CN109857524B - Stream computing method, device, equipment and computer readable storage medium - Google Patents
Stream computing method, device, equipment and computer readable storage medium Download PDFInfo
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Abstract
The invention discloses a stream computing method, which comprises the following steps: reading service flow data generated by a service system in real time; reading event identification fields from the service flow data, wherein different event identification fields are used for identifying different events; shunting the business flow data to corresponding events according to the event identification field; and in each event, calling a preset rule package to perform rule calculation on the shunted business flow data, and obtaining a corresponding flow type calculation result. The invention also discloses a streaming computing device, equipment and a computer readable storage medium. The invention can realize flexible configuration of service rules in stream type calculation, and the event stream can be extended in parallel, thereby meeting the increasingly updated service processing requirement.
Description
Technical Field
The present invention relates to the field of big data processing technologies, and in particular, to a streaming computing method, a streaming computing device, and a streaming computing device.
Background
Streaming computing is a special incremental computation that, unlike offline computing, derives from the mining appeal of business to the "aging" value of mass data, which can well analyze large-scale streaming data in real-time during constantly changing movements, capturing potentially useful information, and sending the results to the next compute node.
The existing flow type calculation flow is as follows: the service system reports the event to the message middleware, and after the real-time computing system RCS (Real Time Computing System) consumes the message of the message middleware, the service system prepares computing parameters by combining the reported message and the data source, then sequentially computes according to a series of hard-coded rules, and finally outputs a computing result. The disadvantage of this calculation is that: (1) event streams cannot be expanded in parallel; (2) The flexible configuration of the service rules cannot be achieved, namely, each time an event is added, the corresponding processing rule needs to be hard-coded again, and the same event stream is processed, so that multiplexing cannot be achieved. Thus, existing streaming computing approaches have failed to meet the increasingly newer business processing needs.
Disclosure of Invention
The main purpose of the present invention is to propose a streaming computing method, a device and a computer readable storage medium, which aim to realize flexible configuration of business rules in streaming computing, and event streams can be expanded in parallel, so as to meet increasingly updated business processing requirements.
In order to achieve the above object, the present invention provides a streaming computing method, which includes the steps of:
reading service flow data generated by a service system in real time;
reading event identification fields from the service flow data, wherein different event identification fields are used for identifying different events;
shunting the business flow data to corresponding events according to the event identification field;
and in each event, calling a preset rule package to perform rule calculation on the shunted business flow data, and obtaining a corresponding flow type calculation result.
Preferably, the step of reading service flow data generated by the service system in real time includes:
and reading the service flow data generated by the service system from the distributed message queue in real time, wherein the distributed message queue is used for storing the service flow data sent by the service system.
Preferably, in each event, the step of calling a preset rule package to perform rule calculation on the service flow data after the splitting, and obtaining a corresponding streaming calculation result includes:
converting the business flow data which are distributed into each event into a preset standard data format;
acquiring preset incoming parameters from service flow data subjected to standard data format conversion;
and calling a preset rule package to perform rule calculation on the input parameters to obtain a corresponding streaming calculation result.
Preferably, before the step of reading the service flow data generated by the service system in real time, the method further includes:
and receiving a rule package configuration instruction, configuring a rule package according to the rule package configuration instruction, and setting an incoming parameter corresponding to the rule package.
Preferably, in each event, after the step of calling a preset rule packet to perform rule calculation on the service flow data after the splitting to obtain a corresponding streaming calculation result, the method further includes:
and receiving a modification instruction based on the configured rule package and the input parameters, and modifying the configured rule package and the configured input parameters according to the modification instruction.
In addition, to achieve the above object, the present invention also provides a streaming computing device, including:
the first reading module is used for reading service flow data generated by the service system in real time;
the second reading module is used for reading event identification fields from the business flow data, wherein different event identification fields are used for identifying different events;
the distribution module is used for distributing the business flow data to the corresponding event according to the event identification field;
and the calculation module is used for calling a preset rule package to perform rule calculation on the shunted business flow data in each event so as to obtain a corresponding streaming calculation result.
Preferably, the first reading module is further configured to:
and reading the service flow data generated by the service system from the distributed message queue in real time, wherein the distributed message queue is used for storing the service flow data sent by the service system.
Preferably, the computing module is further configured to:
converting the business flow data which are distributed into each event into a preset standard data format;
acquiring preset incoming parameters from service flow data subjected to standard data format conversion;
and calling a preset rule package to perform rule calculation on the input parameters to obtain a corresponding streaming calculation result.
Preferably, the streaming computing device further comprises:
the configuration module is used for receiving a rule package configuration instruction, configuring a rule package according to the rule package configuration instruction, and setting an incoming parameter corresponding to the rule package.
