CN110704402A - Data analysis system, method and equipment for multiple data sources - Google Patents
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Abstract
The embodiment of the application discloses a data analysis system, a method and equipment for multiple data sources, which comprises the following steps: the data splitting unit is used for splitting data acquired from a product into user data and user behavior data; a data storage unit for storing the user data and the user behavior data; the data storage unit comprises a low-speed writing storage unit and a high-speed writing storage unit, the low-speed writing storage unit is used for storing the user data, and the high-speed writing storage unit is used for storing the user behavior data; and the query engine unit is used for accessing the low-speed writing storage unit and the high-speed writing storage unit, acquiring the user data and the user behavior data, and transmitting the acquired user data and the user behavior data to an OLAP (on-line analytical processing) system or an off-line analytical system. The data storage and use method and the data storage and use device solve the problems that in the prior art, data storage and use cost is too high, and maintainability is poor.
Description
Technical Field
The present application relates to the field of data analysis technologies, and in particular, to a data analysis system, method, and device for multiple data sources.
Background
Data analysis of user behavior data for a product mostly needs to go through two phases: the first stage is as follows: when a new function is just on line, in order to quickly verify the new function, real-time behavior analysis is required, and then a product is adjusted more quickly, and generally, analysis delay is required to be in the order of minutes or even seconds. And a second stage: after the functions are on line for a period of time, the user data are stable, data analysis is needed according to a data analysis report sent out in the early stage, the requirement on real-time performance of the analysis is not high, statistics is usually carried out according to days, and the analysis is generally summarized and summarized of the first-stage analysis.
For the first stage, high latency requirement is usually completed by using online analytical processing OLAP, where OLAP has high real-time requirement and relatively weak stability; in the second stage, an off-line analysis mode is usually used because the time delay requirement is not high.
In the prior art, user behavior data is usually copied into two copies and sent to the OLAP system and the offline analysis system, respectively. This is relatively expensive in terms of data storage space and maintenance cost, because two completely different data processing paths are used, and a lot of cost is also required in terms of maintaining data consistency. For an analyst using the data analysis system, the two systems also need to be switched at the same time, so that the use cost is increased.
Disclosure of Invention
The embodiment of the application provides a data analysis system, method and device for multiple data sources, and solves the problems of high data storage and use cost and poor maintainability in the prior art.
In view of the above, the present application provides, in a first aspect, a data analysis system with multiple data sources, the system including:
the data splitting unit is used for splitting data acquired from a product into user data and user behavior data;
a data storage unit for storing the user data and the user behavior data;
the data storage unit comprises a low-speed writing storage unit and a high-speed writing storage unit, the low-speed writing storage unit is used for storing the user data, and the high-speed writing storage unit is used for storing the user behavior data;
and the query engine unit is used for accessing the low-speed writing storage unit and the high-speed writing storage unit, acquiring the user data and the user behavior data, and transmitting the acquired user data and the user behavior data to an OLAP (on-line analytical processing) system or an off-line analytical system.
Preferably, the visual operation unit is used for providing an operable interface for an operator and displaying an analysis result of the OLAP system or the offline analysis system.
Preferably, the query engine unit is further configured to obtain an operation instruction of the visualization operation unit, obtain the user data and/or the user behavior data from the data storage unit according to the operation instruction, and transmit the user data and/or the user behavior data to the OLAP system or the offline analysis system.
Preferably, the query engine unit comprises an instruction generation unit;
the instruction generating unit is used for generating a unified data access instruction according to the operation instruction, wherein the unified data access instruction is an instruction for calling the user data and/or the user behavior data by an OLAP (on-line analytical processing) system or an offline analysis system.
Preferably, the user data includes identity information of the user and tag information, and the user behavior data includes behavior data of the user.
A second aspect of the present application provides a data analysis method for multiple data sources, the method including:
acquiring an operation instruction for analyzing a product function;
selecting a needed OLAP system or an off-line analysis system according to the operation instruction;
selecting user data and/or user behavior data required by the OLAP system or the off-line analysis system according to an operation instruction; the user data is stored separately from the user behavior data;
and displaying the analysis result of the OLAP system or the off-line analysis system.
Preferably, the user data is stored in a data source which can be randomly accessed, edited and deleted, and the user behavior data is stored in a data source which can be randomly accessed, can not edit the historical data and can not delete the historical data.
