statistics/node_availability.r

   1 source("functions.r");
   2
   3
   4 available_nodes <- function (ns, from, to, type, fmt="%b")
   5 {
   6     # find 'type' range of days
   7     dates <-seq(as.Date(from), as.Date(to), type)
   8     months <- format(dates, fmt)
   9     hbreaks<-unclass(as.POSIXct(dates))
  10
  11     xx<-NULL;
  12     yy<-NULL;
  13
  14     for ( i in seq(1,length(hbreaks)-1) )
  15     {
  16         # get range from ns
  17         ns_sub <- ns[which(ns$date > hbreaks[i] & ns$date <= hbreaks[i+1] & ns$status == 'BOOT'),]
  18         nodes <- length(ns_sub$date)
  19
  20         xx<- c(xx, hbreaks[i])
  21         yy<- c(yy, nodes)
  22
  23     }
  24     m<- months[1:length(months)-1]
  25     return (rbind(xx,yy,m))
  26 }
  27
  28
  29
  30 open_tickets <- function (t, from, to, type, fmt="%b")
  31 {
  32     # find 'type' range of days
  33     dates <-seq(as.Date(from), as.Date(to), type)
  34     months <- format(dates, fmt)
  35     hbreaks<-unclass(as.POSIXct(dates))
  36
  37     xx<-NULL;
  38     yy<-NULL;
  39
  40     for ( i in seq(1,length(hbreaks)-1) )
  41     {
  42         # identify any tickets with a start time in range, lastreply in range
  43         # or where both start is less and lastreply is greater than the range
  44         t_sub <- t[which( (t$start < hbreaks[i] & t$lastreply > hbreaks[i+1]) |
  45                           (t$start > hbreaks[i] & t$start <= hbreaks[i+1]) |
  46                           (t$lastreply > hbreaks[i] & t$lastreply <= hbreaks[i+1]) ),]
  47         tickets <- length(t_sub$start)
  48         #if ( nrow(t_sub) > 0 ){
  49         #    for ( j in seq(1,nrow(t_sub)) )
  50         #    {
  51         #        #print(sprintf("id %s, date %s", t_sub[i,'ticket_id'], t_sub[i,'s1']))
  52         #        print(sprintf("id %s, date %s", t_sub[j,]$ticket_id, t_sub[j, 's1']))
  53         #    }
  54         #}
  55
  56         xx<- c(xx, hbreaks[i])
  57         yy<- c(yy, tickets)
  58
  59     }
  60     m<- months[1:length(months)-1]
  61     return (rbind(xx,yy,m))
  62 }
  63
  64 online_nodes <- function (fb)
  65 {
  66     breaks <- unique(fb$timestamp)
  67     n<-NULL
  68     o<-NULL
  69     x<-NULL
  70     for (i in seq(1,length(breaks)) )
  71     {
  72         ts <- breaks[i]
  73         sub <- fb[which(fb$timestamp == ts),]
  74         node_count   <- length(unique(sub$hostname))
  75         online_count <- length(unique(sub$hostname[which(sub$state=='BOOT')]))
  76         x<-c(x,ts)
  77         n<-c(n,node_count)
  78         o<-c(o,online_count)
  79     }
  80     print(length(x))
  81     print(length(n))
  82     print(length(o))
  83     return (rbind(x,n,o))
  84 }
  85
  86 lowess_smooth <- function (x, y, delta=(60*60*24), f=0.02)
  87 {
  88     a<-lowess(x, y, delta=delta, f=f)
  89     return (a);
  90 }
  91
  92 #####
  93
  94 ns <- read.csv('node-status-jun09-feb10.csv', sep=',', header=TRUE)
  95 an <- available_nodes(ns, "2009-06-10", "2010-02-28", 'day')
  96
  97 an_x<-an[1,][which(as.numeric(an[2,]) > 100)]
  98 an_y<-an[2,][which(as.numeric(an[2,]) > 100)]
  99
 100 ####
 101 #fb7 <- read.csv('findbad_raw_2007.csv', sep=',', header=TRUE)
 102 #fb8 <- read.csv('findbad_raw_2008.csv', sep=',', header=TRUE)
 103 #fb9 <- read.csv('findbad_raw_2009.csv', sep=',', header=TRUE)
 104 #fball <- rbind(fb7,fb8,fb9)
 105
 106 z7<- online_nodes(fb7)
 107 z8<- online_nodes(fb8)
 108 z9<- online_nodes(fb9)
 109
 110 zx <- c(z7[1,],z8[1,],z9[1,])
 111 zy_reg <- c(z7[2,], z8[2,],z9[2,])
 112 zy_avail <- c(z7[3,], z8[3,],z9[3,])
 113
 114 start_image("node_availability.png")
 115 par(mfrow=c(2,1))
 116 par(mai=c(0.1,1,0.1,0.1))
 117
 118 a_reg<-lowess_smooth(zx, zy_reg)
 119 plot(a_reg$x, a_reg$y,
 120      ylim=c(0,700), xlim=c(min(x1[length(x1)/2]), max(x1)), type='l', pch='.', axes=F,
 121      ylab="Online Node Count", xlab="")
 122
 123 sx <- zx[which(zy_avail > 330)]
 124 sy <- zy_avail[which(zy_avail > 330)]
 125 sx <- c(sx[1:2037],sx[2061:length(sx)])
 126 sy <- c(sy[1:2037],sy[2061:length(sy)])
 127
 128 sx <- c(sx[1:1699],sx[1701:1707],sx[1709:length(sx)])
 129 sy <- c(sy[1:1699],sy[1701:1707],sy[1709:length(sy)])
 130
 131 lines(sx, sy, col='grey80', pch='.')
