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experiment-functions.R
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experiment-functions.R
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### Note: all path args should not end in "/"
getSingleDataset = function(startingThread, endingThread, logPath) {
print(startingThread)
data = as.data.frame(read.csv(file=paste(logPath,"/Thread-", startingThread, ".csv", sep="")))
for (i in (startingThread+1):endingThread) {
print(i)
newdata = as.data.frame(read.csv(file=paste(logPath,"/Thread-", i, ".csv", sep="")))
data = rbind(data, newdata)
}
return(data)
}
getTrainingData = function(startingThread, endingThread, basePath) {
print("Reading in training data...")
data = getSingleDataset(startingThread, endingThread, paste(basePath,"/training-logs", sep=""))
print("Saving training data...")
save(data, file=paste(basePath, "/trainingData.RData", sep=""))
return(data)
}
getValidationStats = function(startingThread, endingThread, basePath, numValidationRuns, latencyQuantile) {
validationStats = matrix(nrow=numValidationRuns, ncol=2)
colnames(validationStats) = c("latencyQuantile", "numQueries")
for (j in 1:numValidationRuns) {
print(paste("Reading in validation data from run ", j, "...", sep=""))
vdata = getSingleDataset(startingThread, endingThread, paste(basePath,"/validation-logs/validation",j,"-logs", sep=""))
validationStats[j,"latencyQuantile"] = quantile(vdata$latency_ms[vdata$opLevel==3], latencyQuantile)
validationStats[j,"numQueries"] = length(which(vdata$opLevel==3))
}
print("Saving validation stats...")
save(validationStats, file=paste(basePath, "/validationStats.RData", sep=""))
return(validationStats)
}
getMultipleValidationLatencyQuantiles = function(startingThread, endingThread, basePath, numValidationRuns, latencyQuantiles) {
validationStats = matrix(nrow=numValidationRuns, ncol=length(latencyQuantiles))
for (j in 1:numValidationRuns) {
print(paste("Reading in validation data from run ", j, "...", sep=""))
vdata = getSingleDataset(startingThread, endingThread, paste(basePath,"/validation-logs/validation",j,"-logs", sep=""))
for (i in 1:length(latencyQuantiles))
validationStats[j,] = quantile(vdata$latency_ms[vdata$opLevel==3], probs=latencyQuantiles)
}
print("Saving validation latency quantiles...")
save(validationStats, file=paste(basePath, "/validationLatencyMultipleQuantiles.RData", sep=""))
return(validationStats)
}
createAndSaveThoughtstreamOpHistograms = function(basePath) {
print("Loading training data...")
load(file=paste(basePath, "/trainingData.RData", sep="")) # => "data"
print("Creating histograms...")
# #breaks may be an interesting thing to adjust. What if I had 50 breaks per hist? 10?
h1 = hist(data[data$opLevel == 2 & data$opType == 1,"latency_ms"], breaks=25)
h3 = hist(data[data$opLevel == 2 & data$opType == 3,"latency_ms"], breaks=25)
h4 = hist(data[data$opLevel == 2 & data$opType == 4,"latency_ms"], breaks=25)
h5 = hist(data[data$opLevel == 2 & data$opType == 5,"latency_ms"], breaks=25)
h6 = hist(data[data$opLevel == 2 & data$opType == 6,"latency_ms"], breaks=25)
h7 = hist(data[data$opLevel == 2 & data$opType == 7,"latency_ms"], breaks=20)
h8 = hist(data[data$opLevel == 2 & data$opType == 8,"latency_ms"], breaks=20)
h9 = hist(data[data$opLevel == 2 & data$opType == 9,"latency_ms"], breaks=20)
print("Saving histograms...")
save(h1, h3, h4, h5, h6, h7, h8, h9, file=paste(basePath,"/histograms.RData", sep=""))
}
createAndSaveUserByEmailOpHistograms = function(basePath) {
print("Loading training data...")
load(file=paste(basePath, "/trainingData.RData", sep="")) # => "data"
print("Creating histograms...")
h2 = hist(data[data$opLevel == 2 & data$opType == 2,"latency_ms"], breaks=25)
h3 = hist(data[data$opLevel == 2 & data$opType == 3,"latency_ms"], breaks=25)
h6 = hist(data[data$opLevel == 2 & data$opType == 6,"latency_ms"], breaks=25)
print("Saving histograms...")
