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  • Is there a way to split the results of a select query into two equal halfs?

    - by Matthias
    I'd like to have a query returning two ResultSets each of which holding exactly half of all records matching a certain criteria. I tried using TOP 50 PERCENT in conjunction with an Order By but if the number of records in the table is odd, one record will show up in both resultsets. Example: I've got a simple table with TheID (PK) and TheValue fields (varchar(10)) and 5 records. Skip the where clause for now. SELECT TOP 50 PERCENT * FROM TheTable ORDER BY TheID asc results in the selected id's 1,2,3 SELECT TOP 50 PERCENT * FROM TheTable ORDER BY TheID desc results in the selected id's 3,4,5 3 is a dup. In real life of course the queries are fairly complicated with a ton of where clauses and subqueries.

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  • postgres - group by on multiple columns - master/detail type table

    - by smpillay
    I have a table order(orderid, ordernumber, clientid, orderdesc etc.,) and a corresponding status for that order on an order_status table ( statusid, orderid, statusdesc, statusNote, statustimestamp) say I have a record in order as below orderid orderumber clientid orderdesc 1111 00980065 ABC blah..blah.. and a corresponding status entries statusid orderid statusdesc statusNote statustimestamp 11 1111 recvd status blah yyyy-mm-dd:10:00 12 1111 clientproce status blah yyyy-mm-dd:11:00 13 1111 clientnotice status blah yyyy-mm-dd:15:00 14 1111 notified status blah yyyy-mm-dd:17:00 How can I get the following result (latest timestamp along with multiple columns) 1111 14 00980065 ABC blah..blah.. notified status blah yyyy-mm-dd:17:00

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  • Visual Studio build and deploy ordering

    - by mthornal
    We have a VS 2010 solution that includes a few class library projects, a SQL Server 2008 database project and a Wix setup project. We are trying to get to a point where the following happens in the order specified: Build the class library projects and the database project Deploy the database project to generate the deploy .sql script Build the Wix setup project. The reason for the desired order is that the setup project requires the deployment .sql scripts as it will use these to generate/update the database on the machine that the msi is run. It seems that there is no way within a Visual Studio solution file to create this type of build/deploy/build order. Is this correct? Thanks

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  • Call an action from another controller

    - by Brian
    I have two different objects: contracts, and task orders. My requirements specify that in order to view the Details for either object, the Url should be "http://.../Contract/Details" or "http://.../TaskOrder/Details" depending on which type. They are both very similar and the details pages are almost identical, so I made a class that can either be a contract or a task order, and has a variable "objectTypeID" that says which type it is. I wrote the action "Details" in the task order controller, but now I want to call that from the contract controller instead of recopying the code. So is there any way to have the url still say ".../Contract/Details" but call the action in the TaskOrder controller instead? I tried using TaskOrderController TOController = new TaskOrderController(); TOController.Details(id); This would have worked except that I can't use the HttpContext.Session anymore, which I used several times in the action.

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  • Selected number of records from database in DB2.

    - by Abhi
    Hi All, I have to fetch only 50 records at a time from database(DB2), for this I have been usig Row_Number but now the persons are telling that this Row_Number is not stable and has bugs in it so now I have to write a different querry for the same as I have to fetch only 50 records at a time. so please can any body help me out for the same. Thanks in advance. The Query which I have been using is SELECT PLC.* FROM ( SELECT ROW_NUMBER() OVER (ORDER BY PRDLN_CTLG_OID) AS Row, PRDLN_CTLG_OID, PRODUCT_LINE_OID AS PRODUCT_LINE_OID, RTRIM(CATALOG_ID) AS CATALOG_ID, FROM PROD_LINE_CATALOG WHERE PRODUCT_LINE_OID=:productLineOID AND ACTV_IND = 1 ORDER BY CATALOG_ID) PLC WHERE Row >= :startIndex AND Row <= :endIndex ORDER BY PLC.CATALOG_ID DESC WITH UR

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  • JSON Response {"d":"128.00"} but displaying "128"

    - by TGuimond
    Hi all, I have been working on a shopping cart that the user can add/remove order items as they please and am returning an updated sub-total via a webservice using jQuery $.ajax Here is how I am calling the webservice and setting the sub-total with the response. //perform the ajax call $.ajax({ url: p, data: '{' + s + '}', success: function(sTotal) { //order was updated: set span to new sub-total $("#cartRow" + orderID).find(".subTotal").text(sTotal); }, failure: function() { //if the orer was not saved //console.log('Error: Order not deleted'); } }); The response I am getting seems perfectly fine: {"d":"128.00"} When I display the total on the page it displays as 128 rather than 128.00 I am fully sure it is something very simple and silly but I am so deep into it now I need someone with a fresh brain to help me out!! Cheers :)

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  • c# How to make linq master detail query for 0..n relationship?

    - by JK
    Given a classic DB structure of Orders has zero or more OrderLines and OrderLine has exactly one Product, how do I write a linq query to express this? The output would be OrderNumber - OrderLine - Product Name Order-1 null null // (this order has no lines) Order-2 1 Red widget I tried this query but is not getting the orders with no lines var model = (from po in Orders from line in po.OrderLines select new { OrderNumber = po.Id, OrderLine = line.LineNumber, ProductName = line.Product.ProductDescription, } ) I think that the 2nd from is limiting the query to only those that have OrderLines, but I dont know another way to express it. LINQ is very non-obvious if you ask me!

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  • MySQL "IS IN" equivalent?

    - by nute
    A while ago I worked on a MS-SQL project and I remember a "IS IN" thing. I tried it on a MySQL project and it did not work. Is there an equivalent? Workaround? Here is the full query I am trying to run: SELECT * FROM product_product, product_viewhistory, product_xref WHERE ( (product_viewhistory.productId = product_xref.product_id_1 AND product_xref.product_id_2 = product_product.id) OR (product_viewhistory.productId = product_xref.product_id_2 AND product_xref.product_id_1 = product_product.id) ) AND product_product.id IS IN (SELECT DISTINCT pvh.productId FROM product_viewhistory AS pvh WHERE pvh.cookieId = :cookieId ORDER BY pvh.viewTime DESC LIMIT 10) AND product_viewhistory.cookieId = :cookieId AND product_product.outofstock='N' ORDER BY product_xref.hits DESC LIMIT 10 It's pretty big ... but the part I am interested in is: AND product_product.id IS IN (SELECT DISTINCT pvh.productId FROM product_viewhistory AS pvh WHERE pvh.cookieId = :cookieId ORDER BY pvh.viewTime DESC LIMIT 10) Which basically says I want the products to be in the "top 10" of that sub-query. How would you achieve that with MySQL (while trying to be efficient)?

