CN107391257B - Method, device and server for estimating memory capacity required by service - Google Patents
Method, device and server for estimating memory capacity required by service Download PDFInfo
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- CN107391257B CN107391257B CN201710555958.0A CN201710555958A CN107391257B CN 107391257 B CN107391257 B CN 107391257B CN 201710555958 A CN201710555958 A CN 201710555958A CN 107391257 B CN107391257 B CN 107391257B
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Abstract
The invention discloses a method and a device for estimating memory capacity required by a service, a server and a computer storage medium. Wherein the method comprises the following steps: acquiring a sample case corresponding to a service, and loading the acquired sample case into a memory; acquiring parameter information corresponding to the sample case; and calculating the memory capacity required by the service based on the obtained sample case and the parameter information. Based on the scheme of the embodiment of the invention, the memory capacity required by the service can be accurately calculated, so that the user can apply for the memory capacity as required, the defect that the prior art scheme cannot pre-estimate the memory capacity required by the service in advance, so that the user applies for the maximum memory each time, and the resource waste is caused is overcome, and the server resource is saved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for estimating memory capacity required by a service, a server and a computer storage medium.
Background
Database (Database) is a data processing system that organizes, stores and manages data according to a data structure, and as the internet develops, the Database is more widely used and its performance requirements are higher and higher. At present, the database system must have higher transaction processing speed and application reliability.
For a memory-based database such as Redis, a user is required to apply for a corresponding memory capacity according to a service requirement, and generally, the user does not consider how large the memory capacity is actually required by the service, and the larger the memory capacity is, the better the memory capacity is, so that the maximum memory capacity is always applied, and many users may not need the memory capacity as large as possible, thereby causing resource waste; however, many times, the user does not know how much memory capacity the service needs, and applies for the maximum memory capacity in order to ensure that sufficient memory is available.
Disclosure of Invention
In view of the above, the present invention has been made to provide a method for estimating a memory capacity required for a service, an apparatus for estimating a memory capacity required for a service, a server, and a computer storage medium that overcome or at least partially solve the above problems.
According to an aspect of the present invention, a method for estimating memory capacity required by a service is provided, including:
acquiring a sample case corresponding to a service, and loading the acquired sample case into a memory;
acquiring parameter information corresponding to the sample case;
and calculating the memory capacity required by the service based on the obtained sample case and the parameter information.
According to another aspect of the present invention, there is provided an apparatus for estimating memory capacity required for a service, including:
the sample case acquisition module is suitable for acquiring a sample case corresponding to the service;
the loading module is suitable for loading the obtained sample case into the memory;
the parameter information acquisition module is suitable for acquiring the parameter information corresponding to the sample case;
and the calculation module is suitable for calculating the memory capacity required by the service based on the obtained sample case and the parameter information.
According to still another aspect of the present invention, there is provided a server including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the prediction method of the memory capacity required by the service.
According to still another aspect of the present invention, a computer storage medium is provided, where at least one executable instruction is stored in the storage medium, and the executable instruction causes a processor to perform an operation corresponding to the method for estimating memory capacity required by the above-mentioned service.
According to the scheme provided by the invention, the parameter information corresponding to the sample case is obtained by obtaining the sample case corresponding to the service and loading the obtained sample case into the memory, and the memory capacity required by the service is calculated based on the obtained sample case and the parameter information. Based on the scheme of the embodiment of the invention, the memory capacity required by the service can be accurately calculated, so that the user can apply for the memory capacity as required, the defect that the prior art scheme cannot pre-estimate the memory capacity required by the service in advance, so that the user applies for the maximum memory each time, and the resource waste is caused is overcome, and the server resource is saved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a schematic flow chart illustrating a method for estimating a memory capacity required by a service according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a method for estimating a memory capacity required by a service according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram illustrating an apparatus for predicting memory capacity required by a service according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram illustrating an apparatus for predicting memory capacity required by a service according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of a server according to a sixth embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Fig. 1 is a flowchart illustrating a method for estimating a memory capacity required by a service according to an embodiment of the present invention. As shown in fig. 1, the method comprises the steps of:
step S100, obtaining a sample case corresponding to the service, and loading the obtained sample case into a memory.
Specifically, the following method may be adopted to obtain a sample case corresponding to a service:
the method comprises the following steps: the user provides a corresponding sample case, for example, an interface can be provided for the user, the interface provides a sample case uploading function, after the user clicks an uploading button, a sample case uploading dialog box is popped up, the user selects the sample case to be uploaded, the uploading button in the dialog box is clicked, the sample case uploading is completed, and thus, the sample case corresponding to the service can be obtained, and then the obtained sample case is loaded into the memory to analyze the memory capacity required by the service.
The second method comprises the following steps: the method can provide an interface for a user, the interface is provided with a sample case selection function, different sample cases for analyzing the memory capacity can be provided for different services, for example, one or more default sample cases are provided, any user can select one or more sample cases according to the needs of the user, for example, for an email service, the user selects one sample case to be used as the sample case for analyzing the memory capacity, in addition, a sample case editing function can be provided, the user can adjust a default value according to needs, so that the sample case corresponding to the service is obtained, and then the obtained sample case is loaded into the memory to analyze the memory capacity needed by the service.
