CN107861981B - Data processing method and device - Google Patents
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Abstract
The invention provides a data processing method and device, and belongs to the technical field of computers. According to the data processing method and device provided by the embodiment of the invention, the structured query statement instruction can be generated according to the selection operation of the user on the preset multiple dimensions and is sent to the data server, so that the data server can conveniently execute the query operation on the data in the data server according to the structured query statement instruction and send the query result to the terminal. The user only needs to select the dimension which needs to be analyzed in the preset interface, so that the data query operation is simplified, and the data query time is shortened. Meanwhile, the metric value of the dimension attribute subset can represent an analysis result obtained after data analysis is performed on the original data corresponding to the dimension attribute subset. Therefore, after the user obtains the query result, the query result does not need to be analyzed, the step of analyzing by the user is omitted, and the data processing efficiency is improved.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a data processing method and device.
Background
With the widespread use of internet technology, a large amount of user data is generated. In order to understand the user's needs, the user data is usually stored in a server, and a developer can query the server for the required data to analyze, etc.
In the prior art, a developer can write a corresponding query statement instruction according to actual requirements to query in a server, and the server converts the query statement instruction into a task and then searches corresponding data.
However, since actual requirements are various, different query statements need to be written for different requirements, and since writing of the query statements needs to consume a lot of time and effort, data query is time-consuming, inefficient, and costly. Meanwhile, the server in the prior art stores all the original data, so the query result in the prior art is the original data, and in practical application, a user often needs to analyze the original data obtained by the query result in the query to obtain effective information from the data. In the prior art, the query mode can only show the query result, so that the user needs to analyze the data after obtaining the query result, and the operation is complicated.
Disclosure of Invention
In view of the above, the present invention has been made to provide a data processing method and apparatus that overcome or at least partially solve the above problems.
According to a first aspect of the present invention, there is provided a data processing method applied to a terminal, the method including:
detecting selection operations of a user for multiple preset dimensions in a preset interface;
generating a structured query statement instruction according to the selection operation;
sending the structural query statement instruction to a data server so that the data server can execute query operation on data in the data server according to the structural query statement instruction and send a query result to the terminal;
the data in the data server is a plurality of key value pairs, the plurality of key value pairs are obtained by pre-analyzing original data based on a plurality of preset dimensions, and the key value pairs are composed of a dimension attribute subset and a metric value corresponding to the dimension attribute subset.
Optionally, the step of generating a structured query statement instruction according to the selection operation includes:
converting the selection operation into a first statement instruction by using a preset first analysis mode;
converting the first statement instruction into a structured query statement instruction by using a preset second analysis mode;
the preset first analysis mode and the preset second analysis mode adopt the same framework; the architecture is written according to the dimensions set in the preset data.
According to a second aspect of the present invention, there is provided a data processing method applied to a data server, the method may include:
receiving a structural query statement instruction sent by a terminal;
querying the data in the data server according to the structured query statement instruction; the data in the data server is a plurality of key value pairs, the plurality of key value pairs are obtained by pre-analyzing original data based on a plurality of preset dimensions, and the key value pairs consist of a dimension attribute subset and a metric value corresponding to the dimension attribute subset;
and sending the query result to the terminal.
Optionally, the method further includes:
acquiring original data, and classifying the original data according to a plurality of preset dimensions;
taking the classified original data as a data set, and respectively performing pre-analysis on the data in the data set according to each preset dimension in the multiple dimensions;
and generating data in the data server according to the result obtained by the pre-analysis.
Optionally, the step of performing pre-analysis on the data in the data set according to the preset multiple dimensions by using the classified original data as the data set includes:
determining at least one metric corresponding to the preset plurality of dimensions;
combining the preset multiple dimensions according to a preset mode to obtain multiple dimension sets; wherein each set of dimensions consists of at least one dimension;
combining the dimension attribute subsets included in each dimension set in the dimension sets to obtain a plurality of dimension attribute subsets;
analyzing data corresponding to the dimension attribute subsets in the data set aiming at each dimension attribute subset in the dimension attribute subsets to obtain metric values corresponding to the dimension attribute subsets and the metrics;
correspondingly, the step of generating the data in the data server according to the result obtained by the pre-analysis comprises:
and storing the metric values and the dimensional attribute subsets corresponding to the metric values into the data server in a key-value pair mode.
Optionally, the preset multiple dimensions include: at least one of a day, an hour, a product area, a platform, a version, and a channel;
the at least one metric includes: at least one of number of clicks, viewing duration, and number of long clicks.
According to a third aspect of the present invention, there is provided a data processing apparatus, which may include:
the detection module is used for detecting the selection operation of a user for multiple preset dimensions in a preset interface;
the instruction generating module is used for generating a structural query statement instruction according to the selection operation;
the instruction sending module is used for sending the structured query statement instruction to a data server so that the data server can conveniently execute query operation on data in the data server according to the structured query statement instruction and send a query result to the terminal;
the data in the data server is a plurality of key value pairs, the plurality of key value pairs are obtained by pre-analyzing original data based on a plurality of preset dimensions, and the key value pairs are composed of a dimension attribute subset and a metric value corresponding to the dimension attribute subset.
