CN108241692A - The querying method and device of data - Google Patents
The querying method and device of data Download PDFInfo
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- CN108241692A CN108241692A CN201611220548.2A CN201611220548A CN108241692A CN 108241692 A CN108241692 A CN 108241692A CN 201611220548 A CN201611220548 A CN 201611220548A CN 108241692 A CN108241692 A CN 108241692A
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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
- G06F16/2453—Query optimisation
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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
- G06F16/2425—Iterative querying; Query formulation based on the results of a preceding query
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Abstract
The invention discloses the querying methods and device of a kind of data.Wherein, this method includes：The big dimension in metadata is obtained, wherein, big dimension is more than or equal to the dimension of predetermined threshold value for unique value, metadata includes the unique value of dimension and dimension, the metadata record correspondence of dimension and unique value；The target dimension identical with big dimension is searched, and using the corresponding index of the target dimension identical with big dimension as the corresponding index of big dimension in inquiry records, wherein, inquiry has recorded the correspondence of target dimension and index in recording；The data for choosing preset quantity from fact table according to big dimension and its corresponding index generate Aggregation Table；Inquiry and big dimension and the relevant data of index in Aggregation Table.The present invention is solved carries out the technical issues of efficiency data query is low according to caused by big dimension directly generates Aggregation Table by Aggregation Table.
Description
Technical field
The present invention relates to data processing field, in particular to the querying method and device of a kind of data.
Background technology
In on-line analytical processing field, we are through being commonly encountered some big dimensions, such as channel (channel), url (nets
Page) etc., many times we need to do these big dimensions grouping anatomy, then go to see the top n of corresponding index, that is, see
The value and index of this dimension of top n, such as see dimension url, from the point of view of page browsing figureofmerit, see preceding 10 pageviews most
Which more url pages are, for instructing website operation or service optimization.
It is that big dimension does the performance cost being grouped when dissecting, common tradition side that on-line analytical processing, which suffers from a problem that,
Formula is solved by the thinking of the on-line analytical processing of Aggregation Table, but after prepolymerization, due to the unique value of the big dimension such as url
Too much, cause its query performance slow.
For it is above-mentioned the problem of, currently no effective solution has been proposed.
Invention content
An embodiment of the present invention provides the querying methods and device of a kind of data, directly raw according to big dimension at least to solve
The technical issues of efficiency data query is low is carried out by Aggregation Table caused by into Aggregation Table.
One side according to embodiments of the present invention provides a kind of data query method, including：It obtains in metadata
Big dimension, wherein, the big dimension is more than or equal to the dimension of predetermined threshold value for unique value, and the metadata includes dimension and dimension
Unique value, the metadata record dimension and the correspondence of the unique value；Lookup and institute in inquiry records
The identical target dimension of big dimension is stated, and using the target dimension corresponding index identical with the big dimension as described big
The corresponding index of dimension, wherein, the inquiry has recorded the correspondence of the target dimension and the index in recording；According to
The big dimension and its corresponding index choose the data generation Aggregation Table of preset quantity from fact table；In the polymerization
Inquiry and the big dimension and the relevant data of the index in table.
Further, the data of preset quantity are chosen from fact table according to the big dimension and its corresponding index
Generation Aggregation Table includes：The fact table is grouped according to the big dimension；According to the index to the fact
Data in tables of data are ranked up, and obtain the data after packet sequencing；Sequence is selected from the data after packet sequencing to lean on
The data of preceding preset quantity；Aggregation Table is generated according to the data of the preset quantity selected.
Further, the big dimension obtained in metadata includes：The metadata is obtained, wherein, the metadata includes
The unique value of dimension and dimension, the dimension are corresponding with the unique value；Unique value described in the metadata is searched to be more than
Equal to the dimension of predetermined threshold value；Unique value described in the metadata is more than or equal to the dimension of predetermined threshold value as the big dimension
Degree.
Further, it inquires in the Aggregation Table and includes with the big dimension and the relevant data of the index：It obtains
Querying condition, the querying condition include inquiry dimension and inquiry index；Judge whether include looking into described in the Aggregation Table
Ask the consistent big dimension of dimension；If it is judged that the Aggregation Table includes the big dimension consistent with the inquiry dimension, then sentence
Whether the disconnected inquiry index and index when generating the Aggregation Table are consistent, and judge the ordering of the inquiry index
It is whether consistent with the ordering of the index during generation Aggregation Table；If it is judged that the inquiry index is described poly- with generating
Index when closing table is consistent, and judges the ordering of the inquiry index and the row of the index during generation Aggregation Table
Row order is consistent, then inquires data in the Aggregation Table according to the inquiry dimension and the inquiry index.
