CN116561162A - Method for carrying out data query analysis by dynamically constructing cubes - Google Patents
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- CN116561162A CN116561162A CN202310591187.6A CN202310591187A CN116561162A CN 116561162 A CN116561162 A CN 116561162A CN 202310591187 A CN202310591187 A CN 202310591187A CN 116561162 A CN116561162 A CN 116561162A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/242—Query formulation
- G06F16/2433—Query languages
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/242—Query formulation
- G06F16/2433—Query languages
- G06F16/2448—Query languages for particular applications; for extensibility, e.g. user defined types
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention discloses a method for carrying out data query analysis by dynamically constructing cubes, which relates to the technical field of data processing, wherein the method in the invention constructs the newly added custom field of a user in a mode of converting SQL (structured query language) or combining the SQL mode with the cube mode to inquire, so as to finish the calculation of the custom field, thereby realizing basic data processing, and realizing row-column conversion and aggregation sequencing results by carrying out multidimensional analysis on the dragging of dimensionality and measurement, and is simple, convenient and flexible to use; the multidimensional calculation process is carried out in the memory, the grammar difference between databases can be shielded, and the invention avoids the direct de-assembly of a plurality of dimensions by adopting a Cartesian product mode when the MDX statement is assembled in an SQL mode for preprocessing the data, thereby improving the query efficiency.
Description
The application is a divisional application which is proposed on the basis of the original application (application number: 201911412349.5), and the application date of the original application is: 12 months 31 days 2019, the application number is: 201911412349.5, the invention is: a method for data processing or querying by dynamically constructing cubes.
Technical Field
The invention relates to the technical field of data processing, in particular to a method for carrying out data query analysis by dynamically constructing cubes.
Background
Along with the rapid development of society, rules and change trends of data are analyzed, a complete solution is formulated, the existing data in enterprises are effectively integrated, intelligent business operation decisions are helped to be made by the enterprises, the construction of cubes is more and more important as a basic link of data analysis, and the construction of complex and flexible cubes brings great challenges to data analysis.
In the traditional data analysis system, the following method is mainly adopted to meet the requirements of complex, flexible and personalized data analysis: multidimensional analysis is carried out by writing SQL script modes (SQL sentences and stored procedures), pre-configuring a cube mode according to business.
In the traditional data analysis system, the SQL script mode (stored procedure) has high requirements on writers, grammar differences exist between databases, and SQL is inconvenient to migrate between databases.
The main disadvantage of pre-configuring cubes from business is its inflexibility compared to relational models, and the associated changes are very difficult to make once the model is built.
Disclosure of Invention
The present invention aims to provide a method for data processing or querying by dynamically constructing cubes, thereby solving the aforementioned problems in the prior art.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a method of data processing by dynamically building cubes, comprising the steps of:
s1, constructing a model, creating a custom field, carrying out data processing on the custom field through a local query grammar, and confirming whether the data processing can be carried out in an SQL mode or not;
s2, when the newly built custom field can be used for data processing in an SQL mode, the data processing is directly performed in a database mode, and otherwise, the step S3 is performed;
s3, constructing a data processing cube by adopting the newly-built custom field;
s4, data processing is carried out on the constructed data processing cube.
Preferably, the building a model in step S1 specifically includes: constructing a model through various files, a relational database, other data and an application interface;
the step of confirming whether the data processing can be performed in the SQL mode specifically comprises the following steps: judging whether the custom field has a nested relation or not, and if the custom field does not have the nested relation, only judging whether the custom field supports SQL grammar of a corresponding database or not; if other custom fields exist for nesting, whether all the custom fields can process data in an SQL mode or not needs to be judged.
Preferably, step S2 specifically includes:
s21, when no nested custom field supports SQL grammar of a corresponding database, if yes, directly calculating in a database mode, otherwise, entering step S3;
s22, when all the custom fields in the nested custom fields can be subjected to data processing in an SQL mode, sequencing the calculation sequence of all the custom fields, preferentially processing the nested custom fields, and finally calculating all the custom fields in the SQL mode; otherwise, step S3 is entered.
Preferably, the dynamically constructing the cube in step S3 specifically includes:
s31, acquiring all basic fields in the custom field, and processing the basic fields to acquire a basic field list;
s32, obtaining a physical table of the model, and constructing a cube table; if the filtering condition exists, converting the filtering condition into grammar which can be identified by the local query language, and constructing a query table of the cube;
constructing a cube dimension for a corresponding basic field data type in the basic field list query model, and constructing a cube measure for the digital type;
s33, constructing a calculation member;
s34, constructing a cube by operating an XML mode through the constructed entity object.
