CN115098378A - Method and device for classifying and aggregating log fragments based on abnormal breakpoints - Google Patents
Method and device for classifying and aggregating log fragments based on abnormal breakpoints Download PDFInfo
- Publication number
- CN115098378A CN115098378A CN202210743227.XA CN202210743227A CN115098378A CN 115098378 A CN115098378 A CN 115098378A CN 202210743227 A CN202210743227 A CN 202210743227A CN 115098378 A CN115098378 A CN 115098378A
- Authority
- CN
- China
- Prior art keywords
- log
- abnormal
- playback
- segments
- classifying
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/362—Software debugging
- G06F11/366—Software debugging using diagnostics
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/17—Details of further file system functions
- G06F16/172—Caching, prefetching or hoarding of files
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/18—File system types
- G06F16/1805—Append-only file systems, e.g. using logs or journals to store data
- G06F16/1815—Journaling file systems
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Computer Hardware Design (AREA)
- Quality & Reliability (AREA)
- Debugging And Monitoring (AREA)
Abstract
The application provides a method for classifying and aggregating log segments based on abnormal breakpoints. The method comprises the following steps: responding to an access request of a worker to execute flow playback; monitoring the flow playback event; when a flow playback failure event is monitored, extracting playback thread information of current execution playback from a playback context acquired by the monitoring event; acquiring a preset log path, wherein the log path is used for storing logs; acquiring a log according to the preset log path; performing content matching and interception from the log according to the playback thread information to obtain a plurality of corresponding abnormal information sections; processing the plurality of abnormal information segments to obtain specific abnormal log segments; and storing the specific abnormal log segments in a memory. The positioning of workers is assisted, the problem solving is more efficient, and the working efficiency is improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method for classifying and aggregating log segments based on abnormal breakpoints and a device for classifying and aggregating log segments based on abnormal breakpoints.
Background
The logs often play an important role in enterprise IT services, and the generated log information has great value in the development, test and operation stages of software products. Many internet companies output logs through page burial points to obtain user operation information to assist operation. Meanwhile, the log information also has other more important functions, particularly in the troubleshooting stage, when the system encounters an abnormality with an unknown reason in the running process, development and operation and maintenance colleagues can be helped to see the error reporting condition of the current system abnormality through the log, so that the abnormal complete stack information can be quickly found, and the abnormal code segment can be positioned.
Generally, log files of applications are recorded in a certain directory of a server deploying the applications, when a log is queried, a log server needs to be logged in to find a relevant log file for querying, and a user wants to accurately query and also needs to master a relevant vi operation shell script. If the application is deployed in a production environment, the server authority is required to be applied for layer-by-layer approval, even if other log collection tools such as ELK, FLUME and the like are docked, the log can be queried by accessing a visual interface, but the abnormal information is difficult to be timely and accurately positioned in consideration of the real-time performance and the abnormal positioning accuracy of the log. Therefore, when an abnormality occurs, the factors are not beneficial to the problem of quick positioning through the log, and therefore the working efficiency is influenced.
Disclosure of Invention
The application provides a method for classifying and aggregating log fragments based on abnormal breakpoints and a device for classifying and aggregating log fragments based on abnormal breakpoints, which help workers to locate problems and improve working efficiency.
In a first aspect, an embodiment of the present application provides a method for classifying aggregated log segments based on abnormal breakpoints. The method for classifying and aggregating log segments based on abnormal breakpoints comprises the following steps: responding to an access request of a worker to execute flow playback; monitoring the flow playback event; when a flow playback failure event is monitored, extracting playback thread information of current execution playback from a playback context acquired by the monitoring event; acquiring a preset log path, wherein the log path is used for storing logs; acquiring a log according to the preset log path; performing content matching and interception from the log according to the playback thread information to obtain a plurality of corresponding abnormal information segments; processing the plurality of abnormal information segments to obtain specific abnormal log segments; and storing the specific abnormal log segments in a memory.
