Search Results

Search found 529 results on 22 pages for 'avg'.

Page 15/22 | < Previous Page | 11 12 13 14 15 16 17 18 19 20 21 22  | Next Page >

• USB-based AV application that restores compromised Windows files from CD/DVD?

  - by overtherainbow

  Hello I just tried a couple of rescue disks (AVG and Kaspersky), and I was wondering if someone knew of a solution that would work like this: The AV solution boots from a USB key, and works entirely from RAM (where the latest virus DB is downloaded) The user inserts his Windows CD/DVD in the drive If any Windows file is compromised, the AV application fetches a clean version from the CD/DVD and restores it on the hard drive optionally, any compromised drive/user-land application is quarantied, and it is up to the user to reinstall those after he successfully rebooted into a restored Windows setup Have you heard of a solution like this? It seems silly to reinstall a whole Windows computer just because one or a few system files were compromised. Thank you.

  Read the article

• Does multi-platter hard-drive use all of their heads to read simultaneously?

  - by WiSaGaN

  Suppose we have a harddisk with 2 platters with characteristics below: Rotational rate: 10, 000 RPM Avg sectors/track: 1000 Surfaces: 4 Sector size: 512 bytes I was reading "Computer Systems: A Programmer's Perspective 2ed" when I found that it calculates transfer time as if it only uses ONE head to read a sector. If that's the case, why not use 4 heads to write(read) on 4 surfaces? So when I write a 2K bytes file, each head should only need to wait for the platters to rotate just one sector length instead of 4, thus reducing the transfer time by a factor of 4. Or even redesign sector to make each sector on one cylinder but on 4 tracks residing same position respectively on 4 surfaces. Each one of (512/4) bytes. So when the hd needs to read a sector of 512 bytes, we only need the disk to rotate roughly 1/4 compare to original time. The idea looks like RAID 0.

  Read the article

• Windows 7 100% Memory Usage (without any process listed as using that much memory)

  - by Paul Tarjan

  When I plug my external USB 2TB hard drive into my windows 7 box, my RAM usage climbs up to all 4 Gigs (but in task manager it shows that all process are small) and the hard drive is churning like crazy. My CPU is only about 20% utilized All I can think of is there is a Virus scanner or an indexer running like crazy. I've tried to kill all virus scanners (AVG and Windows Security Essentials) and it still keeps going. My computer is completely unusable as everything is constantly swapping. I've tried leaving it on for 2 days now and it still hasn't finished whatever it was doing. Any ideas?

  Read the article

• Disabling checkboxes based on selection of another checkbox in jquery

  - by Prady

  Hi, I want to disable a set of checkbox based on selection of one textbox and enable the disabled ones if the checkbox is unchecked. In the code below. If someone checks the checkbox for project cost under change this parameter then checkbox for project cost under Generate simulated value for this param should be disabled and all the checkboxes under change this parameter should be disabled except for checked one. Similarly this should be done each parameter like Project cost,avg hours,Project completion date, hourly rate etc. One way i could think of was of on the click function disable each checkbox by the id. Is there a better way of doing it? <table> <tr> <td></td> <td></td> <td>Change this parameter</td> <td>Generate simulated value for this param</td> </tr> <tr> <td>Project cost</td> <td><input type ="text" id ="pc"/></td> <td><input class="change" type="checkbox" name="chkBox" id="chkBox"></input></td> <td><input class="sim" type="checkbox" name="chkBox1" id="chkBox1"></input></td> </tr> <tr> <td>Avg hours</td> <td><input type ="text" id ="avghrs"/></td> <td><input class="change" type="checkbox" name="chkBoxa" id="chkBoxa"></input></td> <td><input class="sim" type="checkbox" name="chkBox1a" id="chkBox1a"></input></td> </tr> <tr> <td>Project completion date</td> <td><input type ="text" id ="cd"/></td> <td><input class="change" type="checkbox" name="chkBoxb" id="chkBoxb"></input></td> <td><input class="sim" type="checkbox" name="chkBox1b" id="chkBox1b"></input></td> </tr> <tr> <td>Hourly rate</td> <td><input type ="text" id ="hr"/></td> <td><input class="change" type="checkbox" name="chkBoxc" id="chkBoxc"></input></td> <td><input class="sim" type="checkbox" name="chkBox1c" id="chkBox1c"></input></td> </tr> </table> Thanks Prady

