CN113282557A - Big data log analysis method and system based on Spring framework - Google Patents
Big data log analysis method and system based on Spring framework Download PDFInfo
- Publication number
- CN113282557A CN113282557A CN202110458196.9A CN202110458196A CN113282557A CN 113282557 A CN113282557 A CN 113282557A CN 202110458196 A CN202110458196 A CN 202110458196A CN 113282557 A CN113282557 A CN 113282557A
- Authority
- CN
- China
- Prior art keywords
- agent1
- big data
- log
- sink1
- logback
- 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
- 238000004458 analytical method Methods 0.000 title claims abstract description 40
- 238000000034 method Methods 0.000 claims abstract description 20
- 238000012423 maintenance Methods 0.000 claims abstract description 14
- 230000006870 function Effects 0.000 claims abstract description 12
- 238000012544 monitoring process Methods 0.000 claims description 20
- 230000006399 behavior Effects 0.000 claims description 13
- 238000007418 data mining Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 230000003542 behavioural effect Effects 0.000 claims description 3
- 238000007405 data analysis Methods 0.000 claims description 3
- 238000010801 machine learning Methods 0.000 claims description 3
- 230000004044 response Effects 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 238000013480 data collection Methods 0.000 claims description 2
- 238000004519 manufacturing process Methods 0.000 abstract description 4
- 238000011161 development Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 6
- 238000002955 isolation Methods 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 238000005096 rolling process Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
Images
Classifications
-
- 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
-
- 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
- G06F16/182—Distributed file systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Probability & Statistics with Applications (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Debugging And Monitoring (AREA)
Abstract
The invention discloses a big data log analysis method and a big data log analysis system based on a Spring framework, which belong to the technical field of information safety, and comprise the following steps: logs are collected uniformly, in different Spring frames, a logback dependency is newly added in a POM file, and meanwhile, a logback. The invention provides a unified management platform aiming at internal WEB services of different Spring frameworks; the data are analyzed and mined from offline and real-time dimensions, so that the accuracy and consistency of the data are ensured; the method provides the functions of searching time, service names, log levels and keywords, and enables development, operation and maintenance personnel to quickly locate the production environment.
Description
Technical Field
The invention relates to a big data log analysis method and system based on a Spring framework, and belongs to the technical field of information security.
Background
In the current complex and various enterprise WEB services, various service frameworks of Spring MVC, Spring Boot and Spring Cloud are integrated, and meanwhile, the log recording mode is various and different. Not only the functions of log filing and management are lost, but also unified alarm management and risk early warning cannot be made on each WEB service; whether developers or operation and maintenance personnel can not accurately position services and various problems on a server, and a mode for efficiently searching log contents to quickly position the problems does not exist; meanwhile, problems cannot be predicted in advance and timely processed, perception experience of a customer is greatly influenced, and therefore the customer needs to be centralized and independent, log information on each service and on each server is collected and managed, centralized management and timely early warning are achieved, a good UI (user interface) is provided for data display, processing and analysis, data mining is conducted on customer behaviors, and a higher commercial value is extracted.
Disclosure of Invention
The invention mainly aims to solve the defects of the prior art and provide a method and a system for big data log analysis based on a Spring framework.
The purpose of the invention can be achieved by adopting the following technical scheme:
a big data log analysis method based on a Spring framework comprises the following steps:
step 1: logs are collected uniformly, in different Spring frames, a logback dependency is newly added in a POM file, and meanwhile, a logback.xml file is newly built under resource directories of each service;
step 2: the Logstash data is input into the ES, a trace-logging. conf file is newly built under a Logstash config directory, a real-time log is stored into the ES, and then stream processing is carried out through a Flink;
and step 3: storing the flash data in the HDFS, newly building a properties file under a flash conf directory, storing the offline file in the HDFS, and subsequently performing data analysis and mining through HIVE and SPARK;
and 4, step 4: and the Kibana accesses the ES, modifies Kibana.yml, and completely accesses the offline real-time data into a large data platform.
Preferably, in step 1, the newly added logback depends on the following:
preferably, in step 1, creating a logback. xml file under the resources directory of each service as follows:
preferably, in step 2, a trace-logging. conf file is newly created as follows:
preferably, 9601 is a port for logstack to receive data, and the port must be configured in logback, and codec > json _ lines is a json parser to receive json data, and a logstack-codec-json _ lines plug-in is required to be installed; output elastic search points to the ip address and port of the cluster we install; stdout prints the received message.
