A unified platform full-stack observability approach

Abstract

The application provides a unified platform full-stack observability method, which comprises the following steps: collecting log, index and link tracking data by using an application performance monitoring agent, an extended Berkeley packet filter agent and an index collection agent respectively; storing and distributing the log, index and link tracking data; and performing data correlation processing by using request tracking identification and timestamps to obtain a unified storage and cross-data correlation raw data set, and performing format standardization and full-stack tracking integration processing to obtain standardized time series index and full-stack link tracking data respectively. The application can achieve the following effects: all observability data can be observed on the same platform by collecting the data into the same set of observation systems and correlating the observation data by using specific fields; the same set of user interfaces can be used, so that it is not necessary to switch between multiple system pages; and the problem of data silos is solved, and a user-friendly observability platform is created.

Description

Technical Field

[0001] The present invention relates to the field of information technology operation and maintenance technology, and in particular to a full-stack observability method for a unified platform. Background Art

[0002] In the IT operations field, observability refers to obtaining essential information across all layers of infrastructure, orchestration platforms, and service applications to observe any anomalies in the behavior of all systems. Observability is the ability to measure, prevent, discover, locate, and resolve business issues, thereby improving business performance, by correlating and analyzing machine data such as logs, metrics, and links across the entire business operations process, including development and testing, IT operations, business operations, and security compliance.

[0003] Traditional monitoring implementations primarily use Prometheus for metrics, ELK (Elastic Search + Logstash + Kibana) for log collection, and point tracking or code instrumentation to collect application links and business metrics. Metrics, logs, and traces are scattered across different systems, requiring manual correlation, resulting in inefficient troubleshooting. The lack of end-to-end context makes it impossible to correlate front-end user behavior with back-end service anomalies.

[0004] With the development of microservice architecture and cloud-native architecture, traditional monolithic services are gradually being replaced by finer-grained microservices. Service topology is becoming networked. Traditional monitoring cannot track cross-service call links, and single indicator monitoring can no longer meet monitoring needs.

[0005] The emergence of distributed tracing systems such as SkyWalking and OpenTelemetry has addressed some of these issues. SkyWalking collects service performance metrics, link tracing data, and logs, and displays this observability data in a single user interface. Furthermore, it can correlate these three types of data to locate code-level issues. However, SkyWalking lacks detailed information on the system and network layers, making it difficult to locate network issues. Furthermore, SkyWalking cannot collect infrastructure metrics, and its alarm management service is incomplete. Its rule configuration is simple and lacks complex logic. It also lacks features such as alarm severity grading, blocking, and alarm aggregation. The emergence of OpenTelemetry has unified the collection, processing, and export standards for logs, metrics, and traces. It supports multi-language, full-stack data collection, and can integrate with existing tools such as Prometheus, Jaeger, and Grafana. Kubernetes and Service Mesh natively support it, but it still has some flaws. The traces, metrics, and logs collected by OpenTelemetry are stored independently by default and require additional configuration to link them. Data visualization requires the use of multiple tools (such as Jaeger + Prometheus + ELK). Troubleshooting requires manual switching between multiple systems, which is inefficient. Summary of the Invention

[0006] In view of the above situation, the main purpose of this invention is to propose a full-stack observability method for a unified platform to solve the above technical problems.

[0007] The present invention proposes a full-stack observability method for a unified platform, which includes the following steps:

[0008] Step 1: Use the application performance monitoring agent, the extended Berkeley packet filter agent, and the metrics collection agent to collect application performance data, network layer data, and infrastructure data respectively to obtain logs, metrics, and link tracking data, and inject request tracking identifiers into the logs to achieve cross-data type correlation;

[0009] Step 2: Logs, metrics, and link tracking data are stored and allocated, and data association is performed using request tracking identifiers and timestamps to obtain a unified and cross-data-associated original data set.

[0010] Step 3: Standardize the format and perform full-stack tracing integration on the uniformly stored and cross-data-associated original data sets to obtain standardized time series indicators and full-stack link tracing data, respectively. Alarm detection and aggregate calculation are performed on the standardized time series indicators to obtain alarm events and aggregate indicators, respectively.