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  • MySQL Query to receive random combinations from two tables.

    - by Michael
    Alright, here is my issue, I have two tables, one named firstnames and the other named lastnames. What I am trying to do here is to find 100 of the possible combinations from these names for test data. The firstnames table has 5494 entries in a single column, and the lastnames table has 88799 entries in a single column. The only query that I have been able to come up with that has some results is: select * from (select * from firstnames order by rand()) f LEFT JOIN (select * from lastnames order by rand()) l on 1=1 limit 10; The problem with this code is that it selects 1 firstname and gives every lastname that could go with it. While this is plausible, I will have to set the limit to 500000000 in order to get all the combinations possible without having only 20 first names(and I'd rather not kill my server). However, I only need 100 random generations of entries for test data, and I will not be able to get that with this code. Can anyone please give me any advice?

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  • How do I make a recursive list that checks company rankings?

    - by Sera
    I have a set of companies in rank order. I want my rule to check if the companies in a specified list are in rank order, and for the rule to recur until all companies in the list have been checked. I currently have the following: isOrder([]). isOrder([COM1,COM2|T]) :- rank(COM1,D), rank(COM2,E), D<E, print("in order"), isOrder([COM2|T]). However, this does not seem to work. Sometimes, the recursion goes on forever without ending, and sometimes the recursion doesn't work at all. This is when I vary the code to try and get the correct answer. Can anybody help me? I have just started Prolog and my understanding of it is severely limited. Any help would be greatly appreciated.

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  • LINQ Query based on user preferences

    - by Chris Phelps
    How can I do this better (so it actually works : ) I have a LINQ Query that includes an order by that is based on a user preference. The user can decide if they would like the results ordered asc or desc. If fuserclass.SortOrder = "Ascending" Then Dim mydat = (From c In dc.ITRS Order By c.Date Ascending Select c) Else Dim mydat = (From c In dc.ITRS Order By c.Date Descending Select c) End If For each mydata in mydat ***<<<error "mydat is not declared"*** I know I could put my For Each loop inside the If and Else, but that seems silly to have the same code twice. I know you know of a better way : )

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  • Parsing through Arabic / RTL text from left to right

    - by Dan W
    Let's say I have a string in an RTL language such as Arabic with some English chucked in: string s = "Test:?????;?????;?????;a;b" Notice there are semicolons in the string. When I use the Split command like string[] spl = s.Split(';');, then some of the strings are saved in reverse order. This is what happens: ??Test:????? ????? ????? a b The above is out of order compared to the original. Instead, I expect to get this: ?Test: ????? ????? ????? a b I'm prepared to write my own split function. However, the chars in the string also parse in reverse order, so I'm back to square one. I just want to go through each character as it's shown on the screen.

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  • How can I get the count of orders placed from my database?

    - by user1360564
    I am preparing a chart which will display the number of orders placed for a particular day in the current month and year. I wanted the count of orders placed for each day. I am showing the count of orders on the y-axis and the day on the x-axis. In my database, there is table called "order" in which order data is placed: order date, user_id, order_price, etc. For example, if on 4 July, 10 orders are placed, on 5 july, 20 orders are placed, and so on. How can I get the count of orders placed for day of the current month?

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  • Why my mysql DISTINCT doesn't work ?

    - by belaz
    Hello, Why the two query below return duplicate member_id and not the third ? i need the second query to work with distinct. Anytime i run a GROUP BY, this query is incredibly slow and the resultset doesn't return the same value as distinct (the value is wrong). SELECT member_id, id FROM ( SELECT * FROM table1 ORDER BY created_at desc ) as u LIMIT 5 +-----------+--------+ | member_id | id | +-----------+--------+ | 11333 | 313095 | | 141831 | 313094 | | 141831 | 313093 | | 12013 | 313092 | | 60821 | 313091 | +-----------+--------+ SELECT distinct member_id, id FROM ( SELECT * FROM table1 ORDER BY created_at desc ) as u LIMIT 5 +-----------+--------+ | member_id | id | +-----------+--------+ | 11333 | 313095 | | 141831 | 313094 | | 141831 | 313093 | | 12013 | 313092 | | 60821 | 313091 | +-----------+--------+ SELECT distinct member_id FROM ( SELECT * FROM table1 ORDER BY created_at desc ) as u LIMIT 5 +-----------+ | member_id | +-----------+ | 11333 | | 141831 | | 12013 | | 60821 | | 64980 | +-----------+ my table sample CREATE TABLE `table1` ( `id` int(11) NOT NULL AUTO_INCREMENT, `member_id` int(11) NOT NULL, `s_type_id` int(11) NOT NULL, `created_at` datetime DEFAULT NULL, PRIMARY KEY (`id`), KEY `s_FI_1` (`member_id`), KEY `s_FI_2` (`s_type_id`) ) ENGINE=InnoDB AUTO_INCREMENT=313096 DEFAULT CHARSET=utf8;

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  • java.util.Observable, will clients complete executing their update() before continuing

    - by jax
    When I call: setChanged(); notifyObservers() on a java.until.Observable class, will all the listening Observer objects complete execution of their udpate() methods - assuming we are running in the same Thread - before the java.until.Observable class continues running? This is important because I will be sending a few messages through the notifyObservers(Object o) method in quick concession, it is important that each Observer class has finished its method before the new one though. I understand that the order of execution for each Observer class may vary when we call notifyObservers() - it is just important that the order of method execution for each individual instance is in order.

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  • Splitting a UL into three even lists

    - by Andy
    I am printing a menu using UL, the trouble is the order that is generated by my script is ignored because im printing the LI one after the other and they're spanning three across. So the order is 1 , 2 , 3 as opposed to 1 2 3 To counteract this i wanted to split my single UL into three that way the order would be maintained. Here is my code currently which works perfectly to print a single UL. //Category Drop Down Menu $this->CategoryDropDownMenu = '<ul id="subcatmenu">'; foreach($sitemap->CategoryMenu as $val) $this->CategoryDropDownMenu .= '<li><a href="'.$val[host].$val[link].'"><span>'.htmlspecialchars($val[title]).'</span></a></li>'; $this->CategoryDropDownMenu .= '</ul>';

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  • mysql result for pagination

    - by Reteras Remus
    The query is: SELECT * FROM `news` ORDER BY `id` LIMIT ($curr_page * 5), ( ($curr_page * 5) + 5 ) Where $curr_page is a php variable which is getting a value from $_GET['page'] I want to make a pagination (5 news on each page), but I don't know why the mysql is returning me extra values. On the first page the result ok: $curr_page = 0 The query would be: SELECT * FROM `news` ORDER BY `id` LIMIT 0, 5 But on the second page, the result from the query is adding extra news, 10 instead of 5. The query on the second page: SELECT * FROM `news` ORDER BY `id` LIMIT 5, 10 Whats wrong? Why the result has 10 values instead of 5? Thank you!