And S101, acquiring parameter information corresponding to the sample case.
Specifically, for the method used for acquiring the sample case corresponding to the service, the method used for acquiring the parameter information corresponding to the sample case is described as follows:
the method comprises the following steps: the interface is also provided with an input box of parameter information, and a user can fill corresponding parameter information in the input box according to the self business requirement, so that the parameter information corresponding to the sample case can be obtained; of course, other methods may also be adopted, for example, various parameter information may be preset and displayed on the interface, and the user may select appropriate parameter information according to the service requirement of the user.
The second method comprises the following steps: the method comprises the steps that sample cases and parameter information are preset, after a user selects a sample case, the parameter information corresponding to the sample case is correspondingly displayed on an interface, if the user thinks that the sample case and the parameter information can be used for estimating the memory capacity, the user can provide an estimation instruction of the memory capacity, and the memory capacity is estimated according to the instruction. If the user considers that the default parameter information does not accord with the service of the default parameter information, the corresponding parameter information can be adjusted, after the parameter information is adjusted, the user can provide an estimation instruction of the memory capacity, and the memory capacity is estimated according to the instruction.
And step S102, calculating the memory capacity required by the service based on the obtained sample case and the parameter information.
The memory capacity required by the service refers to the size of the memory occupied by the sample case corresponding to the parameter information, that is, how much memory needs to be allocated to store the sample case corresponding to the parameter information. After the obtained sample cases are loaded into the memory, the memory capacity required for the business may be calculated based on the obtained sample cases and the parameter information.
According to the method provided by the embodiment of the invention, the sample case corresponding to the service is obtained, the obtained sample case is loaded into the memory, the parameter information corresponding to the sample case is obtained, and the memory capacity required by the service is calculated based on the obtained sample case and the parameter information. Based on the scheme of the embodiment of the invention, the memory capacity required by the service can be accurately calculated, so that the user can apply for the memory capacity as required, the defect that the prior art scheme cannot pre-estimate the memory capacity required by the service in advance, so that the user applies for the maximum memory each time, and the resource waste is caused is overcome, and the server resource is saved.
Fig. 2 is a flowchart illustrating a method for estimating a memory capacity required by a service according to a second embodiment of the present invention. As shown in fig. 2, the method comprises the steps of:
step S200, obtaining a sample case corresponding to the service, and loading the obtained sample case into a memory.
Step S201, obtaining parameter information corresponding to the sample case.
Wherein, the parameter information may include: the data type and the number of cases of the sample case corresponding to the data type, specifically, the data type includes one or more of the following combinations: hash type (Hash type), sorted Set type (Zset type), List type (List type), Set type (Set type), and/or String type (String type).
Data in sample cases are stored in a data key (K-V) pair format, for example, the user provides a String type sample case: key is dbatest, Value is aaaaaaaaaaaaaaaaaaaaaaaaaaaaa, and the number of sample cases of this type is 5000.
Steps S200 to S201 in the embodiment shown in fig. 2 are similar to steps S100 to S101 in the embodiment shown in fig. 1, and are not described again here.
Step S202, data analysis is carried out on the data in the sample case, and a data structure body of the data value in the sample case is determined.
After the data type corresponding to the sample case is obtained, a data structure of the data value in the sample case can be determined according to the data type, wherein the data structure corresponding to the hash type includes: zipmap structure and Hashtable structure; the data structure body corresponding to the ordered set type comprises: ziplist structures and Skiplist structures; the data structure corresponding to the list type comprises: the data structure body corresponding to the Ziplist structure body and the Linkedlist structure body set type comprises the following components: intset and Hashtable structures; the data structure body corresponding to the character string type comprises: raw and Int structures.
The following lists five data types and methods for determining the data structure of the data values:
(1) if the data type of the data is a Hash type, comparing the number of elements contained in the data value corresponding to each data key with a first preset threshold value, and comparing the data length of each element with a second preset threshold value to obtain a comparison result, and if the comparison result is that the number of the elements is smaller than the first preset threshold value and the data length of each element is smaller than the second preset threshold value, determining that the data structure body of the data value corresponding to the data key is a Zipmap structure body; and if the comparison result is other conditions, determining that the data structure of the data value corresponding to the data key is a Hashtable structure.
It is known that the data value corresponding to the data key may include one or more elements, and the data lengths of the elements may be the same or different, and the Zipmap structure defines an upper limit of the number of elements included in the data value corresponding to the data key and the data length of each element, and specifically, the Zipmap structure defines that the number of elements included in the data value corresponding to the data key needs to be less than 512, and the data length of each element is less than 64 bytes, so that the first preset threshold and the second preset threshold may be set according to the characteristics of the Zipmap structure and the Hashtable structure, for example, the first preset threshold may be set to 512, and the second preset threshold may be set to 64 bytes.
And if the comparison result is that the number of the elements is less than 512 and the data length of each element is less than 64 bytes, determining that the data structure of the data value corresponding to the data key is a Zipmap structure.
