CN111382193A - Method and device for constructing data warehouse topic model - Google Patents
Method and device for constructing data warehouse topic model Download PDFInfo
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Abstract
The application discloses a method and a device for constructing a data warehouse topic model. The method comprises the following steps: creating and executing an ETL task, wherein the ETL task comprises a theme type of business data; the ETL task is used for integrating the business data into a data warehouse; performing topic model calculation according to the business data corresponding to the topic type and a data calculation framework configured in advance according to the data characteristics of the business data to generate a topic model corresponding to the topic type; the topic model includes result set data; and pushing the result set data of the topic model to an ES search platform through an ES interface so that a user can inquire the result set data of the topic model. According to the technical scheme of the embodiment of the application, the computing capacity and the query speed of the data warehouse topic model can be effectively improved.
Description
Technical Field
The present disclosure relates generally to the field of computer technologies, and in particular, to the field of network information security, and in particular, to a method and an apparatus for constructing a data warehouse topic model.
Background
With the rapid development of internet information technology and enterprise market business, the data volume of each large enterprise shows an exponential growth trend, and the data becomes the most important asset of the enterprise. Under the scene of massive business data, how to utilize an enterprise big data warehouse to quickly integrate the business data of the system and mine the data value so as to provide decision support for enterprise market business operation becomes a difficult problem to be solved urgently by enterprises. Therefore, it becomes critical to explore solutions for efficiently building and applying data warehouse topic models.
At present, the construction of a data warehouse topic model mainly comprises a plurality of links such as basic data integration, topic model calculation, modeling result query and the like. For topic model calculation and modeling result query, Mysql, Oracle or hive is still widely used in the industry for storage and calculation query at present, and the implementation method has the following defects:
mysql and oracle have slow calculation speed and cannot support large-scale data calculation; hive has large data computing capacity, but has slow response speed to user query, and is not suitable for being applied to scenes such as instant response of a user to quickly query a data warehouse topic model.
Therefore, how to improve the computing power and query speed of the data warehouse topic model becomes a research topic in the application field of large data processing.
Disclosure of Invention
In view of the above-mentioned shortcomings or drawbacks of the prior art, it is desirable to provide a solution that can effectively improve the computing power and query speed of a data warehouse topic model.
In a first aspect, an embodiment of the present application provides a method for constructing a data warehouse topic model, where the method includes:
creating and executing an ETL task, wherein the ETL task comprises a theme type of business data; the ETL task is used for integrating the business data into a data warehouse;
performing topic model calculation according to the business data corresponding to the topic type and a data calculation framework configured in advance according to the data characteristics of the business data to generate a topic model corresponding to the topic type; the topic model includes result set data;
and pushing the result set data of the topic model to an ES search platform through an ES interface so that a user can inquire the result set data of the topic model.
In a second aspect, an embodiment of the present application further provides an apparatus for constructing a data warehouse topic model, where the apparatus includes:
the ETL unit is used for creating and executing an ETL task, and the ETL task comprises a theme type of the business data; the ETL task is used for integrating the business data into a data warehouse;
the modeling unit is used for performing theme model calculation according to the business data corresponding to the theme type and a data calculation framework configured in advance according to the data characteristics of the business data to generate a theme model corresponding to the theme type; the topic model includes result set data;
and the query unit is used for pushing the result set data of the topic model to an ES search platform through an ES interface so that a user can query the result set data of the topic model.
According to the construction scheme of the data warehouse topic model provided by the embodiment of the application, an ETL task for integrating business data into a data warehouse is created and executed, the ETL task comprises the topic type of the business data, then the topic model is calculated according to the business data corresponding to the topic type and a data calculation framework configured in advance aiming at the data characteristics of the business data, the topic model corresponding to the topic type is generated, the topic model comprises result set data, and then the result set data of the topic model is pushed to an ES search platform through an ES interface so that a user can inquire the result set data of the topic model. According to the technical scheme of the embodiment of the application, on one hand, a data calculation framework adopted in the calculation of the topic model is pre-configured according to the data characteristics of the business data, so that diversified data calculation frameworks can be selected according to the data characteristics of the business data, and the calculation speed of the topic model of the data warehouse is improved; on the other hand, the ES search technology is fused with the data warehouse topic model by adding an ES interface, and the result set data of the topic model is pushed to an ES search platform through the ES interface so that a user can inquire the result set data of the topic model, and therefore the inquiry response speed of the topic model is greatly improved, and a large amount of time cost is saved.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
fig. 1 illustrates an exemplary flowchart of a method for building a data warehouse topic model according to an embodiment of the present application;
fig. 2 shows an exemplary structural block diagram of a device for building a data warehouse topic model provided by an embodiment of the present application; and
FIG. 3 illustrates a schematic diagram of a computer system suitable for use in implementing embodiments of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
As mentioned in the background, the prior art generally makes use of Mysql, Oracle, or hive for storage and computational queries of data warehouse topic models. However, mysql and oracle are slow to compute and cannot support large-scale data computation; hive has large data computing capacity, but has slow response speed to user query, and is not suitable for being applied to scenes such as instant response of a user to quickly query a data warehouse topic model.