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  • Help in restructuring a project

    - by mrblah
    I have a commerce application, asp.net mvc. I want it to be extensible in the sense others can create other payment providers, as long as they adhere to the interfaces. /blah.core /blah.web /blah.Authorize.net (Implementation of a payment provider using interfaces Ipaymentconfig and paymentdata class) Now the problem is this: /blah.core - PaymentData /blah.core.interfaces - IPaymentConfig where Payment Data looks like: using blah.core; public class PaymentData { public Order Order {get;set;} } IPayment data contains classes from blah.core like the Order class. Now I want to use the actual Authorize.net implementation, so when I tried to reference it in the blah.core project I got a circular dependency error. How could I solve this problem? Many have said to break out the interfaces into their own project, but the problem is PaymentData references entities that are found in blah.core also, so there doesn't seem to be a way around this (in my head anyhow). How can I redesign this?

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  • Sending mail with Gmail Account using System.Net.Mail in ASP.NET

    - by Jalpesh P. Vadgama
    Any web application is in complete without mail functionality you should have to write send mail functionality. Like if there is shopping cart application for example then when a order created on the shopping cart you need to send an email to administrator of website for Order notification and for customer you need to send an email of receipt of order. So any web application is not complete without sending email. This post is also all about sending email. In post I will explain that how we can send emails from our Gmail Account without purchasing any smtp server etc. There are some limitations for sending email from Gmail Account. Please note following things. Gmail will have fixed number of quota for sending emails per day. So you can not send more then that emails for the day. Your from email address always will be your account email address which you are using for sending email. You can not send an email to unlimited numbers of people. Gmail ant spamming policy will restrict this. Gmail provide both Popup and SMTP settings both should be active in your account where you testing. You can enable that via clicking on setting link in gmail account and go to Forwarding and POP/Imap. So if you are using mail functionality for limited emails then Gmail is Best option. But if you are sending thousand of email daily then it will not be Good Idea. Here is the code for sending mail from Gmail Account. using System.Net.Mail; namespace Experiement { public partial class WebForm1 : System.Web.UI.Page { protected void Page_Load(object sender,System.EventArgs e) { MailMessage mailMessage = new MailMessage(new MailAddress("[email protected]") ,new MailAddress("[email protected]")); mailMessage.Subject = "Sending mail through gmail account"; mailMessage.IsBodyHtml = true; mailMessage.Body = "<B>Sending mail thorugh gmail from asp.net</B>"; System.Net.NetworkCredential networkCredentials = new System.Net.NetworkCredential("[email protected]", "yourpassword"); SmtpClient smtpClient = new SmtpClient(); smtpClient.EnableSsl = true; smtpClient.UseDefaultCredentials = false; smtpClient.Credentials = networkCredentials; smtpClient.Host = "smtp.gmail.com"; smtpClient.Port = 587; smtpClient.Send(mailMessage); Response.Write("Mail Successfully sent"); } } } That’s run this application and you will get like below in your account. Technorati Tags: Gmail,System.NET.Mail,ASP.NET

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  • Improving Partitioned Table Join Performance