Optionally, the instruction generating module includes:
the first conversion submodule is used for converting the selection operation into a first statement instruction by utilizing a preset first analysis mode;
the second conversion sub-module is used for converting the first statement instruction into a structured query statement instruction by using a preset second analysis mode;
the preset first analysis mode and the preset second analysis mode adopt the same framework; the architecture is written according to the dimensions set in the preset data.
According to a fourth aspect of the present invention, there is provided a data processing apparatus, which may include:
the receiving module is used for receiving a structural query statement instruction sent by a terminal;
the query module is used for querying the data in the data server according to the structured query statement instruction; the data in the data server is a plurality of key value pairs, the plurality of key value pairs are obtained by pre-analyzing original data based on a plurality of preset dimensions, and the key value pairs consist of a dimension attribute subset and a metric value corresponding to the dimension attribute subset;
and the result sending module is used for sending the query result to the terminal.
Optionally, the apparatus further comprises:
the system comprises an acquisition module, a classification module and a display module, wherein the acquisition module is used for acquiring original data and classifying the original data according to each preset dimension of a plurality of dimensions;
the pre-analysis module is used for taking the classified original data as a data set and pre-analyzing the data in the data set according to the preset multiple dimensions;
and the data generation module is used for generating the data in the data server according to the result obtained by pre-analysis.
Optionally, the pre-analysis module includes:
a determining submodule for determining at least one metric corresponding to the preset plurality of dimensions;
the first combination sub-module is used for combining the preset multiple dimensions according to a preset mode to obtain multiple dimension sets; wherein each set of dimensions consists of at least one dimension;
the second combination submodule is used for combining the dimension attribute subsets included in each of the dimension sets to obtain a plurality of dimension attribute subsets;
an analysis submodule, configured to analyze, for each of the plurality of dimension attribute subsets, data of the dimension attribute subset corresponding to the data set, and obtain metric values of the dimension attribute subset corresponding to the metric;
accordingly, the data generation module comprises:
and the storage sub-module is used for storing the metric values and the dimension attribute subsets corresponding to the metric values into the data server in a key-value pair mode.
Optionally, the preset multiple dimensions include: at least one of a day, an hour, a product area, a platform, a version, and a channel;
the at least one metric includes: at least one of number of clicks, viewing duration, and number of long clicks.
Aiming at the prior art, the invention has the following advantages:
the data processing method and the data processing device provided by the embodiment of the invention can generate the structured query statement instruction according to the selection operation of the user on the preset multiple dimensions, so that when the user needs to query the data, only the dimension which needs to be analyzed by the user needs to be selected in the preset interface, and compared with the mode of writing the query statement in the prior art, the data query operation is simplified, and the data query time is further shortened. Further, the terminal sends the generated structured query statement instruction to the data server, so that the data server performs query operation on data in the data server according to the structured query statement instruction, and sends a query result to the terminal. Because the data in the data server is a plurality of key value pairs consisting of the dimension attribute subsets and the metric values corresponding to the dimension attribute subsets, the obtained query result is the dimension attribute subsets and the metric values corresponding to the selection operation of the user. And the metric value of the dimension attribute subset can represent an analysis result obtained after data analysis is performed on the original data corresponding to the dimension attribute subset. Therefore, after the user obtains the query result, the query result does not need to be analyzed, the step of analyzing by the user is omitted, and the data processing efficiency is improved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a schematic diagram of an implementation environment involved in a data processing method provided by an embodiment of the present invention;
FIG. 2 is a flow chart illustrating steps of a data processing method according to an embodiment of the present invention;
FIG. 3 is a flow chart of steps of another data processing method provided by an embodiment of the invention;
FIG. 4 is a flow chart illustrating steps of a further data processing method according to an embodiment of the present invention;
fig. 5 is a block diagram of a data processing apparatus according to an embodiment of the present invention;
FIG. 6 is a block diagram of another data processing apparatus provided by an embodiment of the present invention;
fig. 7 is a block diagram of another data processing apparatus according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 1 is a schematic diagram of an implementation environment related to a data processing method provided in an embodiment of the present invention, and as shown in fig. 1, the implementation environment may include: a data server 110 and at least one terminal 120.
The data server 110 may be a server, a server cluster composed of several servers, or a cloud computing service center. The terminal 120 may be a smartphone, a computer, a wearable device, or the like.
The connection between the data server 110 and the terminal 120 can be established through a wired network or a wireless network, and the user can use the terminal 120 to process the data in the data server 110. Specifically, a user may select multiple dimensions preset in a preset interface provided by the terminal 120, the terminal 120 may generate a structured query statement instruction according to a selection operation of the user and send the structured query statement instruction to the data server 110, and the data server 110 may perform data query according to the received structured query statement instruction and send a query result to the terminal 120.
