Building intelligent gray-scale data system from 0 to 1: Taking vivo game center as an example

author ：

vivo Internet data analysis team -Dong Chenwei vivo

Internet big data team -Qin Cancan、Zeng Kun

This paper introduces vivo Practical experience of game center in gray data analysis system , from “ Experimental ideas - Mathematical methods - Data model - Product plan ” The four levels provide a relatively complete set of intelligent gray-scale data solutions , To ensure the scientificity of version evaluation 、 Fast closed loop of project progress and gray level verification . The highlight of this scheme is , The introduction of index change root cause analysis method and the design of the whole process automation product scheme .

One 、 introduction

The user scale of the game business is large , The service link is long , Data logic is complicated . As the core user product of the game business platform , Version iterations are very frequent , Before each version goes online, a small amount of gray level verification must be carried out .2021 Since then , The average 1~2 Every week, there will be an important version of grayscale , And sometimes there are multiple versions of gray-scale testing online at the same time .

The whole process of grayscale mainly involves 3 A question ：

1. How to ensure the scientificity of version gray evaluation ？
2. How to improve the output efficiency of gray data , Ensure the progress of the project ？
3. When there is an abnormal index problem in the grayscale version , How to quickly locate the problem and complete the closed loop ？

In the past two years , We will gradually systematize the gray-scale evaluation method to agile BI And other data products , At present, the gray data system has solved this problem well 3 A question . This paper first paves the way with the basic concept and development process of version gray data system , Then with “ methodology + Solution ” As the main line, this paper expounds the practice of game center in gray data system , And look to the future .

Two 、 Development of gray data system

2.1 What is grayscale publishing

When the game center develops a new homepage interface , How to verify whether the new homepage is accepted by users , And whether the function is perfect 、 Whether the performance is stable ？

answer ： Grayscale publishing . That is, before the new version is pushed to the full number of users , Select some users according to certain strategies , Let them experience the new homepage first , To get their information about “ The new homepage is easy to use or not ” as well as “ If it's not easy to use , What's wrong ” Using feedback . If there is a major problem , Rollback the old version in time ; On the contrary, it will check and fill the gaps according to the feedback results , And continue to enlarge the launch scope of the new version in due time until the full upgrade .

2.2 Development stage of gray scale evaluation scheme

The key to judge whether grayscale publishing is scientific is to control variables , The process of solving this problem , It is also the process of iteration and development of gray-scale evaluation scheme .

Stage 1 ： Ensure that the comparison time is the same , However, the difference in upgrade speed means that the users who have priority to upgrade and the users who have not upgraded are not homogeneous users , Failed to avoid the impact of sample differences on data result differences .

Stage two ： It ensures that the comparison group is the same , But user behavior may change over time , It is impossible to eliminate the difference of time factors before and after .

Stage three ： At the same time, it ensures that the time is the same as the crowd , It has the following three advantages ：

• Package the old version as a comparison package , Together with the new version of grayscale package , Release to two groups of homogeneous users , The sample properties of gray-scale package and comparison package are guaranteed 、 The time factor is consistent ;
• Calculate a reasonable sample size according to the product target , Avoid too few samples leading to unreliable results 、 Too much leads to waste of resources ;
• Rely on the silent installation function to quickly upgrade , Shorten the time of gray level verification .

2.3 Content of gray data system

Gray scale data systems usually involve Early flow strategy and Later data inspection 2 Parts of .

The former includes sample size calculation and grayscale duration control , The latter includes the comparison of core indicators between new and old versions 、 Data performance of index changes or new functions in product optimization . In addition to conventional gray scale evaluation , The introduction of root cause analysis can improve the interpretation of gray results .

2.4 vivo The practice of game center

We built “ Game center intelligent gray data system ”, And gradually solve the problem mentioned at the beginning of this article through three versions of iteration 3 A question . The data system consists of index test results 、 Dimension drill down interpretation 、 User attribute verification 、 It is composed of subject Kanban such as index anomaly diagnosis and gray conclusion report pushed automatically .

