CN112667635A - Data storage method and system - Google Patents
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Abstract
The invention discloses a data storage method and a system, wherein the method comprises the following steps: reading data through a pyspark task, and performing characteristic calculation on the data to obtain characteristic data; calling python-kafka-sdk to write the feature data into kafka message middleware; processing feature data in kafka message middleware in real time through a spark streaming real-time task; processing the characteristic data and then performing offline storage and/or online storage through a scene _ type field; and sdk, correspondingly inquiring the off-line storage and/or the on-line storage characteristic data and outputting the data. The invention realizes multi-version and dynamic feature updatable data storage of feature data by a technical architecture means, can realize real-time writing of different system feature data of an offline feature storage system and an online feature storage system, can update full or incremental data by a user according to requirements without consuming redundant cluster resources, and can be used in scenes of recommendation, algorithm training, operation analysis and the like.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a data storage method and system based on a recommendation scene.
Background
With the development and popularization of the internet, information resources on the internet expand exponentially, and a recommendation system is generated and gradually applied under the background, so that a plurality of products can recommend interesting contents to users in a targeted manner according to the information of the users, and personalized services are provided for the users.
The recommendation system is used as an information filtering system widely applied, has great success in many fields, and provides personalized products for users and explores potential requirements of the users in electronic commerce; on a search engine, a user is helped to quickly find needed information; on news recommendation, the user is enabled not to miss any interesting piece of information. A large number of off-line features and on-line features cannot be separated behind the accurate recommendations to support a recommendation algorithm model and a recommendation engine, so that the feature storage system becomes an important support of an on-line recommendation system.
The currently common feature storage method is as follows: data are written into the offline feature storage system through feature production calculation, and the synchronous task regularly pulls feature information from the offline feature system in a full scale mode and writes the feature information into the online feature storage system. However, the above method has the following disadvantages:
1. the offline features and the online features are synchronized in a timing mode through a synchronization task, so that the real-time performance of feature information in an online feature system cannot be guaranteed;
2. when the characteristic data is synchronized each time, the synchronization task needs to be started to pull the data in full, so that the cluster resources are consumed;
3. the online characteristic data is imported by the offline characteristic data, and the data cannot be stored in different versions online and offline.
Disclosure of Invention
The invention provides a data storage method and system based on a recommendation scene, aiming at the technical problem that offline and online data cannot be synchronized in real time.
In a first aspect, an embodiment of the present application provides a data storage method based on a recommendation scenario, including:
a characteristic calculation step: reading data through a pyspark task, and performing characteristic calculation on the data to obtain characteristic data;
a data storage step: storing the processed characteristic data;
data query step: the characteristic data is output after being queried through sdk.
The data storage method based on the recommendation scenario, wherein the data storage step includes:
an intermediate storage step: calling python-kafka-sdk to write the feature data into kafka message middleware;
and (3) data processing: processing the feature data in the kafka message middleware in real time by a SparkStreaming real-time streaming task;
a data writing step: and performing offline storage and/or online storage on the processed characteristic data.
In the data storage method based on the recommended scene, the data writing step further includes performing offline storage and/or online storage on the feature data through a scene _ type field.
In the data storage method based on the recommended scenario, the data query step further includes correspondingly querying the feature data stored offline and/or online through sdk and then outputting the feature data.
According to the data storage method based on the recommended scene, the data version information is specified through version.
In a second aspect, an embodiment of the present application provides a data storage system based on a recommendation scenario, including:
a feature calculation unit: reading data through a pyspark task, and performing characteristic calculation on the data to obtain characteristic data;
a data storage unit: storing the processed characteristic data;
a data query unit: the characteristic data is output after being queried through sdk.
The data storage system based on the recommendation scenario, wherein the data storage unit includes:
a feature writing module: calling python-kafka-sdk to write the feature data into kafka message middleware;
a real-time processing module: processing the feature data in the kafka message middleware in real time by a SparkStreaming real-time streaming task;
an offline storage module: performing offline storage on the processed feature data;
an online storage module: and storing the processed characteristic data on line.
In the data storage system based on the recommended scene, the offline storage module and the online storage module perform offline storage and/or online storage on the feature data through the scene _ type field.
The data storage system based on the recommendation scenario, wherein the data query unit includes: and the characteristic reading module is used for correspondingly inquiring the characteristic data of the offline storage module and/or the online storage module through sdk and then outputting the characteristic data.
