CN113888276B - Distributed fragment processing method, system and equipment for batch deduction - Google Patents
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Abstract
The invention discloses a distributed type fragment processing method, a system, equipment and a storage medium for batch deduction, and relates to the technical field of computers. A distributed slicing processing method for batch deduction comprises the following steps: reading a repayment order with the current day deadline, and inputting customer information corresponding to the repayment order into a to-be-deducted customer information table; carrying out a partitioning algorithm on data in a customer information table to be deducted based on the number of nodes of service configuration, and equally dividing the data into a plurality of sections; calculating the total amount of arrearages of each customer in the interval, and recording the customer information and the total amount of arrearages into a to-be-deducted water meter; and reading the flow water meter to be deducted, and calling a payment system to carry out batch deduction. The method provided by the invention solves the problems of low processing efficiency, poor expansibility and low fault tolerance in the prior art, realizes reasonable distribution of physical resources, reduces operation cost and improves processing efficiency.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a distributed type fragment processing method, a system, equipment and a storage medium for batch deduction.
Background
In the prior art, when deducting money for an expired loan order, a multithreading processing scheme is realized based on SpringBatch to realize batch deduction of money for a large number of orders, but when the deduction mode is used for processing tens of millions of data, the processing time can be as long as a plurality of hours, and the efficiency is very low.
Disclosure of Invention
In order to overcome the problems or partially solve the problems, the invention aims to provide a distributed fragment processing method, a system, equipment and a storage medium for Batch deduction, so as to solve the problem that a credit core system based on a Spring Batch processing framework is too long in processing time of mass data, and improve the efficiency of the credit core system for Batch deduction of customers.
The invention is realized by the following technical scheme:
in a first aspect, the present invention provides a distributed slicing processing method for batch deduction, including the following steps: s101, reading a payment order of the current day deadline, and inputting customer information corresponding to the payment order into a customer information table to be paid; s102, carrying out a partitioning algorithm on the data in the to-be-deducted customer information table based on the node number of the service configuration, and equally dividing the data into a plurality of sections; s103, calculating the total amount of arrearages of each customer in the interval, and recording the customer information and the total amount of arrearages into a to-be-deducted flow water meter; s104, reading the to-be-deducted flow water meter, and calling a payment system to carry out batch deduction.
Based on the first aspect, in some embodiments of the present invention, before the current customer information is input into the to-be-deducted customer information table, duplication removing is performed on the current customer information so as to avoid repeated input of the customer information.
Based on the first aspect, in some embodiments of the present invention, the deduplication process includes: matching the current customer information with the customer information in the to-be-deducted customer information table; if the matched customer information exists, the current customer information is not input into the deduction customer information table; if no matched customer information exists, the current customer information is input into a substitute deduction customer information table.
Based on the first aspect, in some embodiments of the present invention, before entering the customer information and the total amount of arrears into the to-be-deducted flow chart, the method further includes: checking whether batch deduction can be carried out on the clients.
Based on the first aspect, in some embodiments of the present invention, the checking manner includes: checking whether the customer has repayment error records; and checking whether the customer has data in repayment processing.
Based on the first aspect, in some embodiments of the present invention, the partition processing method range Partitioning is invoked to partition the customer corresponding to the payment order of the current day deadline.
Based on the first aspect, in some embodiments of the present invention, the reading the to-be-deducted flow meter calls a payment system to perform batch deduction; if the deduction is successful, transmitting deduction confirmation information to the corresponding client; if the deduction fails, marking the corresponding client and sending a deduction failure short message to the client.
Based on the first aspect, in some embodiments of the present invention, the partition processing method range Partitioning is invoked to partition the customer corresponding to the payment order of the current day deadline.
Based on the first aspect, in some embodiments of the present invention, the reading the to-be-deducted flow meter calls a payment system to perform batch deduction; if the deduction is successful, transmitting deduction confirmation information to the corresponding client; if the deduction fails, marking the corresponding client and sending a deduction failure short message.