Fig. 2 is a flowchart of steps of a data processing method provided in an embodiment of the present invention, which is applied to a terminal, and as shown in fig. 2, the method may include:
In the embodiment of the present invention, a plurality of dimensions may be preset and defined in the preset interface, where the dimensions are angles of observing data when analyzing the data in practical application of a user. In a specific implementation, a viewing angle commonly used by a user may be defined as a dimension in the embodiment of the present invention. For example, taking the analysis of the data of the video platform as an example, the preset multiple dimensions may include: day, hour, product area, platform, version, and channel. Each dimension may include multiple attributes, such as the dimension "product area" may include the attribute "xi' an," the attribute "shanghai," and the attribute "beijing," among others; dimension "channel" may include the attribute "channel 1" and the attribute "channel 2", among others. It should be noted that, in practical applications, the preset multiple dimensions may further include other dimensions, such as algorithm classification, video duration, and the like. A developer may define a specific dimension according to an actual requirement, and the specific dimension and the number of dimensions are not limited in the embodiment of the present invention, and meanwhile, an attribute included in each dimension may also be set according to an actual requirement, which is not limited in the embodiment of the present invention.
When a user needs to analyze data, at least one dimension can be selected from a plurality of dimensions preset in a preset interface to serve as a data analysis angle, and then at least one attribute is selected from attributes included in the selected dimension. For example, assume that the user has selected dimension "product area" that includes the attribute "Xian," the attribute "Beijing," and the attribute "Shanghai," and dimension "channel" that includes the attribute "channel 1" and the attribute "channel 2. The user may select the attributes included in the selected dimension, for example, the user may select the attribute "xi 'an" and the attribute "channel 1" to perform data query analysis with "product region-xi' an, channel-channel 1" as the viewing angle.
And 202, generating a structural query statement instruction according to the selection operation.
For example, after the terminal detects a selection operation of a user, the selection operation is converted into a first statement instruction, and then the first statement instruction is converted into a structured query statement instruction which can be recognized by the data server. The structural query statement instruction comprises dimension attribute information selected by a user. For example, assuming that the user selects the attribute "channel 1" corresponding to the attribute "channel 1" and the attribute "west" corresponding to the dimension "product area", the dimension attribute information included in the structured query statement instruction is: attribute "west' corresponding to dimension" product area "and attribute" channel 1 "corresponding to dimension" channel ".
And 203, sending the structural query statement instruction to a data server so that the data server executes query operation on data in the data server according to the structural query statement instruction and sends a query result to the terminal.
The data in the data server is a plurality of key value pairs, the plurality of key value pairs are obtained by pre-analyzing original data based on a plurality of preset dimensions, and the key value pairs are composed of dimension attribute subsets and metric values corresponding to the dimension attribute subsets.
In the embodiment of the present invention, the metric is defined in advance for a preset dimension, and the specific selection and number of the metrics are not limited in the embodiment of the present invention. Values of interest to the user may be generally used as metrics, which may be defined as number of clicks, viewing duration, and number of long clicks, to name a few. The metric value corresponding to the dimension attribute subset refers to a statistical value obtained by analyzing the original data corresponding to the dimension attribute subset, and the statistical value is the metric value. For example, assuming that the subset of the dimension attributes is < product area- "west ampere", and channel- "channel 1" >, and the defined metric is the number of clicks, the metric value corresponding to the subset of the dimension attributes is the number of clicks of channel 1 in west ampere region.
The original data is typically a large amount of accumulated behavior logs, for example, when a user uses a video platform, the behavior logs are generated by clicking and playing a video on the platform. In the prior art, when a user needs to analyze data, a large amount of required behavior logs can be obtained through query, and the user must perform statistical analysis on the behavior logs by himself to obtain a data analysis result and further obtain effective information. However, the analysis operation is complicated, and the requirements of each time are different, so that the query result of each time needs to be analyzed, and the workload is large.
In the embodiment of the invention, because the data in the data server is a plurality of key value pairs consisting of the dimension attribute subsets and the metric values corresponding to the dimension attribute subsets, the data server queries according to the structured query instruction to obtain the query result which is the dimension attribute subsets and the metric values corresponding to the selection operation of the user. The metric values of the dimension attribute subsets are obtained by analyzing the original data corresponding to the dimension attribute subsets, and then the metric values of the dimension attribute subsets can represent the analysis results of the original data corresponding to the dimension attribute subsets after data analysis. Therefore, after the user acquires the query result, the query result does not need to be analyzed, the step of analyzing by the user is omitted, and the data processing efficiency is improved.
In summary, according to the data processing method provided in the embodiment of the present invention, the terminal may generate the structured query statement instruction according to the selection operation of the user on the multiple preset dimensions, so that when the user needs to query the data, only the dimension that needs to be analyzed needs to be selected in the preset interface, and compared with a manner that the query statement needs to be written in the prior art, the operation of data query is simplified, and the time of data query is further shortened. Further, the terminal sends the generated structured query statement instruction to the data server, so that the data server performs query operation on data in the data server according to the structured query statement instruction, and sends a query result to the terminal. Because the data in the data server is a plurality of key value pairs consisting of the dimension attribute subsets and the metric values corresponding to the dimension attribute subsets, the obtained query result is the dimension attribute subsets and the metric values corresponding to the selection operation of the user. And the metric value of the dimension attribute subset can represent an analysis result obtained after data analysis is performed on the original data corresponding to the dimension attribute subset. Therefore, after the user obtains the query result, the query result does not need to be analyzed, the step of analyzing by the user is omitted, and the data processing efficiency is improved.
Fig. 3 is a flowchart of steps of another data processing method provided in an embodiment of the present invention, which is applied to a data server, and as shown in fig. 3, the method may include:
The structural query statement instruction comprises dimension attribute information selected by a user. After receiving the structured query statement instruction, the data server may perform a query according to the structured query statement instruction.
