CN113888276A - Distributed fragment processing method, system and equipment for batch deduction - Google Patents
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Abstract
The invention discloses a distributed fragment processing method, a distributed fragment processing system, distributed fragment processing equipment and a storage medium for batch deduction, and relates to the technical field of computers. A distributed fragment processing method for deduction in batches comprises the following steps: 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 deducted; performing a partition algorithm on data in a client information table to be deducted based on the number of nodes configured by the service, and averagely dividing the data into a plurality of partitions; calculating the total amount of arrears of each client in the interval, and recording the client information and the total amount of arrears into the flow meter to be deducted; and reading the flow meter to be deducted, and calling the payment system to deduct money in batches. The method provided by the invention overcomes the problems of low processing efficiency, poor expansibility and low fault tolerance in the prior art, realizes reasonable distribution of physical resources, reduces the operation cost and improves the processing efficiency.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a distributed fragment processing method, a distributed fragment processing system, distributed fragment processing equipment and a storage medium for batch deduction.
Background
In the prior art, when deducting an expired loan order, a multithread processing scheme is implemented based on spring batch to realize batch deduction of a large number of orders, but the processing time of the deduction mode reaches several hours when tens of millions of data are processed, and the efficiency is very low.
Disclosure of Invention
In order to overcome the above problems or partially solve the above problems, an object of the present invention is to provide a distributed fragment processing method, system, device and storage medium for deducting in batches, so as to solve the problem that the processing time of the credit core system based on the Spring Batch processing framework for mass data is too long, and improve the efficiency of the credit core system for deducting in batches from guests.
The invention is realized by the following technical scheme:
in a first aspect, the present invention provides a distributed fragment processing method for deduction in batches, including the following steps: s101, reading a payment order limited on the current day, and inputting customer information corresponding to the payment order into a customer information table to be deducted; s102, performing a partition algorithm on the data in the customer information table to be deducted based on the number of nodes configured by the service, and averagely dividing the data into a plurality of partitions; s103, calculating the total amount of arrears of each customer in the interval, and recording the customer information and the total amount of arrears into the flow meter to be deducted; and S104, reading the flow water meter to be deducted, and calling a payment system to deduct money in batches.
Based on the first aspect, in some embodiments of the present invention, before the current customer information is entered into the customer information table to be deducted, the current customer information is deduplicated to avoid repeated entry of customer information.
Based on the first aspect, in some embodiments of the present invention, the deduplication processing includes: matching the current customer information with the customer information in the customer information table to be deducted; if the matched customer information exists, the current customer information is not recorded in the withholding customer information table; if the matched customer information does not exist, the current customer information is recorded into the withholding customer information table.
Based on the first aspect, in some embodiments of the present invention, before entering the customer information and the total amount owed into the to-be-deducted fee schedule, the method further includes: and checking whether batch deduction can be carried out on the customers.
Based on the first aspect, in some embodiments of the present invention, the checking includes: checking whether the client has a payment error record or not; and checking whether the client has the data in the payment processing.
Based on the first aspect, in some embodiments of the present invention, partition processing is performed on the customer corresponding to the payment order of the deadline by calling a partition processing method range Partitioning.
Based on the first aspect, in some embodiments of the present invention, the reading of the to-be-deducted money flow meter calls a payment system to deduct money in batches; if the deduction is successful, sending deduction confirmation information to the corresponding client; and 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, partition processing is performed on the customer corresponding to the payment order of the deadline by calling a partition processing method range Partitioning.
Based on the first aspect, in some embodiments of the present invention, the reading of the to-be-deducted money flow meter calls a payment system to deduct money in batches; if the deduction is successful, sending 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 piece processing system for deducting money in batches, including: reading and recording the module: the system is used for reading the absolute repayment order of the day and inputting the customer information corresponding to the repayment order into a customer information table to be deducted; a partition processing module: the system is used for partitioning the repayment orders of the current day deadline based on the key information in the client information table, and gathering all the repayment orders of the same client into one interval; a calculation statistic module: the system is used for calculating the total arrears of all repayment orders in an interval and inputting the customer information and the total arrears into a to-be-deducted flow water meter; and (4) a batch deduction module: and the payment system is used for reading the to-be-deducted money flow meter and calling the payment system to deduct money in batches.