In the data storage system based on the recommended scene, the data version information is specified by version.
Compared with the prior art, the invention has the advantages and positive effects that:
1. the invention provides a data storage method supporting multi-version, multi-feature and updatable feature data of a big data model based on a recommended scene, which meets various service requirements and also ensures real-time and dynamic updating of the feature data by performing off-line and on-line sub-environment multi-version storage on the feature data.
2. The invention realizes multi-version and dynamic feature updatable data storage of feature data by a technical architecture means, can realize real-time writing of different system feature data of an offline feature storage system and an online feature storage system, can update full or incremental data by a user according to requirements without consuming redundant cluster resources, and can be used in scenes of recommendation, algorithm training, operation analysis and the like.
Drawings
FIG. 1 is a schematic diagram illustrating steps of a data storage method based on a recommended scenario according to the present invention;
FIG. 2 is a flowchart based on step S2 in FIG. 1 according to the present invention;
FIG. 3 is a technical schematic diagram of a data storage method based on a recommended scenario according to the present invention;
FIG. 4 is a block diagram of a recommendation scenario based data storage system according to the present invention;
FIG. 5 is a flowchart illustrating a recommended scenario-based data storage system according to the present invention;
fig. 6 is a block diagram of a computer device according to an embodiment of the present application.
Wherein the reference numerals are:
11. a feature calculation unit; 12. a data storage unit; 121. a feature writing module; 122. a real-time processing module; 123. an offline storage module; 124. an online storage module; 13. a data query unit; 131. a feature reading module; 81. a processor; 82. a memory; 83. a communication interface; 80. a bus.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as referred to herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
The present invention is described in detail with reference to the embodiments shown in the drawings, but it should be understood that these embodiments are not intended to limit the present invention, and those skilled in the art should understand that functional, methodological, or structural equivalents or substitutions made by these embodiments are within the scope of the present invention.
Before describing in detail the various embodiments of the present invention, the core inventive concepts of the present invention are summarized and described in detail by the following several embodiments.
The invention provides a data storage method supporting multi-version, multi-feature and updatable feature data of a big data model based on a recommended scene, wherein data subjected to feature calculation is written into kafka message middleware, a spark streaming real-time task processes the feature data in the message middleware in real time and simultaneously writes the feature data into offline storage hudi and/or online storage redis, and a client reads the feature data through corresponding sdk to serve different service scenes.
The first embodiment is as follows:
referring to fig. 1, fig. 1 is a schematic diagram illustrating steps of a data storage method based on a recommended scenario according to the present invention. As shown in fig. 1, the present embodiment discloses a specific implementation of a recommendation scenario-based data storage method (hereinafter referred to as "method").
Specifically, the method disclosed in this embodiment mainly includes the following steps:
step S1: and reading data through a pyspark task, and performing characteristic calculation on the data to obtain characteristic data.
Specifically, PySpark is an API provided by Spark for a Python developer, and a client reads data through a PySpark task and obtains the characteristics of the data after calculation.
Referring to fig. 2, step S2 is performed: and storing the characteristic data after processing.
Wherein, step S2 specifically includes the following contents:
step S21: calling python-kafka-sdk to write the feature data into kafka message middleware;
in particular, the message middleware utilizes an efficient and reliable message passing mechanism for platform-independent data communication and integration of distributed systems based on data communication. kafka is a linkedin open-source distributed publish-subscribe messaging system, currently attributed to the top level project of Apache.
Step S22: processing the feature data in the kafka message middleware in real time by a SparkStreaming real-time streaming task;
step S23: and performing offline storage and/or online storage on the processed characteristic data.
Specifically, the storing process in step S23 writes the feature data into the offline storage hudi and/or the online storage redis through the scene _ type field.
Then, step S3 is executed: the characteristic data is output after being queried through sdk.
Specifically, step S3 queries sdk the feature data of the offline storage hudi and/or the online storage redis, and outputs the query.
Specifically, after the client sends the query request, the data version information is specified through version according to the requirements of the client.
Please refer to fig. 3. Fig. 3 is a technical schematic diagram of a data storage method based on a recommended scenario, which is provided in the present invention, and with reference to fig. 3, an application flow of the method is specifically described as follows:
the client side pyspark task reads data, after feature calculation is carried out, python-kafka-sdk is called to write the data into kafka message middleware, wherein dataframe is structured feature data, scene _ type field is specified to be written into an offline online storage system, and version is specified to be data version information. And then, the feature data in the kafka is processed by a back-end spark streaming real-time streaming task in real time and is written into offline storage hudi and online storage redis. Finally the client queries the corresponding feature data via the correspondence sdk.