[0011] Step 4: Visualize the full-stack link tracking data, alarm events, and aggregated indicators to obtain a visual monitoring screen. Based on the visual monitoring screen, perform abnormal drill-down jumps and cross-data correlation query processing to obtain a visual monitoring screen that includes a drillable fault location path, integrated alarm statistics, and a real-time alarm event wall.

[0012] The present invention proposes to obtain more complete observable information through the collection of multiple collectors, eliminate the blind spots of observability data, introduce the modified alarm management service into the observability platform, support complex alarm rules, silent management, alarm aggregation and other functions, redesign and integrate the user interface display platform, and visualize it on a unified observability platform to realize data association jump and abnormal drill-down functions, and quickly locate faults.

[0013] Compared with the prior art, the present invention has the following beneficial effects:

[0014] 1. Unified observation platform. This invention aggregates data into a single observation system and uses specific fields to associate observation data. This allows all observable data to be observed on the same platform. Using a single user interface eliminates the need to switch between multiple system pages, breaks down data silos, and creates a user-friendly observability platform.

[0015] 2. Rapid fault location: By correlating the collected observation data, displaying visual graphs and tables, and using multi-dimensional and rich icon displays, users are guided step by step from observing the system status in real time to gradually drilling down, narrowing the scope of the problem and finally locating the cause.

[0016] 3. Full-stack distributed tracing supports combining the rich network layer details of the extended Berkeley packet filter agent with the code layer details collected by the application performance monitoring agent. This connects the network layer and the application layer, establishes cross-layer causal relationships, and achieves end-to-end correlation between network performance data and application link tracing, covering the entire call chain from physical network to microservices, thereby more accurately locating full-stack performance bottlenecks.

[0017] 4. Observability indicator alarm engine, alarm rule configuration, alarm event statistics and aggregation noise reduction processing, support for equipment, component, and service performance indicator alarms, allowing development and operation personnel to quickly respond to system failures and improve system stability;

[0018] 5. Multiple data collectors. This invention uses three types of collectors to collect different types of observability data. These include a metrics collection agent that collects various common components and various system hardware facility metrics that are not easily inserted into code. The system also supports custom data collector extensions, allowing users to customize new collectors to collect specific metrics based on actual usage scenarios. The application performance monitoring agent primarily collects service-level performance data through code instrumentation, while the Berkeley Packet Filter agent is extended to add rich network and data layer information to service performance data.

[0019] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flowchart of the steps of the full-stack observability method for a unified platform proposed by the present invention;

[0021] Figure 2 This is a method architecture diagram of a unified platform full-stack observability method proposed in this invention. DETAILED DESCRIPTION

[0022] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0023] These and other aspects of the embodiments of the present invention will become clear with reference to the following description and accompanying drawings. In these descriptions and accompanying drawings, some specific implementations of the embodiments of the present invention are specifically disclosed to illustrate some ways of implementing the principles of the embodiments of the present invention, but it should be understood that the scope of the embodiments of the present invention is not limited thereto.

[0024] See also Figure 1 This embodiment provides a full-stack observability method for a unified platform, comprising the following steps:

[0025] Step 1: Use the application performance monitoring agent, the extended Berkeley packet filter agent, and the indicator collection agent to collect application performance data, network layer data, and infrastructure data respectively to obtain logs, indicators, and link tracking data, and inject request tracking identifiers into the logs to achieve cross-data type association.

[0026] See also Figure 2In step 1, the application performance monitoring agent, the extended Berkeley packet filter agent, and the indicator collection agent are used to collect application performance data, network layer data, and infrastructure data respectively to obtain logs, indicators, and link tracking data. Request tracking identifiers are then injected into the logs to achieve cross-data type correlation. The specific sub-steps include the following:

[0027] Using code instrumentation technology, an application performance monitoring agent is embedded in the application code. The agent is used to capture data, collecting service performance data including logs, link tracking data, and performance indicators. Request tracking identifiers corresponding to link tracking data are then injected into the logs to achieve cross-data type correlation.