Preferably, the streaming computing device further comprises:
and the modification module is used for receiving a modification instruction based on the configured rule package and the input parameter, and modifying the configured rule package and the configured input parameter according to the modification instruction.
In addition, to achieve the above object, the present invention also provides a streaming computing device including: the system comprises a memory, a processor and a streaming computing program stored on the memory and capable of running on the processor, wherein the streaming computing program realizes the steps of the streaming computing method when being executed by the processor.
In addition, to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a streaming calculation program which, when executed by a processor, implements the steps of the streaming calculation method as described above.
The invention reads the service stream data generated by the service system in real time; reading event identification fields from the service flow data, wherein different event identification fields are used for identifying different events; shunting the business flow data to corresponding events according to the event identification field; and in each event, calling a preset rule package to perform rule calculation on the shunted business flow data, and obtaining a corresponding flow type calculation result. By adopting the method for carrying out the streaming calculation, when an event is newly added, the event stream can be expanded in parallel without re-hard coding the corresponding processing rule, and when the business rule is changed, the rule packet is only required to be modified correspondingly, so that the invention can meet the increasingly updated business processing requirement.
Drawings
FIG. 1 is a schematic diagram of a device architecture of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart of a first embodiment of a streaming computing method according to the present invention;
fig. 3 is a schematic diagram illustrating the diversion of service flow data to corresponding events for calculation in an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The main solutions of the embodiments of the present invention are: reading service flow data generated by a service system in real time; reading event identification fields from the service flow data, wherein different event identification fields are used for identifying different events; shunting the business flow data to corresponding events according to the event identification field; and in each event, calling a preset rule package to perform rule calculation on the shunted business flow data, and obtaining a corresponding flow type calculation result.
The existing flow type calculation flow is as follows: the service system reports the event to the message middleware, and after the real-time computing platform RCS (Real Time Computing System) consumes the message of the message middleware, the service system prepares computing parameters by combining the reported message and the data source, and then sequentially computes according to a series of hard-coded rules, and finally outputs a computing result. The disadvantage of this calculation is that: (1) event streams cannot be expanded in parallel; (2) The flexible configuration of the service rules cannot be achieved, namely, each time an event is added, the corresponding processing rule needs to be hard-coded again, and the same event stream is processed, so that multiplexing cannot be achieved. Thus, existing streaming computing approaches have failed to meet the increasingly newer business processing needs.
According to the method, the flow type calculation is carried out, when an event is newly added, the event flow can be expanded in parallel without re-hard coding corresponding processing rules, and when the business rules are changed, only the rule package is required to be modified correspondingly, so that the method can meet the increasingly updated business processing requirements.
As shown in fig. 1, fig. 1 is a schematic diagram of a terminal structure of a hardware running environment according to an embodiment of the present invention.
The streaming computing device according to the embodiment of the invention can be a PC or a server device.
As shown in fig. 1, the streaming computing device may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the device structure shown in fig. 1 is not limiting of the device and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a streaming computer program may be included in the memory 1005, which is a type of computer storage medium.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a background server and performing data communication with the background server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to call a streaming calculation program stored in the memory 1005 and perform the following operations:
reading service flow data generated by a service system in real time;
reading event identification fields from the service flow data, wherein different event identification fields are used for identifying different events;
shunting the business flow data to corresponding events according to the event identification field;
and in each event, calling a preset rule package to perform rule calculation on the shunted business flow data, and obtaining a corresponding flow type calculation result.
Further, the processor 1001 may call a streaming calculation program stored in the memory 1005, and further perform the following operations:
and reading the service flow data generated by the service system from the distributed message queue in real time, wherein the distributed message queue is used for storing the service flow data sent by the service system.
Further, the processor 1001 may call a streaming calculation program stored in the memory 1005, and further perform the following operations:
converting the business flow data which are distributed into each event into a preset standard data format;
acquiring preset incoming parameters from service flow data subjected to standard data format conversion;
and calling a preset rule package to perform rule calculation on the input parameters to obtain a corresponding streaming calculation result.
Further, the processor 1001 may call a streaming calculation program stored in the memory 1005, and further perform the following operations:
and receiving a rule package configuration instruction, configuring a rule package according to the rule package configuration instruction, and setting an incoming parameter corresponding to the rule package.
Further, the processor 1001 may call a streaming calculation program stored in the memory 1005, and further perform the following operations:
and receiving a modification instruction based on the configured rule package and the input parameters, and modifying the configured rule package and the configured input parameters according to the modification instruction.
Based on the above hardware structure, the embodiment of the stream computing method of the invention is provided.