In a second aspect, the present invention provides a distributed tile processing system for batch deduction, comprising: and a reading and inputting module: the payment method comprises the steps of reading a payment order of the current day deadline, and inputting customer information corresponding to the payment order into a customer information table to be paid; partition processing module: the system is used for carrying out partition processing on the payment orders of the current day deadline based on key information in a client information table, and independently gathering all the payment orders of the same client into one interval; and a calculation and statistics module: the method comprises the steps of calculating the total amount of arrearages of all repayment orders in an interval, and recording the customer information and the total amount of arrearages into a to-be-deducted water meter; and a batch deduction module: and the payment system is used for reading the to-be-deducted flow water meter and calling the payment system to carry out batch deduction.
In a third aspect, the present invention provides an electronic device comprising: at least one processor, at least one memory, and a data bus; wherein the processor and the memory communicate with each other via the data bus; the memory stores program instructions executable by the processor that invoke the program instructions to perform the one or more programs or methods, such as performing: s101, reading a payment order of the current day deadline, and inputting customer information corresponding to the payment order into a customer information table to be paid; s102, carrying out a partitioning algorithm on the data in the to-be-deducted customer information table based on the node number of the service configuration, and equally dividing the data into a plurality of sections; s103, calculating the total amount of arrearages of each customer in the interval, and recording the customer information and the total amount of arrearages into a to-be-deducted flow water meter; s104, reading the to-be-deducted flow water meter, and calling a payment system to carry out batch deduction.
In a fourth aspect, the present invention provides a non-transitory computer readable storage medium storing a computer program that causes the computer to execute one or more of the programs or methods described above, for example, to perform: s101, reading a payment order of the current day deadline, and inputting customer information corresponding to the payment order into a customer information table to be paid; s102, carrying out a partitioning algorithm on the data in the to-be-deducted customer information table based on the node number of the service configuration, and equally dividing the data into a plurality of sections; s103, calculating the total amount of arrearages of each customer in the interval, and recording the customer information and the total amount of arrearages into a to-be-deducted flow water meter; s104, reading the to-be-deducted flow water meter, and calling a payment system to carry out batch deduction.
Compared with the prior art, the invention has at least the following advantages and beneficial effects:
The invention solves the problems of low processing efficiency, poor expansibility and low fault tolerance in the prior art, and provides a partition processing scheme based on SpringBatch frames, which performs partition calculation in advance during Spring Batch processing. And the information is filtered and decomposed and then distributed to different servers for processing, so that reasonable distribution of physical resources is realized, the operation cost is reduced, and the processing efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are needed in the examples will be briefly described below, it being understood that the following drawings only illustrate some examples of the present invention and therefore should not be considered as limiting the scope, and that other related drawings may be obtained from these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a flow diagram of one embodiment of a distributed slicing process for batch deduction;
FIG. 2 is a schematic diagram illustrating an embodiment of a distributed slicing processing system for batch deduction;
fig. 3 is a block diagram of an electronic device.
Icon: 1-a processor; 2-memory; 3-a data bus; 100-reading and inputting a module; 200-partitioning the processing module; 300-calculating a statistical module; 400-batch deduction module.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
Example 1
Referring to fig. 1, in an embodiment of the present invention, a distributed slicing processing method for batch deduction is provided, including the following steps:
s101, reading a payment order of the current day deadline, and inputting customer information corresponding to the payment order into a customer information table to be paid;
The server configuration operations also need to be completed before the payment order is read. For example, in the service configuration and deployment stage, the same set of codes distinguishes configuration file information, the main service is set as a Master node, the total node number m is configured, nodeId is n1, and RabbitMQ information monitoring queues queue1 and pre_ pymt _queue_1 are configured; the rest service sets the Slave node nodeId as n2, n3 …, configures RabbitMQ information monitoring queues pre_ pymt _queue_2 and pre_ pymt _queue_3 …; the services are deployed on different servers.
Then carrying out batch buckling initialization: all customers whose repayment day is the order of the current day and whose states are normal (non-overdue) are read regularly every day, customer information is written into a to-be-deducted customer information table auto_ pymt _init, a primary key id is self-increment, which is one of the fields of the table, the type is int, and the values of the fields are accumulated sequentially along with the insertion of data.