After the deployment of the complete scheme goes online , It basically realizes the automatic data production in the gray evaluation stage 、 Effect test 、 Closed loop of data interpretation and decision recommendations , It has greatly released manpower .

3、 ... and 、 Methodology in gray data system

Before introducing the data scheme design , First, introduce the background knowledge and methodology involved in the gray-scale data system , Help you better understand this article .

3.1 Gray scale experiment

Grayscale experiments include Sampling and effect inspection Two parts , Corresponding to the idea of hypothesis testing and the verification of historical differences of samples .

3.1.1 Hypothesis testing

Hypothesis testing is to put forward a hypothetical value for the overall parameters , Then use the sample performance to judge whether this hypothesis is true .

3.1.2 Verification of sample historical differences

Although the grayscale has been passed in advance hash Algorithm for sampling , But because of the randomness of sampling , Generally, statistical test and effect test are conducted at the same time , Verify the historical differences of samples , Eliminate the index fluctuation caused by the difference of the sample itself . The grayscale period is usually 7 God , We used 7 Sampling method of sliding window .

3.2 Root cause analysis

Gray scale indicators are often associated with multidimensional attributes （ Such as user attributes 、 Channel source 、 Page module, etc ） There is a connection , When the test results of indicators have abnormal significant differences , Want to remove the exception , Locating the root cause is a key step . However , This step is often challenging , Especially when the root factor is a combination of multiple dimension attribute values .

To solve this problem , We introduce the method of root cause analysis , In order to make up for the lack of interpretation of gray test results . We combine index logic analysis with Adtributor Algorithm 2 Methods , To ensure the reliability of the analysis results .

3.2.1 Index logical analysis

Because the index system constructed in the gray-scale experiment is basically rate value index or mean value index , These two kinds of indicators can be disassembled into two factors, numerator and denominator, through the indicator formula , The numerator and denominator of the indicator are obtained by adding the dimension values under each dimension . Therefore, it is proposed that Index logical analysis , Based on certain disassembly methods , From the index factor and index dimension 2 The index value is logically disassembled at three levels .

3.2.2 Adtributor Algorithm

In addition to the common dimension drilling method of root cause analysis , We introduced Adtributor Algorithm , To better deal with the situation of multi-dimensional combination impact indicators , And through the cross validation of the two methods to ensure the reliability of the analysis results .

Adtributor The algorithm was developed by Microsoft Research in 2014 An abnormal root cause analysis method of multidimensional time series proposed in , It has good reliability in the scenario of multi-dimensional complex root causes . The whole process of the algorithm includes data preprocessing 、 Anomaly detection 、 Root cause analysis and simulation visualization 4 A step , We mainly learn from the method of root cause analysis .

Four 、 Gray scale intelligent solution

4.1 The overall framework

Version grayscale can be divided into grayscale before - In gray - After grayscale 3 Stages , The overall framework of productization is as follows ：

4.2 Process design

Based on the above framework , How do we design and implement ？

The following is a flowchart describing the whole process ：

4.3 The core content of the scheme

4.3.1 Sample size estimation scheme

Kanban provides ： Under multiple sets of confidence levels and test efficiency standards （ Default display 95% Degree of confidence 、80% Test performance ）, According to the recent performance of the index , Predict the minimum sample size that the index can be detected significantly under different expected ranges of change .

The scheme has 3 Big feature ：

1. Output multiple sets of Standards , Flexibly adjust the expected range ;
2. Automatically select the latest full version of data as data input ;
3. The average index and rate value index adopt differentiated calculation logic .

4.3.2 Significance test scheme of effect index

The question that the index significance test model needs to answer is ： The grayscale version is compared with the contrast version , Is the change of the index statistically believable or unbelievable .

at present , The grayscale version and the contrast version under three confidence levels are realized in 20 Significance judgment on business indicators .

The implementation process is as follows ：

Rate value indicators

... ...
 