Preferably, the step S4 specifically includes:
s41, constructing a data set through a main key of the model for the data set of the MDX statement, and constructing a calculation expression according to the custom field;
s42, constructing a multidimensional query statement according to the dynamic calculation expression and the constructed data set, and performing multidimensional calculation through the cube and the MDX statement;
s43, obtaining the result of multi-dimensional calculation and updating the calculation result into the model.
It is another object of the present invention to provide a method for data query analysis by dynamically building cubes, comprising the steps of:
a1, constructing a data query analysis model, creating a picture chart, and binding the picture chart on an X axis, a Y axis or a Z axis;
a2, judging the number of the binding number axes of the chart, if only 1-2 binding axes are bound and a dynamic expression is not used, directly carrying out query analysis in a database mode, otherwise, entering a step A3;
a3, dynamically constructing a cube when using a dynamic expression or performing display in a three-dimensional manner;
and A4, carrying out data multidimensional query analysis according to the established cube file.
Preferably, the step A3 specifically includes:
a31, acquiring the bound basic fields and all basic fields in the dynamic expression by binding the basic fields;
a32, performing de-duplication on all the basic fields obtained in the step A31 to obtain a basic field list;
a33, obtaining model information to construct a cube table, and converting the filtering condition, the ordering condition and the display line number into grammar which can be identified by a local query language, so as to construct a query view of the cube;
a34, processing the basic field list in the step A32 according to the data type, and constructing cube dimensions and metrics to obtain a data query cube. The method comprises the steps of carrying out a first treatment on the surface of the
Preferably, the binding of the X-axis, the Y-axis and the Z-axis in the step A1 specifically includes: newly creating a dynamic expression or binding by dragging a picture chart;
the steps A1 and A2 also comprise: if screening or sorting is needed, the bound picture charts can be screened or sorted through the drag model field.
Preferably, step A4 specifically includes:
a41, obtaining filtering and sorting information of the bound chart, constructing an SQL sentence for inquiring, obtaining an inquiry result and constructing a multidimensional row data set;
a42, converting the calculation members according to the binding data or the dynamic expression, and constructing a multidimensional statement column data set;
a43, based on the constructed multi-dimensional row dataset, multi-dimensional column dataset, and data query cube,
inquiring through a multidimensional engine so as to obtain an inquiry result;
a44, converting the query result into a format which can be identified by the chart for displaying, and realizing the data analysis result.
Notably, the innovation in the present invention is that:
1. the built custom field establishes the calculation priority and decides the calculation sequence by calculating the nesting relation of the fields in the custom field.
2. The data processing of the model is preferably calculated in an SQL mode, which is favorable for processing complex and personalized requirements, and when the SQL mode can not be adopted to realize the data processing process, the data cube is dynamically built, and the multi-dimensional engine is used for calculating.
3. When multidimensional analysis is performed by constructing a data query cube, the dimensions are preferably ordered in an SQL mode during ordering, so that an axis of an MDX statement is constructed, and the ordering in the mode supports: the ordering of the aggregation mode comprises ordering the query results according to summation, average value, maximum value, minimum value and original value.
4. Processing of the data may be implemented in the built data cube, including single-line processing of the data, or cross-line processing.
5. And the SQL statement and MDX are combined to construct the tuple and the data set of the MDX, so that the data query and processing speed is faster.
The beneficial effects of the invention are as follows:
the invention constructs the inquiry through the newly added custom field of the user and the combination of the SQL mode or the SQL mode and the cube mode, and completes the calculation of the custom field, thereby realizing the basic data processing, and realizing the row-column conversion and the aggregation sequencing result through multidimensional analysis on the dragging of the dimension and the measurement, which is simple, convenient and flexible to use; the multidimensional calculation process is carried out in the memory, the grammar difference between databases can be shielded, and the invention avoids the direct de-assembly of a plurality of dimensions by adopting a Cartesian product mode when the MDX statement is assembled in an SQL mode for preprocessing the data, thereby improving the query efficiency.