In view of the foregoing defects in the prior art, embodiments of the present application provide a solution for constructing a data warehouse topic model. On one hand, the data calculation framework adopted in the theme model calculation is pre-configured according to the data characteristics of the business data, so that diversified data calculation frameworks can be selected according to the data characteristics of the business data, and the calculation speed of the data warehouse theme model is improved; on the other hand, the ES search technology is fused with the data warehouse topic model by adding an ES interface, and the result set data of the topic model is pushed to an ES search platform through the ES interface so that a user can inquire the result set data of the topic model, and therefore the inquiry response speed of the topic model is greatly improved, and a large amount of time cost is saved.
The method of the embodiments of the present application will be described below with reference to a flowchart.
Referring to fig. 1, an exemplary flowchart of a method for building a data warehouse topic model provided by an embodiment of the present application is shown.
The method comprises the following steps:
ETL is an abbreviation used for Extract-Transform-Load in english, and is used to describe the process of extracting (Extract), Transform (Transform), and loading (Load) data from a source end to a destination end. The ETL task in the embodiment of the present application refers to integrating the business data in the business system into the data warehouse through the operations of extraction, interactive conversion and loading.
In addition, the data warehouse in the embodiment of the present application may be a distributed file storage system.
In particular, ETL tasks may be created and executed using ETL tools. In addition, an automatic scheduling time can be set, namely, ETL tasks are created and executed according to the set automatic scheduling time, so that business data can be automatically integrated into a data warehouse.
Currently, ETL tools widely used in the industry, such as informatics, buttons, sqoop and the like, have certain limitations, such as high maintenance service cost (informatics), no incremental synchronization, poor stability, need of regular restart (button), difficulty in operation (sqoop), and the like, and cannot ensure fast integration of data warehouse basic data. In particular, problems such as failure of basic data integration or timeout can occur, and the problems can cause failure of data warehouse topic model construction.
In order to solve the above problems, an automated data integration platform is provided in the embodiment of the present application, and ETL guarantee mechanisms, such as a failure retry mechanism, an overtime early warning mechanism, and a data quality detection mechanism, are added to the automated data integration platform.
Therefore, when the ETL task is created and executed by using the automated data integration platform, the method may further include:
and monitoring the execution condition of the ETL task, and initiating a first abnormal alarm when monitoring that the ETL task is abnormal in execution. Wherein the anomaly comprises one or more of the following conditions:
ETL task execution fails;
ETL task execution timeout;
and after the ETL task is finished, the integration of the business data in the data warehouse fails. And judging whether the integration of the business data in the data warehouse fails or not, wherein the size of a data file block in the data warehouse can be judged according to the shell script.
That is to say, when the execution condition of the ETL task is monitored, one or more of the above three conditions are monitored simultaneously, and the multiple conditions refer to two or more conditions, for example, whether the execution of the ETL task fails is monitored, whether the execution of the ETL task is overtime is monitored, and whether the integration of the business data in the data warehouse fails after the completion of the ETL task is monitored.
In addition, the first abnormal alarm may be an alarm given to the user by the automated data integration platform through an email or a short message.
In the embodiment of the present application, the theme type may be determined according to different types of business data, and for the logistics industry, the theme type may include, for example, a customer theme type, an order theme type, a warehousing theme type, and the like.
The data calculation framework configured in advance for the data characteristics of the service data may be: MR (map reduce) framework, Tez framework, and Spark framework.
Specifically, the data calculation framework may be selected from the three data calculation frameworks according to data characteristics of the service data, for example, an MR framework may be selected for the service data with a large data volume; for traffic data which is not large in data volume but requires high computational efficiency, Tez frames or Spark frames can be selected.
Compared with Mysql, Oracle or hive in the prior art, the three data calculation frameworks greatly improve the calculation speed.
When the theme model budget is carried out, firstly, all business data corresponding to the theme type are determined according to the theme type, then, the theme model calculation is carried out according to all the determined business data and a pre-configured data calculation frame, and a theme model corresponding to the theme type is generated, wherein the theme model comprises result set data. The result set data refers to that all the business data of the subject type are collected and then are shown in forms of tables or diagrams according to different dimensions.
