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  • How do you do real time document tracking?

    - by Nimish
    I was considering diff Document Tracking options and came across DocTracking.com. DocTracking.com allows you to upload documents (PDF Word etc) and adds some kind of invisible tracking to it and returns the document to you which can then be used just like you would use the document otherwise. This tracking tells you when your documents were opened, who opened them (IP), geo-location of opening if they are re-opened or forwarded, what pages were read and how long it was read for, what was printed. Any leads on how this could be done would be appreciated.

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  • MySQL: Order by time (MM:SS)?

    - by Shpigford
    I'm currently storing various metadata about videos and one of those bits of data is the length of a video. So if a video is 10 minutes 35 seconds long, it's saved as "10:35" in the database. But what I'd like to do is retrieve a listing of videos by length (longest first, shortest last). The problem I'm having is that if a video is "2:56", it's coming up as longest because the number 2 is more than the number 1 in. So, how can I order data based on that length field so that "10:35" is recognized as being longer than "2:56" (as per my example)?

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  • C# Date Time Picker to Text?

    - by user3691826
    Im trying to get a text from a file into date format for a label. What i currently have works great for a DateTimePicker however im wanting to now use a label to display the date rather than a DateTimePicker. This is what currently works when getting the value to a DateTimePicker: dateTimeMFR.Value = this.myKeyVault.MFRDate; and this is what im attempting to make work in a label: DateTimePicker myDate = new DateTimePicker(); myDate.Value = myKeyVault.MFRDate; txtMFR.Text = myDate.Text; Thanks for any help on the matter.

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  • Time display query in sql

    - by shanks
    I have following data UserID UserName LogTime LogDate 1 S 9:00 21/5/2010 1 S 10:00 21/5/2010 1 S 11:00 21/5/2010 1 S 12:00 21/5/2010 Need Output as:- 1 s 9:00 10:00 21/5/2010 1 s 11:00 12:00 21/5/2010

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  • Android Convert Central Time to Local Time

    - by chedstone
    I have a MySql database that stores a timestamp for each record I insert. I pull that timestamp into my Android application as a string. My database is located on a server that has a TimeZone of CST. I want to convert that CST timestamp to the Android device's local time. Can someone help with this?

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  • Fedora 17 not saving iptables

    - by Louis W
    For some reason my Fedora is not saving changes made to my iptables. iptables -I INPUT -p tcp --dport 80 -j ACCEPT iptables -I INPUT -p tcp --dport 443 -j ACCEPT service iptables status service iptables restart Redirecting to /bin/systemctl status iptables.service Then when starting, my changes are not there anymore. Also tried saving: [root@VTM01 ~]# service iptables save Redirecting to /bin/systemctl save iptables.service Unknown operation save

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  • Excel Question: I need a date and time formula to convert between time zones

    - by Harold Nottingham
    Hello, I am trying to find a way to calculate a duration in days between my, time zone (Central), and (Pacific; Mountain; Eastern). Just do not know where to start. My criteria would be as follows: Cell C5:C100 would be the timestamps in this format:3/18/2010 23:45 but for different dates and times. Cell D5:D100 would be the corresponding timezone in text form: Pacific; Mountain; Eastern; Central. Cell F5 would be where the duration in days would need to be. Just not sure how to write the formula to give me what I am looking for. I appreciate any assistance in advance. Thanks

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  • Exel Question: I need a date and time formula to convert between time zones

    - by Harold Nottingham
    Hello, I am trying to find a way to calculate a duration in days between my, time zone (Central), and (Pacific; Mountain; Eastern). Just do not know where to start. My criteria would be as follows: Cell C5:C100 would be the timestamps in this format:3/18/2010 23:45 but for different dates and times. Cell D5:D100 would be the corresponding timezone in text form: Pacific; Mountain; Eastern; Central. Cell F5 would be where the duration in days would need to be. Just not sure how to write the formula to give me what I am looking for. I appreciate any assistance in advance. Thanks

