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  • How to measure sum of collected memory of Young Generation?

    - by Marcel
    Hi, I'd like to measure memory allocation data from my java application, i.e. the sum of the size of all objects that were allocated. Since object allocation is done in young generation this seems to be the right place. I know jconsole and I know the JMX beans but I just can't find the right variable... Right at the moment we are parsing the gc log output file but that's quite hard. Ideally we'd like to measure it via JMX... How can I get this value? Thanks, Marcel

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  • Using R to Analyze G1GC Log Files

    - by user12620111
    Using R to Analyze G1GC Log Files body, td { font-family: sans-serif; background-color: white; font-size: 12px; margin: 8px; } tt, code, pre { font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace; } h1 { font-size:2.2em; } h2 { font-size:1.8em; } h3 { font-size:1.4em; } h4 { font-size:1.0em; } h5 { font-size:0.9em; } h6 { font-size:0.8em; } a:visited { color: rgb(50%, 0%, 50%); } pre { margin-top: 0; max-width: 95%; border: 1px solid #ccc; white-space: pre-wrap; } pre code { display: block; padding: 0.5em; } code.r, code.cpp { background-color: #F8F8F8; } table, td, th { border: none; } blockquote { color:#666666; margin:0; padding-left: 1em; border-left: 0.5em #EEE solid; } hr { height: 0px; border-bottom: none; border-top-width: thin; border-top-style: dotted; border-top-color: #999999; } @media print { * { background: transparent !important; color: black !important; filter:none !important; -ms-filter: none !important; } body { 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  Using R to Analyze G1GC Log Files   Using R to Analyze G1GC Log Files Introduction Working in Oracle Platform Integration gives an engineer opportunities to work on a wide array of technologies. My team’s goal is to make Oracle applications run best on the Solaris/SPARC platform. When looking for bottlenecks in a modern applications, one needs to be aware of not only how the CPUs and operating system are executing, but also network, storage, and in some cases, the Java Virtual Machine. I was recently presented with about 1.5 GB of Java Garbage First Garbage Collector log file data. If you’re not familiar with the subject, you might want to review Garbage First Garbage Collector Tuning by Monica Beckwith. The customer had been running Java HotSpot 1.6.0_31 to host a web application server. I was told that the Solaris/SPARC server was running a Java process launched using a commmand line that included the following flags: -d64 -Xms9g -Xmx9g -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -XX:InitiatingHeapOccupancyPercent=80 -XX:PermSize=256m -XX:MaxPermSize=256m -XX:+PrintGC -XX:+PrintGCTimeStamps -XX:+PrintHeapAtGC -XX:+PrintGCDateStamps -XX:+PrintFlagsFinal -XX:+DisableExplicitGC -XX:+UnlockExperimentalVMOptions -XX:ParallelGCThreads=8 Several sources on the internet indicate that if I were to print out the 1.5 GB of log files, it would require enough paper to fill the bed of a pick up truck. Of course, it would be fruitless to try to scan the log files by hand. Tools will be required to summarize the contents of the log files. Others have encountered large Java garbage collection log files. There are existing tools to analyze the log files: IBM’s GC toolkit The chewiebug GCViewer gchisto HPjmeter Instead of using one of the other tools listed, I decide to parse the log files with standard Unix tools, and analyze the data with R. Data Cleansing The log files arrived in two different formats. I guess that the difference is that one set of log files was generated using a more verbose option, maybe -XX:+PrintHeapAtGC, and the other set of log files was generated without that option. Format 1 In some of the log files, the log files with the less verbose format, a single trace, i.e. the report of a singe garbage collection event, looks like this: {Heap before GC invocations=12280 (full 61): garbage-first heap total 9437184K, used 7499918K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 1 young (4096K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. 2014-05-14T07:24:00.988-0700: 60586.353: [GC pause (young) 7324M->7320M(9216M), 0.1567265 secs] Heap after GC invocations=12281 (full 61): garbage-first heap total 9437184K, used 7496533K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 0 young (0K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. } A simple grep can be used to extract a summary: $ grep "\[ GC pause (young" g1gc.log 2014-05-13T13:24:35.091-0700: 3.109: [GC pause (young) 20M->5029K(9216M), 0.0146328 secs] 2014-05-13T13:24:35.440-0700: 3.459: [GC pause (young) 9125K->6077K(9216M), 0.0086723 secs] 2014-05-13T13:24:37.581-0700: 5.599: [GC pause (young) 25M->8470K(9216M), 0.0203820 secs] 2014-05-13T13:24:42.686-0700: 10.704: [GC pause (young) 44M->15M(9216M), 0.0288848 secs] 2014-05-13T13:24:48.941-0700: 16.958: [GC pause (young) 51M->20M(9216M), 0.0491244 secs] 2014-05-13T13:24:56.049-0700: 24.066: [GC pause (young) 92M->26M(9216M), 0.0525368 secs] 2014-05-13T13:25:34.368-0700: 62.383: [GC pause (young) 602M->68M(9216M), 0.1721173 secs] But that format wasn't easily read into R, so I needed to be a bit more tricky. I used the following Unix command to create a summary file that was easy for R to read. $ echo "SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime" $ grep "\[GC pause (young" g1gc.log | grep -v mark | sed -e 's/[A-SU-z\(\),]/ /g' -e 's/->/ /' -e 's/: / /g' | more SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime 2014-05-13T13:24:35.091-0700 3.109 20 5029 9216 0.0146328 2014-05-13T13:24:35.440-0700 3.459 9125 6077 9216 0.0086723 2014-05-13T13:24:37.581-0700 5.599 25 8470 9216 0.0203820 2014-05-13T13:24:42.686-0700 10.704 44 15 9216 0.0288848 2014-05-13T13:24:48.941-0700 16.958 51 20 9216 0.0491244 2014-05-13T13:24:56.049-0700 24.066 92 26 9216 0.0525368 2014-05-13T13:25:34.368-0700 62.383 602 68 9216 0.1721173 Format 2 In some of the log files, the log files with the more verbose format, a single trace, i.e. the report of a singe garbage collection event, was more complicated than Format 1. Here is a text file with an example of a single G1GC trace in the second format. As you can see, it is quite complicated. It is nice that there is so much information available, but the level of detail can be overwhelming. I wrote this awk script (download) to summarize each trace on a single line. #!/usr/bin/env awk -f BEGIN { printf("SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize\n") } ###################### # Save count data from lines that are at the start of each G1GC trace. # Each trace starts out like this: # {Heap before GC invocations=14 (full 0): # garbage-first heap total 9437184K, used 325496K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) ###################### /{Heap.*full/{ gsub ( "\\)" , "" ); nf=split($0,a,"="); split(a[2],b," "); getline; if ( match($0, "first") ) { G1GC=1; IncrementalCount=b[1]; FullCount=substr( b[3], 1, length(b[3])-1 ); } else { G1GC=0; } } ###################### # Pull out time stamps that are in lines with this format: # 2014-05-12T14:02:06.025-0700: 94.312: [GC pause (young), 0.08870154 secs] ###################### /GC pause/ { DateTime=$1; SecondsSinceLaunch=substr($2, 1, length($2)-1); } ###################### # Heap sizes are in lines that look like this: # [ 4842M->4838M(9216M)] ###################### /\[ .*]$/ { gsub ( "\\[" , "" ); gsub ( "\ \]" , "" ); gsub ( "->" , " " ); gsub ( "\\( " , " " ); gsub ( "\ \)" , " " ); split($0,a," "); if ( split(a[1],b,"M") > 1 ) {BeforeSize=b[1]*1024;} if ( split(a[1],b,"K") > 1 ) {BeforeSize=b[1];} if ( split(a[2],b,"M") > 1 ) {AfterSize=b[1]*1024;} if ( split(a[2],b,"K") > 1 ) {AfterSize=b[1];} if ( split(a[3],b,"M") > 1 ) {TotalSize=b[1]*1024;} if ( split(a[3],b,"K") > 1 ) {TotalSize=b[1];} } ###################### # Emit an output line when you find input that looks like this: # [Times: user=1.41 sys=0.08, real=0.24 secs] ###################### /\[Times/ { if (G1GC==1) { gsub ( "," , "" ); split($2,a,"="); UserTime=a[2]; split($3,a,"="); SysTime=a[2]; split($4,a,"="); RealTime=a[2]; print DateTime,SecondsSinceLaunch,IncrementalCount,FullCount,UserTime,SysTime,RealTime,BeforeSize,AfterSize,TotalSize; G1GC=0; } } The resulting summary is about 25X smaller that the original file, but still difficult for a human to digest. SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ... 2014-05-12T18:36:34.669-0700: 3985.744 561 0 0.57 0.06 0.16 1724416 1720320 9437184 2014-05-12T18:36:34.839-0700: 3985.914 562 0 0.51 0.06 0.19 1724416 1720320 9437184 2014-05-12T18:36:35.069-0700: 3986.144 563 0 0.60 0.04 0.27 1724416 1721344 9437184 2014-05-12T18:36:35.354-0700: 3986.429 564 0 0.33 0.04 0.09 1725440 1722368 9437184 2014-05-12T18:36:35.545-0700: 3986.620 565 0 0.58 0.04 0.17 1726464 1722368 9437184 2014-05-12T18:36:35.726-0700: 3986.801 566 0 0.43 0.05 0.12 1726464 1722368 9437184 2014-05-12T18:36:35.856-0700: 3986.930 567 0 0.30 0.04 0.07 1726464 1723392 9437184 2014-05-12T18:36:35.947-0700: 3987.023 568 0 0.61 0.04 0.26 1727488 1723392 9437184 2014-05-12T18:36:36.228-0700: 3987.302 569 0 0.46 0.04 0.16 1731584 1724416 9437184 Reading the Data into R Once the GC log data had been cleansed, either by processing the first format with the shell script, or by processing the second format with the awk script, it was easy to read the data into R. g1gc.df = read.csv("summary.txt", row.names = NULL, stringsAsFactors=FALSE,sep="") str(g1gc.df) ## 'data.frame': 8307 obs. of 10 variables: ## $ row.names : chr "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ... ## $ SecondsSinceLaunch: num 1.16 1.47 1.97 3.83 6.1 ... ## $ IncrementalCount : int 0 1 2 3 4 5 6 7 8 9 ... ## $ FullCount : int 0 0 0 0 0 0 0 0 0 0 ... ## $ UserTime : num 0.11 0.05 0.04 0.21 0.08 0.26 0.31 0.33 0.34 0.56 ... ## $ SysTime : num 0.04 0.01 0.01 0.05 0.01 0.06 0.07 0.06 0.07 0.09 ... ## $ RealTime : num 0.02 0.02 0.01 0.04 0.02 0.04 0.05 0.04 0.04 0.06 ... ## $ BeforeSize : int 8192 5496 5768 22528 24576 43008 34816 53248 55296 93184 ... ## $ AfterSize : int 1400 1672 2557 4907 7072 14336 16384 18432 19456 21504 ... ## $ TotalSize : int 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 ... head(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount ## 1 2014-05-12T14:00:32.868-0700: 1.161 0 ## 2 2014-05-12T14:00:33.179-0700: 1.472 1 ## 3 2014-05-12T14:00:33.677-0700: 1.969 2 ## 4 2014-05-12T14:00:35.538-0700: 3.830 3 ## 5 2014-05-12T14:00:37.811-0700: 6.103 4 ## 6 2014-05-12T14:00:41.428-0700: 9.720 5 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 1 0 0.11 0.04 0.02 8192 1400 9437184 ## 2 0 0.05 0.01 0.02 5496 1672 9437184 ## 3 0 0.04 0.01 0.01 5768 2557 9437184 ## 4 0 0.21 0.05 0.04 22528 4907 9437184 ## 5 0 0.08 0.01 0.02 24576 7072 9437184 ## 6 0 0.26 0.06 0.04 43008 14336 9437184 Basic Statistics Once the data has been read into R, simple statistics are very easy to generate. All of the numbers from high school statistics are available via simple commands. For example, generate a summary of every column: summary(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount FullCount ## Length:8307 Min. : 1 Min. : 0 Min. : 0.0 ## Class :character 1st Qu.: 9977 1st Qu.:2048 1st Qu.: 0.0 ## Mode :character Median :12855 Median :4136 Median : 12.0 ## Mean :12527 Mean :4156 Mean : 31.6 ## 3rd Qu.:15758 3rd Qu.:6262 3rd Qu.: 61.0 ## Max. :55484 Max. :8391 Max. :113.0 ## UserTime SysTime RealTime BeforeSize ## Min. :0.040 Min. :0.0000 Min. : 0.0 Min. : 5476 ## 1st Qu.:0.470 1st Qu.:0.0300 1st Qu.: 0.1 1st Qu.:5137920 ## Median :0.620 Median :0.0300 Median : 0.1 Median :6574080 ## Mean :0.751 Mean :0.0355 Mean : 0.3 Mean :5841855 ## 3rd Qu.:0.920 3rd Qu.:0.0400 3rd Qu.: 0.2 3rd Qu.:7084032 ## Max. :3.370 Max. :1.5600 Max. :488.1 Max. :8696832 ## AfterSize TotalSize ## Min. : 1380 Min. :9437184 ## 1st Qu.:5002752 1st Qu.:9437184 ## Median :6559744 Median :9437184 ## Mean :5785454 Mean :9437184 ## 3rd Qu.:7054336 3rd Qu.:9437184 ## Max. :8482816 Max. :9437184 Q: What is the total amount of User CPU time spent in garbage collection? sum(g1gc.df$UserTime) ## [1] 6236 As you can see, less than two hours of CPU time was spent in garbage collection. Is that too much? To find the percentage of time spent in garbage collection, divide the number above by total_elapsed_time*CPU_count. In this case, there are a lot of CPU’s and it turns out the the overall amount of CPU time spent in garbage collection isn’t a problem when viewed in isolation. When calculating rates, i.e. events per unit time, you need to ask yourself if the rate is homogenous across the time period in the log file. Does the log file include spikes of high activity that should be separately analyzed? Averaging in data from nights and weekends with data from business hours may alias problems. If you have a reason to suspect that the garbage collection rates include peaks and valleys that need independent analysis, see the “Time Series” section, below. Q: How much garbage is collected on each pass? The amount of heap space that is recovered per GC pass is surprisingly low: At least one collection didn’t recover any data. (“Min.=0”) 25% of the passes recovered 3MB or less. (“1st Qu.=3072”) Half of the GC passes recovered 4MB or less. (“Median=4096”) The average amount recovered was 56MB. (“Mean=56390”) 75% of the passes recovered 36MB or less. (“3rd Qu.=36860”) At least one pass recovered 2GB. (“Max.=2121000”) g1gc.df$Delta = g1gc.df$BeforeSize - g1gc.df$AfterSize summary(g1gc.df$Delta) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0 3070 4100 56400 36900 2120000 Q: What is the maximum User CPU time for a single collection? The worst garbage collection (“Max.”) is many standard deviations away from the mean. The data appears to be right skewed. summary(g1gc.df$UserTime) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.040 0.470 0.620 0.751 0.920 3.370 sd(g1gc.df$UserTime) ## [1] 0.3966 Basic Graphics Once the data is in R, it is trivial to plot the data with formats including dot plots, line charts, bar charts (simple, stacked, grouped), pie charts, boxplots, scatter plots histograms, and kernel density plots. Histogram of User CPU Time per Collection I don't think that this graph requires any explanation. hist(g1gc.df$UserTime, main="User CPU Time per Collection", xlab="Seconds", ylab="Frequency") Box plot to identify outliers When the initial data is viewed with a box plot, you can see the one crazy outlier in the real time per GC. Save this data point for future analysis and drop the outlier so that it’s not throwing off our statistics. Now the box plot shows many outliers, which will be examined later, using times series analysis. Notice that the scale of the x-axis changes drastically once the crazy outlier is removed. par(mfrow=c(2,1)) boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(dominated by a crazy outlier)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") crazy.outlier.df=g1gc.df[g1gc.df$RealTime > 400,] g1gc.df=g1gc.df[g1gc.df$RealTime < 400,] boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(crazy outlier excluded)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") box(which = "outer", lty = "solid") Here is the crazy outlier for future analysis: crazy.outlier.df ## row.names SecondsSinceLaunch IncrementalCount ## 8233 2014-05-12T23:15:43.903-0700: 20741 8316 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 8233 112 0.55 0.42 488.1 8381440 8235008 9437184 ## Delta ## 8233 146432 R Time Series Data To analyze the garbage collection as a time series, I’ll use Z’s Ordered Observations (zoo). “zoo is the creator for an S3 class of indexed totally ordered observations which includes irregular time series.” require(zoo) ## Loading required package: zoo ## ## Attaching package: 'zoo' ## ## The following objects are masked from 'package:base': ## ## as.Date, as.Date.numeric head(g1gc.df[,1]) ## [1] "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" ## [3] "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ## [5] "2014-05-12T14:00:37.811-0700:" "2014-05-12T14:00:41.428-0700:" options("digits.secs"=3) times=as.POSIXct( g1gc.df[,1], format="%Y-%m-%dT%H:%M:%OS%z:") g1gc.z = zoo(g1gc.df[,-c(1)], order.by=times) head(g1gc.z) ## SecondsSinceLaunch IncrementalCount FullCount ## 2014-05-12 17:00:32.868 1.161 0 0 ## 2014-05-12 17:00:33.178 1.472 1 0 ## 2014-05-12 17:00:33.677 1.969 2 0 ## 2014-05-12 17:00:35.538 3.830 3 0 ## 2014-05-12 17:00:37.811 6.103 4 0 ## 2014-05-12 17:00:41.427 9.720 5 0 ## UserTime SysTime RealTime BeforeSize AfterSize ## 2014-05-12 17:00:32.868 0.11 0.04 0.02 8192 1400 ## 2014-05-12 17:00:33.178 0.05 0.01 0.02 5496 1672 ## 2014-05-12 17:00:33.677 0.04 0.01 0.01 5768 2557 ## 2014-05-12 17:00:35.538 0.21 0.05 0.04 22528 4907 ## 2014-05-12 17:00:37.811 0.08 0.01 0.02 24576 7072 ## 2014-05-12 17:00:41.427 0.26 0.06 0.04 43008 14336 ## TotalSize Delta ## 2014-05-12 17:00:32.868 9437184 6792 ## 2014-05-12 17:00:33.178 9437184 3824 ## 2014-05-12 17:00:33.677 9437184 3211 ## 2014-05-12 17:00:35.538 9437184 17621 ## 2014-05-12 17:00:37.811 9437184 17504 ## 2014-05-12 17:00:41.427 9437184 28672 Example of Two Benchmark Runs in One Log File The data in the following graph is from a different log file, not the one of primary interest to this article. I’m including this image because it is an example of idle periods followed by busy periods. It would be uninteresting to average the rate of garbage collection over the entire log file period. More interesting would be the rate of garbage collect in the two busy periods. Are they the same or different? Your production data may be similar, for example, bursts when employees return from lunch and idle times on weekend evenings, etc. Once the data is in an R Time Series, you can analyze isolated time windows. Clipping the Time Series data Flashing back to our test case… Viewing the data as a time series is interesting. You can see that the work intensive time period is between 9:00 PM and 3:00 AM. Lets clip the data to the interesting period:     par(mfrow=c(2,1)) plot(g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Complete Log File", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") clipped.g1gc.z=window(g1gc.z, start=as.POSIXct("2014-05-12 21:00:00"), end=as.POSIXct("2014-05-13 03:00:00")) plot(clipped.g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Limited to Benchmark Execution", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") box(which = "outer", lty = "solid") Cumulative Incremental and Full GC count Here is the cumulative incremental and full GC count. When the line is very steep, it indicates that the GCs are repeating very quickly. Notice that the scale on the Y axis is different for full vs. incremental. plot(clipped.g1gc.z[,c(2:3)], main="Cumulative Incremental and Full GC count", xlab="Time of Day", col="#1b9e77") GC Analysis of Benchmark Execution using Time Series data In the following series of 3 graphs: The “After Size” show the amount of heap space in use after each garbage collection. Many Java objects are still referenced, i.e. alive, during each garbage collection. This may indicate that the application has a memory leak, or may indicate that the application has a very large memory footprint. Typically, an application's memory footprint plateau's in the early stage of execution. One would expect this graph to have a flat top. The steep decline in the heap space may indicate that the application crashed after 2:00. The second graph shows that the outliers in real execution time, discussed above, occur near 2:00. when the Java heap seems to be quite full. The third graph shows that Full GCs are infrequent during the first few hours of execution. The rate of Full GC's, (the slope of the cummulative Full GC line), changes near midnight.   plot(clipped.g1gc.z[,c("AfterSize","RealTime","FullCount")], xlab="Time of Day", col=c("#1b9e77","red","#1b9e77")) GC Analysis of heap recovered Each GC trace includes the amount of heap space in use before and after the individual GC event. During garbage coolection, unreferenced objects are identified, the space holding the unreferenced objects is freed, and thus, the difference in before and after usage indicates how much space has been freed. The following box plot and bar chart both demonstrate the same point - the amount of heap space freed per garbage colloection is surprisingly low. par(mfrow=c(2,1)) boxplot(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", horizontal = TRUE, col="red") hist(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", breaks=100, col="red") box(which = "outer", lty = "solid") This graph is the most interesting. The dark blue area shows how much heap is occupied by referenced Java objects. This represents memory that holds live data. The red fringe at the top shows how much data was recovered after each garbage collection. barplot(clipped.g1gc.z[,c("AfterSize","Delta")], col=c("#7570b3","#e7298a"), xlab="Time of Day", border=NA) legend("topleft", c("Live Objects","Heap Recovered on GC"), fill=c("#7570b3","#e7298a")) box(which = "outer", lty = "solid") When I discuss the data in the log files with the customer, I will ask for an explaination for the large amount of referenced data resident in the Java heap. There are two are posibilities: There is a memory leak and the amount of space required to hold referenced objects will continue to grow, limited only by the maximum heap size. After the maximum heap size is reached, the JVM will throw an “Out of Memory” exception every time that the application tries to allocate a new object. If this is the case, the aplication needs to be debugged to identify why old objects are referenced when they are no longer needed. The application has a legitimate requirement to keep a large amount of data in memory. The customer may want to further increase the maximum heap size. Another possible solution would be to partition the application across multiple cluster nodes, where each node has responsibility for managing a unique subset of the data. Conclusion In conclusion, R is a very powerful tool for the analysis of Java garbage collection log files. The primary difficulty is data cleansing so that information can be read into an R data frame. Once the data has been read into R, a rich set of tools may be used for thorough evaluation.