Further, after the big dimension consistent with the inquiry dimension whether is included in the Aggregation Table is judged, institute
The method of stating further includes：If it is judged that the big dimension consistent with the inquiry dimension is not included in the Aggregation Table, then according to institute
It states inquiry dimension and the inquiry index inquires data from the fact table；Or the if it is judged that inquiry index
It is inconsistent or judge that the ordering of the inquiry index described polymerize with generation with index when generating the Aggregation Table
The ordering of index during table is inconsistent, then according to the inquiry dimension and the inquiry index from the fact table
Inquire data.
Another aspect according to embodiments of the present invention additionally provides a kind of data query arrangement, including：Acquiring unit is used
In obtaining the big dimension in metadata, wherein, the big dimension is more than or equal to the dimension of predetermined threshold value, the member number for unique value
According to the unique value for including dimension and dimension, the metadata record dimension and the correspondence of the unique value；It searches
Unit, for searching the target dimension identical with the big dimension in being recorded in inquiry, and by the institute identical with the big dimension
The corresponding index of target dimension is stated as the corresponding index of the big dimension, wherein, have recorded the mesh in the inquiry record
Mark the correspondence of dimension and the index；Polymerized unit, for being engaged in real number according to the big dimension and its corresponding index
Aggregation Table is generated according to the data that preset quantity is chosen in table；Query unit, for the inquiry in the Aggregation Table and the big dimension
Degree and the relevant data of the index.
Further, the polymerized unit includes：Grouping module, for according to the big dimension to the fact table
It is grouped；Sorting module for being ranked up according to the index to the data in the fact table, obtains grouping row
Data after sequence；Selecting module, for selecting the data for the forward preset quantity that sorts from the data after packet sequencing；It is poly-
Block is molded, for generating the Aggregation Table according to the data of the preset quantity selected.
Further, the acquiring unit includes：First acquisition module, for obtaining the metadata, wherein, the member
Data include the unique value of dimension and dimension, and the dimension is corresponding with the unique value；Searching module, for searching the member
Unique value described in data is more than or equal to the dimension of predetermined threshold value；Determining module, for by unique value described in the metadata
More than or equal to predetermined threshold value dimension as the big dimension.
Further, the query unit includes：Second acquisition module, for obtaining querying condition, the querying condition
Including inquiry dimension and inquiry index；First judgment module, for judging whether include tieing up with the inquiry in the Aggregation Table
The consistent big dimension of degree；Second judgment module, for judging that it is consistent with the inquiry dimension that the Aggregation Table includes
During big dimension, judge whether the inquiry index and index when generating the Aggregation Table are consistent, and judge that the inquiry refers to
Whether target ordering is consistent with the ordering of the index during generation Aggregation Table；First enquiry module, for sentencing
Break it is described inquiry index with generate the Aggregation Table when index it is consistent, and judge it is described inquire index ordering
When consistent with the ordering of the index during generation Aggregation Table, according to the inquiry dimension and the inquiry index described
Data are inquired in Aggregation Table.
Further, the query unit further includes：Second enquiry module, for not wrapped in the Aggregation Table is judged
When including the big dimension consistent with the inquiry dimension, according to the inquiry dimension and the index of inquiring from the fact table
Middle inquiry data；Or third enquiry module, for judging the inquiry index with generating the index during Aggregation Table
The ordering of index when ordering that is inconsistent or judging the inquiry index is with generating the Aggregation Table differs
During cause, data are inquired from the fact table according to the inquiry dimension and the inquiry index.
In embodiments of the present invention, using the big dimension obtained in metadata；It is searched and big dimension phase in inquiry records
The same corresponding index of target dimension and target dimension；Preset quantity is chosen from fact table according to big dimension and index
Data generate Aggregation Table；Inquiry and big dimension and the mode of the relevant data of index in Aggregation Table, by pressing index to data
It is ranked up, and chooses the data generation Aggregation Table of preset quantity, achieved the purpose that reduce data dimension in Aggregation Table, so as to
The technique effect for improving the efficiency that data query is carried out by Aggregation Table is realized, and then solves and is directly generated according to big dimension
The technical issues of efficiency data query is low is carried out by Aggregation Table caused by Aggregation Table.