In a second aspect, an embodiment of the present application further provides an apparatus for classifying an aggregated log segment based on an abnormal breakpoint, where a playback main module in the apparatus includes:
the playback unit is used for responding to the access request of the staff to execute flow playback;
the monitoring unit is used for monitoring the flow playback event and calling the playback comparison failure event module when monitoring the flow playback failure event;
the playback comparison failure event module comprises:
the device comprises a path acquisition unit, a log storage unit and a log processing unit, wherein the path acquisition unit is used for acquiring a preset log path which is used for storing logs;
the log obtaining unit is used for obtaining logs according to the preset log path;
the extraction unit is used for carrying out content matching and interception from the log according to the playback thread information to obtain a plurality of corresponding abnormal information sections;
the processing unit is used for processing the plurality of abnormal information segments to obtain specific abnormal log segments; and
and the storage unit is used for storing the specific abnormal log segments in a memory.
In a third aspect, an embodiment of the present application further provides a server, where the server includes:
a memory for storing computer program instructions;
a processor for executing computer program instructions to implement the method for classifying aggregated log segments based on abnormal breakpoints.
According to the method for classifying and aggregating the log segments based on the abnormal breakpoints, the log data, the abnormal information, the playback thread of the program, the error stack and other data information generated in the operation process are obtained by executing the flow playback operation. And acquiring abnormal information segments in the log according to a source program and the generated information, intercepting, aggregating and the like the log segments to generate processed abnormal segments, and storing the processed abnormal segments in a cloud storage to be displayed to a user so that the user can position problems according to the abnormal segments. Meanwhile, the log interception, aggregation and matching logic can be optimized. According to the scheme, the log can be checked without logging in the server when the abnormity occurs, and the permission approval operation of a related trigger is omitted due to the fact that the server needs to be logged in. And after the original logs are accurately matched and aggregated through the information data of the source program codes, the original logs can be stored in cloud storage in real time, and a user can immediately click and check the comparison failure detail page directly. Through the sorted logs, a user can quickly find the abnormal stack from the logs, and the abnormal stack helps analyze the reason of abnormal failure, so that the problem is positioned, the problem is solved, and the working efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
Fig. 1 is a flowchart of a method for classifying and aggregating log segments based on abnormal breakpoints according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of an apparatus for classifying an aggregated log segment based on an abnormal breakpoint according to an embodiment of the present invention.
Fig. 3 is another flowchart of a method for classifying and aggregating log segments based on abnormal breakpoints according to an embodiment of the present application.
Fig. 4 is a schematic internal structural diagram of a computer device for classifying and aggregating log segments based on abnormal breakpoints according to an embodiment of the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," "third," "fourth," and the like in the description and claims of this application and in the above-described drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged under appropriate circumstances, in other words, the described embodiments may be practiced other than as illustrated or described herein. Moreover, the terms "comprises," "comprising," and any other variation thereof, may also include other things, such as processes, methods, systems, articles, or apparatus that comprise a list of steps or elements is not necessarily limited to only those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such processes, methods, articles, or apparatus.
It should be noted that the descriptions relating to "first", "second", etc. in this application are for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present application.
The application provides a method for classifying and aggregating log fragments based on abnormal breakpoints, which is applied to a system for classifying and aggregating log fragments based on abnormal breakpoints, and the system comprises a client and a server. The client communicates with the server over a network. The client may be, but is not limited to, various personal computers, notebook computers, and tablet computers. The server may be implemented as a stand-alone server or as a server cluster consisting of a plurality of servers. The method comprises the steps that a worker sends a request for executing flow playback operation to a server through a client, and the server processes the request according to a preset algorithm and sends a processing result to the client for the worker to check. When software products, network devices, etc. are developed, tested, and operated, log data is generated while the products or devices are operated. The log data records data generated in runtime, and each row of log data includes descriptions of related operations such as date, time, user and action. Before the method is executed, a storage path is configured in advance for a log generated by the server in the operation process so as to be called when needed. And loading a log intercepting plug-in the server for capturing abnormal events, wherein the log intercepting plug-in can intercept and aggregate specific log fragments when the abnormality occurs.
