CN111177193A - Flink-based log streaming processing method and system - Google Patents
Flink-based log streaming processing method and system Download PDFInfo
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- CN111177193A CN111177193A CN201911289752.3A CN201911289752A CN111177193A CN 111177193 A CN111177193 A CN 111177193A CN 201911289752 A CN201911289752 A CN 201911289752A CN 111177193 A CN111177193 A CN 111177193A
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- 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
- G06F16/2455—Query execution
- G06F16/24568—Data stream processing; Continuous queries
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- 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
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
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- 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
- G06F16/2457—Query processing with adaptation to user needs
Abstract
The invention discloses a method and a system for processing logs in a streaming manner based on Flink, wherein the method comprises the following steps: collecting log data in real time; receiving user requirements, wherein the user requirements comprise log information, data types and processing requirements; filtering the log data according to user requirements, and only reserving the data type corresponding to the user requirements to obtain processed data; pulling the processing data into a memory according to a Flink frame, and performing information processing according to a preset rule according to a processing requirement to obtain a real-time processing result; the method and the system are optimized from the processing architecture level, so that the dependence on processor resources and memory resources in the server is greatly reduced, and the resource utilization rate is improved; the condition that special processing is needed in the past is changed, real-time processing can be carried out at any time, and the efficiency of log processing problems is improved. The method and the system only need to be deployed once, and are more convenient and faster than the conventional mode of executing the specific script each time.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a log streaming processing method and system based on Flink.
Background
Data stream processing based on big data technology has a very wide application scene at present, and log processing is always an indispensable part of the data field. The current internet world has an increasingly large and growing base of users, and more businesses are aware that data is the most valuable asset of a company. How to establish an effective statistical strategy, useful information is mined from a web log, and the useful information is fully utilized, which becomes a difficult problem to be solved urgently. When the traffic is not too large, some statistical scripts can be established on the single machine, the log is analyzed at regular time, more processing scripts are established aiming at each functional module, and monitoring scripts are matched to detect the stability of statistical output. In this link, the user needs to maintain dozens of scripts and receive dozens of monitoring mails each day. The development mode has the defects of repeated development, reliability of a statistical method, a large amount of manual operation, processing bottleneck when the data volume is large and the like. The defects are increasingly highlighted along with the increase of the business volume, the increase of the data volume is avoided, even if the mr script is manually written by using hadoop, when the statistical script is too much, the operation monitoring is more difficult, the more manual work is involved, the higher the possibility of generating errors is, and the generation of the report is complicated.
Disclosure of Invention
In order to solve the problems of large data volume and easy error generation of manual operation in log analysis and processing in the background art, the invention provides a log streaming processing method and system based on Flink, wherein the method and system use a Flink frame based on memory calculation as a core for real-time processing, and improve the processing efficiency by directionally consuming logs under the Flink frame according to different topics, and the log streaming processing method based on Flink comprises the following steps:
collecting log data in real time;
receiving user requirements, wherein the user requirements comprise log information, data types and processing requirements;
filtering the log data according to user requirements, and only reserving the data type corresponding to the user requirements to obtain processed data;
and pulling the processing data into a memory according to a Flink frame, and performing information processing according to a preset rule according to a processing requirement to obtain a real-time processing result.
Further, after collecting the log data, the method includes:
pushing the collected log data to a kafka system, and storing the log data in the kafka system;
and accessing the kafka system through a Flink framework to obtain log data corresponding to the log information.
Further, after the log data is stored in the kafka system, the method further includes:
storing the log data under different topics of a kafka system according to a classification corresponding to log analysis according to a preset rule;
and monitoring and acquiring log data under preset topic under the kafka system in real time through a Flink framework.
Further, after obtaining the processing data, the method further comprises:
performing data cleaning on the processed data according to a preset rule to remove abnormal data;
converting the cleaned processing data into a preset data format to obtain standard processing data;
and loading the standard processing data into a memory under a Flink framework.
Further, storing the real-time processing result into a MySQL database through a predefined sink node;
and the user queries the real-time processing result in real time through the MySQL database.
The Flink-based log streaming processing system comprises:
the real-time acquisition unit is used for acquiring log data in real time;
the system comprises a user interaction unit, a processing unit and a processing unit, wherein the user interaction unit is used for receiving user requirements, and the user requirements comprise log information, data types and processing requirements;
the data extraction unit is used for filtering the log data according to user requirements, only reserving the data types corresponding to the user requirements and obtaining processing data;
and the real-time processing unit is used for pulling the processing data into the memory according to a Flink framework, and performing information processing according to a processing requirement and a preset rule to obtain a real-time processing result.