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  • Taming Hopping Windows

    - by Roman Schindlauer
    At first glance, hopping windows seem fairly innocuous and obvious. They organize events into windows with a simple periodic definition: the windows have some duration d (e.g. a window covers 5 second time intervals), an interval or period p (e.g. a new window starts every 2 seconds) and an alignment a (e.g. one of those windows starts at 12:00 PM on March 15, 2012 UTC). var wins = xs     .HoppingWindow(TimeSpan.FromSeconds(5),                    TimeSpan.FromSeconds(2),                    new DateTime(2012, 3, 15, 12, 0, 0, DateTimeKind.Utc)); Logically, there is a window with start time a + np and end time a + np + d for every integer n. That’s a lot of windows. So why doesn’t the following query (always) blow up? var query = wins.Select(win => win.Count()); A few users have asked why StreamInsight doesn’t produce output for empty windows. Primarily it’s because there is an infinite number of empty windows! (Actually, StreamInsight uses DateTimeOffset.MaxValue to approximate “the end of time” and DateTimeOffset.MinValue to approximate “the beginning of time”, so the number of windows is lower in practice.) That was the good news. Now the bad news. Events also have duration. Consider the following simple input: var xs = this.Application                 .DefineEnumerable(() => new[]                     { EdgeEvent.CreateStart(DateTimeOffset.UtcNow, 0) })                 .ToStreamable(AdvanceTimeSettings.IncreasingStartTime); Because the event has no explicit end edge, it lasts until the end of time. So there are lots of non-empty windows if we apply a hopping window to that single event! For this reason, we need to be careful with hopping window queries in StreamInsight. Or we can switch to a custom implementation of hopping windows that doesn’t suffer from this shortcoming. The alternate window implementation produces output only when the input changes. We start by breaking up the timeline into non-overlapping intervals assigned to each window. In figure 1, six hopping windows (“Windows”) are assigned to six intervals (“Assignments”) in the timeline. Next we take input events (“Events”) and alter their lifetimes (“Altered Events”) so that they cover the intervals of the windows they intersect. In figure 1, you can see that the first event e1 intersects windows w1 and w2 so it is adjusted to cover assignments a1 and a2. Finally, we can use snapshot windows (“Snapshots”) to produce output for the hopping windows. Notice however that instead of having six windows generating output, we have only four. The first and second snapshots correspond to the first and second hopping windows. The remaining snapshots however cover two hopping windows each! While in this example we saved only two events, the savings can be more significant when the ratio of event duration to window duration is higher. Figure 1: Timeline The implementation of this strategy is straightforward. We need to set the start times of events to the start time of the interval assigned to the earliest window including the start time. Similarly, we need to modify the end times of events to the end time of the interval assigned to the latest window including the end time. The following snap-to-boundary function that rounds a timestamp value t down to the nearest value t' <= t such that t' is a + np for some integer n will be useful. For convenience, we will represent both DateTime and TimeSpan values using long ticks: static long SnapToBoundary(long t, long a, long p) {     return t - ((t - a) % p) - (t > a ? 