 132 lines(an_x, an_y, col='grey80', pch='.')
 133
 134 a_avail<-lowess_smooth(zx, zy_avail)
 135 lines(a_avail$x, a_avail$y, col='red', pch='.')
 136
 137 a_avail_m3<-lowess_smooth(an_x, an_y)
 138 lines(a_avail_m3$x, a_avail_m3$y, col='red', pch='.')
 139
 140 axis(2, las=1)
 141
 142 x_online_node_list <- c(tstamp("2004-6-1"), tstamp("2005-6-1"), tstamp("2006-6-1"), tstamp("2007-11-1"))
 143 y_online_node_list <- c(330, 480,  500,  550)
 144 lines(x_online_node_list, y_online_node_list, col='grey80')
 145
 146 #abline_at_date('2005-01-01', 'grey60')
 147 #abline_at_date('2006-01-01', 'grey60')
 148 #abline_at_date('2007-01-01', 'grey60')
 149 #abline_at_date('2008-01-01', 'grey60')
 150 #abline_at_date('2009-01-01', 'grey60')
 151 #abline_at_date('2010-01-01', 'grey60')
 152
 153 tstamp_20041112 <-abline_at_date("2004-11-12", col='grey60', lty=2)
 154 tstamp_20050301 <-abline_at_date("2005-03-01", col='grey60', lty=2)
 155 tstamp_20050615 <-abline_at_date("2005-06-15", col='grey60', lty=2)
 156 tstamp_20051023 <-abline_at_date("2005-10-23", col='grey60', lty=2)
 157 tstamp_20070101 <-abline_at_date("2007-01-01", col='grey60', lty=2)
 158 tstamp_20070501 <-abline_at_date("2007-05-01", col='grey60', lty=2)
 159 tstamp_20080601 <-abline_at_date("2008-06-01", col='grey60', lty=2)
 160 tstamp_20080815 <-abline_at_date("2008-08-15", col='grey60', lty=2)
 161 tstamp_20090501 <-abline_at_date("2009-05-01", col='grey60', lty=2)
 162 tstamp_20100201 <-abline_at_date("2010-02-01", col='white', lty=2)
 163
 164
 165 text(x=c( tstamp_20041112+(tstamp_20050301-tstamp_20041112)/2,
 166         tstamp_20050301+(tstamp_20050615-tstamp_20050301)/2,
 167         tstamp_20050615+(tstamp_20051023-tstamp_20050615)/2,
 168         tstamp_20051023+(tstamp_20070101-tstamp_20051023)/2,
 169         tstamp_20070101+(tstamp_20070501-tstamp_20070101)/2,
 170         tstamp_20080601+(tstamp_20080815-tstamp_20080601)/2,
 171         tstamp_20090501+(tstamp_20100201-tstamp_20090501)/2 ),
 172      y=c(700),
 173      labels=c('3.0', '3.1', '3.1S', '3.2', '4.0', '4.2', '4.3'))
 174
 175
 176 l<-length(ot[3,])
 177 #axis(1, labels=ot[3,l/2:l], at=ot[1,l/2:l], cex.axis=0.7)
 178 #axis(2, las=1)
 179 #mtext("2004           2005           2006           2007           2008           2009", 1,2)
 180
 181 uptime_nodes_m3 <- function (uh, from, to)
 182 {
 183     # find 'type' range of days
 184     dates <-seq(as.Date(from), as.Date(to), 'day')
 185     months <- format(dates, '%b')
 186     hbreaks<-unclass(as.POSIXct(dates))
 187
 188     xx<-NULL;
 189     yy<-NULL;
 190     date_index <- NULL;
 191     q_list <- NULL;
 192
 193     print(length(hbreaks))
 194
 195     for ( i in seq(1,length(hbreaks)-1) )
 196     {
 197         print (sprintf("round %s of %s", i, length(hbreaks)-1))
 198         # get range from t
 199         print (sprintf("ts %s ", hbreaks[i] ))
 200         uh_sub <- uh[which(uh$date > hbreaks[i] & uh$date <= hbreaks[i+1] ),]