  Read the article

• Working with PivotTables in Excel

  - by Mark Virtue

  PivotTables are one of the most powerful features of Microsoft Excel.  They allow large amounts of data to be analyzed and summarized in just a few mouse clicks. In this article, we explore PivotTables, understand what they are, and learn how to create and customize them. Note:  This article is written using Excel 2010 (Beta).  The concept of a PivotTable has changed little over the years, but the method of creating one has changed in nearly every iteration of Excel.  If you are using a version of Excel that is not 2010, expect different screens from the ones you see in this article. A Little History In the early days of spreadsheet programs, Lotus 1-2-3 ruled the roost.  Its dominance was so complete that people thought it was a waste of time for Microsoft to bother developing their own spreadsheet software (Excel) to compete with Lotus.  Flash-forward to 2010, and Excel’s dominance of the spreadsheet market is greater than Lotus’s ever was, while the number of users still running Lotus 1-2-3 is approaching zero.  How did this happen?  What caused such a dramatic reversal of fortunes? Industry analysts put it down to two factors:  Firstly, Lotus decided that this fancy new GUI platform called “Windows” was a passing fad that would never take off.  They declined to create a Windows version of Lotus 1-2-3 (for a few years, anyway), predicting that their DOS version of the software was all anyone would ever need.  Microsoft, naturally, developed Excel exclusively for Windows.  Secondly, Microsoft developed a feature for Excel that Lotus didn’t provide in 1-2-3, namely PivotTables.  The PivotTables feature, exclusive to Excel, was deemed so staggeringly useful that people were willing to learn an entire new software package (Excel) rather than stick with a program (1-2-3) that didn’t have it.  This one feature, along with the misjudgment of the success of Windows, was the death-knell for Lotus 1-2-3, and the beginning of the success of Microsoft Excel. Understanding PivotTables So what is a PivotTable, exactly? Put simply, a PivotTable is a summary of some data, created to allow easy analysis of said data.  But unlike a manually created summary, Excel PivotTables are interactive.  Once you have created one, you can easily change it if it doesn’t offer the exact insights into your data that you were hoping for.  In a couple of clicks the summary can be “pivoted” – rotated in such a way that the column headings become row headings, and vice versa.  There’s a lot more that can be done, too.  Rather than try to describe all the features of PivotTables, we’ll simply demonstrate them… The data that you analyze using a PivotTable can’t be just any data – it has to be raw data, previously unprocessed (unsummarized) – typically a list of some sort.  An example of this might be the list of sales transactions in a company for the past six months. Examine the data shown below: Notice that this is not raw data.  In fact, it is already a summary of some sort.  In cell B3 we can see $30,000, which apparently is the total of James Cook’s sales for the month of January.  So where is the raw data?  How did we arrive at the figure of $30,000?  Where is the original list of sales transactions that this figure was generated from?  It’s clear that somewhere, someone must have gone to the trouble of collating all of the sales transactions for the past six months into the summary we see above.  How long do you suppose this took?  An hour?  Ten?  Probably. If we were to track down the original list of sales transactions, it might look something like this: You may be surprised to learn that, using the PivotTable feature of Excel, we can create a monthly sales summary similar to the one above in a few seconds, with only a few mouse clicks.  We can do this – and a lot more too! How to Create a PivotTable First, ensure that you have some raw data in a worksheet in Excel.  A list of financial transactions is typical, but it can be a list of just about anything:  Employee contact details, your CD collection, or fuel consumption figures for your company’s fleet of cars. So we start Excel… …and we load such a list… Once we have the list open in Excel, we’re ready to start creating the PivotTable. Click on any one single cell within the list: Then, from the Insert tab, click the PivotTable icon: The Create PivotTable box appears, asking you two questions:  What data should your new PivotTable be based on, and where should it be created?  Because we already clicked on a cell within the list (in the step above), the entire list surrounding that cell is already selected for us ($A$1:$G$88 on the Payments sheet, in this example).  Note that we could select a list in any other region of any other worksheet, or even some external data source, such as an Access database table, or even a MS-SQL Server database table.  We also need to select whether we want our new PivotTable to be created on a new worksheet, or on an existing one.  In this example we will select a new one: The new worksheet is created for us, and a blank PivotTable is created on that worksheet: Another box also appears:  The PivotTable Field List.  This field list will be shown whenever we click on any cell within the PivotTable (above): The list of fields in the top part of the box is actually the collection of column headings from the original raw data worksheet.  The four blank boxes in the lower part of the screen allow us to choose the way we would like our PivotTable to summarize the raw data.  So far, there is nothing in those boxes, so the PivotTable is blank.  All we need to do is drag fields down from the list above and drop them in the lower boxes.  A PivotTable is then automatically created to match our instructions.  If we get it wrong, we only need to drag the fields back to where they came from and/or drag new fields down to replace them. The Values box is arguably the most important of the four.  The field that is dragged into this box represents the data that needs to be summarized in some way (by summing, averaging, finding the maximum, minimum, etc).  It is almost always numerical data.  A perfect candidate for this box in our sample data is the “Amount” field/column.  Let’s drag that field into the Values box: Notice that (a) the “Amount” field in the list of fields is now ticked, and “Sum of Amount” has been added to the Values box, indicating that the amount column has been summed. If we examine the PivotTable itself, we indeed find the sum of all the “Amount” values from the raw data worksheet: We’ve created our first PivotTable!  Handy, but not particularly impressive.  It’s likely that we need a little more insight into our data than that. Referring to our sample data, we need to identify one or more column headings that we could conceivably use to split this total.  For example, we may decide that we would like to see a summary of our data where we have a row heading for each of the different salespersons in our company, and a total for each.  To achieve this, all we need to do is to drag the “Salesperson” field into the Row Labels box: Now, finally, things start to get interesting!  Our PivotTable starts to take shape….   With a couple of clicks we have created a table that would have taken a long time to do manually. So what else can we do?  Well, in one sense our PivotTable is complete.  We’ve created a useful summary of our source data.  The important stuff is already learned!  For the rest of the article, we will examine some ways that more complex PivotTables can be created, and ways that those PivotTables can be customized. First, we can create a two-dimensional table.  Let’s do that by using “Payment Method” as a column heading.  Simply drag the “Payment Method” heading to the Column Labels box: Which looks like this: Starting to get very cool! Let’s make it a three-dimensional table.  What could such a table possibly look like?  Well, let’s see… Drag the “Package” column/heading to the Report Filter box: Notice where it ends up…. This allows us to filter our report based on which “holiday package” was being purchased.  For example, we can see the breakdown of salesperson vs payment method for all packages, or, with a couple of clicks, change it to show the same breakdown for the “Sunseekers” package: And so, if you think about it the right way, our PivotTable is now three-dimensional.  Let’s keep customizing… If it turns out, say, that we only want to see cheque and credit card transactions (i.e. no cash transactions), then we can deselect the “Cash” item from the column headings.  Click the drop-down arrow next to Column Labels, and untick “Cash”: Let’s see what that looks like…As you can see, “Cash” is gone. Formatting This is obviously a very powerful system, but so far the results look very plain and boring.  For a start, the numbers that we’re summing do not look like dollar amounts – just plain old numbers.  Let’s rectify that. A temptation might be to do what we’re used to doing in such circumstances and simply select the whole table (or the whole worksheet) and use the standard number formatting buttons on the toolbar to complete the formatting.  The problem with that approach is that if you ever change the structure of the PivotTable in the future (which is 99% likely), then those number formats will be lost.  We need a way that will make them (semi-)permanent. First, we locate the “Sum of Amount” entry in the Values box, and click on it.  A menu appears.  We select Value Field Settings… from the menu: The Value Field Settings box appears. Click the Number Format button, and the standard Format Cells box appears: From the Category list, select (say) Accounting, and drop the number of decimal places to 0.  Click OK a few times to get back to the PivotTable… As you can see, the numbers have been correctly formatted as dollar amounts. While we’re on the subject of formatting, let’s format the entire PivotTable.  There are a few ways to do this.  Let’s use a simple one… Click the PivotTable Tools/Design tab: Then drop down the arrow in the bottom-right of the PivotTable Styles list to see a vast collection of built-in styles: Choose any one that appeals, and look at the result in your PivotTable:   Other Options We can work with dates as well.  Now usually, there are many, many dates in a transaction list such as the one we started with.  But Excel provides the option to group data items together by day, week, month, year, etc.  Let’s see how this is done. First, let’s remove the “Payment Method” column from the Column Labels box (simply drag it back up to the field list), and replace it with the “Date Booked” column: As you can see, this makes our PivotTable instantly useless, giving us one column for each date that a transaction occurred on – a very wide table! To fix this, right-click on any date and select Group… from the context-menu: The grouping box appears.  We select Months and click OK: Voila!  A much more useful table: (Incidentally, this table is virtually identical to the one shown at the beginning of this article – the original sales summary that was created manually.) Another cool thing to be aware of is that you can have more than one set of row headings (or column headings): …which looks like this…. You can do a similar thing with column headings (or even report filters). Keeping things simple again, let’s see how to plot averaged values, rather than summed values. First, click on “Sum of Amount”, and select Value Field Settings… from the context-menu that appears: In the Summarize value field by list in the Value Field Settings box, select Average: While we’re here, let’s change the Custom Name, from “Average of Amount” to something a little more concise.  Type in something like “Avg”: Click OK, and see what it looks like.  Notice that all the values change from summed totals to averages, and the table title (top-left cell) has changed to “Avg”: If we like, we can even have sums, averages and counts (counts = how many sales there were) all on the same PivotTable! Here are the steps to get something like that in place (starting from a blank PivotTable): Drag “Salesperson” into the Column Labels Drag “Amount” field down into the Values box three times For the first “Amount” field, change its custom name to “Total” and it’s number format to Accounting (0 decimal places) For the second “Amount” field, change its custom name to “Average”, its function to Average and it’s number format to Accounting (0 decimal places) For the third “Amount” field, change its name to “Count” and its function to Count Drag the automatically created field from Column Labels to Row Labels Here’s what we end up with: Total, average and count on the same PivotTable! Conclusion There are many, many more features and options for PivotTables created by Microsoft Excel – far too many to list in an article like this.  To fully cover the potential of PivotTables, a small book (or a large website) would be required.  Brave and/or geeky readers can explore PivotTables further quite easily:  Simply right-click on just about everything, and see what options become available to you.  There are also the two ribbon-tabs: PivotTable Tools/Options and Design.  It doesn’t matter if you make a mistake – it’s easy to delete the PivotTable and start again – a possibility old DOS users of Lotus 1-2-3 never had. We’ve included an Excel that should work with most versions of Excel, so you can download to practice your PivotTable skills. Download Our Practice Excel File Similar Articles Productive Geek Tips Magnify Selected Cells In Excel 2007Share Access Data with Excel in Office 2010Make Excel 2007 Print Gridlines In Workbook FileMake Excel 2007 Always Save in Excel 2003 FormatConvert Older Excel Documents to Excel 2007 Format TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips Revo Uninstaller Pro Registry Mechanic 9 for Windows PC Tools Internet Security Suite 2010 PCmover Professional Ben & Jerry’s Free Cone Day, 3/23/10 New Stinger from McAfee Helps Remove ‘FakeAlert’ Threats Google Apps Marketplace: Tools & Services For Google Apps Users Get News Quick and Precise With Newser Scan for Viruses in Ubuntu using ClamAV Replace Your Windows Task Manager With System Explorer

