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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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  • iPhone SDK: Data Synchronization

    - by buzzappsoftware
    I am looking for an overview of data synchronization techniques available on the iPhone platform. We need the ability to be able to sync a subset of content from a server to a local database residing on the iPhone. On other projects I have worked on, the data synchronization was handled by the database. Is that available in SQLite? If not, any suggestions on techniques? Rolling our own would not be my first choice. Thanks in advance.

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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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  • 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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  • Does immutability entirely eliminate the need for locks in multi-processor programming?

    - by GlenPeterson
    Part 1 Clearly Immutability minimizes the need for locks in multi-processor programming, but does it eliminate that need, or are there instances where immutability alone is not enough? It seems to me that you can only defer processing and encapsulate state so long before most programs have to actually DO something. If a program performs actions on multiple processors, something needs to collect and aggregate the results. All this involves multi-process communication before, after, and possibly during some transformations. The start and end state of the machines are different. Can this always be done with no locks just by throwing out each object and creating a new one instead of changing the original (a crude view of immutability)? What cases still require locking? I'm interested in both the theoretical/academic answer and the practical/real-world answer. I know a lot of functional programmers like to talk about "no side effect" but in the "real world" everything has a side effect. Every processor click takes time and electricity and machine resources away from other processes. So I understand that there may be more than one perspective to answer this question from. If immutability is safe, given certain bounds or assumptions, I want to know what the borders of the "safety zone" are exactly. Some examples of possible boundaries: I/O Exceptions/errors Interfaces with programs written in other languages Interfaces with other machines (physical, virtual, or theoretical) Special thanks to @JimmaHoffa for his comment which started this question! Part 2 Multi-processor programming is often used as an optimization technique - to make some code run faster. When is it faster to use locks vs. immutable objects? Given the limits set out in Amdahl's Law, when can you achieve better over-all performance (with or without the garbage collector taken into account) with immutable objects vs. locking mutable ones? Summary I'm combining these two questions into one to try to get at where the bounding box is for Immutability as a solution to threading problems.

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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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  • Client-Server MMOG & data structures sync when joining / playing

    - by plang
    After reading a few articles on MMOG architecture, there is still one point on which I cannot find much information: it has to do with how you keep in sync server data on the client, when you join, and while you play. A pretty vague question, I agree. Let me refine it: Let's say we have an MMOG virtual world subdivided into geographical cells. A player in a cell is mostly interested in what happens in the cell itself, and all the surrounding cells, not more. When joining the game for the first time, the only thing we can do is send some sort of "database dump" of the interesting cells to the client. When playing, I guess it would be very inefficient to do the same thing regularly. I imagine the best thing to do is to send "deltas" to the client, which would allow keeping the local database in sync. Now let's say the player moves, and arrives in another cell. Surrounding cells change, and for all the new cells the player subscribes, the same technique as used when joining the game has to be used: some sort of "database dump". This mechanic of joining/moving in a cell-based MMOG virtual world interests me, and I was wondering if there were tried and tested techniques in this domain. Thanks!

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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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  • 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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  • 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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  • NetBeans remote synchronization : failed to save file

    - by yoda
    Hi, I'm using NetBeans in a project, making use of remote sync to save both locally and to a FTP server. This feature works in other projects, but this time is failing when trying to save the file to the remote server. The IDE log tells me that had occurred a unknown error, as well as this : Upload failed: org.netbeans.modules.php.project.connections.TransferInfo [transfered: [], failed: {index.php=Cannot upload file index.php (unknown reason).}, partially failed: {}, ignored: {}, runtime: 61136 ms] Cannot logout from server The version of the IDE is 6.8. Cheers

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  • How can I set the date format to my country setting?

    - by Jamina Meissner
    I am German, but I use only English software. Hence, I am also using English Ubuntu. It's not because I don't know how to install German Ubuntu. It's because I prefer to work with English software environment. However, I would like to keep date & time format in German format, just as I use a German keyboard layout in English Ubuntu. I can set the time format to 24h time. But how can I set the date format to German time format? It is irritating for me to have the day number before the time numbers: In other words, instead of "Oct 14 15:16" I want it to display "14 Okt" or (if only English language is available) "14 Oct 15:16" or "14th Oct 15:16". At least, the number of the day should be displayed before the month. In Windows, it was no problem to choose time/date/currency settings according to a chosen country. Where can I do this in Ubuntu? The best would be if I could freely enter the date/time format myself with variables (DD.MM hh.mm.ss etc). I found answers for Ubuntu 11.04, but not for Ubuntu 12.04. I am using Ubuntu 12.04, 64-bit. Keep in mind that I am a beginner. So I'd like to be able to do this via GUI, if possible. EDIT: I found the answer in a forum. Go to System Settings... and choose Language Support. There are two tabs, Language and Reginal Formats. You are by default on the Language tab. On the Language tab, click Install / Remove Languages. A window with a list of languages opens. Mark the language(s) you want to add for your time/date/currency format. Click Apply Changes. Ubuntu will now download and install the additional language files, as well as help files of other applications in this language. So don't be irritated. When Ubuntu has finished applying the changes, switch to Regional Formats tab. (Do not change the Language for menus and windows on the Language tab if you only want to change the date/time/unit format). There you can choose from the dropdown list the language for your preferred format for date/time/currency/unit. Log out and log in again to have the changes take effect.

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  • Logic - Time measurement

    - by user73384
    To measure the following for tasks- Last execution time and maximum execution time for each task. CPU load/time consumed by each task over a defined period informed by application at run time. Maximum CPU load consumed by each task. Tasks have following characteristics- First task runs as background – Event information for entering only Second task - periodic – Event information for entering and exiting from task Third task is interrupt , can start any time – no information available from this task Forth task highest priority interrupt , can start any time – Event information for entering and exiting from task Should use least possible execution time and memory. 32bit increment timer available for time counting. Lets prepare and discuss the logic, It’s OK to have limitations …! Questions on understanding problem statement are welcome

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  • How to avoid concurrent execution of a time-consuming task without blocking?

    - by Diego V
    I want to efficiently avoid concurrent execution of a time-consuming task in a heavily multi-threaded environment without making threads wait for a lock when another thread is already running the task. Instead, in that scenario, I want them to gracefully fail (i.e. skip its attempt to execute the task) as fast as possible. To illustrate the idea considerer this unsafe (has race condition!) code: private static boolean running = false; public void launchExpensiveTask() { if (running) return; // Do nothing running = true; try { runExpensiveTask(); } finally { running = false; } } I though about using a variation of Double-Checked Locking (consider that running is a primitive 32-bit field, hence atomic, it could work fine even for Java below 5 without the need of volatile). It could look like this: private static boolean running = false; public void launchExpensiveTask() { if (running) return; // Do nothing synchronized (ThisClass.class) { if (running) return; running = true; try { runExpensiveTask(); } finally { running = false; } } } Maybe I should also use a local copy of the field as well (not sure now, please tell me). But then I realized that anyway I will end with an inner synchronization block, that still could hold a thread with the right timing at monitor entrance until the original executor leaves the critical section (I know the odds usually are minimal but in this case we are thinking in several threads competing for this long-running resource). So, could you think in a better approach?

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