0L : p); } How do we find the earliest window including the start time for an event? It’s the window following the last window that does not include the start time assuming that there are no gaps in the windows (i.e. duration < interval), and limitation of this solution. To find the end time of that antecedent window, we need to know the alignment of window ends: long e = a + (d % p); Using the window end alignment, we are finally ready to describe the start time selector: static long AdjustStartTime(long t, long e, long p) {     return SnapToBoundary(t, e, p) + p; } To find the latest window including the end time for an event, we look for the last window start time (non-inclusive): public static long AdjustEndTime(long t, long a, long d, long p) {     return SnapToBoundary(t - 1, a, p) + p + d; } Bringing it together, we can define the translation from events to ‘altered events’ as in Figure 1: public static IQStreamable<T> SnapToWindowIntervals<T>(IQStreamable<T> source, TimeSpan duration, TimeSpan interval, DateTime alignment) {     if (source == null) throw new ArgumentNullException("source");     // reason about DateTime and TimeSpan in ticks     long d = Math.Min(DateTime.MaxValue.Ticks, duration.Ticks);     long p = Math.Min(DateTime.MaxValue.Ticks, Math.Abs(interval.Ticks));     // set alignment to earliest possible window     var a = alignment.ToUniversalTime().Ticks % p;     // verify constraints of this solution     if (d <= 0L) { throw new ArgumentOutOfRangeException("duration"); }     if (p == 0L || p > d) { throw new ArgumentOutOfRangeException("interval"); }     // find the alignment of window ends     long e = a + (d % p);     return source.AlterEventLifetime(         evt => ToDateTime(AdjustStartTime(evt.StartTime.ToUniversalTime().Ticks, e, p)),         evt => ToDateTime(AdjustEndTime(evt.EndTime.ToUniversalTime().Ticks, a, d, p)) -             ToDateTime(AdjustStartTime(evt.StartTime.ToUniversalTime().Ticks, e, p))); } public static DateTime ToDateTime(long ticks) {     // just snap to min or max value rather than under/overflowing     return ticks < DateTime.MinValue.Ticks         ? new DateTime(DateTime.MinValue.Ticks, DateTimeKind.Utc)         : ticks > DateTime.MaxValue.Ticks         ? new DateTime(DateTime.MaxValue.Ticks, DateTimeKind.Utc)         : new DateTime(ticks, DateTimeKind.Utc); } Finally, we can describe our custom hopping window operator: public static IQWindowedStreamable<T> HoppingWindow2<T>(     IQStreamable<T> source,     TimeSpan duration,     TimeSpan interval,     DateTime alignment) {     if (source == null) { throw new ArgumentNullException("source"); }     return SnapToWindowIntervals(source, duration, interval, alignment).SnapshotWindow(); } By switching from HoppingWindow to HoppingWindow2 in the following example, the query returns quickly rather than gobbling resources and ultimately failing! public void Main() {     var start = new DateTimeOffset(new DateTime(2012, 6, 28), TimeSpan.Zero);     var duration = TimeSpan.FromSeconds(5);     var interval = TimeSpan.FromSeconds(2);     var alignment = new DateTime(2012, 3, 15, 12, 0, 0, DateTimeKind.Utc);     var events = this.Application.DefineEnumerable(() => new[]     {         EdgeEvent.CreateStart(start.AddSeconds(0), "e0"),         EdgeEvent.CreateStart(start.AddSeconds(1), "e1"),         EdgeEvent.CreateEnd(start.AddSeconds(1), start.AddSeconds(2), "e1"),         EdgeEvent.CreateStart(start.AddSeconds(3), "e2"),         EdgeEvent.CreateStart(start.AddSeconds(9), "e3"),         EdgeEvent.CreateEnd(start.AddSeconds(3), start.AddSeconds(10), "e2"),         EdgeEvent.CreateEnd(start.AddSeconds(9), start.AddSeconds(10), "e3"),     }).ToStreamable(AdvanceTimeSettings.IncreasingStartTime);     var adjustedEvents = SnapToWindowIntervals(events, duration, interval, alignment);     var query = from win in HoppingWindow2(events, duration, interval, alignment)                 select win.Count();     DisplayResults(adjustedEvents, "Adjusted Events");     DisplayResults(query, "Query"); } As you can see, instead of producing a massive number of windows for the open start edge e0, a single window is emitted from 12:00:15 AM until the end of time: Adjusted Events StartTime EndTime Payload 6/28/2012 12:00:01 AM 12/31/9999 11:59:59 PM e0 6/28/2012 12:00:03 AM 6/28/2012 12:00:07 AM e1 6/28/2012 12:00:05 AM 6/28/2012 12:00:15 AM e2 6/28/2012 12:00:11 AM 6/28/2012 12:00:15 AM e3 Query StartTime EndTime Payload 6/28/2012 12:00:01 AM 6/28/2012 12:00:03 AM 1 6/28/2012 12:00:03 AM 6/28/2012 12:00:05 AM 2 6/28/2012 12:00:05 AM 6/28/2012 12:00:07 AM 3 6/28/2012 12:00:07 AM 6/28/2012 12:00:11 AM 2 6/28/2012 12:00:11 AM 6/28/2012 12:00:15 AM 3 6/28/2012 12:00:15 AM 12/31/9999 11:59:59 PM 1 Regards, The StreamInsight Team

