我正在尝试使用R来解析这些类型的文件,以解析信息并将数据放入数据帧中,如格式：
last_run current_run seconds
------------------------------- ------------------------------- -----------
Jul 4 2016 7:17AM Jul 4 2016 7:21AM 226
Engine Utilization (Tick %) User Busy System Busy I/O Busy Idle
------------------------- ------------ ------------ ---------- ----------
ThreadPool : syb_default_pool
Engine 0 5.0 % 0.4 % 22.4 % 72.1 %
Engine 1 3.9 % 0.5 % 22.8 % 72.8 %
Engine 2 5.6 % 0.3 % 22.5 % 71.6 %
Engine 3 5.1 % 0.4 % 22.7 % 71.8 %
------------------------- ------------ ------------ ---------- ----------
Pool Summary Total 336.1 % 25.6 % 1834.6 % 5803.8 %
Average 4.2 % 0.3 % 22.9 % 72.5 %
------------------------- ------------ ------------ ---------- ----------
Server Summary Total 336.1 % 25.6 % 1834.6 % 5803.8 %
Average 4.2 % 0.3 % 22.9 % 72.5 %
Transaction Profile
-------------------
Transaction Summary per sec per xact count % of total
------------------------- ------------ ------------ ---------- ----------
Committed Xacts 137.3 n/a 41198 n/a
Average Runnable Tasks 1 min 5 min 15 min % of total
------------------------- ------------ ------------ ---------- ----------
ThreadPool : syb_default_pool
Global Queue 0.0 0.0 0.0 0.0 %
Engine 0 0.0 0.1 0.1 0.6 %
Engine 1 0.0 0.0 0.0 0.0 %
Engine 2 0.2 0.1 0.1 2.6 %
------------------------- ------------ ------------ ----------
Pool Summary Total 7.2 5.9 6.1
Average 0.1 0.1 0.1
------------------------- ------------ ------------ ----------
Server Summary Total 7.2 5.9 6.1
Average 0.1 0.1 0.1
Device Activity Detail
----------------------
Device:
/dev/vx/rdsk/sybaserdatadg/datadev_125
datadev_125 per sec per xact count % of total
------------------------- ------------ ------------ ---------- ----------
Total I/Os 0.0 0.0 0 n/a
------------------------- ------------ ------------ ---------- ----------
Total I/Os 0.0 0.0 0 0.0 %
-----------------------------------------------------------------------------
Device:
/dev/vx/rdsk/sybaserdatadg/datadev_126
datadev_126 per sec per xact count % of total
------------------------- ------------ ------------ ---------- ----------
Total I/Os 0.0 0.0 0 n/a
------------------------- ------------ ------------ ---------- ----------
Total I/Os 0.0 0.0 0 0.0 %
-----------------------------------------------------------------------------
Device:
/dev/vx/rdsk/sybaserdatadg/datadev_127
datadev_127 per sec per xact count % of total
------------------------- ------------ ------------ ---------- ----------
Reads
APF 0.0 0.0 5 0.4 %
Non-APF 0.0 0.0 1 0.1 %
Writes 3.8 0.0 1128 99.5 %
------------------------- ------------ ------------ ---------- ----------
Total I/Os 3.8 0.0 1134 0.1 %
Mirror Semaphore Granted 3.8 0.0 1134 100.0 %
Mirror Semaphore Waited 0.0 0.0 0 0.0 %
-----------------------------------------------------------------------------
Device:
/sybaser/database/sybaseR/dev/sybaseR.datadev_000
GPS_datadev_000 per sec per xact count % of total
------------------------- ------------ ------------ ---------- ----------
Reads
APF 7.9 0.0 2372 55.9 %
Non-APF 5.5 0.0 1635 38.6 %
Writes 0.8 0.0 233 5.5 %
------------------------- ------------ ------------ ---------- ----------
Total I/Os 14.1 0.0 4240 0.3 %
Mirror Semaphore Granted 14.1 0.0 4239 100.0 %
Mirror Semaphore Waited 0.0 0.0 2 0.0 %
我需要捕获“2016年7月4日上午7:21”作为日期,
来自“引擎利用率(勾选％)行,服务器摘要 – >平均值”4.2％“
所以,我的数据框应该是这样的：
Date cpu Count Jul 4 2016 7:21AM 4.2 41198
我尝试过这样的事情：
read.table(text=readLines("file.txt")[count.fields("file.txt",blank.lines.skip=FALSE) == 9])
获得这一行：
Average 4.2 % 0.3 % 22.9 % 72.5 %
但我希望能够仅在之后提取平均值
引擎利用率(Tick％),因为可能有许多以Average开头的行.在引擎利用率(Tick％)之后立即显示的平均线是我想要的.
