CN114896248A - Method and device for generating sql by automatic splicing based on association pool data table - Google Patents
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Abstract
The invention relates to a method and a device for generating sql by realizing automatic association and splicing among data tables, wherein the method comprises the following steps: the method comprises the following steps that firstly, all sql related to a table is analyzed based on an association analysis mode, field groups are obtained, the field groups are verified one by one, a table main key is obtained, and a data table main key library is stored; step two, automatically acquiring the association relation among all the sql tables through the sql blood relationship analysis and the association analysis, and generating an association pool according to an association pool association method; and a third step, based on the first step and the second step, dragging and generating table elements by graphical operation executed by a user in the device, completing connection between tables through connecting lines, automatically completing the association relationship between the two tables through an inter-table association pool technology, simultaneously adding customized filtering conditions by the user according to report requirements, manually clicking required field information through a main interface after completing the inter-table association splicing, and automatically completing the automatic sql splicing work.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a method and an apparatus for implementing automatic concatenation of data tables, and a method and an apparatus for implementing automatic generation of sql query statements.
Background
With the appearance of mass data in various industries, various processing needs to be performed on the data in more and more scenes, some enterprises with large scale have thousands of data tables, meanwhile, the demands of business on reports are various, and the splicing processing among the data tables is related.
At present, few instrumental table splicing methods exist, programmers are generally required to write program codes to realize quick splicing of data tables, a special programming language is required to be mastered to write the program codes for table splicing, for example, data table splicing is realized by writing a program by using SQL statements, a user is required to master SQL grammar, the learning cost is high, the technical threshold of data application is improved, and for example, common business personnel (for example, those who cannot be skilled in programming) are difficult to splice data tables conveniently and efficiently.
Disclosure of Invention
The invention aims to provide a method and a device for realizing automatic splicing of data tables, which are used for solving the problem that the splicing of the data tables cannot be conveniently and efficiently realized in the prior art, realizing automatic generation of related query sentences among sql tables in batches, greatly improving the efficiency of writing sql scripts and reducing the labor cost.
The invention is realized by the following steps: the first step, analyzing all sql relevant to the table based on the correlation analysis mode, obtaining field groups, verifying the field groups one by one, obtaining table main keys, and storing a data table main key library.
And a second step of automatically acquiring the association relation among all the sql tables through the sql blood relationship analysis and the association analysis, and generating an association pool according to an association pool association method.
And a third step, based on the first step and the second step, dragging and generating table elements by graphical operation executed by a user in the device, completing connection between tables through connecting lines, automatically completing the association relationship between the two tables through an inter-table association pool technology, simultaneously adding customized filtering conditions by the user according to report requirements, manually clicking required field information through a main interface after completing the inter-table association splicing, and automatically completing the automatic sql splicing work. The specific invention flow is shown in figure 2.
The method for realizing sql splicing between two tables needs to automatically generate an association relation according to information such as a common association pool field between the two tables, a primary key of a secondary table and the like. See flow chart 6 for details.
Drawings
Fig. 1 is a schematic diagram illustrating a patent flow according to the present disclosure.
Fig. 2 is a schematic diagram illustrating a data stitching flow according to an exemplary embodiment of the present disclosure.
FIG. 3 is a block diagram illustrating an implementation of source table lookup box creation in accordance with an illustrative embodiment of the present disclosure.
FIG. 4 is a block diagram illustrating an implementation of a source table entry and its field query in accordance with an exemplary embodiment of the present disclosure.
Fig. 5 is a diagram illustrating an inter-table association connection according to an exemplary embodiment of the present disclosure.
Fig. 6 is a flowchart illustrating a method of implementing data splicing according to an exemplary embodiment of the present disclosure.
FIG. 7 is a block diagram illustrating an inter-table automatic stitching association in accordance with an exemplary embodiment of the present disclosure.
Fig. 8 is a diagram illustrating a manually added field association condition association according to an exemplary embodiment of the present disclosure.
FIG. 9 illustrates an automatically added field association condition association in accordance with an exemplary embodiment of the present disclosure.
Fig. 10 illustrates implementing a group by nested association in accordance with an exemplary embodiment of the present disclosure.
Fig. 11 illustrates sql script content implementing sql automatic splicing work and its generation according to an exemplary embodiment of the present disclosure.
