CN116955063A - Distributed log collection storage management system - Google Patents

Distributed log collection storage management system Download PDF

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Publication number
CN116955063A
CN116955063A CN202310703362.6A CN202310703362A CN116955063A CN 116955063 A CN116955063 A CN 116955063A CN 202310703362 A CN202310703362 A CN 202310703362A CN 116955063 A CN116955063 A CN 116955063A
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data
log
file
management system
module
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方南权
高超
叶宇晖
王文瑞
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Shanghai Corelli Software Co ltd
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Shanghai Corelli Software Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/0706—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment
    • G06F11/0709—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/079—Root cause analysis, i.e. error or fault diagnosis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/3006—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
    • G06F11/3072—Monitoring arrangements determined by the means or processing involved in reporting the monitored data where the reporting involves data filtering, e.g. pattern matching, time or event triggered, adaptive or policy-based reporting
    • 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
    • G06F16/18—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
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G06F16/2228—Indexing structures
    • 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
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G06F16/252—Integrating or interfacing systems involving database management systems between a Database Management System and a front-end application
    • 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
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/069—Management of faults, events, alarms or notifications using logs of notifications; Post-processing of notifications
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1425—Traffic logging, e.g. anomaly detection
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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Abstract

The invention discloses a distributed log collection, storage and management system, which is characterized by comprising the following components: the system comprises an acquisition unit, a data processing unit and a data storage unit, wherein the acquisition unit is used for acquiring production log file data from each node server; the data processing unit adopts a Logstar engine and is used for importing the production log file data acquired by the acquisition unit and exporting the data after processing; the data storage unit adopts an elastic search engine for storing log files exported by the data processing unit. The distributed log collecting, storing and managing system realizes the collection, cleaning, summarizing, inquiring and managing statistics of the logs of each node server, so that the running condition of the system applied to a production line can be seen through the log managing system, and the problem is more simply checked.