Referring to fig. 2, fig. 2 is a flow chart of a first embodiment of a streaming computing method according to the present invention, where the method includes:
step S10, service flow data generated by a service system are read in real time;
the streaming computing method is applied to streaming computing equipment running on a big data platform, wherein the big data platform can be a Spark platform which is a popular big data computing and counting platform at present, and the Spark platform can realize various machine learning and data mining by computing and counting the big data; the business system can be a business system of financial institutions such as banks, securities companies, insurance companies and the like, is used for processing and recording various financial businesses such as deposit, transfer, investment and the like of users and generating corresponding business flow data, and the transaction system of the banks is taken as an example, and the transaction system of the banks can comprise a background transaction system database for storing related business information such as deposit, transfer and the like of bank accounts in a plurality of storage devices in a distributed manner because the daily transaction quantity of the banks is huge. The streaming computing device has a real-time computing system RCS (Real Time Computing System) onboard, and the RCS has a distributed streaming computing component running thereon for streaming computing, including but not limited to: spark Streams, storm, samza, flink, and Kafka Streams.
The step S10 may specifically include: and reading the service flow data generated by the service system from the distributed message queue in real time, wherein the distributed message queue is used for storing the service flow data sent by the service system.
In particular, the service system may send the service flow data to a distributed message queue for storage, taking a distributed message queue Apache Kafka (a distributed publish-subscribe message system, mainly used for processing active streaming data) as an example, the streaming computing device reads the service flow data from the Kafka message queue, which is partially real-time. It should be noted that, the transaction system database may further include a backup library corresponding to the transaction system database, where the backup library is used to backup and store transaction data, and when the Apache Kafka reads online streaming data, the transaction system database may be read from the backup library to avoid occupying transaction system resources. In addition, besides Apache Kafka, the distributed Message queues in the embodiment of the present invention may also be other types of distributed Message queues, such as ActiveMQ (Message Queue), rabbitMQ, rocketMQ, and the like, which may be flexibly set when implemented.
Step S20, reading event identification fields from the service flow data, wherein different event identification fields are used for identifying different events;
the service flow data comprises event identification fields, wherein different event identification fields are used for identifying different events, subsequent newly-added events can be expanded in parallel, and all events cannot be influenced mutually.
Step S30, the business flow data is shunted to the corresponding event according to the event identification field;
referring to fig. 3, fig. 3 is a schematic diagram illustrating the calculation of splitting the service flow data into corresponding events in the embodiment of the present invention. The real-time computing system shunts the service flow data into corresponding events according to the read event identification field, for example, the current service flow data comprises service flow data of 3 different events (event 1, event 2 and event 3), and correspondingly shunts the service flow data into event 1, event 2 and event 3.
And step S40, in each event, calling a preset rule package to perform rule calculation on the distributed business flow data, and obtaining a corresponding flow type calculation result.
After the service data is distributed, in each event, a preset rule packet is called to perform rule calculation on the distributed service flow data, and a corresponding calculation result is obtained and is used as a streaming calculation result.
The step S40 may specifically include: converting the business flow data which are distributed into each event into a preset standard data format; acquiring preset incoming parameters from service flow data subjected to standard data format conversion; and calling a preset rule package to perform rule calculation on the input parameters to obtain a corresponding streaming calculation result.
In order to realize unification of data structures, service flow data which are distributed to all events can be firstly converted into a preset standard data format, such as a JSON format, then preset incoming parameters are obtained from the service flow data which are converted by the standard data format, and a preset rule package is called to perform rule calculation on the incoming parameters, so that a corresponding flow calculation result is obtained. Further, the streaming calculation result can be stored in a preset database, so that the subsequent check can be facilitated.
According to the method, when an event is newly added, the event stream can be expanded in parallel without re-hard coding corresponding processing rules, and when the business rules are changed, only the rule package is modified correspondingly, so that the increasingly updated business processing requirements can be met.
Further, based on the first embodiment of the streaming computing method of the present invention, a second embodiment of the streaming computing method of the present invention is provided.
In this embodiment, before the step S10, the method may further include: and receiving a rule package configuration instruction, configuring a rule package according to the rule package configuration instruction, and setting an incoming parameter corresponding to the rule package.
Specifically, the platform manager may trigger a rule package configuration instruction, and the real-time computing system configures a rule package according to the rule package configuration instruction, and sets an incoming parameter corresponding to the rule package, where the rule package includes service processing logic. Through a preset rule package, a precondition guarantee is provided for subsequent streaming calculation.
Further, after the step S40, the method may further include: and receiving a modification instruction based on the configured rule package and the input parameters, and modifying the configured rule package and the configured input parameters according to the modification instruction.
When the business rule is changed, the platform manager can trigger a modification instruction to modify the configured rule package and the input parameters, and the real-time computing system updates the rule package to enable the new rule package to take effect in real time. The mode is flexible and convenient to operate, and can meet the increasingly updated business processing requirements.