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  • Taming Hopping Windows

    - by Roman Schindlauer
    At first glance, hopping windows seem fairly innocuous and obvious. They organize events into windows with a simple periodic definition: the windows have some duration d (e.g. a window covers 5 second time intervals), an interval or period p (e.g. a new window starts every 2 seconds) and an alignment a (e.g. one of those windows starts at 12:00 PM on March 15, 2012 UTC). var wins = xs     .HoppingWindow(TimeSpan.FromSeconds(5),                    TimeSpan.FromSeconds(2),                    new DateTime(2012, 3, 15, 12, 0, 0, DateTimeKind.Utc)); Logically, there is a window with start time a + np and end time a + np + d for every integer n. That’s a lot of windows. So why doesn’t the following query (always) blow up? var query = wins.Select(win => win.Count()); A few users have asked why StreamInsight doesn’t produce output for empty windows. Primarily it’s because there is an infinite number of empty windows! (Actually, StreamInsight uses DateTimeOffset.MaxValue to approximate “the end of time” and DateTimeOffset.MinValue to approximate “the beginning of time”, so the number of windows is lower in practice.) That was the good news. Now the bad news. Events also have duration. Consider the following simple input: var xs = this.Application                 .DefineEnumerable(() => new[]                     { EdgeEvent.CreateStart(DateTimeOffset.UtcNow, 0) })                 .ToStreamable(AdvanceTimeSettings.IncreasingStartTime); Because the event has no explicit end edge, it lasts until the end of time. So there are lots of non-empty windows if we apply a hopping window to that single event! For this reason, we need to be careful with hopping window queries in StreamInsight. Or we can switch to a custom implementation of hopping windows that doesn’t suffer from this shortcoming. The alternate window implementation produces output only when the input changes. We start by breaking up the timeline into non-overlapping intervals assigned to each window. In figure 1, six hopping windows (“Windows”) are assigned to six intervals (“Assignments”) in the timeline. Next we take input events (“Events”) and alter their lifetimes (“Altered Events”) so that they cover the intervals of the windows they intersect. In figure 1, you can see that the first event e1 intersects windows w1 and w2 so it is adjusted to cover assignments a1 and a2. Finally, we can use snapshot windows (“Snapshots”) to produce output for the hopping windows. Notice however that instead of having six windows generating output, we have only four. The first and second snapshots correspond to the first and second hopping windows. The remaining snapshots however cover two hopping windows each! While in this example we saved only two events, the savings can be more significant when the ratio of event duration to window duration is higher. Figure 1: Timeline The implementation of this strategy is straightforward. We need to set the start times of events to the start time of the interval assigned to the earliest window including the start time. Similarly, we need to modify the end times of events to the end time of the interval assigned to the latest window including the end time. The following snap-to-boundary function that rounds a timestamp value t down to the nearest value t' <= t such that t' is a + np for some integer n will be useful. For convenience, we will represent both DateTime and TimeSpan values using long ticks: static long SnapToBoundary(long t, long a, long p) {     return t - ((t - a) % p) - (t > a ? 