 201         if ( length(uh_sub$uptime ) <= 1 )  { next }
 202
 203         d<- uh_sub$uptime
 204
 205         print (sprintf("min: %s, median: %s, max: %s", min(d), median(d), max(d)))
 206
 207         print (sprintf("length: %s", length(d)))
 208         q<-quantile(d)
 209         print(q)
 210
 211         date_index <- c(date_index, i)
 212
 213         xx<- c(xx, hbreaks[i])
 214         q_list <- rbind(q_list, q)
 215
 216     }
 217     m<- months[date_index]
 218     return (cbind(xx,q_list, m))
 219     #
 220
 221 }
 222
 223 uh <- read.csv('node_uptime_history.csv', header=TRUE, sep=',')
 224
 225
 226 dm <- uptime_nodes_m3(uh, "2009-06-10", "2010-02-28")
 227
 228 par(mai=c(1,1,0.1,0.1))
 229     plot(dm[,1], as.numeric(dm[,5])/(60*60*24), type='l', lty=1, xlab="",
 230             ylim=c(min(as.numeric(dm[,2])/(60*60*24)),max(as.numeric(dm[,5])/(60*60*24))), xlim=c(min(x1[length(x1)/2]), max(x1)), axes=F, ylab="Uptime (days)", col='orange')
 231     lines(dm[,1], as.numeric(dm[,4])/(60*60*24), lty=1, col='red')
 232     lines(dm[,1], as.numeric(dm[,3])/(60*60*24), lty=1, col='black')
 233     lines(dm[,1], as.numeric(dm[,6])/(60*60*24), lty=1, col='orange')
 234     lines(dm[,1], as.numeric(dm[,2])/(60*60*24), lty=1, col='blue')
 235     #axis(1, labels=dm[,7], at=dm[,1])
 236     #axis(2, las=1)
 237     #m<-round(max(as.numeric(dm[,4])/(60*60*24)), 2)
 238     #axis(2, labels=m, at=m, las=1)
 239     #abline(h=m, lty=2, col='grey40')
 240
 241 l<-length(ot[3,])
 242 l2<-l/2
 243 axis(1, labels=ot[3,l2:l], at=ot[1,l2:l], cex.axis=0.7)
 244 axis(2, las=1)
 245 mtext("2007                                    2008                                    2009", 1,2)
 246
 247 tstamp_20041112 <-abline_at_date("2004-11-12", col='grey60', lty=2)
 248 tstamp_20050301 <-abline_at_date("2005-03-01", col='grey60', lty=2)
 249 tstamp_20050615 <-abline_at_date("2005-06-15", col='grey60', lty=2)
 250 tstamp_20051023 <-abline_at_date("2005-10-23", col='grey60', lty=2)
 251 tstamp_20070101 <-abline_at_date("2007-01-01", col='grey60', lty=2)
 252 tstamp_20070501 <-abline_at_date("2007-05-01", col='grey60', lty=2)
 253 tstamp_20080601 <-abline_at_date("2008-06-01", col='grey60', lty=2)
 254 tstamp_20080815 <-abline_at_date("2008-08-15", col='grey60', lty=2)
 255 tstamp_20090501 <-abline_at_date("2009-05-01", col='grey60', lty=2)
 256 tstamp_20100201 <-abline_at_date("2010-02-01", col='white', lty=2)
 257
 258
 259 text(x=c( tstamp_20041112+(tstamp_20050301-tstamp_20041112)/2,
 260         tstamp_20050301+(tstamp_20050615-tstamp_20050301)/2,
 261         tstamp_20050615+(tstamp_20051023-tstamp_20050615)/2,
 262         tstamp_20051023+(tstamp_20070101-tstamp_20051023)/2,
 263         tstamp_20070101+(tstamp_20070501-tstamp_20070101)/2,
 264         tstamp_20080601+(tstamp_20080815-tstamp_20080601)/2,
 265         tstamp_20090501+(tstamp_20100201-tstamp_20090501)/2 ),
 266      y=c(120),
 267      labels=c('3.0', '3.1', '3.1S', '3.2', '4.0', '4.2', '4.3'))
 268
 269 end_image()