  Read the article

• SQL Monitor’s data repository: Alerts

  - by Chris Lambrou

  In my previous post, I introduced the SQL Monitor data repository, and described how the monitored objects are stored in a hierarchy in the data schema, in a series of tables with a _Keys suffix. In this post I had planned to describe how the actual data for the monitored objects is stored in corresponding tables with _StableSamples and _UnstableSamples suffixes. However, I’m going to postpone that until my next post, as I’ve had a request from a SQL Monitor user to explain how alerts are stored. In the SQL Monitor data repository, alerts are stored in tables belonging to the alert schema, which contains the following five tables: alert.Alert alert.Alert_Cleared alert.Alert_Comment alert.Alert_Severity alert.Alert_Type In this post, I’m only going to cover the alert.Alert and alert.Alert_Type tables. I may cover the other three tables in a later post. The most important table in this schema is alert.Alert, as each row in this table corresponds to a single alert. So let’s have a look at it. SELECT TOP 100 AlertId, AlertType, TargetObject, [Read], SubType FROM alert.Alert ORDER BY AlertId DESC;  AlertIdAlertTypeTargetObjectReadSubType 165550397:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,9:SqlServer,1,4:Name,s0:,10 265549387:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,7:Machine,1,4:Name,s0:,10 365548187:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 465547157:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 565546147:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 665545187:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 765544157:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 865543147:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 965542187:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 1065541147:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 11…     So what are we seeing here, then? Well, AlertId is an auto-incrementing identity column, so ORDER BY AlertId DESC ensures that we see the most recent alerts first. AlertType indicates the type of each alert, such as Job failed (6), Backup overdue (14) or Long-running query (12). The TargetObject column indicates which monitored object the alert is associated with. The Read column acts as a flag to indicate whether or not the alert has been read. And finally the SubType column is used in the case of a Custom metric (40) alert, to indicate which custom metric the alert pertains to. Okay, now lets look at some of those columns in more detail. The AlertType column is an easy one to start with, and it brings use nicely to the next table, data.Alert_Type. Let’s have a look at what’s in this table: SELECT AlertType, Event, Monitoring, Name, Description FROM alert.Alert_Type ORDER BY AlertType;  AlertTypeEventMonitoringNameDescription 1100Processor utilizationProcessor utilization (CPU) on a host machine stays above a threshold percentage for longer than a specified duration 2210SQL Server error log entryAn error is written to the SQL Server error log with a severity level above a specified value. 3310Cluster failoverThe active cluster node fails, causing the SQL Server instance to switch nodes. 4410DeadlockSQL deadlock occurs. 5500Processor under-utilizationProcessor utilization (CPU) on a host machine remains below a threshold percentage for longer than a specified duration 6610Job failedA job does not complete successfully (the job returns an error code). 7700Machine unreachableHost machine (Windows server) cannot be contacted on the network. 8800SQL Server instance unreachableThe SQL Server instance is not running or cannot be contacted on the network. 9900Disk spaceDisk space used on a logical disk drive is above a defined threshold for longer than a specified duration. 101000Physical memoryPhysical memory (RAM) used on the host machine stays above a threshold percentage for longer than a specified duration. 111100Blocked processSQL process is blocked for longer than a specified duration. 121200Long-running queryA SQL query runs for longer than a specified duration. 131400Backup overdueNo full backup exists, or the last full backup is older than a specified time. 141500Log backup overdueNo log backup exists, or the last log backup is older than a specified time. 151600Database unavailableDatabase changes from Online to any other state. 161700Page verificationTorn Page Detection or Page Checksum is not enabled for a database. 171800Integrity check overdueNo entry for an integrity check (DBCC DBINFO returns no date for dbi_dbccLastKnownGood field), or the last check is older than a specified time. 181900Fragmented indexesFragmentation level of one or more indexes is above a threshold percentage. 192400Job duration unusualThe duration of a SQL job duration deviates from its baseline duration by more than a threshold percentage. 202501Clock skewSystem clock time on the Base Monitor computer differs from the system clock time on a monitored SQL Server host machine by a specified number of seconds. 212700SQL Server Agent Service statusThe SQL Server Agent Service status matches the status specified. 222800SQL Server Reporting Service statusThe SQL Server Reporting Service status matches the status specified. 232900SQL Server Full Text Search Service statusThe SQL Server Full Text Search Service status matches the status specified. 243000SQL Server Analysis Service statusThe SQL Server Analysis Service status matches the status specified. 253100SQL Server Integration Service statusThe SQL Server Integration Service status matches the status specified. 263300SQL Server Browser Service statusThe SQL Server Browser Service status matches the status specified. 273400SQL Server VSS Writer Service statusThe SQL Server VSS Writer status matches the status specified. 283501Deadlock trace flag disabledThe monitored SQL Server’s trace flag cannot be enabled. 293600Monitoring stopped (host machine credentials)SQL Monitor cannot contact the host machine because authentication failed. 303700Monitoring stopped (SQL Server credentials)SQL Monitor cannot contact the SQL Server instance because authentication failed. 313800Monitoring error (host machine data collection)SQL Monitor cannot collect data from the host machine. 323900Monitoring error (SQL Server data collection)SQL Monitor cannot collect data from the SQL Server instance. 334000Custom metricThe custom metric value has passed an alert threshold. 344100Custom metric collection errorSQL Monitor cannot collect custom metric data from the target object. Basically, alert.Alert_Type is just a big reference table containing information about the 34 different alert types supported by SQL Monitor (note that the largest id is 41, not 34 – some alert types have been retired since SQL Monitor was first developed). The Name and Description columns are self evident, and I’m going to skip over the Event and Monitoring columns as they’re not very interesting. The AlertId column is the primary key, and is referenced by AlertId in the alert.Alert table. As such, we can rewrite our earlier query to join these two tables, in order to provide a more readable view of the alerts: SELECT TOP 100 AlertId, Name, TargetObject, [Read], SubType FROM alert.Alert a JOIN alert.Alert_Type at ON a.AlertType = at.AlertType ORDER BY AlertId DESC;  AlertIdNameTargetObjectReadSubType 165550Monitoring error (SQL Server data collection)7:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,9:SqlServer,1,4:Name,s0:,00 265549Monitoring error (host machine data collection)7:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,7:Machine,1,4:Name,s0:,00 365548Integrity check overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 465547Log backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 565546Backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 665545Integrity check overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 765544Log backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 865543Backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 965542Integrity check overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 1065541Backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 Okay, the next column to discuss in the alert.Alert table is TargetObject. Oh boy, this one’s a bit tricky! The TargetObject of an alert is a serialized string representation of the position in the monitored object hierarchy of the object to which the alert pertains. The serialization format is somewhat convenient for parsing in the C# source code of SQL Monitor, and has some helpful characteristics, but it’s probably very awkward to manipulate in T-SQL. I could document the serialization format here, but it would be very dry reading, so perhaps it’s best to consider an example from the table above. Have a look at the alert with an AlertID of 65543. It’s a Backup overdue alert for the SqlMonitorData database running on the default instance of granger, my laptop. Each different alert type is associated with a specific type of monitored object in the object hierarchy (I described the hierarchy in my previous post). The Backup overdue alert is associated with databases, whose position in the object hierarchy is root → Cluster → SqlServer → Database. The TargetObject value identifies the target object by specifying the key properties at each level in the hierarchy, thus: Cluster: Name = "granger" SqlServer: Name = "" (an empty string, denoting the default instance) Database: Name = "SqlMonitorData" Well, look at the actual TargetObject value for this alert: "7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,". It is indeed composed of three parts, one for each level in the hierarchy: Cluster: "7:Cluster,1,4:Name,s7:granger," SqlServer: "9:SqlServer,1,4:Name,s0:," Database: "8:Database,1,4:Name,s14:SqlMonitorData," Each part is handled in exactly the same way, so let’s concentrate on the first part, "7:Cluster,1,4:Name,s7:granger,". It comprises the following: "7:Cluster," – This identifies the level in the hierarchy. "1," – This indicates how many different key properties there are to uniquely identify a cluster (we saw in my last post that each cluster is identified by a single property, its Name). "4:Name,s14:SqlMonitorData," – This represents the Name property, and its corresponding value, SqlMonitorData. It’s split up like this: "4:Name," – Indicates the name of the key property. "s" – Indicates the type of the key property, in this case, it’s a string. "14:SqlMonitorData," – Indicates the value of the property. At this point, you might be wondering about the format of some of these strings. Why is the string "Cluster" stored as "7:Cluster,"? Well an encoding scheme is used, which consists of the following: "7" – This is the length of the string "Cluster" ":" – This is a delimiter between the length of the string and the actual string’s contents. "Cluster" – This is the string itself. 7 characters. "," – This is a final terminating character that indicates the end of the encoded string. You can see that "4:Name,", "8:Database," and "14:SqlMonitorData," also conform to the same encoding scheme. In the example above, the "s" character is used to indicate that the value of the Name property is a string. If you explore the TargetObject property of alerts in your own SQL Monitor data repository, you might find other characters used for other non-string key property values. The different value types you might possibly encounter are as follows: "I" – Denotes a bigint value. For example, "I65432,". "g" – Denotes a GUID value. For example, "g32116732-63ae-4ab5-bd34-7dfdfb084c18,". "d" – Denotes a datetime value. For example, "d634815384796832438,". The value is stored as a bigint, rather than a native SQL datetime value. I’ll describe how datetime values are handled in the SQL Monitor data repostory in a future post. I suggest you have a look at the alerts in your own SQL Monitor data repository for further examples, so you can see how the TargetObject values are composed for each of the different types of alert. Let me give one further example, though, that represents a Custom metric alert, as this will help in describing the final column of interest in the alert.Alert table, SubType. Let me show you the alert I’m interested in: SELECT AlertId, a.AlertType, Name, TargetObject, [Read], SubType FROM alert.Alert a JOIN alert.Alert_Type at ON a.AlertType = at.AlertType WHERE AlertId = 65769;  AlertIdAlertTypeNameTargetObjectReadSubType 16576940Custom metric7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s6:master,12:CustomMetric,1,8:MetricId,I2,02 An AlertType value of 40 corresponds to the Custom metric alert type. The Name taken from the alert.Alert_Type table is simply Custom metric, but this doesn’t tell us anything about the specific custom metric that this alert pertains to. That’s where the SubType value comes in. For custom metric alerts, this provides us with the Id of the specific custom alert definition that can be found in the settings.CustomAlertDefinitions table. I don’t really want to delve into custom alert definitions yet (maybe in a later post), but an extra join in the previous query shows us that this alert pertains to the CPU pressure (avg runnable task count) custom metric alert. SELECT AlertId, a.AlertType, at.Name, cad.Name AS CustomAlertName, TargetObject, [Read], SubType FROM alert.Alert a JOIN alert.Alert_Type at ON a.AlertType = at.AlertType JOIN settings.CustomAlertDefinitions cad ON a.SubType = cad.Id WHERE AlertId = 65769;  AlertIdAlertTypeNameCustomAlertNameTargetObjectReadSubType 16576940Custom metricCPU pressure (avg runnable task count)7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s6:master,12:CustomMetric,1,8:MetricId,I2,02 The TargetObject value in this case breaks down like this: "7:Cluster,1,4:Name,s7:granger," – Cluster named "granger". "9:SqlServer,1,4:Name,s0:," – SqlServer named "" (the default instance). "8:Database,1,4:Name,s6:master," – Database named "master". "12:CustomMetric,1,8:MetricId,I2," – Custom metric with an Id of 2. Note that the hierarchy for a custom metric is slightly different compared to the earlier Backup overdue alert. It’s root → Cluster → SqlServer → Database → CustomMetric. Also notice that, unlike Cluster, SqlServer and Database, the key property for CustomMetric is called MetricId (not Name), and the value is a bigint (not a string). Finally, delving into the custom metric tables is beyond the scope of this post, but for the sake of avoiding any future confusion, I’d like to point out that whilst the SubType references a custom alert definition, the MetricID value embedded in the TargetObject value references a custom metric definition. Although in this case both the custom metric definition and custom alert definition share the same Id value of 2, this is not generally the case. Okay, that’s enough for now, not least because as I’m typing this, it’s almost 2am, I have to go to work tomorrow, and my alarm is set for 6am – eek! In my next post, I’ll either cover the remaining three tables in the alert schema, or I’ll delve into the way SQL Monitor stores its monitoring data, as I’d originally planned to cover in this post.