Description of the drawings
Attached drawing described herein is used to provide further understanding of the present invention, and forms the part of the application, this hair
Bright illustrative embodiments and their description do not constitute improper limitations of the present invention for explaining the present invention.In the accompanying drawings：
Fig. 1 is a kind of flow chart of the querying method of optional data according to embodiments of the present invention；
Fig. 2 is a kind of schematic diagram of the inquiry unit of optional data according to embodiments of the present invention.
Specific embodiment
In order to which those skilled in the art is made to more fully understand the present invention program, below in conjunction in the embodiment of the present invention
The technical solution in the embodiment of the present invention is clearly and completely described in attached drawing, it is clear that described embodiment is only
The embodiment of a part of the invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill people
Member's all other embodiments obtained without making creative work should all belong to the model that the present invention protects
It encloses.
It should be noted that term " first " in description and claims of this specification and above-mentioned attached drawing, "
Two " etc. be the object for distinguishing similar, and specific sequence or precedence are described without being used for.It should be appreciated that it uses in this way
Data can be interchanged in the appropriate case, so as to the embodiment of the present invention described herein can in addition to illustrating herein or
Sequence other than those of description is implemented.In addition, term " comprising " and " having " and their any deformation, it is intended that cover
Cover it is non-exclusive include, be not necessarily limited to for example, containing the process of series of steps or unit, method, system, product or equipment
Those steps or unit clearly listed, but may include not listing clearly or for these processes, method, product
Or the intrinsic other steps of equipment or unit.
According to embodiments of the present invention, a kind of embodiment of the method for data query method is provided, it should be noted that attached
The step of flow of figure illustrates can perform in the computer system of such as a group of computer-executable instructions, though also,
So show logical order in flow charts, but in some cases, can be performed with the sequence being different from herein shown by
Or the step of description.
Fig. 1 is a kind of flow chart of the querying method of optional data according to embodiments of the present invention, as shown in Figure 1, should
Method includes the following steps：
Step S102 obtains the big dimension in metadata, wherein, big dimension is more than or equal to the dimension of predetermined threshold value for unique value
Degree, metadata include the unique value of dimension and dimension, the metadata record correspondence of dimension and unique value.
The relevant information of data is had recorded in metadata, including the unique value of dimension and dimension, by dimension
Unique value is judged, obtains the big dimension in metadata, wherein, the unique value of big dimension is more than or equal to predetermined threshold value.
Optionally, the big dimension obtained in metadata includes：Metadata is obtained, wherein, metadata includes dimension and dimension
Unique value, dimension is corresponding with unique value；Search the dimension that unique value in metadata is more than or equal to predetermined threshold value；By metadata
Middle unique value is more than or equal to the dimension of predetermined threshold value as big dimension.
Big dimension in metadata is obtained, i.e., in the dimension recorded in metadata, finds out all big dimensions.Specifically
Ground obtains metadata first, due to having recorded the relevant information of data in metadata, wherein including the dimension and dimension of data
The information such as corresponding unique value, and for dimension, the unique value of dimension is bigger, then dimension is bigger, therefore, is obtaining first number
According to a threshold value later, is set, and search the unique value more than or equal to predetermined threshold value in the metadata, then it is more than or waits
Dimension corresponding to unique value in predetermined threshold value is big dimension.
Step S104, searches the target dimension identical with big dimension in inquiry records, and by the mesh identical with big dimension
The corresponding index of dimension is marked as the corresponding index of big dimension, wherein, inquiry has recorded pair of target dimension and index in recording
It should be related to.
After all big dimensions are obtained, the target dimension identical with big dimension is searched in inquiry records.Due to
In each historical query record of inquiry record, in addition to record has the inquiry dimension of this inquiry, it is also recorded for this and looks into
The inquiry index of inquiry, therefore its corresponding index can be found according to target dimension, the index is corresponding as big dimension
Index.
Such as：What is recorded in fact table is sales volume and consumption sum of the specific product in different time,
In, dimension includes：Product type, time and user type, index include：Sales volume and consumption sum.Assuming that pass through metadata
" time " is got as big dimension, and is had recorded in inquiry records to the sales volume of all products in different time sections
Inquiry, then the index corresponding to big dimension " time " is " sales volume ".
Step S106, the data for choosing preset quantity from fact table according to big dimension and its corresponding index generate
Aggregation Table.