Detailed Description
In order to achieve the above object, the following technical solutions are adopted in the embodiments of the present specification.
In the first step, a table primary key acquisition method is provided. In the existing method for acquiring the table main keys, data exploration is manually performed on the table according to the table main keys provided in the table of the data dictionary, or the table is manually performed, and related fields are explored one by one according to experience to acquire the table main keys.
The patent provides a method for mining an sql script, which comprises the steps of obtaining associated fields among tables through association analysis, sorting all the associated fields related to the tables, integrating the associated fields into field groups, sorting and de-duplicating the fields, performing group by running from field combinations with few fields through sorting, and judging whether the associated field combinations are table main keys or not. The specific case is as follows:
Sql1:Select*from testa a left join testb b on a.cust_num＝b.cust_id；
Sql2:Select*from testd a left join testb b on a.cust_id＝b.cust_id and a.cust_acct＝b.cust_acct and b.stat_dt＝’20211231’；
Sql3:Select*from testc a left join testb b on a.cust_acct＝b.cust_acct and a.stat_dt＝b.stat_dt。
the associated field group obtained by the table testb from the sql1 is the cust _ id; the associated field groups obtained from the sql2 are cust _ id, cust _ acct and stat _ dt; the association group obtained from Sql3 is cust _ acct, stat _ dt; sorting the association groups of the three groups according to the number of fields, namely cust _ id, cust _ no, stat _ dt, cust _ id, cust _ no and stat _ dt; the following sql is performed, respectively:
Sql4:select cust_id,count(1)from testb group by cust_id having count(1)>1；
Sql5:select cust_acct,stat_dt,count(1)from testb group by cust_acct,stat_dt having count(1)>1；
Sql6:select cust_id,cust_acct,stat_dt,count(1)from testb group by cust_id,cust_acct,stat_dt having count(1)>1。
assuming that the execution result of sql4 is not null and the execution result of sql5 is null, it can be determined that cut _ acct and stat _ dt are the primary keys of table testb, and the sql6 statement is not executed.
Through the method, all sql scripts can be subjected to association analysis, all associated field groups are obtained, after the duplication is removed, the sql running numbers formed according to the field groups are carried out, and the main keys of the response table are obtained according to the running number results.
In the mode, 2 or more than 2 main keys exist in a table, only one main key can be obtained, and the mode mainly obtains one main key of the table most efficiently; in addition, other table main keys can be executed in this way, namely, after the execution result of the sql6 is null, the sql6 script is continuously executed, all field groups are executed all the time, then the redundant main keys are removed, and other table main keys are obtained. The redundant primary key definition is a field extension of the table primary key, such as the host _ acct, the stat _ dt is the primary key, and the host _ id, the host _ acct and the stat _ dt are redundant primary keys thereof.
And a second step of providing two association pool generation methods, wherein one is an extended association pool derivation case as follows:
Sql7：select*from testa a left join testb b on a.cust_num＝b.cust_id；
Sql8：select*from testb a left join testc b on a.cust_id＝b.cust_no。
according to the association analysis, the testa _ cu _ num and the testb _ cu _ id belong to an association relation, and the testb _ cu _ id and the testc _ cu _ no also belong to an association relation, so that the testa _ cu _ num, the testb _ cu _ id and the testc _ cu _ no fields belong to the same association pool, and the content of the association pool is continuously expanded along with the continuous analysis of the sql script. The initial association pool is as follows:
as shown in the above association pool, the automatic splicing and splicing of testa and testc can obtain two groups of association field sets of testa. cust _ num and testc. cust _ no and testa. acct and testc. acno, and assuming that the main key of testc is cust _ no, under the circumstance of acno, the following sql can be formed by automatic splicing:
Sql9:select*from testa a left join testc b on a.cust_num＝b.cust_no and a.acct＝b.acno。
the association pool derivation scheme is based on sql blood relationship analysis and association analysis technology, wherein through association analysis, all scripts in an sql library are subjected to association analysis and stored in an association comparison library, the word frequency of each association pool is counted through group by, then association comparison relations with association frequencies more than 5 times and more are screened out, and a basic association pool is generated; and then processing the association relation of the association frequency for 4 times, and gradually decreasing to complete the construction of all association pools.