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 complete mutual communication through the data bus; the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the one or more programs or methods, such as: s101, reading a payment order limited on the current day, and inputting customer information corresponding to the payment order into a customer information table to be deducted; s102, performing a partition algorithm on the data in the customer information table to be deducted based on the number of nodes configured by the service, and averagely dividing the data into a plurality of partitions; s103, calculating the total amount of arrears of each customer in the interval, and recording the customer information and the total amount of arrears into the flow meter to be deducted; and S104, reading the flow water meter to be deducted, and calling a payment system to deduct money in batches.
In a fourth aspect, the present invention provides a non-transitory computer readable storage medium storing a computer program, the computer program causing the computer to perform one or more of the procedures or methods described above, such as performing: s101, reading a payment order limited on the current day, and inputting customer information corresponding to the payment order into a customer information table to be deducted; s102, performing a partition algorithm on the data in the customer information table to be deducted based on the number of nodes configured by the service, and averagely dividing the data into a plurality of partitions; s103, calculating the total amount of arrears of each customer in the interval, and recording the customer information and the total amount of arrears into the flow meter to be deducted; and S104, reading the flow water meter to be deducted, and calling a payment system to deduct money in batches.
Compared with the prior art, the invention at least has the following advantages and beneficial effects:
the invention overcomes the problems of low processing efficiency, poor expansibility and low fault tolerance in the prior art, and provides a partitioning processing scheme based on a Spring Batch frame, which performs partitioning calculation in advance during Spring Batch processing. And the messages are filtered and decomposed and then distributed to different servers for processing, so that the 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 required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and that for those skilled in the art, other related drawings can be obtained from these drawings without inventive effort. In the drawings:
FIG. 1 is a block flow diagram of an embodiment of a distributed fragment processing method for deduction of a batch;
FIG. 2 is a block diagram of an embodiment of a batch deduction distributed fragment processing system;
fig. 3 is a block diagram of an electronic device.
Icon: 1-a processor; 2-a memory; 3-a data bus; 100-reading and recording module; 200-a partition processing module; 300-calculating a statistical module; 400-batch deduction module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1
Referring to fig. 1, an embodiment of the present invention provides a distributed fragment processing method for deducting money in batches, including the following steps:
s101, reading a payment order limited on the current day, and inputting customer information corresponding to the payment order into a customer information table to be deducted;
the server configuration operation also needs to be completed before the repayment order is read. Illustratively, in the service configuration and deployment stages, the same set of code distinguishes configuration file information, a main service is set as a Master node, the total node number m is configured, the nodeId is n1, and a RabbitMQ information listening queue1 and pre _ pymt _ queue _1 are configured; the other services set the Slave node nodeId to be n2, n3 …, and configure RabbitMQ information listening queues pre _ pymt _ queue _2 and pre _ pymt _ queue _3 …; the services are deployed on different servers.
Then, batch buckle initialization is carried out: and regularly reading all clients corresponding to the orders with the repayment date of the same day and the normal (non-overdue) states every day, writing the client information into a client information table auto _ pymt _ init to be deducted, wherein the primary key id is self-increment, is one field of the table, has the type of int and is accumulated in sequence along with the insertion of data into the field.
Further, in this embodiment, in order to avoid the burden of subsequent calculation caused by repeatedly inputting the customer information into the customer information table to be deducted, before the current customer information is input into the customer information table to be deducted, the current customer information is subjected to deduplication processing, so as to avoid repeatedly inputting the customer information. For example, the deduplication processing may be performed in the following manner: matching the current customer information with the customer information in the customer information table to be deducted; if the matched customer information exists, the current customer information is not recorded in the withholding customer information table; if the matched customer information does not exist, the current customer information is recorded into the withholding customer information table.