Further, the system also comprises a kafka storage unit;
the kafka storage unit is used for pushing the collected log data to a kafka system and storing the log data in the kafka system;
the data extraction unit is used for accessing the kafka system through a Flink framework to obtain log data corresponding to log information.
Further, the kafka storage unit is used for storing the log data in different topic of the kafka system according to the classification corresponding to the log analysis according to the preset rule;
the data extraction unit is used for monitoring and acquiring log data under preset topic in the kafka system in real time through a Flink framework.
Further, the data extraction unit is used for performing data cleaning on the processing data according to a preset rule to remove abnormal data;
and the data extraction unit is used for converting the cleaned processing data into a preset data format, obtaining standard processing data and loading the standard processing data into an internal memory under a Flink framework.
Further, the real-time processing unit is used for storing the real-time processing result into a MySQL database through a predefined sink node;
and the user queries the real-time processing result in real time through the MySQL database.
The invention has the beneficial effects that: the technical scheme of the invention provides a method and a system for processing logs in a streaming manner based on Flink, wherein the method and the system use a Flink framework based on memory calculation as a core of real-time processing, and improve the processing efficiency by directionally consuming the logs under the Flink framework according to different topics; the method and the system are optimized from the processing architecture level, so that the dependence on processor resources and memory resources in the server is greatly reduced, and the resource utilization rate is improved; the condition that special processing is needed in the past is changed, real-time processing can be carried out at any time, and the efficiency of log processing problems is improved. The method and the system only need to be deployed once, and are more convenient and faster than the conventional mode of executing the specific script each time.
Drawings
A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:
FIG. 1 is a flowchart of a Flink-based log streaming processing method according to an embodiment of the present invention;
fig. 2 is a block diagram of a log streaming processing system based on Flink according to an embodiment of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
FIG. 1 is a flowchart of a Flink-based log streaming processing method according to an embodiment of the present invention; as shown in fig. 1, the method includes:
compared with the traditional timed script or delayed processing in other modes, the method of the embodiment has more real-time performance for processing the log; after real-time log data are acquired, storing the log data; specifically, the method comprises the following steps:
pushing the collected log data to a kafka system, and storing the log data in the kafka system; when data extraction is carried out on data needing to be consumed, the kafka system can be accessed through a Flink framework, and log data corresponding to log information can be obtained.
Further, the collected log data are stored in different topics of the kafka system according to the log analysis and corresponding classification in the kafka system according to preset rules;
and for the determined user processing requirement, the corresponding topic can be matched, and the log data under the preset topic under the kafka system can be monitored and acquired in real time through a Flink framework.
the filtering of the log data is the first preprocessing of the data, unnecessary data is removed through the categories such as data types and projects, and only the data types corresponding to the user requirements are reserved;
further, for the processed data, performing further preprocessing by ETL; specifically, the method comprises the following steps:
performing data cleaning on the processed data according to a preset rule to remove abnormal data;
converting the cleaned processing data into a preset data format to obtain standard processing data;
and loading the standard processing data into a memory under a Flink framework.
And 140, pulling the processing data into a memory according to a Flink frame, and performing information processing according to a preset rule according to a processing requirement to obtain a real-time processing result.
In the embodiment, for information processing modes needing to be carried out by various processing requirements, the information processing is quickly and effectively realized by setting and deploying in advance, and calling the corresponding information processing method when the corresponding processing requirement is received; the information processing described herein includes a variety of scenarios, determined by user requirements.
Further, after a real-time processing result is obtained, the real-time processing result is stored in a database and is consulted or provided for a downstream business processing module by a user through a butt joint UI;
specifically, the real-time processing result can be stored in a MySQL database through a predefined sink node;
and the user queries the real-time processing result in real time through the MySQL database.
In the method, a flink framework based on memory calculation is used as a core of real-time processing, and the calculation is performed in the memory, so that the data calculation processing speed is improved, and in the case of large-batch data, even if an unexpected fault occurs, the final accuracy of the data can be ensured by a fault tolerance mechanism of the flink; the kafka stores the received original data information, and then the logs are directionally consumed to the streaming processing framework flink according to different topics, so that the processing efficiency is improved.
Fig. 2 is a block diagram of a log streaming processing system based on Flink according to an embodiment of the present invention. As shown in fig. 2, the system includes:
the real-time acquisition unit 210, the real-time acquisition unit 210 is used for acquiring log data in real time;
a user interaction unit 220, wherein the user interaction unit 220 is configured to receive a user requirement, and the user requirement includes log information, a data type, and a processing requirement;
the data extraction unit 230 is configured to filter the log data according to a user requirement, and only retain a data type corresponding to the user requirement to obtain processed data;
further, the data extraction unit 230 is configured to perform data cleaning on the processing data according to a preset rule, and remove abnormal data;
the data extraction unit 230 is configured to convert the cleaned processed data into a preset data format, obtain standard processed data, and load the standard processed data into an internal memory under a Flink frame.