The association pool processing logic: the correlation pool processing method based on blood margin analysis mainly aims at the blood margin analysis application of a temporary table and a nested table, and comprises the following specific cases:
wherein the construction of the association pool based on the temporary table comprises the following steps:
Sql10:create table tmp1 as select cust_num cust_id from testa；
Sql11:select*from tmp1 a left join testb b on a.cust_id＝b.cust_id；
by the blood margin analysis, testa. cust _ num and testb. cust _ id form an association relationship.
Secondly, constructing an association pool based on a nested table:
Sql12:select*from testa a left join(select b.cust_id cust_no from testb b)b on a.cust_num＝b.cust_no；
by the blood margin analysis, testa. cust _ num and testb. cust _ id form an association relationship.
The other is a single type association pool, the sql association relationship is directly analyzed, the association relationship is not expanded with other sql association relationships, and the sql7 and the sql8 are used as examples to form the single type association pool as follows:
the single type association relation has no function of deriving connection, for example, the above association pool, the table testa and the table testc belong to one association pool due to no field in the association pool, and can not be spliced automatically.
For tables related to dictionary tables, protocol tables and the like, because the fields of the tables are stored in different systems, the fields do not accord with the conditions of the extended association pool and are included in the single association pool.
Meanwhile, field name analysis can be performed through the extended association pool, fields with the field names distributed in front of 50% are found out, the association pool with the similar field names is constructed, for example, the extended field names of the client number generally have similar field naming methods such as cust _ id, cust _ num, cust _ no and the like, and the association pool with the similar field names can be constructed; in addition, the field annotation names can be analyzed, the field annotations with the field annotation names distributed in front of 50% can be found, a field annotation association pool with similar field names is constructed, and similarly, by taking the client number as an example, in the common client annotation naming, a client id, a client number, a party number, an applicant number and other similar field annotation association pools can be found. The method can be combined with semantic analysis to construct a field annotation close association pool.
And a third step of providing an automatic splicing method of the data table.
Obtaining the associated fields of two tables to be associated in the same associated pool through the associated pool, wherein the fields are the associated fields of the two tables, judging whether the auxiliary table has a primary key, if the auxiliary table has the primary key, the primary key needs to be divided into the associated fields and condition fields, and if all the associated fields of the auxiliary table are in the associated pool, the two tables can be spliced automatically. The specific case is as follows:
the main table guanlian1 is associated with the sub table guanlian2, wherein the main table guanlian1 and the sub table guanlian2 have fields in the same association pool, which are guanlian1. list _ no and guanlian2. list _ id, respectively, and the sub table guanlian2 has main keys of list _ id and stat _ dt, wherein stat _ dt is a condition field, and the two tables are spliced sql as follows:
Sql13:select*from guanlian1 a left join guanlian2 b on a.cust_no＝b.cust_id and b.stat_dt＝。
if the auxiliary table has a main key, but the associated field of the main key is not in the associated pool, or the auxiliary table has no main key, the user can be prompted to be associated in a row _ number () or group by way to form the association relationship of the two tables; the concrete cases are as follows: the main table guanlian1 is associated with the sub table guanlian2, wherein the main table guanlian1 and the sub table guanlian2 have fields in the same association pool, which are guanlian1. list _ no and guanlian2. list _ id, respectively, and the sub table guanlian2 has main keys of list _ acc and stat _ dt, wherein stat _ dt is a condition field, and the two tables are spliced sql as follows:
Sql14:select*from guanlian1 a left join(select cust_id,count(1)ct from guanlian2 where stat_dt＝” group by cust_id)on a.cust_no＝b.cust_id；
Sql15:select*from guanlian1 a left join(select b.*,row_number()over(partition by cust_id order by cust_acct)rk from guanlian2 b where stat_dt＝”)b on a.cust_no＝b.cust_id and b.rk＝1。
in order to better and more quickly screen out the association relationship between the two tables, the tables with 2 and more than 2 associated fields can be sorted by weighting the associated pool fields (for example, weighting by the occurrence frequency in all scripts). The specific case is as follows: the table a has field student id, the student corresponds teacher id, the table b has user id, wherein student id, teacher id and user id all are in same correlation pool, and when table a and table b are correlated, there are two correlation fields that can make the selection according to the suggestion, wherein student id appears more in the probability in the system, and the rank is preceding.