#  The following index data have been obtained 
    variation_visitors  #  Denominator of grayscale version index 
    control_visitors  #  The denominator of the comparison version indicator 
    variation_p  #  Grayscale version index value 
    control_p  #  Compare the version index value 
    z  #  Different confidence levels (90%/95%/99%) Under the z value , Business mainly focuses on 95% Significant test results at confidence level 
 
#  Calculate the standard deviation of the index 
    variation_se = math.sqrt(variation_p * (1 - variation_p))
    control_se = math.sqrt(control_p * (1 - control_p))
 
#  Calculate the index change value and change rate    
    gap = variation_p - control_p
    rate = variation_p / control_p - 1
 
#  Calculate the confidence interval 
    gap_interval_sdown = gap - z * math.sqrt(math.pow(control_se, 2) / control_visitors + math.pow(variation_se, 2) / variation_visitors)  #  Lower bound of confidence interval of change value 
    gap_interval_sup = gap + z * math.sqrt(math.pow(control_se, 2) / control_visitors + math.pow(variation_se, 2) / variation_visitors)  #  Upper bound of confidence interval of change value 
    confidence_interval_sdown = gap_interval_sdown / control_p  #  Lower bound of the confidence interval of the rate of change 
    confidence_interval_sup = gap_interval_sup / control_p  #  Upper bound of confidence interval of change value 
 
#  Significance judgment 
    if (confidence_interval_sdown > 0 and confidence_interval_sup > 0) or (confidence_interval_sdown < 0 and confidence_interval_sup < 0):
       print(" remarkable ")
    elif (confidence_interval_sdown > 0 and confidence_interval_sup < 0) or (confidence_interval_sdown < 0 and confidence_interval_sup > 0):
       print(" No significant ")
... ...

Average index

... ...
 
#  The following index data have been obtained 
    variation_visitors  #  Denominator of grayscale version index 
    control_visitors  #  The denominator of the comparison version indicator 
    variation_p  #  Grayscale version index value 
    control_p  #  Compare the version index value 
    variation_x  #  Gray version single user index value 
    control_x  #  Compare the single user index value of the version 
    z  #  Different confidence levels (90%/95%/99%) Under the z value , Business mainly focuses on 95% Significant test results at confidence level 
 
#  Calculate the standard deviation of the index 
    variation_se = np.std(variation_x, ddof = 1)
    control_se = np.std(control_x, ddof = 1)
 
#  Calculate the index change value and change rate    
    gap = variation_p - control_p
    rate = variation_p / control_p - 1
 
#  Calculate the confidence interval 
    gap_interval_sdown = gap - z * math.sqrt(math.pow(control_se, 2) / control_visitors + math.pow(variation_se, 2) / variation_visitors)  #  Lower bound of confidence interval of change value 
    gap_interval_sup = gap + z * math.sqrt(math.pow(control_se, 2) / control_visitors + math.pow(variation_se, 2) / variation_visitors)  #  Upper bound of confidence interval of change value 
    confidence_interval_sdown = gap_interval_sdown / control_p  #  Lower bound of the confidence interval of the rate of change 
    confidence_interval_sup = gap_interval_sup / control_p  #  Upper bound of confidence interval of change value 
 
#  Significance judgment 
    if (confidence_interval_sdown > 0 and confidence_interval_sup > 0) or (confidence_interval_sdown < 0 and confidence_interval_sup < 0):
       print(" remarkable ")
    elif (confidence_interval_sdown > 0 and confidence_interval_sup < 0) or (confidence_interval_sdown < 0 and confidence_interval_sup > 0):
       print(" No significant ")
... ...

The Kanban is shown below ：

4.3.3 Negative indicator automatic root cause analysis scheme

The automatic root cause analysis scheme for negative indicators of gray-scale scenes includes change detection 、 Verification of sample historical differences 、 Index logic disassembly and Adtributor Automatic root cause analysis 4 A step .

among ,Adtributor Automatic root cause analysis can calculate the factor that contributes the most to the indicator change in the dimension of the same level , We adapt to specific indicator business scenarios by layering indicator dimensions and setting relationships , Build a logical model of multi-level attribution Algorithm , So as to realize the automatic output of business level root cause conclusion .