And 302, inquiring the data in the data server according to the structured inquiry statement instruction.
The data in the data server is a plurality of key value pairs, the plurality of key value pairs are obtained by pre-analyzing original data based on a plurality of preset dimensions, and the key value pairs are composed of dimension attribute subsets and metric values corresponding to the dimension attribute subsets.
In the embodiment of the present invention, after receiving the structured query statement instruction, the data server may first parse the structured query statement instruction to obtain the dimension attribute information in the structured query statement instruction, then search, in the data stored in the data server, the dimension attribute subset matched with the dimension attribute information, and then take the key value pair corresponding to the matched dimension attribute subset as the query result, that is, take the matched dimension attribute subset and the metric value corresponding to the matched dimension attribute subset as the query result. For example, it is assumed that the instruction of the structured query statement includes dimension attribute information: the product area is west ampere, the channel is channel 1, and then the data server may query the dimension attribute subset consisting of "product area" is west ampere and channel is channel 1 "and the metric corresponding to the dimension attribute subset when querying according to the structured query instruction: the number of clicks is 1000 as the query result.
And step 303, sending the query result to the terminal.
For example, the data server may send < product area-west ampere, channel-channel 1, click number 1000> as a query result to the terminal, so that the user can know that the click number of the channel 1 in the west ampere region is 1000 according to the query result received by the terminal.
In summary, in another data processing method provided in the embodiments of the present invention, the data server may receive a structured query statement sent by the terminal, then query the data in the data server according to the instruction of the structured query statement, and finally return the query result to the terminal. Because the data in the data server is a plurality of key value pairs consisting of the dimension attribute subsets and the metric values corresponding to the dimension attribute subsets, the obtained query result is the dimension attribute subsets and the metric values corresponding to the selection operation of the user. And the metric value of the dimension attribute subset can represent an analysis result obtained after data analysis is performed on the original data corresponding to the dimension attribute subset. Therefore, after the user obtains the query result, the query result does not need to be analyzed, the step of analyzing by the user is omitted, and the data processing efficiency is improved.
Fig. 4 is a flowchart of steps of another data processing method according to an embodiment of the present invention, and as shown in fig. 4, the method may include:
Where the raw data is typically a large log of accumulated behavior. Taking a video platform as an example, when a user uses the video platform, a behavior log is generated by clicking and playing the video on the platform. The original data can be stored on the first server, the first server and the data server can be located in the same rack, and therefore the first server and the data server are located in the same rack and are short in distance, the data server can rapidly acquire the original data stored in the first server, and due to the fact that the transmission distance is short, data loss during original data transmission is relatively small, and safe transmission of the original data is guaranteed.
The data server can classify the raw data by using each of a plurality of preset dimensions as a classification standard. Specifically, the attributes included in each dimension may be classified. For example, taking "product area" in a plurality of preset dimensions as an example, assuming that the dimension "product area" includes an attribute "xi ' an", an attribute "beijing", and an attribute "shanghai", when the dimension "product area" is used as a classification standard for classification, all behavior logs generated in xi ' an may be classified into "product area-xi ' an", all behavior logs generated in beijing may be classified into "product area-beijing", and all behavior logs generated in shanghai may be classified into "product area-shanghai".
And step 402, the data server takes the classified original data as a data set, and pre-analyzes the data in the data set according to the preset multiple dimensions.
In the embodiment of the invention, the classified original data is used as the data set for pre-analysis, and the classification is carried out according to each dimension in a plurality of preset dimensions, so that compared with the method of directly using the original data with disordered sequence for pre-analysis, the method can quickly find the required data, and further improve the efficiency of the pre-analysis.
Specifically, step 402 may include:
step 4021, the data server determines at least one metric corresponding to the preset multiple dimensions.
In the embodiment of the present invention, when determining at least one metric corresponding to a plurality of preset dimensions, values that users care more may be used as the metric, and the metric may be defined as click volume, viewing duration, long click volume, and the like. The specific selection and number of metrics are not limited in the embodiments of the present invention.
Step 4022, the data server combines the multiple preset dimensions according to a preset mode to obtain multiple dimension sets; wherein each set of dimensions consists of at least one dimension.
In the embodiment of the present invention, the combining of the preset multiple dimensions in the preset manner is performed by selecting 1 dimension from the preset multiple dimensions, and combining 2 dimensions, where n represents the number of the preset dimensions. Wherein the value range of m is as follows: 1 m n, then by combining we can get a set of dimensions as:
step 4023, the data server combines the dimension attribute subsets included in each of the dimension sets to obtain a plurality of dimension attribute subsets.
Assuming that there are 3 dimension sets, the dimension attribute subsets corresponding to the 3 dimension sets can be obtained by combination. For example, the attribute subset corresponding to each dimension in the dimension set may be determined by the following formula;
wherein the content of the first and second substances,
the representation combines p elements, and p represents the number of attributes included in the dimension.
And then combining the obtained attribute subsets of each dimension to obtain a dimension attribute subset corresponding to the dimension set.