These and/or other aspects and advantages of the present disclosure will become apparent from and more readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings.
Referring to fig. 2, the data splicing task mainly relates to table query box creation, inter-table association, data table splicing relation, query field selection and sql script generation.
Referring to fig. 6, in relation to the association between two tables, it is necessary to generate an association relationship between two tables based on information such as a common association pool field between two tables and a primary key of a secondary table.
Specifically, in step S1, a corresponding number of source table query boxes are created according to the number of primary and secondary tables related to this sql, as shown in detail in fig. 3. Then, the names of the tables involved in the splicing are input in the query box, and a submit button is clicked to obtain the table-related table structure, which is specifically shown in fig. 4. And dragging the connection point to complete the connection between the two tables, establishing the connection relation between the tables, and automatically jumping out of the association comparison relation between the two tables in the association relation column, as shown in detail in fig. 5.
Specifically, in step S2, a splicing relationship between the primary table and the secondary table is automatically formed according to the same association pool field of the primary table and the secondary table, in combination with the primary key relationship of the secondary table, as shown in fig. 7 in detail; meanwhile, specific custom association conditions can be added according to specific filtering requirements, as shown in detail in fig. 8.
When the secondary table primary key is combined with the association field and the condition field, a filtering condition can be automatically generated, for example, the table testd, the primary key is the cut _ id, and the stat _ dt is the condition field, wherein the cut _ id is associated with the primary table as the association field during association, and the stat _ dt is the filtering condition, as shown in fig. 9.
When the association field of the primary key of the secondary table is not in the association pool of the primary table and the secondary table, the device prompts the user to implement association between the two tables through a row _ number or group by nesting function, which is shown in detail in fig. 10.
After the association relationship is confirmed, the device can select the required field by double-clicking, and click the sql generation button to complete the sql automatic splicing work, which is specifically shown in fig. 11.
Claims (16)
1. A method for generating sql by realizing automatic association and splicing between data tables comprises the following operations:
analyzing all sql related to the table based on an association analysis mode, acquiring field groups, verifying the field groups one by one, acquiring a table main key, and storing a first operation of a data table main key library;
automatically acquiring the association relation among all the sql tables through the analysis and the association analysis of the sql blood relationship, and generating a second operation of an association pool according to an association method of the association pool;
the third operation comprises graphical operation executed by a user in the device, table elements are generated by dragging, connection between tables is completed through connecting lines, association relation between the two tables is automatically completed through an association pool technology between the tables, meanwhile, the user can add customized filtering conditions according to report requirements, after association splicing between the tables is completed, field information required by manual clicking is performed through a main interface, and automatic sql splicing work is automatically completed.
2. The method of claim 1, wherein the primary key method of the first operation building table comprises the following: the first way is to maintain the table primary key table manually, and the user integrates the table primary keys into the primary key table according to the table primary keys provided in the table of the data dictionary, or integrates the searched table into the primary key table according to the table search.
3. The second mode is that the sql is mined, the associated fields between tables are automatically obtained through association analysis, and the related associated fields are integrated into field groups; analyzing all sql related to the table, acquiring field groups, reordering, performing run number verification one by one on all the related field groups, and judging whether the related field combination is the main key of the table.
4. The method of claim 1, wherein the second operation build associative pool method comprises: mining the sql, analyzing the blood relationship of the sql and performing association analysis, automatically acquiring the association relation among tables of all the sql, and generating an association pool according to an association pool association method.
5. The association pool has 2 types, one is an extended association pool, and the other is a single association pool.
6. The single type association pool is directly obtained by performing association analysis on the sql to obtain two corresponding fields between the two tables, and the fields are not suitable for expansion due to the diversity of the attributes of the fields, so that the single type association pool is directly constructed.
7. The extended association pool refers to a single association pool with 2 or more than 2, and the extended association pool has a common association field and an extended right.
8. Meanwhile, the table association pool can be manually maintained, a user can read all sql codes one by one, find out the association relation between the two tables and the blood-related relation between the temporary table and the nested table, arrange the relevant association relation between the tables and supplement the table association pool.
9. The method of claim 3, further schemes to build association pools:
one is to construct a field name association pool, analyze the field name through an extended association pool, find out the field of which the field name is distributed in front of 50%, construct an association pool with similar field names, for example, the extended field name of a client number generally has similar field naming methods such as cust _ id, cust _ num, cust _ no, and the like, and can construct an association pool with similar field names;
the other method is to construct a field analysis association pool, analyze the field annotation name through an extended association pool, find out the field annotation of which the field annotation name is distributed at the front of 50%, and construct a field annotation association pool with similar field names, wherein similar field annotation association pools such as client id, client id number, client number, party number, applicant number and the like exist in the common client annotation name.
10. The method can be combined with semantic analysis to construct a field annotation close association pool.
11. The method of claim 1, wherein the third operation comprises:
the method comprises the following steps that firstly, element information of a table to be associated is created in an interface container according to graphical operation executed by a user in a screen, and an association relation between two data tables is established through the graphical operation;
secondly, clicking to set an incidence relation between the two tables, automatically forming the incidence relation between the two tables by the system according to the main key of the auxiliary table and the incidence relation between the two incidence tables, and prompting a user to select a row _ number () function for duplication removal or use a group by function for duplication removal if the incidence relation between the two tables cannot cover the table fields related to the main key of the auxiliary table;
thirdly, after the connection of the incidence relation among the tables is finished, manually clicking required field information through a main interface to realize the generation of the graphical field;
and fourthly, clicking an sql generation button after the field selection is completed, and completing the automatic sql splicing work.
12. The method of claim 5, wherein the step of generating a splicing scheme for two data tables associated by an association pool association relationship between the two tables, in addition to the primary key of the associated table, comprises:
the method comprises the following steps that firstly, the type of an auxiliary table is judged, and the table belongs to a full table, an incremental table, a pull chain table, a flow water meter and a snapshot table;
secondly, acquiring all fields of the two tables which are in the unified association pool id and are related to the unified association pool id through the association pool;
thirdly, judging whether the main key of the auxiliary table exists or not, judging whether the fields related to the main key are in the associated field pool of the second step or not, and if all the associated fields of the main key are in the associated field pool, forming an automatic association relation; if the associated fields related to the primary key are not all in the associated pool in 4, prompting the user to convert to a row _ number () or group by way of association to form an association relationship between the two tables, and particularly recommending the group by way of association for the type of the secondary table being an increment table or a flow meter;
if the table main key does not exist, but the two tables have the associated field, prompting a user to associate in a row _ number () or group by way to form an association relation of the two tables; and if the association pool acquires that all the fields of the two tables are not in the association pool, prompting the user that the automatic association cannot be carried out.
13. The method as claimed in claim 6, wherein the primary key of the secondary table is required to distinguish between the associated field and the filter condition field, only the associated field needs to be judged whether in the associated pool and associated by the associated pool, the filter condition field only needs to add the condition filter, and the association relation does not need to be judged.
14. For example, a table cust _ id and a table b snapshot table are reserved, one copy is reserved every day, the main key of the key b is cust _ id and stat _ dt, wherein stat _ dt is a filtering condition, when the table a is associated with the table b, as long as the cust _ id and the table b cust _ id of the table a are in an association pool, the association relationship between the two tables can be automatically formed, and in addition, stat _ dt is added to the filtering condition; the method is mainly suitable for slice tables and linked lists.
15. The method as claimed in claim 6, wherein the fields related to the primary key of the secondary table have 2 or more than 2 fields corresponding to the primary key, and in order to better and more quickly filter out the association between the two tables, the tables with 2 and more than 2 associated fields can be sorted by assigning weights to the fields of the association pool (for example, by assigning weights to the occurrence frequency in all scripts).
16. The specific case is as follows: the table a has field student id, the student corresponds teacher id, the table b has user id, wherein student id, teacher id and user id all are in same correlation pool, and when table a and table b are correlated, there are two correlation fields that can make the selection according to the suggestion, wherein student id appears more in the probability in the system, and the rank is preceding.
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| CN115827644A (en) * | 2023-02-13 | 2023-03-21 | 明度智云(浙江)科技有限公司 | Report generation method, system and server based on visual view configuration |
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| CN115185982A (en) * | 2022-09-14 | 2022-10-14 | 浩鲸云计算科技股份有限公司 | Data preparation method and system based on data set arrangement |
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| CN117056416B (en) * | 2023-08-16 | 2024-05-07 | 杭州观远数据有限公司 | Flexible construction and management method for visualized data set model |
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