read.table(text=readLines("file.txt")[count.fields("file.txt",blank.lines.skip=FALSE) == 9])
我可以在read.table行中使用grep来搜索某些字符吗？
%%%% Shot 1 – 得到了一些工作
原文链接：https://www.f2er.com/regex/356910.htmlextract <- function(filenam="file.txt"){
txt <- readLines(filenam)
## date of current run:
## assumed to be on 2nd line following the first line matching "current_run"
ii <- 2 + grep("current_run",txt,fixed=TRUE)[1]
line_current_run <- Filter(function(v) v!="",strsplit(txt[ii]," ")[[1]])
date_current_run <- paste(line_current_run[5:8],collapse=" ")
## cpu:
## assumed to be on line following the first line matching "Server Summary"
## which comes after the first line matching "Engine Utilization ..."
jj <- grep("Engine Utilization (Tick %)",fixed=TRUE)[1]
ii <- grep("Server Summary",fixed=TRUE)
ii <- 1 + min(ii[ii>jj])
line_cpu <- Filter(function(v) v!=""," ")[[1]])
cpu <- line_cpu[2]
## Count:
## assumed to be on 2nd line following the first line matching "Transaction Summary"
ii <- 2 + grep("Transaction Summary",fixed=TRUE)[1]
line_count <- Filter(function(v) v!=""," ")[[1]])
count <- line_count[5]
data.frame(Date=date_current_run,cpu=cpu,Count=count,stringsAsFactors=FALSE)
}
print(extract("file.txt"))
##file.list <- dir("./")
file.list <- rep("file.txt",3)
merged <- do.call("rbind",lapply(file.list,extract))
print(merged)
file.list <- rep("file.txt",2000)
print(system.time(merged <- do.call("rbind",extract))))
## runs in about 2.5 secs on my laptop
%%% Shot 2：第一次尝试提取(可能可变)数量的设备列
extractv2 <- function(filenam="file2.txt"){
txt <- readLines(filenam)
## date of current run:
## assumed to be on 2nd line following the first line matching "current_run"
ii <- 2 + grep("current_run"," ")[[1]])
count <- line_count[5]
## Total I/Os
## 1. Each line "Device:" is assumed to be the header of a block of lines
## containing info about a single device (there are 4 such blocks
## in your example);
## 2. each block is assumed to contain one or more lines matching
## "Total I/Os";
## 3. the relevant count data is assumed to be contained in the last
## of such lines (at column 4),for each block.
## Approach: loop on the line numbers of those lines matching "Device:"
## to get: A. counts; B. device names
ii_block_dev <- grep("Device:",fixed=TRUE)
ii_lines_IOs <- grep("Total I/Os",fixed=TRUE)
nblocks <- length(ii_block_dev)
## A. get counts for each device
## for each block,select *last* line matching "Total I/Os"
ii_block_dev_aux <- c(ii_block_dev,Inf) ## just a hack to get a clean code
ii_lines_IOs_dev <- sapply(1:nblocks,function(block){
## select matching liens to "Total I/Os" within each block
IOs_per_block <- ii_lines_IOs[ ii_lines_IOs > ii_block_dev_aux[block ] &
ii_lines_IOs < ii_block_dev_aux[block+1]
]
tail(IOs_per_block,1) ## get the last line of each block (if more than one match)
})
lines_IOs <- lapply(txt[ii_lines_IOs_dev],function(strng){
Filter(function(v) v!="",strsplit(strng," ")[[1]])
})
IOs_counts <- sapply(lines_IOs,function(v) v[5])
## B. get device names:
## assumed to be on lines following each "Device:" match
ii_devices <- 1 + ii_block_dev
device_names <- sapply(ii_devices,function(ii){
Filter(function(v) v!=""," ")[[1]])
})