    - by Paul White
    The query optimizer does not always choose an optimal strategy when joining partitioned tables. This post looks at an example, showing how a manual rewrite of the query can almost double performance, while reducing the memory grant to almost nothing. Test Data The two tables in this example use a common partitioning partition scheme. The partition function uses 41 equal-size partitions: CREATE PARTITION FUNCTION PFT (integer) AS RANGE RIGHT FOR VALUES ( 125000, 250000, 375000, 500000, 625000, 750000, 875000, 1000000, 1125000, 1250000, 1375000, 1500000, 1625000, 1750000, 1875000, 2000000, 2125000, 2250000, 2375000, 2500000, 2625000, 2750000, 2875000, 3000000, 3125000, 3250000, 3375000, 3500000, 3625000, 3750000, 3875000, 4000000, 4125000, 4250000, 4375000, 4500000, 4625000, 4750000, 4875000, 5000000 ); GO CREATE PARTITION SCHEME PST AS PARTITION PFT ALL TO ([PRIMARY]); There two tables are: CREATE TABLE dbo.T1 ( TID integer NOT NULL IDENTITY(0,1), Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T1 PRIMARY KEY CLUSTERED (TID) ON PST (TID) );   CREATE TABLE dbo.T2 ( TID integer NOT NULL, Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T2 PRIMARY KEY CLUSTERED (TID, Column1) ON PST (TID) ); The next script loads 5 million rows into T1 with a pseudo-random value between 1 and 5 for Column1. The table is partitioned on the IDENTITY column TID: INSERT dbo.T1 WITH (TABLOCKX) (Column1) SELECT (ABS(CHECKSUM(NEWID())) % 5) + 1 FROM dbo.Numbers AS N WHERE n BETWEEN 1 AND 5000000; In case you don’t already have an auxiliary table of numbers lying around, here’s a script to create one with 10 million rows: CREATE TABLE dbo.Numbers (n bigint PRIMARY KEY);   WITH L0 AS(SELECT 1 AS c UNION ALL SELECT 1), L1 AS(SELECT 1 AS c FROM L0 AS A CROSS JOIN L0 AS B), L2 AS(SELECT 1 AS c FROM L1 AS A CROSS JOIN L1 AS B), L3 AS(SELECT 1 AS c FROM L2 AS A CROSS JOIN L2 AS B), L4 AS(SELECT 1 AS c FROM L3 AS A CROSS JOIN L3 AS B), L5 AS(SELECT 1 AS c FROM L4 AS A CROSS JOIN L4 AS B), Nums AS(SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS n FROM L5) INSERT dbo.Numbers WITH (TABLOCKX) SELECT TOP (10000000) n FROM Nums ORDER BY n OPTION (MAXDOP 1); Table T1 contains data like this: Next we load data into table T2. The relationship between the two tables is that table 2 contains ‘n’ rows for each row in table 1, where ‘n’ is determined by the value in Column1 of table T1. There is nothing particularly special about the data or distribution, by the way. INSERT dbo.T2 WITH (TABLOCKX) (TID, Column1) SELECT T.TID, N.n FROM dbo.T1 AS T JOIN dbo.Numbers AS N ON N.n >= 1 AND N.n <= T.Column1; Table T2 ends up containing about 15 million rows: The primary key for table T2 is a combination of TID and Column1. The data is partitioned according to the value in column TID alone. Partition Distribution The following query shows the number of rows in each partition of table T1: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T1 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are 40 partitions containing 125,000 rows (40 * 125k = 5m rows). The rightmost partition remains empty. The next query shows the distribution for table 2: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T2 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are roughly 375,000 rows in each partition (the rightmost partition is also empty): Ok, that’s the test data done. Test Query and Execution Plan The task is to count the rows resulting from joining tables 1 and 2 on the TID column: SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; The optimizer chooses a plan using parallel hash join, and partial aggregation: The Plan Explorer plan tree view shows accurate cardinality estimates and an even distribution of rows across threads (click to enlarge the image): With a warm data cache, the STATISTICS IO output shows that no physical I/O was needed, and all 41 partitions were touched: Running the query without actual execution plan or STATISTICS IO information for maximum performance, the query returns in around 2600ms. Execution Plan Analysis The first step toward improving on the execution plan produced by the query optimizer is to understand how it works, at least in outline. The two parallel Clustered Index Scans use multiple threads to read rows from tables T1 and T2. Parallel scan uses a demand-based scheme where threads are given page(s) to scan from the table as needed. This arrangement has certain important advantages, but does result in an unpredictable distribution of rows amongst threads. The point is that multiple threads cooperate to scan the whole table, but it is impossible to predict which rows end up on which threads. For correct results from the parallel hash join, the execution plan has to ensure that rows from T1 and T2 that might join are processed on the same thread. For example, if a row from T1 with join key value ‘1234’ is placed in thread 5’s hash table, the execution plan must guarantee that any rows from T2 that also have join key value ‘1234’ probe thread 5’s hash table for matches. The way this guarantee is enforced in this parallel hash join plan is by repartitioning rows to threads after each parallel scan. The two repartitioning exchanges route rows to threads using a hash function over the hash join keys. The two repartitioning exchanges use the same hash function so rows from T1 and T2 with the same join key must end up on the same hash join thread. Expensive Exchanges This business of repartitioning rows between threads can be very expensive, especially if a large number of rows is involved. The execution plan selected by the optimizer moves 5 million rows through one repartitioning exchange and around 15 million across the other. As a first step toward removing these exchanges, consider the execution plan selected by the optimizer if we join just one partition from each table, disallowing parallelism: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = 1 AND $PARTITION.PFT(T2.TID) = 1 OPTION (MAXDOP 1); The optimizer has chosen a (one-to-many) merge join instead of a hash join. The single-partition query completes in around 100ms. If everything scaled linearly, we would expect that extending this strategy to all 40 populated partitions would result in an execution time around 4000ms. Using parallelism could reduce that further, perhaps to be competitive with the parallel hash join chosen by the optimizer. This raises a question. If the most efficient way to join one partition from each of the tables is to use a merge join, why does the optimizer not choose a merge join for the full query? Forcing a Merge Join Let’s force the optimizer to use a merge join on the test query using a hint: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN); This is the execution plan selected by the optimizer: This plan results in the same number of logical reads reported previously, but instead of 2600ms the query takes 5000ms. The natural explanation for this drop in performance is that the merge join plan is only using a single thread, whereas the parallel hash join plan could use multiple threads. Parallel Merge Join We can get a parallel merge join plan using the same query hint as before, and adding trace flag 8649: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN, QUERYTRACEON 8649); The execution plan is: This looks promising. It uses a similar strategy to distribute work across threads as seen for the parallel hash join. In practice though, performance is disappointing. On a typical run, the parallel merge plan runs for around 8400ms; slower than the single-threaded merge join plan (5000ms) and much worse than the 2600ms for the parallel hash join. We seem to be going backwards! The logical reads for the parallel merge are still exactly the same as before, with no physical IOs. The cardinality estimates and thread distribution are also still very good (click to enlarge): A big clue to the reason for the poor performance is shown in the wait statistics (captured by Plan Explorer Pro): CXPACKET waits require careful interpretation, and are most often benign, but in this case excessive waiting occurs at the repartitioning exchanges. Unlike the parallel hash join, the repartitioning exchanges in this plan are order-preserving ‘merging’ exchanges (because merge join requires ordered inputs): Parallelism works best when threads can just grab any available unit of work and get on with processing it. Preserving order introduces inter-thread dependencies that can easily lead to significant waits occurring. In extreme cases, these dependencies can result in an intra-query deadlock, though the details of that will have to wait for another time to explore in detail. The potential for waits and deadlocks leads the query optimizer to cost parallel merge join relatively highly, especially as the degree of parallelism (DOP) increases. This high costing resulted in the optimizer choosing a serial merge join rather than parallel in this case. The test results certainly confirm its reasoning. Collocated Joins In SQL Server 2008 and later, the optimizer has another available strategy when joining tables that share a common partition scheme. This strategy is a collocated join, also known as as a per-partition join. It can be applied in both serial and parallel execution plans, though it is limited to 2-way joins in the current optimizer. Whether the optimizer chooses a collocated join or not depends on cost estimation. The primary benefits of a collocated join are that it eliminates an exchange and requires less memory, as we will see next. Costing and Plan Selection The query optimizer did consider a collocated join for our original query, but it was rejected on cost grounds. The parallel hash join with repartitioning exchanges appeared to be a cheaper option. There is no query hint to force a collocated join, so we have to mess with the costing framework to produce one for our test query. Pretending that IOs cost 50 times more than usual is enough to convince the optimizer to use collocated join with our test query: -- Pretend IOs are 50x cost temporarily DBCC SETIOWEIGHT(50);   -- Co-located hash join SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (RECOMPILE);   -- Reset IO costing DBCC SETIOWEIGHT(1); Collocated Join Plan The estimated execution plan for the collocated join is: The Constant Scan contains one row for each partition of the shared partitioning scheme, from 1 to 41. The hash repartitioning exchanges seen previously are replaced by a single Distribute Streams exchange using Demand partitioning. Demand partitioning means that the next partition id is given to the next parallel thread that asks for one. My test machine has eight logical processors, and all are available for SQL Server to use. As a result, there are eight threads in the single parallel branch in this plan, each processing one partition from each table at a time. Once a thread finishes processing a partition, it grabs a new partition number from the Distribute Streams exchange…and so on until all partitions have been processed. It is important to understand that the parallel scans in this plan are different from the parallel hash join plan. Although the scans have the same parallelism icon, tables T1 and T2 are not being co-operatively scanned by multiple threads in the same way. Each thread reads a single partition of T1 and performs a hash match join with the same partition from table T2. The properties of the two Clustered Index Scans show a Seek Predicate (unusual for a scan!) limiting the rows to a single partition: The crucial point is that the join between T1 and T2 is on TID, and TID is the partitioning column for both tables. A thread that processes