combining trend terms with dual threshold gating, stable triggering and gentle recycling can be achieved.

[0029] In one embodiment of the present invention, the following steps are specifically included: S310, Weight Configuration and Metric Selection: Assign weights to CPU, memory, IO, connections, lock wait, replication latency, P95 / P99 latency, etc. Adjust according to the scenario to avoid being dominated by a single indicator.

[0030] Define the weight vector and impose constraints. ; Aggregate confidence level: ; To improve sensitivity in high-risk regions, Logit mapping can be applied to the input. Alternatively, segmentation can be used to reduce the impact of extreme outliers and improve the resolution of values ​​near the threshold.

[0031] in, For the first The values ​​of each monitoring metric after min-max or z-score normalization. ; For indicators with unified semantics, stress-type indicators are directly taken. Benefit-oriented indicators ; For the first The weight of each indicator, The allocation is dynamically determined by the scenario strategy, either offline or online. These represent the slope and center point of the Logit mapping, respectively. To control the steepness, Typically, the value is taken as 0.5–0.8. This represents the aggregate confidence level.

[0032] S320, Confidence Calculation and Trend Detection: Calculation and Smooth out instantaneous spikes; set cooldown time and minimum hold duration for jittery scenarios.

[0033] Smoothing and Trend: To suppress spikes, for Exponential smoothing is performed to obtain : ; The trend term can be the discrete derivative or its EMA: ; ; Among them, is the aggregated confidence level, is the smoothing coefficient, is the trend EMA coefficient, is the sampling period, is the timing quantity, is the smoothing confidence level,[[ID=??]] is the smoothing confidence level at the previous moment, is the trend EMA, is the trend EMA at the previous moment, is the expansion confidence level after multi-index aggregation, ; is 's trend or time derivative, used to identify continuous upward / downward trends.

[0034] S330, trigger and recovery gating: Configure with the trend threshold ; When the expansion trigger condition is met, enter the policy engine, and when the recovery condition is met, perform gentle recovery and record the audit.

[0035] Gating conditions (including hysteresis band and cooling / holding): Expansion trigger: ; Gentle recovery: ; Among them, are the upper and lower gating thresholds respectively, is the trend threshold, is the minimum cooling time, is the minimum holding duration, is the trigger / recovery event, is the minimum holding duration; is the time since the last expansion; is the duration of the current low load, is the AND symbol.

[0036] S400, SLO risk assessment and capacity requirement calculation: Calculate the default risk with SLO as the first principle, vectorize and estimate the resource requirements and the number of nodes, and approximately evaluate the compliance of the quantile delay after expansion.

[0037] Note: There seems to be a problem with the tag in the original text as it's not used in a clear way in the provided rules. I've left it as is in the translation but it might need further clarification in the original context.In one embodiment of the present invention, the following steps are specifically included: S410, SLO Default Risk Measurement: Calculation Increase capacity expansion priority when risks rise and the trend is upward.

[0038] in, The current 95th percentile latency; The target delay threshold for SLO; This indicates the intensity of default risk; the higher the value, the closer it is to or exceeds the threshold.

[0039] S420, Demand Vector and Node Count Estimation: Forming the Demand Vector Combined with the available resources of a single node With safety factor calculate .

[0040] in, For the demand vector, This refers to the required capacity (such as CPU cores, memory, I / O, and number of connections). CPU core capacity requirements For memory capacity requirements, For IO capacity requirements, Capacity required for the number of connections; Resources available for a single node; For safety factor (typically 1.1–1.5); The required number of nodes, To round up, for To reserve redundancy.

[0041] S430, Quantile Delay Approximation and Parameter Calibration: Using Assessment of compliance; calibration and To reflect the actual topology and routing costs.

[0042] in, Number of nodes Quantile delay estimation; The quantile level (e.g., 0.95 / 0.99); The baseline delay constant; Estimating future load quantiles; This is the effective processing capacity factor for a single node. Fixed overhead for routing / replication / consistency, etc. To predict the time offset.

[0043] S500, multi-objective optimization decision-making and expansion scale determination: comprehensively considers latency default, cost, risk and change disturbances to perform integer optimization, generate expansion primitives and execution sequences, and ensure idempotency and concurrency safety.

[0044] In one embodiment of the present invention, the following steps are specifically included: S510, Objective Function and Constraint Definition: Set the objective function and constraints. Maintenance window, freeze policy and quota limit.