Assume that each dimension in the set of dimensions < A, B > includes two attributes, namely, dimension A includes attributes a1 and a2, and dimension B includes attributes B1 and B2. Attribute subsets corresponding to the dimension a in the dimension set < a, B > can be obtained as < a1>, < a2> and < a1, a2 >; the attribute subsets corresponding to the dimension B in the dimension set < a, B > are < B1>, < B2>, and < B1, B2 >.
Attribute subsets corresponding to dimension A and dimension B<a1>，<a2>，<a1，a2>，<b1>，<b2>And<b1，b2>the dimension set can be obtained by combination<A，B>The corresponding subset of dimension attributes is<a1，b1>，<a1，b2>，<a1，b1，b2>，<a2，b1>，<a2，b2>，<a2，b1，b2>，<a1，a2，b1>，<a1，a2，b2>，<a1，a2，b1，b1>
Step 4024, the data server analyzes the data corresponding to the dimension attribute subsets in the data set for each dimension attribute subset in the plurality of dimension attribute subsets to obtain the metric values corresponding to the dimension attribute subsets and the metrics.
For example, assuming that there are 10 dimensional attribute subsets, the 10 dimensional attribute subsets can be analyzed respectively to obtain 10 corresponding metric values. When analyzing the metric values corresponding to the dimension attribute subsets, data corresponding to each dimension attribute included in the dimension attribute subsets may be first acquired in a data set, and then the metric values may be analyzed according to the acquired data. Assuming that the subset of the dimension attributes is < product area-west ampere, channel-channel 1>, all behavior logs generated in the west ampere region can be obtained in the data set, and then behavior logs of a channel 1 tag exist in the obtained behavior logs generated in the west ampere region, 2000 behavior logs generated in the west ampere region are assumed to be obtained, wherein the 2000 behavior logs comprise 1500 behavior logs of the channel 1 tag, and if the defined metric is the number of clicks, the metric value corresponding to the metric is the specific number of clicks. Further, it may be determined whether each behavior log includes a click tag, and then the number of behavior logs including the click tag may be determined as the number of clicks. Assuming that 1000 of the 1500 behavior logs include click tags, the metric value corresponding to the dimension attribute subset being < product area-west ampere, channel-channel 1> is determined to be the number of clicks 1000.
It should be noted that, in implementation, multiple metrics may be defined, and when calculating the metric value corresponding to each subset of the dimension attributes, multiple metric values corresponding to the subset of the dimension attributes need to be calculated corresponding to the multiple metrics. For example, assuming that the defined metrics are click times and viewing duration, when calculating the metric value corresponding to the dimensional attribute subset < product region-west ampere, channel-channel 1>, the click times corresponding to the dimensional attribute subset < product region-west ampere, channel-channel 1> and the viewing duration corresponding to the dimensional attribute subset < product region-west ampere, channel-channel 1> may be calculated.
And step 403, the data server generates data in the data server according to the result obtained by the pre-analysis.
In this step, each metric value and the subset of the dimension attributes corresponding to the metric value may be stored in a data server to generate data in the data server. For example, assuming that 10 metric values are obtained through pre-analysis in the above steps, the 10 metric values and the corresponding subset of dimension attributes may be stored in the data server. Specifically, the metric value and the dimension attribute subset corresponding to the metric value may be stored in the data server in the form of a key value pair, and since each key value pair is independent from each other, the storage is performed in the form of a key value pair, which may facilitate changing data in the data server.
Specifically, the implementation process of this step may refer to step 201 described above, and details of the embodiment of the present invention are not described herein.
And 405, the terminal generates a structured query statement instruction according to the selection operation.
Specifically, step 405 may include:
step 4051, the terminal converts the selection operation into a first statement instruction by using a preset first parsing manner.
The preset first parsing manner may be developed based on Saiku, specifically, a preset interface may be set by Saiku to provide a visualization operation for a user, and for a selection operation terminal of the user on the preset interface, dimension attribute information corresponding to the selection operation may be used as an input parameter of the first parsing manner, and a first statement instruction is generated by using a Multidimensional analysis statement (MDX).
Step 4052, the terminal converts the first statement instruction into a structured query statement instruction by using a preset second parsing manner.
The preset second parsing manner may be developed based on Mondrian, and since the data server can only execute Structured Query Language (SQL), the first statement instruction may be converted into a Structured Query statement instruction that the data server can recognize through the second parsing manner. Specifically, the first statement instruction may be analyzed first, and then the analyzed first statement instruction is reconstructed according to the SQL rule, so as to obtain a structured query statement instruction. It should be noted that, in order to ensure that the terminal can convert the initial query instruction into the structured query instruction, the preset second parsing manner and the preset first parsing manner need to have the same architecture. Meanwhile, in order to ensure that the converted structured query instruction can be successfully adopted, the architecture must be written according to preset dimensions.
And step 406, the terminal sends the structural query statement instruction to a data server.
In the step, the terminal sends the structural query statement instruction to the data server, so that the data server executes query operation on the data in the data server according to the structural query statement instruction and sends a query result to the terminal. The data in the data server is a plurality of key value pairs obtained by pre-analyzing original data based on a plurality of preset dimensions, and the key value pairs are composed of a dimension attribute subset and a metric value corresponding to the dimension attribute subset. Specifically, the implementation process of this step may refer to step 203 described above, and details of the embodiment of the present invention are not described herein.