Example two:
with reference to fig. 4 and 5, a data storage method based on a recommendation scenario disclosed in the first embodiment is disclosed, and this embodiment discloses a specific implementation example of a data storage system (hereinafter referred to as "system") based on a recommendation scenario.
Referring to fig. 4, the system includes:
feature calculation unit 11: reading data through a pyspark task, and performing characteristic calculation on the data to obtain characteristic data;
data storage unit 12: storing the processed characteristic data;
the data querying unit 13: the characteristic data is output after being queried through sdk.
The feature calculating unit 11 further includes a feature generating module 111, and the feature generating module 111 is configured to read data and perform feature calculation on the data to generate feature data.
The data storage unit 12 specifically includes:
the feature writing module 121: calling python-kafka-sdk to write the feature data into kafka message middleware;
the real-time processing module 122: processing the feature data in the kafka message middleware in real time by a SparkStreaming real-time streaming task;
the offline storage module 123: performing offline storage on the processed feature data;
the online storage module 124: and storing the processed characteristic data on line.
In particular, feature write module 121 is used for encapsulated write sdk, and feature production module 111 can write feature data into message intermediate storage module 125, i.e., message middleware, only by calling feature write module 121.
Specifically, the offline storage module 123 is configured to store offline data serving a scene of offline computation; the online storage module 124 is used for storing online data, serving a scenario of online computing.
The real-time processing module 122 can read the newly added message in the message intermediate storage module 125 in real time and write the message into the offline storage module 123 and the online storage module 124 simultaneously.
The offline storage module 123 and the online storage module 124 perform offline storage and/or online storage on the feature data through a scene _ type field.
The data query unit 13 includes a feature reading module 131, the feature reading module 131 is used for reading sdk in a package manner, and the client calls different feature reading modules 131 to correspondingly read feature data from the offline storage module 123 and/or the online storage module 124, and specifies data version information for serving different business scenarios through version according to the requirements of the client.
Please refer to fig. 5. Fig. 5 is a call flow chart of the data storage system based on the recommended scenario, which, with reference to fig. 5, specifically illustrates the call steps of the flow chart as follows:
1. the feature generation module 111 reads data to perform feature calculation, and after the calculation is completed, the feature writing module 121 is called to write a feature value into the message intermediate storage module (message middleware) 125.
2. The real-time processing module 122 reads data from the message intermediate storage module 125 in real time while writing to the offline storage module 123 and the online storage module 124.
3. The client calls different feature reading modules 131 to read data from different storage modules to serve different business scenarios.
Please refer to the description of the first embodiment, and details thereof are not repeated herein.
Example three:
referring to fig. 6, the present embodiment discloses an embodiment of a computer device. The computer device may comprise a processor 81 and a memory 82 in which computer program instructions are stored.
Specifically, the processor 81 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
Memory 82 may include, among other things, mass storage for data or instructions. By way of example, and not limitation, memory 82 may include a Hard Disk Drive (Hard Disk Drive, abbreviated to HDD), a floppy Disk Drive, a Solid State Drive (SSD), flash memory, an optical Disk, a magneto-optical Disk, tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 82 may include removable or non-removable (or fixed) media, where appropriate. The memory 82 may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory 82 is a Non-Volatile (Non-Volatile) memory. In particular embodiments, Memory 82 includes Read-Only Memory (ROM) and Random Access Memory (RAM). The ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), Electrically rewritable ROM (EAROM), or FLASH Memory (FLASH), or a combination of two or more of these, where appropriate. The RAM may be a Static Random-Access Memory (SRAM) or a Dynamic Random-Access Memory (DRAM), where the DRAM may be a Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), an Extended data output Dynamic Random-Access Memory (EDODRAM), a Synchronous Dynamic Random-Access Memory (SDRAM), and the like.
The memory 82 may be used to store or cache various data files for processing and/or communication use, as well as possible computer program instructions executed by the processor 81.
The processor 81 realizes any one of the above-described embodiments of the recommendation scenario based data storage method by reading and executing the computer program instructions stored in the memory 82.