Preferably, the Flume data acquisition architecture is a Web Server, and the Flume data acquisition architecture sequentially passes through Source, Channel, Sink and HDFS.
Preferably, in step 3, a new file is created under the Flume conf directory.
#agent1 name
agent1.sources source1
agent1.sinks＝sink1
agent1.channels＝channel1
#Spooling Directory
#set source1
agent1.sources.source1.type＝spooldir
agent1.sources.source1.spoolDir＝/usr/app/flumelog/dir/logdfs
agent1.sources.source1.channe ls:channel1
agent1.sources.source1.fileHeader＝false
agent1.sources.source1.interceptors＝i1.
agent1.sources.source1.interceptors.i1.type＝timestamp
#set sink1
agent1.sinks.sink1.type＝hdfs
agent1.sinks.sink1.hdfs.path＝/user/yuhui/flume
agent1.sinks.sink1.hdfs.fileType DataStream
agent1.sinks.sink1.hdfs.writeFormat＝TEXT
agent1.sinks.sink1.hdfs.rollInterval＝1
agent1.sinks.sink1.channel-channel1
agent1.sinks.sink1.hdfs.filePrefix％Y-％m-％d
#set channel1
agent1.channels.channel1.type file
agent1.channe ls.channel1.checkpointDir-/usr/app/flume log/dir/logdfstmp/point
agent1.channe ls.channe 11.dataDirs-/usr/app/f lume log/dir/logdfs tmp
Preferably, in step 4, kimana. yml is modified as follows:
port 5601# # service port
server.host:"0.0.0.0"
elasticsearch.hosts:["http://11.1.5.69:9200"，"http://11.1.5.70:9200"，"http://11.1.5.71:9200"]
A system of big data log analysis method based on Spring frame comprises log monitoring, alarm management and behavior analysis
The log monitoring comprises a search function and report monitoring
The search function: screening by setting time, service name and log hierarchy dimension;
report monitoring: project operation and maintenance personnel can be informed in time through monitoring the error log, and monitoring is carried out through service, interface calling times and interface response time dimension;
the management of the alarms is carried out by the user,
when the WEB service generates an error log or a certain service is abnormally increased in a certain period of time, the alarm management system can push maintenance personnel in a mail, short message, nail or WeChat mode;
behavioral analysis
PV/UV, DAU/MAU, newly-increased user number, access duration and GMV reuse index can be counted through the calling frequency of the interface;
the clicking behavior of the user is used as the behavior characteristic, the age, sex, address and hobby attribute characteristics of the user are extracted to establish the portrait of the user, and accurate marketing and intelligent recommendation are performed on the user by combining machine learning.
Preferably, the data of the report statistics includes: PV/UV, GMV, DAU/MAU, number of clicks/access duration, and number of newly added users.
The invention has the beneficial technical effects that: according to the big data log analysis method and system based on the Spring framework, a unified management platform is provided for internal WEB services of different Spring frameworks; the data are analyzed and mined from offline and real-time dimensions, so that the accuracy and consistency of the data are ensured; the method provides the functions of searching time, service names, log levels and keywords, so that development, operation and maintenance personnel can quickly locate the production environment; the functions of risk early warning and warning monitoring are provided, and maintenance personnel are pushed in a flexible and various mode through mails, short messages, nails and WeChat; and further analyzing and mining the behavior data of the user, and performing operation decision analysis for operators and data analysts.
Drawings
Fig. 1 is a Spring overall framework diagram of a method and a system for big data log analysis based on a Spring framework according to a preferred embodiment of the present invention.
FIG. 2 is a diagram of a Flume data collection architecture in accordance with a preferred embodiment of the Spring framework based big data log analysis method and system of the present invention;
FIG. 3 is a diagram of a log monitoring framework in accordance with a preferred embodiment of the method and system for Spring framework based big data log analysis in accordance with the present invention;
FIG. 4 is a diagram of an alarm management framework in accordance with a preferred embodiment of the method and system for Spring framework based big data log analysis in accordance with the present invention;
FIG. 5 is a diagram of a behavior analysis framework for a method and system for Spring framework based big data log analysis in accordance with the present invention;
FIG. 6 is a system block diagram of a method and system for Spring-based big data log analysis in accordance with the present invention.
