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  • How do you change the Time Zone in the Windows 8 Consumer Preview?

    - by Rowland Shaw
    It appears that installing Windows 8 on top of XP doesn't give you the option to choose the locale and other settings -- I've got the right keyboard layout restored, and can change the system locale to be for the UK, but there doesn't appear to be any way to change the time zone -- choosing the option to try and change the time zone gives error: Date and Time Unable to continue You do not have permission to perform this task. Please contact your computer administrator for help. [OK]

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  • Why do I have to enter my password every time I activate / deactivate AirPort (WiFi) on my MacBook P

    - by Another Registered User
    I use Snow Leopard, and I'm used to activate / deactivate WiFi like 20 times per day. The reason is that WiFi stops working properly after a few minutes of use. So every time I try to surf, I must stop/reactivate it first. But now, suddenly I have to enter my user password every time I want to do it. It's so annoying! The dialogue details say: Right: com.apple.airport.power Program: SystemUIServer What can I do that the Mac won't ask me for the password every time? It's hard enough that I have to stop/reactivate WiFi all the time (hardware bug). I have a admin account with full rights.

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  • Common Network Administrator Tools

    - by No Time
    I would like to make a custom clump of Network Admin packages, to be able to carry on a thumb drive, to administer Debian based machines. Examples of what I would include so far: nmap traceroute vnstat zenmap * I know every situation may be different, but I would like to build a toolbox I could bring everywhere, and am looking for advice on other tools which would work. (If there is a similar question, I am fine being directed there)

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  • Are elements returned by Linq-to-Entities query streamed from the DB one at the time or are they retrieved all at once?

    - by carewithl
    Are elements returned by Linq-to-Entities query streamed from the database one at the time ( as they are requested ) or are they retrieved all at once: SampleContext context = new SampleContext(); // SampleContext derives from ObjectContext var search = context.Contacts; foreach (var contact in search) { Console.WriteLine(contact.ContactID); // is each Contact retrieved from the DB // only when foreach requests it? } thank you in advance

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  • How to do safely test Biztalk app by manipulating the Windows OS system time w/o breaking the Active Directory?

    - by melaos
    i have a biztalk - window service tied middleware application which talks to other system. recently we had a request to test for scenarios which relates to the date. as we have a lot of places in the application which uses the .net Datetime.Now value, we don't really want to go into the code level and change all these values. so we're looking at the simplest way to test which is to just change the OS time. but what we notice is that sometimes when we change the system date time, we will get account lock out due to Active Directory. So my question is what's a good and safe way that i can test for future dates, etc by changing the windows OS system date time but without causing any issues with the Active Directory. And where can i find out more about AD and how it issues token and what's the correlation with the system date time changes. Thanks! ~m

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  • Python, dictionaries, and chi-square contingency table

    - by rohanbk
    I have a file which contains several lines in the following format (word, time that the word occurred in, and frequency of documents containing the given word within the given instance in time): #inputfile <word, time, frequency> apple, 1, 3 banana, 1, 2 apple, 2, 1 banana, 2, 4 orange, 3, 1 I have Python class below that I used to create 2-D dictionaries to store the above file using as the key, and frequency as the value: class Ddict(dict): ''' 2D dictionary class ''' def __init__(self, default=None): self.default = default def __getitem__(self, key): if not self.has_key(key): self[key] = self.default() return dict.__getitem__(self, key) wordtime=Ddict(dict) # Store each inputfile entry with a <word,time> key timeword=Ddict(dict) # Store each inputfile entry with a <time,word> key # Loop over every line of the inputfile for line in open('inputfile'): word,time,count=line.split(',') # If <word,time> already a key, increment count try: wordtime[word][time]+=count # Otherwise, create the key except KeyError: wordtime[word][time]=count # If <time,word> already a key, increment count try: timeword[time][word]+=count # Otherwise, create the key except KeyError: timeword[time][word]=count The question that I have pertains to calculating certain things while iterating over the entries in this 2D dictionary. For each word 'w' at each time 't', calculate: The number of documents with word 'w' within time 't'. (a) The number of documents without word 'w' within time 't'. (b) The number of documents with word 'w' outside time 't'. (c) The number of documents without word 'w' outside time 't'. (d) Each of the items above represents one of the cells of a chi-square contingency table for each word and time. Can all of these be calculated within a single loop or do they need to be done one at a time? Ideally, I would like the output to be what's below, where a,b,c,d are all the items calculated above: print "%s, %s, %s, %s" %(a,b,c,d)