Please refer to fig. 1 in combination, which is a flowchart illustrating a method for classifying an aggregated log segment based on abnormal breakpoints according to a first embodiment of the present application. The method for classifying the aggregated log segments based on abnormal breakpoints comprises steps S101-S108.
And step S101, responding to the access request of the staff to execute flow playback.
After development is complete, the code needs to be tested. New requirements are also continually added during the code testing process. In the process of adding new requirements, codes can be reconstructed and split, source codes are continuously changed, and in the process of changing the source codes, original functions can be influenced. The flow playback is to store all requests sent to a certain application A in a certain time period, and uniformly send the requests to a different application B, so that the request parameters received by the application A and the application B are kept consistent, and the request received by the application A requests the application B again. And when a new requirement is added in the code test, the request of the operation of the source program code is run on the new code again, and the new function code is determined not to influence the function realized by the source code. The flow segments that the staff member can select to play back at the client can be the flow of a certain case set, such as a large amount of requests initiated by the user at the moment of double eleven shopping, or a single flow, such as a ticket purchased by a user from a ticket purchasing system. And the client sends the request of the staff to the server so that the server executes the flow playback event.
And step S102, monitoring the flow playback event.
The monitoring is to monitor the network running state and data flow and can acquire all information generated in the monitoring process. The server side responds to the request of the client side to operate the flow in a new complete function code, and records data generated in the operation process and related codes, wherein the related codes comprise a thread name Main, a Main calling function, a sub calling function and the like of an opposite code, and the generated data are recorded in a log, and the method comprises the following steps: initiating Exception information generated when playback of time and flow is abnormal, wherein the Exception information is Exception information generated when the execution of a program is judged by the program itself to be that an error cannot be recovered and an error stack in an Exception and program codes is thrown out, abnormal process information printed by an SQL computer language in the execution process and info/war information corresponding to the Exception, and the info/war information is a log type.
Step S103, when a flow playback failure event is monitored, the playback thread information of the current execution playback is extracted from the playback context obtained by the monitoring event.
When the server detects abnormal information such as Exception information generated by an operating program, abnormal process information printed in an SQL computer language and the like, the server judges that the flow playback fails. And acquiring a code segment, a thread name Main, a Main calling function and a sub calling function of the code involved in the flow playback. And acquiring abnormal information generated by the program in the playback process.
And step S104, acquiring a preset log path, wherein the log path is used for storing logs.
And acquiring a pre-configured log path of the playback process, and acquiring a log for recording the playback process according to the path. And the RandomAccessFile operation file is used for reading the log content in a reverse order, the RandomAccessFile is a file content access class which has the richest functions and can be used in the process of inputting or outputting data by Java, the RandomAccessFile operation file can read the file content and output data to the file, and the RandomAccessFile operation file can access the file from any position. Because the log file writing is added from the back according to rows in work, when the log is checked, the log needs to be checked from the latest information, when the file is too large, the file cannot be read and checked completely, and the log is read in the reverse order. All code segments involved in the playback process, the calling process and exception information generated in the calling process are recorded in the log.
And S105, acquiring a log according to the preset log path.
And step S106, performing content matching and interception from the log according to the playback thread information to obtain a plurality of corresponding abnormal information sections.
The thread information is an abnormal code related in the running process of the code, and the log intercepting plug-in is applied to match and acquire a plurality of abnormal code segments corresponding to the thread information in the log.
And step S107, processing the plurality of abnormal information segments to obtain specific abnormal log segments.
The processing includes interception, aggregation, and the like. Dividing each obtained abnormal code segment into a plurality of code information segments, and aggregating the plurality of code information segments obtained by segmentation according to preset conditions to form a plurality of specific abnormal information segments. The preset conditions comprise the starting time of code operation, the thread name of the code, and main calling information and sub calling information contained in the code.
And step S108, storing the specific abnormal log segments in a memory.
The memory is cloud memory, and particularly, the cloud memory can be AmazonS3 cloud storage. And storing the processed log fragments in the AmazonS3 cloud storage. When the flow playback fails, the server sends a playback failure page to the client, and the playback failure page is provided with a plurality of playback options for a user to select. The several options include: playback result options, source code segments involved, log content generated, etc. And if the customer selects the return visit result query option at the page end, displaying the abnormal code segment acquired through processing.