  Read the article

• Wireless Connected But No Internet Connection (Ubuntu 12.04)

  - by Zxy

  I am using same network for 2 days and everything was normal. However, today even though it shows me as connected to the network, I do not have internet connection. If I use ethernet cable instead of wireless, I am still able to connect to the internet. Also my friends are able to connect to the wireless network and they can get internet connection. I did not update or install anything since yesterday. Therefore I do not have any idea why it is happening. Here is some information about my connection: I will be appreciate to any kind of help. root@ghostrider:/etc/resolvconf# ping 127.0.0.1 PING 127.0.0.1 (127.0.0.1) 56(84) bytes of data. 64 bytes from 127.0.0.1: icmp_req=1 ttl=64 time=0.042 ms 64 bytes from 127.0.0.1: icmp_req=2 ttl=64 time=0.023 ms 64 bytes from 127.0.0.1: icmp_req=3 ttl=64 time=0.036 ms 64 bytes from 127.0.0.1: icmp_req=4 ttl=64 time=0.040 ms ^C --- 127.0.0.1 ping statistics --- 4 packets transmitted, 4 received, 0% packet loss, time 2998ms rtt min/avg/max/mdev = 0.023/0.035/0.042/0.008 ms root@ghostrider:/etc/resolvconf# ping 192.168.1.3 PING 192.168.1.3 (192.168.1.3) 56(84) bytes of data. ^C --- 192.168.1.3 ping statistics --- 19 packets transmitted, 0 received, 100% packet loss, time 18143ms root@ghostrider:/etc/resolvconf# ping 8.8.8.8 PING 8.8.8.8 (8.8.8.8) 56(84) bytes of data. ^C --- 8.8.8.8 ping statistics --- 11 packets transmitted, 0 received, 100% packet loss, time 10079ms root@ghostrider:/etc/resolvconf# cat /etc/lsb-release; uname -a DISTRIB_ID=Ubuntu DISTRIB_RELEASE=12.04 DISTRIB_CODENAME=precise DISTRIB_DESCRIPTION="Ubuntu 12.04 LTS" Linux ghostrider 3.2.0-24-generic-pae #39-Ubuntu SMP Mon May 21 18:54:21 UTC 2012 i686 i686 i386 GNU/Linux root@ghostrider:/etc/resolvconf# lspci -nnk | grep -iA2 net 03:00.0 Ethernet controller [0200]: Atheros Communications Inc. AR8131 Gigabit Ethernet [1969:1063] (rev c0) Subsystem: Lenovo Device [17aa:3956] Kernel driver in use: atl1c -- 04:00.0 Network controller [0280]: Broadcom Corporation BCM4313 802.11b/g/n Wireless LAN Controller [14e4:4727] (rev 01) Subsystem: Broadcom Corporation Device [14e4:0510] Kernel driver in use: wl root@ghostrider:/etc/resolvconf# lsusb Bus 001 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub Bus 002 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub Bus 001 Device 002: ID 8087:0020 Intel Corp. Integrated Rate Matching Hub Bus 002 Device 002: ID 8087:0020 Intel Corp. Integrated Rate Matching Hub Bus 001 Device 007: ID 0489:e00d Foxconn / Hon Hai Bus 001 Device 004: ID 1c7a:0801 LighTuning Technology Inc. Fingerprint Reader Bus 001 Device 005: ID 064e:f219 Suyin Corp. Bus 002 Device 010: ID 0424:2412 Standard Microsystems Corp. Bus 002 Device 004: ID 046d:c52b Logitech, Inc. Unifying Receiver Bus 002 Device 011: ID 0403:6010 Future Technology Devices International, Ltd FT2232C Dual USB-UART/FIFO IC root@ghostrider:/etc/resolvconf# iwconfig lo no wireless extensions. eth1 IEEE 802.11 ESSID:"PoliTekno" Mode:Managed Frequency:2.462 GHz Access Point: 00:16:E3:40:C3:E4 Bit Rate=54 Mb/s Tx-Power:24 dBm Retry min limit:7 RTS thr:off Fragment thr:off Power Management:off Link Quality=5/5 Signal level=-52 dBm Noise level=-97 dBm Rx invalid nwid:0 Rx invalid crypt:0 Rx invalid frag:0 Tx excessive retries:0 Invalid misc:0 Missed beacon:0 eth0 no wireless extensions. root@ghostrider:/etc/resolvconf# rfkill list all 0: brcmwl-0: Wireless LAN Soft blocked: no Hard blocked: no 1: ideapad_wlan: Wireless LAN Soft blocked: no Hard blocked: no 2: ideapad_bluetooth: Bluetooth Soft blocked: no Hard blocked: no 5: hci0: Bluetooth Soft blocked: no Hard blocked: no root@ghostrider:/etc/resolvconf# lsmod Module Size Used by nls_iso8859_1 12617 0 nls_cp437 12751 0 vfat 17308 0 fat 55605 1 vfat usb_storage 39646 0 uas 17828 0 snd_hda_codec_realtek 174055 1 rfcomm 38139 12 parport_pc 32114 0 ppdev 12849 0 bnep 17830 2 joydev 17393 0 ftdi_sio 35859 1 usbserial 37173 3 ftdi_sio snd_hda_intel 32765 3 snd_hda_codec 109562 2 snd_hda_codec_realtek,snd_hda_intel snd_hwdep 13276 1 snd_hda_codec acer_wmi 23612 0 hid_logitech_dj 18177 0 snd_pcm 80845 2 snd_hda_intel,snd_hda_codec uvcvideo 67203 0 btusb 17912 2 snd_seq_midi 13132 0 videodev 86588 1 uvcvideo bluetooth 158438 23 rfcomm,bnep,btusb psmouse 72919 0 usbhid 41906 1 hid_logitech_dj snd_rawmidi 25424 1 snd_seq_midi intel_ips 17753 0 serio_raw 13027 0 root@ghostrider:/etc/resolvconf# ping 127.0.0.1 PING 127.0.0.1 (127.0.0.1) 56(84) bytes of data. 64 bytes from 127.0.0.1: icmp_req=1 ttl=64 time=0.042 ms 64 bytes from 127.0.0.1: icmp_req=2 ttl=64 time=0.023 ms 64 bytes from 127.0.0.1: icmp_req=3 ttl=64 time=0.036 ms 64 bytes from 127.0.0.1: icmp_req=4 ttl=64 time=0.040 ms ^C --- 127.0.0.1 ping statistics --- 4 packets transmitted, 4 received, 0% packet loss, time 2998ms rtt min/avg/max/mdev = 0.023/0.035/0.042/0.008 ms root@ghostrider:/etc/resolvconf# ping 192.168.1.3 PING 192.168.1.3 (192.168.1.3) 56(84) bytes of data. ^C --- 192.168.1.3 ping statistics --- 19 packets transmitted, 0 received, 100% packet loss, time 18143ms root@ghostrider:/etc/resolvconf# ping 8.8.8.8 PING 8.8.8.8 (8.8.8.8) 56(84) bytes of data. ^C --- 8.8.8.8 ping statistics --- 11 packets transmitted, 0 received, 100% packet loss, time 10079ms root@ghostrider:/etc/resolvconf# cat /etc/lsb-release; uname -a DISTRIB_ID=Ubuntu DISTRIB_RELEASE=12.04 DISTRIB_CODENAME=precise DISTRIB_DESCRIPTION="Ubuntu 12.04 LTS" Linux ghostrider 3.2.0-24-generic-pae #39-Ubuntu SMP Mon May 21 18:54:21 UTC 2012 i686 i686 i386 GNU/Linux root@ghostrider:/etc/resolvconf# lspci -nnk | grep -iA2 net 03:00.0 Ethernet controller [0200]: Atheros Communications Inc. AR8131 Gigabit Ethernet [1969:1063] (rev c0) Subsystem: Lenovo Device [17aa:3956] Kernel driver in use: atl1c -- 04:00.0 Network controller [0280]: Broadcom Corporation BCM4313 802.11b/g/n Wireless LAN Controller [14e4:4727] (rev 01) Subsystem: Broadcom Corporation Device [14e4:0510] Kernel driver in use: wl root@ghostrider:/etc/resolvconf# lsusb Bus 001 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub Bus 002 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub Bus 001 Device 002: ID 8087:0020 Intel Corp. Integrated Rate Matching Hub Bus 002 Device 002: ID 8087:0020 Intel Corp. Integrated Rate Matching Hub Bus 001 Device 007: ID 0489:e00d Foxconn / Hon Hai Bus 001 Device 004: ID 1c7a:0801 LighTuning Technology Inc. Fingerprint Reader Bus 001 Device 005: ID 064e:f219 Suyin Corp. Bus 002 Device 010: ID 0424:2412 Standard Microsystems Corp. Bus 002 Device 004: ID 046d:c52b Logitech, Inc. Unifying Receiver Bus 002 Device 011: ID 0403:6010 Future Technology Devices International, Ltd FT2232C Dual USB-UART/FIFO IC root@ghostrider:/etc/resolvconf# iwconfig lo no wireless extensions. eth1 IEEE 802.11 ESSID:"PoliTekno" Mode:Managed Frequency:2.462 GHz Access Point: 00:16:E3:40:C3:E4 Bit Rate=54 Mb/s Tx-Power:24 dBm Retry min limit:7 RTS thr:off Fragment thr:off Power Management:off Link Quality=5/5 Signal level=-52 dBm Noise level=-97 dBm Rx invalid nwid:0 Rx invalid crypt:0 Rx invalid frag:0 Tx excessive retries:0 Invalid misc:0 Missed beacon:0 eth0 no wireless extensions. root@ghostrider:/etc/resolvconf# rfkill list all 0: brcmwl-0: Wireless LAN Soft blocked: no Hard blocked: no 1: ideapad_wlan: Wireless LAN Soft blocked: no Hard blocked: no 2: ideapad_bluetooth: Bluetooth Soft blocked: no Hard blocked: no 5: hci0: Bluetooth Soft blocked: no Hard blocked: no root@ghostrider:/etc/resolvconf# lsmod Module Size Used by nls_iso8859_1 12617 0 nls_cp437 12751 0 vfat 17308 0 fat 55605 1 vfat usb_storage 39646 0 uas 17828 0 snd_hda_codec_realtek 174055 1 rfcomm 38139 12 parport_pc 32114 0 ppdev 12849 0 bnep 17830 2 joydev 17393 0 ftdi_sio 35859 1 usbserial 37173 3 ftdi_sio snd_hda_intel 32765 3 snd_hda_codec 109562 2 snd_hda_codec_realtek,snd_hda_intel snd_hwdep 13276 1 snd_hda_codec acer_wmi 23612 0 hid_logitech_dj 18177 0 snd_pcm 80845 2 snd_hda_intel,snd_hda_codec uvcvideo 67203 0 btusb 17912 2 snd_seq_midi 13132 0 videodev 86588 1 uvcvideo bluetooth 158438 23 rfcomm,bnep,btusb psmouse 72919 0 usbhid 41906 1 hid_logitech_dj snd_rawmidi 25424 1 snd_seq_midi intel_ips 17753 0 serio_raw 13027 0 hid 77367 2 hid_logitech_dj,usbhid ideapad_laptop 17890 0 sparse_keymap 13658 2 acer_wmi,ideapad_laptop lib80211_crypt_tkip 17275 0 snd_seq_midi_event 14475 1 snd_seq_midi snd_seq 51567 2 snd_seq_midi,snd_seq_midi_event wl 2646601 0 wmi 18744 1 acer_wmi i915 414672 3 drm_kms_helper 45466 1 i915 snd_timer 28931 2 snd_pcm,snd_seq mac_hid 13077 0 snd_seq_device 14172 3 snd_seq_midi,snd_rawmidi,snd_seq lib80211 14040 2 lib80211_crypt_tkip,wl drm 197692 4 i915,drm_kms_helper i2c_algo_bit 13199 1 i915 snd 62064 15 snd_hda_codec_realtek,snd_hda_intel,snd_hda_codec,snd_hwdep,snd_pcm,snd_rawmidi,snd_se q,snd_timer,snd_seq_device video 19068 1 i915 mei 36570 0 soundcore 14635 1 snd snd_page_alloc 14108 2 snd_hda_intel,snd_pcm lp 17455 0 parport 40930 3 parport_pc,ppdev,lp atl1c 36718 0 root@ghostrider:/etc/resolvconf# nm-tool NetworkManager Tool State: connected (global) - Device: eth1 [PoliTekno] ---------------------------------------------------- Type: 802.11 WiFi Driver: wl State: connected Default: yes HW Address: AC:81:12:7F:6B:B2 Capabilities: Speed: 54 Mb/s Wireless Properties WEP Encryption: yes WPA Encryption: yes WPA2 Encryption: yes Wireless Access Points (* = current AP) CnDStudios: Infra, 00:12:BF:3F:0A:8A, Freq 2412 MHz, Rate 54 Mb/s, Strength 85 WPA AIR_TIES: Infra, 00:1C:A8:6E:84:32, Freq 2462 MHz, Rate 54 Mb/s, Strength 72 WPA2 VKSS: Infra, 00:E0:4D:01:0D:47, Freq 2452 MHz, Rate 54 Mb/s, Strength 62 WPA2 PROGEDA: Infra, 00:1A:2A:60:BF:61, Freq 2462 MHz, Rate 54 Mb/s, Strength 47 WPA MobilAtolye: Infra, 72:2B:C1:65:75:3C, Freq 2422 MHz, Rate 54 Mb/s, Strength 35 WPA WPA2 AIRTIES_WAR-141: Infra, 00:1C:A8:AB:AA:48, Freq 2422 MHz, Rate 54 Mb/s, Strength 35 WPA WPA2 tilda_biri_yeni: Infra, 54:E6:FC:B0:3C:E9, Freq 2437 MHz, Rate 0 Mb/s, Strength 34 WEP *PoliTekno: Infra, 00:16:E3:40:C3:E4, Freq 2462 MHz, Rate 54 Mb/s, Strength 100 WPA2 