[0045] Objective function: ; in, These are the weight hyperparameters for each item; For resource cost functions (related to the number of nodes / specifications / cross-region traffic / storage billing); To change the risk metric (probability of failure × impact); To change the disturbance; For the total number of nodes, The current number of nodes; constraints include and maintenance window / freeze policy / quota, The objective function is denoted as .

[0046] S520, Solution and Selection: Discrete grid search / branch and bound / heuristic search are used to solve the problem. make Minimum, that is get ; make decisions in a tiered manner when necessary (first copy, then shard).

[0047] ; in, To find the optimal number of nodes, so that the objective function... The minimum number of optimal nodes; Discrete grid search: Traversing at a fixed step size Take the smallest of all integer values. ; Branch and bound: systematically partitions and prunes the solution space, suitable for medium-sized integer programming; Heuristic search, such as genetic algorithms and simulated annealing, is used in scenarios with large-scale or high real-time requirements. Layered decision-making: First optimize the number of replicas independently, then optimize the number of shards, and reduce the joint search dimension.

[0048] S530, Expansion Primitive Generation and Orchestration: Generates primitives such as "add replica / expand shard / expand partition / increase quota / adjust route / add agent"; calculates idempotent key, prerequisite dependency, rollback sequence and concurrency limit (priority queue) for each primitive.

[0049] Primitive calculation: Idempotent keys: ensure that repeated commits only take effect once; Prerequisites: such as image preheating, configuration center version number checking; Rollback sequence: Rollback in reverse order in case of failure; Concurrency limiting: The number of primitives executed simultaneously is controlled by a priority queue to prevent cascading failures.

[0050] S600, Canary Access and Rolling Migration: Canary privilege escalation is controlled by representative gating and hotspot coverage constraints. After the health check is passed, the volume is rolled out to ensure a smooth switch between query and write operations.

[0051] In one embodiment of the present invention, the following steps are specifically included: S610, Canary Plan and Sampling Design: Defining Initial Weights With step Sampling is performed by query category / tenant / shard to construct a canary traffic basket and a set of monitoring metrics.

[0052] in, This represents the initial traffic percentage for canaries; The step size for each round of weighting (to control the speed of volume increase) needs to be set in conjunction with the risk tolerance.

[0053] S620, representative gating and hotspot coverage: computing and Gating; when the target is not met, hotspots and shadow queries are injected to supplement representativeness.

[0054] Representative score: ; Hotspot coverage: ; Gating: ; in, For representative scores, ; Jensen–Shannon distance, a measure of distributional difference; The query categories are distributed as Production / Canary; For hotspot coverage; A collection of hotspot shards; Hit the fragment set for the canary; , For representativeness and coverage thresholds; For the gating result, .

[0055] S630, Privilege Escalation and Health Check: Implementation Phased gating is implemented for latency, error rate, replication consistency, and snapshot integrity; the query layer performs progressive flow switching and connection pool emptying, and the write layer implements queue buffering and backpressure protection.

[0056] in, The weight of the canary in the kth round; Let $\frac{k}{k}$ be the error rate in round k. The upper limit of the allowable error rate; This is the gating result; Assign weights for the next round (not exceeding 1). The step size for each round of weighting increases (controlling the speed of volume increase) needs to be set in conjunction with risk tolerance.

[0057] S640, Anomalies and Rollbacks: If a health indicator exceeds the threshold or consistency is abnormal, immediately demote the status and roll back to the previous stable version; record the event and context for subsequent policy adjustments.

[0058] S700, Verification and Writeback: After the expansion is completed, SLO verification and topology writeback are performed to form an auditable closed loop; if the target is not met, the rollback is quickly reversed and the risk is reported.

[0059] In one embodiment of the present invention, the following steps are specifically included: S710, SLO and Metrics Verification: Verify whether P95 / P99 and availability meet the standards; perform end-to-end verification of critical paths (query / write / replication latency) to confirm that there is no performance rollback or data inconsistency.

[0060] S720, Topology and Quota Write-back: Writes replica / shard / partition / quota / routing changes to the orchestration platform or configuration center; updates monitoring tags and template bindings to ensure subsequent data and visual consistency. Figure 1 To.

[0061] S730, Audit and Knowledge Update: Record audit logs for strategy evaluation, execution trajectory, health checks and rollback operations; update dynamic thresholds, weights, baselines and seasonality models; incorporate abnormal cases into the risk library and rollback scripts.

[0062] S740, Rollback and Alarm: When verification fails or an unacceptable risk occurs, rollback is performed according to a predefined script and a high-priority alarm is triggered; the impact and recovery time are assessed and recorded.