If the comparison result is that the number of elements is greater than or equal to 512 and the data length of each element is greater than or equal to 64 bytes, or the number of elements is greater than or equal to 512 and the data length of each element is less than 64 bytes, or the number of elements is less than 512 and the data length of each element is greater than or equal to 64 bytes, determining that the data structure of the data value corresponding to the data key is a Hashtable structure.
(2) If the data type of the data is a Zset type, comparing the number of elements contained in the data value corresponding to each data key with a third preset threshold, and comparing the data length of each element with a fourth preset threshold to obtain a comparison result, and if the comparison result is that the number of the elements is less than the third preset threshold and the data length of each element is less than the fourth preset threshold, determining that the data structure of the data value corresponding to the data key is a Ziplist structure; and if the comparison result is other, determining that the data structure of the data value corresponding to the data key is the Skiplist structure.
The Ziplist structure defines the number of elements included in the data value corresponding to the data key and the upper limit of the data length of each element, and specifically, the Ziplist structure defines that the number of elements included in the data value corresponding to the data key needs to be less than 128, and the data length of each element is less than 64 bytes, so that the third preset threshold and the fourth preset threshold can be set according to the characteristics of the Ziplist structure and the Skiplist structure, for example, the third preset threshold can be set to 128, and the fourth preset threshold can be set to 64 bytes.
And if the comparison result is that the number of the elements is less than 128 and the data length is less than 64 bytes, determining that the data structure of the data value corresponding to the data key is a Ziplist structure.
If the comparison result is that the number of elements is greater than or equal to 128 and the data length of each element is greater than or equal to 64 bytes, or the number of elements is greater than or equal to 128 and the data length of each element is less than 64 bytes, or the number of elements is less than 128 and the data length of each element is greater than or equal to 64 bytes, determining that the data structure of the data value corresponding to the data key is the Skiplist structure.
(3) If the data type of the data is a List type, comparing the number of elements contained in the data value corresponding to each data key with a fifth preset threshold value, and comparing the data length of each element with a sixth preset threshold value to obtain a comparison result, and if the comparison result is that the number of the elements is less than the fifth preset threshold value and the data length of each element is less than the sixth preset threshold value, determining that the data structure body of the data value corresponding to the data key is a Ziplist structure body; and if the comparison result is other, determining that the data structure of the data value corresponding to the data key is a Linkedlist structure.
The zipplist structure defines the number of elements included in the data value corresponding to the data key and the upper limit of the data length of each element, and specifically, the zipplist structure defines that the number of elements included in the data value corresponding to the data key needs to be smaller than 512, and the data length of each element is smaller than 64 bytes, so that the fifth preset threshold and the sixth preset threshold can be set according to the characteristics of the zipplist structure and the Linkedlist structure, for example, the fifth preset threshold can be set to 512, and the sixth preset threshold can be set to 64 bytes.
And if the comparison result is that the number of the elements is less than 512 and the data length of each element is less than 64 bytes, determining that the data structure of the data value corresponding to the data key is a Ziplist structure.
If the comparison result is that the number of elements is greater than or equal to 512 and the data length of each element is greater than or equal to 64 bytes, or the number of elements is greater than or equal to 512 and the data length of each element is less than 64 bytes, or the number of elements is less than 512 and the data length of each element is greater than or equal to 64 bytes, determining that the data structure of the data value corresponding to the data key is a Linkedlist structure.
(4) If the data type of the data is a Set type, comparing the number of elements contained in the data value corresponding to each data key with a seventh preset threshold to obtain a comparison result, and if the comparison result is that the number of elements is less than the seventh preset threshold, determining that the data structure of the data value corresponding to the data key is an Intset structure; and if the comparison result is that the number of the elements is more than or equal to a seventh preset threshold value, determining that the data structure of the data value corresponding to the data key is a Hashtable structure.
The Intset structure defines an upper limit of the number of elements included in the data value corresponding to the data key, and specifically, the Intset structure defines that the number of elements included in the data value corresponding to the data key needs to be less than 512, so that a seventh preset threshold may be set according to characteristics of the Intset structure and the Hashtable structure, for example, the seventh preset threshold may be set to 512.
And if the comparison result is that the number of the elements is smaller than 512, determining that the data structure of the data value corresponding to the data key is an Intset structure.
And if the comparison result is that the number of the elements is more than or equal to 512, determining that the data structure of the data value corresponding to the data key is a Hashtable structure.
(5) If the data type of the data is String type, judging whether each element contained in the data value corresponding to the data key accords with a preset range; if so, determining that the data structure of the data value corresponding to the data key is an Int structure; if not, determining that the data structure of the data value corresponding to the data key is a Raw structure.
For example, the data value corresponding to the data key may include 1, 2, 3, 4, and 5, or may also include a, b, c, d, and e, each element included in the data value corresponding to the data key conforms to a preset range, for example, 0 to 9999, the data structure of the data value corresponding to the data key may be determined to be an Int structure, and if the element included in the data value corresponding to the data key is greater than 9999, or is a character string type of a, b, c, d, and e, the data structure of the data value corresponding to the data key may be determined to be a Raw structure.
Step S203, determining a memory capacity calculation algorithm according to the data structure.