[0028] The extended Berkeley packet filter agent is used to collect data non-intrusively at the network layer to obtain the network layer span and the system layer span;

[0029] The indicator collection agent uses Categraf to collect infrastructure indicators and basic component indicators from system resource data.

[0030] It should be noted that in Figure 2 In this example, APM agent represents the application performance monitoring agent, eBPF agent represents the extended Berkeley Packet Filter agent, and Metric agent represents the metrics collection agent. Span refers to the node in the link tracing data. The application performance monitoring agent collects performance data of application services, mainly including application service logs, link tracing data, and performance metrics. The link tracing data collected by the application performance monitoring agent includes application layer spans.

[0031] The extended Berkeley packet filter agent also collects link tracing data, but the link tracing data collected by the extended Berkeley packet filter agent is incomplete. The tracing of distributed calls only covers the API call and network transmission levels, and there is no tracing data at the function granularity within the application service. This just makes up for the network layer observation blind spot of the link tracing data collected by the application performance monitoring agent. Therefore, by combining the link tracing data collected by the application performance monitoring agent with the link tracing data collected by the extended Berkeley packet filter agent, we can obtain full-stack observable link tracing data, realizing full-stack distributed tracing capabilities that connect applications, systems, and networks.

[0032] Step 2: Logs, indicators, and link tracking data are stored and allocated, and data association is performed using request tracking identifiers and timestamps to obtain a uniformly stored and cross-data associated original data set.

[0033] In step 2, logs, metrics, and link tracking data are stored and allocated, and data association is performed using request tracking identifiers and timestamps to obtain a uniformly stored and cross-data-associated original data set. This specifically includes the following sub-steps:

[0034] Based on the storage routing decision engine, logs, indicators and link tracking data are classified and stored to obtain classified and stored data streams;

[0035] The request tracking identifier and timestamp are used to perform cross-data association processing on the classified and stored data stream to obtain the original data set that is uniformly stored and cross-data associated.

[0036] Furthermore, the logs, indicators, and link tracking data are classified and stored based on the storage routing decision engine to obtain a classified and stored data stream, which specifically includes the following sub-steps:

[0037] Route performance metrics, infrastructure metrics, and basic component metrics to the Victoria Metrics shard cluster and shard them by service name and timestamp;

[0038] Route log and link tracking data to Elastic Search hot and cold tiered storage, shard them according to request tracking ID and date, and generate storage location fingerprints;

[0039] Build a mapping table from request tracking identifiers to storage location fingerprints and store it in Redis;

[0040] Configure alarm rules based on PromQL and store them in MySQL.

[0041] It should be noted that PromQL is the built-in data query language of Prometheus, which provides support for rich query, aggregation and logical operation capabilities of time series data.

[0042] Furthermore, the step of performing cross-data association processing on the classified and stored data stream using the request tracking identifier and the timestamp to obtain a uniformly stored and cross-data associated original data set specifically includes the following sub-steps:

[0043] Use request tracking identifiers to obtain storage location fingerprints from Redis, and simultaneously initiate heterogeneous queries in parallel to query log and link tracking data from Elastic Search and metrics from Victoria Metrics;

[0044] During the query process, the network time protocol is used to fine-tune clock synchronization technology, and the sliding time window alignment algorithm is combined to compensate for clock drift;

[0045] Encapsulate query results into a unified data model stream, including request tracking identifiers, timestamps, logs, metrics, and link tracking data, while automatically associating upstream and downstream data in the service topology;

[0046] During the association process, an exception protection mechanism is implemented to monitor the health status of each storage shard in real time and automatically switch to the backup shard in the event of a failure.

[0047] A hierarchical degradation strategy is initiated for query timeouts, and asynchronous compensation tasks are triggered through data integrity checks to repair missing data.

[0048] Establish an operation audit log to record all related operation details, ensure that the processing process is traceable and rollbackable, and ultimately output a unified storage and cross-data association original data set with complete traceability information.

[0049] Specifically, during the query process, the steps of fine-tuning the clock synchronization technology using the network time protocol and compensating for clock drift in combination with the sliding time window alignment algorithm include the following sub-steps:

[0050] During cross-database queries, a three-level clock synchronization system is built. Basic clock synchronization between servers is achieved through the Network Time Protocol. High-precision software time protocol modules are deployed on core nodes to improve synchronization accuracy to the microsecond level. Furthermore, hardware clock chips are embedded in each agent at the data collection layer.

