Website performance optimization method and related products thereof

Abstract

The invention discloses a website performance optimization method and related products thereof. The method comprises the following steps: creating performance baseline data; performing performance optimization on each website core scene related to the performance data, and outputting a performance test report and an operation information material; determining the performance change condition of the website based on the performance baseline data, the performance test report and the operation information material; and determining whether to end the performance optimization cycle based on the performance change condition. By utilizing the technical scheme provided by the invention, the performance of the website can be optimized, and the overall user experience is improved, so that the user retention rate is improved, and the e-commerce conversion rate is improved.

Description

Technical Field

[0001] This application generally relates to the field of Internet technologies. More specifically, this application relates to a method for optimizing website performance and related products thereof. Background Art

[0002] On the road to the pursuit of high-speed business growth, optimizing the user experience is obviously very important for promoting positive conversion. Among them, the length of the application startup time directly affects the user retention rate. With the continuous increase of startup projects, due to the unreasonable scheduling of the startup framework, the phenomenon of thread blocking occurs during the startup process. At the same time, a large number of reflections are generated by the gson parser relied on for the home page data processing during the parsing process, which makes the data processing slow. In addition, due to the rich content of the home page, the success rate of layout preloading optimization is not high. For example, in some product display scenarios, there are many product labels to be displayed, which not only makes the layout bloated, but also seriously affects the efficiency of Layout Measurement.

[0003] In view of this, there is an urgent need to provide a method for optimizing website performance, so as to optimize the performance of the website, improve the overall user experience, thereby increasing the user retention rate and improving the e-commerce conversion rate. Summary of the Invention

[0004] In order to solve at least one or more of the above-mentioned technical problems, this application proposes a method for optimizing website performance and related products thereof in multiple aspects. The method for optimizing website performance can optimize the performance of the website, improve the overall user experience, thereby increasing the user retention rate and improving the e-commerce conversion rate.

[0005] In a first aspect, this application provides a method for optimizing website performance, including: creating performance baseline data; respectively optimizing the performance of each website core scenario related to performance data, and outputting a performance test report and operation information materials; determining the performance change of the website based on the performance baseline data, the performance test report and the operation information materials; and determining whether to end the performance optimization cycle based on the performance change.

[0006] In some embodiments, creating performance baseline data includes: obtaining the processor frequency, memory information of each user device and the first screen time information of each user device for loading the website; determining the model rating of each user device based on the processor frequency and memory information of each user device; and determining performance baseline data based on the first screen time information of each user device for loading the website and the model rating of each user device.

[0007] In some embodiments, the core scenario of the website is the startup splash screen scenario. Among them, the performance optimization of the startup splash screen scenario includes: optimizing startup tasks, the first-screen link tasks, and thread convergence; preloading the first-screen resources; and configuring corresponding data parsing tools according to the data volume.

[0008] In some embodiments, the core scenario of the website is the long list scenario. Among them, the performance optimization of the long list scenario includes: optimizing data splitting according to the content displayed on the first screen and the query time of business data; preloading the layout resources; and optimizing the layout rendering.

[0009] In some embodiments, the core scenario of the website is the home page tab scenario. Among them, the performance optimization of the home page tab scenario includes: preloading the tab resources.

[0010] In some embodiments, the core scenario of the website is the network dependency scenario. Among them, the performance optimization of the network dependency scenario includes: preferentially processing the status request operation and the callback feedback of the request result subscription message.

[0011] In some embodiments, determining the performance change situation of the website based on the performance baseline data, the performance test report, and the operation information materials includes: determining the performance target gap based on the performance test data and the performance baseline data in the performance test report; determining whether additional performance problems occur based on the performance test report and the previous version of the performance test report of the current performance test report; determining the website performance growth stage based on the index settlement information in the operation information materials; and determining whether there are problems in the program running environment, network request status, and business logic based on the program execution records and operation event records in the operation information materials.

[0012] In some embodiments, determining whether to end the performance optimization cycle based on the performance change situation includes: if the performance change situation is performance optimization, determining to end the performance optimization cycle; if the performance change situation is performance degradation, re-executing the steps of performing performance optimization on each website core scenario related to the performance data respectively, and outputting the performance test report and the operation information materials.