[0063] Based on the above operation and maintenance methods, the following database expansion and real-time monitoring operation and maintenance system based on Zabbix is ​​presented, including the following modules: Module 1: Monitoring, Acquisition and Normalization: In Zabbix, template binding and key monitoring items are enabled, tag semantics are unified and continuously synchronized with the topology, adaptive acquisition window and back pressure control are implemented without increasing database load, the original indicators are denoised and normalized to construct metric vectors, and approximate structures such as bucketing, TDigest, and Count-MinSketch are enabled for aggregation and storage optimization, and data freshness and quality are verified and necessary degradation is handled. Module 2: Load Forecasting and Seasonal Decomposition: This module preprocesses historical data and selects a stable baseline. It trains and optimizes the multiplicative Holt-Winters model to generate point predictions, quantile predictions, and uncertainty measures. It continuously monitors prediction errors and distribution drift and switches to robust statistics or historical maximum window when the drift exceeds the threshold, thereby ensuring the forward-looking and robust nature of expansion decisions.

[0064] Module 3: Confidence Aggregation and Trigger Judgment: This module configures the weights of various indicators according to the scenario, calculates the confidence level and trend of multi-indicator aggregation, and combines uplink / downlink dual thresholds and cooling and minimum hold strategies to execute expansion triggering and gentle recycling gating to suppress jitter and false triggering and output corresponding audit records.

[0065] Module 4: SLO Risk Assessment and Capacity Requirements: This module calculates default risk based on SLO as the first principle, vectorizes resource requirements and the number of required nodes, and combines a quantile delay approximation model. Assess compliance at the target scale, and calibrate the single-node capability factor and fixed overhead parameters to match the real topology and routing costs.

[0066] Module 5: Optimization Decision and Primitive Generation: This module constructs an objective function that integrates latency default, resource cost, change risk and disturbance. It then performs integer optimization to obtain the objective function by combining constraints such as maintenance window, freeze strategy and quota limit. Based on this, it generates executable expansion primitives and orchestration sequences, with idempotent keys, pre-dependencies, rollback scripts and concurrency limits to ensure safe and auditable implementation.

[0067] Module 6: Canary Gating and Rolling Migration: This module formulates canary plans and sampling strategies, controls the pace of volume release based on representativeness and hotspot coverage gating, performs phased health checks on latency, error rate, replication consistency and snapshot integrity, implements gradual flow switching and connection pool emptying, and enables queue buffering and backpressure protection on the write end. If an anomaly is encountered, it immediately demotes the priority and rolls back to the stable version.

[0068] Module 7: Verification and Write-back: After the expansion is completed, perform SLO verifications such as P95 / P99 and availability, perform end-to-end checks on critical paths and confirm that there is no performance rollback or data inconsistency, write back changes such as replicas, shards, partitions, quotas and routes to the configuration center and synchronize monitoring tags, fully record policy evaluation and execution trajectory to update thresholds, weights, baselines and seasonal models, and roll back according to the script and trigger high-priority alarms when necessary.

[0069] In one embodiment of the present invention, a database expansion and real-time monitoring and maintenance method and system based on Zabbix are applied to the following examples, wherein the background and objectives are as follows: Background: E-commerce platforms experience a surge in queries and fluctuations in writes before and after promotional events (such as "Double Eleven"), leading to an expansion of monitoring scale and making the database a bottleneck for latency and throughput.

[0070] Objective: Based on processes S100-S700, complete the closed loop of "monitoring → decision-making → canary → rolling migration → verification → writeback", achieving pre-expansion, zero-downtime flow switching, and SLO compliance.

[0071] The scenario is set as follows: Monitoring: Zabbix has integrated with database, system, and network metrics, and enabled contextual alerts and dynamic thresholds; tags include instance roles, shards, tenants, and regions.

[0072] Data layer: The database has read / write separation and sharding capabilities, and supports online partitioning and replication; the query layer can create materialized views and aggregated indexes to reduce costs and increase speed.

[0073] Control plane: The event bus sends trigger events to the policy engine, the expansion coordinator is responsible for primitive orchestration and idempotent rollback; the validator performs health checks and canary gating.

[0074] Based on the operation and maintenance methods and systems, the specific steps are as follows: Indicator collection and normalization: Unify templates and labels; enable active collection for high-frequency indicators and passive collection for low-frequency indicators; construct metric vectors and perform approximate aggregation.