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  • The Unspoken - The Why of GC Ergonomics

    - by jonthecollector
    Do you use GC ergonomics, -XX:+UseAdaptiveSizePolicy, with the UseParallelGC collector? The jist of GC ergonomics for that collector is that it tries to grow or shrink the heap to meet a specified goal. The goals that you can choose are maximum pause time and/or throughput. Don't get too excited there. I'm speaking about UseParallelGC (the throughput collector) so there are definite limits to what pause goals can be achieved. When you say out loud "I don't care about pause times, give me the best throughput I can get" and then say to yourself "Well, maybe 10 seconds really is too long", then think about a pause time goal. By default there is no pause time goal and the throughput goal is high (98% of the time doing application work and 2% of the time doing GC work). You can get more details on this in my very first blog. GC ergonomics The UseG1GC has its own version of GC ergonomics, but I'll be talking only about the UseParallelGC version. If you use this option and wanted to know what it (GC ergonomics) was thinking, try -XX:AdaptiveSizePolicyOutputInterval=1 This will print out information every i-th GC (above i is 1) about what the GC ergonomics to trying to do. For example, UseAdaptiveSizePolicy actions to meet *** throughput goal *** GC overhead (%) Young generation: 16.10 (attempted to grow) Tenured generation: 4.67 (attempted to grow) Tenuring threshold: (attempted to decrease to balance GC costs) = 1 GC ergonomics tries to meet (in order) Pause time goal Throughput goal Minimum footprint The first line says that it's trying to meet the throughput goal. UseAdaptiveSizePolicy actions to meet *** throughput goal *** This run has the default pause time goal (i.e., no pause time goal) so it is trying to reach a 98% throughput. The lines Young generation: 16.10 (attempted to grow) Tenured generation: 4.67 (attempted to grow) say that we're currently spending about 16% of the time doing young GC's and about 5% of the time doing full GC's. These percentages are a decaying, weighted average (earlier contributions to the average are given less weight). The source code is available as part of the OpenJDK so you can take a look at it if you want the exact definition. GC ergonomics is trying to increase the throughput by growing the heap (so says the "attempted to grow"). The last line Tenuring threshold: (attempted to decrease to balance GC costs) = 1 says that the ergonomics is trying to balance the GC times between young GC's and full GC's by decreasing the tenuring threshold. During a young collection the younger objects are copied to the survivor spaces while the older objects are copied to the tenured generation. Younger and older are defined by the tenuring threshold. If the tenuring threshold hold is 4, an object that has survived fewer than 4 young collections (and has remained in the young generation by being copied to the part of the young generation called a survivor space) it is younger and copied again to a survivor space. If it has survived 4 or more young collections, it is older and gets copied to the tenured generation. A lower tenuring threshold moves objects more eagerly to the tenured generation and, conversely a higher tenuring threshold keeps copying objects between survivor spaces longer. The tenuring threshold varies dynamically with the UseParallelGC collector. That is different than our other collectors which have a static tenuring threshold. GC ergonomics tries to balance the amount of work done by the young GC's and the full GC's by varying the tenuring threshold. Want more work done in the young GC's? Keep objects longer in the survivor spaces by increasing the tenuring threshold. This is an example of the output when GC ergonomics is trying to achieve a pause time goal UseAdaptiveSizePolicy actions to meet *** pause time goal *** GC overhead (%) Young generation: 20.74 (no change) Tenured generation: 31.70 (attempted to shrink) The pause goal was set at 50 millisecs and the last GC was 0.415: [Full GC (Ergonomics) [PSYoungGen: 2048K-0K(26624K)] [ParOldGen: 26095K-9711K(28992K)] 28143K-9711K(55616K), [Metaspace: 1719K-1719K(2473K/6528K)], 0.0758940 secs] [Times: user=0.28 sys=0.00, real=0.08 secs] The full collection took about 76 millisecs so GC ergonomics wants to shrink the tenured generation to reduce that pause time. The previous young GC was 0.346: [GC (Allocation Failure) [PSYoungGen: 26624K-2048K(26624K)] 40547K-22223K(56768K), 0.0136501 secs] [Times: user=0.06 sys=0.00, real=0.02 secs] so the pause time there was about 14 millisecs so no changes are needed. If trying to meet a pause time goal, the generations are typically shrunk. With a pause time goal in play, watch the GC overhead numbers and you will usually see the cost of setting a pause time goal (i.e., throughput goes down). If the pause goal is too low, you won't achieve your pause time goal and you will spend all your time doing GC. GC ergonomics is meant to be simple because it is meant to be used by anyone. It was not meant to be mysterious and so this output was added. If you don't like what GC ergonomics is doing, you can turn it off with -XX:-UseAdaptiveSizePolicy, but be pre-warned that you have to manage the size of the generations explicitly. If UseAdaptiveSizePolicy is turned off, the heap does not grow. The size of the heap (and the generations) at the start of execution is always the size of the heap. I don't like that and tried to fix it once (with some help from an OpenJDK contributor) but it unfortunately never made it out the door. I still have hope though. Just a side note. With the default throughput goal of 98% the heap often grows to it's maximum value and stays there. Definitely reduce the throughput goal if footprint is important. Start with -XX:GCTimeRatio=4 for a more modest throughput goal (%20 of the time spent in GC). A higher value means a smaller amount of time in GC (as the throughput goal).