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  • What features are important in a programming language for young beginners?

    - by NoMoreZealots
    I was talking with some of the mentors in a local robotics competition for 7th and 8th level kids. The robot was using PBASIC and the parallax Basic Stamp. One of the major issues was this was short term project that required building the robot, teaching them to program in PBASIC and having them program the robot. All in only 2 hours or so a week over a couple months. PBASIC is kinda nice in that it has built in features to do everything, but information overload is possible to due this. My thought are simplicity is key. When you have kids struggling to grasp: if X>10 then <DOSOMETHING> There is not much point in throwing "proper" object oriented programming at them. What are the essentials needed to foster an interest in programming?

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  • Are today's young programmers getting wrapped around the axle with patterns and practices?

    - by Robert Harvey
    Recently I have noticed a number of questions on SO that look something like this: I am writing a small program to keep a list of the songs that I keep on my ipod. I'm thinking about writing it as a 3-tier MVC Ruby on Rails web application with TDD, DDD and IOC, using a factory pattern to create the classes and a singleton to store my application settings. Do you think I'm taking the right approach? Do you think that we're handing novice programmers a very sharp knife and telling them, "Don't cut yourself with this"? NOTE: Despite the humorous tone, this is a serious (and programming-related) question.

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  • How to install the latest version of google earth on ubuntu 12.10 64-bit?

    - by user114769
    Chris here with a huge problem with the Google earth latest version. Gosh.. whenever I try to lunch the app this comes out. I'v Tried this web site nothing worked: (THANK YOU SO MUCH FOR EVEN READING THIS. MAY YOU GUYS HAVE A NICE DAY. christopher@christopher-E4300:~$ google-earth Google Earth has caught signal 11. We apologize for the inconvenience, but Google Earth has crashed. This is a bug in the program, and should never happen under normal circumstances. A bug report and debugging data have been written to this text file: /home/christopher/.googleearth/crashlogs/crashlog-50cbd67e.txt Please include this file if you submit a bug report to Google. https://help.ubuntu.com/community/GoogleEarth#Installing_the_.deb_file_downloaded_from_the_Google_Earth_Website Here is the content of /home/christopher/.googleearth/crashlogs/crashlog-50cbd67e.txt Major Version 7 Minor Version 0 Build Number 0001 Build Date Oct 29 2012 Build Time 19:13:39 OS Type 3 OS Major Version 3 OS Minor Version 5 OS Build Version 0 OS Patch Version 0 Crash Signal 11 Crash Time 1355535998 Up Time 0.789556 Stacktrace from glibc: ./libgoogleearth_free.so(+0x1e9cfb)[0xf757dcfb] ./libgoogleearth_free.so(+0x1e9f43)[0xf757df43] [0xf7726400]

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  • Java GC: top object classes promoted (by size)?

    - by Java Geek
    Hello! Please let me know what is the best way to determine composition of young generation memory promoted to old generation, after each young GC event? Ideally I would like to know class names which are responsible say, for 80% of heap in each "young gen - old gen" promotion chunk; Example: I have 600M young gen, each tenure promotes 6M; I want to know which objects compose this 6M. Thank you.

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  • Oracle Employees Support New World Record for IYF Children's Hour

    - by Maria Sandu
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 960 students ‘crouched’, ‘touched’ and ‘set’ under the watchful eye of International Rugby Referee Alain Roland, and supported by Oracle employees, to successfully set a new world record for the World’s Largest Scrum to raise funds and awareness for the Irish Youth Foundation. Last year Oracle Employees supported the Irish Youth Foundation by donating funds from their payroll through the Giving Tree Appeal. We were the largest corporate donor to the IYF by raising €3075. To acknowledge our generosity the IYF asked Oracle Leadership in Society team members to participate in their most recent campaign which was to break the Guinness Book of Records by forming the World’s Largest Rugby Scrum. This was a wonderful opportunity for Oracle’s Leadership in Society to promote the charity, support education and to make a mark in the Corporate Social Responsibility field. The students who formed the scrum also gave up their lunch money and raised a total of €3000. This year we hope Oracle Employees will once again support the IYF with the challenge to match that amount. On the 24th of October the sun shone down on the streaming lines of students entering the field. 480 students were decked out in bright red Oracle T-Shirts against the other 480 in blue and white jerseys - all ready to form a striking scrum. Ryan Tubridy the host of the event made the opening announcement and with the blow of a whistle the Scum began. 960 students locked tight together with the Leinster players also at each side. Leinster Manager Matt O’Connor was there along with presenters Ryan Tubridy and George Hook to assist with getting the boys in line and keeping the shape of the scrum. In accordance with Guinness Book of Records rules, the ball was fed into the scrum properly by Ireland and Leinster scrum-half, Eoin Reddan, and was then passed out the line to his Leinster team mates including Ian Madigan, Brendan Macken and Jordi Murphy, also proudly sporting the Oracle T-Shirt. The new World Record was made, everyone gave a big cheer and thankfully nobody got injured! Thank you to everyone in Oracle who donated last year through the Giving Tree Appeal. Your generosity has gone a long way to support local groups both. Last year’s donation was so substantial that the IYF were able to spread it across two youth groups: The first being Ballybough Youth Project in Dublin. The funding gave them the chance to give 24 young people from their project the chance to get away from the inner city and the problems and issues they face in their daily life by taking a trip to the Cavan Centre to spend a weekend away in a safe and comfortable environment; a very rare holiday in these young people’s lives. The Rahoon Family Centre. Used the money to help secure the long term sustainability of their project. They act as an educational/social/fun project that has been working with disadvantaged children for the past 16 years. Their aim is to change young people’s future with fun /social education and supporting them so they can maximize their creativity and potential. We hope you can help support this worthy cause again this year, so keep an eye out for the Children’s Hour and Giving Tree Appeal! About the Irish Youth Foundation The IYF provides opportunities for marginalised children and young people facing difficult and extreme conditions to experience success in their lives. It passionately believes that achievement starts with opportunity. The IYF’s strategy is based on providing safe places where children can go after school; to grow, to learn and to play; and providing opportunities for teenagers from under-served communities to succeed and excel in their lives. The IYF supports innovative grassroots projects operated by dedicated professionals who understand young people and care about them. This allows the IYF to focus on supporting young people at risk of dropping out of school and, in particular, on the critical transition from primary to secondary school; and empowering teenagers from disadvantaged neighborhoods to become engaged in their local communities. Find out more here www.iyf.ie