0L : p); } How do we find the earliest window including the start time for an event? It’s the window following the last window that does not include the start time assuming that there are no gaps in the windows (i.e. duration < interval), and limitation of this solution. To find the end time of that antecedent window, we need to know the alignment of window ends: long e = a + (d % p); Using the window end alignment, we are finally ready to describe the start time selector: static long AdjustStartTime(long t, long e, long p) {     return SnapToBoundary(t, e, p) + p; } To find the latest window including the end time for an event, we look for the last window start time (non-inclusive): public static long AdjustEndTime(long t, long a, long d, long p) {     return SnapToBoundary(t - 1, a, p) + p + d; } Bringing it together, we can define the translation from events to ‘altered events’ as in Figure 1: public static IQStreamable<T> SnapToWindowIntervals<T>(IQStreamable<T> source, TimeSpan duration, TimeSpan interval, DateTime alignment) {     if (source == null) throw new ArgumentNullException("source");     // reason about DateTime and TimeSpan in ticks     long d = Math.Min(DateTime.MaxValue.Ticks, duration.Ticks);     long p = Math.Min(DateTime.MaxValue.Ticks, Math.Abs(interval.Ticks));     // set alignment to earliest possible window     var a = alignment.ToUniversalTime().Ticks % p;     // verify constraints of this solution     if (d <= 0L) { throw new ArgumentOutOfRangeException("duration"); }     if (p == 0L || p > d) { throw new ArgumentOutOfRangeException("interval"); }     // find the alignment of window ends     long e = a + (d % p);     return source.AlterEventLifetime(         evt => ToDateTime(AdjustStartTime(evt.StartTime.ToUniversalTime().Ticks, e, p)),         evt => ToDateTime(AdjustEndTime(evt.EndTime.ToUniversalTime().Ticks, a, d, p)) -             ToDateTime(AdjustStartTime(evt.StartTime.ToUniversalTime().Ticks, e, p))); } public static DateTime ToDateTime(long ticks) {     // just snap to min or max value rather than under/overflowing     return ticks < DateTime.MinValue.Ticks         ? new DateTime(DateTime.MinValue.Ticks, DateTimeKind.Utc)         : ticks > DateTime.MaxValue.Ticks         ? new DateTime(DateTime.MaxValue.Ticks, DateTimeKind.Utc)         : new DateTime(ticks, DateTimeKind.Utc); } Finally, we can describe our custom hopping window operator: public static IQWindowedStreamable<T> HoppingWindow2<T>(     IQStreamable<T> source,     TimeSpan duration,     TimeSpan interval,     DateTime alignment) {     if (source == null) { throw new ArgumentNullException("source"); }     return SnapToWindowIntervals(source, duration, interval, alignment).SnapshotWindow(); } By switching from HoppingWindow to HoppingWindow2 in the following example, the query returns quickly rather than gobbling resources and ultimately failing! public void Main() {     var start = new DateTimeOffset(new DateTime(2012, 6, 28), TimeSpan.Zero);     var duration = TimeSpan.FromSeconds(5);     var interval = TimeSpan.FromSeconds(2);     var alignment = new DateTime(2012, 3, 15, 12, 0, 0, DateTimeKind.Utc);     var events = this.Application.DefineEnumerable(() => new[]     {         EdgeEvent.CreateStart(start.AddSeconds(0), "e0"),         EdgeEvent.CreateStart(start.AddSeconds(1), "e1"),         EdgeEvent.CreateEnd(start.AddSeconds(1), start.AddSeconds(2), "e1"),         EdgeEvent.CreateStart(start.AddSeconds(3), "e2"),         EdgeEvent.CreateStart(start.AddSeconds(9), "e3"),         EdgeEvent.CreateEnd(start.AddSeconds(3), start.AddSeconds(10), "e2"),         EdgeEvent.CreateEnd(start.AddSeconds(9), start.AddSeconds(10), "e3"),     }).ToStreamable(AdvanceTimeSettings.IncreasingStartTime);     var adjustedEvents = SnapToWindowIntervals(events, duration, interval, alignment);     var query = from win in HoppingWindow2(events, duration, interval, alignment)                 select win.Count();     DisplayResults(adjustedEvents, "Adjusted Events");     DisplayResults(query, "Query"); } As you can see, instead of producing a massive number of windows for the open start edge e0, a single window is emitted from 12:00:15 AM until the end of time: Adjusted Events StartTime EndTime Payload 6/28/2012 12:00:01 AM 12/31/9999 11:59:59 PM e0 6/28/2012 12:00:03 AM 6/28/2012 12:00:07 AM e1 6/28/2012 12:00:05 AM 6/28/2012 12:00:15 AM e2 6/28/2012 12:00:11 AM 6/28/2012 12:00:15 AM e3 Query StartTime EndTime Payload 6/28/2012 12:00:01 AM 6/28/2012 12:00:03 AM 1 6/28/2012 12:00:03 AM 6/28/2012 12:00:05 AM 2 6/28/2012 12:00:05 AM 6/28/2012 12:00:07 AM 3 6/28/2012 12:00:07 AM 6/28/2012 12:00:11 AM 2 6/28/2012 12:00:11 AM 6/28/2012 12:00:15 AM 3 6/28/2012 12:00:15 AM 12/31/9999 11:59:59 PM 1 Regards, The StreamInsight Team