Specifically, the implementation process of this step may refer to step 301, which is not described herein again in this embodiment of the present invention.
And 408, the data server queries the data in the data server according to the structured query statement instruction.
Specifically, the implementation process of this step may refer to step 302 described above, and details of the embodiment of the present invention are not described herein.
And step 409, the data server sends the query result to the terminal.
Specifically, the implementation process of this step may refer to step 303, which is not described herein again in this embodiment of the present invention.
In summary, according to another data processing method provided in the embodiments of the present invention, the data server obtains the original data, classifies and pre-analyzes the original data, and then generates the data in the data server according to the pre-analysis result. After the terminal detects the selection operation of the user, a structured query statement instruction can be generated according to the selection operation and sent to the data server, the data server can query the data in the data server according to the received structured query statement instruction, and finally, a query result is returned to the terminal. Because the data in the data server is a plurality of key value pairs consisting of the dimension attribute subsets and the metric values corresponding to the dimension attribute subsets, the obtained query result is the dimension attribute subsets and the metric values corresponding to the selection operation of the user. And the metric value of the dimension attribute subset can represent an analysis result obtained after data analysis is performed on the original data corresponding to the dimension attribute subset. Therefore, after the user obtains the query result, the query result does not need to be analyzed, the step of analyzing by the user is omitted, and the data processing efficiency is improved.
Fig. 5 is a block diagram of a data processing apparatus according to an embodiment of the present invention, and as shown in fig. 5, the apparatus 50 may include:
the detecting module 501 is configured to detect a selection operation of a user for multiple preset dimensions in a preset interface.
The instruction generating module 502 is configured to generate a structured query statement instruction according to the selection operation.
An instruction sending module 503, configured to send the structural query statement instruction to a data server, so that the data server performs a query operation on data in the data server according to the structural query statement instruction, and sends a query result to the terminal;
the data in the data server is a plurality of key value pairs, the plurality of key value pairs are obtained by pre-analyzing original data based on a plurality of preset dimensions, and the key value pairs are composed of a dimension attribute subset and a metric value corresponding to the dimension attribute subset.
Optionally, the instruction generating module 502 may include:
and the first conversion submodule is used for converting the selection operation into a first statement instruction by utilizing a preset first analysis mode.
And the second conversion sub-module is used for converting the first statement instruction into a structured query statement instruction by using a preset second analysis mode.
The preset first analysis mode and the preset second analysis mode adopt the same framework; the architecture is written according to the dimensions set in the preset data.
In summary, in the data processing apparatus provided in the embodiment of the present invention, the instruction generating module may generate the structured query statement instruction according to the selection operation of the user on the multiple preset dimensions, so that when the user needs to query the data, only the dimension that needs to be analyzed needs to be selected in the preset interface, which simplifies the operation of data query and further shortens the time of data query compared with the manner in which the query statement needs to be written in the prior art. Further, the instruction sending module sends the generated structured query statement instruction to the data server, so that the data server executes query operation on data in the data server according to the structured query statement instruction and sends a query result to the terminal. Because the data in the data server is a plurality of key value pairs consisting of the dimension attribute subsets and the metric values corresponding to the dimension attribute subsets, the obtained query result is the dimension attribute subsets and the metric values corresponding to the selection operation of the user. And the metric value of the dimension attribute subset can represent an analysis result obtained after data analysis is performed on the original data corresponding to the dimension attribute subset. Therefore, after the user obtains the query result, the query result does not need to be analyzed, the step of analyzing by the user is omitted, and the data processing efficiency is improved.
Fig. 6 is a block diagram of another data processing apparatus according to an embodiment of the present invention, and as shown in fig. 6, the apparatus 60 may include:
a receiving module 601, configured to receive a structured query statement instruction sent by a terminal;
the query module 602 is configured to query the data in the data server according to the structured query statement instruction; the data in the data server is a plurality of key value pairs, the plurality of key value pairs are obtained by pre-analyzing original data based on a plurality of preset dimensions, and the key value pairs consist of a dimension attribute subset and a metric value corresponding to the dimension attribute subset;
a result sending module 603, configured to send the query result to the terminal.
In summary, in another data processing apparatus provided in the embodiments of the present invention, the receiving module may receive a structured query statement sent by the terminal, then the querying module may query the data in the data server according to the instruction of the structured query statement, and finally the result sending module sends the query result to the terminal. Because the data in the data server is a plurality of key value pairs consisting of the dimension attribute subsets and the metric values corresponding to the dimension attribute subsets, the obtained query result is the dimension attribute subsets and the metric values corresponding to the selection operation of the user. And the metric value of the dimension attribute subset can represent an analysis result obtained after data analysis is performed on the original data corresponding to the dimension attribute subset. Therefore, after the user obtains the query result, the query result does not need to be analyzed, the step of analyzing by the user is omitted, and the data processing efficiency is improved.
Fig. 7 is a block diagram of another data processing apparatus according to an embodiment of the present invention, and as shown in fig. 7, the apparatus 70 may include:
the obtaining module 701 is configured to obtain original data, and classify the original data according to each preset dimension of a plurality of dimensions.
And the pre-analysis module 702 is configured to use the classified raw data as a data set, and perform pre-analysis on the data in the data set according to the preset multiple dimensions.