Big dimension is determined and index corresponding to big dimension after, according to big dimension and index from fact table
The data of preset quantity are chosen, generate Aggregation Table, wherein, the quantity for the data chosen from fact table is according to practical need
Ask what is set.
Optionally, the data generation for choosing preset quantity from fact table according to big dimension and its corresponding index is poly-
Conjunction table includes：Fact table is grouped according to big dimension；The data in fact table are ranked up according to index,
Obtain the data after packet sequencing；The data for the forward preset quantity that sorts are selected from the data after packet sequencing；According to
The data generation Aggregation Table of the preset quantity selected.
When according to big dimension and index generation Aggregation Table, the data in fact table are divided according to big dimension
Group, while the data in fact table are ranked up according to big dimension corresponding index, in embodiments of the present invention, not
Restriction sequentially is carried out, and in implementation process to above-mentioned grouping and sequence, above-mentioned grouping and sequence can simultaneously into
Row.After grouping and sequence, the data generation Aggregation Table of the forward preset quantity of selected and sorted.
Such as：What is recorded in fact table is sales volume and consumption sum of the specific product in different time,
In, dimension includes：Product type, time and user type, index include：Sales volume and consumption sum.By " product type " and
The dimension combination of " time ", by " consumption sum " as index, one is generated based on the fact table as a big dimension
Aggregation Table is then grouped the data in fact table according to " product type " and " time ", in grouping process, ignores
Other dimensions merge other dimensions according to " product type " dimension and " time " dimension, meanwhile, according to " sale gold
Volume " is ranked up from high to low, obtains the result of packet sequencing, it is assumed that in the example, it is only necessary to 50 data before concern, then
The data that selected and sorted is preceding 50 from packet sequencing result generate Aggregation Table.
Step S108, inquiry and big dimension and the relevant data of index in Aggregation Table.
The number under the big dimension and index of correlation is contained in the Aggregation Table generated due to the big dimension of above-mentioned basis and index
According to when carrying out data query, the data related with the big dimension and index of correlation can be inquired in the Aggregation Table.
In embodiments of the present invention, using the big dimension obtained in metadata；It is searched and big dimension phase in inquiry records
The same corresponding index of target dimension and target dimension；Preset quantity is chosen from fact table according to big dimension and index
Data generate Aggregation Table；Inquiry and big dimension and the mode of the relevant data of index in Aggregation Table, by pressing index to data
It is ranked up, and chooses the data generation Aggregation Table of preset quantity, achieved the purpose that reduce data dimension in Aggregation Table, so as to
The technique effect for improving the efficiency that data query is carried out by Aggregation Table is realized, and then solves and is directly generated according to big dimension
The technical issues of efficiency data query is low is carried out by Aggregation Table caused by Aggregation Table.
Optionally, it inquires in Aggregation Table and includes with big dimension and the relevant data of index：Querying condition is obtained, inquires item
Part includes inquiry dimension and inquiry index；Judge whether include the big dimension consistent with inquiry dimension in Aggregation Table；If it is determined that
Go out Aggregation Table include with the consistent big dimension of inquiry dimension, then judge to inquire index and index during generation Aggregation Table whether one
It causes, and judges to inquire whether the ordering of index is consistent with the ordering of index during generation Aggregation Table；If it is determined that
When it is consistent with index during generation Aggregation Table to go out to inquire index, and judging to inquire the ordering of index with generating Aggregation Table
Index ordering it is consistent, then according to inquiry dimension and inquiry index data are inquired in Aggregation Table.
In embodiments of the present invention, in order to improve the efficiency of inquiry, when carrying out data query, preferentially judging whether can be with
Data query is carried out from Aggregation Table, specifically, first to obtaining querying condition, obtains the inquiry dimension in querying condition and inquiry
Whether index is judged containing the big dimension consistent with the inquiry dimension in Aggregation Table, if Aggregation Table includes and the inquiry dimension
Consistent big dimension, then the inquiry index for continuing to judge the index and current queries being ranked up during generation Aggregation Table to data are
Sortord required by the mode and current queries that are ranked up during no consistent and generation Aggregation Table to data whether one
It causes, if it is consistent with the inquiry index of current queries to the index that data are ranked up during generation Aggregation Table, and would generate polymerization
It is consistent with the sortord required by current queries to the mode that data are ranked up during table, then according to when inquiry dimension and inquiry
Index inquires data in Aggregation Table.