The invention also provides a streaming computing device. The streaming computing device of the embodiment of the invention comprises:
the first reading module is used for reading service flow data generated by the service system in real time;
the second reading module is used for reading event identification fields from the business flow data, wherein different event identification fields are used for identifying different events;
the distribution module is used for distributing the business flow data to the corresponding event according to the event identification field;
and the calculation module is used for calling a preset rule package to perform rule calculation on the shunted business flow data in each event so as to obtain a corresponding streaming calculation result.
Further, the first reading module is further configured to:
and reading the service flow data generated by the service system from the distributed message queue in real time, wherein the distributed message queue is used for storing the service flow data sent by the service system.
Further, the computing module is further configured to:
converting the business flow data which are distributed into each event into a preset standard data format;
acquiring preset incoming parameters from service flow data subjected to standard data format conversion;
and calling a preset rule package to perform rule calculation on the input parameters to obtain a corresponding streaming calculation result.
Further, the streaming computing device further includes:
the configuration module is used for receiving a rule package configuration instruction, configuring a rule package according to the rule package configuration instruction, and setting an incoming parameter corresponding to the rule package.
Further, the streaming computing device further includes:
and the modification module is used for receiving a modification instruction based on the configured rule package and the input parameter, and modifying the configured rule package and the configured input parameter according to the modification instruction.
Operations performed by the above program modules may refer to various embodiments of the streaming computing method of the present invention, and are not described herein.
The invention also provides a computer readable storage medium.
The present invention computer readable storage medium has stored thereon a streaming calculation program which, when executed by a processor, implements the steps of the streaming calculation method as described above.
The method implemented when the streaming computing program running on the processor is executed may refer to various embodiments of the streaming computing method of the present invention, which are not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (8)
1. A streaming computing method, characterized in that the streaming computing method comprises the steps of:
receiving a rule package configuration instruction, configuring a rule package according to the rule package configuration instruction, and setting an incoming parameter corresponding to the rule package;
reading service flow data generated by a service system in real time;
reading event identification fields from the service flow data, wherein different event identification fields are used for identifying different events;
shunting the business flow data to corresponding events according to the event identification field;
converting the business flow data which are distributed into each event into a preset standard data format;
acquiring preset incoming parameters from service flow data subjected to standard data format conversion;
and calling a preset rule package to perform rule calculation on the input parameters to obtain a corresponding streaming calculation result.
2. The streaming computing method of claim 1, wherein the step of reading service pipeline data generated by the service system in real time comprises:
and reading the service flow data generated by the service system from the distributed message queue in real time, wherein the distributed message queue is used for storing the service flow data sent by the service system.
3. The method of streaming calculation according to claim 1, wherein in each event, invoking a preset rule packet to perform rule calculation on the service flow data after being shunted, and after the step of obtaining a corresponding streaming calculation result, further comprising:
and receiving a modification instruction based on the configured rule package and the input parameters, and modifying the configured rule package and the configured input parameters according to the modification instruction.
4. A streaming computing device, the streaming computing device comprising:
the configuration module is used for receiving a rule package configuration instruction, configuring a rule package according to the rule package configuration instruction, and setting an incoming parameter corresponding to the rule package;
the first reading module is used for reading service flow data generated by the service system in real time;
the second reading module is used for reading event identification fields from the business flow data, wherein different event identification fields are used for identifying different events;
the distribution module is used for distributing the business flow data to the corresponding event according to the event identification field;
the computing module is used for converting the business flow data distributed to each event into a preset standard data format; acquiring preset incoming parameters from service flow data subjected to standard data format conversion; and calling a preset rule package to perform rule calculation on the input parameters to obtain a corresponding streaming calculation result.
5. The streaming computing device of claim 4, wherein the first reading module is further to:
and reading the service flow data generated by the service system from the distributed message queue in real time, wherein the distributed message queue is used for storing the service flow data sent by the service system.
6. The streaming computing device according to claim 4, wherein the streaming computing device further comprises:
and the modification module is used for receiving a modification instruction based on the configured rule package and the input parameter, and modifying the configured rule package and the configured input parameter according to the modification instruction.
7. A streaming computing device, the streaming computing device comprising: memory, a processor and a streaming calculation program stored on the memory and executable on the processor, which when executed by the processor implements the steps of the streaming calculation method according to any of claims 1 to 3.
8. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a streaming calculation program, which when executed by a processor, implements the steps of the streaming calculation method according to any of claims 1 to 3.
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| CN111221550B (en) * | 2019-10-24 | 2022-09-06 | 支付宝(杭州)信息技术有限公司 | Rule updating method and device for streaming computing and streaming computing system |
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| CN109857524A (en) | 2019-06-07 |
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