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  • Twitter Tuesday - Top 10 @ArchBeat Tweets - May 27 - June 2, 2014

    - by OTN ArchBeat
    The Top 10 tweets from @OTNArchBeat for the last seven days, May 27- June 2, 2014.. RT @Java_EE: We changed the term from #J2EE and #JEE to Java EE in May 2006. Let's educate all users and especially recruiters. Retweet! May 30, 2014 at 12:00 AM Video: #kscope14 Preview: @timtow on Essbase Java API and @ODTUG Community Jun 02, 2014 at 12:00 AM #GoldenGate and #ODI - A Perfect Match in 12c - Part 1: Getting Started | Michael Rainey Jun 02, 2014 at 12:00 AM Podcast: Developing Enterprise Mobile Apps - Part 2 w/ @chriscmuir @fnimphiu @stevendavelaar @lucb_ May 29, 2014 at 12:00 AM Caveats on Using #WebLogic Server with JDK7 | @JayJayZheng May 28, 2014 at 12:00 AM SOA and Business Processes: You are the Process! @gschmutz @dschmied @t_winterberg et al #industrialsoa May 27, 2014 at 12:00 AM Video: #Kscope14 Preview: Data Modeling and Moving Meditation with @KentGraziano May 28, 2014 at 12:00 AM #Kscope14 Preview: @ericerikson on #HFM Metadata Diagnostics and more @ODTUG Jun 02, 2014 at 12:00 AM Extract Data from #FusionApps via Web Services | Richard Williams May 29, 2014 at 12:00 AM Top 10 @ArchBeat Tweets - May 20-26 #KScope14 #OBIEE #WebLogic #WebCenter May 27, 2014 at 12:00 AM

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  • Java EE 8 update

    - by delabassee
    Planning for Java EE 8 is now well underway. As you know, a few weeks ago, we conducted a three part Java EE 8 Community Survey (you can find the final summary here). The data gathered have been very influential for the next steps. You can now expect over the coming weeks and months to see updates on the various specifications that compose the Java EE platform. Some Specification Leads are busy gathering additional feedback regarding what they should focus their efforts on (e.g. CDI 2 survey). Other Specification Leads have already publicly exposed what they think should be one of the focus for the evolution of the specification they lead.  For example, adding Server-Senet Events (SSE) support in JAX-RS is being discussed here and adding MVC support is being discussed here. Please remember that the fact we are now discussing any feature does not insure that it will be included in the proposal, nor in any particular update to Java EE. We can expect additional enhancements, changes and evolutions as we get closer to the finalisation of the different specifications... and there is still a long way to go with these specification proposals! Linda DeMichiel, Java EE Co-Specification Lead, has recently posted a draft proposal for the Java EE 8 Platform specification. Linda's goal is to recruit people and companies supporting this proposal before submitting it to the JCP.  This draft proposal is very interesting reading as it contains relevant information on the plans for Java EE 8 such as : The themes: Support for the latest web standards (eg. HTTP 2.0)  Continue to work on ease of development Improve the infrastructure for cloud support Alignment with Java SE 8 New JSRs to be added to the platform: J-Cache Java API for JSON Binding Java Configuration Plans for the Web Profile Plans on technologies to prune in Java EE 8, ... So if you haven't done it yet, I really encourage you to read the Java EE 8 draft proposal! Our goal for the Java EE 8 specification is for it to be finalized in the second half of 2016. It is important to note that we are in the early days of Java EE 8 and at this stage everything (themes, content, timing, etc.) is preliminary. Everything still needs to be discussed, challenged and agreed within the different Java Community Process (JCP) Experts Groups (EGs). Some EGs that still need to be formed! It could also means that the roadmap will have to be adjusted to follow the progress being made in the different EGs. This is also a good occasion to remind you that participation within those upcoming JCP Experts Groups is encouraged. Contributing in an EG is an effective lever to influence what Java EE 8 will become! Finally, as things get more concrete, we will share details on how to engage in the different Java EE 8 related Adopt-a-JSR initiatives, another way to contribute. You can also read other posts related to Java EE 8, here at The Aquarium blog. Just look for articles with the 'javaee8' tag.

