守护进程与Clojure / JVM(Daemon with Clojure/JVM)
我想在一个小服务器上运行一个小的（不是太多太多）守护进程，看一个目录添加到它的新文件（以及主程序中的任何目录），并调用另一个Clojure程序来处理那个新文件。
理想情况下，每个文件都会被添加到一个队列（由Clojure中的
ref表示的列表？），并且主进程将以FIFO为基础处理队列中的那些文件。我的问题是：有一个JVM一直在运行这个小程序太多资源吗？ 你有什么建议去做这个吗？
非常感谢你！
编辑：我应该问的另一个问题：我应该将它作为自己的实例运行（使用更少的内存）并让它在看到文件时启动新的JVM，或者在同一个JVM上运行将处理文件的Clojure代码？
I'd like to have a small (not doing too damn much) daemon running on a little server, watching a directory for new files being added to it (and any directories in the main one), and calling another Clojure program to deal with that new file.
Ideally, each file would be added to a queue (a list represented by a
refin Clojure?) and the main process would take care of those files in the queue on a FIFO basis.My question is: is having a JVM up running this little program all the time too much a resource hog? And do you have any suggestions as to how go about doing this?
Thank you very much!
EDIT: Another question I should ask: should I run this as its own instance (using less memory) and have it launch a new JVM when a file is seen, or have it on the same JVM the Clojure code that will process the file?
原文：https://stackoverflow.com/questions/2933280
更新时间：2024-01-18 08:01
最满意答案
拦截部分有一个错误（paramsA）。 其他一切都很好。 我已经实施了Alexey在评论中提出的建议。 这是解决方案：
pow <- function(x,y) { return(x^y) } #### posterior distribution posteriorDistribution <- function(x, y, a, b,s2,N) { sumSqError <- 0.0 for(i in 1:N) { sumSqError <- sumSqError + pow(y[i] - (a + b*x[i]),2) } return((-((N/2)+1) * log(s2)) + ((-0.5/s2) * sumSqError)) } # x <- x values # y <- actual datapoints # N <- sample size # m <- length of chain # sigmaProposalWidth <- width of uniform proposal dist for sigma squared # paramAProposalWidth <- width of uniform proposal dist for intercept # paramBProposalWidth <- width of uniform proposal dist for slope mcmcSampling <- function(x,y,N,m,sigmaProposalWidth,paramAProposalWidth,paramBProposalWidth) { desiredAcc <- 0.44 paramsA <- vector("numeric",length=m) # intercept paramsB <- vector("numeric",length=m) # slope s2 <- vector("numeric",length=m) # sigma squared paramsA[1] <- 0 paramsB[1] <- 0 s2[1] <- 1 accATot <- 0 accBTot <- 0 accS2Tot <- 0 for(i in 2:m) { paramsA[i] <- paramsA[i-1] + runif(1,-paramAProposalWidth,paramAProposalWidth) accA <- 1 if((posteriorDistribution(x,y,paramsA[i],paramsB[i-1],s2[i-1],N) - posteriorDistribution(x,y,paramsA[i-1],paramsB[i-1],s2[i-1],N)) < log(runif(1))) { paramsA[i] <- paramsA[i-1] accA <- 0 } accATot <- accATot + accA paramsB[i] <- paramsB[i-1] + runif(1,-paramBProposalWidth,paramBProposalWidth) accB <- 1 if((posteriorDistribution(x,y,paramsA[i],paramsB[i],s2[i-1],N) - posteriorDistribution(x,y,paramsA[i-1],paramsB[i-1],s2[i-1],N)) < log(runif(1))) { paramsB[i] <- paramsB[i-1] accB <- 0 } accBTot <- accBTot + accB s2[i] <- s2[i-1] + runif(1,-sigmaProposalWidth,sigmaProposalWidth) accS2 <- 1 if((s2[i] < 