Preferably, the selecting, according to the operation instruction, user data and/or user behavior data required by the OLAP system or the offline analysis system specifically includes:
the method comprises the steps that an inquiry engine obtains an operation instruction and converts the operation instruction into a unified data access instruction, and the OLAP system or the off-line analysis system accesses required user data and/or user behavior data according to the unified data access instruction.
Preferably, the user data includes identity information of the user and tag information, and the user behavior data includes behavior data of the user.
A third aspect of the present application provides a data analysis device for multiple data sources, the device comprising a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the steps of the data analysis method for multiple data sources according to the second aspect.
According to the technical scheme, the data analysis system with multiple data sources comprises a data splitting unit, a data analyzing unit and a data analyzing unit, wherein the data splitting unit is used for splitting data acquired from a product into user data and user behavior data; a data storage unit for storing the user data and the user behavior data; the data storage unit comprises a low-speed writing storage unit and a high-speed writing storage unit, the low-speed writing storage unit is used for storing the user data, and the high-speed writing storage unit is used for storing the user behavior data; and the query engine unit is used for accessing the low-speed writing storage unit and the high-speed writing storage unit, acquiring the user data and the user behavior data, and transmitting the acquired user data and the user behavior data to an OLAP (on-line analytical processing) system or an off-line analytical system. According to the method and the device, the user data and the user behavior data source are kept in one part without being divided into two parts to be respectively supplied to the OLAP system and the offline analysis system, so that the data storage space is saved, and the problem of data inconsistency possibly brought by a conventional scheme is solved.
Drawings
FIG. 1 is a system block diagram of one embodiment of a data analysis system for multiple data sources according to the present application;
FIG. 2 is a system block diagram of one embodiment of a data analysis system for multiple data sources according to the present application;
FIG. 3 is a flow chart of a method of one embodiment of a method for data analysis of multiple data sources.
Detailed Description
According to the method and the device, the user data and the user behavior data source are kept in one part without being divided into two parts to be respectively supplied to the OLAP system and the offline analysis system, so that the data storage space is saved, and the problem of data inconsistency possibly brought by a conventional scheme is solved.
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
For easy understanding, the present application is applied to a data analysis system with multiple data sources, please refer to fig. 1, fig. 1 is a system structural diagram of an embodiment of the data analysis system with multiple data sources, as shown in fig. 1, and fig. 1 includes:
the data splitting unit 101 is configured to split data acquired from a product into user data and user behavior data.
It should be noted that, after the product has the new online function, the data obtained from the product generally includes user data and user behavior data, where the user data is used to store the detailed information description of the user and the data of the tag information of the user, and the user behavior data is the recorded data of all behaviors of the user when using the new product function.
And a data storage unit 102 for storing user data and user behavior data.
The data storage unit includes a low-speed writing storage unit 1021 for storing user data, and a high-speed writing storage unit 1022 for storing user behavior data.
It should be noted that, because the amount of user data is relatively small, the capacity and the growth speed can be estimated, and the user data needs to be modified or deleted at any time, the user data can be stored in a low-speed writing storage unit which can be randomly accessed, edited, deleted, and has a low writing performance requirement; the data volume of the user behavior data is huge, the growth speed is difficult to predict, and the data is not allowed to be modified and deleted, so that the data can be stored in a high-speed write storage unit which can be randomly accessed, but can not edit the historical data and can not delete the historical data, and the high-speed write storage unit has high write performance requirement.
And the query engine unit 103 is configured to access the low-speed write storage unit and the high-speed write storage unit, acquire user data and user behavior data, and transmit the acquired user data and user behavior data to the OLAP system or the offline analysis system.
It should be noted that, the OLAP system or the offline analysis system accesses the storage unit through the query engine, so as to obtain the user data and the user behavior data.
The data analysis system with multiple data sources is designed, and the data sources of the user data and the user behavior data are kept in one part instead of being divided into two parts to be respectively supplied to the OLAP system and the offline analysis system, so that the data storage space is saved, and the problem of data inconsistency possibly caused by a conventional scheme is solved.
In the above, for convenience of understanding, a specific embodiment of a data analysis system with multiple data sources is provided, please refer to fig. 2, where fig. 2 is a system structure diagram of an embodiment of a data analysis system with multiple data sources, and specifically:
the data splitting unit 201 is configured to split data acquired from a product into user data and user behavior data.
It should be noted that, after the product has the new online function, the data obtained from the product generally includes user data and user behavior data, where the user data is used to store the detailed information description of the user and the data of the tag information of the user, and the user behavior data is the recorded data of all behaviors of the user when using the new product function.
A data storage unit 202 for storing user data and user behavior data.