[0013] In a second aspect, the present application provides a device for website performance optimization, including: a memory; and at least one processor configured to: create performance baseline data; perform performance optimization on each website core scenario related to the performance data respectively, and output a performance test report and operation information materials; determine the performance change situation of the website based on the performance baseline data, the performance test report, and the operation information materials; and determine whether to end the performance optimization cycle based on the performance change situation.

[0014] In a third aspect, the present application provides a non-transitory machine-readable medium having program code for website performance optimization stored thereon. When the program code is executed by at least one processor, the code guides the execution operations of the at least one processor. The program code includes: creating performance baseline data; performing performance optimization on each website core scenario related to performance data respectively, and outputting a performance test report and operation information materials; determining the performance change situation of the website based on the performance baseline data, the performance test report, and the operation information materials; and determining whether to end the performance optimization cycle based on the performance change situation.

[0015] The technical solutions provided by the present application may include the following beneficial effects:

[0016] The website performance optimization method and related products provided by the present application, by creating performance baseline data, performing performance optimization on each website core scenario related to performance data respectively, and outputting a performance test report and operation information materials, and then determining the performance change situation of the website based on the performance baseline data, the performance test report, and the operation information materials, can monitor the website performance situation, identify and prevent potential performance degradation problems, and then determine whether to end the performance optimization cycle based on the performance change situation, ensuring that each performance optimization iteration will not have a negative impact on the user experience, and improving user satisfaction and retention rate.

[0017] Generally speaking, the present application can optimize the performance of the website, improve the overall user experience, thereby increasing the user retention rate and improving the e-commerce conversion rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present application will become readily understood. In the drawings, several embodiments of the present application are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0019] Figure 1 An exemplary flowchart of a website performance optimization method according to some embodiments of the present application is shown;

[0020] Figure 2 An exemplary flowchart of a website performance optimization method according to some other embodiments of the present application is shown;

[0021] Figure 3 An exemplary flowchart of a website performance optimization method according to still some other embodiments of the present application is shown;

[0022] Figure 4 A block diagram of the hardware configuration of a device 400 for website performance optimization that can implement the website performance optimization method of the embodiments of the present application is shown. Detailed Implementation Manner

[0023] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. For the sake of simplicity and clarity of description, where appropriate, the same reference numerals may be repeated in the drawings to indicate corresponding or similar elements. Additionally, the present application elaborates on many specific details to provide a thorough understanding of the embodiments described herein. However, those of ordinary skill in the art will understand that the embodiments described herein may be practiced without these specific details. In other cases, well-known methods, procedures, and components are not described in detail so as not to obscure the embodiments described herein. Moreover, this description should not be regarded as limiting the scope of the embodiments described herein. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of protection of the present application.

[0024] It should be understood that the possible terms "first" or "second" etc. in the claims, the specification, and the drawings disclosed in the present application are used to distinguish different objects, rather than to describe a specific order. The terms "comprising" and "including" used in the specification and claims of the present application indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0025] It should also be understood that the terms used in the specification of the present application are merely for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and claims of the present application, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms. It should be further understood that the term "and / or" used in the specification and claims of the present application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0026] As used in this specification and the claims, the term "if" may be interpreted as "when", "once", "in response to a determination", or "in response to a detection" depending on the context. Similarly, the phrase "if a determination is made" or "if [the described condition or event] is detected" may be interpreted as meaning "once a determination is made", "in response to a determination", "once [the described condition or event] is detected", or "in response to a detection of [the described condition or event]" depending on the context.

[0027] The length of application startup time directly affects user retention rate. As the number of startup projects continues to increase, due to unreasonable scheduling of the startup framework, thread blocking occurs during the startup process. At the same time, the gson parser relied on for home page data processing generates a large number of reflections during the parsing process, making the data processing slow. In addition, due to the rich content on the home page, the success rate of layout preloading optimization is not high. For example, in some product display scenarios, there are numerous product labels to be displayed, which not only makes the layout bloated but also seriously affects the efficiency of Layout Measurement.