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  • SQL SERVER – Signal Wait Time Introduction with Simple Example – Wait Type – Day 2 of 28

    - by pinaldave
    In this post, let’s delve a bit more in depth regarding wait stats. The very first question: when do the wait stats occur? Here is the simple answer. When SQL Server is executing any task, and if for any reason it has to wait for resources to execute the task, this wait is recorded by SQL Server with the reason for the delay. Later on we can analyze these wait stats to understand the reason the task was delayed and maybe we can eliminate the wait for SQL Server. It is not always possible to remove the wait type 100%, but there are few suggestions that can help. Before we continue learning about wait types and wait stats, we need to understand three important milestones of the query life-cycle. Running - a query which is being executed on a CPU is called a running query. This query is responsible for CPU time. Runnable – a query which is ready to execute and waiting for its turn to run is called a runnable query. This query is responsible for Signal Wait time. (In other words, the query is ready to run but CPU is servicing another query). Suspended – a query which is waiting due to any reason (to know the reason, we are learning wait stats) to be converted to runnable is suspended query. This query is responsible for wait time. (In other words, this is the time we are trying to reduce). In simple words, query execution time is a summation of the query Executing CPU Time (Running) + Query Wait Time (Suspended) + Query Signal Wait Time (Runnable). Again, it may be possible a query goes to all these stats multiple times. Let us try to understand the whole thing with a simple analogy of a taxi and a passenger. Two friends, Tom and Danny, go to the mall together. When they leave the mall, they decide to take a taxi. Tom and Danny both stand in the line waiting for their turn to get into the taxi. This is the Signal Wait Time as they are ready to get into the taxi but the taxis are currently serving other customer and they have to wait for their turn. In other word they are in a runnable state. Now when it is their turn to get into the taxi, the taxi driver informs them he does not take credit cards and only cash is accepted. Neither Tom nor Danny have enough cash, they both cannot get into the vehicle. Tom waits outside in the queue and Danny goes to ATM to fetch the cash. During this time the taxi cannot wait, they have to let other passengers get into the taxi. As Tom and Danny both are outside in the queue, this is the Query Wait Time and they are in the suspended state. They cannot do anything till they get the cash. Once Danny gets the cash, they are both standing in the line again, creating one more Signal Wait Time. This time when their turn comes they can pay the taxi driver in cash and reach their destination. The time taken for the taxi to get from the mall to the destination is running time (CPU time) and the taxi is running. I hope this analogy is bit clear with the wait stats. You can check the Signalwait stats using following query of Glenn Berry. -- Signal Waits for instance SELECT CAST(100.0 * SUM(signal_wait_time_ms) / SUM (wait_time_ms) AS NUMERIC(20,2)) AS [%signal (cpu) waits], CAST(100.0 * SUM(wait_time_ms - signal_wait_time_ms) / SUM (wait_time_ms) AS NUMERIC(20,2)) AS [%resource waits] FROM sys.dm_os_wait_stats OPTION (RECOMPILE); Higher the Signal wait stats are not good for the system. Very high value indicates CPU pressure. In my experience, when systems are running smooth and without any glitch the Signal wait stat is lower than 20%. Again, this number can be debated (and it is from my experience and is not documented anywhere). In other words, lower is better and higher is not good for the system. In future articles we will discuss in detail the various wait types and wait stats and their resolution. Read all the post in the Wait Types and Queue series. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL DMV, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • SQL SERVER – Single Wait Time Introduction with Simple Example – Wait Type – Day 2 of 28

    - by pinaldave
    In this post, let’s delve a bit more in depth regarding wait stats. The very first question: when do the wait stats occur? Here is the simple answer. When SQL Server is executing any task, and if for any reason it has to wait for resources to execute the task, this wait is recorded by SQL Server with the reason for the delay. Later on we can analyze these wait stats to understand the reason the task was delayed and maybe we can eliminate the wait for SQL Server. It is not always possible to remove the wait type 100%, but there are few suggestions that can help. Before we continue learning about wait types and wait stats, we need to understand three important milestones of the query life-cycle. Running - a query which is being executed on a CPU is called a running query. This query is responsible for CPU time. Runnable – a query which is ready to execute and waiting for its turn to run is called a runnable query. This query is responsible for Single Wait time. (In other words, the query is ready to run but CPU is servicing another query). Suspended – a query which is waiting due to any reason (to know the reason, we are learning wait stats) to be converted to runnable is suspended query. This query is responsible for wait time. (In other words, this is the time we are trying to reduce). In simple words, query execution time is a summation of the query Executing CPU Time (Running) + Query Wait Time (Suspended) + Query Single Wait Time (Runnable). Again, it may be possible a query goes to all these stats multiple times. Let us try to understand the whole thing with a simple analogy of a taxi and a passenger. Two friends, Tom and Danny, go to the mall together. When they leave the mall, they decide to take a taxi. Tom and Danny both stand in the line waiting for their turn to get into the taxi. This is the Signal Wait Time as they are ready to get into the taxi but the taxis are currently serving other customer and they have to wait for their turn. In other word they are in a runnable state. Now when it is their turn to get into the taxi, the taxi driver informs them he does not take credit cards and only cash is accepted. Neither Tom nor Danny have enough cash, they both cannot get into the vehicle. Tom waits outside in the queue and Danny goes to ATM to fetch the cash. During this time the taxi cannot wait, they have to let other passengers get into the taxi. As Tom and Danny both are outside in the queue, this is the Query Wait Time and they are in the suspended state. They cannot do anything till they get the cash. Once Danny gets the cash, they are both standing in the line again, creating one more Single Wait Time. This time when their turn comes they can pay the taxi driver in cash and reach their destination. The time taken for the taxi to get from the mall to the destination is running time (CPU time) and the taxi is running. I hope this analogy is bit clear with the wait stats. You can check the single wait stats using following query of Glenn Berry. -- Signal Waits for instance SELECT CAST(100.0 * SUM(signal_wait_time_ms) / SUM (wait_time_ms) AS NUMERIC(20,2)) AS [%signal (cpu) waits], CAST(100.0 * SUM(wait_time_ms - signal_wait_time_ms) / SUM (wait_time_ms) AS NUMERIC(20,2)) AS [%resource waits] FROM sys.dm_os_wait_stats OPTION (RECOMPILE); Higher the single wait stats are not good for the system. Very high value indicates CPU pressure. In my experience, when systems are running smooth and without any glitch the single wait stat is lower than 20%. Again, this number can be debated (and it is from my experience and is not documented anywhere). In other words, lower is better and higher is not good for the system. In future articles we will discuss in detail the various wait types and wait stats and their resolution. Read all the post in the Wait Types and Queue series. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL DMV, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • Change the Default Location for Saving Internet Explorer Favorites