save(h2, h3, h6, file=paste(basePath,"/histograms.RData", sep=""))
}
createAndSaveUserByNameOpHistograms = function(basePath) {
print("Loading training data...")
load(file=paste(basePath, "/trainingData.RData", sep="")) # => "data"
print("Creating histograms...")
h1 = hist(data[data$opLevel == 2 & data$opType == 1,"latency_ms"], breaks=25)
h6 = hist(data[data$opLevel == 2 & data$opType == 6,"latency_ms"], breaks=25)
print("Saving histograms...")
save(h1, h6, file=paste(basePath,"/histograms.RData", sep=""))
}
createAndSaveThoughtsByHashTagOpHistograms = function(basePath) {
print("Loading training data...")
load(file=paste(basePath, "/trainingData.RData", sep="")) # => "data"
print("Creating histograms...")
h2 = hist(data[data$opLevel==2 & data$opType==2,"latency_ms"], breaks=25)
h5 = hist(data[data$opLevel==2 & data$opType==5,"latency_ms"], breaks=25)
h6 = hist(data[data$opLevel==2 & data$opType==6,"latency_ms"], breaks=25)
h8 = hist(data[data$opLevel==2 & data$opType==8,"latency_ms"], breaks=25)
print("Saving histograms...")
save(h2, h5, h6, h8, file=paste(basePath,"/histograms.RData", sep=""))
}
createAndSaveUserByEmailOpHistogramsFromOtherQueries = function(basePathThoughtstream, basePathThoughtsByHashTag, outputPath1, outputPath2) {
print("Loading histograms for thoughtstream...")
load(file=paste(basePathThoughtstream, "/histograms.RData", sep=""))
# => h1, h3, h4, h5, h6, h7, h8, h9
# want to keep h3 & h6
h3.thoughtstream = h3
h6.thoughtstream = h6
print("Loading histograms for thoughtsByHashTag...")
load(file=paste(basePathThoughtsByHashTag, "/histograms.RData", sep=""))
# => h2, h5, h6, h8
# want to keep h2 & h6
h2.thoughtsByHashTag = h2
h6.thoughtsByHashTag = h6
print("Saving histogram files (2 options) for userByEmail...")
# First option: h2=h2.thoughtsByHashTag, h3=h3.thoughtstream, h6=h6.thoughtstream
h2=h2.thoughtsByHashTag
h3=h3.thoughtstream
h6=h6.thoughtstream
save(h2,h3,h6,file=paste(outputPath1,"/histograms.RData", sep=""))
# Second option: h2=h2.thoughtsByHashTag, h3=h3.thoughtstream, h6=h6.thoughtsByHashTag
#h2=h2.thoughtsByHashTag
#h3=h3.thoughtstream
h6=h6.thoughtsByHashTag
save(h2,h3,h6,file=paste(outputPath2,"/histograms.RData", sep=""))
}
## DEPRECATED
## Note more general version which follows.
getPredictedThoughtstreamQueryLatencyQuantiles = function(numSampleSets, basePath, latencyQuantile) {
load(file=paste(basePath, "/histograms.RData", sep="")) # => histograms
predictedQueryLatencyQuantiles = matrix(nrow=1,ncol=numSampleSets)
load(file=paste(basePath, "/validationStats.RData", sep="")) # => validationStats
numSamplesPerSet = floor(mean(validationStats[,"numQueries"]))
print(paste("Using", numSamplesPerSet, "samples per set."))