Step S204, calculating the memory capacity needed by the business in the Redis database based on the memory capacity calculation algorithm, the sample case and the parameter information.
After the data structure of the data value corresponding to the data key is determined, the memory capacity calculation algorithm corresponding to the data structure can be determined, so that the memory capacity required by the business in the Redis database is calculated based on the memory capacity calculation algorithm, the sample case and the parameter information, wherein the memory capacity required by the data mainly comprises: the memory capacity required by the data key, the memory capacity required by the data value corresponding to the data key, and the memory capacity required by the data structure.
For example, taking the data type as String type as an example, where Key is dbatest and Value is aaaaaaaaaaaaaaaaaaaaaaaaaaaaa, the memory capacity required by the data is 24 bytes (dictEntry structure) +16 bytes (redisObject structure) +16 bytes (Key size) +32 bytes (Value size), where the dictEntry structure is responsible for storing a specific Key-Value pair; the redisObject structure is used as a Value object, so that the memory capacity required by one sample case can be calculated, and the product of the memory capacity required by the sample case and the number of cases is the memory capacity required by the service.
Step S205, providing a corresponding memory service to the user according to the memory capacity.
After the memory capacity required by the service is obtained through calculation, the corresponding memory service can be provided for the user according to the calculated memory capacity, and specifically, at least one preset memory capacity which is greater than or equal to the memory capacity can be searched; and providing the memory service corresponding to the minimum preset memory capacity in the at least one preset memory capacity for the user.
Generally, a plurality of memory capacities are preset, for example, the memory capacities are respectively set to 1G, 5G, 10G, and 100G, and the memory capacity required by the service is calculated to be 3.5G based on the sample case and the parameter information, so that the preset memory capacity which is greater than or equal to the memory capacity required by the service and is 3.5G can be found as follows: 5G, 10G, and 100G, and in order to save memory resources, only when the preset memory capacity is 5G, the corresponding memory service is provided to the user.
According to the method provided by the embodiment of the invention, the sample case corresponding to the service is obtained, the obtained sample case is loaded into the memory, the parameter information corresponding to the sample case is obtained, the data in the sample case is subjected to data analysis, the data structure body of the data value in the sample case is determined, the memory capacity calculation algorithm is determined according to the data structure body, and the memory capacity required by the service in the Redis database is calculated based on the memory capacity calculation algorithm, the sample case and the parameter information. Based on the scheme of the embodiment of the invention, different types of data have different memory capacities required during storage, the memory capacity required by the service can be accurately calculated by determining the data structure body of the data, and the calculation accuracy is improved, so that a user can be guided to apply for the memory capacity as required, the defect that the prior art scheme cannot pre-estimate the memory capacity required by the service in advance, the user applies for the maximum memory each time, the resource waste is caused is overcome, and the server resource is saved.
Fig. 3 is a schematic structural diagram illustrating an estimation apparatus of memory capacity required by a service according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes: a sample case acquisition module 300, a loading module 310, a parameter information acquisition module 320, and a calculation module 330.
The sample case obtaining module 300 is adapted to obtain a sample case corresponding to a service.
A loading module 310, adapted to load the obtained sample case into the memory.
The parameter information obtaining module 320 is adapted to obtain parameter information corresponding to the sample case.
And the calculating module 330 is adapted to calculate the memory capacity required by the service based on the obtained sample case and the parameter information.
According to the device provided by the embodiment of the invention, the parameter information corresponding to the sample case is obtained by obtaining the sample case corresponding to the service and loading the obtained sample case into the memory, and the memory capacity required by the service is calculated based on the obtained sample case and the parameter information. Based on the scheme of the embodiment of the invention, the memory capacity required by the service can be accurately calculated, so that the user can apply for the memory capacity as required, the defect that the prior art scheme cannot pre-estimate the memory capacity required by the service in advance, so that the user applies for the maximum memory each time, and the resource waste is caused is overcome, and the server resource is saved.
Fig. 4 is a schematic structural diagram illustrating an estimation apparatus of memory capacity required by a service according to a fourth embodiment of the present invention. As shown in fig. 4, the apparatus includes: a sample case acquisition module 400, a loading module 410, a parameter information acquisition module 420, a calculation module 430, and a memory service module 440.
The sample case obtaining module 400 is adapted to obtain a sample case corresponding to a service.
A loading module 410, adapted to load the obtained sample case into the memory.
The parameter information obtaining module 420 is adapted to obtain parameter information corresponding to the sample case.
Wherein the parameter information includes: the data type and the case number of the sample case corresponding to the data type, wherein the data type comprises one or more of the following combinations: a hash type, an ordered set type, a list type, a set type, and/or a string type.
Storing data in the sample case in a data key value pair mode;
the calculation module 430 further includes: a data analysis unit 431, adapted to perform data analysis on the sample case, and determine a data structure of the data values in the sample case;
wherein, the data structure body that hash type corresponds includes: zipmap structure and Hashtable structure; the data structure body corresponding to the ordered set type comprises: ziplist structures and Skiplist structures; the data structure corresponding to the list type comprises: ziplist structures and Linkedlist structures; the data structure body corresponding to the set type comprises: intset and Hashtable structures; the data structure body corresponding to the character string type comprises: raw and Int structures.