[0028] In view of this, the present application provides a website performance optimization method to optimize the performance of the website, enhance the overall user experience, thereby improving user retention rate and boosting e-commerce conversion rate.

[0029] The following will describe in detail the specific implementation manners of the present application with reference to the accompanying drawings.

[0030] In step S101, performance baseline data is created. In the embodiments of the present application, the aforementioned performance baseline data refers to the reference standard data provided for website performance optimization. For example, the performance baseline data can be FirstScreen Paint (FSP) data. The first screen time is the time required for the user to click on the website entry link until the website page content is fully presented, that is, the time from the start of website page loading to the completion of all drawing of the first screen content, which can be used to measure the website performance. It can be understood that when the user is waiting for the page to load, the rapid presentation of the first screen content can significantly improve user satisfaction. If the first screen loading time is too long, the user may become impatient and even leave the page.

[0031] In the embodiments of the present application, the performance baseline data can be represented by the Top Percentile (TP) index of the first screen time. The TP index of the first screen time means that within a period of time, the time consumed for each first screen loading of the website page is statistically counted, and these times are sorted in ascending order, and the time value corresponding to the value obtained by multiplying the index parameter (for example, the index parameter of TP50 is 50%) by the number of statistical times is taken as the critical value for evaluating the first screen time level. For example, it can be indicated that the website starts extremely fast by TP50 < 500ms, that the website starts quickly in seconds by TP50 < 1000ms, and that the website is close to starting quickly in seconds by TP50 < 1200ms. The above TP50 < 500ms, TP50 < 1000ms, and TP50 < 1200ms constitute the performance baseline data.

[0032] In step S102, performance optimization is performed on each website core scenario related to performance data, and a performance test report and operation information material are output. In the embodiment of the present application, the website core scenario related to performance data can be any one of scenarios such as the startup splash screen scenario, the long list scenario, the home page tab scenario, the network dependency scenario, etc., and each website core scenario matches a corresponding performance optimization strategy. After optimizing each website core scenario respectively, it can be tested and run through the laboratory computer room, so as to output a performance test report and operation information material. Among them, the performance test report contains website performance data obtained from laboratory computer room tests (such as splash screen time, frame rate, resource occupancy, etc.) and screen recording sensory data (screen recording sensory data refers to subjective quality indicators related to user experience, such as stutter detection data, structural similarity, perceptual weighted mean square error, etc.). In addition, the operation information material may include, but is not limited to, program execution records (i.e., logs), operation event records (i.e., traces), and index settlement information (including data on the time consumed in each stage of the splash screen link) and other information materials generated during the operation of the website.

[0033] In step S103, the performance change situation of the website is determined based on the performance baseline data, the performance test report, and the operation information material. It can be understood that during the process of website performance optimization, new performance problems may be introduced, which may instead deteriorate the website performance. Therefore, it is necessary to perform performance change analysis on the website based on the performance baseline data, the performance test report, and the operation information material, so as to effectively prevent the deterioration of website performance.

[0034] In step S104, it is determined whether to end the performance optimization cycle based on the performance change situation. In the embodiment of the present application, the performance change situation may include performance optimization and performance deterioration. If the performance change situation is performance optimization, the current performance optimization cycle can be ended, the iteratively updated website can be released, and the performance test report and operation information material generated after this optimization are stored for performance change analysis in the next performance optimization cycle.

[0035] The embodiment of the present application creates performance baseline data, performs performance optimization on each website core scenario related to performance data respectively, and outputs a performance test report and operation information material. Furthermore, based on the performance baseline data, the performance test report, and the operation information material, the performance change situation of the website is determined, so that the website performance situation can be monitored, potential performance degradation problems can be identified and prevented. Furthermore, it is determined whether to end the performance optimization cycle based on the performance change situation, ensuring that each performance optimization iteration will not have a negative impact on the user experience, improving user satisfaction and retention rate. Generally speaking, the present application can optimize the performance of the website, improve the overall user experience, thereby increasing the user retention rate and improving the e-commerce conversion rate.