Description

Distributed log collection storage management system
Technical Field
The invention relates to the technical field of log management in factory production, in particular to a distributed log collection, storage and management system.
Background
Semiconductor factories use MES systems (production execution systems) to manage factory production. In the current distributed application system, the application log on each node is scattered among the servers as the number of nodes increases. When it is required to check the problem of the production line system, for example, in the aspects of intelligent transportation and storage, when it is required to know that a certain command is sent from the vertical warehouse device, the AGV (automatic guided vehicle) device, the elevator device or the MES system to the MCS (automatic transfer line control system), and the MCS needs to reply a log of the text of the interactive communication to the device or the MES system, then the staff needs to pull the relevant log on each server to analyze and check the problem. But it has major drawbacks: 1) If the production is busy, it may be up to several tens of megabytes or more, and this process of analyzing the log becomes time consuming, it may be difficult to check if restrictions on the production environment are encountered that do not allow large files to be copied from the server. 2) The architecture of the current system is a distributed application, and for the production log of a certain project, a part of the log exists in an A node server, and the other part of the log exists in a B node server, so that log checking needs to be carried out from the A server and the B server respectively when the complete log is required to be obtained, the time cost is high, and the searching and tracking are difficult.
Disclosure of Invention
The invention aims to solve the problems, and provides a distributed log collecting, storing and managing system which is used for collecting, cleaning, summarizing, inquiring and managing logs of each node and inquiring the logs to see the running condition of a production line, so that the problem investigation is simpler.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the invention provides a distributed log collection, storage and management system, which is characterized by comprising the following components: the system comprises an acquisition unit, a data processing unit and a data storage unit, wherein the acquisition unit is used for acquiring production log file data from each node server; the data processing unit is used for importing the production log file data acquired by the acquisition unit, processing the production log file data and exporting the production log file data; and the data storage unit is used for storing the log file derived by the data processing unit.
Further, in the distributed log collection storage management system provided by the present invention, the system may further have the following features: the data processing unit adopts a Logstar engine, and the working flow is as follows: s1: the collected production log file data is input to a data processing unit through a data input plug-in unit inputplug in; s2: analyzing each log file of the input production log file data through a filter fileplug, filtering the data, converting the data, and cleaning the data; s3: and outputting the filtered production log file data through a data export plugin.
Further, in the distributed log collection storage management system provided by the present invention, the system may further have the following features: the data processing unit comprises the following functional modules: the path module is used for monitoring the path of the log file; the type module is used for distinguishing the types of different files and marking the types of the files; an include module for setting log files to be excluded; the start_position module is used for setting where to open the log file; the stat_interval module is used for setting how often to check the monitored file state; the sincedb_write_interval module is used for setting how often to write a sincedb file; the discover_interval module is used for setting how often to monitor whether files exist under the path; the close_old module is used for setting the monitored file, and closing the monitored file handle if the updated content is not available in the time exceeding the set value; and the sincedb_path module is used for setting in which file the read file information is recorded.
Further, in the distributed log collection storage management system provided by the present invention, the system may further have the following features: the data storage unit adopts an elastic search engine and comprises the following architecture: cluster Cluster composed of ES nodes deployed on multiple machines; node nodes, belonging to ES process on machine, can be configured with different types of nodes; a master node for cluster selection; the data node is used for storing index data; index for indexing a logical set of data; the Suard shards are used for indexing the data subsets, and the data lateral expansion is realized by distributing the shards to different nodes of the cluster; primary shard main shards, wherein data shards are carried out in a master-slave mode, and index operation is received by the shards; replicashad copy shards, which are copies of the master shard.
Further, in the distributed log collection storage management system provided by the present invention, the system may further have the following features: wherein the data storage unit comprises the following data models: index model, document model, field model, mapping model, everythisibinded model, queryDSL model.
Further, in the distributed log collection and storage management system provided by the present invention, the system further includes: and the external API interface is used for communicating with the query equipment, and the query equipment calls the external API interface to perform log query.
Further, in the distributed log collection storage management system provided by the present invention, the system may further have the following features: when log inquiry is carried out, log data in the data storage unit is transmitted to a corresponding WebUI webpage interface or Api application program interface or Kibana visual interface, so that a user can inquire from corresponding inquiry equipment conveniently.
The distributed log collection and storage management system has the following beneficial effects:
1) Application performance real-time monitoring: real-time monitoring of the application performance log, such as execution time analysis of an interface API, can be realized, and performance bottleneck analysis can be positioned in time.
2) Operation and maintenance real-time monitoring and alarming and rapid fault positioning: the state of the server and the application program is monitored in real time, and whether the service is normally provided or not. And (3) troubleshooting, wherein the service abnormality is the host cause, the network cause looks around the self problem of the application, and end-to-end service monitoring and troubleshooting are performed.
3) Tracking analysis of Bug during program development: the quick association analyzes a large number of Debug logs generated by each module of the large-scale distributed system. The service request is abnormal, the access speed is low, and the quick positioning can be realized due to which code.
4) Security information and event management: and (3) carrying out tracking analysis on the firewall and the weblog, and discovering port scanning and illegal intrusion log analysis through the weblog security monitoring log.
5) Providing a log query interface to the outside: if the front end queries all log records of communication interactions related to the device through the device, the API can be called for query.
Drawings
FIG. 1 is an overall architecture diagram of a distributed journal collection storage management system in an embodiment of the present invention;
FIG. 2 is a schematic diagram of the operation of a data processing unit in an embodiment of the invention;
FIG. 3 is an analogy of the data model in elastic search and the data model in MySQL in an embodiment of the invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement of the purposes and the effects of the present invention easy to understand, the following embodiments specifically describe the technical scheme of the present invention with reference to the accompanying drawings.
Referring to fig. 1, the present embodiment provides a distributed log collection storage management system, which includes: the device comprises an acquisition unit, a data processing unit and a data storage unit.
The acquisition unit is used for acquiring production log file data from each node server. In this embodiment, the production log file data is collected from each node server through several filebean middleware. The acquisition unit not only can collect the application logs of the system, but also can collect the logs and database logs of log operating systems such as an IIS server, a TOMCAT server, an APACHE server and the like.
The data processing unit is used for importing the production log file data acquired by the acquisition unit, processing the production log file data and exporting the production log file data. In this embodiment, the data processing unit adopts a logstar engine, referring to fig. 2, in which DateSource represents a data source, dateDestination represents a data destination, and logstar provides more input and output options, such as mongo db, mySQL, and the like, and may send data to a specified destination. The workflow of the data processing unit is as follows:
step S1: the collected production log file data is input to a data processing unit through a data input plug-in unit inputplug in;
step S2: analyzing each log file of the input production log file data through a filter fileplug, filtering the data, converting the data, and cleaning the data;
step S3: and outputting the filtered production log file data through a data export plug-in.
The data processing unit comprises the following functional modules: path module, type module, include module, start_position module, start_interval module, sincedb_write_interval module, discover_interval module, close_oled module, sincedb_path module.
The path module is used for monitoring the path of the log file.
the type module is used for distinguishing the types of different files and marking the types of the files.
The include module is used for setting log files to be excluded. The user sets up by oneself as required.
The start_position module is used to set where to open the log file. For example, a file is imported from the header, and its value is set to "begin".
The stat_interval module is used for setting how often the monitored file state is checked. The default value is 1s, and the user can set the value according to the needs.
The sincedb_write_interval module is used for setting how often the sincedb file is written. The default value is 15s, and the user can set the value according to the needs.
The discover_interval module is used for setting how often to monitor whether files exist under the path. The default value is 15s, and the user can set the value according to the needs.
The close_old module is used for setting the file which is monitored, and closing the file handle which is monitored if the content is not updated in the time exceeding the set value. The default value is 3600 seconds, i.e., one hour, and the user can set the value by himself as required.
The sincedb_path module is used for setting in which file the read file information is recorded. The default positions are: /dev/null.
The data storage unit is used for storing the log file derived by the data processing unit. The data storage unit in this embodiment employs an elastic search engine, which includes the following architecture:
cluster: is composed of ES nodes deployed at multiple machines for processing larger data sets and achieving high availability.
Node: belonging to the ES process on the machine, different types of nodes can be configured.
Master node: the method is used for cluster master selection. One of the nodes acts as a master node and is responsible for cluster metadata management, such as index creation, node leaving joining a cluster, and the like.
DataNode data node: for storage of index data.
Index: the logical set used to index the data may be analogous to the DataBase of the relational data.
Suard shards: the method is used for indexing the data subsets, and the data lateral expansion is realized by distributing the fragments to different nodes of the cluster. So as to solve the problem that the processing capacity of the CPU, the memory and the disk of a single node is insufficient.
Primary sharyshard master shards: and carrying out data slicing by adopting a master-slave mode, and receiving indexing operation by the slicing.
Replicashad copy shards: belonging to the copy of the master tile. For improving query throughput and achieving high reliability of data. When the main partition is abnormal, one of the auxiliary partitions can be automatically lifted to be a new main partition
The data storage unit comprises the following data models: index model, document model, field model, mapping model, everythisibinded model, queryDSL model. Referring to FIG. 3, to facilitate understanding of the data model in the elastic search, analogy is made to the relational database MySQL in terms of similarity in model function.
Referring to fig. 1, the distributed log collection storage management system further includes: and an external API interface. The external API interface is used for communicating with the query equipment, and the query equipment calls the external API interface to perform log query. The query device is provided with a corresponding UI interaction interface. When log inquiry is carried out, the log data in the data storage unit is transmitted to a corresponding WebUI webpage interface or Api application program interface or Kibana visual interface, so that a user can inquire from corresponding inquiry equipment conveniently.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, but various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims (7)