Optionally, after whether including inquiry dimension in Aggregation Table is judged, method further includes：If it is judged that Aggregation Table
In do not include with the consistent big dimension of inquiry dimension, then according to the inquiry dimension and the index of inquiring from fact table
Inquire data；Either if it is judged that inquiry index with generate Aggregation Table when index it is inconsistent or judge inquiry index
Ordering and index during generation Aggregation Table ordering it is inconsistent, then according to inquiry dimension and inquiry index from the fact
Data are inquired in tables of data.
In data query, if by judging, current queries dimension is not included in Aggregation Table, then according in querying condition
Inquiry dimension and inquiry index, data are inquired in fact table；If Aggregation Table includes the dimension of current queries, but
Although the index and the inquiry index of current queries that are ranked up when generating Aggregation Table to data be inconsistent or generation Aggregation Table
When it is to the index that data are ranked up consistent with the inquiry index of current queries, but while generating Aggregation Table data are ranked up
Mode and the sortord required by current queries are inconsistent, then data cannot be inquired from Aggregation Table, be according to current queries
Inquiry dimension and inquiry index in condition inquire data in fact table.
According to embodiments of the present invention, a kind of embodiment of the inquiry unit of data, the inquiry unit of the data are additionally provided
It is mainly used for performing the querying method of data that the above of the embodiment of the present invention is provided, Fig. 2 is according to embodiments of the present invention
A kind of optional data inquiry unit schematic diagram, as shown in Fig. 2, the device mainly includes：
Acquiring unit 210, for obtaining the big dimension in metadata, wherein, big dimension is more than or equal to default for unique value
The dimension of threshold value, metadata include the unique value of dimension and dimension, the metadata record correspondence of dimension and unique value；
The relevant information of data is had recorded in metadata, including the unique value of dimension and dimension, acquiring unit 210 is logical
It crosses and the unique value of dimension is judged, obtain the big dimension in metadata, wherein, the unique value of big dimension is more than or equal to
Predetermined threshold value.
Optionally, acquiring unit includes：First acquisition module, for obtaining metadata, wherein, metadata include dimension and
The unique value of dimension, dimension are corresponding with unique value；Searching module is more than or equal to default threshold for searching unique value in metadata
The dimension of value；Determining module, for unique value in metadata to be more than or equal to the dimension of predetermined threshold value as big dimension.
Big dimension in metadata is obtained, i.e., in the dimension recorded in metadata, finds out all big dimensions.Specifically
Ground, the first acquisition module obtains metadata, due to having recorded the relevant information of data in metadata, wherein including the dimension of data
The information such as degree unique value corresponding with dimension, and for dimension, the unique value of dimension is bigger, then dimension is bigger, therefore,
After obtaining metadata, a threshold value is set, and searching module is searched unique more than or equal to predetermined threshold value in the metadata
Value, determining module will be greater than or be determined as big dimension equal to the dimension corresponding to the unique value of predetermined threshold value.
Searching unit 220, for searching the target dimension identical with big dimension, and will be with big dimension phase in inquiry record
With the corresponding index of target dimension as the corresponding index of big dimension, wherein, inquiry record in have recorded target dimension with refer to
Target correspondence；
After all big dimensions are obtained, the target dimension identical with big dimension is searched in inquiry records.Due to
In each historical query record of inquiry record, in addition to record has the inquiry dimension of this inquiry, it is also recorded for this and looks into
The inquiry index of inquiry, therefore searching unit 220 can find its corresponding index according to target dimension, using the index as big
The corresponding index of dimension.
Such as：What is recorded in fact table is sales volume and consumption sum of the specific product in different time,
In, dimension includes：Product type, time and user type, index include：Sales volume and consumption sum.Assuming that pass through metadata
" time " is got as big dimension, and is had recorded in inquiry records to the sales volume of all products in different time sections
Inquiry, then the index corresponding to big dimension " time " is " sales volume ".
Polymerized unit 230, for choosing preset quantity from fact table according to big dimension and its corresponding index
Data generate Aggregation Table；
Big dimension is determined and index corresponding to big dimension after, polymerized unit 230 according to big dimension and index from
The data of preset quantity are chosen in fact table, generate Aggregation Table, wherein, the quantity for the data chosen from fact table
It is set according to actual demand.
Optionally, polymerized unit includes：Grouping module, for being grouped according to big dimension to fact table；Sequence
Module for being ranked up according to index to the data in fact table, obtains the data after packet sequencing；Selecting module,
For selecting the data for the forward preset quantity that sorts from the data after packet sequencing；Aggregation module, for according to selection
The data generation Aggregation Table of the preset quantity gone out.