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  • How To: Drag & Drop in einer APEX Kalenderregion

    - by carstenczarski
    Wussten Sie schon, dass Sie Einträge in einer APEX-Kalenderregion mit Drag & Drop verschieben können - ganz wie in bekannten Anwendungen wie Microsoft Outlook oder Mozilla Thunderbird? Und dazu müssen Sie noch nicht einmal aufwändiges JavaScript programmieren - APEX bringt nahezu alles nötige out-of-the-box mit. Lesen Sie in unserem aktuellen Tipp, wie Sie Drag & Drop in einer APEX-Kalenderregion aktivieren - und das in wenigen Minuten. Übrigens: Schon zum APEX-Entwicklertag angemeldet ...?

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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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  • Join Companies in Web and Telecoms by Adopting MySQL Cluster

    - by Antoinette O'Sullivan
    Join Web and Telecom companies who have adopted MySQL Cluster to facilitate application in the following areas: Web: High volume OLTP eCommerce User profile management Session management and caching Content management On-line gaming Telecoms: Subscriber databases (HLR/HSS) Service deliver platforms VAS: VoIP, IPTV and VoD Mobile content delivery Mobile payments LTE access To come up to speed on MySQL Cluster, take the 3-day MySQL Cluster training course. Events already on the schedule include:  Location  Date  Delivery Language  Berlin, Germany  16 December 2013  German  Munich, Germany  2 December 2013  German  Budapest, Hungary  4 December 2013  Hungarian  Madrid, Spain  9 December 2013  Spanish  Jakarta Barat, Indonesia  27 January 2014  English  Singapore  20 December 2013  English  Bangkok, Thailand  28 January 2014  English  San Francisco, CA, United States  28 May 2014  English  New York, NY, United States  17 December 2013  English For more information about this course or to request an additional event, go to the MySQL Curriculum Page (http://education.oracle.com/mysql).

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  • Introducing Exam Preparation Seminars on iPad

    - by Brandye Barrington
    Oracle University announced last week, the availability of the new Oracle Training On Demand app for iPad. This means that Oracle Certification's Exam Preparation Seminars, which are in the Training On Demand format are conveniently available for viewing on your iPad. The app is supplemental to the Web browser version. Features include: Access to your Oracle Training On Demand course titles High-quality video playback Video download and offline playback Interactive Table of Contents Course search Ability to search and preview available courses The app is available for free on the Apple App Store.

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  • UI Design Patterns : Are you developing a Fusion Apps extension, an ADF or Webcenter App?

    - by asantaga
    A big question I get asked when speaking to partners who are developing Oracle ADF, or Webcenter, Apps is how to make it look nice.. Some of the big SIs ask me, "Do we have any design patterns/guidelines we can use?". .. Alas website design is a very personal thing and each website will have different requirements and needs, however I am now pleased to say we've just launched "Oracle Fusion Applications Design Patterns" website.   The website is the result of many years of Oracle R&D into user interface design for Fusion applications and features a really cool web app which allows you to visualise the UI components in action. Although many of the design patterns are related to ADF , its worth noting that ADF took its lead from Oracle Fusion Applications User Interface needs - not the other way around, its just taken us a while to publish these. Coupled together with the dashboard patterns this makes are really cool extra asset for your kit bag Design Patterns Oracle dashboard patterns and guidelines Usable Apps.oracle.com Enjoy

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  • Amazon CloudFormations and Oracle Virtual Assembly Builder

    - by llaszews
    Yesterday I blogged about AWS AMIs and Oracle VM templates. These are great mechanisms to stand up an initial cloud environment. However, they don't provide the capability to manage, provision and update an environment once it is up and running. This is where AWS Cloud Formations and Oracle Virtual Assembly Builder comes into play. In a way, these tools/frameworks pick up where AMIs and VM templates leave off. Once again, there a similar offers from AWS and Oracle that compliant and also overlap with each other. Let's start by looking at the definitions: AWS CloudFormation gives developers and systems administrators an easy way to create and manage a collection of related AWS resources, provisioning and updating them in an orderly and predictable fashion. AWS CloudFormations Oracle Virtual Assembly Builder - Oracle Virtual Assembly Builder makes it possible for administrators to quickly configure and provision entire multi-tier enterprise applications onto virtualized and cloud environments. Oracle VM Builder As with the discussion around should you use AMI or VM Templates, there are pros and cons to each: 1. CloudFormation is JSON, Assembly Builder is GUI and CLI 2. VM Templates can be used in any private or public cloud environment. Of course, CloudFormations is tied to AWS public cloud