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  • Understanding G1 GC Logs

    - by poonam
    The purpose of this post is to explain the meaning of GC logs generated with some tracing and diagnostic options for G1 GC. We will take a look at the output generated with PrintGCDetails which is a product flag and provides the most detailed level of information. Along with that, we will also look at the output of two diagnostic flags that get enabled with -XX:+UnlockDiagnosticVMOptions option - G1PrintRegionLivenessInfo that prints the occupancy and the amount of space used by live objects in each region at the end of the marking cycle and G1PrintHeapRegions that provides detailed information on the heap regions being allocated and reclaimed. We will be looking at the logs generated with JDK 1.7.0_04 using these options. Option -XX:+PrintGCDetails Here's a sample log of G1 collection generated with PrintGCDetails. 0.522: [GC pause (young), 0.15877971 secs] [Parallel Time: 157.1 ms] [GC Worker Start (ms): 522.1 522.2 522.2 522.2 Avg: 522.2, Min: 522.1, Max: 522.2, Diff: 0.1] [Ext Root Scanning (ms): 1.6 1.5 1.6 1.9 Avg: 1.7, Min: 1.5, Max: 1.9, Diff: 0.4] [Update RS (ms): 38.7 38.8 50.6 37.3 Avg: 41.3, Min: 37.3, Max: 50.6, Diff: 13.3] [Processed Buffers : 2 2 3 2 Sum: 9, Avg: 2, Min: 2, Max: 3, Diff: 1] [Scan RS (ms): 9.9 9.7 0.0 9.7 Avg: 7.3, Min: 0.0, Max: 9.9, Diff: 9.9] [Object Copy (ms): 106.7 106.8 104.6 107.9 Avg: 106.5, Min: 104.6, Max: 107.9, Diff: 3.3] [Termination (ms): 0.0 0.0 0.0 0.0 Avg: 0.0, Min: 0.0, Max: 0.0, Diff: 0.0] [Termination Attempts : 1 4 4 6 Sum: 15, Avg: 3, Min: 1, Max: 6, Diff: 5] [GC Worker End (ms): 679.1 679.1 679.1 679.1 Avg: 679.1, Min: 679.1, Max: 679.1, Diff: 0.1] [GC Worker (ms): 156.9 157.0 156.9 156.9 Avg: 156.9, Min: 156.9, Max: 157.0, Diff: 0.1] [GC Worker Other (ms): 0.3 0.3 0.3 0.3 Avg: 0.3, Min: 0.3, Max: 0.3, Diff: 0.0] [Clear CT: 0.1 ms] [Other: 1.5 ms] [Choose CSet: 0.0 ms] [Ref Proc: 0.3 ms] [Ref Enq: 0.0 ms] [Free CSet: 0.3 ms] [Eden: 12M(12M)->0B(10M) Survivors: 0B->2048K Heap: 13M(64M)->9739K(64M)] [Times: user=0.59 sys=0.02, real=0.16 secs] This is the typical log of an Evacuation Pause (G1 collection) in which live objects are copied from one set of regions (young OR young+old) to another set. It is a stop-the-world activity and all the application threads are stopped at a safepoint during this time. This pause is made up of several sub-tasks indicated by the indentation in the log entries. Here's is the top most line that gets printed for the Evacuation Pause. 0.522: [GC pause (young), 0.15877971 secs] This is the highest level information telling us that it is an Evacuation Pause that started at 0.522 secs from the start of the process, in which all the regions being evacuated are Young i.e. Eden and Survivor regions. This collection took 0.15877971 secs to finish. Evacuation Pauses can be mixed as well. In which case the set of regions selected include all of the young regions as well as some old regions. 1.730: [GC pause (mixed), 0.32714353 secs] Let's take a look at all the sub-tasks performed in this Evacuation Pause. [Parallel Time: 157.1 ms] Parallel Time is the total elapsed time spent by all the parallel GC worker threads. The following lines correspond to the parallel tasks performed by these worker threads in this total parallel time, which in this case is 157.1 ms. [GC Worker Start (ms): 522.1 522.2 522.2 522.2Avg: 522.2, Min: 522.1, Max: 522.2, Diff: 0.1] The first line tells us the start time of each of the worker thread in milliseconds. The start times are ordered with respect to the worker thread ids – thread 0 started at 522.1ms and thread 1 started at 522.2ms from the start of the process. The second line tells the Avg, Min, Max and Diff of the start times of all of the worker threads. [Ext Root Scanning (ms): 1.6 1.5 1.6 1.9 Avg: 1.7, Min: 1.5, Max: 1.9, Diff: 0.4] This gives us the time spent by each worker thread scanning the roots (globals, registers, thread stacks and VM data structures). Here, thread 0 took 1.6ms to perform the root scanning task and thread 1 took 1.5 ms. The second line clearly shows the Avg, Min, Max and Diff of the times spent by all the worker threads. [Update RS (ms): 38.7 38.8 50.6 37.3 Avg: 41.3, Min: 37.3, Max: 50.6, Diff: 13.3] Update RS gives us the time each thread spent in updating the Remembered Sets. Remembered Sets are the data structures that keep track of the references that point into a heap region. Mutator threads keep changing the object graph and thus the references that point into a particular region. We keep track of these changes in buffers called Update Buffers. The Update RS sub-task processes the update buffers that were not able to be processed concurrently, and updates the corresponding remembered sets of all regions. [Processed Buffers : 2 2 3 2Sum: 9, Avg: 2, Min: 2, Max: 3, Diff: 1] This tells us the number of Update Buffers (mentioned above) processed by each worker thread. [Scan RS (ms): 9.9 9.7 0.0 9.7 Avg: 7.3, Min: 0.0, Max: 9.9, Diff: 9.9] These are the times each worker thread had spent in scanning the Remembered Sets. Remembered Set of a region contains cards that correspond to the references pointing into that region. This phase scans those cards looking for the references pointing into all the regions of the collection set. [Object Copy (ms): 106.7 106.8 104.6 107.9 Avg: 106.5, Min: 104.6, Max: 107.9, Diff: 3.3] These are the times spent by each worker thread copying live objects from the regions in the Collection Set to the other regions. [Termination (ms): 0.0 0.0 0.0 0.0 Avg: 0.0, Min: 0.0, Max: 0.0, Diff: 0.0] Termination time is the time spent by the worker thread offering to terminate. But before terminating, it checks the work queues of other threads and if there are still object references in other work queues, it tries to steal object references, and if it succeeds in stealing a reference, it processes that and offers to terminate again. [Termination Attempts : 1 4 4 6 Sum: 15, Avg: 3, Min: 1, Max: 6, Diff: 5] This gives the number of times each thread has offered to terminate. [GC Worker End (ms): 679.1 679.1 679.1 679.1 Avg: 679.1, Min: 679.1, Max: 679.1, Diff: 0.1] These are the times in milliseconds at which each worker thread stopped. [GC Worker (ms): 156.9 157.0 156.9 156.9 Avg: 156.9, Min: 156.9, Max: 157.0, Diff: 0.1] These are the total lifetimes of each worker thread. [GC Worker Other (ms): 0.3 0.3 0.3 0.3Avg: 0.3, Min: 0.3, Max: 0.3, Diff: 0.0] These are the times that each worker thread spent in performing some other tasks that we have not accounted above for the total Parallel Time. [Clear CT: 0.1 ms] This is the time spent in clearing the Card Table. This task is performed in serial mode. [Other: 1.5 ms] Time spent in the some other tasks listed below. The following sub-tasks (which individually may be parallelized) are performed serially. [Choose CSet: 0.0 ms] Time spent in selecting the regions for the Collection Set. [Ref Proc: 0.3 ms] Total time spent in processing Reference objects. [Ref Enq: 0.0 ms] Time spent in enqueuing references to the ReferenceQueues. [Free CSet: 0.3 ms] Time spent in freeing the collection set data structure. [Eden: 12M(12M)->0B(13M) Survivors: 0B->2048K Heap: 14M(64M)->9739K(64M)] This line gives the details on the heap size changes with the Evacuation Pause. This shows that Eden had the occupancy of 12M and its capacity was also 12M before the collection. After the collection, its occupancy got reduced to 0 since everything is evacuated/promoted from Eden during a collection, and its target size grew to 13M. The new Eden capacity of 13M is not reserved at this point. This value is the target size of the Eden. Regions are added to Eden as the demand is made and when the added regions reach to the target size, we start the next collection. Similarly, Survivors had the occupancy of 0 bytes and it grew to 2048K after the collection. The total heap occupancy and capacity was 14M and 64M receptively before the collection and it became 9739K and 64M after the collection. Apart from the evacuation pauses, G1 also performs concurrent-marking to build the live data information of regions. 1.416: [GC pause (young) (initial-mark), 0.62417980 secs] ….... 2.042: [GC concurrent-root-region-scan-start] 2.067: [GC concurrent-root-region-scan-end, 0.0251507] 2.068: [GC concurrent-mark-start] 3.198: [GC concurrent-mark-reset-for-overflow] 4.053: [GC concurrent-mark-end, 1.9849672 sec] 4.055: [GC remark 4.055: [GC ref-proc, 0.0000254 secs], 0.0030184 secs] [Times: user=0.00 sys=0.00, real=0.00 secs] 4.088: [GC cleanup 117M->106M(138M), 0.0015198 secs] [Times: user=0.00 sys=0.00, real=0.00 secs] 4.090: [GC concurrent-cleanup-start] 4.091: [GC concurrent-cleanup-end, 0.0002721] The first phase of a marking cycle is Initial Marking where all the objects directly reachable from the roots are marked and this phase is piggy-backed on a fully young Evacuation Pause. 2.042: [GC concurrent-root-region-scan-start] This marks the start of a concurrent phase that scans the set of root-regions which are directly reachable from the survivors of the initial marking phase. 2.067: [GC concurrent-root-region-scan-end, 0.0251507] End of the concurrent root region scan phase and it lasted for 0.0251507 seconds. 2.068: [GC concurrent-mark-start] Start of the concurrent marking at 2.068 secs from the start of the process. 3.198: [GC concurrent-mark-reset-for-overflow] This indicates that the global marking stack had became full and there was an overflow of the stack. Concurrent marking detected this overflow and had to reset the data structures to start the marking again. 4.053: [GC concurrent-mark-end, 1.9849672 sec] End of the concurrent marking phase and it lasted for 1.9849672 seconds. 4.055: [GC remark 4.055: [GC ref-proc, 0.0000254 secs], 0.0030184 secs] This corresponds to the remark phase which is a stop-the-world phase. It completes the left over marking work (SATB buffers processing) from the previous phase. In this case, this phase took 0.0030184 secs and out of which 0.0000254 secs were spent on Reference processing. 