[0075] Load forecasting and seasonal decomposition: Daily / weekly seasonality is decomposed and short-term forecasts are made; conservative quantiles are used for promotion windows, and drift detection and rollback strategies are set.

[0076] Confidence aggregation and trigger judgment: Based on the confidence and trend of CPU / IO / connection / lock wait and percentile latency, expansion is triggered when the threshold is exceeded and the trend is upward; gentle recycling is performed when the load decreases.

[0077] SLO Risk and Capacity Estimation: With P95 / P99 and availability as the core objectives, calculate the risks and estimate the required nodes and resource quotas; assess whether the per-unit latency can be achieved after expansion.

[0078] Multi-objective optimization and scale determination: Determine the scaling scale and order (replica first, then sharding) under constraints of cost, risk, and change disturbances, and generate primitives and rollback scripts.

[0079] Canary and Rolling Migration: Canary traffic needs to pass representative gating and hotspot coverage checks; after passing the health check, privileges are elevated in stages, the query layer gradually switches traffic, and the write layer uses queue buffering and backpressure protection.

[0080] Verification and Write-back: Complete SLO verification, topology and quota write-back, audit log and knowledge update; if not met, quickly roll back according to the script and report the risk.

[0081] The embodiments of the present invention have been described above, but the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention, all of which are within the protection scope of the present invention.

Claims

1. A database expansion and real-time monitoring and maintenance method based on Zabbix, characterized in that, Includes the following steps: S100, Metrics Collection and Normalization: In Zabbix, monitor items and labels are uniformly configured, metric vectors are constructed, and approximate aggregation and collection window control are performed. S200, Load Forecasting and Seasonal Decomposition: Based on multiplicative Holt-Winters, seasonal decomposition and short-term forecasting of load are performed to generate near-future estimates of key quantiles; S300, multi-indicator confidence aggregation and trigger judgment: normalized indicators are aggregated into expanded confidence scores according to weights, and combined with trend items and dual threshold gating to achieve stable triggering and gentle recovery; S400, SLO Risk Assessment and Capacity Requirement Calculation: Calculate default risk based on SLO as the first principle, estimate resource requirements and number of nodes in a vectorized manner, and approximate the quantile latency compliance after capacity expansion. S500, multi-objective optimization decision-making and expansion scale determination: comprehensively considers latency default, cost, risk and change disturbances to perform integer optimization, generate expansion primitives and execution sequences, and ensure idempotency and concurrency safety; S600, Canary Access and Rolling Migration: Canary privilege escalation is controlled by representative gating and hotspot coverage constraints. After the health check is passed, the volume is rolled out to ensure a smooth switch between query and write operations. S700, Verification and Writeback: After the expansion is completed, SLO verification and topology writeback are performed to form an auditable closed loop; if the target is not met, the rollback is quickly reversed and the risk is reported.

2. The method for database expansion and real-time monitoring and maintenance based on Zabbix according to claim 1, characterized in that, The steps for indicator collection and normalization are as follows: S110, Monitoring Template and Monitoring Item Configuration: Create / bind ZabbixTemplate for database, system, network and application respectively; enable key monitoring items, set collection period and heartbeat; enable differentiated items for different instance roles; Key monitoring items include connection count, active transactions, buffer hits, IO, replication latency, and P95 / P99 latency; instance roles include primary router and backup router. S120, Tag Semantic Alignment and Topology Synchronization: Unified tag keys; Timely synchronization of instance-shard-tenant-region mapping from topology source; Monitoring data is aggregated according to the "metric-role-topology" dimension; Tag keys include role, shard, partition, tenant, region, and env; topology sources include orchestration platform / CMDB; S130, Acquisition Window and Back Pressure Control: Set active acquisition for high-frequency indicators and passive acquisition for low-frequency indicators; Set adaptive acquisition window and back pressure threshold according to database load; S140, Indicator Normalization and Measurement Vector Construction: Denoise, standardize, and Min-Max normalize the original indicators to construct the measurement vector; S150, Approximate Aggregation and Storage Optimization: Enables approximate structures such as bucketing, TDigest, and Count-MinSketch for high cardinality / high throughput metrics, retaining quantile information to support alarms and decisions, while reducing storage and computing costs; S160, Quality and Integrity Verification: Interpolate and label data gaps and anomalies; set data freshness alarms for key indicators; trigger degradation and retry mechanisms when Agent or Proxy is unreachable.

