CN107943976B - Account-based hot spot transaction identification method and system in massive transaction logs - Google Patents
Account-based hot spot transaction identification method and system in massive transaction logs Download PDFInfo
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Abstract
The embodiment of the application discloses a hotspot transaction identification method and system based on accounts in a mass transaction log, wherein the method comprises the following steps: obtaining accounts with transaction times larger than a threshold value M in each T minute time period from the transaction log, and storing the accounts in a high-frequency account table; extracting all transactions of the accounts in the high-frequency account table in the corresponding time period, and storing the transactions in a hot spot transaction table; calculating the heat value of each transaction in the hot spot transaction table to obtain the heat value of each transaction in the hot spot transaction table; and counting the transactions with the heat value larger than the threshold value N, and identifying hotspot transactions in the transaction log.
Description
Technical Field
The application relates to the technical field of internet, in particular to a hotspot transaction identification method and system based on an account in a mass transaction log.
Background
High-frequency accounts and hot spot transactions thereof are ubiquitous in a core system of large online transactions. If the peripheral system continuously and concurrently calls the core in an asynchronous mode for the same account, hot account transactions are easily formed, the performance of the database of the core system is seriously influenced, and even the database is overtime or deadlock is caused.
Currently, in log analysis of a large online transaction core system, a high-frequency account is regarded as a hotspot account. On one hand, some hotspot accounts have a small total number of transactions, but hotspot transactions are formed densely at a certain time point, and the hotspot accounts and the transactions thereof are missed; on the other hand, some accounts are large in transaction amount, but the transactions are not dense and are also mistakenly classified as hot accounts. Therefore, the prior art cannot accurately identify the hot account and the transaction thereof, cannot discover a transaction source system, and cannot perform quantitative analysis on the crowdedness and the performance of the transaction of the hot account.
Disclosure of Invention
The technical scheme solves the technical problem of how to quickly and accurately identify hotspot transactions from the massive transaction logs of the transaction system, can accurately identify hotspot accounts and transactions thereof, and determines the source and performance problems of the hotspot transactions so as to facilitate a peripheral system to take control measures.
In order to achieve the above object, an embodiment of the present application provides an account-based transaction identification method for hotspots in a mass transaction log, including:
obtaining accounts with transaction times larger than a threshold value M in each T minute time period from the transaction log, and storing the accounts in a high-frequency account table;
extracting all transactions of the accounts in the high-frequency account table in the corresponding time period, and storing the transactions in a hot spot transaction table;
calculating the heat value of each transaction in the hot spot transaction table to obtain the heat value of each transaction in the hot spot transaction table;
and counting the transactions with the heat value larger than the threshold value N, and identifying hotspot transactions in the transaction log.
Preferably, the method further comprises the following steps: and when the hotspot transaction in the transaction log is identified, determining scene information and performance information of the hotspot transaction.
Preferably, the method for calculating the hot value of each transaction in the hot transaction table is as follows:
and traversing the hot spot transaction table, and counting the transaction number overlapped with the same account number and the transaction time of the transaction, wherein the transaction number is the heat value of the transaction.
Preferably, the method for traversing the hot spot transaction table comprises: and traversing the transaction corresponding to the account in the hot spot transaction table in the current time period T, the transaction corresponding to the account in the previous time period T and the transaction corresponding to the account in the later time period T.
Preferably, the fields of the high frequency account table include: date, time period, account number, transaction number.
Preferably, the fields of the hotspot transaction table comprise: date, start timestamp, end timestamp, heat value, and fields of the transaction log.
Preferably, the fields of the transaction log include: transaction code, serial number, channel identification, return code.
Preferably, the scene information includes: a transaction initiation system and a transaction code.
Preferably, the performance information includes: transaction amount, average processing time.
In order to achieve the above object, an embodiment of the present application further provides an account-based hotspot transaction identification system in a mass transaction log, including:
the high-frequency account table scanning unit is used for acquiring accounts with transaction times larger than a threshold value M in each T-minute time period from the transaction log and storing the accounts in the high-frequency account table;
the hotspot transaction table scanning unit is used for extracting all transactions of the account in the high-frequency account table in the corresponding time period and storing the transactions in the hotspot transaction table;
the hot value calculation unit is used for calculating the hot value of each transaction in the hot transaction table to obtain the hot value of each transaction in the hot transaction table;
and the identification unit is used for counting the transactions with the heat value larger than the threshold value N and identifying the hotspot transactions in the transaction log.