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  • Exalogic 2.0.1 Tea Break Snippets - Creating and using Distribution Groups

    - by The Old Toxophilist
    By default running your Exalogic in a Virtual provides you with, what to Cloud Users, is a single large resource and they can just create vServers and not care about how they are laid down on the the underlying infrastructure. All the Cloud Users will know is that they can create vServers. For example if we have a Quarter Rack (8 Nodes) and our Cloud User creates 8 vServers those 8 vServers may run on 8 distinct nodes or may all run on the same node. Although in many cases we, as Cloud Users, may not be to worried how the Virtualisation Algorithm decides where to place our vServers there are cases where it is extremely important that vServers run on distinct physical compute nodes. For example if we have a Weblogic Cluster we will want the Servers with in the cluster to run on distinct physical node to cover for the situation where one physical node is lost. To achieve this the Exalogic Virtualised implementation provides Distribution Groups that define and anti-aliasing policy that the underlying Virtualisation Algorithm will take into account when placing vServers. It should be noted that Distribution Groups must be created before you create vServers because a vServer can only be added to a Distribution Group at creation time. Creating A Distribution Group To create a Distribution Groups we will first need to select the Account in which we want the Distribution Group to be created. Once we have selected the account we will see the Interface update and Account specific Actions will be displayed within the Action Panes. From the Action pane (or Right-Click on the Account) select the "Create Distribution Group" action. This will initiate the create wizard as follows. Distribution Group Details Within the first Step of the Wizard we can specify the name of the distribution group and this should be unique. In addition we can provide a detailed description of the group. Distribution Group Configuration The second step of the configuration wizard allows you to specify the number of elements that are required within this group and will specify a maximum of the number of nodes within you Exalogic. At this point it is always better to specify a group with spare capacity allowing for future expansion. As vServers are added to group the available slots decrease. Summary Finally the last step of the wizard display a summary of the information entered.

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  • The Best Data Integration for Exadata Comes from Oracle

    - by maria costanzo
    Oracle Data Integrator and Oracle GoldenGate offer unique and optimized data integration solutions for Oracle Exadata. For example, customers that choose to feed their data warehouse or reporting database with near real-time throughout the day, can do so without decreasing  performance or availability of source and target systems. And if you ask why real-time, the short answer is: in today’s fast-paced, always-on world, business decisions need to use more relevant, timely data to be able to act fast and seize opportunities. A longer response to "why real-time" question can be found in a related blog post. If we look at the solution architecture, as shown on the diagram below,  Oracle Data Integrator and Oracle GoldenGate are both uniquely designed to take full advantage of the power of the database and to eliminate unnecessary middle-tier components. Oracle Data Integrator (ODI) is the best bulk data loading solution for Exadata. ODI is the only ETL platform that can leverage the full power of Exadata, integrate directly on the Exadata machine without any additional hardware, and by far provides the simplest setup and fastest overall performance on an Exadata system. We regularly see customers achieving a 5-10 times boost when they move their ETL to ODI on Exadata. For  some companies the performance gain is even much higher. For example a large insurance company did a proof of concept comparing ODI vs a traditional ETL tool (one of the market leaders) on Exadata. The same process that was taking 5hrs and 11 minutes to complete using the competing ETL product took 7 minutes and 20 seconds with ODI. Oracle Data Integrator was 42 times faster than the conventional ETL when running on Exadata.This shows that Oracle's own data integration offering helps you to gain the most out of your Exadata investment with a truly optimized solution. GoldenGate is the best solution for streaming data from heterogeneous sources into Exadata in real time. Oracle GoldenGate can also be used together with Data Integrator for hybrid use cases that also demand non-invasive capture, high-speed real time replication. Oracle GoldenGate enables real-time data feeds from heterogeneous sources non-invasively, and delivers to the staging area on the target Exadata system. ODI runs directly on Exadata to use the database engine power to perform in-database transformations. Enterprise Data Quality is integrated with Oracle Data integrator and enables ODI to load trusted data into the data warehouse tables. Only Oracle can offer all these technical benefits wrapped into a single intelligence data warehouse solution that runs on Exadata. Compared to traditional ETL with add-on CDC this solution offers: §  Non-invasive data capture from heterogeneous sources and avoids any performance impact on source §  No mid-tier; set based transformations use database power §  Mini-batches throughout the day –or- bulk processing nightly which means maximum availability for the DW §  Integrated solution with Enterprise Data Quality enables leveraging trusted data in the data warehouse In addition to Starwood Hotels and Resorts, Morrison Supermarkets, United Kingdom’s fourth-largest food retailer, has seen the power of this solution for their new BI platform and shared their story with us. Morrisons needed to analyze data across a large number of manufacturing, warehousing, retail, and financial applications with the goal to achieve single view into operations for improved customer service. The retailer deployed Oracle GoldenGate and Oracle Data Integrator to bring new data into Oracle Exadata in near real-time and replicate the data into reporting structures within the data warehouse—extending visibility into operations. Using Oracle's data integration offering for Exadata, Morrisons produced financial reports in seconds, rather than minutes, and improved staff productivity and agility. You can read more about Morrison’s success story here and hear from Starwood here. From an Irem Radzik article.