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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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  • DataTables warning (table id = 'example-advanced'): Cannot reinitialise DataTable while using treetable and datatable at the same time

    - by Nyaro
    DataTables warning (table id = 'example-advanced'): Cannot reinitialise DataTable while using treetable and datatable at the same time. Here is my code: <script src="jquery-1.7.2.min.js"></script> <script src='jquery.dataTables.min.js'></script> <script src="jquery.treetable.js"></script> <script> $("#example-advanced").treetable({ expandable: true }); </script> <script> $('#example-advanced').dataTable( { "bSort": false } ); </script> Actually I wanted to get rid of the sorting part of the datatable coz it was giving error in treetable display so i want the sorting part from the datatable out and keep other functions like search and pagination. Please help me out. Thanks in advance.

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  • ANTS CLR and Memory Profiler In Depth Review (Part 1 of 2 &ndash; CLR Profiler)

    - by ToStringTheory
    One of the things that people might not know about me, is my obsession to make my code as efficient as possible.  Many people might not realize how much of a task or undertaking that this might be, but it is surely a task as monumental as climbing Mount Everest, except this time it is a challenge for the mind…  In trying to make code efficient, there are many different factors that play a part – size of project or solution, tiers, language used, experience and training of the programmer, technologies used, maintainability of the code – the list can go on for quite some time. I spend quite a bit of time when developing trying to determine what is the best way to implement a feature to accomplish the efficiency that I look to achieve.  One program that I have recently come to learn about – Red Gate ANTS Performance (CLR) and Memory profiler gives me tools to accomplish that job more efficiently as well.  In this review, I am going to cover some of the features of the ANTS profiler set by compiling some hideous example code to test against. Notice As a member of the Geeks With Blogs Influencers program, one of the perks is the ability to review products, in exchange for a free license to the program.  I have not let this affect my opinions of the product in any way, and Red Gate nor Geeks With Blogs has tried to influence my opinion regarding this product in any way. Introduction The ANTS Profiler pack provided by Red Gate was something that I had not heard of before receiving an email regarding an offer to review it for a license.  Since I look to make my code efficient, it was a no brainer for me to try it out!  One thing that I have to say took me by surprise is that upon downloading the program and installing it you fill out a form for your usual contact information.  Sure enough within 2 hours, I received an email from a sales representative at Red Gate asking if she could help me to achieve the most out of my trial time so it wouldn’t go to waste.  After replying to her and explaining that I was looking to review its feature set, she put me in contact with someone that setup a demo session to give me a quick rundown of its features via an online meeting.  After having dealt with a massive ordeal with one of my utility companies and their complete lack of customer service, Red Gates friendly and helpful representatives were a breath of fresh air, and something I was thankful for. ANTS CLR Profiler The ANTS CLR profiler is the thing I want to focus on the most in this post, so I am going to dive right in now. Install was simple and took no time at all.  It installed both the profiler for the CLR and Memory, but also visual studio extensions to facilitate the usage of the profilers (click any images for full size images): The Visual Studio menu options (under ANTS menu) Starting the CLR Performance Profiler from the start menu yields this window If you follow the instructions after launching the program from the start menu (Click File > New Profiling Session to start a new project), you are given a dialog with plenty of options for profiling: The New Session dialog.  Lots of options.  One thing I noticed is that the buttons in the lower right were half-covered by the panel of the application.  If I had to guess, I would imagine that this is caused by my DPI settings being set to 125%.  This is a problem I have seen in other applications as well that don’t scale well to different dpi scales. The profiler options give you the ability to profile: .NET Executable ASP.NET web application (hosted in IIS) ASP.NET web application (hosted in IIS express) ASP.NET web application (hosted in Cassini Web Development Server) SharePoint web application (hosted in IIS) Silverlight 4+ application Windows Service COM+ server XBAP (local XAML browser application) Attach to an already running .NET 4 process Choosing each option provides a varying set of other variables/options that one can set including options such as application arguments, operating path, record I/O performance performance counters to record (43 counters in all!), etc…  All in all, they give you the ability to profile many different .Net project types, and make it simple to do so.  