    - by Lori Kaufman
    By default, in Windows 7, Favorites for Internet Explorer are saved in the C:\Users\[username]\Favorites folder. However, you may want them in a different location so they are easier to backup or even on a drive where Windows is not installed. This article shows you how to change the location of the Internet Explorer Favorites folder in two ways: by changing the properties of the Favorites folder and by making changes to the registry. HTG Explains: Why You Only Have to Wipe a Disk Once to Erase It HTG Explains: Learn How Websites Are Tracking You Online Here’s How to Download Windows 8 Release Preview Right Now

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  • Developing web application with time zones support

    - by outcoldman
    When you develop web application you should know that client PCs can be located anywhere on earth. Even if you develop app just for your country users you should remember it (in Russia now we have 9 time zones, before 28 of March we had 11 time zones). On big sites with many members do it very easy – you can place field “time zone” in member profile, in Sharepoint I saw this solution, and many enterprise app do it like this. But if we have simple website with blog publications or website with news and we don’t have member profiles on server, how we can support user’s time zones? I thought about this question because I wanted to develop time zone support on my own site. My case is ASP.NET MVC app and MS SQL Server DB. First, I started from learning which params we have at HTTP headers, but it doesn’t have information about it. So we can’t use regional settings and methods DateTime.ToLocalTime and DateTime.ToUniversalTime until we get user time zone on server. If we used our app before without time zones support we need to change dates from local time zone to UTC time zone (something like Greenwich Mean Time). Read more...(Redirect to http://outcoldman.ru)

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  • Opening Time-Machine OSX backup files on Windows 7?

    - by user39279
    Hi, Have Time Machine backups on a Western Digital External HD. The Time Machine backups were done on my now dead Mac G4 running OSX Leopard- I am waiting on a new iMac but in the meantime I need to access some of my backup files urgently. I have a laptop running Windows 7 so is there any safe way of accessing some of the files from the Time Machine backup on my laptop and still be able to do a full restore when the iMac arrives? Thanks -

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  • Unity3d generating a file in iOS and saving it on a linux machine

    - by N0xus
    I've done a little research and don't know if the following is possible. At the moment I have created a small application in Unity that generates an XML file. This file will be used to help set up my game. It's done in Unity due to it being cross platform with no need to re-write a single line of code. Eventually this will run on an iPad. However, my game will be running on a linux computer and I need to pass over the XML file to the computer that will be running the final game (please don't ask why I'm doing that, it's something I need to do). So what I want to know is the following: Can I generate my XML file on an iPad and have that XML file be saved, and transmitted to a linux machine, without the need to manually copy the file over. If so, how is this possible?