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  • 6 Facts About GlassFish Announcement

    - by Bruno.Borges
    To help clarify the message about the recent roadmap for GlassFish, I decided to put together 6 facts about the announcement, future of GlassFish, and the Java EE platform as a whole:  "Since Oracle announced the end of commercial support for future Oracle GlassFish Server versions, the Java EE world has started wondering what will happen to GlassFish Server Open Source Edition. Unfortunately, there's a lot of misleading information going around. So let me clarify some things with facts, not FUD." Read full story here

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  • Data Governance (Veri Yönetisimi)

    - by Arda Eralp
    Data governance,veri ile ilgili islemler için bir sorumluluklar sistemidir. Bu sistemin temelini ise politikalar, standartlar ve prosedürler olusturur. Sistem politikalar, standartlar ve prosedürler sayesinde verinin ne zaman, hangi sartlar altinda, hangi eylemlerde, hangi yöntemler ile kimler tarafindan kullanilacagina karar verir. Sistemin kurumda basarili bir sekilde islemesi için öncelikle kurumda farkindalik saglanmasi gereklidir. Farkindalik saglandiktan sonra ise kurum governance ve mimari kültürünü benimsemelidir. Ancak bu sartlar altinda sistem basarili bir sekilde isleyebilecektir. Bu sebeplerden dolayidir ki data governance kisa bir süreç degil, aksine kurum varligini sürdürdügü sürece isleyecek olan bir süreçtir. Bu durum bize data governance in bir proje degil bir program oldugunu açiklamaktadir. Programin baslangicinda kurumun ihtiyaçlarinin netlesmesi ve farkindaligin saglanmasi temeldir. Hedef kitle ise, veri ile dogrudan ve ya dolayli olarak iliski içerisinde olan herkesdir. Bu sebeple programin baslangicinda hedef kitleyi içeren ekipler ile toplantilar düzenlenecektir. Bu toplantilar sayesinde hem farkindalik saglanacak hemde ekiplerin ihtiyaçlari birebir ekipler tarafindan aktarilarak netlesecektir. Hedef kitlenin ihtiyaçlari netlestirildikten sonra ise devamli isleyecek olan bu sürecin planlamasi yapilacaktir. Bu sürecin planlanmasinda ihtiyaçlarin önceliklendirilmesi gerekmektedir. Sebebi ise her ekibin ihtiyaçlarinin farkli olabilecegi ve bütün ihtiyaçlara ayni anda karsilik verilemeyebileceginin öngörülmesidir. Bu öngörünün temeli ise ekiplerin ihtiyaçlarinin birbirleriyle olan baglantisidir. Süreç planlamasinda ihtiyaçlarin önceliklendirilmesinin ardindan kurumun büyüklügünün gözönünde bulundurulmasi gerekmedir. Kurumun büyüklügünün önemi ise eger kurum bir bütün olarak ayni anda govern edilemeyecek kadar büyük ise ihtiyaçlari öncelikli olarak bulunan ekipler ile govern edilmesine baslanarak sürecin belirli bir hiz ile bütün kurumda isler hale getirilmesini saglamaktir. Ihtiyaçlar belirlendikten ve ilgili ekipler seçildikten sonra artik programin planlanmasina geçilebilecek. Programin planlama asamasinda öncelikli olarak sürecin asamalarini kontrol edecek ve süreç kurum içerisinde isleyise geçtiginde kontrolü saglayacak olan Data Governance Office in planlanmasidir. Office in planlanmasiyla birlikte süreçteki roller ve bu rollerin sorumluluklari belirlenecektir. Planlama asamasinda Data governance office, roller ve sorumluluklar, güvenlik ve veri saklanan sistemler ele alinacak konulardir. Planlama asamasi tamamlandiginda ise belirlenen ekipler ve ihtiyaçlar dogrultusunda programin isleyis asamasina geçilebilecektir. Isleyis kisminda ekibin ihtiyaçlari dogrultusunda güvenlik konusunda ve veri saklanan sistemler üzerinde çalismalar yapilacaktir. Bu yapilan çalismalar bir süreç olarak dökümante edilecek ve süreç sona erdiginde baska bir ekiple baska bir ihtiyaç dogrultusunda çalisma yapilarak ayni süreç isletilecek ve böylece kurum içesinde ilgili süreçte standartlasma saglanacaktir. Güvenlik konusunda verinin erisim güvenligi ve kullanim güvenligi ele alinacaktir. Veri saklanan sistemler üzerindeki çalismalar ise saklanan sistemlerin program dahilinde belirlenen standartlar ile olusturulmasi ve yönetilmesi saglanacaktir. Isleyis kisminin ardindan ise programin izleme kismina geçilecektir. Bu kisimda artik Data Governance Office olusmus, politikalar, standartlar ve prosedürler belirlenmistir. Ve Data Governance Office çalisanlari rolleri ve sorumluluklari dahilinde programin isleyisini izleyecek ve gerek gördügünde politikalar standartlar ve prosedürler üzerinde degisiklikler yapacaklardir.

