A method and system for web interface data caching

Abstract

The present invention proposes a method and system for web interface data caching, comprising: sequentially normalizing and weighting a parameter set to obtain an enhanced cache key; retrieving cache data matching the enhanced cache key from a client cache to obtain a matching result; based on the matching result, obtaining a dynamic cache capacity upper limit after network delay compensation based on a maximum cache capacity and real-time network delay; then further obtaining a final cache capacity upper limit; judging the current total cache capacity of the server based on the final cache capacity upper limit to obtain a judgment result; dynamically adjusting the final cache capacity upper limit based on the judgment result; eliminating cached data based on a predicted number of accesses and a last access timestamp, and generating an elimination result; and re-adjusting the final capacity upper limit based on the elimination result. The present invention dynamically adjusts the capacity upper limit based on the average response time, thereby reducing the range of response time fluctuations.

Description

Technical Field

[0001] The present invention relates to the field of Internet and cache technology, and in particular to a method and system for caching web interface data. Background Art

[0002] With the rapid development of internet technology, the user base and data volume of web applications are growing exponentially. In high-concurrency scenarios, interface response speed and system stability have become core indicators of user experience. However, traditional data processing models rely on direct database access, leading to the following increasingly prominent problems.

[0003] Excessive database load: Frequent query operations can make the database a performance bottleneck. Statistics show that in systems without caching, a single user request can trigger dozens of database I / O operations. In high-concurrency scenarios, this can exhaust the database connection pool, increase response latency, and even cause service avalanches.

[0004] Mixed hot and cold data: The server caches all data, causing infrequently accessed data to occupy cache space for a long time, reducing memory utilization. Summary of the Invention

[0005] In view of the above situation, the main purpose of the present invention is to provide a method and system for web interface data caching to solve the above technical problems.

[0006] The present invention provides a method for caching web interface data, the method comprising the following steps:

[0007] Step 1: Group the query parameters of the HTTP request into a parameter set;

[0008] Standardize the parameter set to obtain standardized parameters;

[0009] Perform weight correction on the standardized parameters to obtain enhanced cache keys;

[0010] Step 2: Retrieve cache data matching the enhanced cache key from the client cache.

[0011] If the match is successful, the last access timestamp of the matching cache data is updated and output;

[0012] If the match is not successful, a cache hit signal is generated;

[0013] Step 3: After receiving the cache hit signal, the server records the average response time;

[0014] The maximum cache size is calculated using the average response time and the current amount of available memory;

[0015] Based on the maximum cache capacity, combined with the real-time network delay and the maximum tolerable delay, the upper limit of the dynamic cache capacity after network delay compensation is obtained;

[0016] The final cache capacity limit is obtained by using the dynamic cache capacity limit after network delay compensation and the current available memory;

[0017] The total amount of the server's current cache is determined based on the final cache capacity limit. When the total amount of the server's current cache does not exceed the final cache capacity limit, the cache write instruction is executed.

[0018] Step 4: When the total amount of the current cache on the server exceeds the upper limit of the final cache capacity, the predicted number of visits is obtained through ARIMA model prediction;

[0019] Feedback the prediction results to the server and dynamically adjust the final cache capacity upper limit to obtain the dynamically adjusted final cache capacity upper limit;

[0020] Step 5: Get the enhanced value of the cached data based on the predicted number of accesses and the last access timestamp;

[0021] The cached data is judged based on the enhancement value, and the data is eliminated, and then the elimination result is generated;

[0022] The elimination results are fed back to the server and the upper limit of the final cache capacity is readjusted.

[0023] The present invention also provides a system for caching web interface data, the system comprising:

[0024] Parameter processing module, used to:

[0025] The query parameters of the HTTP request are grouped into a parameter set;

[0026] Standardize the parameter set to obtain standardized parameters;

[0027] Perform weight correction on the standardized parameters to obtain enhanced cache keys;

[0028] Data matching module, used to:

[0029] Retrieve cache data matching the enhanced cache key from the client cache;

[0030] If the match is successful, the last access timestamp of the matching cache data is updated and output;

[0031] If the match is not successful, a cache hit signal is generated;

[0032] Adaptive capacity module for:

[0033] After receiving the cache hit signal, the server records the average response time;

[0034] The maximum cache size is calculated using the average response time and the current amount of available memory;

[0035] Based on the maximum cache capacity, combined with the real-time network delay and the maximum tolerable delay, the upper limit of the dynamic cache capacity after network delay compensation is obtained;

[0036] The final cache capacity limit is obtained by using the dynamic cache capacity limit after network delay compensation and the current available memory;

[0037] The total amount of the server's current cache is determined based on the final cache capacity limit. When the total amount of the server's current cache does not exceed the final cache capacity limit, the cache write instruction is executed.