partition ‘n’ is guaranteed to see all rows that can possibly join on TID for that partition. In addition, no other thread will see rows from that partition, so this removes the need for repartitioning exchanges. CPU and Memory Efficiency Improvements The collocated join has removed two expensive repartitioning exchanges and added a single exchange processing 41 rows (one for each partition id). Remember, the parallel hash join plan exchanges had to process 5 million and 15 million rows. The amount of processor time spent on exchanges will be much lower in the collocated join plan. In addition, the collocated join plan has a maximum of 8 threads processing single partitions at any one time. The 41 partitions will all be processed eventually, but a new partition is not started until a thread asks for it. Threads can reuse hash table memory for the new partition. The parallel hash join plan also had 8 hash tables, but with all 5,000,000 build rows loaded at the same time. The collocated plan needs memory for only 8 * 125,000 = 1,000,000 rows at any one time. Collocated Hash Join Performance The collated join plan has disappointing performance in this case. The query runs for around 25,300ms despite the same IO statistics as usual. This is much the worst result so far, so what went wrong? It turns out that cardinality estimation for the single partition scans of table T1 is slightly low. The properties of the Clustered Index Scan of T1 (graphic immediately above) show the estimation was for 121,951 rows. This is a small shortfall compared with the 125,000 rows actually encountered, but it was enough to cause the hash join to spill to physical tempdb: A level 1 spill doesn’t sound too bad, until you realize that the spill to tempdb probably occurs for each of the 41 partitions. As a side note, the cardinality estimation error is a little surprising because the system tables accurately show there are 125,000 rows in every partition of T1. Unfortunately, the optimizer uses regular column and index statistics to derive cardinality estimates here rather than system table information (e.g. sys.partitions). Collocated Merge Join We will never know how well the collocated parallel hash join plan might have worked without the cardinality estimation error (and the resulting 41 spills to tempdb) but we do know: Merge join does not require a memory grant; and Merge join was the optimizer’s preferred join option for a single partition join Putting this all together, what we would really like to see is the same collocated join strategy, but using merge join instead of hash join. Unfortunately, the current query optimizer cannot produce a collocated merge join; it only knows how to do collocated hash join. So where does this leave us? CROSS APPLY sys.partitions We can try to write our own collocated join query. We can use sys.partitions to find the partition numbers, and CROSS APPLY to get a count per partition, with a final step to sum the partial counts. The following query implements this idea: SELECT row_count = SUM(Subtotals.cnt) FROM ( -- Partition numbers SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1 ) AS P CROSS APPLY ( -- Count per collocated join SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals; The estimated plan is: The cardinality estimates aren’t all that good here, especially the estimate for the scan of the system table underlying the sys.partitions view. Nevertheless, the plan shape is heading toward where we would like to be. Each partition number from the system table results in a per-partition scan of T1 and T2, a one-to-many Merge Join, and a Stream Aggregate to compute the partial counts. The final Stream Aggregate just sums the partial counts. Execution time for this query is around 3,500ms, with the same IO statistics as always. This compares favourably with 5,000ms for the serial plan produced by the optimizer with the OPTION (MERGE JOIN) hint. This is another case of the sum of the parts being less than the whole – summing 41 partial counts from 41 single-partition merge joins is faster than a single merge join and count over all partitions. Even so, this single-threaded collocated merge join is not as quick as the original parallel hash join plan, which executed in 2,600ms. On the positive side, our collocated merge join uses only one logical processor and requires no memory grant. The parallel hash join plan used 16 threads and reserved 569 MB of memory:   Using a Temporary Table Our collocated merge join plan should benefit from parallelism. The reason parallelism is not being used is that the query references a system table. We can work around that by writing the partition numbers to a temporary table (or table variable): SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   CREATE TABLE #P ( partition_number integer PRIMARY KEY);   INSERT #P (partition_number) SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1;   SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals;   DROP TABLE #P;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; Using the temporary table adds a few logical reads, but the overall execution time is still around 3500ms, indistinguishable from the same query without the temporary table. The problem is that the query optimizer still doesn’t choose a parallel plan for this query, though the removal of the system table reference means that it could if it chose to: In fact the optimizer did enter the parallel plan phase of query optimization (running search 1 for a second time): Unfortunately, the parallel plan found seemed to be more expensive than the serial plan. This is a crazy result, caused by the optimizer’s cost model not reducing operator CPU costs on the inner side of a nested loops join. Don’t get me started on that, we’ll be here all night. In this plan, everything expensive happens on the inner side of a nested loops join. Without a CPU cost reduction to compensate for the added cost of exchange operators, candidate parallel plans always look more expensive to the optimizer than the equivalent serial plan. Parallel Collocated Merge Join We can produce the desired parallel plan using trace flag 8649 again: SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: One difference between this plan and the collocated hash join plan is that a Repartition Streams exchange operator is used instead of Distribute Streams. The effect is similar, though not quite identical. The Repartition uses round-robin partitioning, meaning the next partition id is pushed to the next thread in sequence. The Distribute Streams exchange seen earlier used Demand partitioning, meaning the next partition id is pulled across the exchange by the next thread that is ready for more work. There are subtle performance implications for each partitioning option, but going into that would again take us too far off the main point of this post. Performance The important thing is the performance of this parallel collocated merge join – just 1350ms on a typical run. The list below shows all the alternatives from this post (all timings include creation, population, and deletion of the temporary table where appropriate) from quickest to slowest: Collocated parallel merge join: 1350ms Parallel hash join: 2600ms Collocated serial merge join: 3500ms Serial merge join: 5000ms Parallel merge join: 8400ms Collated parallel hash join: 25,300ms (hash spill per partition) The parallel collocated merge join requires no memory grant (aside from a paltry 1.2MB used for exchange buffers). This plan uses 16 threads at DOP 8; but 8 of those are (rather pointlessly) allocated to the parallel scan of the temporary table. These are minor concerns, but it turns out there is a way to address them if it bothers you. Parallel Collocated Merge Join with Demand Partitioning This final tweak replaces the temporary table with a hard-coded list of partition ids (dynamic SQL could be used to generate this query from sys.partitions): SELECT row_count = SUM(Subtotals.cnt) FROM ( VALUES (1),(2),(3),(4),(5),(6),(7),(8),(9),(10), (11),(12),(13),(14),(15),(16),(17),(18),(19),(20), (21),(22),(23),(24),(25),(26),(27),(28),(29),(30), (31),(32),(33),(34),(35),(36),(37),(38),(39),(40),(41) ) AS P (partition_number) CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: The parallel collocated hash join plan is reproduced below for comparison: The manual rewrite has another advantage that has not been mentioned so far: the partial counts (per partition) can be computed earlier than the partial counts (per thread) in the optimizer’s collocated join plan. The earlier aggregation is performed by the extra Stream Aggregate under the nested loops join. The performance of the parallel collocated merge join is unchanged at around 1350ms. Final Words It is a shame that the current query optimizer does not consider a collocated merge join (Connect item closed as Won’t Fix). The example used in this post showed an improvement in execution time from 2600ms to 1350ms using a modestly-sized data set and limited parallelism. In addition, the memory requirement for the query was almost completely eliminated  – down from 569MB to 1.2MB. The problem with the parallel hash join selected by the optimizer is that it attempts to process the full data set all at once (albeit using eight threads). It requires a large memory grant to hold all 5 million rows from table T1 across the eight hash tables, and does not take advantage of the divide-and-conquer opportunity offered by the common partitioning. The great thing about the collocated join strategies is that each parallel thread works on a single partition from both tables, reading rows, performing the join, and computing a per-partition subtotal, before moving on to a new partition. From a thread’s point of view… If you have trouble visualizing what is happening from just looking at the parallel collocated merge join execution plan, let’s look at it again, but from the point of view of just one thread operating between the two Parallelism (exchange) operators. Our thread picks up a single partition id from the Distribute Streams exchange, and starts a merge join using ordered rows from partition 1 of table T1 and partition 1 of table T2. By definition, this is all happening on a single thread. As rows join, they are added to a (per-partition) count in the Stream Aggregate immediately above the Merge Join. Eventually, either T1 (partition 1) or T2 (partition 1) runs out of rows and the merge join stops. The per-partition count from the aggregate passes on through the Nested Loops join to another Stream Aggregate, which is maintaining a per-thread subtotal. Our same thread now picks up a new partition id from the exchange (say it gets id 9 this time). The count in the per-partition aggregate is reset to zero, and the processing of partition 9 of both tables proceeds just as it did for partition 1, and on the same thread. Each thread picks up a single partition id and processes all the data for that partition, completely independently from other threads working on other partitions. One thread might eventually process partitions (1, 9, 17, 25, 33, 41) while another is concurrently processing partitions (2, 10, 18, 26, 34) and so on for the other six threads at DOP 8. The point is that all 8 threads can execute independently and concurrently, continuing to process new partitions until the wider job (of which the thread has no knowledge!) is done. This divide-and-conquer technique can be much more efficient than simply splitting the entire workload across eight threads all at once. Related Reading Understanding and Using Parallelism in SQL Server Parallel Execution Plans Suck © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

