我们有一个大的日志文件，用于存储用户与应用程序的交互。日志文件中的条目遵循以下架构：{userId，timestamp，actionType}，其中actionType是两个可能值之一：[open，close]
对于此问题，我们需要实现一个类/类，以计算每个用户在打开和关闭之间花费的平均时间。请记住，某些用户缺少条目，因此在进行计算时，我们将不得不选择如何处理这些条目。关于我们如何做出选择，代码应遵循一致的政策。
对于日志文件中的所有用户，解决方案的期望输出应为[{userId，timeSpent}，....]。
示例日志文件（逗号分隔的文本文件）
1,1435456566,open
2,1435457643,open
3,1435458912,open
1,1435459567,close
4,1435460345,open
1,1435461234,open
2,1435462567,close
1,1435463456,open
3,1435464398,close
4,1435465122,close
1,1435466775,close
下面是我用Python和Scala编写的代码，它似乎效率不高，无法达到给定场景的期望，我想从这个论坛的开发人员社区中反馈我们如何更好地优化此代码根据给定的方案。
import java.io.FileInputStream
import java.util.{Scanner, Map, LinkedList}
import java.lang.Long
import scala.collection.mutable
object UserMetrics extends App {
if (args.length == 0) {
println("Please provide input data file name for processing")
}
val userMetrics = new UserMetrics()
userMetrics.readInputFile(args(0),if (args.length == 1) 600000 else args(1).toInt)
}
case class UserInfo(userId: Integer, prevTimeStamp: Long, prevStatus: String, timeSpent: Long, occurence: Integer)
class UserMetrics {
val usermap = mutable.Map[Integer, LinkedList[UserInfo]]()
def readInputFile(stArr:String, timeOut: Int) {
var inputStream: FileInputStream = null
var sc: Scanner = null
try {
inputStream = new FileInputStream(stArr);
sc = new Scanner(inputStream, "UTF-8");
while (sc.hasNextLine()) {
val line: String = sc.nextLine();
processInput(line, timeOut)
}
for ((key: Integer, userLs: LinkedList[UserInfo]) <- usermap) {
val userInfo:UserInfo = userLs.get(0)
val timespent = if (userInfo.occurence>0) userInfo.timeSpent/userInfo.occurence else 0
println("{" + key +","+timespent + "}")
}
if (sc.ioException() != null) {
throw sc.ioException();
}
} finally {
if (inputStream != null) {
inputStream.close();
}
if (sc != null) {
sc.close();
}
}
}
def processInput(line: String, timeOut: Int) {
val strSp = line.split(",")
val userId: Integer = Integer.parseInt(strSp(0))
val curTimeStamp = Long.parseLong(strSp(1))
val status = strSp(2)
val uInfo: UserInfo = UserInfo(userId, curTimeStamp, status, 0, 0)
val emptyUserInfo: LinkedList[UserInfo] = new LinkedList[UserInfo]()
val lsUserInfo: LinkedList[UserInfo] = usermap.getOrElse(userId, emptyUserInfo)
if (lsUserInfo != null && lsUserInfo.size() > 0) {
val lastUserInfo: UserInfo = lsUserInfo.get(lsUserInfo.size() - 1)
val prevTimeStamp: Long = lastUserInfo.prevTimeStamp
val prevStatus: String = lastUserInfo.prevStatus
if (prevStatus.equals("open")) {
if (status.equals(lastUserInfo.prevStatus)) {
val timeSelector = if ((curTimeStamp - prevTimeStamp) > timeOut) timeOut else curTimeStamp - prevTimeStamp
val timeDiff = lastUserInfo.timeSpent + timeSelector
lsUserInfo.remove()
lsUserInfo.add(UserInfo(userId, curTimeStamp, status, timeDiff, lastUserInfo.occurence + 1))
} else if(!status.equals(lastUserInfo.prevStatus)){
val timeDiff = lastUserInfo.timeSpent + curTimeStamp - prevTimeStamp
lsUserInfo.remove()
lsUserInfo.add(UserInfo(userId, curTimeStamp, status, timeDiff, lastUserInfo.occurence + 1))
}
} else if(prevStatus.equals("close")) {
if (status.equals(lastUserInfo.prevStatus)) {
lsUserInfo.remove()
val timeSelector = if ((curTimeStamp - prevTimeStamp) > timeOut) timeOut else curTimeStamp - prevTimeStamp
lsUserInfo.add(UserInfo(userId, curTimeStamp, status, lastUserInfo.timeSpent + timeSelector, lastUserInfo.occurence+1))
}else if(!status.equals(lastUserInfo.prevStatus))
{
lsUserInfo.remove()
lsUserInfo.add(UserInfo(userId, curTimeStamp, status, lastUserInfo.timeSpent, lastUserInfo.occurence))
}
}
}else if(lsUserInfo.size()==0){
lsUserInfo.add(uInfo)
}
usermap.put(userId, lsUserInfo)
}
}
import sys
def fileBlockStream(fp, number_of_blocks, block):
#A generator that splits a file into blocks and iterates over the lines of one of the blocks.