3. The method for database expansion and real-time monitoring and maintenance based on Zabbix according to claim 1, characterized in that, The steps involved in load forecasting and seasonality decomposition are as follows: S210, Data Preprocessing and Baseline Selection: Delineate the training window, remove the abnormal effects of holidays and activity peaks, and model them separately; A stable period was selected as the historical baseline; S220, Model Training and Parameter Optimization: Train Holt–Winters and optimize parameters using grid search / Bayesian; validate multi-period seasonality, and model different scenarios if necessary; S230, Quantile Forecasting and Uncertainty Assessment: Estimating quantiles and interval confidence based on point forecasting; using more conservative quantile thresholds for key business windows; S240, Drift Detection and Rollback Strategy: Monitors prediction error and distribution drift; when the drift exceeds the threshold, switches to robust statistics and historical maximum value window to avoid misleading expansion.

4. The method for database expansion and real-time monitoring and maintenance based on Zabbix according to claim 3, characterized in that, The multiplicative Holt-Winters model is used for model training and parameter optimization as follows: ； ； ； ； in, The length of the seasonal cycle; To predict the step size, ; Let t be the horizontal component; The trend component at time t; Let t be the seasonal component. These are the smoothing coefficients for horizontal, trend, and seasonality, respectively; the larger the coefficient, the higher the weight of the latest observation. A balance between response speed and stability is achieved through validation set grid search and Bayesian optimization. The horizontal component from the previous moment; This represents the trend component from the previous moment; This represents the seasonal component corresponding to the previous cycle. For the seasonal component corresponding to the seasonal index of the prediction time, These are the results of multiplicative Holt-Winters predictions; In quantile forecasting and uncertainty assessment, quantiles and intervals are defined as follows: ； in, For confidence level, To predict window offset, , The maximum allowable forward forecast span for the business. To predict the load metric within the forecast window, For future quantile estimation, Let be the confidence interval. The confidence bias is calculated using the empirical CDF of historical prediction errors at confidence levels. To obtain.

5. The method for database expansion and real-time monitoring and maintenance based on Zabbix according to claim 1, characterized in that, The steps for multi-indicator confidence aggregation and trigger judgment are as follows: S310, Weight Configuration and Indicator Selection: Set weights for CPU, memory, IO, connection, lock wait, replication latency, and P95 / P99 latency; Adjust according to the scenario to avoid being dominated by a single indicator; S320, Confidence Calculation and Trend Detection: Calculates the expanded confidence score and its time derivative after multi-indicator aggregation, and smooths instantaneous spikes; sets cooling time and minimum hold duration for jitter scenarios; S330, Trigger and Recycling Gating: Configure upper and lower thresholds and trend thresholds for gating; when the expansion trigger condition is met, enter the policy engine; when the recycling condition is met, perform gentle recycling and record audits.

6. The method for database expansion and real-time monitoring and maintenance based on Zabbix according to claim 1, characterized in that, The steps for SLO risk assessment and capacity requirement calculation are as follows: S410, SLO Default Risk Measurement: Calculates the intensity of default risk; increases expansion priority when risk rises and the trend is upward. S420, Demand Vector and Node Count Estimation: Form a demand vector and calculate the required number of nodes by combining the available resources of a single node and the safety factor; S430, Quantile Delay Approximation and Parameter Calibration: Use quantile delay estimation to assess compliance; calibrate the single-node effective processing capacity factor and fixed overhead to reflect the true topology and routing costs.

7. The method for database expansion and real-time monitoring and maintenance based on Zabbix according to claim 1, characterized in that, The steps for multi-objective optimization decision-making and determining the expansion scale are as follows: S510, Objective Function and Constraint Definition: Set the objective function, number of constraint nodes, maintenance window, freeze strategy and quota limit; S520, Solution and Selection: Use a planning algorithm to minimize the objective function with the optimal number of nodes; hierarchical decision-making may be necessary. The hierarchical decision-making process prioritizes replicating before sharding. Planning algorithms include discrete grid search, branch and bound, and heuristic search; S530, Expansion Primitive Generation and Orchestration: Generates primitives for "adding replicas / expanding shards / expanding partitions / increasing quotas / adjusting routes / adding proxies"; calculates idempotent keys, prerequisite dependencies, rollback sequences, and concurrency limits for each primitive; The primitives are calculated as follows: Idempotent keys: ensure that repeated commits only take effect once; Prerequisites: such as image preheating, configuration center version number checking; Rollback sequence: Rollback in reverse order in case of failure; Concurrency limiting: The number of primitives executed simultaneously is controlled by a priority queue to prevent cascading failures.