[0036] In some embodiments, the performance baseline data corresponding to each user device can be evaluated by collecting the device information of the user, and the performance optimization strategies corresponding to each core website scenario can be further designed. The following will be combined with Figure 2 to elaborate in detail on the determination process of the performance baseline data and the performance optimization process of each core website scenario. Figure 2 An exemplary flowchart of the website performance optimization method according to some other embodiments of the present application is shown. Please refer to Figure 2 , the website performance optimization method shown in the embodiments of the present application may include:

[0037] In step S201, the processor frequency, memory information of each user device, and the first-screen time information of each user device for loading the website are obtained. In the embodiments of the present application, the processor frequency and memory information of the user device can be obtained by the user reporting the processor frequency and memory information of their user device. The first-screen time information of each user device for loading the website can be monitored when each user device uses the app or web to start the website. It can be understood that the acquisition methods of the processor frequency, memory information of each user device, and the first-screen time information of each user device for loading the website are diverse. In practical applications, a suitable acquisition method needs to be adopted according to the actual application situation, and the present application does not impose any restrictions in this regard.

[0038] In step S202, the model rating of each user device is determined based on the processor frequency and memory information of each user device. In the embodiments of the present application, weighted scoring can be performed based on the processor frequency and memory information of each user device, and the scores can be converged to the 1-10 score range. Then, the proportion of users in each score range is statistically analyzed. For example, user devices with a score of 5 or less can be defined as low-end devices, user devices with a score above 5 and below 8 can be defined as mid-end devices, user devices with a score above 8 can be defined as high-end devices, and all rated user devices can be defined as the overall market.

[0039] In step S203, performance baseline data is determined based on the first-screen time information of each user device for loading a website and the model rating of each user device. In the embodiments of the present application, for mid-range devices, the first-screen time information corresponding to each mid-range device can be extracted from the first-screen time information of each user device for loading a website. Then, the multiple extracted first-screen time information is sorted in ascending order, and the multiple first-screen time information is divided into four equal parts. Then, the first quartile, the second quartile, and the third quartile are extracted. For example, the first quartile is 500 ms, the second quartile is 1000 ms, and the third quartile is 1200 ms. Then, if the TP metric is used to represent, the website can be considered to start extremely fast by TP50 < 500 ms, start quickly by TP50 < 1000 ms, and start nearly quickly by TP50 < 1200 ms. The above TP50 < 500 ms, TP50 < 1000 ms, and TP50 < 1200 ms constitute the performance baseline data corresponding to mid-range devices.

[0040] It can be understood that the above description of the performance baseline data corresponding to mid-range devices is only exemplary. In actual applications, the performance baseline data corresponding to user devices of each rating needs to be determined according to the actual application situation, and the present application does not make any restrictions in this regard.

[0041] In step S204, performance optimization is performed on each website core scenario related to the performance data.

[0042] When the core scenario of the website is the startup splash screen scenario, the performance optimization of the startup splash screen scenario can be specifically as follows: First, the startup tasks, the splash screen link tasks, and the thread convergence can be optimized. Among them, during the optimization of the startup tasks, all startup tasks can be incorporated into the launcher for management. The launcher reads the startup task configuration and constructs a directed acyclic graph based on the mutual dependencies between the tasks, and schedules them to different threads according to whether they are executed synchronously. And according to the business attributes, the core startup tasks are placed in the core launcher that executes earlier, and the non-core startup tasks are placed in the non-core launcher that executes in the background. In addition, during the optimization of the splash screen link tasks, a task scheduler can be built to postpone the non-splash-screen required tasks to the splash screen idle stage for scheduling according to the priority. In addition, during the optimization of the thread convergence, a suitable thread pool can be customized. Through bytecode instrumentation technology, during the process of scanning bytecode, when the bytecode of thread creation is read, the bytecode of unreasonably creating threads is modified to create asynchronous threads using the customized thread pool to reduce the consumption of system resources. Then, the splash screen resources can be pre-loaded, and the splash screen required resources can be persisted, including splash screen data, splash screen pictures, splash screen widgets, and splash screen Class caches, etc., which are loaded at the startup of the splash screen to accelerate the content presentation. Next, corresponding data parsing tools can be configured according to the data volume. Specifically, for example, in the case of a fixed data model and a small data volume, a more efficient parsing method such as Parcelable can be configured, which optimizes the performance by implementing the serialization and deserialization of objects. For the Gson framework that depends on a large amount of data parsing, the GsonTypeFactoryAdapter corresponding to the model can be generated through compile-time code generation technology and registered to the Gson framework to reduce the reflection loss during data parsing. Generally speaking, the performance optimization of the startup splash screen scenario can reduce unnecessary resource consumption, significantly accelerate the startup speed of the website's splash screen, and is beneficial to improving the impression and retention rate when users use it for the first time.