Further, in this embodiment, in order to avoid the repeated entry of the customer information into the customer information table to be deducted, a burden of subsequent calculation is caused, so that, before the current customer information is entered into the customer information table to be deducted, a duplicate processing is performed on the current customer information, so as to avoid the repeated entry of the customer information. Illustratively, the manner of deduplication may be: matching the current customer information with the customer information in the to-be-deducted customer information table; if the matched customer information exists, the current customer information is not input into the deduction customer information table; if no matched customer information exists, the current customer information is input into a substitute deduction customer information table.
S102, carrying out a partitioning algorithm on data in a customer information table to be deducted based on the number of nodes of service configuration, and equally dividing the data into a plurality of sections;
in this step, the operation of performing the partition processing on the customer of the daily limited payment order includes the steps of:
Step 1: and carrying out partition processing, namely uniformly dividing clients in the client information table to be deducted into a plurality of intervals. The Master node reads the summary point m in the configuration file, acquires the minimum value a and the maximum value b of the auto_ pymt _init table id field of the client information to be deducted, calls the partition processing method range Partitioning (m, a, b), outputs a partition calculation result of List < Map < String, object >,
[ { Nodeid=1, range= [ a, a1] }, { nodeid=2, range= [ a1+1, a2] }, …, { nodeid=m, range= [ am, b ] }; example m=3, a=1, b=100, and after invoking partition processing method range Partitioning (3,1,100), the output result is:
[ { nodeid=1, range= [1,33] }, { nodeid=2, range= [34,66] }, { nodeid=3, range= [67,100] }. I.e. 100 clients are allocated to 3 intervals.
Step 2: the Master node order message gateway OrderGateway processes the results in step 1 according to the NodeID dimension node, pushes the results to RabbitMQ queue1, json message example:
{“msg”:
“[{“NodeID”:’1’,”range”:’[a,a1]’},
{“NodeID”:’2’,”range”:’[a1+1,a2]’},…]”
}
Step 3: master node order message decomposition OrderSplitter monitors queque1 messages, pushes different messages to different queues pre_ pymt _queue_m according to NodeID values in the messages, wherein m is the NodeID of a node, and the message body is { "range": '[ a, a1]' };
examples:
the message pushed to the queue pre_ pymt _queue_1 monitored by the Master node is { "range": '[ a, a1]' },
The message pushed to the queue pre_ pymt _queue_2 monitored by the Slave1 node is { "range": '[ a1+1, a2]' }
The other nodes and so on.
S103, calculating the total amount of arrearages of each customer in the interval, and recording the customer information and the total amount of arrearages into a to-be-deducted water meter;
The Master node and the Slave node consume the monitored queues pre_ pymt _queue_m respectively, read the maximum value a and the minimum value a1 of the to-be-deducted client information table auto_ pymt _init processed by the Master node, start multithreading to calculate client debt, calculate the expiration amounts of all orders of the client on the same day, write the expiration amounts into the client-level to-be-deducted flow table cure_ pymt, and obtain the state as to-be-deducted D.
Furthermore, before the customer information and the total amount of the arrearages are input into the flow list of the to-be-deducted money, whether the customers can be deducted in batches is checked, the occurrence of the situation of false deduction can be prevented, and the customer dissatisfaction is avoided. The verification method comprises the following steps: 1. checking whether the customer has repayment error records; 2. and checking whether the customer has data in repayment processing.
S104, reading the to-be-deducted flow water meter, and calling a payment system to carry out batch deduction.
Reading the data of the to-be-deducted flow list cure_ pymt with the state of the to-be-deducted D, calling a payment system to deduct money one by one, modifying the state into S after the deduction is successful, simultaneously sending a deduction confirmation short message to the client, modifying the state into F if the deduction confirmation short message fails, recording the deduction failure reason, and sending a short message of deduction failure to the client.