In most cases of my using this application, I would be using the built in Visual Studio extensions, as they automatically start a new profiling project in ANTS with the options setup, and start your program, however RedGate has made it easy enough to profile outside of Visual Studio as well. On the flip side of this, as someone who lives most of their work life in Visual Studio, one thing I do wish is that instead of opening an entirely separate application/gui to perform profiling after launching, that instead they would provide a Visual Studio panel with the information, and integrate more of the profiling project information into Visual Studio.  So, now that we have an idea of what options that the profiler gives us, its time to test its abilities and features. Horrendous Example Code – Prime Number Generator One of my interests besides development, is Physics and Math – what I went to college for.  I have especially always been interested in prime numbers, as they are something of a mystery…  So, I decided that I would go ahead and to test the abilities of the profiler, I would write a small program, website, and library to generate prime numbers in the quantity that you ask for.  I am going to start off with some terrible code, and show how I would see the profiler being used as a development tool. First off, the IPrimes interface (all code is downloadable at the end of the post): interface IPrimes { IEnumerable<int> GetPrimes(int retrieve); } Simple enough, right?  Anything that implements the interface will (hopefully) provide an IEnumerable of int, with the quantity specified in the parameter argument.  Next, I am going to implement this interface in the most basic way: public class DumbPrimes : IPrimes { public IEnumerable<int> GetPrimes(int retrieve) { //store a list of primes already found var _foundPrimes = new List<int>() { 2, 3 }; //if i ask for 1 or two primes, return what asked for if (retrieve <= _foundPrimes.Count()) return _foundPrimes.Take(retrieve); //the next number to look at int _analyzing = 4; //since I already determined I don't have enough //execute at least once, and until quantity is sufficed do { //assume prime until otherwise determined bool isPrime = true; //start dividing at 2 //divide until number is reached, or determined not prime for (int i = 2; i < _analyzing && isPrime; i++) { //if (i) goes into _analyzing without a remainder, //_analyzing is NOT prime if (_analyzing % i == 0) isPrime = false; } //if it is prime, add to found list if (isPrime) _foundPrimes.Add(_analyzing); //increment number to analyze next _analyzing++; } while (_foundPrimes.Count() < retrieve); return _foundPrimes; } } This is the simplest way to get primes in my opinion.  Checking each number by the straight definition of a prime – is it divisible by anything besides 1 and itself. I have included this code in a base class library for my solution, as I am going to use it to demonstrate a couple of features of ANTS.  This class library is consumed by a simple non-MVVM WPF application, and a simple MVC4 website.  I will not post the WPF code here inline, as it is simply an ObservableCollection<int>, a label, two textbox’s, and a button. Starting a new Profiling Session So, in Visual Studio, I have just completed my first stint developing the GUI and DumbPrimes IPrimes class, so now I want to check my codes efficiency by profiling it.  All I have to do is build the solution (surprised initiating a profiling session doesn’t do this, but I suppose I can understand it), and then click the ANTS menu, followed by Profile Performance.  I am then greeted by the profiler starting up and already monitoring my program live: You are provided with a realtime graph at the top, and a pane at the bottom giving you information on how to proceed.  I am going to start by asking my program to show me the first 15000 primes: After the program finally began responding again (I did all the work on the main UI thread – how bad!), I stopped the profiler, which did kill the process of my program too.  One important thing to note, is that the profiler by default wants to give you a lot of detail about the operation – line hit counts, time per line, percent time per line, etc…  The important thing to remember is that this itself takes a lot of time.  When running my program without the profiler attached, it can generate the 15000 primes in 5.18 seconds, compared to 74.5 seconds – almost a 1500 percent increase.  While this may seem like a lot, remember that there is a trade off.  