Detailed Description
In order to make the technical solutions of the present invention more clear and definite for those skilled in the art, the present invention is further described in detail below with reference to the examples and the accompanying drawings, but the embodiments of the present invention are not limited thereto.
As shown in fig. 1 to fig. 5, the method and system for big data log analysis based on a Spring framework provided in this embodiment is a method for big data log analysis based on a Spring framework, and the method includes the following steps:
step 1: logs are collected uniformly, in different Spring frames, a logback dependency is newly added in a POM file, and meanwhile, a logback.xml file is newly built under resource directories of each service;
step 2: the Logstash data is input into the ES, a trace-logging. conf file is newly built under a Logstash config directory, a real-time log is stored into the ES, and then stream processing is carried out through a Flink;
and step 3: storing the flash data in the HDFS, newly building a properties file under a flash conf directory, storing the offline file in the HDFS, and subsequently performing data analysis and mining through HIVE and SPARK;
and 4, step 4: and the Kibana accesses the ES, modifies Kibana.yml, and completely accesses the offline real-time data into a large data platform.
In step 1, the logback is newly added depending on the following:
<dependency>
<groupId>net.logstash.logback</groupid>
<artifactId>logstash-logback-encoder</artifactId>
<version>4.11</version>
</dependency>
in step 1, creating a logback. xml file under the resources directory of each service as follows:
<？xml version＝""1.0"encoding＝"UTF-8"？>
<configuration>
< l- -this name will reflect the beginning of each log >
<contextName>car-trace-logging</contextName>
< | A! - -set a variable, used below, meaning the Log saving Path- - ]
<property name＝"log.path"value＝"D:/log/CarTrace"/>
< | A! Output to a console >
<appendername＝"console"class＝"ch.qos.logback.core.ConsoleAppender">
< | A! -level filtering >
<filterclass＝"ch.qos.logback.classic.filter.LevelFilter">
<level>INFO</level>
<onMatch>ACCEPT</onMatch>
<onMismatch>DENY</onMismatch>
</filter>
< | A! Log output format- - > -)
<encoder>
<pattern>％d{HH:mm:ss.ssS}％contextName[％thread]％-5level％logger{36}-kmsgKn</pattern.
</encoder>
</appender>
< | A! - - -export to File- - > -)
<appendername＝"file"class＝"ch.qos.logback.core.rolling.RollingFileAppender">
< l- -Log name, using the above configured route- - ]
<file>${log.path}/car-trace.log</file>
< I- -according to yyyy- -dd- - >)
<rollingPolicyclass＝"ch.qos.logback.core.rolling.TimeBasedRollingPolicy">
<fileNamePattern>${log.path}/car-trace.％d{yyyy-M-dd}.log.zip</fileNamePattern></rollingPolicy>
<encoder>
<pattern>％d{H:mm:ss.SSS]％contextName[％thread]％-5level％logger{36}-‰msg％n</pattern>
</encoder>
</appender>
</appendername＝"LOGSTASH"class＝"net.logstash.logback.appender.LogstashTcpSocketAppender">
< destination >192.168.253.6:9601</destination > -designation of logstaship: listening port tcpAppender can implement transmission like kafka by itself >
<encodercharset＝"UTF-8"class＝"net.logstash.logback.encoder.LogstashEncoder"/>
</appender>
< | A! - -set Log isolation level- - > -, and
<root level＝"info">
<appender-ref ref＝"console"/>
<appender-ref ref＝""file">
<appender-ref ref＝"LOGSTASH”/>
</root>
< | A! - -setting the isolation level of a particular package- - >
<logger name＝"cn.theUnit4.Mapper"level＝"debug"/>
</configuration>
In step 2, a trace-logging. conf file is newly created as follows:
input{
tcp{
port＝>9601
codec＝>json_lines
}
output{
<elasticsearch{
action＝>"index"
hosts [ "11.1.5.69:8088", "11.1.5.70:8088", "11.1.5.71:8088" ] # ES cluster ip address and port
index > "% { [ appName ] } -% { + yyyy.mm.dd }" # is indexed by an item name
document_type＝>"applog”
}
stdout{codec＝>rubydebug}
}
9601 is a port for logstack to receive data, which must be configured in logback, and codec > json _ lines is a json parser, and a plug-in for logstack-codec-json _ lines is required to be installed to receive json data; output elastic search points to the ip address and port of the cluster we install; stdout prints the received message.