[0051] The synchronization process uses a two-way timestamp exchange mechanism to dynamically calibrate the time base. The clock offset is automatically corrected by calculating the average time difference between requests and responses, forming a hierarchical clock synchronization network covering the infrastructure to the application layer.

[0052] A dynamically adjusted time window is designed to achieve drift compensation. The initial window size is the maximum value of the average network delay and the protection threshold. Clock offset, network jitter, and packet loss rate parameters are collected in real time. The window range is dynamically expanded or contracted through a feedback control model.

[0053] A high-precision interpolation algorithm is used to align data points within the window. A continuous function model is constructed for time series indicators to fit the actual observed values. This eliminates association breaks caused by clock jumps or network delays and ensures the temporal consistency of cross-database data.

[0054] A multi-level verification process was developed to ensure association accuracy. After data alignment, three core checks were performed: a cross-data type distribution matrix was constructed to verify tracking identity consistency, a time series cross-correlation function was calculated to confirm the strength of the association between logs and indicators, and a backtracking process was automatically triggered when an abnormal association was detected.

[0055] The backtracking mechanism expands the time window to three times its original size and calls the historical best compensation parameter library for iterative optimization until the preset confidence threshold is met, forming a self-correcting closed-loop verification system;

[0056] Deploy dedicated clock synchronization accelerator cards in core network nodes, integrate hardware timestamp engines to eliminate operating system scheduling delays, use built-in intelligent prediction algorithms to learn historical drift patterns and pre-generate compensation parameters, and equip real-time monitoring modules to detect sudden clock offsets.

[0057] When severe drift occurs, it automatically switches to the hardware acceleration channel, achieves instantaneous calibration of millions of data points through a parallel processing architecture, completes clock compensation in sub-millisecond time, and ensures data association reliability in large-scale distributed environments.

[0058] Step 3: Standardize the format and perform full-stack tracing integration processing on the original data sets that are uniformly stored and associated across data to obtain standardized time series indicators and full-stack link tracing data, respectively. Perform alarm detection and aggregation calculation processing on the standardized time series indicators to obtain alarm events and aggregate indicators, respectively.

[0059] In step 3, the format of the uniformly stored and cross-data-associated original data sets is standardized and integrated with full-stack tracing to obtain standardized time series indicators and full-stack link tracing data, respectively. The standardized time series indicators are then subjected to alarm detection and aggregate calculation to obtain alarm events and aggregate indicators, respectively. This specifically includes the following sub-steps:

[0060] Based on the original data set that is uniformly stored and associated across data, performance indicators are converted into Prometheus format and business dimension labels are added to obtain standardized time series indicators.

[0061] Based on request tracing identifier matching, the network layer spans and system layer spans collected by the Extended Berkeley Packet Filter agent, as well as the application layer spans collected by the Application Performance Monitoring agent, are aggregated to generate a call chain covering the entire stack. This generates full-stack chain trace data and stores it in Elastic Search.

[0062] Concatenate the network layer span, application layer span, and system layer span in sequence to generate a flame graph.

[0063] Based on alarm rules and standardized time series indicators, real-time monitoring, alarm generation, aggregation and noise reduction, and alarm notification processing are carried out in sequence to obtain alarm events;

[0064] Aggregate and calculate the standardized time series metrics according to the aggregation tags configured in the aggregation rules to obtain aggregate metrics, which are then stored in Victoria Metrics.

[0065] Furthermore, the network layer span, application layer span, and system layer span are sequentially spliced ​​together to generate a full-stack flame graph, which specifically includes the following sub-steps:

[0066] Obtain the request tracking identifier, segment identifier, and span identifier of the application layer span, and combine and convert the segment identifier and span identifier of the application layer span to obtain the span identifier corresponding to the network layer span;

[0067] Based on the SQL API, the span identifier corresponding to the network layer span is used as the query condition to query the network layer span and system layer span collected by the extended Berkeley packet filter agent;

[0068] The application layer span, network layer span, and system layer span are correlated to obtain complete link tracing data, and flame graphs are drawn to obtain flame graphs.