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  • Let Me Show You Something: Instagram, Vine and Snapchat for Brands

    - by Mike Stiles
    While brands are well aware of how much more impactful images are than text-only posts on social channels, today you’re additionally being presented with platform after additional platform for hosting, doctoring and sharing photos and videos.  Can you play in every sandbox? And if you do, can you be brilliant on all of them? As has usually been the case, so far brands are sticking their toes into new platforms while not actually committing to them, or strategizing for them, or resourcing them. TrackMaven found of the 123 F500 companies using Instagram, only 22% of them are active on it. Likewise, research from Simply Measured found brands are indeed jumping in, with the number establishing a presence on Instagram up 55% over the past year. Users want them there…brand engagement has exploded 350%, and over 1/3 of the top brands have at least 10,000 followers. BUT…the top 10 brands are generating 33% of all posts, reaping 83% of all engagement. Things are also growing on Twitter’s Vine, the 6-second looping video app that hit 40 million users in August. The 7th Chamber says 5 tweets a second contain a Vine link. Other studies say branded Vines are 4 times more likely to be shared and seen than rank-and-file branded videos. Why? Users know that even if a video is pure junk, they won’t get robbed of too much of their valuable time. Vine is always upgrading so you can make sure your videos are worth viewers’ time. You can now edit videos, and save & work on several projects concurrently. What you can’t do is upload a finely crafted video into Vine, but you can do that with Instagram. The key to success? Same as with all other content; make it of value. Deliver a laugh or a lesson or both. How-to, behind the scenes peeks, contests, demos, all make sense in the short video format. Or follow Nash Grier’s example, which is to just have fun with and connect to your viewers, earning their trust that your next Vine will be as good as the last. Nash is only 15, has over 1.4 million followers, and adds about 100,000 a week. He broke out when one of his videos was re-Vined by some other kid with 300,000 followers. Make good stuff, get it in front of influencers, and your brand Vines could break out as well. Then there’s Snapchat, the “this photo will self destruct” platform. How can that be of use to brands besides offering coupons that really expire? The jury is out. But with an audience of over 100 million and a valuation of $800 million, media-with-a-time-limit is compelling. Now there’s “Snapchat Stories” that can last 24 hours and be shared to the public at large. You might be able to capitalize on how much more focus gets put on content when there’s a time limit on its availability. The underlying truth to all of this is, these are all tools. Very cool, feature rich tools, but tools. You can give the exact same art kit to 5 different people and you’d get back 5 very different works, ranging from worthless garbage to masterpiece. Brands are being called upon to be still and moving image artists. That’s what your customers are used to seeing, from a variety of sources. Commit to communicating with them accordingly. @mikestiles Photo: stock.xchng