It may be WAY more inefficient, however, I am able to drill down and make improvements to specific problem areas, and then decrease execution time all around. Analyzing the Profiling Session After clicking ‘Stop Profiling’, the process running my application stopped, and the entire execution time was automatically selected by ANTS, and the results shown below: Now there are a number of interesting things going on here, I am going to cover each in a section of its own: Real Time Performance Counter Bar (top of screen) At the top of the screen, is the real time performance bar.  As your application is running, this will constantly update with the currently selected performance counters status.  A couple of cool things to note are the fact that you can drag a selection around specific time periods to drill down the detail views in the lower 2 panels to information pertaining to only that period. After selecting a time period, you can bookmark a section and name it, so that it is easy to find later, or after reloaded at a later time.  You can also zoom in, out, or fit the graph to the space provided – useful for drilling down. It may be hard to see, but at the top of the processor time graph below the time ticks, but above the red usage graph, there is a green bar. This bar shows at what times a method that is selected in the ‘Call tree’ panel is called. Very cool to be able to click on a method and see at what times it made an impact. As I said before, ANTS provides 43 different performance counters you can hook into.  Click the arrow next to the Performance tab at the top will allow you to change between different counters if you have them selected: Method Call Tree, ADO.Net Database Calls, File IO – Detail Panel Red Gate really hit the mark here I think. When you select a section of the run with the graph, the call tree populates to fill a hierarchical tree of method calls, with information regarding each of the methods.   By default, methods are hidden where the source is not provided (framework type code), however, Red Gate has integrated Reflector into ANTS, so even if you don’t have source for something, you can select a method and get the source if you want.  Methods are also hidden where the impact is seen as insignificant – methods that are only executed for 1% of the time of the overall calling methods time; in other words, working on making them better is not where your efforts should be focused. – Smart! Source Panel – Detail Panel The source panel is where you can see line level information on your code, showing the code for the currently selected method from the Method Call Tree.  If the code is not available, Reflector takes care of it and shows the code anyways! As you can notice, there does seem to be a problem with how ANTS determines what line is the actual line that a call is completed on.  I have suspicions that this may be due to some of the inline code optimizations that the CLR applies upon compilation of the assembly.  In a method with comments, the problem is much more severe: As you can see here, apparently the most offending code in my base library was a comment – *gasp*!  Removing the comments does help quite a bit, however I hope that Red Gate works on their counter algorithm soon to improve the logic on positioning for statistics: I did a small test just to demonstrate the lines are correct without comments. For me, it isn’t a deal breaker, as I can usually determine the correct placements by looking at the application code in the region and determining what makes sense, but it is something that would probably build up some irritation with time. Feature – Suggest Method for Optimization A neat feature to really help those in need of a pointer, is the menu option under tools to automatically suggest methods to optimize/improve: Nice feature – clicking it filters the call tree and stars methods that it thinks are good candidates for optimization.  I do wish that they would have made it more visible for those of use who aren’t great on sight: Process Integration I do think that this could have a place in my process.  After experimenting with the profiler, I do think it would be a great benefit to do some development, testing, and then after all the bugs are worked out, use the profiler to check on things to make sure nothing seems like it is hogging more than its fair share.  For example, with this program, I would have developed it, ran it, tested it – it works, but slowly. After looking at the profiler, and seeing the massive amount of time spent in 1 method, I might go ahead and try to re-implement IPrimes (I actually would probably rewrite the offending code, but so that I can distribute both sets of code easily, I’m just going to make another implementation of IPrimes).  Using two pieces of knowledge about prime numbers can make this method MUCH more efficient – prime numbers fall into two buckets 6k+/-1 , and a number is prime if it is not divisible by any other primes before it: public class SmartPrimes : IPrimes { public IEnumerable<int> GetPrimes(int retrieve) { //store a list of primes already found var _foundPrimes = new List<int>() { 2, 3 }; //if i ask for 1 or two primes, return what asked for if (retrieve <= _foundPrimes.Count()) return _foundPrimes.Take(retrieve); //the next number to look at int _k = 1; //since I already determined I don't have enough //execute at least once, and until quantity is sufficed do { //assume prime until otherwise determined bool isPrime = true; int potentialPrime; //analyze 6k-1 //assign the value to potential potentialPrime = 6 * _k - 1; //if there are any primes that divise this, it is NOT a prime number //using PLINQ for quick boost isPrime = !