4.088: [GC cleanup 117M->106M(138M), 0.0015198 secs] Cleanup phase which is again a stop-the-world phase. It goes through the marking information of all the regions, computes the live data information of each region, resets the marking data structures and sorts the regions according to their gc-efficiency. In this example, the total heap size is 138M and after the live data counting it was found that the total live data size dropped down from 117M to 106M. 4.090: [GC concurrent-cleanup-start] This concurrent cleanup phase frees up the regions that were found to be empty (didn't contain any live data) during the previous stop-the-world phase. 4.091: [GC concurrent-cleanup-end, 0.0002721] Concurrent cleanup phase took 0.0002721 secs to free up the empty regions. Option -XX:G1PrintRegionLivenessInfo Now, let's look at the output generated with the flag G1PrintRegionLivenessInfo. This is a diagnostic option and gets enabled with -XX:+UnlockDiagnosticVMOptions. G1PrintRegionLivenessInfo prints the live data information of each region during the Cleanup phase of the concurrent-marking cycle. 26.896: [GC cleanup ### PHASE Post-Marking @ 26.896### HEAP committed: 0x02e00000-0x0fe00000 reserved: 0x02e00000-0x12e00000 region-size: 1048576 Cleanup phase of the concurrent-marking cycle started at 26.896 secs from the start of the process and this live data information is being printed after the marking phase. Committed G1 heap ranges from 0x02e00000 to 0x0fe00000 and the total G1 heap reserved by JVM is from 0x02e00000 to 0x12e00000. Each region in the G1 heap is of size 1048576 bytes. ### type address-range used prev-live next-live gc-eff### (bytes) (bytes) (bytes) (bytes/ms) This is the header of the output that tells us about the type of the region, address-range of the region, used space in the region, live bytes in the region with respect to the previous marking cycle, live bytes in the region with respect to the current marking cycle and the GC efficiency of that region. ### FREE 0x02e00000-0x02f00000 0 0 0 0.0 This is a Free region. ### OLD 0x02f00000-0x03000000 1048576 1038592 1038592 0.0 Old region with address-range from 0x02f00000 to 0x03000000. Total used space in the region is 1048576 bytes, live bytes as per the previous marking cycle are 1038592 and live bytes with respect to the current marking cycle are also 1038592. The GC efficiency has been computed as 0. ### EDEN 0x03400000-0x03500000 20992 20992 20992 0.0 This is an Eden region. ### HUMS 0x0ae00000-0x0af00000 1048576 1048576 1048576 0.0### HUMC 0x0af00000-0x0b000000 1048576 1048576 1048576 0.0### HUMC 0x0b000000-0x0b100000 1048576 1048576 1048576 0.0### HUMC 0x0b100000-0x0b200000 1048576 1048576 1048576 0.0### HUMC 0x0b200000-0x0b300000 1048576 1048576 1048576 0.0### HUMC 0x0b300000-0x0b400000 1048576 1048576 1048576 0.0### HUMC 0x0b400000-0x0b500000 1001480 1001480 1001480 0.0 These are the continuous set of regions called Humongous regions for storing a large object. HUMS (Humongous starts) marks the start of the set of humongous regions and HUMC (Humongous continues) tags the subsequent regions of the humongous regions set. ### SURV 0x09300000-0x09400000 16384 16384 16384 0.0 This is a Survivor region. ### SUMMARY capacity: 208.00 MB used: 150.16 MB / 72.19 % prev-live: 149.78 MB / 72.01 % next-live: 142.82 MB / 68.66 % At the end, a summary is printed listing the capacity, the used space and the change in the liveness after the completion of concurrent marking. In this case, G1 heap capacity is 208MB, total used space is 150.16MB which is 72.19% of the total heap size, live data in the previous marking was 149.78MB which was 72.01% of the total heap size and the live data as per the current marking is 142.82MB which is 68.66% of the total heap size. Option -XX:+G1PrintHeapRegions G1PrintHeapRegions option logs the regions related events when regions are committed, allocated into or are reclaimed. COMMIT/UNCOMMIT events G1HR COMMIT [0x6e900000,0x6ea00000]G1HR COMMIT [0x6ea00000,0x6eb00000] Here, the heap is being initialized or expanded and the region (with bottom: 0x6eb00000 and end: 0x6ec00000) is being freshly committed. COMMIT events are always generated in order i.e. the next COMMIT event will always be for the uncommitted region with the lowest address. G1HR UNCOMMIT [0x72700000,0x72800000]G1HR UNCOMMIT [0x72600000,0x72700000] Opposite to COMMIT. The heap got shrunk at the end of a Full GC and the regions are being uncommitted. Like COMMIT, UNCOMMIT events are also generated in order i.e. the next UNCOMMIT event will always be for the committed region with the highest address. GC Cycle events G1HR #StartGC 7G1HR CSET 0x6e900000G1HR REUSE 0x70500000G1HR ALLOC(Old) 0x6f800000G1HR RETIRE 0x6f800000 0x6f821b20G1HR #EndGC 7 This shows start and end of an Evacuation pause. This event is followed by a GC counter tracking both evacuation pauses and Full GCs. Here, this is the 7th GC since the start of the process. G1HR #StartFullGC 17G1HR UNCOMMIT [0x6ed00000,0x6ee00000]G1HR POST-COMPACTION(Old) 0x6e800000 0x6e854f58G1HR #EndFullGC 17 Shows start and end of a Full GC. This event is also followed by the same GC counter as above. This is the 17th GC since the start of the process. ALLOC events G1HR ALLOC(Eden) 0x6e800000 The region with bottom 0x6e800000 just started being used for allocation. In this case it is an Eden region and allocated into by a mutator thread. G1HR ALLOC(StartsH) 0x6ec00000 0x6ed00000G1HR ALLOC(ContinuesH) 0x6ed00000 0x6e000000 Regions being used for the allocation of Humongous object. The object spans over two regions. G1HR ALLOC(SingleH) 0x6f900000 0x6f9eb010 Single region being used for the allocation of Humongous object. G1HR COMMIT [0x6ee00000,0x6ef00000]G1HR COMMIT [0x6ef00000,0x6f000000]G1HR COMMIT [0x6f000000,0x6f100000]G1HR COMMIT [0x6f100000,0x6f200000]G1HR ALLOC(StartsH) 0x6ee00000 0x6ef00000G1HR ALLOC(ContinuesH) 0x6ef00000 0x6f000000G1HR ALLOC(ContinuesH) 0x6f000000 0x6f100000G1HR ALLOC(ContinuesH) 0x6f100000 0x6f102010 Here, Humongous object allocation request could not be satisfied by the free committed regions that existed in the heap, so the heap needed to be expanded. Thus new regions are committed and then allocated into for the Humongous object. G1HR ALLOC(Old) 0x6f800000 Old region started being used for allocation during GC. G1HR ALLOC(Survivor) 0x6fa00000 Region being used for copying old objects into during a GC. Note that Eden and Humongous ALLOC events are generated outside the GC boundaries and Old and Survivor ALLOC events are generated inside the GC boundaries. Other Events G1HR RETIRE 0x6e800000 0x6e87bd98 Retire and stop using the region having bottom 0x6e800000 and top 0x6e87bd98 for allocation. Note that most regions are full when they are retired and we omit those events to reduce the output volume. A region is retired when another region of the same type is allocated or we reach the start or end of a GC(depending on the region). So for Eden regions: For example: 1. ALLOC(Eden) Foo2. ALLOC(Eden) Bar3. StartGC At point 2, Foo has just been retired and it was full. At point 3, Bar was retired and it was full. If they were not full when they were retired, we will have a RETIRE event: 1. ALLOC(Eden) Foo2. RETIRE Foo top3. ALLOC(Eden) Bar4. StartGC G1HR CSET 0x6e900000 Region (bottom: 0x6e900000) is selected for the Collection Set. The region might have been selected for the collection set earlier (i.e. when it was allocated). However, we generate the CSET events for all regions in the CSet at the start of a GC to make sure there's no confusion about which regions are part of the CSet. G1HR POST-COMPACTION(Old) 0x6e800000 0x6e839858 POST-COMPACTION event is generated for each non-empty region in the heap after a full compaction. A full compaction moves objects around, so we don't know what the resulting shape of the heap is (which regions were written to, which were emptied, etc.). To deal with this, we generate a POST-COMPACTION event for each non-empty region with its type (old/humongous) and the heap boundaries. At this point we should only have Old and Humongous regions, as we have collapsed the young generation, so we should not have eden and survivors. POST-COMPACTION events are generated within the Full GC boundary. G1HR CLEANUP 0x6f400000G1HR CLEANUP 0x6f300000G1HR CLEANUP 0x6f200000 These regions were found empty after remark phase of Concurrent Marking and are reclaimed shortly afterwards. G1HR #StartGC 5G1HR CSET 0x6f400000G1HR CSET 0x6e900000G1HR REUSE 0x6f800000 At the end of a GC we retire the old region we are allocating into. Given that its not full, we will carry on allocating into it during the next GC. This is what REUSE means. In the above case 0x6f800000 should have been the last region with an ALLOC(Old) event during the previous GC and should have been retired before the end of the previous GC. G1HR ALLOC-FORCE(Eden) 0x6f800000 A specialization of ALLOC which indicates that we have reached the max desired number of the particular region type (in this case: Eden), but we decided to allocate one more. Currently it's only used for Eden regions when we extend the young generation because we cannot do a GC as the GC-Locker is active. G1HR EVAC-FAILURE 0x6f800000 During a GC, we have failed to evacuate an object from the given region as the heap is full and there is no space left to copy the object. This event is generated within GC boundaries and exactly once for each region from which we failed to evacuate objects. When Heap Regions are reclaimed ? It is also worth mentioning when the heap regions in the G1 heap are reclaimed. All regions that are in the CSet (the ones that appear in CSET events) are reclaimed at the end of a GC. The exception to that are regions with EVAC-FAILURE events. All regions with CLEANUP events are reclaimed. After a Full GC some regions get reclaimed (the ones from which we moved the objects out). But that is not shown explicitly, instead the non-empty regions that are left in the heap are printed out with the POST-COMPACTION events.