Drawings
FIG. 1 is a block diagram of the basic cube of the prior art;
FIG. 2 is a flow chart of a method of data processing by dynamically building cubes in example 1;
FIG. 3 is a flow chart of a method of data query analysis by dynamically building cubes in example 2.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the detailed description is presented by way of example only and is not intended to limit the invention.
Example 1
The embodiment provides a method for data processing by dynamically constructing a cube, which comprises the following steps:
s1, constructing a data processing model through a file (Excel/CSV/JSON/XML), a relational database, other data (ES/REDIS/INFLUX), an application interface (WebService, restful) and the like, creating a custom field, carrying out data processing on the custom field through a local query grammar, and confirming whether the data processing can be carried out in an SQL mode;
judging whether the custom field has a nested relation or not, and if the custom field does not have the nested relation, only judging whether the custom field supports SQL grammar of a corresponding database or not; if other custom fields exist for nesting, whether all the custom fields can process data in an SQL mode or not needs to be judged.
S2, when the newly built custom field can be used for data processing in an SQL mode, the data processing is directly performed in a database mode, and otherwise, the step S3 is performed;
s3, constructing a cube by adopting the newly-built custom field, wherein the process for constructing the cube specifically comprises the following steps:
s31, acquiring all basic fields in the custom field, and processing the basic fields to acquire a basic field list;
s32, obtaining a physical table of the model, and constructing a cube table; if the filtering condition exists, converting the filtering condition into grammar which can be identified by the local query language, and constructing a query table of the cube;
constructing a cube dimension for a corresponding basic field data type in the basic field list query model, and constructing a cube measure for the digital type;
s33, constructing a calculation member;
s34, constructing a cube by operating an XML mode through the constructed entity object.
S4, carrying out data processing on the built cube, wherein the method specifically comprises the following steps:
s41, constructing a data set through a main key of the model for the data set of the MDX statement, and constructing a calculation expression according to the custom field;
s42, constructing a multidimensional query statement according to the dynamic calculation expression and the constructed data set, and performing multidimensional calculation through the constructed cube and the MDX statement;
s43, obtaining the result of multi-dimensional calculation and updating the calculation result into the model.
It should be noted that, in the present embodiment, whether the data processing in step S1 can be performed in the SQL manner specifically includes: judging whether the custom field has a nested relation or not, and if the custom field does not have the nested relation, only judging whether the custom field supports SQL grammar of a corresponding database or not; if other custom fields exist for nesting, whether all the custom fields can process data in an SQL mode or not needs to be judged.
Step S2 in this embodiment specifically includes:
s21, when no nested custom field supports SQL grammar of a corresponding database, if yes, directly calculating in a database mode, otherwise, entering step S3;
s22, when all the custom fields in the nested custom fields can be subjected to data processing in an SQL mode, sequencing the calculation sequence of all the custom fields, preferentially processing the nested custom fields, and finally calculating all the custom fields in the SQL mode; otherwise, step S3 is entered.
In the embodiment, when constructing a computing member, two modes are included, firstly, aiming at a custom field which is calculated in an SQL mode, if the custom field is calculated in the SQL mode, when constructing a cube, according to the type of the custom field, directly inquiring the corresponding basic field data type in a model for a basic field list, taking the custom field as a common field, constructing cube measurement for a character string, a date, a dimension of a building cube and a digital type, thereby directly constructing the computing member of the cube;
if the custom field is the custom field which can not be calculated in the SQL mode, the expression in the custom field is converted into the expression which can be identified by the corresponding cube through processing in a mode of constructing a calculation member, and the expression is used as a measure.
Example 2
The embodiment provides a method for data query analysis by dynamically constructing cubes, as shown in fig. 3, comprising the following steps:
a1, constructing a data query analysis model, creating a picture chart, binding an X-axis, a Y-axis and a Z-axis of the newly created dynamic expression or the picture chart through dragging, and if screening or sorting is needed, screening or sorting the bound picture chart through a dragging model field;
a2, judging the number of the chart binding number axes, if only 1-2 chart binding number are bound and dynamic expressions are not used, directly carrying out query analysis by a local query or corresponding database mode, otherwise, entering a step A3;
a3, when the dynamic expression is used or displayed in a three-dimensional mode, the data query cube is dynamically constructed, and the method specifically comprises the following steps:
a31, acquiring the bound basic fields and all basic fields in the dynamic expression by binding the basic fields;
a32, performing de-duplication on all the basic fields obtained in the step A31 to obtain a basic field list;
a33, obtaining model information to construct a cube table, and converting the filtering condition, the ordering condition and the display line number into grammar which can be identified by a local query language, so as to construct a query view of the cube;
a34, processing the basic field list in the step A32 according to the data type, and constructing cube dimensions and metrics to obtain a data query cube.