assert 0 <= block and block < number_of_blocks #Assertions to validate number of blocks given
assert 0 < number_of_blocks
fp.seek(0,2) #seek to end of file to compute block size
file_size = fp.tell()
ini = file_size * block / number_of_blocks #compute start & end point of file block
end = file_size * (1 + block) / number_of_blocks
if ini <= 0:
fp.seek(0)
else:
fp.seek(ini-1)
fp.readline()
while fp.tell() < end:
yield fp.readline() #iterate over lines of the particular chunk or block
def computeResultDS(chunk,avgTimeSpentDict,defaultTimeOut):
countPos,totTmPos,openTmPos,closeTmPos,nextEventPos = 0,1,2,3,4
for rows in chunk.splitlines():
if len(rows.split(",")) != 3:
continue
userKeyID = rows.split(",")[0]
try:
curTimeStamp = int(rows.split(",")[1])
except ValueError:
print("Invalid Timestamp for ID:" + str(userKeyID))
continue
curEvent = rows.split(",")[2]
if userKeyID in avgTimeSpentDict.keys() and avgTimeSpentDict[userKeyID][nextEventPos]==1 and curEvent == "close":
#Check if already existing userID with expected Close event 0 - Open; 1 - Close
#Array value within dictionary stores [No. of pair events, total time spent (Close tm-Open tm), Last Open Tm, Last Close Tm, Next expected Event]
curTotalTime = curTimeStamp - avgTimeSpentDict[userKeyID][openTmPos]
totalTime = curTotalTime + avgTimeSpentDict[userKeyID][totTmPos]
eventCount = avgTimeSpentDict[userKeyID][countPos] + 1
avgTimeSpentDict[userKeyID][countPos] = eventCount
avgTimeSpentDict[userKeyID][totTmPos] = totalTime
avgTimeSpentDict[userKeyID][closeTmPos] = curTimeStamp
avgTimeSpentDict[userKeyID][nextEventPos] = 0 #Change next expected event to Open
elif userKeyID in avgTimeSpentDict.keys() and avgTimeSpentDict[userKeyID][nextEventPos]==0 and curEvent == "open":
avgTimeSpentDict[userKeyID][openTmPos] = curTimeStamp
avgTimeSpentDict[userKeyID][nextEventPos] = 1 #Change next expected event to Close
elif userKeyID in avgTimeSpentDict.keys() and avgTimeSpentDict[userKeyID][nextEventPos]==1 and curEvent == "open":
curTotalTime,closeTime = missingHandler(defaultTimeOut,avgTimeSpentDict[userKeyID][openTmPos],curTimeStamp)
totalTime = curTotalTime + avgTimeSpentDict[userKeyID][totTmPos]
avgTimeSpentDict[userKeyID][totTmPos]=totalTime
avgTimeSpentDict[userKeyID][closeTmPos]=closeTime
avgTimeSpentDict[userKeyID][openTmPos]=curTimeStamp
eventCount = avgTimeSpentDict[userKeyID][countPos] + 1
avgTimeSpentDict[userKeyID][countPos] = eventCount
elif userKeyID in avgTimeSpentDict.keys() and avgTimeSpentDict[userKeyID][nextEventPos]==0 and curEvent == "close":