It is to be understood that if the traffic playback operation is successful, a client next playback request is received and the playback operation is performed.
The foregoing embodiment further provides an apparatus 1 for sorting aggregated log segments based on abnormal breakpoints, which is characterized by comprising a playback main module 2 and a playback comparison failure event module 3.
The playback main module 2 includes:
the playback unit 3 is used for responding to an access request of a worker to execute flow playback; the flow segments that the staff member can select to play back at the client can be the flow of a certain case set, such as a large amount of requests initiated by the user at the moment of double eleven shopping, or a single flow, such as a ticket purchased by a user from a ticket purchasing system. And the client sends the request of the staff to the server so that the server executes the flow playback event.
The monitoring unit 4 is used for monitoring the flow playback event and calling the playback comparison failure event module when monitoring the flow playback failure event; the server side responds to the request of the client side to operate the flow in new complete function codes and records the data generated in the operation process and the related codes
The playback comparison failure event module 2 includes:
a path obtaining unit 5, configured to obtain a preset log path, where the log path is used to store a log; and acquiring a pre-configured log path of the playback process, and acquiring a log for recording the playback process according to the path.
The log obtaining unit 6 is used for obtaining logs according to the preset log path;
the extraction unit 7 is used for carrying out content matching and interception from the log according to the playback thread information to obtain a plurality of corresponding abnormal information sections; the thread information is an abnormal code related to the running process of the code, and the log intercepting plug-in is applied to match and obtain a plurality of abnormal code segments corresponding to the thread information in the log.
The processing unit 8 is configured to process the plurality of abnormal information segments to obtain specific abnormal log segments; and dividing each acquired abnormal code segment into a plurality of code information segments, and aggregating the plurality of code information segments acquired by segmentation according to preset conditions to form a plurality of specific abnormal information segments.
And the storage unit 9 is used for storing the specific abnormal log segments in a memory. And storing the processed log segments in the AmazonS3 cloud storage.
As shown in fig. 3, the method for classifying and aggregating log segments based on abnormal breakpoints provided by the present application includes: the staff initiates a request from the client, and the server responds to the access request of the staff to execute flow playback. The server listens for the traffic playback event. When a flow playback failure event is monitored, the server extracts the playback thread information currently executing playback from the playback context acquired by the monitoring event. And when the playback operation is successful, the server responds to the access request of the next worker and executes the flow playback operation. The server acquires a preset log path, wherein the log path is used for storing logs. And the server acquires the log according to the preset log path. The server performs content matching and interception from the log according to the playback thread information to obtain a plurality of corresponding abnormal information sections, and the server processes the plurality of abnormal information sections to obtain each specific abnormal log section. And the server stores the specific abnormal log segments in a memory. The worker may retrieve exception information from the memory page.
Please refer to fig. 4, which is a schematic diagram of an internal structure of the computer apparatus 900 according to the first embodiment of the present application. Further, the computer embedded device is a HUD. The computer device 900 comprises at least a memory 901, a processor 902. Specifically, a memory 901 for storing program instructions of a method for classifying an aggregated log segment based on an abnormal breakpoint. A processor 902 for executing program instructions to cause the computer device 900 to implement the above-described method for classifying aggregated log segments based on abnormal breakpoints.
The memory 901 includes at least one type of readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like. The memory 901 may be used not only to store application software installed in the computer apparatus 900 and various types of data, for example, control instructions of a method of classifying an aggregated log segment based on an abnormal breakpoint, etc., but also to temporarily store data that has been output or is to be output.
Processor 902 may be, in some embodiments, a Central Processing Unit (CPU), controller, microcontroller, microprocessor or other data Processing chip that executes program instructions or processes data stored in memory 901. In particular, the processor 902 executes program instructions of a method of classifying aggregated log segments based on abnormal breakpoints to control the computer device 900 to implement the method of classifying aggregated log segments based on abnormal breakpoints.