Example 2
Referring to fig. 2, the present invention provides a distributed slice processing system for batch deduction, which includes: the read entry module 100: the payment method comprises the steps of reading a payment order of the current day deadline, and inputting customer information corresponding to the payment order into a customer information table to be paid; partition processing module 200: the system is used for carrying out partition processing on the payment orders of the current day deadline based on key information in a client information table, and independently gathering all the payment orders of the same client into one interval; the calculation statistics module 300: the method comprises the steps of calculating the total amount of arrearages of all repayment orders in an interval, and recording the customer information and the total amount of arrearages into a to-be-deducted water meter; batch deduction module 400: and the payment system is used for reading the to-be-deducted flow water meter and calling the payment system to carry out batch deduction.
The system provided by the present invention may be used to execute the method or step described in the foregoing embodiment 1, and the details are described in embodiment 1 and are not repeated here.
Example 3
Referring to fig. 3, the present invention provides an electronic device, including: at least one processor 1, at least one memory 2 and a data bus 3; wherein the processor 1 and the memory 2 complete communication with each other through the data bus 3; the memory 2 stores program instructions executable by the processor 1, and the processor 1 invokes the program instructions to perform the method provided in the above embodiment, for example, to perform: s101, reading a payment order of the current day deadline, and inputting customer information corresponding to the payment order into a customer information table to be paid; s102, carrying out a partitioning algorithm on the data in the to-be-deducted customer information table based on the node number of the service configuration, and equally dividing the data into a plurality of sections; s103, calculating the total amount of arrearages of each customer in the interval, and recording the customer information and the total amount of arrearages into a to-be-deducted flow water meter; s104, reading the to-be-deducted flow water meter, and calling a payment system to carry out batch deduction.
Fig. 3 is a schematic block diagram of an electronic device according to an embodiment of the present application. The electronic device comprises a memory 2, a processor 1 and a data bus 3, wherein the memory 2, the processor 1 and the data bus 3 are directly or indirectly electrically connected with each other to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 2 may be used for storing software programs and modules, such as program instructions/modules corresponding to the electronic device provided in the embodiments of the present application, and the processor 1 executes the software programs and modules stored in the memory 2, thereby performing various functional applications and data processing. The data bus 3 may be used for communication of signalling or data with other node devices.
The Memory 2 may be, but is not limited to, a random access Memory (Random Access Memory, RAM), a Read Only Memory (ROM), a programmable Read Only Memory (Programmable Read-Only Memory, PROM), an erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.
The processor 1 may be an integrated circuit chip with signal processing capabilities. The processor 1 may be a general-purpose processor including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but may also be a digital signal processor (DIGITAL SIGNAL Processing, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components.
Example 4
A non-transitory computer-readable storage medium storing a computer program that causes a computer to execute the method provided by the above embodiments, for example, to perform: s101, reading a payment order of the current day deadline, and inputting customer information corresponding to the payment order into a customer information table to be paid; s102, carrying out a partitioning algorithm on the data in the to-be-deducted customer information table based on the node number of the service configuration, and equally dividing the data into a plurality of sections; s103, calculating the total amount of arrearages of each customer in the interval, and recording the customer information and the total amount of arrearages into a to-be-deducted flow water meter; s104, reading the to-be-deducted flow water meter, and calling a payment system to carry out batch deduction.
In the embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other manners as well. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. The distributed slicing processing method for batch deduction is characterized by comprising the following steps of:
Reading a repayment order with the current day deadline, and inputting customer information corresponding to the repayment order into a to-be-deducted customer information table;
Carrying out a partitioning algorithm on the data in the to-be-deducted customer information table based on the node number of service configuration, and equally dividing the data into a plurality of intervals, wherein in the step, the operation of partitioning the customers of the daily limited repayment order comprises the steps 1-3;
Step 1: partition processing, namely uniformly placing clients in a client information table to be deducted into a plurality of intervals, reading a summary point m in a configuration file by a Master node, acquiring the minimum value a and the maximum value b of an auto_ pymt _init table id field of the client information to be deducted, calling a partition processing method range Partitioning (m, a, b), outputting a partition computing result as List < Map < String, object >,
[{NodeID=1,range=[a,a1]},{NodeID=2,range=[a1+1,a2]},…,{NodeID=m,range=[am,b]}]；
Step 2: the Master node order message gateway OrderGateway processes the result in the step 1 according to the node of NodeID dimension and pushes the result to the RabbitMQ queue1;
Step 3: master node order message decomposition OrderSplitter monitors queque1 messages, pushes different messages to different queues pre_ pymt _queue_m according to NodeID values in the messages, wherein m is the NodeID of a node, and the message body is { "range": '[ a, a1]' };
calculating the total amount of arrearages of each customer in the interval, and recording the customer information and the total amount of arrearages into a to-be-deducted water meter;
and reading the to-be-deducted flow water meter, and calling a payment system to carry out batch deduction.