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  • Customers Deploying Sun Oracle Database Machine

    - by kimberly.billings
    Philippine Savings Bank (PS Bank) recently deployed the Sun Oracle Database Machine to underpin its enterprise-wide analytics platform. Now, the response times for queries and requests that used to take from three hours to several days is completed in less than one minute with near real-time updates. Read the press release. EFU General Insurance also announced this week that they have deployed the Sun Oracle Database Machine. With Oracle, EFU will be able to open more sales channels via the Web and facilitate integration with other companies. As a result, more quality services can be offered to its customers via the Web because of the more agile and reliable IT infrastructure. In addition, a centralized IT environment will offer the EFU management a real time view of key information, enabling EFU to analyze business trends and make timely decisions. Read the press release. Let us know about your Sun Oracle Database deployment! var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www."); document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E")); try { var pageTracker = _gat._getTracker("UA-13185312-1"); pageTracker._trackPageview(); } catch(err) {}

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  • The Simplicity of the Oracle Stack

    - by user801960
    For many retailers, technology is something they know they need to optimise business operations, but do they really understand it and how can they select the solutions they need from the many vendors on the market? Retail is a data heavy industry, with the average retailer managing thousands of SKUs and hundreds of categories through multiple channels. Add to this the exponential growth in data driven by social media and mobile activities, and the process can seem overwhelming. Handling data of this magnitude and analyzing it effectively to gain actionable insight is a huge task, and needs several IT components to work together harmoniously to make the best use of the data available and make smarter decisions. With this in mind, Oracle has produced a video to make it easier for businesses to understand its global data IT solutions and how they integrate seamlessly with Oracle’s other solutions to enable organisations to operate as effectively as possible. The video uses an orchestra as an analogy for IT solutions and clever illustration to demonstrate the value of the Oracle brand. This video can be viewed at http://medianetwork.oracle.com/video/player/1622148401001. To find out more about how Oracle’s products and services can help retailers to deliver better results, visit the Oracle Retail website.

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  • Video Did Not Kill the Podcast Star

    - by Justin Kestelyn
    Who says video killed the podcast star? We're seeing more favorites out there than ever before. For example, the OTN team is proud to be supporters of the Java Spotlight Podcasts, straight from the official Java Evangelist Team at Oracle (lots of great insider info); the OurSQL: The MySQL Database Podcasts, produced by MySQL maven (and Oracle ACE Director) Sheeri Cabral; and The GlassFish Podcast, always a reliable source. And we'd add The Java Posse and The Basement Coders to our personal playlist. And although we're on a video kick ourselves at the moment, you can still get the audio of our TechCast Live shows, if you think we have "faces for radio."

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  • Oracle Solaris at OpenWorld SF 2012

    - by Markus Weber
    SAVE THE DATE !Oracle OpenWorld will be from Sep 30 to Oct 4 in San Francisco this year.Register paying early bird prices, plan for your travel, and plan for your hotel !Get ready to learn about the latest of Oracle Solaris, Oracle Solaris Cluster, and Oracle Solaris Studio. The external Call For Papers just closed, which means many people will work hard over the next few weeks to make sure you will get the best possible sessions, demos, hands on labs, etc.Early signs show that we will have great Solaris coverage, similar to last year. Read this nice recap about it, or to refresh your memory of what we managed to cover last year even more, check out the 2011 Focus On Oracle Solaris document (pdf). So stay tuned. As it's true for all other Oracle products, we will keep you posted on OpenWorld 2012 news as they become available.