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  • Getting the innermost .NET Exception

    - by Rick Strahl
    Here's a trivial but quite useful function that I frequently need in dynamic execution of code: Finding the innermost exception when an exception occurs, because for many operations (for example Reflection invocations or Web Service calls) the top level errors returned can be rather generic. A good example - common with errors in Reflection making a method invocation - is this generic error: Exception has been thrown by the target of an invocation In the debugger it looks like this: In this case this is an AJAX callback, which dynamically executes a method (ExecuteMethod code) which in turn calls into an Amazon Web Service using the old Amazon WSE101 Web service extensions for .NET. An error occurs in the Web Service call and the innermost exception holds the useful error information which in this case points at an invalid web.config key value related to the System.Net connection APIs. The "Exception has been thrown by the target of an invocation" error is the Reflection APIs generic error message that gets fired when you execute a method dynamically and that method fails internally. The messages basically says: "Your code blew up in my face when I tried to run it!". Which of course is not very useful to tell you what actually happened. If you drill down the InnerExceptions eventually you'll get a more detailed exception that points at the original error and code that caused the exception. In the code above the actually useful exception is two innerExceptions down. In most (but not all) cases when inner exceptions are returned, it's the innermost exception that has the information that is really useful. It's of course a fairly trivial task to do this in code, but I do it so frequently that I use a small helper method for this: /// <summary> /// Returns the innermost Exception for an object /// </summary> /// <param name="ex"></param> /// <returns></returns> public static Exception GetInnerMostException(Exception ex) { Exception currentEx = ex; while (currentEx.InnerException != null) { currentEx = currentEx.InnerException; } return currentEx; } This code just loops through all the inner exceptions (if any) and assigns them to a temporary variable until there are no more inner exceptions. The end result is that you get the innermost exception returned from the original exception. It's easy to use this code then in a try/catch handler like this (from the example above) to retrieve the more important innermost exception: object result = null; string stringResult = null; try { if (parameterList != null) // use the supplied parameter list result = helper.ExecuteMethod(methodToCall,target, parameterList.ToArray(), CallbackMethodParameterType.Json,ref attr); else // grab the info out of QueryString Values or POST buffer during parameter parsing // for optimization result = helper.ExecuteMethod(methodToCall, target, null, CallbackMethodParameterType.Json, ref attr); } catch (Exception ex) { Exception activeException = DebugUtils.GetInnerMostException(ex); WriteErrorResponse(activeException.Message, ( HttpContext.Current.IsDebuggingEnabled ? ex.StackTrace : null ) ); return; } Another function that is useful to me from time to time is one that returns all inner exceptions and the original exception as an array: /// <summary> /// Returns an array of the entire exception list in reverse order /// (innermost to outermost exception) /// </summary> /// <param name="ex">The original exception to work off</param> /// <returns>Array of Exceptions from innermost to outermost</returns> public static Exception[] GetInnerExceptions(Exception ex) {     List<Exception> exceptions = new List<Exception>();     exceptions.Add(ex);       Exception currentEx = ex;     while (currentEx.InnerException != null)     {         exceptions.Add(ex);     }       // Reverse the order to the innermost is first     exceptions.Reverse();       return exceptions.ToArray(); } This function loops through all the InnerExceptions and returns them and then reverses the order of the array returning the innermost exception first. This can be useful in certain error scenarios where exceptions stack and you need to display information from more than one of the exceptions in order to create a useful error message. This is rare but certain database exceptions bury their exception info in mutliple inner exceptions and it's easier to parse through them in an array then to manually walk the exception stack. It's also useful if you need to log errors and want to see the all of the error detail from all exceptions. None of this is rocket science, but it's useful to have some helpers that make retrieval of the critical exception info trivial. Resources DebugUtils.cs utility class in the West Wind Web Toolkit© Rick Strahl, West Wind Technologies, 2005-2011Posted in CSharp  .NET  

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  • SQL SERVER – SSMS: Backup and Restore Events Report

    - by Pinal Dave
    A DBA wears multiple hats and in fact does more than what an eye can see. One of the core task of a DBA is to take backups. This looks so trivial that most developers shrug this off as the only activity a DBA might be doing. I have huge respect for DBA’s all around the world because even if they seem cool with all the scripting, automation, maintenance works round the clock to keep the business working almost 365 days 24×7, their worth is knowing that one day when the systems / HDD crashes and you have an important delivery to make. So these backup tasks / maintenance jobs that have been done come handy and are no more trivial as they might seem to be as considered by many. So the important question like: “When was the last backup taken?”, “How much time did the last backup take?”, “What type of backup was taken last?” etc are tricky questions and this report lands answers to the same in a jiffy. So the SSMS report, we are talking can be used to find backups and restore operation done for the selected database. Whenever we perform any backup or restore operation, the information is stored in the msdb database. This report can utilize that information and provide information about the size, time taken and also the file location for those operations. Here is how this report can be launched.   Once we launch this report, we can see 4 major sections shown as listed below. Average Time Taken For Backup Operations Successful Backup Operations Backup Operation Errors Successful Restore Operations Let us look at each section next. Average Time Taken For Backup Operations Information shown in “Average Time Taken For Backup Operations” section is taken from a backupset table in the msdb database. Here is the query and the expanded version of that particular section USE msdb; SELECT (ROW_NUMBER() OVER (ORDER BY t1.TYPE))%2 AS l1 ,       1 AS l2 ,       1 AS l3 ,       t1.TYPE AS [type] ,       (AVG(DATEDIFF(ss,backup_start_date, backup_finish_date)))/60.0 AS AverageBackupDuration FROM backupset t1 INNER JOIN sys.databases t3 ON ( t1.database_name = t3.name) WHERE t3.name = N'AdventureWorks2014' GROUP BY t1.TYPE ORDER BY t1.TYPE On my small database the time taken for differential backup was less than a minute, hence the value of zero is displayed. This is an important piece of backup operation which might help you in planning maintenance windows. Successful Backup Operations Here is the expanded version of this section.   This information is derived from various backup tracking tables from msdb database.  Here is the simplified version of the query which can be used separately as well. SELECT * FROM sys.databases t1 INNER JOIN backupset t3 ON (t3.database_name = t1.name) LEFT OUTER JOIN backupmediaset t5 ON ( t3.media_set_id = t5.media_set_id) LEFT OUTER JOIN backupmediafamily t6 ON ( t6.media_set_id = t5.media_set_id) WHERE (t1.name = N'AdventureWorks2014') ORDER BY backup_start_date DESC,t3.backup_set_id,t6.physical_device_name; The report does some calculations to show the data in a more readable format. For example, the backup size is shown in KB, MB or GB. I have expanded first row by clicking on (+) on “Device type” column. That has shown me the path of the physical backup file. Personally looking at this section, the Backup Size, Device Type and Backup Name are critical and are worth a note. As mentioned in the previous section, this section also has the Duration embedded inside it. Backup Operation Errors This section of the report gets data from default trace. You might wonder how. One of the event which is tracked by default trace is “ErrorLog”. This means that whatever message is written to errorlog gets written to default trace file as well. Interestingly, whenever there is a backup failure, an error message is written to ERRORLOG and hence default trace. This section takes advantage of that and shows the information. We can read below message under this section, which confirms above logic. No backup operations errors occurred for (AdventureWorks2014) database in the recent past or default trace is not enabled. Successful Restore Operations This section may not be very useful in production server (do you perform a restore of database?) but might be useful in the development and log shipping secondary environment, where we might be interested to see restore operations for a particular database. Here is the expanded version of the section. To fill this section of the report, I have restored the same backups which were taken to populate earlier sections. Here is the simplified version of the query used to populate this output. USE msdb; SELECT * FROM restorehistory t1 LEFT OUTER JOIN restorefile t2 ON ( t1.restore_history_id = t2.restore_history_id) LEFT OUTER JOIN backupset t3 ON ( t1.backup_set_id = t3.backup_set_id) WHERE t1.destination_database_name = N'AdventureWorks2014' ORDER BY restore_date DESC,  t1.restore_history_id,t2.destination_phys_name Have you ever looked at the backup strategy of your key databases? Are they in sync and do we have scope for improvements? Then this is the report to analyze after a week or month of maintenance plans running in your database. Do chime in with what are the strategies you are using in your environments. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Backup and Restore, SQL Query, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL Tagged: SQL Reports

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  • [GEEK SCHOOL] Network Security 1: Securing User Accounts and Passwords in Windows