Further, the bus 903 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 4, but this does not indicate only one bus or one type of bus.
Further, computer device 900 may also include a display component 904. The display component 904 may be an LED (Light Emitting Diode) display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light Emitting Diode) touch panel, or the like. The display component 904 may also be referred to as a display device or display unit, as appropriate, for displaying information processed in the computer device 900 and for displaying a visual user interface, among other things.
Further, the computer device 900 may further include a communication component 905, and the communication component 905 may optionally include a wired communication component and/or a wireless communication component (e.g., WI-FI communication component, bluetooth communication component, etc.), typically for establishing a communication connection between the computer device 900 and other computer devices.
While FIG. 4 illustrates only a computer device 900 having components 901-905 and program instructions implementing a method for classifying an aggregated log segment based on an abnormal breakpoint, those skilled in the art will appreciate that the architecture illustrated in FIG. 4 is not intended to be limiting of the computer device 900 and may include fewer or more components than illustrated, or may combine certain components, or be arranged in different components.
It will be apparent to those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, apparatuses and units described above, and in particular, the processor 902 of the computer device 900 executes the program instructions of the method of classifying the aggregated log segments based on abnormal breakpoints to control the computer device 900 to implement the detailed processes of the prediction method of the motion trajectory of the movable object. Reference may be made to the corresponding process in the above method embodiment, which is not described herein again.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, and it is intended that the present application cover the modifications and variations of this invention provided they come within the scope of the appended claims and their equivalents.
The above-mentioned embodiments are only examples of the present invention, and the scope of the claims of the present invention should not be limited by these examples, so that the claims of the present invention should be construed as equivalent and still fall within the scope of the present invention.
Claims (10)
1. A method for classifying aggregated log segments based on abnormal breakpoints, the method comprising:
responding to an access request of a worker to execute flow playback;
monitoring the flow playback event;
when a flow playback failure event is monitored, extracting playback thread information of current execution playback from a playback context acquired by the monitoring event;
acquiring a preset log path, wherein the log path is used for storing logs;
acquiring a log according to the preset log path;
performing content matching and interception from the log according to the playback thread information to obtain a plurality of corresponding abnormal information sections;
processing the plurality of abnormal information segments to obtain specific abnormal log segments; and
and storing the specific abnormal log segments in a memory.
2. The method of classifying an aggregated log segment based on abnormal breakpoints according to claim 1, wherein the method further comprises:
and if the playback operation is successful, responding to the access request of the next worker and executing the flow playback operation.
3. The method of classifying aggregate log fragments based on abnormal breakpoints according to claim 1, wherein the method further comprises:
providing a playback failure page, the playback failure page providing a number of playback options for selection by a user;
and responding to the playback result option selected by the user to inquire out a corresponding abnormal log segment and display the abnormal log segment to the user.
4. The method of classifying the aggregated log segments based on abnormal breakpoints according to claim 1, wherein processing the plurality of abnormal information segments to obtain a specific abnormal log segment comprises:
segmenting each abnormal information segment to form a plurality of abnormal information segments;
and aggregating the plurality of abnormal information segments according to preset conditions to form each specific abnormal log segment.
5. The method of classifying the aggregated log segments based on abnormal breakpoints according to claim 4, wherein the preset conditions include initiation time, thread name, main call information, sub call information.
6. The method for classifying and aggregating log fragments based on abnormal breakpoints according to claim 4, wherein the obtaining of the log according to the preset log path specifically comprises:
and reading the log contents in a reverse order by using the RandomAccessFile operation file.
7. The method of classifying an aggregated log segment based on abnormal breakpoints according to claim 4, wherein the memory is a cloud memory.