S102, performing a partition algorithm on data in a client information table to be deducted based on the number of nodes configured by the service, and averagely dividing the data into a plurality of partitions;
in this step, the operation of performing partition processing on the client with the deadline payment order on the current day includes the following steps:
step 1: and (4) partition processing, namely uniformly placing the 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 a 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 ] } ]; when the partition processing method is called, the output result is as follows, i.e., m is 3, a is 1, b is 100:
{ NodeID ═ 1, range [1,33] }, { NodeID ═ 2, range [ [34,66] }, { NodeID ═ 3, and range [ [67,100] }. I.e. 100 clients are allocated in 3 intervals.
Step 2: the Master node order message gateway Ordergateway, the result in step 1 is processed according to NodeID dimension node, and is pushed to RabbitMQ queue1, Json message example:
{“msg”:
“[{“NodeID”:’1’,”range”:’[a,a1]’},
{“NodeID”:’2’,”range”:’[a1+1,a2]’},…]”
}
and step 3: decomposing OrderSplitter by the Master node order message, monitoring the message of the queue1, pushing different messages to pre _ pymt _ queue _ m in different queues according to the NodeID value in the message, wherein m is the NodeID of the node, and the message body is { "range": a, a 1' };
example (c):
the message pushed to the queue pre _ pymt _ queue _1 listened by the Master node is { "range" [ a, a1 ]' },
the message pushed to the queue pre _ pymt _ queue _2 listened by the Slave1 node is { "range" [ a1+1, a2 ]' }
And the other nodes are analogized in turn.
S103, calculating the total amount of arrears of each client in the interval, and recording the client information and the total amount of arrears into the flow meter to be deducted;
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 client information table auto _ pymt _ init to be deducted processed by the nodes, start multithreading to carry out client arrearage calculation, calculate the due amount of all orders of the client on the day, write the due amount into the client-level running table to be deducted, and obtain the state of D to be deducted.
Further, before the customer information and the total amount of arrears are input into the to-be-deducted money flow table, whether batch deduction can be carried out on the customers is verified, the situation of mistaken deduction can be prevented, and customer dissatisfaction is avoided. The checking mode comprises the following steps: 1. checking whether the client has a payment error record or not; 2. and checking whether the client has the data in the payment processing.
And S104, reading the flow water meter to be deducted, and calling a payment system to deduct money in batches.
Reading data of a deduction D to be paid in the client-level deduction flow table cust _ pymt, calling a payment system to deduct money one by one, modifying the state to S after the money deduction is successful, simultaneously sending a deduction confirmation short message to the client, modifying the state to F when the money deduction is successful, recording the reason of the money deduction failure, and sending the short message of the money deduction failure to the client.
Example 2
Referring to fig. 2, the present invention provides a distributed fragment processing system for deduction in batches, including: the read entry module 100: the system is used for reading the absolute repayment order of the day and inputting the customer information corresponding to the repayment order into a customer information table to be deducted; partition processing module 200: the system is used for partitioning the repayment orders of the current day deadline based on the key information in the client information table, and gathering all the repayment orders of the same client into one interval; the calculation statistics module 300: the system is used for calculating the total arrears of all repayment orders in an interval and inputting the customer information and the total arrears into a to-be-deducted flow water meter; batch deduction module 400: and the payment system is used for reading the to-be-deducted money flow meter and calling the payment system to deduct money in batches.
The system provided by the present invention can be used to execute the method or steps described in embodiment 1, and specific contents are shown in embodiment 1 and will not be described herein again.
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 the communication with each other through the data bus 3; the memory 2 stores program instructions executable by the processor 1, and the processor 1 calls the program instructions to execute the method provided by the above embodiment, for example, to execute: s101, reading a payment order limited on the current day, and inputting customer information corresponding to the payment order into a customer information table to be deducted; s102, performing a partition algorithm on the data in the customer information table to be deducted based on the number of nodes configured by the service, and averagely dividing the data into a plurality of partitions; s103, calculating the total amount of arrears of each customer in the interval, and recording the customer information and the total amount of arrears into the flow meter to be deducted; and S104, reading the flow water meter to be deducted, and calling a payment system to deduct money in batches.