for (j in 1:numSampleSets) {
print(paste("Sample Set", j))
samples=matrix(data=0, nrow=1, ncol=numSamplesPerSet)
for (i in 1:numSamplesPerSet) {
samples[i] = samples[i] + sum(sample(h1$mids, 2, replace=TRUE, prob=h1$density))
samples[i] = samples[i] + sample(h3$mids, 1, replace=TRUE, prob=h3$density)
samples[i] = samples[i] + sum(sample(h4$mids, 2, replace=TRUE, prob=h4$density))
samples[i] = samples[i] + sample(h5$mids, 1, replace=TRUE, prob=h5$density)
samples[i] = samples[i] + sum(sample(h6$mids, 5, replace=TRUE, prob=h6$density))
samples[i] = samples[i] + sample(h7$mids, 1, replace=TRUE, prob=h7$density)
samples[i] = samples[i] + sample(h8$mids, 1, replace=TRUE, prob=h8$density)
samples[i] = samples[i] + sample(h9$mids, 1, replace=TRUE, prob=h9$density)
}
predictedQueryLatencyQuantiles[j]=quantile(samples, latencyQuantile) }
save(predictedQueryLatencyQuantiles, file=paste(basePath,"/predictedStats.RData", sep=""))
return(predictedQueryLatencyQuantiles)
}
getPredictedQueryLatencyQuantiles = function(queryType, numSampleSets, basePath, latencyQuantile) {
predictedQueryLatencyQuantiles = matrix(nrow=1,ncol=numSampleSets)
load(file=paste(basePath, "/validationStats.RData", sep="")) # => validationStats
numSamplesPerSet = floor(mean(validationStats[,"numQueries"]))
print(paste("Using", numSamplesPerSet, "samples per set."))
for (j in 1:numSampleSets) {
print(paste("Sample Set", j))
if (queryType == "thoughtstream") {
samples=thoughtstreamSampler(basePath, j, numSamplesPerSet)
} else if (queryType == "userByEmail") {
samples=userByEmailSampler(basePath, j, numSamplesPerSet)
} else if (queryType == "userByName") {
samples=userByNameSampler(basePath, j, numSamplesPerSet)
} else if (queryType == "thoughtsByHashTag") {
samples=thoughtsByHashTagSampler(basePath, j, numSamplesPerSet)
} else {
print("Incorrect queryType specified.")
}
predictedQueryLatencyQuantiles[j]=quantile(samples, latencyQuantile) }
save(predictedQueryLatencyQuantiles, file=paste(basePath,"/predictedStats.RData", sep=""))
return(predictedQueryLatencyQuantiles)
}
# Allows validationStats.RData & histogram.RData to be stored in different directories.
# Output is saved in directory represented by basePathHistograms.
getPredictedQueryLatencyQuantiles2 = function(queryType, numSampleSets, basePathValidationStats, basePathHistograms, latencyQuantile) {
predictedQueryLatencyQuantiles = matrix(nrow=1,ncol=numSampleSets)
load(file=paste(basePathValidationStats, "/validationStats.RData", sep="")) # => validationStats
numSamplesPerSet = floor(mean(validationStats[,"numQueries"]))
print(paste("Using", numSamplesPerSet, "samples per set."))
for (j in 1:numSampleSets) {
print(paste("Sample Set", j))
if (queryType == "thoughtstream") {
samples=thoughtstreamSampler(basePathHistograms, j, numSamplesPerSet)
} else if (queryType == "userByEmail") {
samples=userByEmailSampler(basePathHistograms, j, numSamplesPerSet)
} else if (queryType == "userByName") {
samples=userByNameSampler(basePathHistograms, j, numSamplesPerSet)
} else if (queryType == "thoughtsByHashTag") {
samples=thoughtsByHashTagSampler(basePathHistograms, j, numSamplesPerSet)
} else {
print("Incorrect queryType specified.")
}
predictedQueryLatencyQuantiles[j]=quantile(samples, latencyQuantile) }
save(predictedQueryLatencyQuantiles, file=paste(basePathHistograms,"/predictedStats.RData", sep=""))
return(predictedQueryLatencyQuantiles)
}
getPredictedQueryLatencyMultipleQuantiles = function(queryType, numSampleSets, basePathValidationStats, basePathHistograms, latencyQuantiles) {
predictedQueryLatencyQuantiles = matrix(nrow=numSampleSets,ncol=length(latencyQuantiles))
load(file=paste(basePathValidationStats, "/validationStats.RData", sep="")) # => validationStats
numSamplesPerSet = floor(mean(validationStats[,"numQueries"]))
print(paste("Using", numSamplesPerSet, "samples per set."))
for (j in 1:numSampleSets) {
print(paste("Sample Set", j))
if (queryType == "thoughtstream") {
samples=thoughtstreamSampler(basePathHistograms, j, numSamplesPerSet)
} else if (queryType == "userByEmail") {
samples=userByEmailSampler(basePathHistograms, j, numSamplesPerSet)
} else if (queryType == "userByName") {
samples=userByNameSampler(basePathHistograms, j, numSamplesPerSet)
} else if (queryType == "thoughtsByHashTag") {
samples=thoughtsByHashTagSampler(basePathHistograms, j, numSamplesPerSet)
} else {
print("Incorrect queryType specified.")