When according to big dimension and index generation Aggregation Table, grouping module is by the data in fact table according to big dimension
It is grouped, while sorting module is ranked up the data in fact table according to the corresponding index of big dimension, in this hair
In bright embodiment, the execution of above-mentioned module sequence is not defined, and in implementation process, above-mentioned module can be simultaneously
It performs.After grouping and sequence, the data of the forward preset quantity of selecting module selected and sorted, aggregation module is according to selecting
Preset quantity data generation Aggregation Table.
Such as：What is recorded in fact table is sales volume and consumption sum of the specific product in different time,
In, dimension includes：Product type, time and user type, index include：Sales volume and consumption sum.By " product type " and
The dimension combination of " time ", by " consumption sum " as index, one is generated based on the fact table as a big dimension
Aggregation Table is then grouped the data in fact table according to " product type " and " time ", in grouping process, ignores
Other dimensions merge other dimensions according to " product type " dimension and " time " dimension, meanwhile, according to " sale gold
Volume " is ranked up from high to low, obtains the result of packet sequencing, it is assumed that in the example, it is only necessary to 50 data before concern, then
The data that selected and sorted is preceding 50 from packet sequencing result generate Aggregation Table.
Query unit 240, for the inquiry in Aggregation Table and big dimension and the relevant data of index.
The number under the big dimension and index of correlation is contained in the Aggregation Table generated due to the big dimension of above-mentioned basis and index
According to when carrying out data query, query unit 240 inquires the number related with the big dimension and index of correlation in the Aggregation Table
According to.
In embodiments of the present invention, using the big dimension obtained in metadata；It is searched and big dimension phase in inquiry records
The same corresponding index of target dimension and target dimension；Preset quantity is chosen from fact table according to big dimension and index
Data generate Aggregation Table；Inquiry and big dimension and the mode of the relevant data of index in Aggregation Table, by pressing index to data
It is ranked up, and chooses the data generation Aggregation Table of preset quantity, achieved the purpose that reduce data dimension in Aggregation Table, so as to
The technique effect for improving the efficiency that data query is carried out by Aggregation Table is realized, and then solves and is directly generated according to big dimension
The technical issues of efficiency data query is low is carried out by Aggregation Table caused by Aggregation Table.
Optionally, query unit includes：Second acquisition module, for obtaining querying condition, querying condition, which includes inquiry, to be tieed up
Degree and inquiry index；First judgment module, for judging whether include the big dimension consistent with inquiry dimension in Aggregation Table；Second
Judgment module, for when judging that Aggregation Table includes the big dimension consistent with inquiry dimension, judging to inquire index and generation
Whether index during Aggregation Table is consistent, and judges to inquire the ordering of index and the arrangement time of index during generation Aggregation Table
Whether sequence is consistent；First enquiry module, for judging that inquiry index is consistent with index when generating Aggregation Table, and judges
Go out to inquire the ordering of index it is consistent with the ordering of the index during generation Aggregation Table when, refer to according to inquiry dimension and inquiry
It is marked in Aggregation Table and inquires data.
In embodiments of the present invention, in order to improve the efficiency of inquiry, when carrying out data query, preferentially judging whether can be with
Data query is carried out from Aggregation Table, specifically, the second acquisition module obtains the inquiry in querying condition to obtaining querying condition
Whether dimension and inquiry index, the first judgment module judge to look into if Aggregation Table includes this containing the inquiry dimension in Aggregation Table
Dimension is ask, then the second judgment module continues to judge the inquiry for the index and current queries being ranked up during generation Aggregation Table to data
Sortord required by whether index consistent and when generation Aggregation Table is ranked up data mode and current queries is
It is no consistent, if it is consistent with the inquiry index of current queries to the index that data are ranked up during generation Aggregation Table, and generate
It is consistent with the sortord required by current queries to the mode that data are ranked up during Aggregation Table, then the first enquiry module according to
When inquiry dimension and inquiry index inquire data in Aggregation Table.
Optionally, query unit further includes：Second enquiry module, for not including tieing up with inquiry in Aggregation Table is judged
When spending consistent big dimension, data are inquired from fact table according to the inquiry dimension and the inquiry index；Or the
Three enquiry modules, for judge inquire index with generate Aggregation Table when index it is inconsistent or judge inquiry index
Ordering and during the inconsistent ordering of index during generation Aggregation Table, according to inquiry dimension and inquiry index from the fact
Data are inquired in tables of data.