1. A distributed log collection storage management system, comprising: the acquisition unit, the data processing unit and the data storage unit,
the acquisition unit is used for acquiring production log file data from each node server;
the data processing unit is used for importing the production log file data acquired by the acquisition unit, processing the production log file data and exporting the production log file data;
the data storage unit is used for storing the log file exported by the data processing unit.
2. The distributed log collection storage management system of claim 1, wherein:
the data processing unit adopts a Logstar engine, and the working flow is as follows:
s1: the collected production log file data is input to a data processing unit through a data input plug-in unit inputplug in;
s2: analyzing each log file of the input production log file data through a filter fileplug, filtering the data, converting the data, and cleaning the data;
s3: and outputting the filtered production log file data through a data export plug-in.
3. The distributed log collection storage management system of claim 2, wherein:
wherein, the data processing unit comprises the following functional modules:
the path module is used for monitoring the path of the log file;
the type module is used for distinguishing the types of different files and marking the types of the files;
an include module for setting log files to be excluded;
the start_position module is used for setting where to open the log file;
the stat_interval module is used for setting how often to check the monitored file state;
the sincedb_write_interval module is used for setting how often to write a sincedb file;
the discover_interval module is used for setting how often to monitor whether files exist under the path;
the close_old module is used for setting the monitored file, and closing the monitored file handle if the updated content is not available in the time exceeding the set value;
and the sincedb_path module is used for setting in which file the read file information is recorded.
4. The distributed log collection storage management system of claim 1, wherein:
the data storage unit adopts an elastic search engine and comprises the following architecture:
cluster Cluster composed of ES nodes deployed on multiple machines;
node nodes, belonging to ES process on machine, can be configured with different types of nodes;
a master node for cluster selection;
the data node is used for storing index data;
index for indexing a logical set of data;
the Suard shards are used for indexing the data subsets, and the data lateral expansion is realized by distributing the shards to different nodes of the cluster;
primary shard main shards, wherein data shards are carried out in a master-slave mode, and index operation is received by the shards;
replicashad copy shards, which are copies of the master shard.
5. The distributed log collection storage management system according to claim 4, wherein:
wherein the data storage unit comprises the following data models: index model, document model, field model, mapping model, everythisibinded model, queryDSL model.
6. The distributed log collection storage management system of claim 1, further comprising: an external API interface is provided with a plurality of interfaces,
the external API interface is used for communication connection with the query equipment, and the query equipment calls the external API interface to perform log query.
7. The distributed log collection storage management system according to claim 6, wherein:
when the log query is performed, the log data in the data storage unit is transmitted to a corresponding WebUI webpage interface or Api application program interface or kimana visual interface, so that the user can conveniently query from the corresponding query device.
CN202310703362.6A 2023-06-14 2023-06-14 Distributed log collection storage management system Pending CN116955063A (en)

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