[0038] Prediction module, used to:

[0039] When the total amount of the server's current cache exceeds the upper limit of the final cache capacity, the predicted number of visits is obtained through ARIMA model prediction;

[0040] Feedback the prediction results to the server and dynamically adjust the final cache capacity upper limit to obtain the dynamically adjusted final cache capacity upper limit;

[0041] Cache value evaluation module, used to:

[0042] Get enhanced value of cached data based on predicted access count and last access timestamp;

[0043] The cached data is judged based on the enhancement value, and the data is eliminated, and then the elimination result is generated;

[0044] The elimination results are fed back to the server and the upper limit of the final cache capacity is readjusted.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. The present invention uses a parameter standardization hash algorithm to sort parameter key values according to ASCII codes to generate a fixed-length unique identifier, thereby improving the cache hit rate;

[0047] 2. The present invention reduces the risk of memory overflow by monitoring available memory in real time and setting safety thresholds;

[0048] 3. The present invention dynamically adjusts the capacity upper limit according to the average response time, thereby reducing the fluctuation range of the response time. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1This is a flow chart of a method for caching web interface data proposed by the present invention;

[0050] Figure 2 This is a framework diagram of a system for web interface data caching proposed by the present invention. DETAILED DESCRIPTION

[0051] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0052] These and other aspects of the embodiments of the present invention will become clear with reference to the following description and accompanying drawings. In these descriptions and accompanying drawings, some specific implementations of the embodiments of the present invention are specifically disclosed to illustrate some ways of implementing the principles of the embodiments of the present invention, but it should be understood that the scope of the embodiments of the present invention is not limited thereto.

[0053] See also Figure 1 The embodiment of the present invention provides a method for caching web interface data, the method comprising the following steps:

[0054] Step 1: Group the query parameters of the HTTP request into a parameter set;

[0055] Standardize the parameter set to obtain standardized parameters;

[0056] Perform weight correction on the standardized parameters to obtain enhanced cache keys;

[0057] In step 1, the query parameters of the HTTP request are grouped into a parameter set. The corresponding relationship is:

[0058] ;

[0059] in, Represents a set of parameters, Both represent parameter keys. Both represent the values corresponding to the parameter keys. Indicates the total number of Query parameters;

[0060] The parameter set is standardized to obtain the standardized parameters. The corresponding relationship in the process is:

[0061] ;

[0062] in, Indicates standardized function processing, Indicates that it has been processed by the sorting function. are keys that represent normalized parameters, They all represent the values corresponding to the keys of the standardized parameters;

[0063] The standardized parameters are weighted to obtain the enhanced cache key. The corresponding relationship is:

[0064] ;

[0065] in, Represents an enhanced cache key, Indicates that it has been processed by the SHA-256 hash algorithm. represents the index of the normalization parameter, Indicates the contribution of the standardized parameters when the cache hits. Indicates the The key of the standardized parameters, Indicates the The values corresponding to the keys of the normalized parameters.

[0066] Step 2: Retrieve cache data matching the enhanced cache key from the client cache.

[0067] If the match is successful, the last access timestamp of the matching cache data is updated and output;

[0068] If no match is successful, a cache hit signal is generated.

[0069] Step 3: After receiving the cache hit signal, the server records the average response time;

[0070] The maximum cache size is calculated using the average response time and the current amount of available memory;

[0071] Based on the maximum cache capacity, combined with the real-time network delay and the maximum tolerable delay, the upper limit of the dynamic cache capacity after network delay compensation is obtained;

[0072] The final cache capacity limit is obtained by using the dynamic cache capacity limit after network delay compensation and the current available memory;

[0073] The total amount of the server's current cache is determined based on the final cache capacity limit. When the total amount of the server's current cache does not exceed the final cache capacity limit, the cache write instruction is executed.