}
predictedQueryLatencyQuantiles[j,]=quantile(samples, probs=latencyQuantiles) }
save(predictedQueryLatencyQuantiles, file=paste(basePathHistograms,"/predictedStats-multipleQuantiles.RData", sep=""))
return(predictedQueryLatencyQuantiles)
}
# Samplers produce & save a single sample set.
# Called from "getPredictedQueryLatencyQuantiles" function.
thoughtstreamSampler = function(basePath, sampleID, numSamples) {
load(file=paste(basePath, "/histograms.RData", sep="")) # => histograms
samples=matrix(data=0, nrow=1, ncol=numSamples)
for (i in 1:numSamples) {
samples[i] = samples[i] + sum(sample(h1$mids, 2, replace=TRUE, prob=h1$density))
samples[i] = samples[i] + sample(h3$mids, 1, replace=TRUE, prob=h3$density)
samples[i] = samples[i] + sum(sample(h4$mids, 2, replace=TRUE, prob=h4$density))
samples[i] = samples[i] + sample(h5$mids, 1, replace=TRUE, prob=h5$density)
samples[i] = samples[i] + sum(sample(h6$mids, 5, replace=TRUE, prob=h6$density))
samples[i] = samples[i] + sample(h7$mids, 1, replace=TRUE, prob=h7$density)
samples[i] = samples[i] + sample(h8$mids, 1, replace=TRUE, prob=h8$density)
samples[i] = samples[i] + sample(h9$mids, 1, replace=TRUE, prob=h9$density)
}
save(samples, file=paste(basePath,"/sample", sampleID,".RData",sep=""))
return(samples)
}
userByEmailSampler = function(basePath, sampleID, numSamples) {
load(file=paste(basePath, "/histograms.RData", sep="")) # => histograms
samples=matrix(data=0, nrow=1, ncol=numSamples)
for (i in 1:numSamples) {
samples[i] = samples[i] + sample(h2$mids, 1, replace=TRUE, prob=h2$density)
samples[i] = samples[i] + sample(h3$mids, 1, replace=TRUE, prob=h3$density)
samples[i] = samples[i] + sample(h6$mids, 1, replace=TRUE, prob=h6$density)
}
save(samples, file=paste(basePath,"/sample", sampleID,".RData",sep=""))
return(samples)
}
# Saves predicted query & op latencies (rather than just sampled query latencies)
userByEmailQueryAndOpSampler = function(basePath, sampleID, numSamples) {
load(file=paste(basePath, "/histograms.RData", sep="")) # => histograms
samples = matrix(nrow=numSamples, ncol=1)
opSamples = matrix(nrow=numSamples, ncol=3)
colnames(opSamples) = c("op2", "op3", "op6")
for (i in 1:numSamples) {
opSamples[i,"op2"] = sample(h2$mids, 1, replace=TRUE, prob=h2$density)
opSamples[i,"op3"] = sample(h3$mids, 1, replace=TRUE, prob=h3$density)
opSamples[i,"op6"] = sample(h6$mids, 1, replace=TRUE, prob=h6$density)
samples[i] = sum(opSamples[i,])
}
save(samples, opSamples, file=paste(basePath,"/sample", sampleID,"-queriesAndOps.RData",sep=""))
#return(samples)
}
userByNameSampler = function(basePath, sampleID, numSamples) {
load(file=paste(basePath, "/histograms.RData", sep="")) # => histograms
samples=matrix(data=0, nrow=1, ncol=numSamples)
for (i in 1:numSamples) {
samples[i] = samples[i] + sample(h1$mids, 1, replace=TRUE, prob=h1$density)
samples[i] = samples[i] + sample(h6$mids, 1, replace=TRUE, prob=h6$density)
}
save(samples, file=paste(basePath,"/sample", sampleID,".RData",sep=""))
return(samples)
}
thoughtsByHashTagSampler = function(basePath, sampleID, numSamples) {
load(file=paste(basePath, "/histograms.RData", sep="")) # => histograms
samples=matrix(data=0, nrow=1, ncol=numSamples)
for (i in 1:numSamples) {
samples[i] = samples[i] + sample(h2$mids, 1, replace=TRUE, prob=h2$density)
samples[i] = samples[i] + sample(h5$mids, 1, replace=TRUE, prob=h5$density)
samples[i] = samples[i] + sum(sample(h6$mids, 2, replace=TRUE, prob=h6$density))
samples[i] = samples[i] + sample(h8$mids, 1, replace=TRUE, prob=h8$density)
}
save(samples, file=paste(basePath,"/sample", sampleID,".RData",sep=""))
return(samples)
}
## Note: just for testing
testSwitch = function(queryType) {
if(queryType == "thoughtstream") {
print("thoughtstream")
} else if (queryType == "userByEmail") {
print("userByEmail")
} else if (queryType == "userByName") {
print("userByName")
} else {
print("no")