The Kanban is shown below ：

4.3.4 Gray scale report intelligent splicing push scheme

Automatic acquisition of version information content ：

Get the version number through the publishing platform 、 Actual loading 、 Cumulative number of days of release and version related content , As the beginning of the grayscale report .

The conclusion is that ：

According to whether the indicators are all positive / Partially negative / All negative 、 Whether the statistical results such as uneven samples are automatically combined and mapped to the preset conclusion document , A total of 10 Various conclusion templates .

Interpretation of significance test of core indicators （ According to different gray levels , Interpret different types of indicators ）：

• T+1～T+2： Performance indicators 、 Activity rate index
• T+3～T+6： Active performance indicators 、 Distribute performance indicators 、 Download installation process conversion indicators
• T+7： Active performance indicators 、 Distribute performance indicators 、 Download installation process conversion indicators 、 The latter transformation indicators

Interpretation of the attribution of the first level module dimension ：

If the grayscale version has clearly input the specific change points to a level-1 module in the early stage , The module will be interpreted automatically , And output the data of other modules with index differences ; If the grayscale version does not input the change points at the module level , On the output indicators, the effect is remarkable （ Positive significant 、 Negative significant ） The interpretation conclusion of the first level module .

Interpretation of sample size uniformity ：

Business indicators , Judge whether the distribution is uniform by significance test ; Non business indicators , Judge by distribution differences .

Interpretation of negative diagnosis ：

According to the results of the multi-level automated root cause model , Modifiers mapped according to different dimension types 、 Dimension quantity positioning （ One dimension / multidimensional ） And the conclusion of sample historical difference verification , Corresponding to different templates , Finally, the negative diagnosis copy is spliced .

5、 ... and 、 At the end

Requirements for scientific evaluation and rapid decision-making in business gray-scale publishing , We combine a variety of methods , from “ Experimental ideas - Mathematical methods - Data model - Product plan ” The four levels provide a relatively complete solution of intelligent gray-scale data system .

This paper hopes to provide reference for the construction of gray data system in business , However, it should be reasonably designed in combination with the characteristics of each business . The data model design involved in the scheme is not introduced in detail here , Interested students are welcome to discuss learning with the author .

Besides , Gray data system still needs to be improved , Throw it out here first , Some of them are also being studied and solved ：

1. When gray traffic is grouped , It usually adopts the method of random grouping . But because of completely random uncertainty , After grouping ,2 Group samples may naturally be unevenly distributed in some index characteristics . Compared with the post sample uniformity verification method , Stratified sampling can also be considered to avoid this problem ;
2. In the process of gray index analysis , There is still room for improvement in the automatic multidimensional root cause analysis model , At present, the model is very dependent on the comprehensiveness of the dimensions in its own data source , And only quantitative reasons can be detected . Later, we hope to put the quantitative root cause model , Combine qualitative factors for a more comprehensive and accurate interpretation ;
3. At present, the whole gray-scale solution of the game center is essentially based on 2 sample-test Test model , However, the model needs to improve the core indicators according to the expected improvement of the gray version compared with the comparative version , To estimate the minimum sample size in advance , In the actual grayscale process, the core index may not reach the expected situation . Try it in the future mSPRT And other inspection methods , Weaken the limitation of minimum sample size on significant results .

reference ：

1. Mao Shisong , Wang Jinglong , Pu Xiaolong . 《 Advanced mathematical statistics （ The second edition ）》
2. It's Lao Li, that's right . 《 Five minutes to master AB Principle of experiment and sample size calculation 》. CSDN Blog
3. Ranjita Bhagwan, Rahul Kumar, Ramachandran Ramjee, et al. 《Adtributor: Revenue Debugging in Advertising Systems》