2. The distributed slicing processing method of batch deduction according to claim 1, wherein prior to entering the current customer information into the to-be-deducted customer information table, the current customer information is subjected to duplication elimination processing to avoid repeated entry of customer information.
3. The distributed sharding processing method of bulk deductions according to claim 2, wherein the deduplication processing includes:
matching the current customer information with the customer information in the to-be-deducted customer information table;
If the matched customer information exists, the current customer information is not input into the deduction customer information table;
If no matched customer information exists, the current customer information is input into a substitute deduction customer information table.
4. The method for distributed slicing of batch deductions according to claim 1, wherein before entering the customer information and the total amount of arrearages into the line list of pending deductions, further comprising:
Checking whether batch deduction can be carried out on the clients.
5. The distributed sharding processing method of batch deductions according to claim 4, wherein the verification means comprises:
checking whether the customer has repayment error records;
And checking whether the customer has data in repayment processing.
6. The distributed sharding method of batch payment according to claim 1, wherein the client corresponding to the payment order of the current day deadline is partitioned by calling a partition processing method range Partitioning.
7. The distributed slicing processing method of batch deduction according to claim 1, wherein the reading of the to-be-deducted flow water meter invokes a payment system to carry out batch deduction;
If the deduction is successful, transmitting deduction confirmation information to the corresponding client;
If the deduction fails, marking the corresponding client and sending a deduction failure short message to the client.
8. A distributed slice processing system for batch deduction, comprising:
And a reading and inputting module: the payment method comprises the steps of reading a payment order of the current day deadline, and inputting customer information corresponding to the payment order into a customer information table to be paid;
partition processing module: the system is used for carrying out partition processing on the payment orders of the current day deadline based on key information in a client information table, and independently gathering all the payment orders of the same client into one interval;
And a calculation and statistics module: the method comprises the steps of calculating the total amount of arrearages of all repayment orders in an interval, and recording the customer information and the total amount of arrearages into a to-be-deducted water meter;
and a batch deduction module: the payment system is used for reading the to-be-deducted flow water meter and calling the payment system to carry out batch deduction;
The operation of the partition processing module for carrying out partition processing on clients of the payment order with limited current day comprises the steps 1-3;
Step 1: partition processing, namely uniformly placing clients in a client information table to be deducted into a plurality of intervals, reading a summary point m in a configuration file by a Master node, acquiring the minimum value a and the maximum value b of an auto_ pymt _init table id field of the client information to be deducted, calling a partition processing method range Partitioning (m, a, b), outputting a partition computing result as List < Map < String, object >,
[{NodeID=1,range=[a,a1]},{NodeID=2,range=[a1+1,a2]},…,{NodeID=m,range=[am,b]}]；
Step 2: the Master node order message gateway OrderGateway processes the result in the step 1 according to the node of NodeID dimension and pushes the result to the RabbitMQ queue1;
step 3: master node order message decomposition OrderSplitter monitors queque1 messages, pushes different messages to different queues pre_ pymt _queue_m according to NodeID values in the messages, wherein m is the NodeID of a node, and the message body is { "range": '[ a, a1]' }.
9. An electronic device, comprising: at least one processor, at least one memory, and a data bus;
Wherein the processor and the memory complete communication with each other through the data bus; the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1-7.
10. A non-transitory computer readable storage medium storing a computer program that causes a computer to perform the method of any of claims 1-7.
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| CN107967650A (en) * | 2017-11-08 | 2018-04-27 | 中国银行股份有限公司 | A kind of batch accounting data processing method and processing device of core banking system |
| CN109101296A (en) * | 2018-07-10 | 2018-12-28 | 上海维信荟智金融科技有限公司 | Distributed auto deduction method and system |
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