[0074] In step 3, the maximum cache size is calculated using the average response time and the current amount of available memory. The corresponding relationship is:

[0075] ;

[0076] in, Indicates the maximum cache size. express The memory health factor at the moment, Indicates the current amount of available memory. express The response time weight factor at the moment, represents the average response time, express The memory health factor at the moment, express The response time weight factor at the moment, express Cache hit rate at the moment, Indicates processing by Q-learning algorithm;

[0077] Based on the maximum cache capacity, combined with the real-time network delay and the maximum tolerable delay, the upper limit of the dynamic cache capacity after network delay compensation is obtained. The specific steps are as follows:

[0078] Collect the delay data of different time periods and servers to construct the network delay time-space matrix. The relationship between the corresponding process is:

[0079] ;

[0080] in, represents the network delay space-time matrix, Indicates the Period The network latency of each server node, Indicates the total length of the time period, Indicates the total number of server nodes;

[0081] Based on the network delay spatiotemporal matrix, the predicted delay matrix is obtained by using the graph convolutional network prediction. The relationship between the corresponding process is:

[0082] ;

[0083] in, represents the prediction delay matrix, Indicates prediction processing through graph convolutional network, Represents the topological relationship matrix;

[0084] The predicted network delay is obtained based on the predicted delay matrix. Combined with the real-time network delay and the maximum tolerable delay, the upper limit of the dynamic cache capacity after network delay compensation is obtained. The corresponding relationship is:

[0085] ;

[0086] in, Indicates the upper limit of dynamic cache capacity after network delay compensation. Indicates the real-time network delay, represents the maximum tolerable delay, Represents the predicted network delay;

[0087] The final cache capacity limit is obtained by combining the dynamic cache capacity limit after network delay compensation and the current available memory. The relationship at the macro level is:

[0088] ;

[0089] in, Indicates the final cache capacity limit, Indicates the total amount of system memory. Indicates the overall system load; Indicates the load safety threshold, that is, the maximum load critical value that the system can withstand; Indicates the load sensitivity factor. The larger the value, the faster the capacity shrinks when the load exceeds the threshold.

[0090] At the micro level, the relationship is:

[0091] ;

[0092] in, Represents cache value density, that is, the data value efficiency per unit cache space; Indicates the current number of cache entries, Indicates the The enhanced value of cached data, Indicates the total cache size.

[0093] In this step, in the graph convolutional network (GCN), spatial convolution is used to aggregate adjacent node delays, and temporal convolution is used to extract timing features.

[0094] In this step, the macro level represents the system level and the micro level represents the data level;

[0095] At the micro level, when cache value density Below the cache value density threshold When the system is running, it triggers the active elimination of low-value data instead of passive expansion.

[0096] Step 4: When the total amount of the current cache on the server exceeds the upper limit of the final cache capacity, the predicted number of visits is obtained through ARIMA model prediction;

[0097] Feedback the prediction results to the server and dynamically adjust the final cache capacity upper limit to obtain the dynamically adjusted final cache capacity upper limit;

[0098] In step 4, when the total amount of the current cache on the server exceeds the upper limit of the final cache capacity, the predicted number of visits is obtained through the ARIMA model prediction. The corresponding relationship is:

[0099] ;

[0100] in, Represents the predicted number of visits, Indicates that it has been processed by the ARIMA model forecast. Indicates the prediction time.

[0101] Step 5: Get the enhanced value of the cached data based on the predicted number of accesses and the last access timestamp;

[0102] The cached data is judged based on the enhancement value, and the data is eliminated, and then the elimination result is generated;

[0103] Feedback the elimination results to the server and readjust the final cache capacity limit;

[0104] The enhanced value of cached data is obtained based on the predicted number of accesses and the last access timestamp. The corresponding relationship is:

[0105] ;

[0106] in, Indicates enhanced value, and Both represent dynamic weight coefficients, Indicates the current number of visits. Indicates the total number of visits. Indicates the current timestamp, Indicates the last access timestamp, Indicates the preset expiration timestamp. Indicates the adjustment factor for controlling load sensitivity.

[0107] Furthermore, in this step, the generated elimination result includes the actual elimination rate. When the actual elimination rate is higher than the false elimination rate threshold, the adjustment coefficient for controlling the load sensitivity is automatically adjusted. The relationship in the corresponding process is:

[0108] ;

[0109] in, Indicates the adjustment coefficient of the controlled load sensitivity after adjustment, Indicates the adjustment coefficient of the control load sensitivity before adjustment, Indicates the actual elimination rate;

[0110] The false elimination rate is obtained by dividing the number of data reloads by the total number of eliminations.

