pacemaker recall

The biggest difference between Heartbeat 3 and 2.x is that 3 split the original 2.x by module into multiple sub-projects and provides a cluster-glue component dedicated to the management of local ResourceManager. namely Heartbeat + Cluster-glue + resouce-agent three parts:Reference(1) hearbeat itself is the base of the whole cluster (cluster messaging layer), which is responsible for maintaining the information of the nodes of the cluster and communicating with them before;(2) Cluster-glue equiv

monitoring the master. MHA is only used for failover interaction. 3. non-interactive failover is also available (master is not monitored and Failover is automatically performed ). This feature is very useful, especially when you have installed other software to monitor the master. For example, the Pacemaker (Heartbeat) is used to monitor master faults and vip take over, and MHA failover and slave improvement are used. 4 online master switching to dif

] [4] When a 'nameserver 'node goes down, the entire cluster is still available. According to the above configuration, only node-level HA can be implemented. When the Nameserver process on the node where the virtual IP address is located goes down directly, failover cannot be implemented, in addition, the Nameserver process cannot be restarted after the host is restarted. To implement HA at the Resource level, you need to use CRM (Cluster Resource Manager). The most famous is

interface: command-line client Crmsh,gui client Hb-gui)Heartbeat v3 = heartbeat + pacemaker + Cluster-gluePackmaker:Cli:crm (SuSE), PCsGui:hawk, LCMC, PACEMAKER-MGMTCman + Rgmanager:Resource group Manager:failover Domain, node priorityTo configure the interface:Clustat, Cman_toolConga:luci+ricciLrm:local Resource ManagerProvided by CRMRa:resource AgentHeartbeat legacy:heartbeat and traditional types, usual

monitoring MASTER,MHA only as a failover interaction. 3 non-interactive failover is also provided (without monitoring master, automatic failover). This feature is useful, especially if you have installed other software monitoring master. For example, use Pacemaker (Heartbeat) to monitor master faults and VIP takeover with MHA failover and slave elevation. 4 Switch master to different host online in many cases, it is necessary to transfer master to ot

This article introduces the content of the detailed classification evaluation indicators and regression evaluation indicators and Python code implementation, has a certain reference value, now share to everyone, there is a need for friends to refer to. 1. Concept Performance measurement (evaluation) indicators, the main divided into two major categories:1) Classification Evaluation Index (classification), main analysis, discrete, integer. Specific indicators include accuracy (accuracy rate), pre

7 machine learning System Design Content 7 Machine Learning System Design 7.1 Prioritizing 7.2 Error Analysis 7.3 Error Metrics for skewed classed 7.3.1 Precision/recall 7.3.2 Trading off precision and RECALL:F1 score 7.4 Data for machine learning 7.1 PrioritizingWhen we set out to design a machine learning system for a practical problem, in what ways should we spend more time making the system less error? To

-slave synchronization of the database to be the same, actually not, but the configuration file, the configuration database this piece to be the same. 2, from the server, to add relay_log_purge=0, if not added, will be reported warning,relay_log_purge=0 is not set on slave Six, corosync pacemaker MySQL replication configuration Please refer to:corosync pacemaker MySQL replication for high availability Confi

used to do only failover, without monitoring MASTER,MHA only as a failover interaction. 3 non-interactive failover Non-interactive failover is also available (do not monitor master, automatic failover). This feature is useful, especially if you have installed other software monitoring master. For example, use Pacemaker (Heartbeat) to monitor master faults and VIP takeover with MHA failover and slave elevation. 4 online switch master to di

machine learning model is really "good"?In this article, we'll look at some common scenarios where seemingly good machine learning models still make mistakes, and discuss how to evaluate these model problems with metrics such as bias (bias) vs variance (variance), precision (precision) vs recall (recall). and propose some solutions for you to use when you encounter such situations.High deviation or high va