AIRTIES_RJY: Infra, 00:1A:2A:BD:85:16, Freq 2462 MHz, Rate 54 Mb/s, Strength 55 WEP IPv4 Settings: Address: 0.0.0.0 Prefix: 24 (255.255.255.0) Gateway: 192.168.1.1 DNS: 192.168.1.1 - Device: eth0 ----------------------------------------------------------------- Type: Wired Driver: atl1c State: unavailable Default: no HW Address: F0:DE:F1:6C:90:65 Capabilities: Carrier Detect: yes Speed: 100 Mb/s Wired Properties Carrier: off root@ghostrider:/etc/resolvconf# sudo iwlist scan lo Interface doesn't support scanning. eth1 Scan completed : Cell 01 - Address: 00:16:E3:40:C3:E4 ESSID:"PoliTekno" Mode:Managed Frequency:2.462 GHz (Channel 11) Quality:5/5 Signal level:-48 dBm Noise level:-98 dBm IE: IEEE 802.11i/WPA2 Version 1 Group Cipher : CCMP Pairwise Ciphers (1) : CCMP Authentication Suites (1) : PSK Encryption key:on Bit Rates:1 Mb/s; 2 Mb/s; 5.5 Mb/s; 11 Mb/s; 18 Mb/s 24 Mb/s; 36 Mb/s; 54 Mb/s; 6 Mb/s; 9 Mb/s 12 Mb/s; 48 Mb/s Cell 02 - Address: 00:E0:4D:01:0D:47 ESSID:"VKSS" Mode:Managed Frequency:2.452 GHz (Channel 9) Quality:4/5 Signal level:-64 dBm Noise level:-98 dBm IE: IEEE 802.11i/WPA2 Version 1 Group Cipher : CCMP Pairwise Ciphers (1) : CCMP Authentication Suites (1) : PSK Encryption key:on Bit Rates:1 Mb/s; 2 Mb/s; 5.5 Mb/s; 11 Mb/s; 6 Mb/s 9 Mb/s; 12 Mb/s; 18 Mb/s; 24 Mb/s; 36 Mb/s 48 Mb/s; 54 Mb/s Cell 03 - Address: 00:1C:A8:AB:AA:48 ESSID:"AIRTIES_WAR-141" Mode:Managed Frequency:2.422 GHz (Channel 3) Quality:2/5 Signal level:-77 dBm Noise level:-95 dBm IE: IEEE 802.11i/WPA2 Version 1 Group Cipher : TKIP Pairwise Ciphers (2) : CCMP TKIP Authentication Suites (1) : PSK IE: Unknown: DDB20050F204104A0001101049001E007FC5100018DE7CF0D8B70223A62711C18926AC290E30303030303139631044000102103B0001031047001076B31BC241E953CB99C3872554425A28102100194169725469657320576972656C657373204E6574776F726B73102300074169723534343010240008312E322E302E31321042000F4154303939313131383030323832351054000800060050F20400011011000741697235343430100800020084103C000103 IE: WPA Version 1 Group Cipher : TKIP Pairwise Ciphers (2) : CCMP TKIP Authentication Suites (1) : PSK Encryption key:on Bit Rates:1 Mb/s; 2 Mb/s; 5.5 Mb/s; 11 Mb/s; 18 Mb/s 24 Mb/s; 36 Mb/s; 54 Mb/s; 6 Mb/s; 9 Mb/s 12 Mb/s; 48 Mb/s Cell 04 - Address: 72:2B:C1:65:75:3C ESSID:"MobilAtolye" Mode:Managed Frequency:2.422 GHz (Channel 3) Quality:2/5 Signal level:-78 dBm Noise level:-92 dBm IE: IEEE 802.11i/WPA2 Version 1 Group Cipher : TKIP Pairwise Ciphers (2) : TKIP CCMP Authentication Suites (1) : PSK IE: Unknown: DDA20050F204104A0001101044000102103B00010310470010BC329E001DD811B28601722BC165753C1021001D48756177656920546563686E6F6C6F6769657320436F2E2C204C74642E1023001C48756177656920576972656C6573732041636365737320506F696E74102400065254323836301042000831323334353637381054000800060050F204000110110009487561776569415053100800020084103C000100 IE: WPA Version 1 Group Cipher : TKIP Pairwise Ciphers (2) : TKIP CCMP Authentication Suites (1) : PSK Encryption key:on Bit Rates:1 Mb/s; 2 Mb/s; 5.5 Mb/s; 11 Mb/s; 9 Mb/s 18 Mb/s; 36 Mb/s; 54 Mb/s; 6 Mb/s; 12 Mb/s 24 Mb/s; 48 Mb/s Cell 05 - Address: 00:12:BF:3F:0A:8A ESSID:"CnDStudios" Mode:Managed Frequency:2.412 GHz (Channel 1) Quality:5/5 Signal level:-47 dBm Noise level:-95 dBm IE: WPA Version 1 Group Cipher : TKIP Pairwise Ciphers (1) : TKIP Authentication Suites (1) : PSK Encryption key:on Bit Rates:1 Mb/s; 2 Mb/s; 5.5 Mb/s; 11 Mb/s; 22 Mb/s 6 Mb/s; 9 Mb/s; 12 Mb/s; 18 Mb/s; 24 Mb/s 36 Mb/s; 48 Mb/s; 54 Mb/s Cell 06 - Address: 00:1C:A8:6E:84:32 ESSID:"AIR_TIES" Mode:Managed Frequency:2.462 GHz (Channel 11) Quality:5/5 Signal level:-56 dBm Noise level:-98 dBm IE: IEEE 802.11i/WPA2 Version 1 Group Cipher : CCMP Pairwise Ciphers (1) : CCMP Authentication Suites (1) : PSK Encryption key:on Bit Rates:1 Mb/s; 2 Mb/s; 5.5 Mb/s; 11 Mb/s; 22 Mb/s 6 Mb/s; 9 Mb/s; 12 Mb/s; 18 Mb/s; 24 Mb/s 36 Mb/s; 48 Mb/s; 54 Mb/s Cell 07 - Address: 54:E6:FC:B0:3C:E9 ESSID:"tilda_biri_yeni" Mode:Managed Frequency:2.437 GHz (Channel 6) Quality:1/5 Signal level:-85 dBm Noise level:-99 dBm Encryption key:on Bit Rates:1 Mb/s; 2 Mb/s; 5.5 Mb/s; 11 Mb/s; 6 Mb/s 12 Mb/s; 24 Mb/s; 36 Mb/s; 9 Mb/s; 18 Mb/s 48 Mb/s; 54 Mb/s Cell 08 - Address: 18:28:61:16:57:C3 ESSID:"obilet" Mode:Managed Frequency:2.437 GHz (Channel 6) Quality:1/5 Signal level:-88 dBm Noise level:-99 dBm IE: IEEE 802.11i/WPA2 Version 1 Group Cipher : TKIP Pairwise Ciphers (2) : CCMP TKIP Authentication Suites (1) : PSK IE: WPA Version 1 Group Cipher : TKIP Pairwise Ciphers (2) : CCMP TKIP Authentication Suites (1) : PSK Encryption key:on Bit Rates:1 Mb/s; 2 Mb/s; 5.5 Mb/s; 11 Mb/s; 18 Mb/s 24 Mb/s; 36 Mb/s; 54 Mb/s; 6 Mb/s; 9 Mb/s 12 Mb/s; 48 Mb/s Cell 09 - Address: 00:1A:2A:60:BF:61 ESSID:"PROGEDA" Mode:Managed Frequency:2.462 GHz (Channel 11) Quality:2/5 Signal level:-75 dBm Noise level:-98 dBm IE: WPA Version 1 Group Cipher : TKIP Pairwise Ciphers (1) : TKIP Authentication Suites (1) : PSK Encryption key:on Bit Rates:1 Mb/s; 2 Mb/s; 5.5 Mb/s; 11 Mb/s; 22 Mb/s 6 Mb/s; 9 Mb/s; 12 Mb/s; 18 Mb/s; 24 Mb/s 36 Mb/s; 48 Mb/s; 54 Mb/s eth0 Interface doesn't support scanning.