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  • Issues running commands

    - by Joel
    Every time I run a command I get this back. E: Could not open lock file /var/lib/apt/lists/lock - open (13: Permission denied) E: Unable to lock directory /var/lib/apt/lists/ E: Could not open lock file /var/lib/dpkg/lock - open (13: Permission denied) E: Unable to lock the administration directory (/var/lib/dpkg/), are you root? christopher@christopher:~$ This didn't start happening until I changed my device name.

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  • Tuning garbage collections for low latency

    - by elec
    I'm looking for arguments as to how best to size the young generation (with respect to the old generation) in an environment where low latency is critical. My own testing tends to show that latency is lowest when the young generation is fairly large (eg. -XX:NewRatio <3), however I cannot reconcile this with the intuition that the larger the young generation the more time it should take to garbage collect. The application runs on linux, jdk 6 before update 14, i.e G1 not available.

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  • Silverlight Cream for May 02, 2010 -- #854

    - by Dave Campbell
    In this Issue: Michael Washington, Jason Young(-2-, -3-), Phil Middlemiss, Jeremy Likness, Victor Gaudioso, Kunal Chowdhury, Antoni Dol, and Jacek Ciereszko(-2-). Shoutout: Victor Gaudioso has aggregated All of My Silverlight Video Tutorials in One Place (revised again 05.02.10) From SilverlightCream.com: Unit Testing A Silverlight 'Simplified MVVM' Modal Popup Michael Washington's latest 'Simplified MVVM' post is published at The Code Project and is on Unit Testing with MVVM. Input Localization in Silverlight without IValueConverter Jason Young sent me some links to posts I've not seen... this first one is on localization by using the Language property of the Root Visual. MVVM – The Model - Part 1 – INotifyPropertyChanged Jason Young's next archive post is the first of a series on MVVM and Silverlight 4 ... implementing a simple ViewModel base class. Silverlight, WCF, and ASP.Net Configuration Gotchas Jason Young worked at tracking down the answers to some forum questions and in the process has produced a post of 'gotchas' with using WCF in Silverlight. A Chrome and Glass Theme - Part 5 Phil Middlemiss has part 5 of his Chrome and Glass Theme tutorial up ... in this one, he's looking at the Progress Bar and Slider. Download the files and play along. Silverlight Out of Browser (OOB) Versions, Images, and Isolated Storage Jeremy Likness has a post up responding to his 3 major questions about OOB apps, and he has to code up for the sample too. New Silverlight Video Tutorial: How to Make a Slide In/Out Navigation Bar – All in Blend Victor Gaudioso's latest video tutorial is on building a Behavior for a Slide in/out Navigation bar... kinda like the menu sliders on my GlyphMap Utility... only easier! Command Binding in Silverlight 4 (Step-by-Step) Kunal Chowdhury has another post up at DotNetFunda, and this time he's talking about Command Binding in Silverlight 4 with an eye toward MVVM usage. The Silverlight PageCurl implementation Antoni Dol has a post up about doing a Page Curl effect in Silverlight. He has a manual up on the effect and full application code. How to center and scale Silverlight applications using ViewBox control Jacek Ciereszko has a couple posts up about centering and scaling your app with the ViewBox control. This first one is a code solution. Source is available, as is a Polish version. Silverlight Center And Scale Behavior Jacek Ciereszko's 2nd post, he provides a Behavior that handles the scaling and centering of the previous post. Stay in the 'Light! Twitter SilverlightNews | Twitter WynApse | WynApse.com | Tagged Posts | SilverlightCream Join me @ SilverlightCream | Phoenix Silverlight User Group Technorati Tags: Silverlight    Silverlight 3    Silverlight 4    Windows Phone MIX10

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  • Trainee programs for foreigner in EU [closed]

    - by user63970
    Maybe this is the wrong site for asking that, but I didn't find better. I heard a lot about programs for young IT specialists in EU from other countries, my residence is Ukraine. We have several organizations that provide info about them, but you must pay quite a lot for them to only show you the list of vacancies. Maybe someone knows about companies in EU that are willing to take young programmers for trainee or junior vacancies from non-EU countries? I am interested in C++ development.

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  • Why is this PHP loop rendering every row twice?

    - by Christopher
    I'm working on a real frankensite here not of my own design. There's a rudimentary CMS and one of the pages shows customer records from a MySQL DB. For some reason, it has no probs picking up the data from the DB - there's no duplicate records - but it renders each row twice. The page PHP is viewable at http://christopher.pastebin.com/DQkjjG3s (attempted to include in this post but it was horribly mangled, think it's important to have it all in context). I'm not the world's best PHP expert but I think I can see an error in a for loop when there is one... But everything looks ok to me. You'll notice that the customer name is clickable; clicking takes you to another page where you can view their full info as held in the DB - and for both rows, the customer ID is identical, and manually checking the DB shows there's no duplicate entries. The code is definitely rendering each row twice, but for what reason I have no idea. All pointers / advice appreciated.