_foundPrimes.AsParallel() .Any(prime => potentialPrime % prime == 0); //if it is prime, add to found list if (isPrime) _foundPrimes.Add(potentialPrime); if (_foundPrimes.Count() == retrieve) break; //analyze 6k+1 //assign the value to potential potentialPrime = 6 * _k + 1; //if there are any primes that divise this, it is NOT a prime number //using PLINQ for quick boost isPrime = !_foundPrimes.AsParallel() .Any(prime => potentialPrime % prime == 0); //if it is prime, add to found list if (isPrime) _foundPrimes.Add(potentialPrime); //increment k to analyze next _k++; } while (_foundPrimes.Count() < retrieve); return _foundPrimes; } } Now there are definitely more things I can do to help make this more efficient, but for the scope of this example, I think this is fine (but still hideous)! Profiling this now yields a happy surprise 27 seconds to generate the 15000 primes with the profiler attached, and only 1.43 seconds without.  One important thing I wanted to call out though was the performance graph now: Notice anything odd?  The %Processor time is above 100%.  This is because there is now more than 1 core in the operation.  A better label for the chart in my mind would have been %Core time, but to each their own. Another odd thing I noticed was that the profiler seemed to be spot on this time in my DumbPrimes class with line details in source, even with comments..  Odd. Profiling Web Applications The last thing that I wanted to cover, that means a lot to me as a web developer, is the great amount of work that Red Gate put into the profiler when profiling web applications.  In my solution, I have a simple MVC4 application setup with 1 page, a single input form, that will output prime values as my WPF app did.  Launching the profiler from Visual Studio as before, nothing is really different in the profiler window, however I did receive a UAC prompt for a Red Gate helper app to integrate with the web server without notification. After requesting 500, 1000, 2000, and 5000 primes, and looking at the profiler session, things are slightly different from before: As you can see, there are 4 spikes of activity in the processor time graph, but there is also something new in the call tree: That’s right – ANTS will actually group method calls by get/post operations, so it is easier to find out what action/page is giving the largest problems…  Pretty cool in my mind! Overview Overall, I think that Red Gate ANTS CLR Profiler has a lot to offer, however I think it also has a long ways to go.  3 Biggest Pros: Ability to easily drill down from time graph, to method calls, to source code Wide variety of counters to choose from when profiling your application Excellent integration/grouping of methods being called from web applications by request – BRILLIANT! 3 Biggest Cons: Issue regarding line details in source view Nit pick – Processor time vs. Core time Nit pick – Lack of full integration with Visual Studio Ratings Ease of Use (7/10) – I marked down here because of the problems with the line level details and the extra work that that entails, and the lack of better integration with Visual Studio. Effectiveness (10/10) – I believe that the profiler does EXACTLY what it purports to do.  Especially with its large variety of performance counters, a definite plus! Features (9/10) – Besides the real time performance monitoring, and the drill downs that I’ve shown here, ANTS also has great integration with ADO.Net, with the ability to show database queries run by your application in the profiler.  This, with the line level details, the web request grouping, reflector integration, and various options to customize your profiling session I think create a great set of features! Customer Service (10/10) – My entire experience with Red Gate personnel has been nothing but good.  their people are friendly, helpful, and happy! UI / UX (8/10) – The interface is very easy to get around, and all of the options are easy to find.  With a little bit of poking around, you’ll be optimizing Hello World in no time flat! Overall (8/10) – Overall, I am happy with the Performance Profiler and its features, as well as with the service I received when working with the Red Gate personnel.  I WOULD recommend you trying the application and seeing if it would fit into your process, BUT, remember there are still some kinks in it to hopefully be worked out. My next post will definitely be shorter (hopefully), but thank you for reading up to here, or skipping ahead!  Please, if you do try the product, drop me a message and let me know what you think!  I would love to hear any opinions you may have on the product. Code Feel free to download the code I used above – download via DropBox