  Read the article

• Django ORM: Ordering w/ aggregate functions — None special treatment

  - by deno

  Hi, I'm doing query like that: SomeObject.objects.annotate(something=Avg('something')).order_by(something).all() Now, I normally have an aggregate field in my model that I use with Django signals to keep in sync, however in this case perfomance isn't an issue so I thought I'd keep it simple and just use subqueries. This approach, however, presented an unexpected issue: It all works grate if aggregate function results are like this: [5.0, 4.0, 6.0 … (etc, just numbers)] However if you mix in some Nones than it's being ordered like this: [None, 5.0, 4.0 …] The issue is that None has higher value than any number, while it should have value at most of 0. I'm using PostgreSQL and haven't tested w/ other DBs. I haven't actually checked what query is generated etc. I worked it around by just sorting in memory: sorted(…, key=lambda _:_.avg_rating if _.avg_rating is not None else 0) So I'm just curious if you know a way to do it w/ just Django ORM. Perhaps .where? or something… Kind regards

  Read the article

• Using GIT Smart HTTP via IIS

  - by Andrew Matthews

  I recently read Scott Chacon's post "Smart HTTP Transport", and I was hoping that it might have become possible via IIS (windows 7) since that post was written. I haven't been able to find anything showing how it can be done, and Apache is not an option in my IIS 7 based environment. So, I'm at a loss (git daemon was foiled for me by a combination of AVG anti-virus and AD). I want to provide LDAP authenticated read/write access for selected users. So this question seems not to be relevant. Do you know of a way to provide access to GIT via IIS?

  Read the article

• Django aggregation query on related one-to-many objects

  - by parxier

  Here is my simplified model: class Item(models.Model): pass class TrackingPoint(models.Model): item = models.ForeignKey(Item) created = models.DateField() data = models.IntegerField() In many parts of my application I need to retrieve a set of Item's and annotate each item with data field from latest TrackingPoint from each item ordered by created field. For example, instance i1 of class Item has 3 TrackingPoint's: tp1 = TrackingPoint(item=i1, created=date(2010,5,15), data=23) tp2 = TrackingPoint(item=i1, created=date(2010,5,14), data=21) tp3 = TrackingPoint(item=i1, created=date(2010,5,12), data=120) I need a query to retrieve i1 instance annotated with tp1.data field value as tp1 is the latest tracking point ordered by created field. That query should also return Item's that don't have any TrackingPoint's at all. If possible I prefer not to use QuerySet's extra method to do this. That's what I tried so far... and failed :( Item.objects.annotate(max_created=Max('trackingpoint__created'), data=Avg('trackingpoint__data')).filter(trackingpoint__created=F('max_created')) Any ideas?

  Read the article

• What's the right way to calculate derived data in a Flex AdvancedDataGrid using summaries?

  - by Chris R

  Here's the gist of the problem: I have a set of rows of data with (say) field1 to field4 in them. I'm using a GroupingCollection to group on field1 and field2. So, I have something like this: f1.1 f2.1 f3.1 f4.1 f3.2 f4.2 f2.2 f3.3 f4.3 f3.4 f4.4 f3.5 f4.5 f1.2 f2.1 f3.6 f4.6 f2.2 f3.7 f4.7 f3.8 f4.8 f3.9 f4.9 (or at least, I hope that's clear enough) I need to calculate some derived values for each leaf row, for example f3, that is the ratio of f3 to the average of all f3 in that particular part of the tree. So, for f3.7 I need to calculate f3.7 / avg(f3.7..f3.9) and fill that into the f3_index property on the row, displaying that in lieu of f3 itself. So, basically, what it looks like I have to do is add source field values in the summarizeFunction implementation. It seems to me that there must be a better way of doing this. Is there?

  Read the article

• Consolidate data from many different databases into one with minimum latency

  - by NTDLS

  I have 12 databases totaling roughly 1.0TB, each on a different physical server running SQL 2005 Enterprise - all with the same exact schema. I need to offload this data into a separate single database so that we can use for other purposes (reporting, web services, ect) with a maximum of 1 hour latency. It should also be noted that these servers are all in the same rack, connected by gigabit connections and that the inserts to the databases are minimal (Avg. 2500 records/hour). The current method is very flakey: The data is currently being replicated (SQL Server Transactional Replication) from each of the 12 servers to a database on another server (yes, 12 different employee tables from 12 different servers into a single employee table on a different server). Every table has a primary key and the rows are unique across all tables (there is a FacilityID in each table). What are my options, these has to be a simple way to do this.

  Read the article

• Optimising SQL distance query

  - by Alex

  I'm running an MySQL query that returns results based on location. However I have noticed recently that its really slowing down my PHP app. I used CodeIgniter and the profiler shows the query taking 4.2seconds. The geoname table has 500,000 rows. I have some indexes on the key columns, how else can speed up this query? Here is my SQL: SELECT `products`.`product_name`, `geoname`.`geonameid`, `geoname`.`latitude`, `geoname`.`longitude`, `products`.`product_id`, AVG(ratings.vote) as rating, count(comments.comment_id) as total_comments, (6371 * acos(cos(radians(38.7666667)) * cos(radians(geoname.latitude)) * cos(radians(geoname.longitude) - radians(-3.3833333)) + sin(radians(38.7666667)) * sin(radians(geoname.latitude)))) AS distance FROM (`foods`) JOIN `geoname` ON `geoname`.`geonameid` = `products`.`geoname_id` LEFT JOIN `ratings` ON `ratings`.`var_id` = `products`.`product_id` LEFT JOIN `comments` ON `comments`.`var_id` = `products `.`product_id` WHERE `products`.`product_id` != 82 GROUP BY `products`.`product_id` HAVING `distance` < 99 ORDER BY `distance` LIMIT 10

  Read the article

• SQL Server 2008 - Conditional Range

  - by user208662

  Hello, I have a database that has two tables. These two tables are defined as: Movie ----- ID (int) Title (nvchar) MovieReview ----------- ID (int) MovieID (int) StoryRating (decimal) HumorRating (decimal) ActingRating (decimal) I have a stored procedure that allows the user to query movies based on other user's reviews. Currently, I have a temporary table that is populated with the following query: SELECT m.*, (SELECT COUNT(ID) FROM MovieReivew r WHERE r.MovieID=m.ID) as 'TotalReviews', (SELECT AVG((r.StoryRating + r.HumorRating + r.ActingRating) / 3) FROM MovieReview r WHERE r.MovieID=m.ID) as 'AverageRating' FROM Movie m In a later query in my procedure, I basically want to say: SELECT * FROM MyTempTable t WHERE t.AverageRating >= @lowestRating AND t.AverageRating <= @highestRating My problem is, sometimes AverageRating is zero. Because of this, I'm not sure what to do. How do I handle this scenario in SQL?

  Read the article

• MPI4Py Scatter sendbuf Argument Type?