How the data analysis unit determines the data structure of the data will be described in detail below:
preferably, the data analysis unit 431 is further adapted to: if the data type of the data is the Hash type, comparing the number of elements contained in the data value corresponding to each data key with a first preset threshold value, and comparing the data length of each element with a second preset threshold value to obtain a comparison result;
if the comparison result is that the number of the elements is smaller than a first preset threshold and the data length of each element is smaller than a second preset threshold, determining that the data structure of the data value corresponding to the data key is a Zipmap structure;
and if the comparison result is other conditions, determining that the data structure of the data value corresponding to the data key is a Hashtable structure.
Preferably, the data analysis unit 431 is further adapted to: if the data type of the data is an ordered set type, comparing the number of elements contained in the data value corresponding to each data key with a third preset threshold value, and comparing the data length of each element with a fourth preset threshold value to obtain a comparison result;
if the comparison result is that the number of the elements is smaller than a third preset threshold and the data length of each element is smaller than a fourth preset threshold, determining that the data structure of the data value corresponding to the data key is a Ziplist structure;
and if the comparison result is other, determining that the data structure of the data value corresponding to the data key is the Skiplist structure.
Preferably, the data analysis unit 431 is further adapted to: if the data type of the data is a list type, comparing the number of elements contained in the data value corresponding to each data key with a fifth preset threshold value, and comparing the data length of each element with a sixth preset threshold value to obtain a comparison result;
if the comparison result is that the number of the elements is smaller than a fifth preset threshold and the data length of each element is smaller than a sixth preset threshold, determining that the data structure of the data value corresponding to the data key is a Ziplist structure;
and if the comparison result is other, determining that the data structure of the data value corresponding to the data key is a Linkedlist structure.
Preferably, the data analysis unit 431 is further adapted to: if the data type of the data is the set type, comparing the number of elements contained in the data value corresponding to each data key with a seventh preset threshold value to obtain a comparison result;
if the comparison result is that the number of the elements is smaller than a seventh preset threshold value, determining that the data structure of the data value corresponding to the data key is an Intset structure;
and if the comparison result is that the number of the elements is more than or equal to a seventh preset threshold value, determining that the data structure of the data value corresponding to the data key is a Hashtable structure.
Preferably, the data analysis unit 431 is further adapted to: if the data type of the data is a character string type, judging whether each element contained in the data value corresponding to the data key conforms to a preset range or not;
if so, determining that the data structure of the data value corresponding to the data key is an Int structure;
if not, determining that the data structure of the data value corresponding to the data key is a Raw structure.
A determining unit 432 adapted to determine a memory capacity calculation algorithm according to the data structure;
the calculating unit 433 is adapted to calculate the memory capacity required by the service based on the memory capacity calculation algorithm, the sample case and the parameter information.
In a preferred embodiment of the invention, the calculation module is further adapted to: and calculating the memory capacity required by the service in the Redis database based on the obtained sample case and the parameter information.
The memory service module 440 is adapted to provide a corresponding memory service to the user according to the memory capacity.
The memory service module 440 further includes: the searching unit 441 is adapted to search at least one preset memory capacity greater than or equal to the memory capacity;
the memory service unit 442 is adapted to provide a memory service corresponding to a minimum preset memory capacity of the at least one preset memory capacity to the user.
According to the device provided by the embodiment of the invention, the sample case corresponding to the service is obtained, the obtained sample case is loaded into the memory, the parameter information corresponding to the sample case is obtained, the data in the sample case is subjected to data analysis, the data structure body of the data value in the sample case is determined, the memory capacity calculation algorithm is determined according to the data structure body, and the memory capacity required by the service in the Redis database is calculated based on the memory capacity calculation algorithm, the sample case and the parameter information. Based on the scheme of the embodiment of the invention, different types of data have different memory capacities required during storage, the memory capacity required by the service can be accurately calculated by determining the data structure body of the data, and the calculation accuracy is improved, so that a user can be guided to apply for the memory capacity as required, the defect that the prior art scheme cannot pre-estimate the memory capacity required by the service in advance, the user applies for the maximum memory each time, the resource waste is caused is overcome, and the server resource is saved.
EXAMPLE five
An embodiment of the present application provides a nonvolatile computer storage medium, where the computer storage medium stores at least one executable instruction, and the computer executable instruction may execute the method for estimating the memory capacity required by the service in any method embodiment.
EXAMPLE six
Fig. 5 is a schematic structural diagram of a server according to a sixth embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the server.
As shown in fig. 5, the server may include: a processor (processor)502, a Communications Interface 504, a memory 506, and a communication bus 508.
Wherein:
the processor 502, communication interface 504, and memory 506 communicate with one another via a communication bus 508.
A communication interface 504 for communicating with network elements of other devices, such as clients or other servers.
The processor 502 is configured to execute the program 510, and may specifically execute the relevant steps in the embodiment of the method for estimating the memory capacity required by the service.
In particular, program 510 may include program code that includes computer operating instructions.