In data query, if by judging, current queries dimension is not included in Aggregation Table, then the second enquiry module is pressed
According to the inquiry dimension in querying condition and inquiry index, data are inquired in fact table；If Aggregation Table is included currently
The dimension of inquiry, but while generating Aggregation Table index that data are ranked up and the inquiry index of current queries it is inconsistent or
Although generation Aggregation Table when it is consistent with the inquiry index of current queries to the index that data are ranked up, generate Aggregation Table when pair
Sortord required by mode and current queries that data are ranked up is inconsistent, then data cannot be inquired from Aggregation Table,
Data are inquired in fact table according to the inquiry dimension in current queries condition and inquiry index by third enquiry module.
The embodiments of the present invention are for illustration only, do not represent the quality of embodiment.
In the above embodiment of the present invention, all emphasize particularly on different fields to the description of each embodiment, do not have in some embodiment
The part of detailed description may refer to the associated description of other embodiment.
In several embodiments provided herein, it should be understood that disclosed technology contents can pass through others
Mode is realized.Wherein, the apparatus embodiments described above are merely exemplary, such as the division of the unit, Ke Yiwei
A kind of division of logic function, can there is an other dividing mode in actual implementation, for example, multiple units or component can combine or
Person is desirably integrated into another system or some features can be ignored or does not perform.Another point, shown or discussed is mutual
Between coupling, direct-coupling or communication connection can be INDIRECT COUPLING or communication link by some interfaces, unit or module
It connects, can be electrical or other forms.
The unit illustrated as separating component may or may not be physically separate, be shown as unit
The component shown may or may not be physical unit, you can be located at a place or can also be distributed to multiple
On unit.Some or all of unit therein can be selected according to the actual needs to realize the purpose of this embodiment scheme.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, it can also
That each unit is individually physically present, can also two or more units integrate in a unit.Above-mentioned integrated list
The form that hardware had both may be used in member is realized, can also be realized in the form of SFU software functional unit.
If the integrated unit is realized in the form of SFU software functional unit and is independent product sale or uses
When, it can be stored in a computer read/write memory medium.Based on such understanding, technical scheme of the present invention is substantially
The part to contribute in other words to the prior art or all or part of the technical solution can be in the form of software products
It embodies, which is stored in a storage medium, is used including some instructions so that a computer
Equipment (can be personal computer, server or network equipment etc.) perform each embodiment the method for the present invention whole or
Part steps.And aforementioned storage medium includes：USB flash disk, read-only memory (ROM, Read-Only Memory), arbitrary access are deposited
Reservoir (RAM, Random Access Memory), mobile hard disk, magnetic disc or CD etc. are various can to store program code
Medium.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications also should
It is considered as protection scope of the present invention.
Claims (10)
1. a kind of querying method of data, which is characterized in that including：
The big dimension in metadata is obtained, wherein, the big dimension is more than or equal to the dimension of predetermined threshold value, the member for unique value
Data include the unique value of dimension and dimension, metadata record correspondence of the dimension with the unique value；
Search the target dimension identical with the big dimension in inquiry records, and by the target identical with the big dimension
The corresponding index of dimension as the corresponding index of the big dimension, wherein, have recorded the target dimension in the inquiry record
With the correspondence of the index；
The data for choosing preset quantity from fact table according to the big dimension and its corresponding index generate Aggregation Table；
Inquiry and the big dimension and the relevant data of the index in the Aggregation Table.
2. according to the method described in claim 1, it is characterized in that, real number is engaged according to the big dimension and its corresponding index
Include according to the data generation Aggregation Table that preset quantity is chosen in table：
The fact table is grouped according to the big dimension；
The data in the fact table are ranked up according to the index, obtain the data after packet sequencing；
The data for the forward preset quantity that sorts are selected from the data after packet sequencing；
Aggregation Table is generated according to the data of the preset quantity selected.
3. according to the method described in claim 1, it is characterized in that, the big dimension obtained in metadata includes：
The metadata is obtained, wherein, the metadata includes the unique value of dimension and dimension, the dimension and the unique value
It is corresponding；
Search the dimension that unique value described in the metadata is more than or equal to predetermined threshold value；
Unique value described in the metadata is more than or equal to the dimension of predetermined threshold value as the big dimension.