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  • How do tight timelines and scheduling pressure affect TCO and delivery time?

    - by JonathanHayward
    A friend's father, who is a software engineering manager, said, emphatically, "The number one cause of scheduling overruns is scheduling pressure." Where does the research stand? Is a moderate amount of scheduling pressure invigorating, or is the manager I mentioned right or wrong, or is it a matter of "the more scheduling pressure you have, the longer the delivery time and the more TCO?" Is it one of those things where ideally software engineering would work without scheduling pressure but practically we have to work with constraints of real-world situations? Any links to software engineering literature would be appreciated.

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  • Should I be paid for time spent learning a framework?

    - by nate-bit
    To give light to the situation: I am currently one of two programmers working in a small startup software company. Part of my job requires me to learn a Web development framework that I am not currently familiar with. I get paid by the hour. So the question is: Is it wholly ethical to spend multiple hours of the day reading through documentation and tutorials and be paid for this time where I am not actively developing for our product? Or should the bulk of this learning be done at home, or otherwise off hours, to allow for more full-on development of our application during the work day?

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  • Does Google include the time to load images, for a single page, as part of the page speed?

    - by Pure.Krome
    we all know that Google's affects your page rank with the load time of a page. How? That's part of the secret sauce. But we know that page speed is a serious factor. So - what is considered the speed of a page? Is it just the first (and main) html file which the GET receives? Or does it also include loading of images as part of that speed. so for example... GET /index.htm <- takes 0.45 seconds to retrieve (including DNS lookup before). robot parses page.. see's there's a single main image.... GET /img/main.png <- takes 5 seconds to download. is the page speed for that resource, 0.45 seconds OR 5.45 seconds? I understand Javascript is not fired .. but are any of these external resources all downloaded and part of the page speed?

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  • How to concentrate on one project at a time. Divide and Conquer doesn't work for me [closed]

    - by refhat
    Possible Duplicate: Tips for staying focused and motivated on a project I have serious issues on concentrating on one project at a time. I cant even follow the Divide and Conquer Approach. Once I start a project, I try to get the things done as neatly as possible but very soon I end up messing so many components of it. I try to do divide and conquer, but my approach doesn't work smoothly, and then I then wonder here and there in other projects. Sometimes I try spending so many hours for some trivial issues, which in-fact are not even issues. How do I avoid this jargon and be a smooth developer and have a nice workflow around my projects. I tend to loose my concentration on the current project and wonder in another project.