  - by Noel

  I'm having trouble with the Scatter function in the MPI4Py Python module. My assumption is that I should be able to pass it a single list for the sendbuffer. However, I'm getting a consistent error message when I do that, or indeed add the other two arguments, recvbuf and root: File "code/step3.py", line 682, in subbox_grid i = mpi_communicator.Scatter(station_range, station_data) File "Comm.pyx", line 427, in mpi4py.MPI.Comm.Scatter (src/ mpi4py_MPI.c:44993) File "message.pxi", line 321, in mpi4py.MPI._p_msg_cco.for_scatter (src/mpi4py_MPI.c:14497) File "message.pxi", line 232, in mpi4py.MPI._p_msg_cco.for_cco_send (src/mpi4py_MPI.c:13630) File "message.pxi", line 36, in mpi4py.MPI.message_simple (src/ mpi4py_MPI.c:11904) ValueError: message: expecting 2 or 3 items Here is the relevant code snipped, starting a few lines above 682 mentioned above. for station in stations #snip--do some stuff with station station_data = [] station_range = range(1,len(station)) mpi_communicator = MPI.COMM_WORLD i = mpi_communicator.Scatter(station_range, nsm) #snip--do some stuff with station[i] nsm = combine(avg, wt, dnew, nf1, nl1, wti[i], wtm, station[i].id) station_data = mpi_communicator.Gather(station_range, nsm) I've tried a number of combinations initializing station_range, but I must not be understanding the Scatter argument types properly. Does a Python/MPI guru have a clarification this?

  Read the article

• MySQL easy question CURDATE()

  - by Tristan

  I want to compare two results one is stored in the first query, and the other is exactly the same as the first, but i want only to recieve data < today "SELECT s.GSP_nom as nom, timestamp, COUNT(s.GSP_nom) as nb_votes, AVG(v.vote+v.prix+v.serviceClient+v.interface+v.interface+v.services)/6 as moy FROM votes_serveur AS v INNER JOIN serveur AS s ON v.idServ = s.idServ WHERE s.valide = 1 AND v.date < CURDATE() ROUP BY s.GSP_nom HAVING nb_votes > 9 ORDER BY moy DESC LIMIT 0,15"; is that correct ? thank you

  Read the article

• Add column from another table matching results from first MySQL query

  - by Nemi

  This is my query for available rooms in choosen period: SELECT rooms.room_id FROM rooms WHERE rooms.room_id NOT IN ( SELECT reservations.room_id FROM reservations WHERE ( reservations.arrivaldate >= $arrival_datetime AND reservations.departuredate <= $departure_datetime) OR ( reservations.arrivaldate <= $arrival_datetime AND reservations.departuredate >= $arrival_datetime ) OR ( reservations.arrivaldate <= $departure_datetime AND reservations.departuredate >= $departure_datetime ) ); How to add average room price column for selected period(from $arrival_datetime to $departure_datetime) from another table (room_prices_table), for every room_id returned from above query. So I need to look in columns whos name is same as room_id... room_prices_table: date room0001 room0002 room0003 ... Something like SELECT AVG(room0003) FROM room_prices_table WHERE datum IS BETWEEN $arrival_datetime AND $departure_datetime ??

  Read the article

• The use of GROUP BY in MySQL

  - by Gustav Bertram

  I'm fishing for a comprehensive and canonical answer for the typical "mysql group by?" question. Here is some sample data: TABLE A +------+------+----------+-----+ | id | foo | bar | baz | +------+------+----------+-----+ | 1 | 1 | hello | 42 | | 2 | 0 | apple | 96 | | 3 | 20 | boot | 11 | | 4 | 31 | unicorn | 99 | | 5 | 19 | pumpkin | 11 | | 6 | 88 | orange | 13 | +------+------+----------+-----+ TABLE B +------+------+ | id | moo | +------+------+ | 1 | 1 | | 2 | 99 | | 3 | 11 | +------+------+ Demonstrate and explain the correct use of the GROUP BY clause in MySQL. Touch upon the following points: The use of MIN, MAX, SUM, AVG The use of HAVING Grouping by date, and ranges of dates Grouping with an ORDER BY Grouping with a JOIN Grouping on multiple columns Bonus points for references to other great answers, the MySQL online manual, and online tutorials on GROUP BY.

  Read the article

• USing Min/Max with conditional operator

  - by user638501

  Hello All, I am trying to run a query to find max and min values, and then use a conditional operator. however when I try to run the following query, it gives me error - "misuse of aggregate: min()". My query is: SELECT a.prim_id, min(b.new_len*36) as min_new_len, max(b.new_len*36) as max_new_len FROM tb_first a, tb_second b WHERE a.sec_id = b.sec_id AND min_new_len > 1900 AND max_new_len < 75000 GROUP BY a.prim_id ORDER BY avg(b.new_len*36); Any suggestions ?

  Read the article

• JPQL: What kind of objects contains a result list when querying multiple columns?

  - by Bunkerbewohner

  Hello! I'm trying to do something which is easy as pie in PHP & Co: SELECT COUNT(x) as numItems, AVG(y) as average, ... FROM Z In PHP I would get a simple array like [{ numItems: 0, average: 0 }] which I could use like this: echo "Number of Items: " . $result[0]['numItems']; Usually in JPQL you only query single objects or single columns and get Lists types, for example List or List. But what do you get, when querying multiple columns?

  Read the article

• Float as DateTime

  - by lp1

  SQL Server 2008 I almost have, I think, what I'm looking to do. I'm just trying to fine tune the result. I have a table that stores timestamps of all transactions that occur on the system. I'm writing a query to give an average transaction time. This is what I have so far: With TransTime AS ( select endtime-starttime AS Totaltime from transactiontime where starttime > '2010-05-12' and endtime < '2010-05-13') Select CAST(AVG(CAST(TotalTime As Float))As Datetime) from TransTime I'm getting the following result: 1900-01-01 00:00:00.007 I can't figure out how to strip the date off and just display the time, 00:00:00:007. Any help would be appreciated. Thank you.

  Read the article

• sql insert statement with a lot of same where clause and one different where cluase

  - by william

  I m sry if the title is not clear. Here's my proble. I created a new table which will show total, average and maximum values. I have to insert the results into that table. That table will have only 4 rows. No Appointment, Appointment Early, Appointment Late and Appointment Punctual. So.. I have sth like.. insert into newTable select 'No Appointment' as 'Col1', avg statement, total statement, max statement from orgTable where (general conditions) and (unique condition to check NO APPOINTMENT); I have to do that same thing for another 3 rows.. where only the unique condition is different to check early, punctual or late.. So..the statement is super long. I wanna reduce the size.. How can I achieve that?

  Read the article

• Calculate average in each group

  - by Gokul

  I am using the following class class Country { int CountryID {get; set;} List<City> city {get; set;} } class City { int CountryID {get; set; } string city {get; set;} int sqkm {get; set;} } Here's is some sample data for Country and City Country US UK Canada City CityC CityF CityA CityB CityG CityD CityE I am populating using List<Country> countries = new List<Country> { new Country() { CountryID = "US", city = new List<City> { new City() {CountryID = "US", City ="CityF", sqkm = 2803 }, and so on Question 1: I want to use LINQ to find avg sq. km of land per country Eg: Canada - 2459 UK - 3243 US - 3564

  Read the article

• MySQL Rating system (calculating average from two tables).

  - by MussuR

  I have two tables, videos and videos_ratings. The videos table has an int videoid field (and many others but those fields are not important I think) and many records. The videos_ratings table has 3 int fields: videoid, rating, rated_by which has many records (multiple records for each fields from the videos table) but not for all records from the videos table. Currently I have the following mysql query: SELECT `videos`.*, avg(`videos_ratings`.`vote`) FROM `videos`, `videos_ratings` WHERE `videos_ratings`.`videoid` = `videos`.`videoid` GROUP BY `videos_ratings`.`videoid` ORDER BY RAND() LIMIT 0, 12 It selects all the records from table videos that have a rating in table video_ratings and calculates the average correctly. But what I need is to select all records from the videos table, no matter if there is a rating for that record or not. And if there aren't any records in the videos_ratings table for that particular videos record, the average function should show 0. Hope someone could understand what I want... :) Thanks!

  Read the article

• fetch some data from two tables

  - by user1753971

  i have site like imdb and we provide movie information sin site..and our website have option to rate all movies for every users. I have two tables 1 . imdb (its for store movie details) id,name,actors,vote 2. ratings (its for store users rating details) id,rating_id(its same as id from first table),rating_num,IP now what am doing is..when anyone rating a movie take the avg of that movie rating by using rating tables (total ratings/number of ratings) and insert that value into "vote" column in first table..my demands this..thats why done like this.. Now my problem is..i want to fetch top rated movies..i mean in vote column which movie have top rating which want to list and one more condition is that that movie should rated by 10 users(use ratings table for that) thanks in advance

  Read the article

< Previous Page | 11 12 13 14 15 16 17 18 19 20 21 22  | Next Page >