Fig. 3 is a schematic structural 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, the memory 2, the processor 1 and the data bus 3 being electrically connected to each other, directly or indirectly, to enable transmission or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 2 can 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 executing various functional applications and data processing. The data bus 3 can be used for signaling or data communication with other node devices.
The Memory 2 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 1 may be an integrated circuit chip having signal processing capabilities. The Processor 1 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
Example 4
A non-transitory computer readable storage medium storing a computer program that causes a computer to perform the method provided by the embodiments, such as performing: s101, reading a payment order limited on the current day, and inputting customer information corresponding to the payment order into a customer information table to be deducted; s102, performing a partition algorithm on the data in the customer information table to be deducted based on the number of nodes configured by the service, and averagely dividing the data into a plurality of partitions; s103, calculating the total amount of arrears of each customer in the interval, and recording the customer information and the total amount of arrears into the flow meter to be deducted; and S104, reading the flow water meter to be deducted, and calling a payment system to deduct money in batches.
In the embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures 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 above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A distributed fragment processing method for deduction in batches is characterized by comprising the following steps:
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 deducted;
performing a partition algorithm on the data in the client information table to be deducted based on the number of nodes configured by the service, and averagely dividing the data into a plurality of partitions;
calculating the total amount of arrears of each client in the interval, and recording the client information and the total amount of arrears into the flow meter to be deducted;
and reading the to-be-deducted money flow meter, and calling a payment system to deduct money in batches.
2. The distributed fragment processing method for the batch deduction according to claim 1, wherein before the current customer information is entered into the customer information table to be deducted, the current customer information is deduplicated to avoid repeated entry of customer information.
3. The distributed piece processing method for the batch deduction according to claim 2, wherein the de-duplication process comprises:
matching the current customer information with the customer information in the customer information table to be deducted;
if the matched customer information exists, the current customer information is not recorded in the withholding customer information table;
if the matched customer information does not exist, the current customer information is recorded into the withholding customer information table.
4. The distributed fragment processing method for deduction in batches according to claim 1, wherein before entering the customer information and the total amount owed into the schedule for deduction, the method further comprises:
and checking whether batch deduction can be carried out on the customers.
5. The distributed piece processing method for the batch deduction as claimed in claim 4, wherein the verification comprises:
checking whether the client has a payment error record or not;
and checking whether the client has the data in the payment processing.
6. The distributed fragment processing method for batch deduction according to claim 1, wherein the partition processing is performed on the client corresponding to the daily deadline repayment order by calling a partition processing method range Partitioning.
7. The distributed piece processing method for deduction in batches according to claim 1, wherein the flow meter to be deducted is read and a payment system is called to carry out batch deduction;
if the deduction is successful, sending deduction confirmation information to the corresponding client;
and if the deduction fails, marking the corresponding client and sending a deduction failure short message to the client.
8. A distributed fragment processing system for deduction in batches is characterized by comprising:
reading and recording the module: the system comprises a payment order table, a payment information table and a payment information table, wherein the payment order table is used for reading a payment order of the current day deadline and inputting customer information corresponding to the payment order into the payment information table to be deducted;
a partition processing module: the system is used for partitioning the repayment orders of the current day deadline based on the key information in the customer information table, and gathering all the repayment orders of the same customer in an interval;
a calculation statistic module: the system is used for calculating the total arrears of all repayment orders in an interval and inputting the customer information and the total arrears into a to-be-deducted flow water meter;
and (4) a batch deduction module: and the payment system is used for reading the to-be-deducted money flow meter and calling the payment system to deduct money in batches.
9. An electronic device, comprising: at least one processor, at least one memory, and a data bus;
the processor and the memory complete mutual communication through the data bus; the memory stores program instructions executable by the processor, the processor calling 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 one 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 |
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