8. The device for classifying the aggregated log segments based on the abnormal breakpoints is characterized by comprising a playback main module and a playback comparison failure event module,
the playback main module includes:
the playback unit is used for responding to the access request of the staff to execute flow playback;
the monitoring unit is used for monitoring the flow playback event and calling the playback comparison failure event module when monitoring the flow playback failure event;
the playback comparison failure event module comprises:
the device comprises a path acquisition unit, a log storage unit and a log processing unit, wherein the path acquisition unit is used for acquiring a preset log path which is used for storing logs;
the log obtaining unit is used for obtaining logs according to the preset log path;
the extraction unit is used for carrying out content matching and interception from the log according to the playback thread information to obtain a plurality of corresponding abnormal information sections;
the processing unit is used for processing the plurality of abnormal information segments to obtain specific abnormal log segments; and
and the storage unit is used for storing each specific abnormal log segment in a memory.
9. Computer-readable storage medium, characterized in that it is used for storing program instructions executable by a processor for implementing a method for classifying an aggregated log segment based on abnormal breakpoints according to any one of claims 1 to 8.
10. A server, characterized in that the server comprises:
a memory for storing computer program instructions;
a processor for executing computer program instructions to implement the method of classifying an aggregated log segment based on abnormal breakpoints according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210743227.XA CN115098378A (en) | 2022-06-28 | 2022-06-28 | Method and device for classifying and aggregating log fragments based on abnormal breakpoints |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210743227.XA CN115098378A (en) | 2022-06-28 | 2022-06-28 | Method and device for classifying and aggregating log fragments based on abnormal breakpoints |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115098378A true CN115098378A (en) | 2022-09-23 |
Family
ID=83294660
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210743227.XA Pending CN115098378A (en) | 2022-06-28 | 2022-06-28 | Method and device for classifying and aggregating log fragments based on abnormal breakpoints |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115098378A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117194566A (en) * | 2023-08-21 | 2023-12-08 | 泽拓科技(深圳)有限责任公司 | Multi-storage engine data copying method, system and computer equipment |
-
2022
- 2022-06-28 CN CN202210743227.XA patent/CN115098378A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117194566A (en) * | 2023-08-21 | 2023-12-08 | 泽拓科技(深圳)有限责任公司 | Multi-storage engine data copying method, system and computer equipment |
| CN117194566B (en) * | 2023-08-21 | 2024-04-19 | 泽拓科技(深圳)有限责任公司 | Multi-storage engine data copying method, system and computer equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10303533B1 (en) | Real-time log analysis service for integrating external event data with log data for use in root cause analysis | |
| US10810074B2 (en) | Unified error monitoring, alerting, and debugging of distributed systems | |
| US9612936B2 (en) | Correlation of source code with system dump information | |
| US9495234B1 (en) | Detecting anomalous behavior by determining correlations | |
| US20140136692A1 (en) | Diagnosing distributed applications using application logs and request processing paths | |
| CN112491602B (en) | Behavior data monitoring method and device, computer equipment and medium | |
| US10474565B2 (en) | Root cause analysis of non-deterministic tests | |
| US11429574B2 (en) | Computer system diagnostic log chain | |
| CN107003931B (en) | Decoupling test validation from test execution | |
| US11436133B2 (en) | Comparable user interface object identifications | |
| US9164857B2 (en) | Scalable structured data store operations | |
| CN113014445B (en) | Operation and maintenance method, device and platform for server and electronic equipment | |
| CN112650658A (en) | Application program exception recording and reproduction method, device and storage medium | |
| US9563541B2 (en) | Software defect detection identifying location of diverging paths | |
| US10915510B2 (en) | Method and apparatus of collecting and reporting database application incompatibilities | |
| CN115098378A (en) | Method and device for classifying and aggregating log fragments based on abnormal breakpoints | |
| US11188449B2 (en) | Automated exception resolution during a software development session based on previous exception encounters | |
| CN107644041B (en) | Policy settlement processing method and device | |
| CN113708986B (en) | Server monitoring apparatus, method and computer-readable storage medium | |
| JP2014170277A (en) | Information processing device, information processing method and program | |
| CN110750416A (en) | Method and device for automatically processing fault information | |
| CN113377719B (en) | System abnormal shutdown time acquisition method and system | |
| CN115310011A (en) | Page display method and system and readable storage medium | |
| CN112162954A (en) | User operation log generation method, user operation log generation device | |
| US10102109B2 (en) | Exception resolution in a software development session |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |