CN116303610A - Database data query method, device, electronic equipment and storage medium based on state dictionary - Google Patents

Abstract

The invention discloses a database data query method and device based on a state dictionary, electronic equipment and a storage medium. The method is characterized by comprising the following steps: receiving a data query instruction aiming at a target database, and determining target query data corresponding to the data query instruction; performing hash calculation on the target query data according to a hash function set stored in a state dictionary, and determining a query hash value of the target query data; and if the hash bit number group stored in the state dictionary has the same target hash value as the query hash value, sending the data query instruction to a target database for data query. The method and the device realize the pre-query of the data stored in the database, can filter the data which does not exist in the database through the state dictionary, reduce the times of querying the database by invalid data and improve the query efficiency.

Description

Database data query method, device, electronic equipment and storage based on state dictionary medium

technical field

The invention relates to the field of database query, in particular to a database data query method, device, electronic equipment and storage medium based on a state dictionary.

Background technique

In the information society, the scene of information interaction can be seen everywhere. The user requests the server to obtain data on the application side, and the server queries the corresponding data in the database system according to the user's request, and feeds back the queried data. The existing The server usually integrates multiple functions, the business is complex and the access volume is high, and the data access volume increases exponentially. When faced with high concurrent data requests, it is difficult for a database with low performance to respond to data requests in a timely manner and respond quickly. In order to save hardware resources and costs, the existing technology usually optimizes and preprocesses the information in the data request of the server, and then sends the processed data request to the database, so that the database can quickly respond to the data request. However, although the prior art improves the response speed of the database, it cannot filter data requests, and still needs to query one by one in the database, which wastes database resources.

Contents of the invention

The invention provides a database data query method, device, electronic equipment and storage medium based on a state dictionary, so as to filter the data requests of the database and improve the response speed of the database.

According to one aspect of the present invention, a kind of database data query method based on state dictionary is provided, comprising:

receiving a data query instruction for the target database, and determining the target query data corresponding to the data query instruction;

Perform hash calculation on the target query data according to the hash function group stored in the state dictionary, and determine the query hash value of the target query data;

If the hash bit array stored in the state dictionary has the same target hash value as the query hash value, the data query instruction is sent to the target database for data query.

According to another aspect of the present invention, a kind of database data query device based on state dictionary is provided, comprising:

A data query module, configured to receive a data query instruction for the target database, and determine the target query data corresponding to the data query instruction;

a hash calculation module, configured to perform hash calculation on the target query data according to the hash function group stored in the state dictionary, and determine the query hash value of the target query data;

The hash verification module is configured to send the data query instruction to the target database for data query if the hash bit array stored in the state dictionary has the same target hash value as the query hash value.

According to another aspect of the present invention, an electronic device is provided, and the electronic device includes:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor, so that the at least one processor can execute the method described in any embodiment of the present invention. A database data query method based on a state dictionary.

According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement any of the embodiments of the present invention when executed. The database data query method based on the state dictionary.

The technical solution of the embodiment of the present invention can improve the accuracy of data query by receiving the data query instruction for the target database, determining the target query data corresponding to the data query instruction, and determining the target query data before entering the database; according to the state dictionary The stored hash function group performs hash calculation on the target query data, determines the query hash value of the target query data, and can confirm the existence status of the target query data in the database by querying the hash value, thereby improving the response speed of the database, Reduce the waste of database resources; if the hash bit array stored in the state dictionary has the same target hash value as the query hash value, send the data query instruction to the target database for data query, and confirm in the state dictionary There is target query data in the database, and the data query command is sent to the target database, which realizes the rapid response of the database, improves the response speed of the database, solves the technical problem that it is difficult to filter the request for database data in the prior art, and improves the performance of the database. Feedback efficiency saves database resources.

It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will be easily understood from the following description.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings that need to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those skilled in the art, other drawings can also be obtained based on these drawings without creative effort.

Fig. 1 is a flow chart of a database data query method based on a state dictionary provided by Embodiment 1 of the present invention;

FIG. 2 is a schematic diagram of a state dictionary disclosed in an embodiment of the present invention;

FIG. 3 is a schematic diagram of a counting linked list disclosed in an embodiment of the present invention;

4 is a flow chart of another state dictionary-based database data query method provided in Embodiment 2 of the present invention;

5 is a flow chart of another state dictionary-based database data query method provided by Embodiment 3 of the present invention;

FIG. 6 is a schematic diagram of the relationship between a prediction success rate, a preset expansion coefficient, and the number of hash functions disclosed in an embodiment of the present invention;

7 is a schematic diagram of updating a state dictionary when adding data to a target database disclosed by an embodiment of the present invention;

8 is a schematic diagram of updating a state dictionary when a target database deletes data according to an embodiment of the present invention;

9 is a schematic structural diagram of a database data query device based on a state dictionary provided by Embodiment 3 of the present invention;

FIG. 10 is a schematic structural diagram of an electronic device implementing a state dictionary-based database data query method according to an embodiment of the present invention.

Detailed ways

In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is an embodiment of a part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

Embodiment one

Fig. 1 is a flowchart of a database data query method based on a state dictionary provided by Embodiment 1 of the present invention. This embodiment is applicable to the situation where the server performs data query to the database. The database data query device based on the state dictionary can be implemented in the form of hardware and/or software, and the database data query device based on the state dictionary can be configured in electronic equipment. As shown in Figure 1, the method includes:

S110. Receive a data query instruction for the target database, and determine target query data corresponding to the data query instruction.

Wherein, the target database is a database for data requested by the server; the target database is used to store and manage multiple target query data. The target database may include at least one of MySQL database, SQL (Structured Query Language) Server, MongoDB database and other non-relational databases.

Wherein, the data query instruction may be a query instruction for the client to request the target query data from the server; the data query instruction is used to obtain the target query data in the database through the server.

Optionally, the server is to provide services and resources to the client, and the server may be a server or any program capable of providing services. Wherein, the server and the client may be in the same device.

Wherein, the target query data may be the data that the client requests to query. It should be noted that, in the embodiment of the present invention, when the client requests the target query data from the server, the client and the server cannot know whether the target query data exists in the target database. After querying in the target database according to the target query instruction, In order to know whether the target query data is stored in the target database.

Specifically, when the user requests the service from the server using the service provided by the server, the user issues a data query command for the target server, and the server determines the target query data queried by the data query command after receiving the data query command.

Optionally, after receiving the data query instruction, the server may analyze the data query instruction, determine the target query data queried by the data query command, and determine the target database corresponding to the target query data.

S120. Perform hash calculation on the target query data according to the hash function group stored in the state dictionary, and determine a query hash value of the target query data.

Wherein, the hash function group may store multiple hash functions in the state dictionary. The hash function group can be used to calculate the hash value of the target query data. The algorithm corresponding to the hash function used in the embodiment of the present invention may be MD5 algorithm, secure hash algorithm, direct addressing method, digital analysis method, folding method, square method, subtraction method, base conversion method, and division and remainder method , at least one of random multiplier method and string value hash method.

Wherein, the state dictionary may be a data structure created on the server side for recording data in the target database. Optionally, in this embodiment of the present invention, the state dictionary is composed of three parts: a hash function group, a hash bit array, and a count linked list, and the state dictionary, the hash bit array, and the count linked list have the same length. Wherein, the hash bit array can be a bit array recording the hash value, and the hash bit array can be a one-dimensional array; the counting linked list can be a linked list recording the number of digits of each hash bit array, and the counting linked list can be a two-dimensional array .

Exemplarily, FIG. 2 is a schematic diagram of a state dictionary disclosed by an embodiment of the present invention. Fig. 3 is a schematic diagram of a counting linked list disclosed by an embodiment of the present invention. As shown in Figure 2 and Figure 3, the length of the state dictionary is represented by L, the length of the hash bit array and the count list are the same as the length of the state dictionary, and both are L, and the count list is a two-dimensional array, represented by Count_List.

Wherein, the query hash value may be a hash value obtained by calculating the target query data through a hash function; the query hash value is used to determine whether the target query data exists in the target database.

Specifically, the server sends the target query data to the state dictionary, and the hash function group stored in the state dictionary performs hash calculation on the target query data to obtain a query hash value corresponding to the target query data.

S130. If the hash bit array stored in the state dictionary has the same target hash value as the query hash value, send the data query instruction to the target database for data query.

Wherein, the target hash value may be a hash value stored in the hash bit array; the target hash value may be used to determine whether the target query data exists in the target database. Optionally, in this embodiment of the present invention, whether there is a target hash value identical to the query hash value in the hash bit array is queried, and the position of the data in the hash bit array is determined according to the query hash value. If there is already existing data at the location corresponding to the hash value, it is considered that the hash bit array has the same target hash value as the query hash value; on the contrary, if there is no data at the location corresponding to the query hash value, the hash bit is considered to be The same target hash as the query hash does not exist in the array.

Specifically, get the query hash value corresponding to the target query data in the state dictionary, pass the query hash value into the hash bit array, and judge whether the hash bit array has the same target hash value as the query hash value , if there is a target hash value identical to the query hash value in the hash bit array, it is determined that the target query data exists in the target database, and then the data query instruction is sent to the target database for data query; if there is no If there is a target hash value identical to the query hash value, it is determined that the target query data does not exist in the target database, and the target query data does not exist in the target database is returned to the server.

The technical solution of the embodiment of the present invention can improve the accuracy of data query by receiving the data query instruction for the target database, determining the target query data corresponding to the data query instruction, and determining the target query data before entering the database; according to the state dictionary The stored hash function group performs hash calculation on the target query data, determines the query hash value of the target query data, and can confirm the existence status of the target query data in the database by querying the hash value, thereby improving the response speed of the database, Reduce the waste of database resources; if the hash bit array stored in the state dictionary has the same target hash value as the query hash value, send the data query instruction to the target database for data query, and confirm in the state dictionary There is target query data in the database, and the data query command is sent to the target database, which realizes the rapid response of the database, improves the response speed of the database, solves the technical problem that it is difficult to filter the request for database data in the prior art, and improves the performance of the database. Feedback efficiency saves database resources.

Embodiment two

Fig. 4 is a flow chart of another state dictionary-based database data query method provided by Embodiment 2 of the present invention. The relationship between this embodiment and the above-mentioned embodiment is the specific method of calculating the query hash value through the hash function group . As shown in Figure 4, the database data query method based on the state dictionary includes:

S210. Receive a data query instruction for the target database, and determine target query data corresponding to the data query instruction.

S220. Determine the target query character corresponding to the target query data according to the target query data; divide the target query data into multiple target query sub-data according to the target query character; The hash calculation is performed on the sub-data to determine the query hash value of the target query data.

Wherein, the hash function group includes at least one hash function. In the state dictionary, at least one hash function can be set according to the requirements of hash calculation.

Wherein, the target query character may be a plurality of characters corresponding to the target query data. It should be noted that the target query data is usually composed of multiple characters, and the corresponding target query characters are determined according to the target query data. Wherein, characters include letters, numbers, operation symbols, punctuation marks and other symbols.

Wherein, the target query sub-data may be data composed of each character of the target query data.

Optionally, in the embodiment of the present invention, after the target query character of the target query data is obtained, the target query data is divided according to each character, each character is divided into a target query sub-data, and multiple target query sub-data are obtained.

Optionally, in this embodiment of the present invention, the number of unit characters of the divided target query data can be set, and the target query data is divided according to the number of unit characters, and the number of characters is divided into multiple target query subdata with the number of unit characters. Wherein, the number of unit characters is at least one character.

Specifically, the state dictionary obtains the target query data, determines the target query character corresponding to the target query data, divides the target query data into characters according to the target query character, and divides the target query data into multiple target query sub-data, and then uses the hash function to Perform hash calculation on the target query sub-data to determine the query hash value of the target query data.

Optionally, in another optional embodiment of the present invention, the query hash value includes at least one hash bit value; performing hash calculation on a plurality of target query sub-data according to the hash function , to determine the query hash value of the target query data, including:

According to the preset random number generation method, a multi-dimensional random number is generated according to the number of hash functions and the number of target query sub-data; wherein, the dimension number of the multi-dimensional random number is used to indicate the number of hash functions, and the multi-dimensional random number of each dimension The number of random numbers is used to indicate the number of target query subdata;

For each hash function, perform hash calculation according to the multidimensional random number of the dimension corresponding to the hash function and a plurality of target query sub-data, and determine the hash bit value corresponding to each hash function;

The query hash value of the target query data is determined according to the hash bit value corresponding to each hash function.

Wherein, the preset random number generation method may be a method preset for generating random numbers.

Wherein, the multidimensional random number may be multiple sets of random numbers with the same number. It should be noted that there is no correlation between random numbers and they are unpredictable. Multidimensional random numbers can improve the randomness of hash calculations and improve the accuracy of queries.

Wherein, the number of dimensions of the multi-dimensional random number is used to indicate the number of hash functions, and the number of random numbers in the multi-dimensional random number of each dimension is used to indicate the number of target query sub-data; When the method generates a multidimensional random number, since the multidimensional random number participates in the calculation of the hash function, and each target query subdata requires a random number, the number of random numbers in each dimension of the multidimensional random number needs to be equal to the target query subdata Each hash function can perform hash calculation through a random number of one dimension and the target query sub-data, and then the dimension of the multi-dimensional random number is the same as the number of hash functions in the hash function group.

Wherein, the hash bit value may indicate a position of the hash bit value in the hash bit array. It should be noted that the hash bit array is a binary one-dimensional array, and the value of each bit is 1 or 0. The hash bit value is used to indicate the number of bits corresponding to the hash bit array, which can be queried through the hash bit value The value of the number of bits corresponding to the hash bit array.

Optionally, in this embodiment of the present invention, after performing hash calculation on the multidimensional random number and multiple target query subdata through a hash function, the multidimensional random number corresponding to each target query subdata is determined, and for each target query subdata , determine the product of the target query sub-data and the corresponding multi-dimensional random number, and calculate the sum of the products of all the target query sub-data to obtain the hash bit value of the hash function. Among them, the formula for calculating the hash bit value through the hash function is shown in the following formula (1):

hash＝X _i Key[i]+X _i-1 Key[i-1]+...+X ₁ Key[1]

Among them, hash is the hash bit value obtained by hash calculation; X _i is the i-th random number, and Key[i] is the i-th target query sub-data.

Specifically, for multiple target query sub-data of the target query data, according to the preset random number generation method, the number of dimensions equal to the number of hash functions and the number of random numbers in the multi-dimensional random numbers of each dimension are equal to the number of target query sub-data Each hash function performs hash calculations on multidimensional random numbers corresponding to one dimension and multiple target query sub-data, and determines the hash bit value corresponding to each hash function. According to each hash function The hash bit value of determines the query hash value for the target query data.

S230. If the hash bit array stored in the state dictionary has the same target hash value as the query hash value, send the data query instruction to the target database for data query.

The technical solution of the embodiment of the present invention determines the target query data corresponding to the data query command by receiving the data query command for the target database; determines the target query character corresponding to the target query data according to the target query data; The character divides the target query data into a plurality of target query sub-data; performs hash calculation on a plurality of target query sub-data according to the hash function, determines the query hash value of the target query data, and performs Character division, and multi-dimensional random numbers are added to improve the randomness of the hash function calculation, which can obtain more accurate query hash values and improve the accuracy of the state dictionary to determine the target query data; if the hash bit array stored in the state dictionary exists If the target hash value is the same as the query hash value, the data query instruction is sent to the target database for data query, which realizes the rapid response of the database, improves the response speed of the database, and solves the problem that is difficult in the prior art. Filter the technical problems of the database data request, improve the feedback efficiency of the database, and save the database resources.

Embodiment Three

Fig. 5 is a flow chart of another state dictionary-based database data query method provided by Embodiment 3 of the present invention. Create a state dictionary corresponding to the target server in the terminal. As shown in Figure 5, the method includes:

S310. Obtain the maximum storage capacity of the target database.

Wherein, the maximum storage capacity is the maximum amount of data that the target database can store; the maximum storage capacity may be the maximum storage capacity estimated by the database manager when the target database is created.

Specifically, when the state dictionary is created on the server side, the maximum storage capacity of the target database is obtained.

S320. Determine the number of hash functions and a preset expansion factor in the state dictionary according to a preset prediction success rate requirement.

Wherein, the preset expansion factor may be a preset expansion factor. The preset expansion factor can be used to determine whether the target database needs to be expanded.

Wherein, the predicted success rate requirement may be a preset requirement for judging the query success rate of the state dictionary. It should be noted that, in the embodiment of the present invention, when the data query instruction arrives at the state dictionary, it is judged through the state dictionary whether the target query data corresponding to the data query instruction exists in the database, and the prediction success rate requirement is used to constrain the success of the state dictionary judgment. Rate.

Specifically, obtain the preset prediction success rate requirements of the server, and select the preset expansion coefficient and hash function that meet the prediction success rate requirements according to the relationship between the prediction success rate requirements, the preset expansion coefficient, and the number of hash functions. number of Hich functions

Optionally, in the embodiment of the present invention, the relationship between the prediction success rate, the dictionary length and the maximum storage capacity is shown in the following formula (2):

Among them, P_true is the prediction success rate; M is the amount of data added to the target database; n is the number of hash functions; L is the length of the state dictionary, and e is the predicted expected value. Further, in order to ensure the accuracy of the state dictionary and the rationality of memory usage, the dictionary length of the state dictionary needs to meet the restrictions of the maximum storage capacity, the number of hash functions, and the preset expansion coefficient. The dictionary length of the state dictionary and the maximum storage capacity , the number of hash functions and the preset expansion coefficient are shown in the following formula (3):

L=max*n*k

Among them, max is the maximum storage capacity; k is the preset expansion factor.

Based on the fact that the amount of data added to the target database must be less than the maximum storage capacity of the target database, the prediction success rate, preset expansion coefficient, and number of hash functions can be calculated according to the above formulas (2) and (3) The association relationship is shown in formula (4):

Fig. 6 is a schematic diagram of the relationship between a prediction success rate, a preset expansion coefficient, and the number of hash functions disclosed in an embodiment of the present invention. As shown in Fig. 6, based on formula (4), in the input target database The amount of data must be less than the maximum storage capacity of the target database, and when the maximum storage capacity and the amount of data input into the target database are fixed values, the preset expansion coefficient and the number of hash functions will affect the prediction success rate of the state dictionary. rate, the preset expansion coefficient and the number of hash functions, and then determine the preset expansion coefficient and the number of hash functions according to the requirements of the predicted success rate.

S330. Determine the dictionary length of the state dictionary according to the number of hash functions, the maximum storage capacity, and the preset expansion factor.

Wherein, the dictionary length may be the length of the state dictionary.

Specifically, the dictionary length of the state dictionary is determined according to the number of hash functions, the maximum storage capacity and the preset expansion factor.

S340. Set a state dictionary according to the dictionary length and the number of hash functions

Specifically, the dictionary length of the state dictionary and the number of hash functions are obtained, and the state dictionary is set according to the dictionary length and the number of hash functions.

Optionally, in another optional embodiment of the present invention, setting the state dictionary according to the dictionary length and the number of hash functions includes:

Set the hash bit array and the count linked list according to the dictionary length, set the hash function according to the preset hash processing algorithm and the number of hash functions, and determine the hash function group; according to the preset hash function group, the described The hash bit array and the count linked list set the state dictionary.

Wherein, the length of the hash bit array and the length of the count linked list are the same as the length of the state dictionary;

Wherein, the hash processing algorithm may be a hash algorithm preset for a hash function. Hash processing algorithms can include MD5 algorithm, secure hash algorithm, direct addressing method, number analysis method, folding method, square method, subtraction method, radix conversion method, division and remainder method, random multiplier method and string At least one of numerical hashing methods.

Specifically, obtain the dictionary length of the state dictionary and the number of the hash functions, set the hash bit array and the counting list with the same length as the dictionary length according to the dictionary length of the state dictionary, and according to the preset hash processing algorithm and hash function The number of hash functions sets the hash function, determines the hash function group of the state dictionary, and sets the state dictionary according to the preset hash function group, hash bit array and counting linked list.

Optionally, in another optional embodiment of the present invention, the method further includes:

Acquiring target update data updated by the target database; performing hash calculation on the target update data according to the hash function group to determine an update hash value of the target update data; updating the hash according to the update hash value Hi bit array and count linked list.

Wherein, the target update data may be the data updated by the server in the target database. It should be noted that, when the server updates the target database, there are two cases. The first case is to add data to the target database , when adding data on the server side, the added data is the target update data; the second case is to delete the data in the target database, and when deleting data on the server side, the deleted data is the target update data.

Wherein, the update hash value object updates the hash value calculated by the hash function.

Specifically, when the server adds data to or deletes data from the target database, the target update data updated by the target database is obtained, and the hash calculation is performed on the target update data through the hash function group to obtain the update hash of the target update data value, and update the hash bit array and count linked list according to the updated hash value.

Optionally, in another optional embodiment of the present invention, the updated hash value includes at least one hash bit value; the updating of the hash bit array and the count linked list according to the updated hash value ,include:

Querying the corresponding binary digit value in the hash digit array according to the hash digit value;

If the binary digit value is a preset value, the corresponding count value in the count linked list is updated according to the hash digit value.

Wherein, the binary bit value can be the value recorded by each bit in the hash bit array; the hash bit array is a binary one-dimensional array, the value of each bit is 1 or 0, and the hash bit value is used to indicate the hash bit The number of bits corresponding to the array.

Wherein, the preset value may be a preset binary value for judging whether the binary value needs to be updated. Exemplarily, it is usually set to 1 in the hash bit array.

Wherein, the count value may be the count value of each count record in the count linked list. The count value can be used to record the number of binary digits whose value is a preset value.

Specifically, obtain the updated hash value, query the corresponding binary digit value in the hash digit array according to the hash digit value in the updated hash value; determine whether the binary digit value is a preset value, and if the binary digit value is a preset value , then update the corresponding count value in the count linked list according to the hash bit value; if the binary bit value is not a preset value, then update the hash bit value to a preset value.

Exemplarily, FIG. 7 is a schematic diagram of updating a state dictionary when data is added to a target database according to an embodiment of the present invention. As shown in Figure 7: Add data to the target database, obtain the added target update data as data 1, calculate the corresponding update hash value through three hash functions in the hash function group, and update the hash value including three Hash bit values are Hash1(), Hash2() and Hash3(), which are divided into three cases from top to bottom when updating the state dictionary. The first case: Hash1(), Hash2() in the hash bit array When the three target bit values corresponding to Hash3() are all 0, adjust the three target bit values to 1; the second case: in the hash bit array, Hash1(), Hash2() and Hash3( ) when there are 0, 0, and 1 in the three target digit values corresponding respectively, then the target digit value with the target digit value of 0 is adjusted to 1, and the count value in the count linked list corresponding to the target digit value of 1 is adjusted to 1; The third case: when the three target bit values corresponding to Hash1() and Hash3() in the hash bit array respectively exist 1 and 1, the target bit value is 1, and the count value is 0 corresponding to the count value in the count list 0 is adjusted to 1, the target bit value is 1, and the count value in the count list corresponding to the count value 1 is adjusted from 1 to 2.

Exemplarily, FIG. 8 is a schematic diagram of updating a state dictionary when a target database deletes data according to an embodiment of the present invention. As shown in Figure 8: delete data in the target database, obtain the deleted target update data as data 1, calculate the corresponding update hash value through the three hash functions in the hash function group, and update the hash value including three Hash bit values are Hash1(), Hash2() and Hash3(), which are divided into three cases from top to bottom when updating the state dictionary. The first case: Hash1(), Hash2() in the hash bit array When the three target digit values corresponding to Hash3() are 1, 1, and 1, then determine that the corresponding count values in the calculation linked list are 1, 3, and 5 respectively, and adjust the corresponding count values in the calculation linked list to 0, 2 , 4; the second case: when the three target digit values corresponding to Hash1(), Hash2() and Hash3() in the hash bit array respectively exist 1, 1, 1, then determine and calculate the corresponding count value in the linked list They are 0, 1, and 4 respectively, set the target digit value to 1, adjust the target digit value of count value 0 to 0, adjust the target digit value to 1, and adjust the count value in the counting list corresponding to the target digit value of count value 1 to 0 , adjust the target bit value to 1, and adjust the count value of the count value 4 to 3; the third case: when the two target bit values corresponding to Hash1() and Hash3() in the hash bit array are 1 and 1 respectively, Determine the calculated values corresponding to the two target digit values of 0 and 3, set the target digit value to 1, and adjust the target digit value corresponding to the target digit value of the count value 0 from 1 to 0, set the target digit value to 1, and the count value to 3 The counter value corresponding to the target bit value is adjusted from 3 to 2.

The technical solution of the embodiment of the present invention acquires the maximum storage capacity of the target database by receiving; for the storage capacity of the target database, it is possible to determine a state dictionary that is more in line with the target database, improve the performance of the state dictionary, and reduce the space of the state dictionary; according to the preset The predictive success rate requires determining the number of hash functions in the state dictionary and the preset expansion coefficient; for the expected success rate of the state dictionary, the preset expansion coefficient of the target database can be determined, and the performance of the database and the response speed of the state dictionary can be further improved; Determine the dictionary length of the state dictionary according to the number of hash functions, the maximum storage capacity and the preset expansion factor; setting a suitable dictionary length can ensure the accuracy of the state dictionary and the rationality of occupying memory, according to the dictionary Length and the quantity setting state dictionary of described hash function, have realized that corresponding state dictionary is created to target database, have further improved the response speed of database, solved the technical problem that is difficult to filter to database data request in the prior art, improved This improves the feedback efficiency of the database and saves database resources.

Optionally, the embodiment of the present invention discloses another database data query method based on a state dictionary, wherein the method includes:

S1. Create a state dictionary Dist for the target database. The initial length of the state dictionary Dist is L, the length of the hash bit array Bloom and the count list Count_List are both L, the data structure of the state dictionary is shown in Figure 2, and its internal representation is in binary, where Bloom is a one-dimensional array and Count_List is a two-dimensional array. The array is shown in Figure 3. At the same time, a set of independent hash function groups Hash_Group() should be determined.

Optionally, the specific process of creating a state dictionary Dist is as follows:

In order to ensure the accuracy of the state dictionary and the rationality of occupying memory, the initial length L of the state dictionary Dise can follow the formula (3):

L=max*n*k

Further, the relationship between the prediction success rate, the dictionary length L and the maximum storage capacity max is shown in the following formula (2):

Among them, P_true is the prediction success rate; M is the amount of data added to the target database; n is the number of hash functions; L is the length of the state dictionary, and e is the predicted expected value.

Based on the fact that the amount of data added to the target database must be less than the maximum storage capacity of the target database, the prediction success rate, preset expansion coefficient, and number of hash functions can be calculated according to the above formulas (2) and (3) The association relationship is shown in formula (4):

Fig. 6 is a schematic diagram of the relationship between a prediction success rate, a preset expansion coefficient, and the number of hash functions disclosed in an embodiment of the present invention. As shown in Fig. 6, based on formula (4), the data in the input target database The amount must be less than the maximum storage capacity of the target database, and when the maximum storage capacity and the amount of data input into the target database are fixed values, the preset expansion coefficient and the number of hash functions will affect the prediction success rate of the state dictionary. By predicting the success rate , the preset expansion coefficient and the number of hash functions, and then determine the preset expansion coefficient and the number of hash functions according to the requirements of the predicted success rate. Furthermore, the dictionary length of the state dictionary is determined according to the number of hash functions, the maximum storage capacity and the preset expansion factor. The dictionary length of the state dictionary and the number of hash functions are obtained, and the state dictionary is set according to the dictionary length and the number of hash functions.

S2. When adding or deleting data to the database, the internal data of the state dictionary is also updated at the same time.

Specifically, when new data is added to the database to update the state dictionary, hash calculation is performed on the newly added target update data data in Hash_Group(), and the calculated updated hash value is shown in formula (5):

Wherein, {N ₁ , N ₂ ,...N _n } are updated hash values obtained by hash calculation, and N ₁ , N ₂ ,...Nn are hash bit values. Update the hash bit array Bloom and the count list in the state dictionary according to the hash bit value,

Query the target bit value at the position of the hash bit array corresponding to the hash bit value. If the target bit value is originally 0, update it to 1. If it is already 1, update the counting list value at this position and add 1, and finally get updated The subsequent bit array Bloom{L, 0/1} and the counting list Count_List{L, N|N>=0}.

When deleting data from the database to update the state dictionary, hash calculation is performed on the deleted target update data data in Hash_Group(), and the calculated update hash value is shown in formula (5):

Wherein, {N ₁ , N ₂ ,...N _n } are updated hash values obtained by hash calculation, and N ₁ , N ₂ ,...Nn are hash bit values. Update the hash bit array Bloom and the count list in the state dictionary according to the hash bit value,

Query the target bit value at the position of the hash bit array where the hash bit value is mapped, and query the count value of the count list Count_List corresponding to the target bit value. If the count value is greater than 0, decrement 1. If the count value is 0, then Update the target bit value Bloom value of the hash bit array corresponding to the count value to 0, and obtain the updated state dictionary.

S3. When querying the target query data through the state dictionary, first enter the state dictionary to determine whether the target query data exists in the target database. If there is no target query data, directly return the query to null, otherwise enter the target database for query.

Specifically, hash calculation is performed on the target query data data in Hash_Group() to obtain the query hash value, and the calculated query hash value is shown in formula (5):

Among them, {N ₁ , N ₂ ,...N _n } is the query hash value obtained by hash calculation, and then judge whether the query hash value has the same target hash value as the query hash value in the hash bit array. The Greek value, the judgment formula is shown in the following formula (6):

Res=Bloom(N ₁ )&Bloom(N ₂ )&...&Bloom(N _n )

Among them, N _n represents the value of the _N nth bit in the hash bit array, and Res represents the calculation result. If the result Res is equal to 1, it means that the target query data data may exist in the target database, and Res is equal to 0, which means that the target query data data must not be in the target database. , if it is judged that the target query data data may exist in the target database, then enter the target database query, if it is judged that the target query data data does not exist in the target database, then directly return null.

The technical scheme of the embodiment of the present invention can realize the rapid response of the database, improve the response speed of the database, solve the technical problem that it is difficult to filter the data request to the database in the prior art, improve the feedback efficiency of the database, and save database resources.

Embodiment four

FIG. 9 is a schematic structural diagram of a database data query device based on a state dictionary provided by Embodiment 3 of the present invention. As shown in Figure 9, the device includes: a data query module 910, a hash calculation module 920 and a hash verification module 930, wherein,

The data query module 910 is configured to receive a data query instruction for the target database, and determine the target query data corresponding to the data query instruction;

Hash calculation module 920, configured to perform hash calculation on the target query data according to the hash function group stored in the state dictionary, and determine the query hash value of the target query data;

The hash verification module 930 is used to send the data query instruction to the target database for data query if the hash bit array stored in the state dictionary has the same target hash value as the query hash value;

The technical solution of the embodiment of the present invention can improve the accuracy of data query by receiving the data query instruction for the target database, determining the target query data corresponding to the data query instruction, and determining the target query data before entering the database; according to the state dictionary The stored hash function group performs hash calculation on the target query data, determines the query hash value of the target query data, and can confirm the existence status of the target query data in the database by querying the hash value, thereby improving the response speed of the database, Reduce the waste of database resources; if the hash bit array stored in the state dictionary has the same target hash value as the query hash value, send the data query instruction to the target database for data query, and confirm in the state dictionary There is target query data in the database, and the data query command is sent to the target database, which realizes the rapid response of the database, improves the response speed of the database, solves the technical problem that it is difficult to filter the request for database data in the prior art, and improves the performance of the database. Feedback efficiency saves database resources.

Optionally, the hash function group includes at least one hash function; the hash calculation module is specifically configured to: determine a target query character corresponding to the target query data according to the target query data;

dividing the target query data into a plurality of target query sub-data according to the target query characters;

Hash calculation is performed on a plurality of target query sub-data according to the hash function to determine a query hash value of the target query data.

Optionally, the query hash value includes at least one hash bit value; the hash calculation module is also specifically used for:

According to the preset random number generation method, a multi-dimensional random number is generated according to the number of hash functions and the number of target query sub-data; wherein, the dimension number of the multi-dimensional random number is used to indicate the number of hash functions, and the multi-dimensional random number of each dimension The number of random numbers is used to indicate the number of target query subdata;

For each hash function, perform hash calculation according to the multidimensional random number of the dimension corresponding to the hash function and a plurality of target query sub-data, and determine the hash bit value corresponding to each hash function;

The query hash value of the target query data is determined according to the hash bit value corresponding to each hash function.

Optionally, the device further includes: a state dictionary setting module; the state dictionary setting module is specifically used for: a data acquisition unit for obtaining the maximum storage capacity of the target database; a parameter calculation unit for predicting according to a preset The success rate requires determining the number of hash functions and the preset expansion coefficient in the state dictionary; the dictionary length calculation unit is used to determine the dictionary length of the state dictionary according to the number of hash functions, the maximum storage capacity and the preset expansion coefficient ; A dictionary setting unit, configured to set a state dictionary according to the dictionary length and the number of hash functions.

Optionally, the state dictionary setting module is also specifically used for:

According to the length of the dictionary, the hash bit array and the count linked list are set, wherein the length of the hash bit array and the length of the count linked list are the same as the length of the state dictionary;

Set the hash function according to the preset hash processing algorithm and the number of hash functions, and determine the hash function group;

The state dictionary is set according to the preset hash function group, the hash bit array and the count linked list.

Optionally, the device further includes: a state dictionary update module and a hash update module; wherein,

The state dictionary update module is used to obtain target update data for updating the target database;

The hash calculation module is further configured to perform hash calculation on the target update data according to the hash function group, and determine the update hash value of the target update data;

The hash update module is configured to update the hash bit array and count linked list according to the updated hash value.

Optionally, the updated hash value includes at least one hash bit value; the hash update module is specifically used for:

Querying the corresponding binary digit value in the hash digit array according to the hash digit value;

If the binary digit value is a preset value, the corresponding count value in the count linked list is updated according to the hash digit value.

The database data query device based on the state dictionary provided by the embodiment of the present invention can execute the database data query method based on the state dictionary provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

Embodiment four

FIG. 10 shows a schematic structural diagram of an electronic device 10 that can be used to implement an embodiment of the present invention. Electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (eg, helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are by way of example only, and are not intended to limit implementations of the inventions described and/or claimed herein.

As shown in FIG. 10 , the electronic device 10 includes at least one processor 11, and a memory communicatively connected with the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores There is a computer program executable by at least one processor, and the processor 11 can operate according to a computer program stored in a read-only memory (ROM) 12 or loaded from a storage unit 18 into a random access memory (RAM) 13. Various appropriate actions and processes are performed. In the RAM 13, various programs and data necessary for the operation of the electronic device 10 are also stored. The processor 11 , ROM 12 , and RAM 13 are connected to each other through a bus 14 . An input/output (I/O) interface 15 is also connected to the bus 14 .

Multiple components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunication networks.

Processor 11 may be various general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various dedicated artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, digital signal processing processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes various methods and processes described above, such as a database data query method based on a state dictionary.

In some embodiments, the database data query method based on the state dictionary can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18 . In some embodiments, part or all of the computer program may be loaded and/or installed on the electronic device 10 via the ROM 12 and/or the communication unit 19 . When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the state dictionary-based database data query method described above can be executed. Alternatively, in other embodiments, the processor 11 may be configured in any other appropriate way (for example, by means of firmware) to execute the database data query method based on the state dictionary.

Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips Implemented in a system of systems (SOC), load programmable logic device (CPLD), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs executable and/or interpreted on a programmable system including at least one programmable processor, the programmable processor Can be special-purpose or general-purpose programmable processor, can receive data and instruction from storage system, at least one input device, and at least one output device, and transmit data and instruction to this storage system, this at least one input device, and this at least one output device an output device.

Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, so that the computer program causes the functions/operations specified in the flowcharts and/or block diagrams to be implemented when executed by the processor. A computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus or device. A computer readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium may be a machine readable signal medium. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer discs, hard drives, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.

In order to provide interaction with the user, the systems and techniques described herein can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display)) for displaying information to the user. monitor); and a keyboard and pointing device (eg, a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and can be in any form (including Acoustic input, speech input or, tactile input) to receive input from the user.

The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., as a a user computer having a graphical user interface or web browser through which a user can interact with embodiments of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system. The components of the system can be interconnected by any form or medium of digital data communication, eg, a communication network. Examples of communication networks include: local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

A computing system can include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or a cloud host. It is a host product in the cloud computing service system to solve the problems of difficult management and weak business expansion in traditional physical hosts and VPS services. defect.

It should be understood that steps may be reordered, added or deleted using the various forms of flow shown above. For example, each step described in the present invention may be executed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution of the present invention can be achieved, there is no limitation herein.

Embodiment five

This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of the database data query method based on the state dictionary provided by any embodiment of the present invention are implemented. The method includes:

receiving a data query instruction for the target database, and determining the target query data corresponding to the data query instruction;

Perform hash calculation on the target query data according to the hash function group stored in the state dictionary, and determine the query hash value of the target query data;

If the hash bit array stored in the state dictionary has the same target hash value as the query hash value, the data query instruction is sent to the target database for data query.

The computer storage medium in the embodiments of the present invention may use any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer-readable storage medium may be, for example but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (non-exhaustive list) of computer readable storage media include: electrical connections with one or more leads, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), Erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a data signal carrying computer readable program code in baseband or as part of a carrier wave. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. .

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out the operations of the present invention may be written in one or more programming languages, or combinations thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional Procedural Programming Language - such as "C" or a similar programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as through the Internet using an Internet service provider). connect).

Those of ordinary skill in the art should understand that each module or each step of the present invention described above can be realized by a general-purpose computing device, and they can be concentrated on a single computing device, or distributed on a network formed by multiple computing devices. Optionally, they can be implemented with executable program codes of computer devices, so that they can be stored in storage devices and executed by computing devices, or they can be made into individual integrated circuit modules, or a plurality of modules in them Or the steps are fabricated into a single integrated circuit module to realize. As such, the present invention is not limited to any specific combination of hardware and software.

It should be understood that steps may be reordered, added or deleted using the various forms of flow shown above. For example, each step described in the present invention may be executed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution of the present invention can be achieved, there is no limitation herein.

The above specific implementation methods do not constitute a limitation to the protection scope of the present invention. It should be apparent to those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made depending on design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims (10)

1. A method for querying database data based on a state dictionary, comprising:

receiving a data query instruction aiming at a target database, and determining target query data corresponding to the data query instruction;

performing hash calculation on the target query data according to a hash function set stored in a state dictionary, and determining a query hash value of the target query data;

and if the hash bit number group stored in the state dictionary has the same target hash value as the query hash value, sending the data query instruction to a target database for data query.

2. The method of claim 1, wherein the set of hash functions comprises at least one hash function; the hash calculation is performed on the target query data according to the hash function set stored in the state dictionary, and the determining of the query hash value of the target query data includes:

determining target query characters corresponding to the target query data according to the target query data;

dividing target query data into a plurality of target query sub-data according to the target query characters;

and carrying out hash calculation on a plurality of target query sub-data according to the hash function, and determining a query hash value of the target query data.

3. The method of claim 2, wherein the query hash value comprises at least one hash bit value; performing hash calculation on the plurality of target query sub-data according to the hash function, and determining a query hash value of the target query data, including:

generating a multi-dimensional random number according to the number of the hash functions and the number of the target query sub-data according to a preset random number generation method; the number of the dimensions of the multi-dimensional random numbers is used for indicating the number of the hash functions, and the number of the random numbers in the multi-dimensional random numbers of each dimension is used for indicating the number of target query sub-data;

aiming at each hash function, carrying out hash calculation according to the multidimensional random number of the dimension corresponding to the hash function and a plurality of target query sub-data, and determining a hash bit value corresponding to each hash function;

and determining a query hash value of the target query data according to the hash bit number value corresponding to each hash function.

4. The method of claim 1, wherein before receiving the data query command for the target database and determining the target query data corresponding to the data query command, further comprises:

obtaining the maximum storage capacity of a target database;

Determining the number of hash functions in a state dictionary and a preset expansion coefficient according to a preset prediction success rate requirement;

determining the dictionary length of a state dictionary according to the number of hash functions, the maximum storage amount and the preset expansion coefficient;

and setting a state dictionary according to the dictionary length and the number of the hash functions.

5. The method of claim 4, wherein said setting said state dictionary according to said dictionary length and said number of hash functions comprises:

setting Ha Xiwei arrays and counting linked lists according to the dictionary length, wherein the length of the Ha Xiwei arrays and the length of the counting linked lists are the same as the state dictionary length;

setting hash functions according to a preset hash processing algorithm and the number of the hash functions, and determining a hash function set;

and setting the state dictionary according to a preset hash function set, the Ha Xiwei set and the counting chain table.

6. The method of claim 5, wherein the method further comprises:

acquiring target update data updated by the target database;

performing hash calculation on the target update data according to the Ha Xihan array, and determining an update hash value of the target update data;

And updating the Ha Xiwei array and the counting linked list according to the updated hash value.

7. The method of claim 6, wherein the updated hash value comprises at least one hash bit value; the updating the Ha Xiwei array and the count linked list according to the updated hash value comprises:

inquiring a corresponding binary bit value in the hash bit array according to the hash bit value;

and if the binary digit value is a preset value, updating a corresponding count value in the count chain table according to the hash digit value.

8. A state dictionary-based database data query apparatus, comprising:

the data query module is used for receiving a data query instruction aiming at the target database and determining target query data corresponding to the data query instruction;

the hash calculation module is used for carrying out hash calculation on the target query data according to a preset hash function set and determining a query hash value of the target query data;

and the hash verification module is used for sending the data query instruction to a target database for data query if the hash bit number group stored in the state dictionary has the same target hash value as the query hash value.

9. An electronic device, the electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the state dictionary based database data querying method of any of claims 1-7.

10. A computer readable storage medium storing computer instructions for causing a processor to perform the state dictionary based database data querying method of any of claims 1-7.

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