Preferably, the identification unit is further configured to determine scene information and performance information of the hotspot transaction when the hotspot transaction in the transaction log is identified.
Preferably, the heat value calculation unit traverses the hot spot transaction table, and counts the number of transaction strokes overlapped with the transaction with the account number and the transaction time, wherein the number of transaction strokes is the heat value of the transaction.
Preferably, the method for traversing the hot spot transaction table by the heat value calculation unit is as follows: and traversing the transaction corresponding to the account in the hot spot transaction table in the current time period T, the transaction corresponding to the account in the previous time period T and the transaction corresponding to the account in the later time period T.
Therefore, compared with the prior art, the technical scheme adopts the heat value index to identify the hot spot transaction, measures the intensity of the hot spot transaction, and counts the source and the performance of the hot spot transaction on the basis. The method has important practical significance for maintenance work such as online performance monitoring and risk assessment of the application system.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a hot spot transaction identification method based on an account in a mass transaction log according to an embodiment of the present application;
fig. 2 is a schematic diagram of an account-based hotspot transaction identification system in a mass transaction log according to an embodiment of the present disclosure;
fig. 3 is a second schematic diagram of an account-based hotspot transaction identification system in a mass transaction log according to an embodiment of the present disclosure.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art without any inventive work based on the embodiments in the present application shall fall within the scope of protection of the present application.
In mass online transaction log data with daily transaction volume of more than hundred million, only high-frequency accounts can be mined, but the high-frequency accounts cannot be accurately identified to form hot spot transactions, the crowding degree and performance of the hot spot transactions cannot be quantitatively analyzed, and the hot spot transactions generated by a peripheral system cannot be determined.
Based on this, the present application provides an account-based hotspot transaction identification method in a massive transaction log, and in this embodiment, hotspot transactions are identified from a high-frequency account according to the steps shown in fig. 1. The method can be applied to terminal equipment with a data processing function. The terminal device may be, for example, a desktop computer, a notebook computer, a tablet computer, a workstation, a smart phone, and the like. The method may comprise the steps of:
s11: and obtaining accounts with transaction times larger than the threshold value M in each T-minute time period from the transaction log, and storing the accounts in the high-frequency account table.
In this embodiment, from the transaction log of one day, the accounts whose transaction number is greater than the threshold M every T minutes are identified and stored in the high-frequency account table. The high-frequency account table key fields comprise date, time period, account number and transaction times. The parameter T and the threshold M are dynamically adjusted according to the actual conditions of the transaction data. The threshold value M is greater than 1, and the higher the value of T, the higher the value of M.
S12: and extracting all transactions of the accounts in the high-frequency account table in the corresponding time period, and storing the transactions in the hot spot transaction table.
In this embodiment, the accounts in the high frequency account table are extracted from all transactions in each period of the transaction log of a day, and are guaranteed to be in the hot transaction table. Wherein, the key fields in the hotspot transaction table comprise: date, start timestamp, end timestamp, heat value, and other fields from the transaction log such as: a transaction code, serial number, channel identification, return code, etc.
S13: and calculating the heat value of each transaction in the hot spot transaction table to obtain the heat value of each transaction in the hot spot transaction table.
In this embodiment, a heat value calculation is performed for each transaction in the hot spot transaction table. The calculation method comprises the following steps: and traversing the hot transaction table according to the account information aiming at each transaction in the hot transaction table, and counting the transaction number overlapped with the account number and the transaction time as the heat value of the transaction. The method comprises the following specific steps: let the start timestamp of the current transaction be ST1 and the end timestamp be ET 1. The total transaction amount of all the transactions of the account in the hot spot transaction table, with the start time stamp ST between (ST1, ET1) or the end time stamp ET between (ST1, ET1), is the heat value of the current transaction.
Because the transaction processing time is far shorter than the time period length T minutes, only the transaction in the time period of the exchange in the hot spot transaction table can be traversed, so that the traversal efficiency is improved. Meanwhile, in order to avoid missing hot spot transactions near the start point and the end point of the current time period, the previous time period and the next time period of the current time period are also included in the traversal range. The implementation method can accurately calculate the heat value of the current transaction, simultaneously avoids full-table traversal, and greatly improves the operation efficiency.