[0069] Step 4: Visualize the full-stack link tracking data, alarm events, and aggregated indicators to obtain a visual monitoring screen. Based on the visual monitoring screen, perform abnormal drill-down jumps and cross-data correlation query processing to obtain a visual monitoring screen that includes a drillable fault location path, integrated alarm statistics, and a real-time alarm event wall.

[0070] In step 4, visualization is performed based on full-stack link tracing data, alarm events, and aggregated indicators to obtain a visual monitoring dashboard. Based on the visual monitoring dashboard, exception drill-down jumps and cross-data correlation query processing are performed to obtain a visual monitoring dashboard that includes drillable fault location paths, integrated alarm statistics, and a real-time alarm event wall. This specifically includes the following sub-steps:

[0071] Based on full-stack link tracking data, alarm events, and aggregated indicators, PromQL query, visual rendering, and dynamic refresh processing are performed in sequence to obtain a visual monitoring screen that includes a global view and a real-time alarm event wall. The global view displays global indicators, and the real-time alarm event wall displays currently active alarms.

[0072] Based on abnormal indicators of user clicks, we extract service names and timestamps, generate dynamic routes, query associated logs by request tracking ID in ElasticSearch, and query indicators by timestamp in Victoria Metrics to obtain a drill-down fault location path.

[0073] Alarm rules and alarm events are processed sequentially through rule configuration, alarm display and processing, and historical alarm query processing to obtain integrated alarm statistics including alarm rule configuration form, active alarm list, processing operation panel and historical alarm query results;

[0074] Embed drillable fault location paths and integrated alarm statistics into the visual monitoring screen.

[0075] Furthermore, the global view displays global indicators, which includes the following sub-steps:

[0076] Deploy the metrics collection agent on the server, use it to collect infrastructure metrics including CPU, memory, disk, and network, and write them directly to Victoria Metrics;

[0077] Use SkyWalking's application performance monitoring agent to collect service performance data and report performance indicators to the data processing layer using the ProtoBuf protocol;

[0078] Convert service performance data into Prometheus standard indicator format;

[0079] Downsample the second-level data in performance indicators to the minute level to reduce storage pressure;

[0080] The processed data is uniformly written into Victoria Metrics to form a global indicator data set. Each module of the large screen directly queries Victoria Metrics through PromQL to calculate the indicators required for monitoring the large screen in real time.

[0081] It should be noted that if Figure 2 As shown in the figure, the data storage layer uses MySQL, Redis, Victoria Metrics, and Elastic Search as storage media. MySQL stores observability platform alerts, users, data sources, and other information. Redis is used to cache some core observability system data to improve query performance. Victoria Metrics is mainly used to store indicators, including performance indicators, infrastructure indicators, and basic component indicators. Elastic Search is used to store various service interface indicators, link tracking data, logs, and service performance indicator data that has undergone format conversion and downsampling.

[0082] The data processing layer includes functions such as data format conversion, an alert engine, metric aggregation, and full-stack tracing. Format conversion exports performance metrics collected by the application performance monitoring agent, converts them, downsamples them, and stores them in Victoria Metrics for centralized management. The alert engine implements real-time metric detection, checks for anomalies, and aggregates and reduces the noise of alert events. Metric aggregation integrates and calculates distributed, multi-source data metrics using specific rules or algorithms to generate higher-level, comprehensive metrics. These metrics include project average request success rate, load and request latency, overall CPU and memory usage across different clusters, infrastructure quantity, and the number of system-wide alert events. These metrics are displayed uniformly on the monitoring dashboard to reveal overall trends or issues, simplify complex data, extract key information, and provide a holistic perspective to support decision-making and monitoring. Full-stack tracing augments the link trace data collected by the application performance monitoring agent with link trace data collected by the extended Berkeley Packet Filter agent. This provides more comprehensive link trace data covering the entire system network and application stack, enabling multi-dimensional, blind-spot-free problem tracking.