The data storage unit comprises a low-speed writing storage unit 2021 and a high-speed writing storage unit 2022, wherein the low-speed writing storage unit 2021 is used for storing user data, and the high-speed writing storage unit 2022 is used for storing user behavior data.
It should be noted that, because the amount of user data is relatively small, the capacity and the growth speed can be estimated, and the user data needs to be modified or deleted at any time, the user data can be stored in a low-speed writing storage unit which can be randomly accessed, edited, deleted, and has a low writing performance requirement; the data volume of the user behavior data is huge, the growth speed is difficult to predict, and the data is not allowed to be modified and deleted, so that the data can be stored in a high-speed write storage unit which can be randomly accessed, but can not edit the historical data and can not delete the historical data, and the high-speed write storage unit has high write performance requirement.
And the query engine unit 203 is configured to access the low-speed write storage unit and the high-speed write storage unit, acquire user data and user behavior data, and transmit the acquired user data and user behavior data to the OLAP system or the offline analysis system.
It should be noted that, the OLAP system or the offline analysis system accesses the storage unit through the query engine, so as to obtain the user data and the user behavior data.
In a specific embodiment, the query engine unit is further configured to obtain an operation instruction of the visual operation unit, obtain user data and/or user behavior data from the data storage unit according to the operation instruction, and transmit the user data and/or the user behavior data to the OLAP system or the offline analysis system. The query engine unit comprises an instruction generation unit; the instruction generating unit is used for generating a unified data access instruction according to the operation instruction, wherein the unified data access instruction is an instruction for calling user data and/or user behavior data by the OLAP system or the offline analysis system.
It should be noted that, the OLAP and the offline analysis system can simultaneously access the user data and the user behavior data from the data storage unit in a unified data access manner, both access manners are in a manner of adding a custom function to the standard SQL, and the SQL can be shared between the two. On the other hand, both the OLAP and the offline analysis system employ a unified query engine, and the query engine is connected to the visual operation unit and is used for converting the operation instruction transmitted by the visual operation unit into a unified data access instruction of SQL or similar data operation interacted between the OLAP/offline analysis system and the database. The visual operation unit simultaneously accesses data of the OLAP and the offline analysis system through the query engine, and user experience is kept consistent when the data of the OLAP and the data of the offline analysis system are accessed.
And the visualization operation unit 204 is used for providing an operable interface for an operator and displaying an analysis result of the OLAP system or the offline analysis system.
It should be noted that the visual operation unit 204 may provide a corresponding operation platform for the operator to select the content to be analyzed and to select the OLAP or offline analysis system, after the selection is completed, the visual operation unit transmits the operation instruction to the query engine, the query engine generates a corresponding unified data access instruction for the OLAP or offline analysis system according to the operation instruction, acquires the required data from the data storage unit for analysis, and after the analysis of the OLAP or offline analysis system is completed, displays the analysis result on the visual operation unit 204.
According to the data analysis system for the multiple data sources, the user data and the user behavior data sources are kept in one part instead of being divided into two parts to be respectively supplied to the OLAP system and the offline analysis system, so that the data storage space is saved, and the problem of data inconsistency possibly caused by a conventional scheme is solved. Through unified data visualization, unified query engine and unified data access mode for the analysis report form that verifies in OLAP stage can directly be taken to off-line analysis and use, when not losing the real-time of A stage, the off-line analysis report form of completion B stage that again can be quick, make operating personnel only need be familiar with one set of data visualization system and just can accomplish the analytic process, reduced data analysis system user's use cost.
Referring to fig. 3, fig. 3 is a flowchart of a method of an embodiment of the data analysis method for multiple data sources, as shown in fig. 3, where fig. 3 includes:
301. and acquiring an operation instruction of the function of the analysis product.
302. And selecting a required OLAP system or an off-line analysis system according to the operation instruction.
303. Selecting user data and/or user behavior data required by an OLAP system or an offline analysis system according to an operation instruction; the user data is stored separately from the user behavior data.
304. And displaying the analysis result of the OLAP system or the off-line analysis system.
It should be noted that, the user data includes the identity information of the user, the tag information, and the user behavior data includes the behavior data of the user, so that the user data can be stored in the low-speed writing storage unit which is randomly accessible, editable, deletable, and has a lower writing performance requirement; the user behavior data can be stored in a high-speed writing storage unit which can be randomly accessed, but can not edit the history data and can not delete the writing performance requirement in the history data.