[0111] See also Figure 2 , an embodiment of the present invention further provides a system for caching web interface data, the system comprising:

[0112] Parameter processing module, used to:

[0113] The query parameters of the HTTP request are grouped into a parameter set;

[0114] Standardize the parameter set to obtain standardized parameters;

[0115] Perform weight correction on the standardized parameters to obtain enhanced cache keys;

[0116] Data matching module, used to:

[0117] Retrieve cache data matching the enhanced cache key from the client cache;

[0118] If the match is successful, the last access timestamp of the matching cache data is updated and output;

[0119] If the match is not successful, a cache hit signal is generated;

[0120] Adaptive capacity module for:

[0121] After receiving the cache hit signal, the server records the average response time;

[0122] The maximum cache size is calculated based on the average response time and the current amount of available memory;

[0123] Based on the maximum cache capacity, combined with the real-time network delay and the maximum tolerable delay, the upper limit of the dynamic cache capacity after network delay compensation is obtained;

[0124] The final cache capacity limit is obtained by using the dynamic cache capacity limit after network delay compensation and the current available memory;

[0125] The total amount of the server's current cache is determined based on the final cache capacity limit. When the total amount of the server's current cache does not exceed the final cache capacity limit, the cache write instruction is executed.

[0126] Prediction module, used to:

[0127] When the total amount of the server's current cache exceeds the upper limit of the final cache capacity, the predicted number of visits is obtained through ARIMA model prediction;

[0128] Feedback the prediction results to the server and dynamically adjust the final cache capacity upper limit to obtain the dynamically adjusted final cache capacity upper limit;

[0129] Cache value evaluation module, used to:

[0130] Get enhanced value of cached data based on predicted access count and last access timestamp;

[0131] The cached data is judged based on the enhancement value, and the data is eliminated, and then the elimination result is generated;

[0132] The elimination results are fed back to the server and the upper limit of the final cache capacity is readjusted.

[0133] It should be understood that various components of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0134] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0135] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for caching web interface data, characterized in that: The method comprises the following steps: Step 1: Group the query parameters of the HTTP request into a parameter set; Standardize the parameter set to obtain standardized parameters; Perform weight correction on the standardized parameters to obtain enhanced cache keys; Step 2: Retrieve cache data matching the enhanced cache key from the client cache. If the match is successful, the last access timestamp of the matching cache data is updated and output; If the match is not successful, a cache hit signal is generated; Step 3: After receiving the cache hit signal, the server records the average response time; The maximum cache size is calculated based on the average response time and the current amount of available memory; Based on the maximum cache capacity, combined with the real-time network delay and the maximum tolerable delay, the upper limit of the dynamic cache capacity after network delay compensation is obtained; The final cache capacity limit is obtained by using the dynamic cache capacity limit after network delay compensation and the current available memory; The total amount of the server's current cache is determined based on the final cache capacity limit. When the total amount of the server's current cache does not exceed the final cache capacity limit, the cache write instruction is executed. Step 4: When the total amount of the current cache on the server exceeds the upper limit of the final cache capacity, the predicted number of visits is obtained through ARIMA model prediction; Feedback the prediction results to the server and dynamically adjust the final cache capacity upper limit to obtain the dynamically adjusted final cache capacity upper limit; Step 5: Get the enhanced value of the cached data based on the predicted number of accesses and the last access timestamp; The cached data is judged based on the enhancement value, and the data is eliminated, and then the elimination result is generated; The elimination results are fed back to the server and the upper limit of the final cache capacity is readjusted.

2. A method for caching web interface data according to claim 1, characterized in that: In step 1, the query parameters of the HTTP request are grouped into a parameter set. The corresponding relationship is: ； in, Represents a parameter set, Both represent parameter keys. Both represent the values corresponding to the parameter keys. Indicates the total number of query parameters.

3. A method for caching web interface data according to claim 2, characterized in that: In step 1, the parameter set is standardized to obtain standardized parameters. The corresponding relationship is: ； in, Indicates standardized function processing, Indicates that it has been processed by the sorting function. are keys that represent normalized parameters, They all represent the values corresponding to the keys of the standardized parameters.

4. A method for caching web interface data according to claim 3, characterized in that: In step 1, the standardized parameters are weighted to obtain an enhanced cache key. The corresponding relationship is: ； in, Represents an enhanced cache key, Indicates that it has been processed by the SHA-256 hash algorithm. represents the index of the normalization parameter, Indicates the contribution of the standardized parameters when the cache hits. Indicates the The key of the standardized parameters, Indicates the The values corresponding to the keys of the normalized parameters.