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  • Java Spotlight Episode 138: Paul Perrone on Life Saving Embedded Java

    - by Roger Brinkley
    Interview with Paul Perrone, founder and CEO of Perrone Robotics, on using Java Embedded to test autonomous vehicle operations for the Insurance Institute for Highway Safety that will save lives. Right-click or Control-click to download this MP3 file. You can also subscribe to the Java Spotlight Podcast Feed to get the latest podcast automatically. If you use iTunes you can open iTunes and subscribe with this link: Java Spotlight Podcast in iTunes. Show Notes News JDK 8 is Feature Complete Java SE 7 Update 25 Released What should the JCP be doing? 2013 Duke's Choice Award Nominations Another Quick update to Code Signing Article on OTN Events June 24, Austin JUG, Austin, TX June 25, Virtual Developer Day - Java, EMEA, 10AM CEST Jul 16-19, Uberconf, Denver, USA Jul 22-24, JavaOne Shanghai, China Jul 29-31, JVM Summit Language, Santa Clara Sep 11-12, JavaZone, Oslo, Norway Sep 19-20, Strange Loop, St. Louis Sep 22-26 JavaOne San Francisco 2013, USA Feature Interview Paul J. Perrone is founder/CEO of Perrone Robotics. Paul architected the Java-based general-purpose robotics and automation software platform known as “MAX”. Paul has overseen MAX’s application to rapidly field self-driving robotic cars, unmanned air vehicles, factory and road-side automation applications, and a wide range of advanced robots and automaton applications. He fielded a self-driving autonomous robotic dune buggy in the historic 2005 Grand Challenge race across the Mojave desert and a self-driving autonomous car in the 2007 Urban Challenge through a city landscape. His work has been featured in numerous televised and print media including the Discovery Channel, a theatrical documentary, scientific journals, trade magazines, and international press. Since 2008, Paul has also been working as the chief software engineer, CTO, and roboticist automating rock star Neil Young’s LincVolt, a 1959 Lincoln Continental retro-fitted as a fully autonomous extended range electric vehicle. Paul has been an engineer, author of books and articles on Java, frequent speaker on Java, and entrepreneur in the robotics and software space for over 20 years. He is a member of the Java Champions program, recipient of three Duke Awards including a Gold Duke and Lifetime Achievement Award, has showcased Java-based robots at five JavaOne keynotes, and is a frequent JavaOne speaker and show floor participant. He holds a B.S.E.E. from Rutgers University and an M.S.E.E. from the University of Virginia. What’s Cool Shenandoah: A pauseless GC for OpenJDK

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  • Rhythmbox not saving the album art permanently

    - by Nik
    I run ubuntu 10.10 and use rhythmbox (version 0.13.1) regularly with the albumartsearch plugin installed. However when I change the album art it is only temporary. On moving to the next song it automatically removes the previous song's album art cover. (I do know about banshee but would like to use rhythmbox). The cover art plugin is also installed by default however it cannot display some of the album covers since the songs are in my local language (tamil) hence it cannot retrieve the album cover from the internet. However the albumartsearch plugin seems to do the job although only temporarily. Any reason why it might be? I have tried looking for other rhythmbox plugins which might be similar to albumartsearch but in vain. Any help would be appreciated. I have filed a bug in the albumartsearch plugin's website. Waiting for the reply.

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  • How do I restore a non-system hard drive using Time Machine under OSX?

    - by richardtallent
    I dropped one of the external drives on my Mac Pro and it started making noises... so I bought a replacement drive. No biggie, that's why I have Time Machine, right? So now that I have the new drive up and initialized, how do I actually restore the drive from backup? Time Machine is intuitive when it comes to restoring the system drive or restoring individual folders/files on the same literal device, but I'm a bit stuck in how to properly restore an entire drive that is not the boot drive. I saw one suggestion to use the same volume name as the old drive and then go into Time Machine. Haven't tried that since the information is unconfirmed. For now, I just went to the Time Machine volume, found the latest backup folder for that volume, and I'm copying the files via Finder. Of couse, I expect this to work just fine, but I feel like I'm missing something if that's the "proper" way to do this.

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  • Saving game data to server [on hold]

    - by Eugene Lim
    What's the best method to save the player's data to the server? Method to store the game saves Which one of the following method should I use ? Using a database structure(e.g.. mySQL) to store the game data as blobs? Using the server hard disk to store the saved game data as binary data files? Method to send saved game data to server What method should I use ? socketIO web socket a web-based scripting language to receive the game data as binary? for example, a php script to handle binary data and save it to file Meta-data I read that some games store saved game meta-data in database structures. What kind of meta data is useful to store?

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