In a specific embodiment, selecting user data and/or user behavior data required by the OLAP system or the offline analysis system according to the operation instruction specifically includes: the query engine acquires the operation instruction and converts the operation instruction into a unified data access instruction, and the OLAP system or the offline analysis system accesses the required user data and/or user behavior data according to the unified data access instruction.
It should be noted that, the OLAP and the offline analysis system can simultaneously access the user data and the user behavior data from the data storage unit in a unified data access manner, both access manners are in a manner of adding a custom function to the standard SQL, and the SQL can be shared between the two. On the other hand, both the OLAP and the offline analysis system employ a unified query engine, and the query engine is connected to the visual operation unit and is used for converting the operation instruction transmitted by the visual operation unit into a unified data access instruction of SQL or similar data operation interacted between the OLAP/offline analysis system and the database. The visual operation unit simultaneously accesses data of the OLAP and the offline analysis system through the query engine, and user experience is kept consistent when the data of the OLAP and the data of the offline analysis system are accessed.
In addition, the visual operation unit can provide a corresponding operation platform for an operator to select contents to be analyzed and select an OLAP or offline analysis system, when the selection is completed, the visual operation unit transmits an operation instruction to the query engine, the query engine generates a corresponding unified data access instruction for the OLAP or offline analysis system according to the operation instruction, the required data is obtained from the data storage unit to be analyzed, and after the analysis of the OLAP or offline analysis system is completed, an analysis result is displayed on the visual operation unit.
An embodiment of the present application further provides a computer-readable storage device, where the device includes a processor and a memory: the memory is used for storing the program codes and transmitting the program codes to the processor; the processor is configured to execute a data analysis method of multiple data sources according to instructions in the program code.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A data analysis system for multiple data sources, comprising:
the data splitting unit is used for splitting data acquired from a product into user data and user behavior data;
a data storage unit for storing the user data and the user behavior data;
the data storage unit comprises a low-speed writing storage unit and a high-speed writing storage unit, the low-speed writing storage unit is used for storing the user data, and the high-speed writing storage unit is used for storing the user behavior data;
and the query engine unit is used for accessing the low-speed writing storage unit and the high-speed writing storage unit, acquiring the user data and the user behavior data, and transmitting the acquired user data and the user behavior data to an OLAP (on-line analytical processing) system or an off-line analytical system.
2. The data analysis system for multiple data sources of claim 1, further comprising:
and the visual operation unit is used for providing an operable interface for an operator and displaying the analysis result of the OLAP system or the off-line analysis system.
3. The data analysis system of claim 2, wherein the query engine unit is further configured to obtain an operation instruction of a visual operation unit, obtain the user data and/or the user behavior data from the data storage unit according to the operation instruction, and transmit the user data and/or the user behavior data to the OLAP system or the offline analysis system.
4. The data analysis system for multiple data sources of claim 3, wherein said query engine unit comprises an instruction generation unit;
the instruction generating unit is used for generating a unified data access instruction according to the operation instruction, wherein the unified data access instruction is an instruction for calling the user data and/or the user behavior data by an OLAP (on-line analytical processing) system or an offline analysis system.
5. The data analysis system with multiple data sources as claimed in claim 1, wherein the user data includes identity information of the user, tag information, and the user behavior data includes behavior data of the user.
6. A method for data analysis from multiple data sources, comprising:
acquiring an operation instruction for analyzing a product function;
selecting a needed OLAP system or an off-line analysis system according to the operation instruction;
selecting user data and/or user behavior data required by the OLAP system or the off-line analysis system according to an operation instruction; the user data is stored separately from the user behavior data;
and displaying the analysis result of the OLAP system or the off-line analysis system.
7. The method for data analysis from multiple data sources as recited in claim 6, comprising: the user data is stored in a data source which can be randomly accessed, edited and deleted, and the user behavior data is stored in a data source which can be randomly accessed, can not edit the historical data and can not delete the historical data.
8. The data analysis method for multiple data sources according to claim 6, wherein the selecting the user data and/or the user behavior data required by the OLAP system or the offline analysis system according to the operation instruction specifically includes:
the method comprises the steps that an inquiry engine obtains an operation instruction and converts the operation instruction into a unified data access instruction, and the OLAP system or the off-line analysis system accesses required user data and/or user behavior data according to the unified data access instruction.
9. The method for data analysis from multiple data sources of claim 6, wherein the user data includes identity information of the user, tag information, and the user behavior data includes behavior data of the user.
10. A data analysis device for multiple data sources, the device comprising a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute a method for data analysis from multiple data sources as claimed in any one of claims 6-9 according to instructions in the program code.
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