4. according to claim 1-3 any one of them methods, which is characterized in that inquiry and the big dimension in the Aggregation Table
Degree and the relevant data of the index include：
Querying condition is obtained, the querying condition includes inquiry dimension and inquiry index；
Judge whether include the big dimension consistent with the inquiry dimension in the Aggregation Table；
If it is judged that including the big dimension consistent with the inquiry dimension, then judge the inquiry index with generating described polymerize
Whether index during table is consistent, and judges the ordering of the inquiry index and the row of the index during generation Aggregation Table
Whether row order is consistent；
If it is judged that the inquiry index is consistent with index when generating the Aggregation Table, and judge the inquiry index
Ordering it is consistent with the ordering of index when generating the Aggregation Table, then according to the inquiry dimension and inquiry
Index inquires data in the Aggregation Table.
5. according to the method described in claim 4, it is characterized in that, whether include and the inquiry in the Aggregation Table is judged
After the consistent big dimension of dimension, the method further includes：
If it is judged that the big dimension consistent with the inquiry dimension is not included in the Aggregation Table, then according to the inquiry dimension
With the inquiry index data are inquired from the fact table；Or
If it is judged that it is described inquiry index with generate the Aggregation Table when index it is inconsistent or judge it is described inquire refer to
Target ordering and the ordering of index when generating the Aggregation Table are inconsistent, then according to the inquiry dimension and described
Inquiry index inquires data from the fact table.
6. a kind of inquiry unit of data, which is characterized in that including：
Acquiring unit, for obtaining the big dimension in metadata, wherein, the big dimension is more than or equal to predetermined threshold value for unique value
Dimension, the metadata includes the unique value of dimension and dimension, the metadata record dimension and the unique value
Correspondence；
Searching unit, for searching the target dimension identical with the big dimension, and will be with the big dimension in inquiry record
The identical corresponding index of the target dimension as the corresponding index of the big dimension, wherein, recorded in the inquiry record
The correspondence of the target dimension and the index；
Polymerized unit, for choosing the data of preset quantity from fact table according to the big dimension and its corresponding index
Generate Aggregation Table；
Query unit, for the inquiry in the Aggregation Table and the big dimension and the relevant data of the index.
7. device according to claim 6, which is characterized in that the polymerized unit includes：
Grouping module, for being grouped according to the big dimension to the fact table；
Sorting module, for being ranked up according to the index to the data in the fact table, after obtaining packet sequencing
Data；
Selecting module, for selecting the data for the forward preset quantity that sorts from the data after packet sequencing；
Aggregation module, for generating Aggregation Table according to the data of the preset quantity selected.
8. device according to claim 6, which is characterized in that the acquiring unit includes：
First acquisition module, for obtaining the metadata, wherein, the metadata includes the unique value of dimension and dimension, institute
It is corresponding with the unique value to state dimension；
Searching module, for searching the dimension that unique value described in the metadata is more than or equal to predetermined threshold value；
Determining module, for unique value described in the metadata to be more than or equal to the dimension of predetermined threshold value as the big dimension
Degree.
9. device according to claim 6, which is characterized in that the query unit includes：
Second acquisition module, for obtaining querying condition, the querying condition includes inquiry dimension and inquiry index；
First judgment module, for judging whether include the big dimension consistent with the inquiry dimension in the Aggregation Table；
Second judgment module, for when judging that the Aggregation Table includes the inquiry dimension, judging the inquiry index
It is whether consistent with index when generating the Aggregation Table, and judge the ordering of the inquiry index with generating described polymerize
Whether the ordering of index during table is consistent；
First enquiry module, for judging that the inquiry index is consistent with index when generating the Aggregation Table, and sentences
When the ordering for the inquiry index that breaks is consistent with the ordering of the index during generation Aggregation Table, looked into according to described
It askes dimension and the inquiry index inquires data in the Aggregation Table.
10. device according to claim 9, which is characterized in that the query unit further includes：
Second enquiry module, for when not including in judging the Aggregation Table with the inquiry consistent big dimension of dimension,
According to the inquiry dimension and the inquiry index data are inquired from the fact table；Or
Third enquiry module, for judge it is described inquiry index with generation the Aggregation Table when index it is inconsistent or
When judging the ordering of the inquiry index and the inconsistent ordering of the index during generation Aggregation Table, according to institute
It states inquiry dimension and the inquiry index inquires data from the fact table.
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