The processor 502 may be a central processing unit CPU, or an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement an embodiment of the invention. The server comprises one or more processors, which can be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And a memory 506 for storing a program 510. The memory 506 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 510 may specifically be used to cause the processor 502 to perform the following operations: acquiring a sample case corresponding to a service, and loading the acquired sample case into a memory; acquiring parameter information corresponding to the sample case; and calculating the memory capacity required by the service based on the obtained sample case and the parameter information.
In an alternative embodiment, the data in the sample case is stored in a data key-value pair manner;
the program 510 is further configured to cause the processor 502, when calculating the memory capacity required for the business based on the obtained sample cases and the parameter information: performing data analysis on the data in the sample case to determine a data structure body of the data value in the sample case;
determining a memory capacity calculation algorithm according to the data structure;
and calculating the memory capacity required by the service based on the memory capacity calculation algorithm, the sample case and the parameter information.
In an alternative embodiment, the parameter information includes: the data type and the number of cases of the sample case corresponding to the data type.
In an alternative embodiment, the data types include one or more of the following combinations: a hash type, an ordered set type, a list type, a set type, and/or a string type.
In an optional implementation manner, the data structure corresponding to the hash type includes: zipmap structure and Hashtable structure;
the data structure body corresponding to the ordered set type comprises: ziplist structures and Skiplist structures;
the data structure corresponding to the list type comprises: ziplist structure and Linkedlist structure
The data structure body corresponding to the set type comprises: intset and Hashtable structures;
the data structure body corresponding to the character string type comprises: raw and Int structures.
In an alternative embodiment, the program 510 is further configured to cause the processor 502, when performing data analysis on a sample case, determining a data structure of data in the sample case:
if the data type of the data is the Hash type, comparing the number of elements contained in the data value corresponding to each data key with a first preset threshold value, and comparing the data length of each element with a second preset threshold value to obtain a comparison result;
if the comparison result is that the number of the elements is smaller than a first preset threshold and the data length of each element is smaller than a second preset threshold, determining that the data structure of the data value corresponding to the data key is a Zipmap structure;
and if the comparison result is other conditions, determining that the data structure of the data value corresponding to the data key is a Hashtable structure.
In an alternative embodiment, the program 510 is further configured to cause the processor 502, when performing data analysis on a sample case, determining a data structure of data in the sample case:
if the data type of the data is an ordered set type, comparing the number of elements contained in the data value corresponding to each data key with a third preset threshold value, and comparing the data length of each element with a fourth preset threshold value to obtain a comparison result;
if the comparison result is that the number of the elements is smaller than a third preset threshold and the data length of each element is smaller than a fourth preset threshold, determining that the data structure of the data value corresponding to the data key is a Ziplist structure;
and if the comparison result is other, determining that the data structure of the data value corresponding to the data key is the Skiplist structure.
In an alternative embodiment, the program 510 is further configured to cause the processor 502, when performing data analysis on a sample case, determining a data structure of data in the sample case:
if the data type of the data is a list type, comparing the number of elements contained in the data value corresponding to each data key with a fifth preset threshold value, and comparing the data length of each element with a sixth preset threshold value to obtain a comparison result;
if the comparison result is that the number of the elements is smaller than a fifth preset threshold and the data length of each element is smaller than a sixth preset threshold, determining that the data structure of the data value corresponding to the data key is a Ziplist structure;
and if the comparison result is other, determining that the data structure of the data value corresponding to the data key is a Linkedlist structure.
In an alternative embodiment, the program 510 is further configured to cause the processor 502, when performing data analysis on a sample case, determining a data structure of data in the sample case:
if the data type of the data is the set type, comparing the number of elements contained in the data value corresponding to each data key with a seventh preset threshold value to obtain a comparison result;
if the comparison result is that the number of the elements is smaller than a seventh preset threshold value, determining that the data structure of the data value corresponding to the data key is an Intset structure;
and if the comparison result is that the number of the elements is more than or equal to a seventh preset threshold value, determining that the data structure of the data value corresponding to the data key is a Hashtable structure.
In an alternative embodiment, the program 510 is further configured to cause the processor 502, when performing data analysis on a sample case, determining a data structure of data in the sample case:
if the data type of the data is a character string type, judging whether each element contained in the data value corresponding to the data key conforms to a preset range or not;
if so, determining that the data structure of the data value corresponding to the data key is an Int structure;
if not, determining that the data structure of the data value corresponding to the data key is a Raw structure.
In an alternative embodiment, the program 510 is further configured to cause the processor 502 to: and providing corresponding memory service for the user according to the memory capacity.
In an alternative embodiment, the program 510 is further configured to enable the processor 502, when providing the corresponding memory service to the user according to the memory capacity:
searching at least one preset memory capacity which is larger than or equal to the memory capacity;
and providing the memory service corresponding to the minimum preset memory capacity in the at least one preset memory capacity for the user.