[0043] When the core scenario of the website is a long list scenario (referring to a list page containing a large number of data items in the application, such as a product list or a message list), the performance optimization of the long list scenario can be specifically as follows: First, data splitting optimization can be performed based on the first screen display content and the time consumed for business data query. Among them, non-first screen redundant data can be split into subsequent queries based on the first screen display content to reduce the time consumed for first screen data query and parsing. At the same time, according to the query time consumed for different business attribute data, data with high query efficiency can be returned first to participate in rendering to evenly distribute the rendering load. Then, the layout resources can be preloaded, wherein commonly used layout resources can be extracted and pre-requested based on the widget's PV (page views) funnel, and pooled management can be performed based on the production, consumption, supply and demand relationship of resources, and the exhausted resources can be replenished and stored in the cache pool in a timely manner. Next, the layout rendering can be optimized, wherein bytecode technology can be used to create a section for the measurement logic of key layout containers in the layout hierarchy, output measurement information at the point of intersection, detect whether there is a problem of repeated measurement and optimize it. In general, performance optimization for long list scenarios can solve the problem of frequent measurement and achieve layout optimization for cache-free scenarios, improve the response speed and rendering efficiency of the page, and solve the problem of operation delays caused by bloated layout.

[0044] When the core scenario of the website is the home page tab scenario, the performance optimization of the home page tab scenario can be specifically: preloading the tab page resources. The page resources of the tab page may include but are not limited to tab page data, tab page images, tab page widgets, and tab page Class caches can be gradually loaded during the idle period after the first screen display. In general, performance optimization of the home page tab scenario can achieve fast page loading and smooth interface interaction.

[0045] When the core scenario of the website is a network-dependent scenario, the performance optimization of the network-dependent scenario can be specifically as follows: give priority to the callback feedback of the status request operation and the request result subscription message. Among them, the priority processing of the status request operation can specifically extend the existing network framework, and after the extension, it supports the feedback of the request callback (callback), request result, request exception information and other states after the management request is sent. Change the request initiated after the page is initialized to the request initiated after the user clicks, and directly bind the subscriber of the request result to the pre-request when the page is initialized, thereby improving the data return time. In addition, the priority processing of the callback feedback of the request result subscription message can specifically extend the network framework, and after the extension, it supports the request result subscription message to be placed at the front of the main thread message loop, shortening the data preparation time. In general, performance optimization of network-dependent scenarios can effectively reduce user waiting time and enhance the fluency of page interaction.

[0046] In some embodiments, the performance change situation can be used to prevent the deterioration of website performance. The following will be combined with Figure 3 to elaborate in detail on the process of preventing website performance deterioration. Figure 3 FIG. shows an exemplary flowchart of a website performance optimization method according to still some embodiments of the present application. Please refer to Figure 3 , the website performance optimization method shown in the embodiments of the present application may include:

[0047] In step S301, a performance target gap is determined based on the performance test data and performance baseline data in the performance test report. In the embodiments of the present application, the performance test data in the performance test report can be compared with the performance baseline data, and after the comparison, the gap between the performance test data and the performance baseline data, that is, the performance target gap, can be obtained.

[0048] In step S302, it is determined whether additional performance problems occur based on the performance test report and the previous version of the performance test report of the current performance test report. In the embodiments of the present application, the website performance data and screencast sensory data in the current performance test report can be compared one by one with those in the previous version of the performance test report to determine whether data deterioration occurs, and based on this, it is determined whether additional performance problems occur.