And a data generating module 703, configured to generate data in the data server according to a result obtained by pre-analysis.
Optionally, the pre-analysis module 702 may include:
a determination submodule for determining at least one metric corresponding to the preset plurality of dimensions.
The first combination sub-module is used for combining the preset multiple dimensions according to a preset mode to obtain multiple dimension sets; wherein each set of dimensions consists of at least one dimension.
And the second combination submodule is used for combining the dimension attribute subsets included in each of the plurality of dimension sets to obtain a plurality of dimension attribute subsets.
And the analysis submodule is used for analyzing the data of the dimension attribute subsets in the data set aiming at each dimension attribute subset in the dimension attribute subsets to obtain the metric values of the dimension attribute subsets corresponding to the metric.
Optionally, the data generating module 703 may include:
and the storage sub-module is used for storing the metric values and the dimension attribute subsets corresponding to the metric values into the data server in a key-value pair mode.
A receiving module 704, configured to receive a structural query statement instruction sent by a terminal.
The query module 705 is configured to query the data in the data server according to the structured query statement instruction; the data in the data server is a plurality of key value pairs, the plurality of key value pairs are obtained by pre-analyzing original data based on a plurality of preset dimensions, and the key value pairs are composed of a dimension attribute subset and a metric value corresponding to the dimension attribute subset.
And a result sending module 706, configured to send the query result to the terminal.
In summary, in another data processing apparatus provided in the embodiments of the present invention, the obtaining module obtains the original data, classifies the original data, then the pre-analysis module performs pre-analysis, and then the data generation module generates the data in the data server according to the result obtained by the pre-analysis. The query module can query the data in the data server according to the received instruction of the structured query statement, and finally the result sending module returns the query result to the terminal. Because the data in the data server is a plurality of key value pairs consisting of the dimension attribute subsets and the metric values corresponding to the dimension attribute subsets, the obtained query result is the dimension attribute subsets and the metric values corresponding to the selection operation of the user. And the metric value of the dimension attribute subset can represent an analysis result obtained after data analysis is performed on the original data corresponding to the dimension attribute subset. Therefore, after the user obtains the query result, the query result does not need to be analyzed, the step of analyzing by the user is omitted, and the data processing efficiency is improved.
As is readily imaginable to the person skilled in the art: any combination of the above embodiments is possible, and thus any combination between the above embodiments is an embodiment of the present invention, but the present disclosure is not necessarily detailed herein for reasons of space.
The data processing methods provided herein are not inherently related to any particular computer, virtual machine system, or other apparatus. Various general purpose systems may also be used with the teachings herein. The structure required to construct a system incorporating aspects of the present invention will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components of the data processing method according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (12)
1. A data processing method is applied to a terminal, and is characterized in that the method comprises the following steps:
detecting selection operations of a user for multiple preset dimensions in a preset interface; each of the dimensions is an angle of data analysis;
generating a structured query statement instruction according to the selection operation;
sending the structural query statement instruction to a data server so that the data server can execute query operation on data in the data server according to the structural query statement instruction and send a query result to the terminal; the query result is a dimension attribute subset and a metric value corresponding to the selected dimension of the user;
the data in the data server is a plurality of key value pairs, the plurality of key value pairs are obtained by pre-analyzing original data based on a plurality of preset dimensions, and the key value pairs consist of a dimension attribute subset and a metric value corresponding to the dimension attribute subset; the metric values corresponding to the subset of dimension attributes are statistical values of the metrics defined for the corresponding subset of dimension attributes.
2. The method of claim 1, wherein the step of generating a structured query statement instruction according to the selection operation comprises:
converting the selection operation into a first statement instruction by using a preset first analysis mode;
converting the first statement instruction into a structured query statement instruction by using a preset second analysis mode;
the preset first analysis mode and the preset second analysis mode adopt the same framework; the architecture is written according to dimensions set in preset data.
3. A data processing method is applied to a data server, and is characterized by comprising the following steps:
receiving a structural query statement instruction sent by a terminal;
querying the data in the data server according to the structured query statement instruction;
the data in the data server is a plurality of key value pairs, the plurality of key value pairs are obtained by pre-analyzing original data based on a plurality of preset dimensions, and the key value pairs consist of a dimension attribute subset and a metric value corresponding to the dimension attribute subset; the metric values corresponding to the subset of dimension attributes are statistical values of the metrics defined for the corresponding subset of dimension attributes;
sending the query result to the terminal; the query result is a dimension attribute subset and a metric value corresponding to the selected dimension of the user; each of the dimensions is an angle of data analysis.
4. The method of claim 3, further comprising:
acquiring original data, and classifying the original data according to each preset dimension in a plurality of dimensions;
taking the classified original data as a data set, and performing pre-analysis on the data in the data set according to the preset multiple dimensions;
and generating data in the data server according to the result obtained by the pre-analysis.