S14: and counting the transactions with the heat value larger than the threshold value N, and identifying hotspot transactions in the transaction log.
In this embodiment, by counting the transactions with the heat value greater than the threshold N, it can be known which systems initiate the hotspot transactions, which transaction codes and other scene information, and the performance information such as the transaction amount and the average processing time. In the technical scheme, N is larger than 1.
The hot value calculation is actually a self-associated query of the hotspot transaction table. Since the data volume of the hot spot transaction table is much smaller than that of the transaction log, and the query range is concentrated in the current time period and the adjacent time period of the transaction, the efficiency of self-correlation query is much higher. According to the technical scheme, the hot value index is adopted to identify the hot transaction, measure the intensity of the hot transaction and count the source and performance of the hot transaction on the basis. Therefore, the technical scheme can accurately identify the hotspot account and the transaction thereof, and clearly determine the source and performance problems of the hotspot transaction so as to facilitate the peripheral system to take control measures. Meanwhile, the scheme adopts a step-by-step screening method, so that the efficiency of batch tasks can be ensured.
As shown in fig. 2, one of schematic diagrams of a hotspot transaction identification system based on an account in a mass transaction log is further provided for the embodiment of the present application. The method comprises the following steps:
the high-frequency account table scanning unit 201 is configured to obtain accounts with transaction times greater than a threshold M in each T-minute time period from the transaction log, and store the accounts in the high-frequency account table;
a hotspot transaction table scanning unit 202, configured to extract all transactions of an account in the high-frequency account table in a corresponding time period, and store the extracted transactions in a hotspot transaction table;
the hot value calculation unit 203 is configured to calculate a hot value of each transaction in the hot transaction table to obtain a hot value of each transaction in the hot transaction table;
the identifying unit 204 is configured to count the transactions with the heat value greater than the threshold N, and identify a hotspot transaction in the transaction log.
Preferably, the identifying unit 204 is further configured to determine scene information and performance information of the hotspot transaction when the hotspot transaction in the transaction log is identified.
Preferably, the heat value calculation unit 203 traverses the hot spot transaction table for each transaction, and counts the number of transactions overlapping with the account number and the transaction time, where the number of transactions is the heat value of the transaction.
Fig. 3 is a second schematic diagram of an account-based hotspot transaction identification system in a mass transaction log according to the embodiment of the present application. The system comprises: a memory a and a processor b, wherein the memory a stores a computer program, and the computer program realizes the following functions when being executed by the processor b:
obtaining accounts with transaction times larger than a threshold value M in each T minute time period from the transaction log, and storing the accounts in a high-frequency account table;
extracting all transactions of the accounts in the high-frequency account table in the corresponding time period, and storing the transactions in a hot spot transaction table;
calculating the heat value of each transaction in the hot spot transaction table to obtain the heat value of each transaction in the hot spot transaction table;
and counting the transactions with the heat value larger than the threshold value N, and identifying hotspot transactions in the transaction log.
In this embodiment, the computer program further realizes the following functions when executed by the processor b:
and when the hotspot transaction in the transaction log is identified, determining scene information and performance information of the hotspot transaction.
In this embodiment, the Memory includes, but is not limited to, a Random Access Memory (RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard Disk Drive (HDD), or a Memory Card (Memory Card).
In this embodiment, the processor may be implemented in any suitable manner. For example, the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium that stores computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, an embedded microcontroller, and so forth.
The specific functions implemented by the memory and the processor of the system for identifying hot spot transactions in mass transaction logs provided by the embodiments of the present description may be explained in comparison with the foregoing embodiments of the present description, and can achieve the technical effects of the foregoing embodiments, and will not be described herein again.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Language Description Language), traffic, pl (core unified Programming Language), HDCal, JHDL (Java Hardware Description Language), langue, Lola, HDL, laspam, hardbyscript Description Language (vhr Description Language), and the like, which are currently used by Hardware compiler-software (Hardware Description Language-software). It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
Those skilled in the art will also appreciate that, in addition to implementing a client, server as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the client, server are in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a client, server may be considered as a hardware component, and the means included therein for implementing various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present application may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present application.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the embodiments of the client, reference may be made to the introduction of the embodiments of the method described above for a comparative explanation.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
Although the present application has been described in terms of embodiments, those of ordinary skill in the art will recognize that there are numerous variations and permutations of the present application without departing from the spirit of the application, and it is intended that the appended claims encompass such variations and permutations without departing from the spirit of the application.