}
}
# Q: is this a good way to measure error? Could ask PB then MJ.
getPredictionError = function(basePath) {
load(file=paste(basePath,"/validationStats.RData",sep="")) # => validationStats
load(file=paste(basePath,"/predictedStats.RData",sep="")) # => predictedQueryLatencyQuantiles
#error = abs(mean(validationStats[,"latencyQuantile"]) - mean(predictedQueryLatencyQuantiles))/mean(validationStats[,"latencyQuantile"])
error = abs(median(validationStats[,"latencyQuantile"]) - median(predictedQueryLatencyQuantiles))/median(validationStats[,"latencyQuantile"])
return(error)
}
getPredictionError2 = function(basePathValidationStats, basePathPredictedStats) {
load(file=paste(basePathValidationStats,"/validationStats.RData",sep="")) # => validationStats
load(file=paste(basePathPredictedStats,"/predictedStats.RData",sep="")) # => predictedQueryLatencyQuantiles
error = abs(median(validationStats[,"latencyQuantile"]) - median(predictedQueryLatencyQuantiles))/median(validationStats[,"latencyQuantile"])
return(error)
}
plotPredictionErrorMultipleQuantiles = function(basePathValidationStats, basePathPredictedStats, queryType, latencyQuantiles) {
load(file=paste(basePathValidationStats, "/validationLatencyMultipleQuantiles.RData", sep="")) # => validationStats; row=>validation run, column=>quantile
load(file=paste(basePathPredictedStats, "/predictedStats-multipleQuantiles.RData", sep="")) # => predictedQueryLatencyQuantiles; row=>sample set, column=>quantile
actualMedians = matrix(nrow=1, ncol=length(latencyQuantiles))
predictedMedians = matrix(nrow=1, ncol=length(latencyQuantiles))
# Get medians for each quantile
for (i in 1:length(latencyQuantiles)) {
actualMedians[i] = median(validationStats[,i])
predictedMedians[i] = median(predictedQueryLatencyQuantiles[,i])
}
save(actualMedians, predictedMedians, file=paste(basePathPredictedStats,"/actualAndPredictedQuantiles.RData",sep=""))
pdf(file=paste(basePathPredictedStats, "/quantiles.pdf", sep=""), height=6, width=6)
par(mar=c(5,5,4,2)+0.1)
plot(latencyQuantiles, actualMedians, ylim=c(0,max(actualMedians, predictedMedians)), col=0, xlab="Latency Quantiles", ylab="Latency (ms)", main="Comparing Actual & Predicted Latency Quantiles")
lines(latencyQuantiles, actualMedians, lw=2, col="red")
lines(latencyQuantiles, predictedMedians, lw=2, col="blue")
legend("topleft", legend=c("Actual", "Predicted"), col=c("red", "blue"), lwd=2)
dev.off()
}
plotActualAndPredictedLatencyQuantiles = function(basePathValidationStats, basePathPredictedStats, queryType) {
load(file=paste(basePathValidationStats,"/validationStats.RData",sep="")) # => validationStats
load(file=paste(basePathPredictedStats,"/predictedStats.RData",sep="")) # => predictedQueryLatencyQuantiles
median.actual = median(validationStats[,"latencyQuantile"])
median.predicted = median(predictedQueryLatencyQuantiles)
ub.predicted = max(predictedQueryLatencyQuantiles) - median.predicted
lb.predicted = median.predicted - min(predictedQueryLatencyQuantiles)
ub.actual = max(validationStats[,"latencyQuantile"]) - median.actual
lb.actual = median.actual - min(validationStats[,"latencyQuantile"])