8. The method for database expansion and real-time monitoring and maintenance based on Zabbix according to claim 1, characterized in that, The steps for canary access and rolling migration are as follows: S610, Canary Plan and Sampling Design: Define initial weights and weighting step size; Sampling by query category / tenant / shard to construct canary traffic baskets and monitoring metric sets; S620, Representative Gating and Hotspot Coverage: Calculate representativeness score and hotspot coverage rate, and gating; when the target is not met, inject hotspots and re-query shadow reads / replays to supplement representativeness; S630, Privilege Escalation and Health Check: Performs weight updates; performs phased gating of latency, error rate, replication consistency, and snapshot integrity; implements progressive flow switching and connection pool emptying in the query layer, and implements queue buffering and backpressure protection in the write layer; S640, Anomalies and Rollbacks: If health indicators exceed the threshold or consistency is abnormal, immediately demote the system and roll back to the previous stable version; record the event and context for subsequent policy adjustments.

9. The method for database expansion and real-time monitoring and maintenance based on Zabbix according to claim 1, characterized in that, The verification and write-back steps are as follows: S710, SLO and Metrics Verification: Verify whether P95 / P99 and availability meet the standards; perform end-to-end verification of the critical path to confirm that there is no performance rollback and data inconsistency; S720, Topology and Quota Writeback: Writes replica / fragment / partition / quota / routing changes to the orchestration platform and configuration center; Update the monitoring tags and template bindings to ensure consistency between subsequent data and views; S730, Audit and Knowledge Update: Record audit logs for strategy evaluation, execution trajectory, health checks and rollback operations; update dynamic thresholds, weights, baselines and seasonality models; incorporate abnormal cases into the risk library and rollback scripts; S740, Rollback and Alarm: When verification fails or an unacceptable risk occurs, rollback is performed according to a predefined script and a high-priority alarm is triggered. Assess and record the scope of impact and recovery time.

10. A database expansion and real-time monitoring and maintenance system based on Zabbix, characterized in that: The method for performing the steps of the Zabbix-based database scaling and real-time monitoring and maintenance method as described in any one of claims 1-9 includes: Module 1: Complete template binding and enable key monitoring items in Zabbix, unify tag semantics and continuously synchronize with the topology, implement adaptive acquisition window and back pressure control without increasing database load, denoise and normalize the original indicators to construct metric vectors, enable the approximate structure of bucketing, TDigest, and Count-Min Sketch for aggregation and storage optimization, and verify data freshness and quality and perform necessary degradation processing. Module 2: This module preprocesses historical data and selects a stable baseline, trains and optimizes the multiplicative Holt-Winters model to generate point predictions, quantile predictions and uncertainty measures, continuously monitors prediction errors and distribution drift, and switches to robust statistics and historical maximum window when the drift exceeds the threshold, thereby ensuring the forward-looking and robust nature of expansion decisions. Module 3: This module configures the weights of various indicators according to the scenario, calculates the aggregate confidence and trend of multiple indicators, and combines uplink / downlink dual thresholds and cooling and minimum hold strategies to execute expansion triggering and gentle recycling gating to suppress jitter and false triggering and output corresponding audit records; Module 4: This module calculates default risk with SLO as the first principle, vectorizes the resource requirements and the number of nodes required, and combines a quantile delay approximation model to evaluate the compliance under the target scale. At the same time, it completes the calibration of single-node capability factor and fixed overhead parameters to match the real topology and routing cost. Module 5: This module constructs an objective function that integrates latency default, resource cost, change risk and disturbance. It is then solved by integer optimization in combination with the constraints of maintenance window, freeze strategy and quota limit. Based on this, it generates executable expansion primitives and orchestration sequences, with idempotent keys, pre-dependencies, rollback scripts and concurrency limits to ensure safe and auditable implementation. Module 6: This module formulates the canary plan and sampling strategy, controls the release pace based on representativeness and hot spot coverage gating, performs health checks on latency, error rate, replication consistency and snapshot integrity in stages, implements gradual flow switching and connection pool emptying, and enables queue buffering and back pressure protection on the write end. If an anomaly is encountered, it immediately demotes the priority and rolls back to the stable version. Module 7: After the expansion is completed, perform P95 / P99 and availability SLO verification, perform end-to-end checks on critical paths and confirm that there is no performance rollback or data inconsistency, write back changes to replicas, shards, partitions, quotas and routes to the configuration center and synchronize monitoring tags, fully record policy evaluation and execution trajectory to update thresholds, weights, baselines and seasonal models, and roll back according to the script and trigger high-priority alarms when necessary.

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