11.1 What to do first11.2 Error AnalysisError measurement for class 11.3 skew11.4 The tradeoff between recall and precision11.5 Machine-Learning data11.1 what to do firstThe next video will talk about the design of the machine learning system. These videos will talk about the major problems you will encounter when designing a complex machine learning system. Also try to give some advice on how to cleverly build a complex machine learning system. The f

" disappear ", but is actually recorded by Emacs, so you can also find back, while the deleted things may still be in memory, but has been" abandoned "by Emacs, so can not find back. "Reinsert the removed text is called recall (Yank)." In general, those commands that may eliminate a lot of text will record the erased text (they are set to be "recalled"), while those that eliminate only one characterOr just eliminate blank commands and not record what

") #计算Nagelkerke拟合优度R2 =r2/(1-exp (-pre$null.deviance)/N) Cat ("Nagelkerke r2=", R2, "\ n") #Nagelkerke r2= 0.7379711 #模型其他指标 #residuals (PRE) #残差 #coefficients (PRE) #系数 #anova (pre) #方差　　4. Accuracy and precisionTRUE_VALUE=AUS_TEST[,15]PREDICT_VALUE=AUS_TEST[,16] #计算模型精度error =predict_value-true_value# to determine the proportion of the correct number in the total Accuracy= (Nrow (aus_test)-sum (ABS (Error)))/nrow (aus_test) #混淆矩阵中的量 (The confusion matrix is explained on the

calculated as the predictive accuracy.①: Rating prediction: General mean-square error (RMSE) and mean absolute error (MAE) calculationRMSE: Recommended system->USERCF Algorithm _ Recommendation System "> MAE:Recommended system->USERCF Algorithm _ Recommendation System "> Import Math def RMSE (Records): Return math.sqrt (sum ([(RUI-PUI) * (RUI-PUI) to U,i,rui,pui in Records])/float ( Len (Records)) def MAE (Records): Return sum ([Math.fabs (RUI-PUI) to U,i,rui,pui in Records])/float (L

: 0.890909090909pos recall: 0.98neg precision: 0.977777777778neg recall: 0.88Most Informative Features magnificent = True pos : neg = 15.0 : 1.0 outstanding = True pos : neg = 13.6 : 1.0 insulting = True neg : pos = 13.0 : 1.0 vulnerable = True pos : neg = 12.3 : 1.0

sixth week. Design of learning curve and machine learning system Learning Curve and machine learning System Design Key Words Learning curve, deviation variance diagnosis method, error analysis, numerical evaluation of machine learning system, big Data principle Overview This week's content is divided into two: First talk. Advice for applying machine learning, the main content is about the deviation, variance and the learning curve as the representative of the diagnostic method, in order to impro

subpoena, search other time is slow, such as search cake recipes on the site of dozens of Or hundreds of cake recipes . When you configure SOLR, you should combine tradeoffs against other factors such as timeliness and ease of use. Two important concepts of relevance: Precision (precision) : Returns the result, The percentage of the document's relevance. Recall : is the percentage of results that are return

11.1 What to do first11.2 Error AnalysisError measurement for class 11.3 skew11.4 The tradeoff between recall and precision11.5 Machine-Learning data 11.1 what to do firstIn the next video, I'll talk about the design of the machine learning system. These videos will talk about the major problems you will encounter when designing a complex machine learning system. At the same time we'll try to give some advice on how to build a sophisticated mach

: The number of matched items is not found; Fp: False positive. The matching result is incorrect; TN: Non-matched logarithm correctly rejected; So we can get the matrix on the left and define the accuracy.(Precision)And recall rate(Recall ). Precision: Correct prediction of positive samples/All predicted positive samples; Recall: Correct prediction of po

Skewed classesSkewed classes: The number of a species is much higher (or less than) another class, that is, two extreme cases.Predicting the classification model of cancer, if there is only a 1% classification error on test set, at first glance is a good result, actually if we predict all Y to be 0 (that is, none is cancer), the classification error is 0.5% (because the ratio of cancer is 0.5%). The error is reduced, is this an improvement to the algorithm? Obviously not. Because the latter meth