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  • Backpacks and Booth Paint: TechEd 2012

    - by The Un-T Guy
    Arriving in the parking lot of the Orange County Convention Center, I immediately knew I was in the right place. As far as the eye could see, the acres of asphalt were awash in backpacks, quirky (to be kind) outfits, and bad haircuts. This was the place. This was Microsoft Mecca v2012 for geeks and nerds, the Central Florida event of the year, a gathering of high tech professionals whose skills I both greatly respect and, frankly, fear a little. I was wholly and completely out of element, a dork in a vast sea of geek jumbo. It like was wearing dockers and a golf shirt walking into a RenFaire, but one with really crappy costumes and no turkey legs...save those attached to some of the attendees. Of course the corporate whores...errrr, vendors were in place, ready to parlay the convention's fre-nerd-ic energy into millions of dollars by convincing the big-brained and under-sexed in the crowd (i.e., virtually all of them...present company excluded, of course) that their product or service was the only thing standing between them and professional success, industry fame, and clear skin. "With KramTech 2012," they seemed to scream, "you will be THE ROCK STAR of your company's IT department!" As car shows and tattoo parlors learned long ago, Tech companies seem to believe that the best way to attract the attention of this crowd is through the hint of the promise of sex. They recruit and deploy an army of "sales reps" whose primary qualifications appear to be long hair, short skirts, high heels, and a vagina. Unlike their distant cousins in the car and body art industries, however, this sub-species of booth paint (semi-gloss decoration that adds nothing to the substance of the product) seems torn between committing to being all-out sex objects and recognition that they are in the presence of intelligent, discerning people. People who are smart enough to know exactly what these vendors are doing. Also unlike their distant car show and tattoo shop cousins, these young women (what…are there no gay tech professionals who could use some eye candy?) seem to realize that while IT remains a male-dominated field, there are ever-increasing numbers of intelligent, capable, strong professional women – women who’ve battled to make it in this field through hard work and work performance rather than a hard body and performing after work. This is not to say that all of the young female sales reps are there only because of their physical attributes. Many are competent, intelligent, and driven -- not to mention attractive. They're working hard on the front lines of delivering the next generation of technology. The distinction is pretty clear, however, between these young professionals and the booth paint. The former enthusiastically deliver credible information about the products they’re hawking. The latter are positioned in the aisles, uncomfortably avoiding eye contact as they struggle to operate the badge readers. Surprisingly, not all of the women in attendance seemed to object to the objectification of their younger sisters. One IT professional woman who came of age in the industry (mostly in IT marketing) said, “I have no problem with it. I was a ‘booth babe’ for years and it doesn’t bother me at all.” Others, however, weren’t quite so gracious. One woman I spoke with, an IT manager from Cheyenne, Wyoming, said it was demeaning and frankly, as more and more women grow into IT management positions, not a great marketing idea. “Using these young women is, to me, no different than vendors giving out t-shirts to attract attention. It’s sad because it’s still hard for a woman to be respected in the IT field and this just perpetuates the outdated notion that IT is a male-dominated field.” She went on to say that decisions by vendors to employ these young women in this “inappropriate way” could impact her purchasing decisions. “I might be swayed toward a vendor who has women on staff who are intelligent and dynamic rather than the vendors who use the ‘decoration’ girls.” So in many ways, the IT industry is no different than most other industries as it struggles to maximize performance by finding and developing talent – all of the talent, not just the 50% with a penis. Women in IT, like their brethren, struggle to find their niche in the field, to grow professionally, and reach for the brass ring, struggling to overcome obstacles as they climb the mountain of professional success in a never-ending cycle of economic uncertainty. But as (generally) well-educated and highly-trained professionals, they are probably better positioned than those in many other industries. Beside, they’ve got one other advantage over their non-IT counterparts as they attempt their ascent to the summit: They’ve already got the backpacks.

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  • How to update the text of a tag in XML using Elementree

    - by Christopher
    Using elementree, the easiest way to read the text of a tag is to do the following: import elementtree.ElementTree as ET sKeyMap = ET.parse("KeyMaps/KeyMap_Checklist.xml") host = sKeyMap.findtext("/BrowserInformation/BrowserSetup/host") Now I want to update the text in the same file, hopefully without having to re-write it with something easy like: host = "4444" sKeyMap.replacetext("/BrowserInformation/BrowserSetup/host") Any ideas? Thx in advance Christopher

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  • How to create dynamic Scatter Plot/Matrix with labels and categories on both axis in Excel 2010?

    - by user1581900
    Let us consider a following data set: Name | Age | Hair Color ----------------------------- John | Young | Brown Sophie | Old | Blond Adam | Mature| Blond Mark | Teen | Dark Jeremy | Old | Grey Alex | Young | Brown etc... Both Age and Hair Color, can take only defined values(Young/teen/mature/old and Blond/brown/Dark/Grey). Name is the only real variable here. I want to create a Scatter Plot / Matrix that will look something like that: I know that I schould use this tool to add labels to the scatter plot. I also found this youtube video that explains how to display categories on Y-axis Moreover I need the chart to be dynamic as explained in another youtube video. How do I combine all these approaches to get a Scatter Plot with categories as values on both axis?

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  • I am the one who needs 5000 cd's!!!!!!

    - by cabey
    I didn't realise how world wide the users of the site are. I am based in England and will be helping out at an International Camp for young people in Finland this summer. I will be in charge off an game where we will have 1500 young people searcing for these CD's that will be hidden all over the camp site. They will have to find them and bringing them back to base one at a time. The young people will be divided into 5 teams and the team that brings back the most gets a prize. Hope this helps and allows me to put the reequest back on the site. I have tried to source them in Finland but have had no success.

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  • Simple XML variable returns "0"

    - by Warren Haskins
    Ok, so im working with tumblr's API and im using SimpleXMLElement with php and ive run into a problem. Basically, when I call on a variable it just prints a zero. for instance : <?php echo $data->posts->post[0]->photo-url[5]; ?> This just breaks the page, and when I leave out the [5] it just prints zero I want to echo the photo-url with the number [5] but I cant get my head around this and its killing me. I just dont understand why it wont work. Heres a sample of the simpleXMLElement print-out : SimpleXMLElement Object ( [@attributes] = Array ( [version] = 1.0 ) [posts] => SimpleXMLElement Object ( [@attributes] => Array ( [type] => photo ) [post] => Array ( [0] => SimpleXMLElement Object ( [@attributes] => Array ( [id] => 574166180 [url] => http://young-n-reckless.tumblr.com/post/574166180 [url-with-slug] => http://young-n-reckless.tumblr.com/post/574166180 [type] => photo [date-gmt] => 2010-05-05 19:52:46 GMT [date] => Wed, 05 May 2010 15:52:46 [unix-timestamp] => 1273089166 [format] => html [reblog-key] => vc9EfT7d [slug] => [tumblelog] => young-n-reckless [bookmarklet] => true ) [tumblelog] => SimpleXMLElement Object ( [@attributes] => Array ( [title] => Young & Reckless [name] => young-n-reckless [url] => http://young-n-reckless.tumblr.com/ [timezone] => US/Eastern [avatar-url-16] => http://24.media.tumblr.com/avatar_424f776dd55c_16.png [avatar-url-24] => http://24.media.tumblr.com/avatar_424f776dd55c_24.png [avatar-url-30] => http://24.media.tumblr.com/avatar_424f776dd55c_30.png [avatar-url-40] => http://25.media.tumblr.com/avatar_424f776dd55c_40.png [avatar-url-48] => http://24.media.tumblr.com/avatar_424f776dd55c_48.png [avatar-url-64] => http://26.media.tumblr.com/avatar_424f776dd55c_64.png [avatar-url-96] => http://25.media.tumblr.com/avatar_424f776dd55c_96.png [avatar-url-128] => http://24.media.tumblr.com/avatar_424f776dd55c_128.png [avatar-url-512] => http://29.media.tumblr.com/avatar_424f776dd55c_512.png ) ) [photo-link-url] => http://www.flickr.com/photos/katiemabey/3429588812/ [photo-url] => Array ( [0] => http://30.media.tumblr.com/tumblr_l1w1qd7Zcg1qatgoto1_500.jpg [1] => http://30.media.tumblr.com/tumblr_l1w1qd7Zcg1qatgoto1_500.jpg [2] => http://25.media.tumblr.com/tumblr_l1w1qd7Zcg1qatgoto1_400.jpg [3] => http://28.media.tumblr.com/tumblr_l1w1qd7Zcg1qatgoto1_250.jpg [4] => http://30.media.tumblr.com/tumblr_l1w1qd7Zcg1qatgoto1_100.jpg [5] => http://30.media.tumblr.com/tumblr_l1w1qd7Zcg1qatgoto1_75sq.jpg ) ) Any help would be greatly appreciated, and thanks in advance. Warren

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  • Jquery getJSON cross domain problems