In an alternative embodiment, the program 510 is further configured to cause the processor 502, when calculating the memory capacity required for the service based on the obtained sample cases and the parameter information:
and calculating the memory capacity required by the service in the Redis database based on the obtained sample case and the parameter information.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in a predictive device of memory capacity required for services in accordance with embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (26)
1. A method for predicting memory capacity required by service comprises the following steps:
acquiring a sample case corresponding to a service, and loading the acquired sample case into a memory;
acquiring parameter information corresponding to the sample case; wherein the parameter information includes: a data type;
calculating the memory capacity required by the service based on the obtained sample case and the parameter information;
wherein the data in the sample case is stored in a data key-value pair manner;
the calculating the memory capacity required for the service based on the obtained sample case and the parameter information further comprises:
performing data analysis on the data in the sample case, and determining a data structure body of the data value in the sample case according to the data type;
determining a memory capacity calculation algorithm according to the data structure body;
calculating the memory capacity required by the service based on the memory capacity calculation algorithm, the sample case and the parameter information, wherein the memory capacity required by the service mainly comprises: the memory capacity required by the data key, the memory capacity required by the data value corresponding to the data key, and the memory capacity required by the data structure.
2. The method of claim 1, wherein the parameter information further comprises: number of cases of sample cases corresponding to the data type.
3. The method of claim 2, wherein the data types include one or more of the following in combination: a hash type, an ordered set type, a list type, a set type, and/or a string type.
4. The method of claim 3, wherein the hash-type corresponding data structure comprises: zipmap structure and Hashtable structure;
the data structure body corresponding to the ordered set type comprises: ziplist structures and Skiplist structures;
the data structure body corresponding to the list type comprises: ziplist structures and Linkedlist structures;
the data structure body corresponding to the set type comprises: intset and Hashtable structures;
the data structure body corresponding to the character string type comprises: raw and Int structures.
5. The method of any of claims 1-4, wherein the analyzing the data for the sample case, determining a data structure of the data in the sample case further comprises:
if the data type of the data is the Hash type, comparing the number of elements contained in the data value corresponding to each data key with a first preset threshold value, and comparing the data length of each element with a second preset threshold value to obtain a comparison result;
if the comparison result is that the number of the elements is smaller than a first preset threshold and the data length of each element is smaller than a second preset threshold, determining that the data structure of the data value corresponding to the data key is a Zipmap structure;
and if the comparison result is other conditions, determining that the data structure of the data value corresponding to the data key is a Hashtable structure.
6. The method of any of claims 1-4, wherein the analyzing the data for the sample case, determining a data structure of the data in the sample case further comprises:
if the data type of the data is an ordered set type, comparing the number of elements contained in the data value corresponding to each data key with a third preset threshold value, and comparing the data length of each element with a fourth preset threshold value to obtain a comparison result;
if the comparison result is that the number of the elements is smaller than a third preset threshold and the data length of each element is smaller than a fourth preset threshold, determining that the data structure of the data value corresponding to the data key is a Ziplist structure;
and if the comparison result is other, determining that the data structure of the data value corresponding to the data key is the Skiplist structure.
7. The method of any of claims 1-4, wherein the analyzing the data for the sample case, determining a data structure of the data in the sample case further comprises:
if the data type of the data is a list type, comparing the number of elements contained in the data value corresponding to each data key with a fifth preset threshold value, and comparing the data length of each element with a sixth preset threshold value to obtain a comparison result;
if the comparison result is that the number of the elements is smaller than a fifth preset threshold and the data length of each element is smaller than a sixth preset threshold, determining that the data structure of the data value corresponding to the data key is a Ziplist structure;
and if the comparison result is other, determining that the data structure of the data value corresponding to the data key is a Linkedlist structure.
8. The method of any of claims 1-4, wherein the analyzing the data for the sample case, determining a data structure of the data in the sample case further comprises:
if the data type of the data is the set type, comparing the number of elements contained in the data value corresponding to each data key with a seventh preset threshold value to obtain a comparison result;
if the comparison result is that the number of the elements is smaller than a seventh preset threshold value, determining that the data structure of the data value corresponding to the data key is an Intset structure;
and if the comparison result is that the number of the elements is more than or equal to a seventh preset threshold value, determining that the data structure of the data value corresponding to the data key is a Hashtable structure.
9. The method of any of claims 1-4, wherein the analyzing the data for the sample case, determining a data structure of the data in the sample case further comprises:
if the data type of the data is a character string type, judging whether each element contained in the data value corresponding to the data key conforms to a preset range or not;
if so, determining that the data structure of the data value corresponding to the data key is an Int structure;
if not, determining that the data structure of the data value corresponding to the data key is a Raw structure.
10. The method of any of claims 1-4, further comprising:
and providing corresponding memory service for the user according to the memory capacity.
11. The method of claim 10, wherein the providing the corresponding memory service to the user according to the memory capacity further comprises:
searching at least one preset memory capacity which is larger than or equal to the memory capacity;
and providing the memory service corresponding to the minimum preset memory capacity in the at least one preset memory capacity for the user.
12. The method of any of claims 1-4, wherein the calculating the memory capacity required for the service based on the obtained sample cases and parameter information further comprises:
and calculating the memory capacity required by the service in the Redis database based on the obtained sample case and the parameter information.
