[英]R CPU Utilisation Heat map in data center
I have readings from 100 servers in the data center. 我从数据中心的100台服务器中读取了数据。 The readings are in a data frame format of time having 3 columns
Time
, host name
, CPU Utilization
. 读数采用时间的数据帧格式,具有3列
Time
, host name
, CPU Utilization
。 The readings are every 10 mins generated by a monitoring system. 监测系统每10分钟产生一次读数。 I need to plot a heat map of CPU utilization, with
time
on X axis and % of servers
on Y axis with CPU utilization
range in Heat map. 我需要绘制CPU使用率的热图,在X轴上显示
time
,在Y轴上% of servers
并在Heat map中绘制CPU utilization
范围。
For example : If total number of servers is 5
. 例如:如果服务器总数为
5
。 The input data is as follows 输入数据如下
Time CPU Hostname
1/25/2015 10:15 19% H1
1/25/2015 10:15 90% H2
1/25/2015 10:15 90% H3
1/25/2015 10:15 50% H4
1/25/2015 10:15 25% H5
1/25/2015 10:25 30% H1
1/25/2015 10:25 85% H2
1/25/2015 10:25 30% H3
1/25/2015 10:25 21% H4
1/25/2015 10:25 21% H5
The output required is a stacked chart to depict the following figures in a heat map. 所需的输出是一个堆叠图,以热图显示以下图形。
For example at 10:15
there are 2
servers in range of 80-100%
utilization and hence value is 40%
例如,在
10:15
,有2
台服务器的利用率为80-100%
,因此价值为40%
Range 10:15 10:25
0-20 20% 0%
20-40 20% 80%
40-60 20% 0%
60-80 0% 0%
80-100 40% 20%
Need help on the functions in R to plot this kind of heat map. 在R中的功能上需要帮助以绘制此类热图。 Have tried to use
xts
but I am not clear on this use case of how to apply the xts
package. 曾尝试使用
xts
但在此应用案例中我不清楚如何应用xts
包。
You just need to: 您只需要:
cut
values into the groups you need cut
价值分成所需的组 expand
out missing entries expand
缺少的条目 geom_tile
for your heatmap geom_tile
作为您的热图 Many of the components of following code are in many SO posts: 以下代码的许多组件都在许多SO帖子中:
library(dplyr)
library(ggplot2)
library(tidyr)
library(scales)
dat <- read.table(text="Time,CPU,Hostname
1/25/2015 10:15,19%,H1
1/25/2015 10:15,90%,H2
1/25/2015 10:15,90%,H3
1/25/2015 10:15,50%,H4
1/25/2015 10:15,25%,H5
1/25/2015 10:25,30%,H1
1/25/2015 10:25,85%,H2
1/25/2015 10:25,30%,H3
1/25/2015 10:25,21%,H4
1/25/2015 10:25,21%,H5", header=TRUE, sep=",", stringsAs=FALSE)
total_hosts <-length(unique(dat$Hostname))
dat %>%
mutate(Time=as.POSIXct(Time, format="%m/%d/%Y %H:%M"),
Day=format(Time, format="%Y-%m-%d"),
HM=format(Time, format="%H:%M"),
CPU=as.numeric(gsub("%", "", CPU)),
`CPU Range`=as.character(cut(CPU,
breaks=c(0,20,40,60,80,100),
labels=c("0-20", "20-40", "40-60",
"60-80", "80-100")))) %>%
group_by(Day, `CPU Range`, HM) %>%
summarise(Pct=n()/total_hosts) %>%
merge(expand(., `CPU Range`, HM, Day), all.y=TRUE) -> dat
gg <- ggplot(dat, aes(x=HM, y=`CPU Range`))
gg <- gg + geom_tile(aes(fill=Pct), color="#7f7f7f")
gg <- gg + scale_fill_distiller(palette="RdPu", na.value="white",
label=percent, name="% Hosts")
gg <- gg + coord_equal()
gg <- gg + labs(x=NULL)
gg <- gg + theme_bw()
gg <- gg + theme(panel.border=element_blank())
gg <- gg + theme(panel.grid=element_blank())
gg
I left the Day
in the data frame in case you want/need to facet_wrap
by it or aggregate by it. 我将“
Day
保留在数据框中,以防您需要/需要进行facet_wrap
或进行汇总。
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