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  • Web Seminar - The Oracle Database Appliance: How to Sell a Unique Product!

    - by swalker
    Dear partner, You are exclusively invited to join us for a webcast, dedicated to Oracle’s EMEA Partners, on the Oracle Database Appliance value proposition, positioning and ecosystem – to help you capture new business and help your customers roll out their solutions fast, easily, safely and with maximum cost efficiency! Join us to learn about: ODA Benefits: Fast, Easy, Cost Efficient, Highly Reliable Feedback from early Customer Wins: What can we Learn? Objection Handling: Overcoming the most common customer questions Going beyond the Database: The ODA ECO System for applications, backup & more… When combined with your high-value services (e.g., migration, consolidation), the end result is a database system that you can use to grow the business in your existing accounts, or capture new business. Join us at the EMEA partner webcast hosted by Robert Van Espelo Cloud and Virtualization Leader, EMEA Business Development on Thursday, April 12, at 9:00am UK / 10:00am CET. The presentation will be given in English. To register for this webcast click here We look forward to talking to you on April 12! Best regards,Giuseppe Facchetti EMEA Partner Business Development Manager Oracle EMEA, Hardware Sales Paul LeonardEMEA Partner Marketing Manager Oracle EMEA, Systems Marketing

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  • Book to Help OBI11g Developers by Mark Rittman

    - by Mike.Hallett(at)Oracle-BI&EPM
    Normal 0 false false false EN-GB X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Mark Rittman has published an extensive up to date Developer’s Guide for Oracle Business Intelligence 11g. For a great summary of what you can get from this new book have a quick look at the review posted here by Abhinav Agarwal.

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  • Today's Links (6/24/2011)

    - by Bob Rhubart
    Fusion Applications - How we look at the near future | Domien Bolmers Bolmers recaps a Logica pow-wow around Fusion Applications. Who invented e-mail? | Nicholas Carr IT apparently does matter to Nicholas Carr as he shares links to Errol Morris's 5-part NYT series about the origins of email. David Sprott's Blog: Service Oriented Cloud (SOC) "Whilst all the really good Cloud environments are Service Oriented," says Sprott, "it’s very much the minority of consumer SaaS that is today." Fast, Faster, JRockit | René van Wijk Oracle ACE René van Wijk tells you "everything you ever wanted to know about the JRockit JVM, well quite a lot anyway." Creating an XML document based on my POJO domain model – how will JAXB help me? | Lucas Jellema "I thought that adding a few JAXB annotations to my existing POJO model would do the trick," says Jellema, "but no such luck." Announcing Oracle Environmental Accounting and Reporting | Theresa Hickman Oracle Environmental Accounting and Reporting is designed to help companies track and report greenhouse emissions. Yoga framework for REST-like partial resource access | William Vambenepe Vambenepe says: "A tweet by Stefan Tilkov brought Yoga to my attention, 'a framework for supporting REST-like URI requests with field selectors.'" InfoQ: Pragmatic Software Architecture and the Role of the Architect "Joe Wirtley introduces software architecture and the role of the architect in software development along with techniques, tips and resources to help one get started thinking as an architect."

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  • Bouygues Telecom Gains a 360-Degree Overview of Customers and Improves Offer Acceptance Rates

    - by user511693
    With more than 10 million mobile customers and 500,000 landline customers, the mission of Bouygues Telecom is to become the premier mobile, landline, television, and internet brand in France, by focusing on customer acquisition, advice, service, and support. Project challenges included: Leverage every customer relationship and increase customer loyalty through personalized offers or promotions on landline or mobile phone contracts Build on marketing campaigns and take advantage of incoming contacts from the company’s call center, Web, and retail stores Improve acceptance rates of communication service offers “Thanks to Oracle’s Siebel CRM solutions and Oracle Real-Time Decisions, we can now meet customer requests faster, personalize offers to improve the services we provide, and gain feedback on responses to offers. This enhances our knowledge of our customers before our next contact with them, whether through the Web site, call center, or our Club retail stores.” – Eric Dobremer, IT Manager - Grand Public CRM Development, Bouygues Telecom Read about results here.