0) || (posteriorDistribution(x,y,paramsA[i],paramsB[i],s2[i],N) - posteriorDistribution(x,y,paramsA[i],paramsB[i],s2[i-1],N)) < log(runif(1))) { s2[i] <- s2[i-1] accS2 <- 0 } accS2Tot <- accS2Tot + accS2 if(i%%100==0) { paramAProposalWidth <- paramAProposalWidth * ((accATot/100)/desiredAcc) paramBProposalWidth <- paramBProposalWidth * ((accBTot/100)/desiredAcc) sigmaProposalWidth <- sigmaProposalWidth * ((accS2Tot/100)/desiredAcc) accATot <- 0 accBTot <- 0 accS2Tot <- 0 } } res <- data.frame(paramsA,paramsB,s2) return(res) }There was one mistake in the intercept section (paramsA). Everything else was fine. I've implemented what Alexey suggested in his comments. Here's the solution:
pow <- function(x,y) { return(x^y) } #### posterior distribution posteriorDistribution <- function(x, y, a, b,s2,N) { sumSqError <- 0.0 for(i in 1:N) { sumSqError <- sumSqError + pow(y[i] - (a + b*x[i]),2) } return((-((N/2)+1) * log(s2)) + ((-0.5/s2) * sumSqError)) } # x <- x values # y <- actual datapoints # N <- sample size # m <- length of chain # sigmaProposalWidth <- width of uniform proposal dist for sigma squared # paramAProposalWidth <- width of uniform proposal dist for intercept # paramBProposalWidth <- width of uniform proposal dist for slope mcmcSampling <- function(x,y,N,m,sigmaProposalWidth,paramAProposalWidth,paramBProposalWidth) { desiredAcc <- 0.44 paramsA <- vector("numeric",length=m) # intercept paramsB <- vector("numeric",length=m) # slope s2 <- vector("numeric",length=m) # sigma squared paramsA[1] <- 0 paramsB[1] <- 0 s2[1] <- 1 accATot <- 0 accBTot <- 0 accS2Tot <- 0 for(i in 2:m) { paramsA[i] <- paramsA[i-1] + runif(1,-paramAProposalWidth,paramAProposalWidth) accA <- 1 if((posteriorDistribution(x,y,paramsA[i],paramsB[i-1],s2[i-1],N) - posteriorDistribution(x,y,paramsA[i-1],paramsB[i-1],s2[i-1],N)) < log(runif(1))) { paramsA[i] <- paramsA[i-1] accA <- 0 } accATot <- accATot + accA paramsB[i] <- paramsB[i-1] + runif(1,-paramBProposalWidth,paramBProposalWidth) accB <- 1 if((posteriorDistribution(x,y,paramsA[i],paramsB[i],s2[i-1],N) - posteriorDistribution(x,y,paramsA[i-1],paramsB[i-1],s2[i-1],N)) < log(runif(1))) { paramsB[i] <- paramsB[i-1] accB <- 0 } accBTot <- accBTot + accB s2[i] <- s2[i-1] + runif(1,-sigmaProposalWidth,sigmaProposalWidth) accS2 <- 1 if((s2[i] < 0) || (posteriorDistribution(x,y,paramsA[i],paramsB[i],s2[i],N) - posteriorDistribution(x,y,paramsA[i],paramsB[i],s2[i-1],N)) < log(runif(1))) { s2[i] <- s2[i-1] accS2 <- 0 } accS2Tot <- accS2Tot + accS2 if(i%%100==0) { paramAProposalWidth <- paramAProposalWidth * ((accATot/100)/desiredAcc) paramBProposalWidth <- paramBProposalWidth * ((accBTot/100)/desiredAcc) sigmaProposalWidth <- sigmaProposalWidth * ((accS2Tot/100)/desiredAcc) accATot <- 0 accBTot <- 0 accS2Tot <- 0 } } res <- data.frame(paramsA,paramsB,s2) return(res) }
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