And the Flume data acquisition architecture is a Web Server, and the Flume data acquisition architecture sequentially passes through Source, Channel, Sink and HDFS.
In step 3, a new property file is created under the Flume conf directory as follows:
#agent1 name
agent1.sources source1
agent1.sinks＝sink1
agent1.channels＝channel1
#Spooling Directory
#set source1
agent1.sources.source1.type＝spooldir
agent1.sources.source1.spoolDir＝/usr/app/flumelog/dir/logdfs
agent1.sources.source1.channe ls:channel1
agent1.sources.source1.fileHeader＝false
agent1.sources.source1.interceptors＝i1.
agent1.sources.source1.interceptors.i1.type＝timestamp
#set sink1
agent1.sinks.sink1.type＝hdfs
agent1.sinks.sink1.hdfs.path＝/user/yuhui/flume
agent1.sinks.sink1.hdfs.fileType DataStream
agent1.sinks.sink1.hdfs.writeFormat＝TEXT
agent1.sinks.sink1.hdfs.rollInterval＝1
agent1.sinks.sink1.channel-channel1
agent1.sinks.sink1.hdfs.filePrefix％Y-％m-％d
#set channel1
agent1.channels.channel1.type file
agent1.channe ls.channel1.checkpointDir-/usr/app/flume log/dir/logdfstmp/point
agent1.channe ls.channe 11.dataDirs-/usr/app/f lume log/dir/logdfs tmp
yml, kibana, was modified as follows in step 4:
port 5601# # service port
server.host:"0.0.0.0"
elasticsearch.hosts:["http://11.1.5.69:9200"，"http://11.1.5.70:9200"，"http://11.1.5.71:9200"]
A system of big data log analysis method based on Spring frame comprises log monitoring, alarm management and behavior analysis
The log monitoring comprises a search function and report monitoring
The search function: by setting time, service names and log level dimension screening, keyword search enables operation and maintenance and developers to quickly analyze and locate problems of the production environment;
report monitoring: project operation and maintenance personnel can be informed in time through monitoring of the error log, and the service, interface calling times and interface response time dimension are monitored, so that the user experience can be improved, and references are provided for subsequent capacity expansion and performance optimization;
the management of the alarms is carried out by the user,
when a WEB service generates an error log or a certain service is abnormally increased in a certain period of time, the alarm management system can push maintenance personnel in a flexible and various mode through mails, short messages, nails and WeChat, and the operation and maintenance personnel can not only find problems in time, but also predict the occurrence of the problems in advance and expand the capacity in time;
behavioral analysis
PV/UV, DAU/MAU, newly-increased user number, access duration and GMV reuse index can be counted through the calling frequency of the interface, and operation decisions are made for operation personnel and data analysts;
the clicking behavior of the user is used as the behavior characteristic, the age, sex, address and hobby attribute characteristics of the user are extracted to establish the portrait of the user, and accurate marketing and intelligent recommendation are performed on the user by combining machine learning. The data of report statistics comprises: PV/UV, GMV, DAU/MAU, number of clicks/access duration, and number of newly added users.
In summary, in this embodiment, according to the method and system for big data log analysis based on the Spring framework of this embodiment, the method and system for big data log analysis based on the Spring framework of this embodiment provide a uniform management platform for internal WEB services of different Spring frameworks; the data are analyzed and mined from offline and real-time dimensions, so that the accuracy and consistency of the data are ensured; the method provides the functions of searching time, service names, log levels and keywords, so that development, operation and maintenance personnel can quickly locate the production environment; the functions of risk early warning and warning monitoring are provided, and maintenance personnel are pushed in a flexible and various mode through mails, short messages, nails and WeChat; and further analyzing and mining the behavior data of the user, and performing operation decision analysis for operators and data analysts.
The above description is only a further embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can substitute or change the technical solution and the concept of the present invention within the scope of the present invention.
Claims (10)
1. A big data log analysis method based on a Spring framework is characterized by comprising the following steps:
step 1: logs are collected uniformly, in different Spring frames, a logback dependency is newly added in a POM file, and meanwhile, a logback.xml file is newly built under resource directories of each service;
step 2: the Logstash data is input into the ES, a trace-logging. conf file is newly built under a Logstash config directory, a real-time log is stored into the ES, and then stream processing is carried out through a Flink;
and step 3: storing the flash data in the HDFS, newly building a properties file under a flash conf directory, storing the offline file in the HDFS, and subsequently performing data analysis and mining through HIVE and SPARK;
and 4, step 4: and the Kibana accesses the ES, modifies Kibana.yml, and completely accesses the offline real-time data into a large data platform.