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  • Oracle Weblogic 12c Launch

    - by Robert Baumgartner
    Am 1. Dezember 2011 wird Oracle WebLogic Server 12c weltweit vorgestellt. Um 19:00 findet ein Execuite Overview mit Hasan Rizvi, Senior Vice President, Product Development, statt. Um 20:00 findet ein Developer Deep-Dive mit Will Lyons, Director, Oracle WebLogic Server Product Management, statt. The new release of Oracle WebLogic Server is: • Designed to help customers seamlessly move into the public or private cloud with an open, standards-based platform • Built to drive higher value for customers’ current infrastructure and significantly reduce development time and cost • Enhanced with transformational platforms and technologies such as Java EE 6, Oracle’s Active GridLink for RAC, Oracle Traffic Director, and Oracle Virtual Assembly Builder Hier geht es zur Anmeldung: Anmeldung

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  • Extending the Value of Your Oracle Financials Applications Investment with Document Capture, Imaging and Workflow

    Learn how Oracles end-to-end document imaging system extends the value and increases the automation of your Oracle Financials applications by using intelligent capture and imaging technologies to streamline high volume operations like accounts payable. Oracle Imaging and Process Management 11g (Oracle I/PM 11g) offers an integrated system that digitizes paper invoices, intelligently extracts header information and line item details, initiates automated workflows, and enables in-context access to imaged invoices directly from Oracle Applications, including Oracle E-Business Suite Financials and PeopleSoft Enterprise Financial Management. Come hear more about these certfied, standards-based application integrations as well as how document imaging can help your organization achieve quick, measurable ROI, by increasing efficiencies across financial departments, and reducing costs related to paper storage and handling.

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  • EBS – ATG Webcast 9/11 - 9/12

    - by cwarticki
    EBS – ATG Webcast in September 2012 EBS – Multiple Language Support (MLS) Agenda :EBS is MLS Ready                                                                                 NLS / MLS Basic ArchitectureNLS / MLS InstallationNLS / MLS Configuration Settings                                                                    TroubleshootingQuestion and AnswersEMEA Session : September 11, 2012 at 09:00 UK / 10:00 CET / 13:30 India / 17:00 Japan / 18:00 Sydney (Australia) Details & Registration : Note 1480084.1 Direct link to register in WebEx US Session : September 12, 2012 at 18:00 UK / 19:00 CET / 10:00 AM Pacific / 11:00 AM Mountain/ 01:00 PM Eastern ·      Details & Registration : Note 1480085.1 ·      Direct link to register in WebEx ·         Schedules, recordings and the Presentations of the Advisor Webcast drove under the EBS Applications Technology area can be found in Note 1186338.1. ·         Current Schedules of Advisor Webcast for all Oracle Products can be found on Note 740966.1 ·         Post Presentation Recordings of the Advisor Webcasts for all Oracle Products can be found on Note 740964.1 If you have any question about the schedules or if you have a suggestion for an Advisor Webcast to be planned in future, please send an E-Mail to Ruediger Ziegler.

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  • How to Set Up a MongoDB NoSQL Cluster Using Oracle Solaris Zones

    - by Orgad Kimchi
    This article starts with a brief overview of MongoDB and follows with an example of setting up a MongoDB three nodes cluster using Oracle Solaris Zones. The following are benefits of using Oracle Solaris for a MongoDB cluster: • You can add new MongoDB hosts to the cluster in minutes instead of hours using the zone cloning feature. Using Oracle Solaris Zones, you can easily scale out your MongoDB cluster. • In case there is a user error or software error, the Service Management Facility ensures the high availability of each cluster member and ensures that MongoDB replication failover will occur only as a last resort. • You can discover performance issues in minutes versus days by using DTrace, which provides increased operating system observability. DTrace provides a holistic performance overview of the operating system and allows deep performance analysis through cooperation with the built-in MongoDB tools. • ZFS built-in compression provides optimized disk I/O utilization for better I/O performance. In the example presented in this article, all the MongoDB cluster building blocks will be installed using the Oracle Solaris Zones, Service Management Facility, ZFS, and network virtualization technologies. Figure 1 shows the architecture:

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