And A4, carrying out data multidimensional query analysis according to the established data query cube.
The multidimensional query analysis of data in step A4 in this embodiment specifically includes:
a41, acquiring filtering and sorting information of the bound chart, constructing an SQL sentence for inquiring, and constructing a multidimensional row data set by adopting the acquired inquiring result;
a42, converting according to the bound data or dynamic expression, so as to construct a multidimensional column data set;
a43, based on the constructed multi-dimensional row dataset, multi-dimensional column dataset, and data query cube,
inquiring through a multidimensional engine so as to obtain an inquiry result;
a44, converting the query result into a format which can be identified by the chart for displaying, and realizing the data analysis result.
By adopting the technical scheme disclosed by the invention, the following beneficial effects are obtained:
the invention constructs the inquiry through the newly added custom field of the user and the combination of the SQL mode or the SQL mode and the cube mode, and completes the calculation of the custom field, thereby realizing the basic data processing, and realizing the row-column conversion and the aggregation sequencing result through multidimensional analysis on the dragging of the dimension and the measurement, which is simple, convenient and flexible to use; the multidimensional calculation process is carried out in the memory, the grammar difference between databases can be shielded, and the invention avoids the direct de-assembly of a plurality of dimensions by adopting a Cartesian product mode when the MDX statement is assembled in an SQL mode for preprocessing the data, thereby improving the query efficiency.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which is also intended to be covered by the present invention.
Claims (4)
1. A method for data query analysis by dynamically building cubes, comprising the steps of:
a1, constructing a data query analysis model, creating a picture chart, and binding the picture chart on an X axis, a Y axis or a Z axis;
a2, judging the number of the binding number axes of the chart, if only 1-2 binding axes are bound and a dynamic expression is not used, directly carrying out query analysis in a database mode, otherwise, entering a step A3;
a3, dynamically constructing a cube when using a dynamic expression or performing display in a three-dimensional manner;
and A4, carrying out data multidimensional query analysis according to the established cube file.
2. The method for data query analysis by dynamically constructing cubes of claim 1, wherein step A3 comprises:
a31, acquiring the bound basic fields and all basic fields in the dynamic expression by binding the basic fields;
a32, performing de-duplication on all the basic fields obtained in the step A31 to obtain a basic field list;
a33, obtaining model information to construct a cube table, and converting the filtering condition, the ordering condition and the display line number into grammar which can be identified by a local query language, so as to construct a query view of the cube;
a34, processing the basic field list in the step A32 according to the data type, and constructing cube dimensions and metrics to obtain a data query cube.
3. The method of claim 1, wherein binding X-axis, Y-axis and Z-axis in step A1 comprises: newly creating a dynamic expression or binding by dragging a picture chart;
the steps A1 and A2 also comprise: if screening or sorting is needed, the bound picture charts can be screened or sorted through the drag model field.
4. The method for data query analysis by dynamically constructing cubes of claim 1, wherein step A4 comprises:
a41, obtaining filtering and sorting information of the bound chart, constructing an SQL sentence for inquiring, obtaining an inquiry result and constructing a multidimensional row data set;
a42, converting the calculation members according to the binding data or the dynamic expression, and constructing a multidimensional statement column data set;
a43, inquiring through a multidimensional engine based on the constructed multidimensional row dataset, the multidimensional column dataset and the data inquiry cube, thereby obtaining an inquiry result;
a44, converting the query result into a format which can be identified by the chart for displaying, and realizing the data analysis result.
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| CN201911412349.5A CN111159221B (en) | 2019-12-31 | 2019-12-31 | Method for data processing or query through dynamic cube construction |
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| US7310637B2 (en) * | 2004-05-05 | 2007-12-18 | International Business Machines Corporation | Dynamic database access via standard query language and abstraction technology |
| CN105205085A (en) * | 2014-06-30 | 2015-12-30 | 中兴通讯股份有限公司 | Multi-dimensional analysis method and device for mass data |
| CN110347755A (en) * | 2019-07-08 | 2019-10-18 | 南威软件股份有限公司 | A kind of big data multidimensional data analysis method and system based on Hadoop and HBase |
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