In addition, in order to further guarantee the accuracy of constructing the topic model, the embodiment of the present application may further include:
detecting result set data of the theme model through the shell script and/or the hive script;
and when the result set data of the topic model is detected to be abnormal, initiating a second abnormal alarm.
The second abnormal alarm can be sent to the user by mail or short message for the user to view.
And step 130, pushing the result set data of the topic model to an ES search platform through an ES interface so that a user can inquire the result set data of the topic model.
Specifically, after the result set data of the topic model is pushed to the ES search platform through the ES interface, the ES search platform establishes an index according to the result set data of the topic model, so that a user can inquire the result set data of the topic model. When the index is established, a proper field can be selected as the index according to the characteristics of the table field in the result set data.
Therefore, the user can search the result and the data of the data warehouse topic model based on the established index to obtain millisecond response, and therefore the application efficiency of the data warehouse topic model is greatly improved.
According to the construction scheme of the data warehouse topic model provided by the embodiment of the application, an ETL task for integrating business data into a data warehouse is created and executed, the ETL task comprises the topic type of the business data, then the topic model is calculated according to the business data corresponding to the topic type and a data calculation framework configured in advance aiming at the data characteristics of the business data, the topic model corresponding to the topic type is generated, the topic model comprises result set data, and then the result set data of the topic model is pushed to an ES search platform through an ES interface so that a user can inquire the result set data of the topic model. According to the technical scheme of the embodiment of the application, on one hand, a data calculation framework adopted in the calculation of the topic model is pre-configured according to the data characteristics of the business data, so that diversified data calculation frameworks can be selected according to the data characteristics of the business data, and the calculation speed of the topic model of the data warehouse is improved; on the other hand, the ES search technology is fused with the data warehouse topic model by adding an ES interface, and the result set data of the topic model is pushed to an ES search platform through the ES interface so that a user can inquire the result set data of the topic model, and therefore the inquiry response speed of the topic model is greatly improved, and a large amount of time cost is saved.
It should be noted that while the operations of the method of the present invention are depicted in the drawings in a particular order, this does not require or imply that the operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Rather, the steps depicted in the flowcharts may change the order of execution. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
Further referring to fig. 2, it shows an exemplary structural block diagram of a data warehouse topic model building apparatus provided in an embodiment of the present application.
The device includes:
an ETL unit 21, configured to create and execute an ETL task, where the ETL task includes a subject type of business data; the ETL task is used for integrating the business data into a data warehouse;
the modeling unit 22 is configured to perform topic model calculation according to the service data corresponding to the topic type and a data calculation framework configured in advance for data characteristics of the service data, and generate a topic model corresponding to the topic type; the topic model includes result set data;
and the query unit 23 is configured to push the result set data of the topic model to an ES search platform through an ES interface, so that a user may query the result set data of the topic model.
Optionally, the apparatus further comprises:
a monitoring unit 24, configured to monitor an execution condition of the ETL task; when monitoring that the ETL task is abnormal in execution, initiating a first abnormal alarm; the anomalies include one or more of: the ETL task fails to execute, the ETL task is overtime to execute, and the business data fails to be integrated in the data warehouse after the ETL task is finished.
Optionally, the apparatus further comprises:
a detection unit 25, configured to detect result set data of the topic model through a shell script and/or a hive script; and when the result set data of the topic model is detected to be abnormal, initiating a second abnormal alarm.
Optionally, the querying unit 23 is configured to:
and pushing the result set data of the topic model to an ES (ES) search platform through an ES interface, and establishing an index by the ES search platform according to the result set data of the topic model so that a user can inquire the result set data of the topic model.
Optionally, the data calculation framework includes: MR framework, Tez framework, and Spark framework.
The device for constructing the data warehouse topic model provided by the embodiment of the application can execute the embodiment of the method for constructing the data warehouse topic model, and the implementation principle and the technical effect are similar, and are not described herein again.
Referring now to FIG. 3, shown is a block diagram of a computer system suitable for use in implementing a server according to embodiments of the present application.
As shown in fig. 3, the computer system includes a Central Processing Unit (CPU)301 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)302 or a program loaded from a storage section 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data necessary for the operation of the system 300 are also stored. The CPU301, ROM 302, and RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
The following components are connected to the I/O interface 305: an input portion 306 including a keyboard, a mouse, and the like; an output section 307 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN card, a modem, or the like. The communication section 309 performs communication processing via a network such as the internet. A drive 310 is also connected to the I/O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 310 as necessary, so that a computer program read out therefrom is mounted into the storage section 308 as necessary.