2. The method for big data log analysis based on a Spring framework according to claim 1, wherein in step 1, the newly added logback depends on the following:
<dependency>
<groupId>net.logstash.logback</groupid>
<artifactId>logstash-logback-encoder</artifactId>
<version>4.11</version>
</dependency>
3. the method for big data log analysis based on Spring framework according to claim 1, wherein in step 1, a logback.xml file is created under the resources directory of each service as follows:
4. the method for big data log analysis based on a Spring framework according to claim 1, wherein in step 2, a trace-logging.conf file is created as follows:
5. a method for big data log analysis based on a Spring framework as claimed in claim 1, wherein 9601 is a port for logstack to receive data, and the port must be configured in logback, and codec > json lines is a json parser to receive json data, and a logstack-codec-json lines plug-in is required to be installed; output elastic search points to the ip address and port of the cluster we install; stdout prints the received message.
6. The Spring framework-based big data log analysis method and system according to claim 1, wherein the Flume data collection architecture is a Web server.
7. The method for big data log analysis based on Spring framework as claimed in claim 1, wherein in step 3, a new property file is created under a Flume conf directory as follows:
#agent1 name
agent1.sources source1
agent1.sinks＝sink1
agent1.channels＝channel1
#Spooling Directory
#set source1
agent1.sources.source1.type＝spooldir
agent1.sources.source1.spoolDir＝/usr/app/flumelog/dir/logdfs
agent1.sources.source1.channe ls:channel1
agent1.sources.source1.fileHeader＝false
agent1.sources.source1.interceptors＝i1.
agent1.sources.source1.interceptors.i1.type＝timestamp
#set sink1
agent1.sinks.sink1.type＝hdfs
agent1.sinks.sink1.hdfs.path＝/user/yuhui/flume
agent1.sinks.sink1.hdfs.fileType DataStream
agent1.sinks.sink1.hdfs.writeFormat＝TEXT
agent1.sinks.sink1.hdfs.rollInterval＝1
agent1.sinks.sink1.channel-channel1
agent1.sinks.sink1.hdfs.filePrefix％Y-％m-％d
#set channel1
agent1.channels.channel1.type file
agent1.channe ls.channel1.checkpointDir-/usr/app/flume log/dir/logdfstmp/point
agent1.channe ls.channe 11.dataDirs-/usr/app/f lume log/dir/logdfs tmp
8. a method for big data log analysis based on Spring framework according to claim 1, characterized in that in step 4, yml is modified kibana.yml as follows:
port 5601# # service port
server.host:"0.0.0.0"
elasticsearch.hosts:["http://11.1.5.69:9200"，"http://11.1.5.70:9200"，"http://11.1.5.71:9200"]
9. A system of a big data log analysis method based on a Spring framework is characterized in that the system comprises log monitoring, alarm management and behavior analysis
The log monitoring comprises a search function and report monitoring
The search function: screening by setting time, service name and log hierarchy dimension;
report monitoring: project operation and maintenance personnel can be informed in time through monitoring the error log, and monitoring is carried out through service, interface calling times and interface response time dimension;
the management of the alarms is carried out by the user,
when the WEB service generates an error log or a certain service is abnormally increased in a certain period of time, the alarm management system can push maintenance personnel in a mail, short message, nail or WeChat mode;
behavioral analysis
PV/UV, DAU/MAU, newly-increased user number, access duration and GMV reuse index can be counted through the calling frequency of the interface;
the clicking behavior of the user is used as the behavior characteristic, the age, sex, address and hobby attribute characteristics of the user are extracted to establish the portrait of the user, and accurate marketing and intelligent recommendation are performed on the user by combining machine learning.