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  • Do More, Spend Less, Speed Time to Market – All with Oracle Database Appliance.

    - by jgelhaus
    Do More, Spend Less, Speed Time to Market – All with Oracle Database Appliance. Join Oracle for a first hand experience that will highlight how your business can lower TCO for hardware and software, do more with your existing personnel and resources, and get your products to market faster with Oracle Database Appliance. Learn how you can take advantage of the world's most popular database – Oracle Database 11g – in a single solution that's affordable, provides automated installation, is easy to manage, and is supported end-to-end by Oracle. Oracle Database Appliance is the complete package: software, server, storage, and networking, all designed by Oracle to simplify your technology and let you get down to business. Webcast Schedule Wednesday, April 4 1:00pm Eastern Webcast Link Teleconference: 1-866-753-5684 Conference Code: 61908866 Passcode: oda Add meeting to your calendar Wednesday, April 11 1:00pm Eastern Webcast Link Teleconference: 1-866-753-5684 Conference Code: 61909590 Passcode: oda Add meeting to your calendar Wednesday, April 18 1:00pm Eastern Webcast Link Teleconference: 1-866-753-5684 Conference Code: 61910385 Passcode: oda Add meeting to your calendar

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  • What should be a fair amount of time in an interview before rejecting a candidate?

    - by Danish
    As a panelist for technical interviews, you often come across candidates who have all the requried educational qualifications, skill sets and experience level on resumes, but struggle to answer even the most basic questions. Ideally, technical interviews should try to check different aspects of a candidate and test them on various skills. So, if the candidate falters on one aspect, one should test the other ones before coming to a conclusion. But often, if a candidates falters on the first few questions, the red flag rises up pretty quickly. What in your opinion should be the bare minimum time spent with a candidate before making a fair accessment of his/her skills and suitability for the job?

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  • Two internships at the same time -- good or bad?

    - by Karl
    I had no internship a few months ago, so I basically went on a 'resume mailing' spree and emailed a lot of companies that I was interested in working for and that had my line of work. This didn't prove futile until a company accepted me into their internship program but said that I would be working remotely. I had no problem with that, the project was good and I was interested. Now I have another internship at a company that is close to my home and I don't want to miss it at all! I can manage both internships side-by-side. In the day, I will do the internship that is closer to my home and at night (and other times), I can manage the remote internship. My question is -- should I both? I am particularly interested in how two internships at the same time are viewed. Would it look good or bad? PS: Neither is paying me anything, so money is not a factor.

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  • Auto-mount CD/DVD drive to single, specific mount point every time?

    - by Christopher Parker
    Currently, whenever I insert a CD or DVD into my DVD drive, it mounts to a location such as /media/<LABEL>, where <LABEL> is the arbitrary label assigned to the optical disc. I remember, once upon a time, CD and DVD media being reliably located at /media/cdrom0 or something similar. Why was this changed? And how do I get this old behavior back for this drive? I can understand this behavior for USB sticks. It makes sense for those. But not for CD/DVD media, in my opinion. For example, because of this, I have no way to configure Wine to point to my DVD drive, as the mount point changes with every single CD I insert. TL;DR: How do I make CD/DVD media always mount to /media/cdrom0?

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  • Where can I get feedback and support from other programmers in real time?

    - by cypherblue
    I used to work in an office surrounded by a large team of programmers where we all used the same languages and had different expertises. Now that I am on my own forming a startup at home, my productivity is suffering because I miss having people I can talk to for specific help, inspiration and reality checks when working on a coding problem. I don't have access to business incubators or shared (co-working) office spaces for startups so I need to chat with people virtually. Where can I go for real-time chat with other programmers and developers (currently I'm looking for people developing for the web, javascript and python) for live debugging and problem-solving of the tasks I am working on? And what other resources can I use to get fellow programmer support?

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