13. An apparatus for predicting memory capacity required by a service, comprising:
the sample case acquisition module is suitable for acquiring a sample case corresponding to the service;
the loading module is suitable for loading the obtained sample case into the memory;
the parameter information acquisition module is suitable for acquiring parameter information corresponding to the sample case, wherein the parameter information comprises: a data type;
the calculation module is suitable for calculating the memory capacity required by the business based on the obtained sample cases and the parameter information;
wherein the data in the sample case is stored in a data key-value pair manner;
the calculation module further comprises: the data analysis unit is suitable for carrying out data analysis on the data in the sample case and determining a data structure body of the data value in the sample case according to the data type;
the determining unit is suitable for determining a memory capacity calculation algorithm according to the data structure body;
a calculating unit adapted to calculate a memory capacity required for a service based on the memory capacity calculating algorithm, the sample case, and the parameter information, the memory capacity required for the service mainly including: the memory capacity required by the data key, the memory capacity required by the data value corresponding to the data key, and the memory capacity required by the data structure.
14. The apparatus of claim 13, wherein the parameter information further comprises: number of cases of sample cases corresponding to the data type.
15. The apparatus of claim 14, wherein the data types comprise one or more of the following in combination: a hash type, an ordered set type, a list type, a set type, and/or a string type.
16. The apparatus of claim 15, wherein the hash-type corresponding data structure comprises: zipmap structure and Hashtable structure;
the data structure body corresponding to the ordered set type comprises: ziplist structures and Skiplist structures;
the data structure body corresponding to the list type comprises: ziplist structures and Linkedlist structures;
the data structure body corresponding to the set type comprises: intset and Hashtable structures;
the data structure body corresponding to the character string type comprises: raw and Int structures.
17. The apparatus according to any of claims 13-16, wherein the data analysis unit is further adapted to: if the data type of the data is the Hash type, comparing the number of elements contained in the data value corresponding to each data key with a first preset threshold value, and comparing the data length of each element with a second preset threshold value to obtain a comparison result;
if the comparison result is that the number of the elements is smaller than a first preset threshold and the data length of each element is smaller than a second preset threshold, determining that the data structure of the data value corresponding to the data key is a Zipmap structure;
and if the comparison result is other conditions, determining that the data structure of the data value corresponding to the data key is a Hashtable structure.
18. The apparatus according to any of claims 13-16, wherein the data analysis unit is further adapted to: if the data type of the data is an ordered set type, comparing the number of elements contained in the data value corresponding to each data key with a third preset threshold value, and comparing the data length of each element with a fourth preset threshold value to obtain a comparison result;
if the comparison result is that the number of the elements is smaller than a third preset threshold and the data length of each element is smaller than a fourth preset threshold, determining that the data structure of the data value corresponding to the data key is a Ziplist structure;
and if the comparison result is other, determining that the data structure of the data value corresponding to the data key is the Skiplist structure.
19. The apparatus according to any of claims 13-16, wherein the data analysis unit is further adapted to: if the data type of the data is a list type, comparing the number of elements contained in the data value corresponding to each data key with a fifth preset threshold value, and comparing the data length of each element with a sixth preset threshold value to obtain a comparison result;
if the comparison result is that the number of the elements is smaller than a fifth preset threshold and the data length of each element is smaller than a sixth preset threshold, determining that the data structure of the data value corresponding to the data key is a Ziplist structure;
and if the comparison result is other, determining that the data structure of the data value corresponding to the data key is a Linkedlist structure.
20. The apparatus according to any of claims 13-16, wherein the data analysis unit is further adapted to: if the data type of the data is the set type, comparing the number of elements contained in the data value corresponding to each data key with a seventh preset threshold value to obtain a comparison result;
if the comparison result is that the number of the elements is smaller than a seventh preset threshold value, determining that the data structure of the data value corresponding to the data key is an Intset structure;
and if the comparison result is that the number of the elements is more than or equal to a seventh preset threshold value, determining that the data structure of the data value corresponding to the data key is a Hashtable structure.
21. The apparatus according to any of claims 13-16, wherein the data analysis unit is further adapted to: if the data type of the data is a character string type, judging whether each element contained in the data value corresponding to the data key conforms to a preset range or not;
if so, determining that the data structure of the data value corresponding to the data key is an Int structure;
if not, determining that the data structure of the data value corresponding to the data key is a Raw structure.
22. The apparatus of any one of claims 13-16, wherein the apparatus further comprises: and the memory service module is suitable for providing corresponding memory service for the user according to the memory capacity.
23. The apparatus of claim 22, wherein the memory service module further comprises:
the searching unit is suitable for searching at least one preset memory capacity which is larger than or equal to the memory capacity;
and the memory service unit is suitable for providing the memory service corresponding to the minimum preset memory capacity in the at least one preset memory capacity for the user.
24. The apparatus of any of claims 13-16, wherein the computing module is further adapted to: and calculating the memory capacity required by the service in the Redis database based on the obtained sample case and the parameter information.
25. A server, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the prediction method of the memory capacity required by the service according to any one of claims 1-12.
26. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the method for estimating memory capacity required for a service according to any one of claims 1 to 12.
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| CN109039801B (en) * | 2018-06-29 | 2021-09-28 | 北京奇虎科技有限公司 | Package overuse detection method and device of distributed cluster and computing equipment |
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