5. A method for caching web interface data according to claim 4, characterized in that: In step 3, the maximum cache capacity is calculated by using the average response time and the current available memory capacity. The corresponding relationship is: ； in, Indicates the maximum cache size. express The memory health factor at the moment, Indicates the current amount of available memory. express The response time weight factor at the moment, represents the average response time, express The memory health factor at the moment, express The response time weight factor at the moment, express Cache hit rate at the moment, Indicates processing by the Q-learning algorithm.

6. A method for caching web interface data according to claim 5, characterized in that: In step 3, based on the maximum cache capacity, combined with the real-time network delay and the maximum tolerable delay, the upper limit of the dynamic cache capacity after network delay compensation is obtained. The specific steps are as follows: Collect the delay data of different time periods and servers to construct the network delay time-space matrix. The relationship between the corresponding process is: ； in, represents the network delay space-time matrix, Indicates the Period The network latency of each server node, Indicates the total length of the time period, Indicates the total number of server nodes; Based on the network delay spatiotemporal matrix, the predicted delay matrix is obtained by using the graph convolutional network prediction. The relationship between the corresponding process is: ； in, represents the prediction delay matrix, Indicates prediction processing through graph convolutional network, Represents the topological relationship matrix; The predicted network delay is obtained based on the predicted delay matrix. Combined with the real-time network delay and the maximum tolerable delay, the upper limit of the dynamic cache capacity after network delay compensation is obtained. The corresponding relationship is: ； in, Indicates the upper limit of dynamic cache capacity after network delay compensation. Indicates the real-time network delay, represents the maximum tolerable delay, Represents the predicted network delay.

7. A method for caching web interface data according to claim 6, characterized in that: In step 3, the final cache capacity upper limit is obtained by the dynamic cache capacity upper limit after network delay compensation and the current available memory amount, wherein the relationship at the macro level is: ； in, Indicates the final cache capacity limit, Indicates the total system memory. Indicates the overall system load. Indicates the load safety threshold, represents the load sensitivity factor; At the micro level, the relationship is: ； in, represents the cache value density, Indicates the current number of cache entries, Indicates the The enhanced value of cached data, Indicates the total cache size.

8. A method for caching web interface data according to claim 7, characterized in that: In step 4, when the total amount of the current cache on the server exceeds the upper limit of the final cache capacity, the predicted number of visits is obtained through ARIMA model prediction. The relationship between the corresponding process is: ； in, Represents the predicted number of visits, Indicates that it has been processed by the ARIMA model forecast. Indicates the prediction time.

9. A method for caching web interface data according to claim 8, characterized in that: In step 5, the enhanced value of the cached data is obtained based on the predicted number of accesses and the last access timestamp. The corresponding relationship is: ； in, Indicates enhanced value, and Both represent dynamic weight coefficients, Indicates the current number of visits. Indicates the total number of visits. Indicates the current timestamp, Indicates the last access timestamp, Indicates the preset expiration timestamp. Indicates the adjustment factor for controlling load sensitivity.

10. A system for caching web interface data, characterized in that: The system applies the method for web interface data caching according to any one of claims 1 to 9, and the system includes: Parameter processing module, used to: The query parameters of the HTTP request are grouped into a parameter set; Standardize the parameter set to obtain standardized parameters; Perform weight correction on the standardized parameters to obtain enhanced cache keys; Data matching module, used to: Retrieve cache data matching the enhanced cache key from the client cache; If the match is successful, the last access timestamp of the matching cache data is updated and output; If the match is not successful, a cache hit signal is generated; Adaptive capacity module for: After receiving the cache hit signal, the server records the average response time; The maximum cache size is calculated based on the average response time and the current amount of available memory; Based on the maximum cache capacity, combined with the real-time network delay and the maximum tolerable delay, the upper limit of the dynamic cache capacity after network delay compensation is obtained; The final cache capacity limit is obtained by using the dynamic cache capacity limit after network delay compensation and the current available memory; The total amount of the server's current cache is determined based on the final cache capacity limit. When the total amount of the server's current cache does not exceed the final cache capacity limit, the cache write instruction is executed. Prediction module, used to: When the total amount of the server's current cache exceeds the upper limit of the final cache capacity, the predicted number of visits is obtained through ARIMA model prediction; Feedback the prediction results to the server and dynamically adjust the final cache capacity upper limit to obtain the dynamically adjusted final cache capacity upper limit; Cache value evaluation module, used to: Get enhanced value of cached data based on predicted access count and last access timestamp; The cached data is judged based on the enhancement value, and the data is eliminated, and then the elimination result is generated; The elimination results are fed back to the server and the upper limit of the final cache capacity is readjusted.

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