pdf(file=paste(basePathPredictedStats,"/error.pdf",sep=""), height=6, width=6)
par(mar=c(5,5,4,2)+0.1)
x.abscissa = barplot(c(median.predicted,median.actual), ylim=c(0,1.1*max(validationStats[,"latencyQuantile"], predictedQueryLatencyQuantiles)), names.arg=c("Sampled", "Actual"), col=c("blue","cyan"), ylab="Latency (ms)", main=paste(queryType, "Query: Sampled vs. Actual Latency"))
superpose.eb(x.abscissa,c(median.predicted,median.actual),c(lb.predicted,lb.actual),c(ub.predicted,ub.actual),col="green",lwd=2)
text()
dev.off()
}
superpose.eb = function (x, y, ebl, ebu = ebl, length.arg = 0.08, col, lwd) {
arrows(x, y + ebu, x, y - ebl, angle = 90, code = 3, length=length.arg, col=col, lwd=lwd)
}
histWithMedianToPdf = function(histData, filename, breaks, xlab, main, col, legendLocation) {
pdf(file=filename, width=5, height=5)
par(mar=c(5,5,4,)1)
hist(histData, breaks=breaks, xlab=xlab, main=main)
medianVal = median(histData)
abline(v=medianVal, col=col, lw=2)
legend(legendLocation, legend=c(paste("median = ", round(medianVal, digits=2), "ms", sep="")), lwd=2, col=col)
dev.off()
}
# Quantile latency => center of bin
# Percent of quantile => width of bin
# Return ops corresponding to queries that fall within the specified bin
# Just for actual data; not for samples
binActualDataByDifferenceFromQuantileLatency = function(data, quantileLatency, percentDifferenceFromQuantileLatency) {
quantileValidationLatency = quantile(data$latency_ms[data$opLevel==3], probs=quantileLatency)
targetBinDeviationFromQuantile = percentDifferenceFromQuantileLatency/100*quantileValidationLatency
queriesWithinBin = data[which(data$opLevel==3 & abs(medianValidationLatency - data$latency_ms) < targetBinDeviationFromQuantile),]
#print("dim:")
#print(dim(queriesWithinBin))
#print("range:")
#print(range(queriesWithinBin$latency_ms))
#print("mtx:")
#print(queriesWithinBin[1:10,])
# => (threadNum, queryNum) id of each query whose latency falls within this range.
#data[data$threadNum==51 & data$queryNum==12 & data$opLevel==2,]
# Next, get its ops and add those rows to another array.
currentThreadNum = queriesWithinBin[1,"threadNum"]
currentQueryNum = queriesWithinBin[1,"queryNum"]
print(1)
opsWithinBin = data[data$threadNum==currentThreadNum & data$queryNum==currentQueryNum & data$opLevel==2,]
for (i in 2:nrow(queriesWithinBin)) {
print(i)
currentThreadNum = queriesWithinBin[i,"threadNum"]
currentQueryNum = queriesWithinBin[i,"queryNum"]
ops = data[data$threadNum==currentThreadNum & data$queryNum==currentQueryNum & data$opLevel==2,]
opsWithinBin = rbind(opsWithinBin, ops)
}
dim(opsWithinBin)
opsWithinBin[1:10,]
return(opsWithinBin)
}
binSamplesByDifferenceFromQuantileLatency = function(querySamples, opSamples, quantileLatency, percentDifferenceFromQuantileLatency) {
binCenter = quantile(querySamples, probs=quantileLatency)
targetBinDeviationFromQuantile = percentDifferenceFromQuantileLatency/100*binCenter
samplesWithinBin = samples[which(abs(samples - binCenter) < targetBinDeviationFromQuantile)]
return(opSamples[which(abs(samples - binCenter) < targetBinDeviationFromQuantile),])
}