And the real-time processing unit 240 is configured to pull the processing data into the memory according to a Flink framework, perform information processing according to a preset rule according to a processing requirement, and obtain a real-time processing result.
Further, the real-time processing unit 240 is configured to store the real-time processing result into a MySQL database through a predefined sink node;
and the user queries the real-time processing result in real time through the MySQL database.
Further, the system also comprises a kafka storage unit;
the kafka storage unit is used for pushing the collected log data to a kafka system and storing the log data in the kafka system;
the data extraction unit 230 is configured to access the kafka system through a Flink framework to obtain log data corresponding to log information.
Further, the kafka storage unit is used for storing the log data in different topic of the kafka system according to the classification corresponding to the log analysis according to the preset rule;
the data extraction unit 230 is configured to monitor and acquire log data under a preset topic in the kafka system in real time through a Flink framework.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the disclosure may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Reference to step numbers in this specification is only for distinguishing between steps and is not intended to limit the temporal or logical relationship between steps, which includes all possible scenarios unless the context clearly dictates otherwise.
Moreover, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the disclosure and form different embodiments. For example, any of the embodiments claimed in the claims can be used in any combination.
Various component embodiments of the disclosure may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. The present disclosure may also be embodied as device or system programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present disclosure may be stored on a computer-readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the disclosure, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The disclosure may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several systems, several of these systems may be embodied by one and the same item of hardware.
The foregoing is directed to embodiments of the present disclosure, and it is noted that numerous improvements, modifications, and variations may be made by those skilled in the art without departing from the spirit of the disclosure, and that such improvements, modifications, and variations are considered to be within the scope of the present disclosure.
Claims (10)
1. A method for Flink-based log streaming, the method comprising:
collecting log data in real time;
receiving user requirements, wherein the user requirements comprise log information, data types and processing requirements;
filtering the log data according to user requirements, and only reserving the data type corresponding to the user requirements to obtain processed data;
and pulling the processing data into a memory according to a Flink frame, and performing information processing according to a preset rule according to a processing requirement to obtain a real-time processing result.
2. The method of claim 1, wherein after collecting log data, the method comprises:
pushing the collected log data to a kafka system, and storing the log data in the kafka system;
and accessing the kafka system through a Flink framework to obtain log data corresponding to the log information.
3. The method of claim 2, wherein after log data is stored in the kafka system, the method further comprises:
storing the log data under different topics of a kafka system according to a classification corresponding to log analysis according to a preset rule;
and monitoring and acquiring log data under preset topic under the kafka system in real time through a Flink framework.
4. The method of claim 1, wherein after obtaining the processed data, the method further comprises:
performing data cleaning on the processed data according to a preset rule to remove abnormal data;
converting the cleaned processing data into a preset data format to obtain standard processing data;
and loading the standard processing data into a memory under a Flink framework.
5. The method of claim 1, wherein:
storing the real-time processing result into a MySQL database through a predefined sink node;
and the user queries the real-time processing result in real time through the MySQL database.
6. A Flink-based log streaming system, the system comprising:
the real-time acquisition unit is used for acquiring log data in real time;
the system comprises a user interaction unit, a processing unit and a processing unit, wherein the user interaction unit is used for receiving user requirements, and the user requirements comprise log information, data types and processing requirements;
the data extraction unit is used for filtering the log data according to user requirements, only reserving the data types corresponding to the user requirements and obtaining processing data;
and the real-time processing unit is used for pulling the processing data into the memory according to a Flink framework, and performing information processing according to a processing requirement and a preset rule to obtain a real-time processing result.
7. The system of claim 6, wherein: the system further comprises a kafka storage unit;
the kafka storage unit is used for pushing the collected log data to a kafka system and storing the log data in the kafka system;
the data extraction unit is used for accessing the kafka system through a Flink framework to obtain log data corresponding to log information.
8. The system of claim 7, wherein:
the kafka storage unit is used for storing the log data in different topic of the kafka system according to the classification corresponding to log analysis according to preset rules;
the data extraction unit is used for monitoring and acquiring log data under preset topic in the kafka system in real time through a Flink framework.
9. The system of claim 6, wherein:
the data extraction unit is used for cleaning the processed data according to a preset rule and removing abnormal data;
and the data extraction unit is used for converting the cleaned processing data into a preset data format, obtaining standard processing data and loading the standard processing data into an internal memory under a Flink framework.
10. The system of claim 6, wherein:
the real-time processing unit is used for storing the real-time processing result into a MySQL database through a predefined sink node;
and the user queries the real-time processing result in real time through the MySQL database.
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