[0049] In step S303, a website performance growth stage is determined based on the index settlement information in the operation information material. In the embodiments of the present application, the index settlement information may include, but is not limited to, the time-consuming data of each stage in the first-screen link (such as: routing jump stage, page initialization stage, network request stage, data processing stage, image loading stage, etc.). In the embodiments of the present application, the stage in which the performance is improved and optimized can be confirmed by analyzing the time-consuming data of each stage.

[0050] In step S304, it is determined whether there are problems with the program running environment, network request status, and business logic based on the program execution record and running event record in the operation information material. In the embodiments of the present application, the memory water level, GC (Garbage Collection) information, current running processes, and threads collected from the logs of the above growth stage can be analyzed to determine whether there are problems with the program running environment by judging the main thread message loop. In addition, the time consumption of each stage of image and network loading can be analyzed to determine whether image loading and network requests are affected. Furthermore, the time consumption of method execution on the trace can be analyzed to determine whether the business logic is affected.

[0051] In step S305, it is determined whether to end the performance optimization cycle based on the performance change situation. It can be understood that if any performance degradation situation is found during the analysis process of the above steps S301 to S304, it is determined that the performance change situation is performance degradation. If the performance change situation is performance degradation, the steps of respectively performing performance optimization on each website core scenario related to the performance data and outputting a performance test report and operation information materials are re-executed. On the contrary, it is determined that the performance change situation is performance optimization. If the performance change situation is performance optimization, it is determined to end the performance optimization cycle.

[0052] Corresponding to the foregoing embodiments of the application function implementation method, the present application further provides a device for website performance optimization and corresponding embodiments.

[0053] Figure 4 A block diagram showing the hardware configuration of a device 400 for website performance optimization that can implement the website performance optimization method of the embodiments of the present application. As Figure 4 shown, the device 400 for website performance optimization may include a processor 410 and a memory 420. In Figure 4 the device 400 for website performance optimization, only the constituent elements related to this embodiment are shown. Therefore, it is obvious to those of ordinary skill in the art that: the device 400 for website performance optimization may further include common constituent elements different from Figure 4 the constituent elements shown therein. For example: a fixed-point arithmetic unit.

[0054] The device 400 for website performance optimization may correspond to a computing device having various processing functions. For example, functions for generating a neural network, training or learning a neural network, quantifying a floating-point neural network into a fixed-point neural network, or retraining a neural network. For example, the device 400 for website performance optimization may be implemented as various types of devices, such as a personal computer (PC), a server device, a mobile device, etc.

[0055] The processor 410 controls all functions of the device 400 for website performance optimization. For example, the processor 410 controls all functions of the device 400 for website performance optimization by executing a program stored in the memory 420 of the device 400 for website performance optimization. The processor 410 may be implemented by a central processing unit (CPU), a graphics processing unit (GPU), an application processor (AP), an artificial intelligence processor chip (IPU), etc. provided in the device 400 for website performance optimization. However, the present application is not limited thereto.

[0056] In some embodiments, the processor 410 may include an input / output (I / O) unit 411 and a computing unit 412. The I / O unit 411 may be used to receive various data, such as performance baseline data. Exemplarily, the computing unit 412 may be used to perform performance optimization for each website core scenario related to performance data respectively, and output a performance test report and operation information materials; determine the performance change of the website based on the performance baseline data, the performance test report and the operation information materials; and determine whether to end the performance optimization cycle based on the performance change. The result of whether to end the performance optimization cycle may be output by the I / O unit 411, for example. The output data may be provided to the memory 420 for other devices (not shown) to read and use, or may be directly provided to other devices for use.