Claims (9)
1. A hotspot transaction identification method based on accounts in a mass transaction log is characterized by comprising the following steps:
obtaining accounts with transaction times larger than a threshold value M in each T minute time period from the transaction log, and storing the accounts in a high-frequency account table;
extracting all transactions of the accounts in the high-frequency account table in the corresponding time period, and storing the transactions in a hot spot transaction table;
calculating the heat value of each transaction in the hot spot transaction table to obtain the heat value of each transaction in the hot spot transaction table;
counting the transactions with the heat value larger than the threshold value N, and identifying hotspot transactions in a transaction log;
the method for calculating the heat value of each transaction in the hot spot transaction table comprises the following steps: traversing the hot spot transaction table, and counting the transaction number overlapped with the same account number and the transaction time of the transaction, wherein the transaction number is the heat value of the transaction; the method specifically comprises the following steps: setting the starting time stamp of the current transaction as ST1 and the ending time stamp as ET1, wherein in all transactions of the account in the hotspot transaction table, the total transaction amount of the starting time stamp ST between (ST1 and ET1) or the ending time stamp ET between (ST1 and ET1) is the heat value of the current transaction;
the traversing the hotspot transaction table comprises: and traversing the transaction corresponding to the account in the current time period T, the transaction corresponding to the previous time period T and the transaction corresponding to the next time period T in the hot spot transaction table.
2. The method of claim 1, further comprising: and when the hotspot transaction in the transaction log is identified, determining scene information and performance information of the hotspot transaction.
3. The method of claim 1, wherein the fields of the high frequency account table comprise: date, time period, account number, transaction number.
4. The method of claim 1, wherein the fields of the hotspot transaction table comprise: date, start timestamp, end timestamp, heat value, and fields of the transaction log.
5. The method of claim 4, wherein the fields of the transaction log comprise: transaction code, serial number, channel identification, return code.
6. The method of claim 2, wherein the scene information comprises: a transaction initiation system and a transaction code.
7. The method of claim 2, wherein the performance information comprises: transaction amount, average processing time.
8. An account-based hotspot transaction identification system in a massive transaction log is characterized by comprising:
the high-frequency account table scanning unit is used for acquiring accounts with transaction times larger than a threshold value M in each T-minute time period from the transaction log and storing the accounts in the high-frequency account table;
the hotspot transaction table scanning unit is used for extracting all transactions of the account in the high-frequency account table in the corresponding time period and storing the transactions in the hotspot transaction table;
the hot value calculation unit is used for calculating the hot value of each transaction in the hot transaction table to obtain the hot value of each transaction in the hot transaction table;
the identification unit is used for counting the transactions with the heat value larger than the threshold value N and identifying hotspot transactions in the transaction log;
the heat value calculation unit traverses the hot transaction table, and counts the transaction number overlapped with the transaction account number and the transaction time, wherein the transaction number is the heat value of the transaction; the method specifically comprises the following steps: setting the starting time stamp of the current transaction as ST1 and the ending time stamp as ET1, wherein in all transactions of the account in the hotspot transaction table, the total transaction amount of the starting time stamp ST between (ST1 and ET1) or the ending time stamp ET between (ST1 and ET1) is the heat value of the current transaction;
the method for traversing the hot spot transaction table by the heat value calculation unit comprises the following steps: and traversing the transaction corresponding to the account in the current time period T, the transaction corresponding to the previous time period T and the transaction corresponding to the next time period T in the hot spot transaction table.
9. The system of claim 8, wherein the identification unit is further configured to determine context information and performance information for the hotspot transaction upon identifying the hotspot transaction in a transaction log.
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| CN111915340B (en) * | 2019-05-09 | 2023-08-18 | 腾讯科技（深圳）有限公司 | Method, device, equipment and storage medium for identifying merchant type |
| CN112134766B (en) * | 2020-10-27 | 2021-08-03 | 拉卡拉支付股份有限公司 | Method and device for detecting high concurrency service |
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