10. The system of a Spring framework-based big data log analysis method according to claim 9, wherein the report statistic data includes: PV/UV, GMV, DAU/MAU, number of clicks/access duration, and number of newly added users.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110458196.9A CN113282557A (en) | 2021-04-27 | 2021-04-27 | Big data log analysis method and system based on Spring framework |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110458196.9A CN113282557A (en) | 2021-04-27 | 2021-04-27 | Big data log analysis method and system based on Spring framework |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113282557A true CN113282557A (en) | 2021-08-20 |
Family
ID=77277440
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110458196.9A Pending CN113282557A (en) | 2021-04-27 | 2021-04-27 | Big data log analysis method and system based on Spring framework |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113282557A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116611663A (en) * | 2023-06-07 | 2023-08-18 | 广州三七极梦网络技术有限公司 | Art outsourcing management system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20030015061A (en) * | 2001-08-14 | 2003-02-20 | (주) 이씨마이너 | Method of CRM on comparison with web-log and user ID |
| CN109614553A (en) * | 2018-12-21 | 2019-04-12 | 北京博明信德科技有限公司 | PaaS platform for log collection |
| CN110457178A (en) * | 2019-07-29 | 2019-11-15 | 江苏艾佳家居用品有限公司 | A kind of full link monitoring alarm method based on log collection analysis |
| US10810110B1 (en) * | 2018-01-25 | 2020-10-20 | Intuit Inc. | Methods, systems, and articles of manufacture for testing web services using a behavior-driven development domain specific language framework |
-
2021
- 2021-04-27 CN CN202110458196.9A patent/CN113282557A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20030015061A (en) * | 2001-08-14 | 2003-02-20 | (주) 이씨마이너 | Method of CRM on comparison with web-log and user ID |
| US10810110B1 (en) * | 2018-01-25 | 2020-10-20 | Intuit Inc. | Methods, systems, and articles of manufacture for testing web services using a behavior-driven development domain specific language framework |
| CN109614553A (en) * | 2018-12-21 | 2019-04-12 | 北京博明信德科技有限公司 | PaaS platform for log collection |
| CN110457178A (en) * | 2019-07-29 | 2019-11-15 | 江苏艾佳家居用品有限公司 | A kind of full link monitoring alarm method based on log collection analysis |
Non-Patent Citations (2)
| Title |
|---|
| 赵阳;王春喜;: "基于Storm框架结构的分布式实时日志分析系统的设计研究", 信息与电脑(理论版), no. 08 * |
| 陈涛;叶荣华;: "基于Spring Boot和MongoDB的数据持久化框架研究", 电脑与电信, no. 1 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116611663A (en) * | 2023-06-07 | 2023-08-18 | 广州三七极梦网络技术有限公司 | Art outsourcing management system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liu et al. | Monitoring and analyzing big traffic data of a large-scale cellular network with Hadoop | |
| CN111092852B (en) | Network security monitoring method, device, equipment and storage medium based on big data | |
| US10108411B2 (en) | Systems and methods of constructing a network topology | |
| US20180365085A1 (en) | Method and apparatus for monitoring client applications | |
| Chen et al. | CauseInfer: Automated end-to-end performance diagnosis with hierarchical causality graph in cloud environment | |
| CN108200111B (en) | Resource configuration information updating method and device and resource interface equipment | |
| US10567409B2 (en) | Automatic and scalable log pattern learning in security log analysis | |
| US20160306613A1 (en) | Code routine performance prediction using test results from code integration tool | |
| Sang et al. | Precise, scalable, and online request tracing for multitier services of black boxes | |
| US11042525B2 (en) | Extracting and labeling custom information from log messages | |
| US10810216B2 (en) | Data relevancy analysis for big data analytics | |
| CN114363042B (en) | Log analysis method, device, equipment and readable storage medium | |
| CN105743730A (en) | Method and system used for providing real-time monitoring for webpage service of mobile terminal | |
| CN112905548B (en) | Security audit system and method | |
| CN113760652B (en) | Method, system, device and storage medium for full link monitoring based on application | |
| CN113448812A (en) | Monitoring alarm method and device under micro-service scene | |
| CN113760677A (en) | Abnormal link analysis method, device, equipment and storage medium | |
| CN113420032A (en) | Classification storage method and device for logs | |
| Astekin et al. | Incremental analysis of large-scale system logs for anomaly detection | |
| CN107204868B (en) | Task operation monitoring information acquisition method and device | |
| CN113282557A (en) | Big data log analysis method and system based on Spring framework | |
| JP6002849B2 (en) | Monitoring device, monitoring method, and recording medium | |
| US10706108B2 (en) | Field name recommendation | |
| Kubacki et al. | Multidimensional log analysis | |
| CN108959041B (en) | Method for transmitting information, server and computer readable storage medium |
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 |