[0083] The application layer provides a unified user interface for data retrieval, alarm rule configuration management, alarm event management, and correlation and jump between indicators and link tracking data and logs. The monitoring screen provides a global perspective, observes system status, and supports drill-down on exceptions.

[0084] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0085] It should be understood that various components of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0086] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0087] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A full-stack observability approach for a unified platform, characterized by: The method comprises the following steps: Step 1: Use the application performance monitoring agent, the extended Berkeley packet filter agent, and the metrics collection agent to collect application performance data, network layer data, and infrastructure data respectively to obtain logs, metrics, and link tracking data, and inject request tracking identifiers into the logs to achieve cross-data type correlation; Step 2: Logs, metrics, and link tracking data are stored and allocated, and data association is performed using request tracking identifiers and timestamps to obtain a unified and cross-data-associated original data set. Step 3: Standardize the format and perform full-stack tracing integration on the uniformly stored and cross-data-associated original data sets to obtain standardized time series indicators and full-stack link tracing data, respectively. Alarm detection and aggregate calculation are performed on the standardized time series indicators to obtain alarm events and aggregate indicators, respectively. Step 4: Visualize the full-stack link tracing data, alarm events, and aggregated indicators to create a visual monitoring dashboard. Perform drill-down and cross-data correlation query processing on the dashboard to create a visual monitoring dashboard that includes drillable fault location paths, integrated alarm statistics, and a real-time alarm event wall. In step 2, logs, indicators, and link tracking data are stored and allocated, and data association processing is performed using request tracking identifiers and timestamps to obtain a uniformly stored and cross-data-associated original data set, which specifically includes the following sub-steps: Intelligently classify and store logs, metrics, and link tracking data based on the storage routing decision engine to obtain classified and stored data streams; Use request tracking identifiers and timestamps to perform cross-data association processing on the classified and stored data stream to obtain a uniformly stored and cross-data associated original data set; The step of performing cross-data association processing on the classified and stored data stream using the request tracking identifier and the timestamp to obtain a uniformly stored and cross-data associated original data set specifically includes the following sub-steps: Use request tracking identifiers to obtain storage location fingerprints from Redis, and simultaneously initiate heterogeneous queries in parallel to query log and link tracking data from ElasticSearch and metrics from Victoria Metrics; During the query process, the network time protocol is used to fine-tune clock synchronization technology, and the sliding time window alignment algorithm is combined to compensate for clock drift; Encapsulate query results into a unified data model stream, including request tracking identifiers, timestamps, logs, metrics, and link tracking data, while automatically associating upstream and downstream data in the service topology; During the association process, an exception protection mechanism is implemented to monitor the health status of each storage shard in real time and automatically switch to the backup shard in the event of a failure. A hierarchical degradation strategy is initiated for query timeouts, and asynchronous compensation tasks are triggered through data integrity checks to repair missing data. Establish an operation audit log to record all related operation details, ensure that the processing process is traceable and rollbackable, and ultimately output a unified storage and cross-data association original data set with complete traceability information; The step of intelligently classifying and storing logs, indicators, and link tracking data based on the storage routing decision engine to obtain a classified and stored data stream specifically includes the following sub-steps: Route performance metrics, infrastructure metrics, and basic component metrics to the Victoria Metrics shard cluster and shard them by service name and timestamp; Route log and link tracking data to Elastic Search hot and cold tiered storage, shard them according to request tracking ID and date, and generate storage location fingerprints; Build a mapping table from request tracking identifiers to storage location fingerprints and store it in Redis; Configure alarm rules based on PromQL and store them in MySQL.

2. The full-stack observability method for a unified platform according to claim 1, characterized in that: In step 1, application performance monitoring agent, extended Berkeley packet filter agent and indicator collection agent are used to collect application performance data, network layer data and infrastructure data respectively to obtain logs, indicators and link tracking data, and request tracking identifiers are injected into the logs to achieve cross-data type correlation. Specifically, the following sub-steps are included: Using code instrumentation technology, an application performance monitoring agent is embedded in the application code. The agent is used to capture data, collecting service performance data including logs, link tracking data, and performance indicators. Request tracking identifiers corresponding to link tracking data are then injected into the logs to achieve cross-data type correlation. The extended Berkeley packet filter agent is used to collect data non-intrusively at the network layer to obtain the network layer span and the system layer span; The indicator collection agent uses Categraf to collect infrastructure indicators and basic component indicators from system resource data.

3. The full-stack observability method for a unified platform according to claim 1, characterized in that: In step 3, the original data sets that are uniformly stored and associated across data are subjected to format standardization and full-stack tracing integration processing to obtain standardized time series indicators and full-stack link tracing data, respectively. The standardized time series indicators are subjected to alarm detection and aggregation calculation processing to obtain alarm events and aggregate indicators, respectively. The specific sub-steps include the following: Based on the original data set that is uniformly stored and associated across data, performance indicators are converted into Prometheus format and business dimension labels are added to obtain standardized time series indicators. Based on request tracing identifier matching, the network layer spans and system layer spans collected by the Extended Berkeley Packet Filter agent, as well as the application layer spans collected by the Application Performance Monitoring agent, are aggregated to generate a call chain covering the entire stack. This generates full-stack chain trace data and stores it in Elastic Search. Concatenate the network layer span, application layer span, and system layer span in sequence to generate a flame graph. Based on alarm rules and standardized time series indicators, real-time monitoring, alarm generation, aggregation and noise reduction, and alarm notification processing are carried out in sequence to obtain alarm events; Aggregate and calculate the standardized time series metrics according to the aggregation tags configured in the aggregation rules to obtain aggregate metrics, which are then stored in Victoria Metrics.

4. The full-stack observability method for a unified platform according to claim 3, characterized in that: The network layer span, application layer span, and system layer span are sequentially spliced ​​together to generate a flame graph. This process includes the following sub-steps: Obtain the request tracking identifier, segment identifier, and span identifier of the application layer span, and combine and convert the segment identifier and span identifier of the application layer span to obtain the span identifier corresponding to the network layer span; Based on the SQL API, the span identifier corresponding to the network layer span is used as the query condition to query the network layer span and system layer span collected by the extended Berkeley packet filter agent; The application layer span, network layer span, and system layer span are correlated to obtain complete link tracing data, and flame graphs are drawn to obtain flame graphs.

5. The full-stack observability method for a unified platform according to claim 4, characterized in that: In step 4, visualization processing is performed based on the full-stack link tracing data, alarm events, and aggregated indicators to obtain a visualization monitoring screen. Based on the visualization monitoring screen, abnormal drill-down jumps and cross-data correlation query processing are performed to obtain a visualization monitoring screen that includes a drillable fault location path, integrated alarm statistics, and a real-time alarm event wall. The specific sub-steps include the following: Based on full-stack link tracking data, alarm events, and aggregated indicators, PromQL query, visual rendering, and dynamic refresh processing are performed in sequence to obtain a visual monitoring screen that includes a global view and a real-time alarm event wall. The global view displays global indicators, and the real-time alarm event wall displays currently active alarms. Based on abnormal user click indicators, we extract service names and timestamps, generate dynamic routes, query associated logs by request tracking ID in Elastic Search, and query indicators by timestamp in Victoria Metrics to obtain a drill-down fault location path. Alarm rules and alarm events are processed sequentially through rule configuration, alarm display and processing, and historical alarm query processing to obtain an alarm management interface that includes an alarm rule configuration form, an active alarm list, a processing operation panel, and historical alarm query results; Embed the drillable fault location path and alarm management interface into the visual monitoring screen.

6. The full-stack observability method for a unified platform according to claim 5, characterized in that: The global view displays global indicators and includes the following sub-steps: Deploy the metrics collection agent on the server, use it to collect infrastructure metrics including CPU, memory, disk, and network, and write them directly to Victoria Metrics; Use SkyWalking's application performance monitoring agent to collect service performance data and report performance indicators to the data processing layer using the ProtoBuf protocol; Convert service performance data into Prometheus format; Downsample the second-level data in performance indicators to the minute level to reduce storage pressure; The processed data is written uniformly to Victoria Metrics. Each module of the large screen directly queries Victoria Metrics through PromQL to calculate the indicators required for monitoring the large screen in real time.

Images