In some of these embodiments, the computer device may also include a communication interface 83 and a bus 80. As shown in fig. 6, the processor 81, the memory 82, and the communication interface 83 are connected via the bus 80 to complete communication therebetween.
The communication interface 83 is used for implementing communication between modules, devices, units and/or equipment in the embodiment of the present application. The communication port 83 may also be implemented with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.
In addition, in combination with the data storage method based on the recommended scenario in the foregoing embodiment, the embodiment of the present application may provide a computer-readable storage medium to implement. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the recommendation scenario-based data storage methods of the above embodiments.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
In summary, the beneficial effects of the invention are that the invention provides a data storage method supporting multi-version, multi-feature and updatable feature data of a big data model based on a recommended scene, and by performing offline and online multi-version storage of sub-environments on the feature data, various business requirements are met, and real-time and dynamic updating of the feature data is also ensured. The data storage with multiple versions and updatable dynamic characteristics of the characteristic data is realized through a technical architecture means, the real-time writing of different system characteristic data of an offline characteristic storage system and an online characteristic storage system can be realized, a user can update full or incremental data according to requirements without consuming redundant cluster resources, and the method can be used in scenes such as recommendation, algorithm training, operation analysis and the like.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A data storage method is characterized by comprising the following steps based on a recommendation scene:
a characteristic calculation step: reading data through a pyspark task, and performing characteristic calculation on the data to obtain characteristic data;
a data storage step: storing the processed characteristic data;
data query step: the characteristic data is output after being queried through sdk.
2. The data storage method of claim 1, wherein the data storage step comprises:
an intermediate storage step: calling python-kafka-sdk to write the feature data into kafka message middleware;
and (3) data processing: processing the feature data in the kafka message middleware in real time by a SparkStreaming real-time streaming task;
a data writing step: and performing offline storage and/or online storage on the processed characteristic data.
3. The data storage method according to claim 2, wherein the data writing step further comprises storing the feature data offline and/or online through a scene _ type field.
4. The data storage method according to claim 2, wherein the data query step further comprises correspondingly querying and outputting the characteristic data stored offline and/or online through sdk.
5. The data storage method according to any one of claims 1 to 4, wherein the data version information is specified by version.
6. A data storage system, based on a recommendation scenario, comprising:
a feature calculation unit: reading data through a pyspark task, and performing characteristic calculation on the data to obtain characteristic data;
a data storage unit: storing the processed characteristic data;
a data query unit: the characteristic data is output after being queried through sdk.
7. The data storage system of claim 6, wherein the data storage unit comprises:
a feature writing module: calling python-kafka-sdk to write the feature data into kafka message middleware;
a real-time processing module: processing the feature data in the kafka message middleware in real time by a SparkStreaming real-time streaming task;
an offline storage module: performing offline storage on the processed feature data;
an online storage module: and storing the processed characteristic data on line.
8. The data storage system of claim 7, wherein the offline storage module and the online storage module store the feature data offline and/or online through a scene _ type field.
9. The data storage system of claim 7, wherein the data query unit comprises: and the characteristic reading module is used for correspondingly inquiring the characteristic data of the offline storage module and/or the online storage module through sdk and then outputting the characteristic data.
10. The data storage system according to any one of claims 6 to 9, wherein the data version information is specified by version.
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| CN106909598A (en) * | 2016-07-01 | 2017-06-30 | 阿里巴巴集团控股有限公司 | It is a kind of to ensure processing method, the apparatus and system for calculating data consistency |
| CN111651524A (en) * | 2020-06-05 | 2020-09-11 | 第四范式（北京）技术有限公司 | Auxiliary implementation method and device for online prediction by using machine learning model |
| US20200311568A1 (en) * | 2019-03-26 | 2020-10-01 | Microsoft Technology Licensing, Llc | Filtering content using generalized linear mixed models |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106909598A (en) * | 2016-07-01 | 2017-06-30 | 阿里巴巴集团控股有限公司 | It is a kind of to ensure processing method, the apparatus and system for calculating data consistency |
| US20200311568A1 (en) * | 2019-03-26 | 2020-10-01 | Microsoft Technology Licensing, Llc | Filtering content using generalized linear mixed models |
| CN111651524A (en) * | 2020-06-05 | 2020-09-11 | 第四范式（北京）技术有限公司 | Auxiliary implementation method and device for online prediction by using machine learning model |
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