5. The method according to claim 4, wherein the step of pre-analyzing the data in the data set according to the preset multiple dimensions by using the classified original data as the data set comprises:
determining at least one metric corresponding to the preset plurality of dimensions;
combining the preset multiple dimensions according to a preset mode to obtain multiple dimension sets; wherein each set of dimensions consists of at least one dimension;
combining the dimension attribute subsets included in each dimension set in the dimension sets to obtain a plurality of dimension attribute subsets;
analyzing data corresponding to the dimension attribute subsets in the data set aiming at each dimension attribute subset in the dimension attribute subsets to obtain metric values corresponding to the dimension attribute subsets and the metrics;
correspondingly, the step of generating the data in the data server according to the result obtained by the pre-analysis comprises:
and storing the metric values and the dimensional attribute subsets corresponding to the metric values into the data server in a key-value pair mode.
6. The method of claim 5, wherein the predetermined plurality of dimensions comprise at least one of days, hours, product areas, platforms, versions, and channels;
the at least one metric includes: at least one of number of clicks and viewing duration.
7. A data processing apparatus, characterized in that the apparatus comprises:
the detection module is used for detecting the selection operation of a user for multiple preset dimensions in a preset interface; each of the dimensions is an angle of data analysis; the instruction generating module is used for generating a structural query statement instruction according to the selection operation;
the instruction sending module is used for sending the structured query statement instruction to a data server so that the data server can conveniently execute query operation on data in the data server according to the structured query statement instruction and send a query result to a terminal; the query result is a dimension attribute subset and a metric value corresponding to the selected dimension of the user;
the data in the data server is a plurality of key value pairs, the plurality of key value pairs are obtained by pre-analyzing original data based on a plurality of preset dimensions, and the key value pairs consist of a dimension attribute subset and a metric value corresponding to the dimension attribute subset; the metric values corresponding to the subset of dimension attributes are statistical values of the metrics defined for the corresponding subset of dimension attributes.
8. The apparatus of claim 7, wherein the instruction generation module comprises:
the first conversion submodule is used for converting the selection operation into a first statement instruction by utilizing a preset first analysis mode;
the second conversion sub-module is used for converting the first statement instruction into a structured query statement instruction by using a preset second analysis mode;
the preset first analysis mode and the preset second analysis mode adopt the same framework; the architecture is written according to dimensions set in preset data.
9. A data processing apparatus, characterized in that the apparatus comprises:
the receiving module is used for receiving a structural query statement instruction sent by a terminal;
the query module is used for querying the data in the data server according to the structured query statement instruction; the data in the data server is a plurality of key value pairs, the plurality of key value pairs are obtained by pre-analyzing original data based on a plurality of preset dimensions, and the key value pairs consist of a dimension attribute subset and a metric value corresponding to the dimension attribute subset; the metric values corresponding to the subset of dimension attributes are statistical values of the metrics defined for the corresponding subset of dimension attributes;
the result sending module is used for sending the query result to the terminal; the query result is a dimension attribute subset and a metric value corresponding to the selected dimension of the user; each of the dimensions is an angle of data analysis.
10. The apparatus of claim 9, further comprising:
the system comprises an acquisition module, a classification module and a display module, wherein the acquisition module is used for acquiring original data and classifying the original data according to each preset dimension of a plurality of dimensions;
the pre-analysis module is used for taking the classified original data as a data set and pre-analyzing the data in the data set according to the preset multiple dimensions;
and the data generation module is used for generating the data in the data server according to the result obtained by pre-analysis.
11. The apparatus of claim 10, wherein the pre-analysis module comprises:
a determining submodule for determining at least one metric corresponding to the preset plurality of dimensions;
the first combination sub-module is used for combining the preset multiple dimensions according to a preset mode to obtain multiple dimension sets; wherein each set of dimensions consists of at least one dimension;
the second combination submodule is used for combining the dimension attribute subsets included in each of the dimension sets to obtain a plurality of dimension attribute subsets;
an analysis submodule, configured to analyze, for each of the plurality of dimension attribute subsets, data of the dimension attribute subset corresponding to the data set, and obtain metric values of the dimension attribute subset corresponding to the metric;
accordingly, the data generation module comprises:
and the storage sub-module is used for storing the metric values and the dimension attribute subsets corresponding to the metric values into the data server in a key-value pair mode.
12. The apparatus of claim 11, wherein the predetermined plurality of dimensions comprise at least one of days, hours, product areas, platforms, versions, and channels;
the at least one metric includes: at least one of number of clicks and viewing duration.
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| CN109542953B (en) * | 2018-11-29 | 2021-01-05 | 成都四方伟业软件股份有限公司 | Presto-based data processing method and device |
| CN109977139B (en) * | 2019-03-18 | 2022-12-02 | 京东科技控股股份有限公司 | Data processing method and device based on class structured query statement |
| CN110633293A (en) * | 2019-09-02 | 2019-12-31 | 深圳数位传媒科技有限公司 | Off-line computing method and device, storage medium and computer equipment |
| CN111090708B (en) * | 2019-10-11 | 2023-07-14 | 支付宝（中国）网络技术有限公司 | User characteristic output method and system based on data warehouse |
| CN111221891A (en) * | 2019-11-18 | 2020-06-02 | 北京明略软件系统有限公司 | Data query method and system |
| CN113407791A (en) * | 2021-06-18 | 2021-09-17 | 南方电网数字电网研究院有限公司 | Data query system, method, device, computer equipment and storage medium |
| CN117540107A (en) * | 2024-01-09 | 2024-02-09 | 浙江同花顺智能科技有限公司 | Data processing method and device, electronic equipment and storage medium |
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