[0057] The memory 420 is hardware for storing various data processed in the device 400 for website performance optimization. For example, the memory 420 may store processed data and data to be processed in the device 400 for website performance optimization. The memory 420 may store data sets involved in the process of the website performance optimization method that the processor 410 has processed or is to process, such as performance baseline data, etc. In addition, the memory 420 may store applications, drivers, etc. to be driven by the device 400 for website performance optimization. For example, the memory 420 may store various programs related to the website performance optimization method to be executed by the processor 410. The memory 420 may be DRAM, but this application is not limited thereto. The memory 420 may include at least one of volatile memory or non-volatile memory. The non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, phase change RAM (PRAM), magnetic RAM (MRAM), resistive RAM (RRAM), ferroelectric RAM (FRAM), etc. The volatile memory may include dynamic RAM (DRAM), static RAM (SRAM), synchronous DRAM (SDRAM), PRAM, MRAM, RRAM, ferroelectric RAM (FeRAM), etc. In an embodiment, the memory 420 may include at least one of a hard disk drive (HDD), a solid state drive (SSD), a high density flash (CF) card, a secure digital (SD) card, a micro secure digital (Micro-SD) card, a mini secure digital (Mini-SD) card, an extreme digital (xD) card, caches, or a memory stick.

[0058] In summary, the specific functions implemented by the memory 420 and the processor 410 of the device 400 for website performance optimization provided in the embodiments of this specification may be interpreted in contrast to the foregoing embodiments in this specification, and can achieve the technical effects of the foregoing embodiments, which will not be elaborated here.

[0059] In the present embodiment, the processor 410 may be implemented in any suitable manner. For example, the processor 410 may take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, and so on.

[0060] It should also be understood that any module, unit, component, server, computer, terminal, or device that executes instructions as exemplified herein may include or otherwise access a computer-readable medium, such as a storage medium, a computer storage medium, or a data storage device (removable) and / or non-removable), such as a magnetic disk, an optical disk, or a magnetic tape. The computer storage medium may include volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data.

[0061] The foregoing may be better understood in accordance with the following clauses:

[0062] Clause A1. A method for optimizing website performance, which includes: creating performance baseline data; respectively performing performance optimization on each website core scenario related to performance data, and outputting a performance test report and operation information materials; determining the performance change situation of the website based on the performance baseline data, the performance test report, and the operation information materials; and determining whether to end the performance optimization cycle based on the performance change situation.

[0063] Clause A2. The method for optimizing website performance according to Clause A1, wherein the creating of the performance baseline data includes: obtaining the processor frequency, memory information of each user device, and the first-screen time information of each user device for loading the website; determining the model rating of each user device based on the processor frequency and memory information of each user device; and determining the performance baseline data based on the first-screen time information of each user device for loading the website and the model rating of each user device.

[0064] Clause A3. The method for optimizing website performance according to Clause A1, wherein the website core scenario is the startup first-screen scenario, and the performance optimization of the startup first-screen scenario includes: optimizing startup tasks, first-screen link tasks, and thread convergence; preloading first-screen resources; and configuring corresponding data parsing tools according to the data volume size.

[0065] Article A4. The website performance optimization method according to Article A1, wherein the core scenario of the website is a long list scenario, and the performance optimization of the long list scenario includes: optimizing data splitting according to the content displayed on the first screen and the time-consuming of business data query; preloading layout resources; and optimizing layout rendering.

[0066] Article A5. The website performance optimization method according to Article A1, wherein the core scenario of the website is the home page tab scenario, and the performance optimization of the home page tab scenario includes: preloading tab resources.

[0067] Article A6. The website performance optimization method according to Article A1, wherein the core scenario of the website is a network-dependent scenario, and the performance optimization of the network-dependent scenario includes: preferentially processing the callback feedback of the status request operation and the request result subscription message.

[0068] Article A7. The determination of the performance change of the website based on the performance baseline data, the performance test report, and the operation information material includes: determining the performance target gap based on the performance test data in the performance test report and the performance baseline data; determining whether additional performance problems occur based on the performance test report and the previous version of the performance test report of the current performance test report; determining the website performance growth stage based on the index settlement information in the operation information material; and determining whether there are problems in the program running environment, network request status, and business logic based on the program execution record and the running event record in the operation information material.

[0069] Article A8. The determination of whether to end the performance optimization cycle based on the performance change situation includes: if the performance change situation is performance optimization, determining to end the performance optimization cycle; if the performance change situation is performance degradation, re-executing the steps of respectively performing performance optimization on each website core scenario related to performance data, and outputting a performance test report and operation information material.