curTotalTime,openTime = missingHandler(defaultTimeOut,avgTimeSpentDict[userKeyID][closeTmPos],curTimeStamp)
totalTime = curTotalTime + avgTimeSpentDict[userKeyID][totTmPos]
avgTimeSpentDict[userKeyID][totTmPos]=totalTime
avgTimeSpentDict[userKeyID][openTmPos]=openTime
eventCount = avgTimeSpentDict[userKeyID][countPos] + 1
avgTimeSpentDict[userKeyID][countPos] = eventCount
elif curEvent == "open":
#Initialize userid with Open event
avgTimeSpentDict[userKeyID] = [0,0,curTimeStamp,0,1]
elif curEvent == "close":
#Initialize userid with missing handler function since there is no Open event for this User
totaltime,OpenTime = missingHandler(defaultTimeOut,0,curTimeStamp)
avgTimeSpentDict[userKeyID] = [1,totaltime,OpenTime,curTimeStamp,0]
def missingHandler(defaultTimeOut,curTimeVal,lastTimeVal):
if lastTimeVal - curTimeVal > defaultTimeOut:
return defaultTimeOut,curTimeVal
else:
return lastTimeVal - curTimeVal,curTimeVal
def computeAvg(avgTimeSpentDict,defaultTimeOut):
resDict = {}
for k,v in avgTimeSpentDict.iteritems():
if v[0] == 0:
resDict[k] = 0
else:
resDict[k] = v[1]/v[0]
return resDict
if __name__ == "__main__":
avgTimeSpentDict = {}
if len(sys.argv) < 2:
print("Please provide input data file name for processing")
sys.exit(1)
fileObj = open(sys.argv[1])
number_of_chunks = 4 if len(sys.argv) < 3 else int(sys.argv[2])
defaultTimeOut = 60000 if len(sys.argv) < 4 else int(sys.argv[3])
for chunk_number in range(number_of_chunks):
for chunk in fileBlockStream(fileObj, number_of_chunks, chunk_number):
computeResultDS(chunk, avgTimeSpentDict, defaultTimeOut)
print (computeAvg(avgTimeSpentDict,defaultTimeOut))
avgTimeSpentDict.clear() #Nullify dictionary
fileObj.close #Close the file object
以上两个程序都提供了所需的输出，但是效率对于这种特定情况至关重要。让我知道您是否有更好的建议或对现有实施的任何建议。
预先感谢！
答案 0 :(得分：0)
您追求的是迭代器的使用。我不会重新编写您的代码，但是这里的技巧可能是使用iterator。幸运的是，Scala为这项工作提供了开箱即用的工具。
import scala.io.Source
object ReadBigFiles {
def read(fileName: String): Unit = {
val lines: Iterator[String] = Source.fromFile(fileName).getLines
// now you get iterator semantics for the file line traversal
// that means you can only go through the lines once, but you don't incur a penalty on heap usage
}
}
对于您的用例，您似乎需要一个lastUser，因此您要处理2个条目的组。我认为您有两种选择，要么选择iterator.sliding(2)，它会为每对生成迭代器，或者简单地使用选项将递归添加到混合中。
def navigate(source: Iterator[String], last: Option[User]): ResultType = {
if (source.hasNext) {
val current = source.next()
last match {
case Some(existing) => // compare with previous user etc
case None => navigate(source, Some(current))
}
} else {
// exit recursion, return result
}
}
您可以避免编写用于读取文件的所有代码，等等。如果您需要计算发生次数，只需在递归内部构建一个Map，然后根据您的业务逻辑在每个步骤增加发生次数即可。