    - by Charlie
    I cant seem to get my JSON file to work when pulling it in from another domain using JQuerys getJSON. I have placed the callback part at the end of the url but still have no joy. Firebug tells me its a cross domain issue, which seems to make sense as if I place the json file locally the below code (excluding the ?jsoncallback=? works fine) Heres the Jquery part $.getJSON("http://anotherdomain/js/morearticles.js?jsoncallback=?", function(json){ if (show5More.nextSetCount ' + this.titletext + '' + this.paratext + '').appendTo("#lineupswitch"); } else { $('' + this.titletext + '' + this.paratext + '').appendTo("#lineupswitch"); } }); return false; } }); } }); } And the JSON, which I have validated. { "items": [ [ { "href": "/edinburgh/video/news-090415-s2-squalor-edinburgh/", "thumbimg": "http://brightcove.vo.llnwd.net/d7/unsecured/media/1486976045/1486976045_19721015001_asset-1239819553334.jpg?pubId=1486976045", "titletext": "Cannabis plants found in house with neglected children", "paratext": "A court has heard four young children lived in", "cname": "" }, { "href": "/edinburgh/video/news-090414-s2-waverley-station-edinburgh/", "thumbimg": "http://brightcove.vo.llnwd.net/d7/unsecured/media/1486976045/1486976045_19537855001_asset-1239732920496.jpg?pubId=1486976045", "titletext": "Multi-million pound revamp for Waverley Station", "paratext": "Edinburgh's Waverley Station is set for a", "cname": "" }, { "href": "/edinburgh/video/news-s2-natal-20090408/", "thumbimg":"http://brightcove.vo.llnwd.net/d7/unsecured/media/1486976045/1486976045_18948154001_asset-1239206353135.jpg?pubId=1486976045", "titletext": "Stillbirth charity on the road to raise awareness", "paratext": "SANDS Lothian are hoping to highlight their", "cname": "" }, { "href": "/edinburgh/video/news-090407-l2-rbs/", "thumbimg":"http://brightcove.vo.llnwd.net/d7/unsecured/media/1486976045/1486976045_18827378001_asset-1239110600777.jpg?pubId=1486976045", "titletext": "Thousands of jobs to go at Royal Bank of Scotland", "paratext": "Edinburgh-based bank to cut 4,500 positions in the", "cname": "" }, { "href": "/edinburgh/video/news-090415-s2-squalor-edinburgh/", "thumbimg": "http://brightcove.vo.llnwd.net/d7/unsecured/media/1486976045/1486976045_19721015001_asset-1239819553334.jpg?pubId=1486976045", "titletext": "1", "paratext": "A court has heard four young children lived in", "cname": "lastlineup" } ], [ { "href": "/edinburgh/video/news-090415-s2-squalor-edinburgh/", "thumbimg": "http://brightcove.vo.llnwd.net/d7/unsecured/media/1486976045/1486976045_19721015001_asset-1239819553334.jpg?pubId=1486976045", "titletext": "1", "paratext": "A court has heard four young children lived in", "cname": "" }, { "href": "/edinburgh/video/news-090414-s2-waverley-station-edinburgh/", "thumbimg": "http://brightcove.vo.llnwd.net/d7/unsecured/media/1486976045/1486976045_19537855001_asset-1239732920496.jpg?pubId=1486976045", "titletext": "2", "paratext": "Edinburgh's Waverley Station is set for a", "cname": "" }, { "href": "/edinburgh/video/news-s2-natal-20090408/", 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  • Natural talent vs experience [on hold]

    - by Tord Johansson Munk
    Hi i have a question for you guys if you had a choice of hiring one of two programmers. One of them is a natural born programming talent, he has been programming since he was 14 year old and he has been programming all sorts of things by him self, 3d renders,games,his own frameworks, he is really good at algorithms and problem solving. He is now about 25 years old and is looking for a job after some unchallenged years of college the only experience he has is working on his own/university stuff and some open source project. This guy spends all his free time programming and has several pet projects at home. The other person is a 37 year old career programmer. He has been programming since he graduated from university at the age of 26 and have been working since then. He did not have an interest in programming before university. During his studies he discovered that programming was fun and challenging but it never was a "passion". During his career he mainly worked with "enterprise" platforms such as .net or javaEE. He mainly have done database business applications and thus is lacking skills of the young talent like abstract problem solving or algorithms. But he know the tools he has been using during the years and is reliable and almost always makes his boss happy. He keeps him self updated in the platform and tools he has and is using. But outside the office walls he don't touch any code at all. Witch one would you hire? Would you favor one of them in certain projects? Do you think that if the young talent learns his tools he will be a better programmer than the older one? Would your decision be different if both of them where lacking a degree? or if only one of them was lacking a degree be the old and experienced or the young genius.

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  • MySQL 5.5 is GA!

    - by rob.young(at)oracle.com
    It is my pleasure to announce that MySQL 5.5 is now GA and ready for production deployment.  You can read Oracle's official press release here. I am excited about 5.5 because of the performance and scalability gains, new replication enhancements and overall improved technical efficiencies.  Congratulations and a sincere "Thanks!" go out to the entire MySQL Community and product engineering teams for making 5.5 the best release of MySQL to date.Please join us for today's MySQL Technology Update webcast where Tomas Ulin and I will cover what's new in MySQL 5.5 and provide an update on the other technologies we are working on. You can download MySQL 5.5 here.  All of the documentation and what's new information is here.  There is also a great article on MySQL 5.5 and the MySQL community here.Thanks for reading, and as always, THANKS for your support of MySQL!

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  • Form, function and complexity in rule processing

    - by Charles Young
    Tim Bass posted on ‘Orwellian Event Processing’. I was involved in a heated exchange in the comments, and he has more recently published a post entitled ‘Disadvantages of Rule-Based Systems (Part 1)’. Whatever the rights and wrongs of our exchange, it clearly failed to generate any agreement or understanding of our different positions. I don't particularly want to promote further argument of that kind, but I do want to take the opportunity of offering a different perspective on rule-processing and an explanation of my comments. For me, the ‘red rag’ lay in Tim’s claim that “...rules alone are highly inefficient for most classes of (not simple) problems” and a later paragraph that appears to equate the simplicity of form (‘IF-THEN-ELSE’) with simplicity of function.   It is not the first time Tim has expressed these views and not the first time I have responded to his assertions.   Indeed, Tim has a long history of commenting on the subject of complex event processing (CEP) and, less often, rule processing in ‘robust’ terms, often asserting that very many other people’s opinions on this subject are mistaken.   In turn, I am of the opinion that, certainly in terms of rule processing, which is an area in which I have a specific interest and knowledge, he is often mistaken. There is no simple answer to the fundamental question ‘what is a rule?’ We use the word in a very fluid fashion in English. Likewise, the term ‘rule processing’, as used widely in IT, is equally difficult to define simplistically. The best way to envisage the term is as a ‘centre of gravity’ within a wider domain. That domain contains many other ‘centres of gravity’, including CEP, statistical analytics, neural networks, natural language processing and so much more. Whole communities tend to gravitate towards and build themselves around some of these centres. The term 'rule processing' is associated with many different technology types, various software products, different architectural patterns, the functional capability of many applications and services, etc. There is considerable variation amongst these different technologies, techniques and products. Very broadly, a common theme is their ability to manage certain types of processing and problem solving through declarative, or semi-declarative, statements of propositional logic bound to action-based consequences. It is generally important to be able to decouple these statements from other parts of an overall system or architecture so that they can be managed and deployed independently.  As a centre of gravity, ‘rule processing’ is no island. It exists in the context of a domain of discourse that is, itself, highly interconnected and continuous.   Rule processing does not, for example, exist in splendid isolation to natural language processing.   On the contrary, an on-going theme of rule processing is to find better ways to express rules in natural language and map these to executable forms.   Rule processing does not exist in splendid isolation to CEP.   On the contrary, an event processing agent can reasonably be considered as a rule engine (a theme in ‘Power of Events’ by David Luckham).   Rule processing does not live in splendid isolation to statistical approaches such as Bayesian analytics. On the contrary, rule processing and statistical analytics are highly synergistic.   Rule processing does not even live in splendid isolation to neural networks. For example, significant research has centred on finding ways to translate trained nets into explicit rule sets in order to support forms of validation and facilitate insight into the knowledge stored in those nets. What about simplicity of form?   Many rule processing technologies do indeed use a very simple form (‘If...Then’, ‘When...Do’, etc.)   However, it is a fundamental mistake to equate simplicity of form with simplicity of function.   It is absolutely mistaken to suggest that simplicity of form is a barrier to the efficient handling of complexity.   There are countless real-world examples which serve to disprove that notion.   Indeed, simplicity of form is often the key to handling complexity. Does rule processing offer a ‘one size fits all’. No, of course not.   No serious commentator suggests it does.   Does the design and management of large knowledge bases, expressed as rules, become difficult?   Yes, it can do, but that is true of any large knowledge base, regardless of the form in which knowledge is expressed.   The measure of complexity is not a function of rule set size or rule form.  It tends to be correlated more strongly with the size of the ‘problem space’ (‘search space’) which is something quite different.   Analysis of the problem space and the algorithms we use to search through that space are, of course, the very things we use to derive objective measures of the complexity of a given problem. This is basic computer science and common practice. Sailing a Dreadnaught through the sea of information technology and lobbing shells at some of the islands we encounter along the way does no one any good.   Building bridges and causeways between islands so that the inhabitants can collaborate in open discourse offers hope of real progress.

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  • Advise on career development [closed]

    - by Mike Young
    I am an amateur programmer working at a start-up. I didn't try coding at college. I've been working for 2 months now on web development. I'm satisfied with my progress. My project will go live soon. I work on front-end and my colleague integrates my work in his. So I decided to learn back-end technologies so that I would be able to work on a project from scratch, help my company build up. I recently got to know about the technologies used by fb and was fascinated to learn ,work on them,keep motivating myself. Now I want to work on building a product from scratch, be good at database concepts, a language like ruby or python, and get to know load balancing, dynamic requests from servers, hosting a website, real time communication, secured login, implementing sophisticated search feature for the app, using git by the end of the project.I would like to be a full stack developer in due course of time and learn everything in detail. I decide to keep myself out of time frame, learn every concept in detail.I would like to use both rdbms and non relational dbm for the project. I have no experience except some beginner knowledge in html5,css and JavaScript. I would like to get some advice on how to proceed forward step by step,flow what technologies to pick up and project idea which includes all the above.

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