## Create a data.frame with "device_names" as column names and "IOs_counts" as
## the values of a single row.
## Sorting the device names by order() will help produce the same column names
## if different sysmon files list the devices in different order
ord <- order(device_names)
devices <- as.data.frame(structure(as.list(IOs_counts[ord]),names=device_names[ord]),check.names=FALSE) ## Prevent R from messing with our device names
data.frame(stringsAsFactors=FALSE,check.names=FALSE,Date=date_current_run,devices)
}
print(extractv2("file2.txt"))
## WATCH OUT:
## merging will ONLY work if all devices have the same names across sysmon files!!
file.list <- rep("file2.txt",extractv2))
print(merged)
%%%%%%% Shot 3：提取两个表,一个包含一行,另一个包含可变行数(取决于每个sysmon文件中列出的设备).
extractv3 <- function(filenam="file2.txt"){
txt <- readLines(filenam)
## date of current run:
## assumed to be on 2nd line following the first line matching "current_run"
ii <- 2 + grep("current_run"," ")[[1]])
count <- line_count[5]
## first part of output: fixed three-column structure
fixed <- data.frame(stringsAsFactors=FALSE,Count=count)
## Total I/Os
## 1. Each line "Device:" is assumed to be the header of a block of lines
## containing info about a single device (there are 4 such blocks
## in your example);
## 2. each block is assumed to contain one or more lines matching
## "Total I/Os";
## 3. the relevant count data is assumed to be contained in the last
## of such lines (at column 4),fixed=TRUE)
if(length(ii_block_dev)==0){
variable <- data.frame(stringsAsFactors=FALSE,date_current_run=date_current_run,device_names=NA,IOs_counts=NA)
}else{
ii_lines_IOs <- grep("Total I/Os",fixed=TRUE)
nblocks <- length(ii_block_dev)
if(length(ii_block_dev)==0){
sprintf("WEIRD datapoint at date %s: I have %d devices but 0 I/O lines??")
##stop()
}
## A. get counts for each device
## for each block,select *last* line matching "Total I/Os"
ii_block_dev_aux <- c(ii_block_dev,Inf) ## just a hack to get a clean code
ii_lines_IOs_dev <- sapply(1:nblocks,function(block){
## select matching lines to "Total I/Os" within each block
IOs_per_block <- ii_lines_IOs[ ii_lines_IOs > ii_block_dev_aux[block ] &
ii_lines_IOs < ii_block_dev_aux[block+1]
]
tail(IOs_per_block,1) ## get the last line of each block (if more than one match)
})
lines_IOs <- lapply(txt[ii_lines_IOs_dev],function(strng){
Filter(function(v) v!=""," ")[[1]])
})
IOs_counts <- sapply(lines_IOs,function(v) v[5])
## B. get device names:
## assumed to be on lines following each "Device:" match
ii_devices <- 1 + ii_block_dev
device_names <- sapply(ii_devices,function(ii){
Filter(function(v) v!=""," ")[[1]])
})
## Create a data.frame with three columns: date,device,counts
variable <- data.frame(stringsAsFactors=FALSE,date_current_run=rep(date_current_run,length(IOs_counts)),device_names=device_names,IOs_counts=IOs_counts)
}
list(fixed=fixed,variable=variable)
}
print(extractv3("file2.txt"))
file.list <- c("file.txt","file2.txt","file3.txt")
res <- lapply(file.list,extractv3)
fixed.merged <- do.call("rbind",lapply(res,function(r) r$fixed))
print(fixed.merged)
variable.merged <- do.call("rbind",function(r) r$variable))
print(variable.merged)