In particular, the process described above with reference to fig. 1 may be implemented as a computer software program, according to an embodiment of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program containing program code for performing a method of building a data warehouse topic model of FIG. 1. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 309, and/or installed from the removable medium 311.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. 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 units or modules described in the embodiments of the present application may be implemented by software or hardware. The described units or modules may also be provided in a processor, and may be described as: a processor includes an ETL unit, a modeling unit, and a query unit. Where the names of these units or modules do not in some cases constitute a limitation of the unit or module itself, for example, an ETL unit may also be described as a "unit for creating and performing ETL tasks".
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs, which when executed by an electronic device, cause the electronic device to implement the method for constructing the data warehouse topic model as described in the above embodiments.
For example, the electronic device may implement the following as shown in fig. 1: s110, creating and executing an ETL task, wherein the ETL task comprises a theme type of business data; s120, performing topic model calculation according to the business data corresponding to the topic type and a data calculation framework configured in advance aiming at the data characteristics of the business data to generate a topic model corresponding to the topic type; the topic model includes result set data; s130, pushing the result set data of the topic model to an ES search platform through an ES interface so that a user can inquire the result set data of the topic model.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by a person skilled in the art that the scope of the invention as referred to in the present application is not limited to the embodiments with a specific combination of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (10)
1. A method for constructing a data warehouse topic model, the method comprising:
creating and executing an ETL task, wherein the ETL task comprises a theme type of business data; the ETL task is used for integrating the business data into a data warehouse;
performing topic model calculation according to the business data corresponding to the topic type and a data calculation framework configured in advance according to the data characteristics of the business data to generate a topic model corresponding to the topic type; the topic model includes result set data;
and pushing the result set data of the topic model to an ES search platform through an ES interface so that a user can inquire the result set data of the topic model.
2. The method of claim 1, wherein after creating and executing the ETL task, the method further comprises:
monitoring the execution condition of the ETL task;
when monitoring that the ETL task is abnormal in execution, initiating a first abnormal alarm; the anomalies include one or more of: the ETL task fails to execute, the ETL task is overtime to execute, and the business data fails to be integrated in the data warehouse after the ETL task is finished.
3. The method according to claim 1 or 2, wherein after generating the topic model corresponding to the topic type, the method further comprises:
detecting result set data of the theme model through a shell script and/or a hive script;
and when the result set data of the topic model is detected to be abnormal, initiating a second abnormal alarm.
4. The method of claim 1 or 2, wherein pushing the result set data of the topic model to an ES search platform through an ES interface for a user to query the result set data of the topic model comprises:
and pushing the result set data of the topic model to an ES (ES) search platform through an ES interface, and establishing an index by the ES search platform according to the result set data of the topic model so that a user can inquire the result set data of the topic model.
5. The method of claim 1 or 2, wherein the data computation framework comprises: MR framework, Tez framework, and Spark framework.
6. An apparatus for constructing a data warehouse topic model, the apparatus comprising:
the ETL unit is used for creating and executing an ETL task, and the ETL task comprises a theme type of the business data; the ETL task is used for integrating the business data into a data warehouse;
the modeling unit is used for performing theme model calculation according to the business data corresponding to the theme type and a data calculation framework configured in advance according to the data characteristics of the business data to generate a theme model corresponding to the theme type; the topic model includes result set data;
and the query unit is used for pushing the result set data of the topic model to an ES search platform through an ES interface so that a user can query the result set data of the topic model.
7. The apparatus of claim 6, further comprising:
the monitoring unit is used for monitoring the execution condition of the ETL task; when monitoring that the ETL task is abnormal in execution, initiating a first abnormal alarm; the anomalies include one or more of: the ETL task fails to execute, the ETL task is overtime to execute, and the business data fails to be integrated in the data warehouse after the ETL task is finished.
8. The apparatus of claim 6 or 7, further comprising:
the detection unit is used for detecting the result set data of the theme model through the shell script and/or the hive script; and when the result set data of the topic model is detected to be abnormal, initiating a second abnormal alarm.
9. The apparatus according to claim 6 or 7, wherein the querying unit is configured to:
and pushing the result set data of the topic model to an ES (ES) search platform through an ES interface, and establishing an index by the ES search platform according to the result set data of the topic model so that a user can inquire the result set data of the topic model.
10. The apparatus of claim 6 or 7, wherein the data computation framework comprises: MR framework, Tez framework, and Spark framework.
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