[0070] Article A9. A device for website performance optimization, including: a memory; and at least one processor configured to: create performance baseline data; respectively perform performance optimization on each website core scenario related to performance data, and output a performance test report and operation information material; determine the performance change situation of the website based on the performance baseline data, the performance test report, and the operation information material; and determine whether to end the performance optimization cycle based on the performance change situation.

[0071] Clause A10. A non-transitory machine-readable medium stores program code for website performance optimization. When the program code is executed by at least one processor, the code guides the execution operations of the at least one processor. The program code includes: creating performance baseline data; performing performance optimization on each website core scenario related to performance data respectively, and outputting a performance test report and operation information materials; determining the performance change situation of the website based on the performance baseline data, the performance test report, and the operation information materials; and determining whether to end the performance optimization cycle based on the performance change situation.

Claims

1. A website performance optimization method, characterized in that: include: Create performance baseline data; Optimize the performance of each core scenario of the website related to performance data, and output performance test reports and operation information materials; Determine the performance change of the website based on the performance baseline data, the performance test report and the operation information material; as well as Determine whether to end the performance optimization cycle based on the performance change.

2. The website performance optimization method according to claim 1, characterized in that: The creation of performance baseline data includes: Obtain the processor frequency, memory information and first screen time information of each user's device when loading the website; Determine the model rating of each user device based on the processor frequency and memory information of each user device; and The performance baseline data is determined based on the first screen time information of each user device loading the website and the model rating of each user device.

3. The website performance optimization method according to claim 1, characterized in that: The core scenario of the website is the first screen startup scenario, wherein the performance optimization of the first screen startup scenario includes: Optimize startup tasks, first-screen link tasks, and thread convergence; Preload first-screen resources; and Configure corresponding data analysis tools according to the amount of data.

4. The website performance optimization method according to claim 1, characterized in that: The core scenario of the website is a long list scenario, wherein the performance optimization of the long list scenario includes: Optimize data splitting based on the content displayed on the first screen and the time consumed in business data query; Preloading layout resources; and Optimize layout rendering.

5. The website performance optimization method according to claim 1, characterized in that: The core scenario of the website is the home page tab scenario, wherein the performance optimization of the home page tab scenario includes: Preload tab resources.

6. The website performance optimization method according to claim 1, characterized in that: The core scenario of the website is a network-dependent scenario, wherein the performance optimization of the network-dependent scenario includes: Prioritize callback feedback of status request operations and request result subscription messages.

7. The website performance optimization method according to claim 1, characterized in that: Determining the performance change of the website based on the performance baseline data, the performance test report and the operation information material includes: Determine a performance target gap based on the performance test data in the performance test report and the performance baseline data; Determining whether additional performance issues are generated based on the performance test report and a previous version of the performance test report of the current performance test report; Determine the website performance growth stage based on the indicator settlement information in the operation information material; Based on the program execution records and operation event records in the operation information material, determine whether there are problems with the program operation environment, network request status and business logic judgment.

8. The website performance optimization method according to claim 1, characterized in that: The determining whether to end the performance optimization cycle based on the performance change includes: If the performance change is performance optimization, then the performance optimization cycle is determined to be ended; If the performance change is performance degradation, the steps of optimizing the performance of each core scenario of the website related to the performance data and outputting a performance test report and operation information materials are re-executed.

9. A device for optimizing website performance, characterized in that: include: Memory; as well as at least one processor configured to: Create performance baseline data; Optimize the performance of each core scenario of the website related to performance data, and output performance test reports and operation information materials; Determine the performance change of the website based on the performance baseline data, the performance test report and the operation information material; as well as Determine whether to end the performance optimization cycle based on the performance change.

10. A non-transitory machine-readable medium having stored thereon a program code for optimizing website performance, wherein when the program code is executed by at least one processor, the code guides the execution operation of the at least one processor, the program code comprising: Create performance baseline data; Optimize the performance of each core scenario of the website related to performance data, and output performance test reports and operation information materials; Determine the performance change of the website based on the performance baseline data, the performance test report and the operation information material; as well as Determine whether to end the performance optimization cycle based on the performance change.

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