    - by Matt Klein
    This How-To Geek School class is intended for people who want to learn more about security when using Windows operating systems. You will learn many principles that will help you have a more secure computing experience and will get the chance to use all the important security tools and features that are bundled with Windows. Obviously, we will share everything you need to know about using them effectively. In this first lesson, we will talk about password security; the different ways of logging into Windows and how secure they are. In the proceeding lesson, we will explain where Windows stores all the user names and passwords you enter while working in this operating systems, how safe they are, and how to manage this data. Moving on in the series, we will talk about User Account Control, its role in improving the security of your system, and how to use Windows Defender in order to protect your system from malware. Then, we will talk about the Windows Firewall, how to use it in order to manage the apps that get access to the network and the Internet, and how to create your own filtering rules. After that, we will discuss the SmartScreen Filter – a security feature that gets more and more attention from Microsoft and is now widely used in its Windows 8.x operating systems. Moving on, we will discuss ways to keep your software and apps up-to-date, why this is important and which tools you can use to automate this process as much as possible. Last but not least, we will discuss the Action Center and its role in keeping you informed about what’s going on with your system and share several tips and tricks about how to stay safe when using your computer and the Internet. Let’s get started by discussing everyone’s favorite subject: passwords. The Types of Passwords Found in Windows In Windows 7, you have only local user accounts, which may or may not have a password. For example, you can easily set a blank password for any user account, even if that one is an administrator. The only exception to this rule are business networks where domain policies force all user accounts to use a non-blank password. In Windows 8.x, you have both local accounts and Microsoft accounts. If you would like to learn more about them, don’t hesitate to read the lesson on User Accounts, Groups, Permissions & Their Role in Sharing, in our Windows Networking series. Microsoft accounts are obliged to use a non-blank password due to the fact that a Microsoft account gives you access to Microsoft services. Using a blank password would mean exposing yourself to lots of problems. Local accounts in Windows 8.1 however, can use a blank password. On top of traditional passwords, any user account can create and use a 4-digit PIN or a picture password. These concepts were introduced by Microsoft to speed up the sign in process for the Windows 8.x operating system. However, they do not replace the use of a traditional password and can be used only in conjunction with a traditional user account password. Another type of password that you encounter in Windows operating systems is the Homegroup password. In a typical home network, users can use the Homegroup to easily share resources. A Homegroup can be joined by a Windows device only by using the Homegroup password. If you would like to learn more about the Homegroup and how to use it for network sharing, don’t hesitate to read our Windows Networking series. What to Keep in Mind When Creating Passwords, PINs and Picture Passwords When creating passwords, a PIN, or a picture password for your user account, we would like you keep in mind the following recommendations: Do not use blank passwords, even on the desktop computers in your home. You never know who may gain unwanted access to them. Also, malware can run more easily as administrator because you do not have a password. Trading your security for convenience when logging in is never a good idea. When creating a password, make it at least eight characters long. Make sure that it includes a random mix of upper and lowercase letters, numbers, and symbols. Ideally, it should not be related in any way to your name, username, or company name. Make sure that your passwords do not include complete words from any dictionary. Dictionaries are the first thing crackers use to hack passwords. Do not use the same password for more than one account. All of your passwords should be unique and you should use a system like LastPass, KeePass, Roboform or something similar to keep track of them. When creating a PIN use four different digits to make things slightly harder to crack. When creating a picture password, pick a photo that has at least 10 “points of interests”. Points of interests are areas that serve as a landmark for your gestures. Use a random mixture of gesture types and sequence and make sure that you do not repeat the same gesture twice. Be aware that smudges on the screen could potentially reveal your gestures to others. The Security of Your Password vs. the PIN and the Picture Password Any kind of password can be cracked with enough effort and the appropriate tools. There is no such thing as a completely secure password. However, passwords created using only a few security principles are much harder to crack than others. If you respect the recommendations shared in the previous section of this lesson, you will end up having reasonably secure passwords. Out of all the log in methods in Windows 8.x, the PIN is the easiest to brute force because PINs are restricted to four digits and there are only 10,000 possible unique combinations available. The picture password is more secure than the PIN because it provides many more opportunities for creating unique combinations of gestures. Microsoft have compared the two login options from a security perspective in this post: Signing in with a picture password. In order to discourage brute force attacks against picture passwords and PINs, Windows defaults to your traditional text password after five failed attempts. The PIN and the picture password function only as alternative login methods to Windows 8.x. Therefore, if someone cracks them, he or she doesn’t have access to your user account password. However, that person can use all the apps installed on your Windows 8.x device, access your files, data, and so on. How to Create a PIN in Windows 8.x If you log in to a Windows 8.x device with a user account that has a non-blank password, then you can create a 4-digit PIN for it, to use it as a complementary login method. In order to create one, you need to go to “PC Settings”. If you don’t know how, then press Windows + C on your keyboard or flick from the right edge of the screen, on a touch-enabled device, then press “Settings”. The Settings charm is now open. Click or tap the link that says “Change PC settings”, on the bottom of the charm. In PC settings, go to Accounts and then to “Sign-in options”. Here you will find all the necessary options for changing your existing password, creating a PIN, or a picture password. To create a PIN, press the “Add” button in the PIN section. The “Create a PIN” wizard is started and you are asked to enter the password of your user account. Type it and press “OK”. Now you are asked to enter a 4-digit pin in the “Enter PIN” and “Confirm PIN” fields. The PIN has been created and you can now use it to log in to Windows. How to Create a Picture Password in Windows 8.x If you log in to a Windows 8.x device with a user account that has a non-blank password, then you can also create a picture password and use it as a complementary login method. In order to create one, you need to go to “PC settings”. In PC Settings, go to Accounts and then to “Sign-in options”. Here you will find all the necessary options for changing your existing password, creating a PIN, or a picture password. To create a picture password, press the “Add” button in the “Picture password” section. The “Create a picture password” wizard is started and you are asked to enter the password of your user account. You are shown a guide on how the picture password works. Take a few seconds to watch it and learn the gestures that can be used for your picture password. You will learn that you can create a combination of circles, straight lines, and taps. When ready, press “Choose picture”. Browse your Windows 8.x device and select the picture you want to use for your password and press “Open”. Now you can drag the picture to position it the way you want. When you like how the picture is positioned, press “Use this picture” on the left. If you are not happy with the picture, press “Choose new picture” and select a new one, as shown during the previous step. After you have confirmed that you want to use this picture, you are asked to set up your gestures for the picture password. Draw three gestures on the picture, any combination you wish. Please remember that you can use only three gestures: circles, straight lines, and taps. Once you have drawn those three gestures, you are asked to confirm. Draw the same gestures one more time. If everything goes well, you are informed that you have created your picture password and that you can use it the next time you sign in to Windows. If you don’t confirm the gestures correctly, you will be asked to try again, until you draw the same gestures twice. To close the picture password wizard, press “Finish”. Where Does Windows Store Your Passwords? Are They Safe? All the passwords that you enter in Windows and save for future use are stored in the Credential Manager. This tool is a vault with the usernames and passwords that you use to log on to your computer, to other computers on the network, to apps from the Windows Store, or to websites using Internet Explorer. By storing these credentials, Windows can automatically log you the next time you access the same app, network share, or website. Everything that is stored in the Credential Manager is encrypted for your protection.

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  • Internet Protocol Suite: Transition Control Protocol (TCP) vs. User Datagram Protocol (UDP)

    How do we communicate over the Internet?  How is data transferred from one machine to another? These types of act ivies can only be done by using one of two Internet protocols currently. The collection of Internet Protocol consists of the Transition Control Protocol (TCP) and the User Datagram Protocol (UDP).  Both protocols are used to send data between two network end points, however they both have very distinct ways of transporting data from one endpoint to another. If transmission speed and reliability is the primary concern when trying to transfer data between two network endpoints then TCP is the proper choice. When a device attempts to send data to another endpoint using TCP it creates a direct connection between both devices until the transmission has completed. The direct connection between both devices ensures the reliability of the transmission due to the fact that no intermediate devices are needed to transfer the data. Due to the fact that both devices have to continuously poll the connection until transmission has completed increases the resources needed to perform the transmission. An example of this type of direct communication can be seen when a teacher tells a students to do their homework. The teacher is talking directly to the students in order to communicate that the homework needs to be done.  Students can then ask questions about the assignment to ensure that they have received the proper instructions for the assignment. UDP is a less resource intensive approach to sending data between to network endpoints. When a device uses UDP to send data across a network, the data is broken up and repackaged with the destination address. The sending device then releases the data packages to the network, but cannot ensure when or if the receiving device will actually get the data.  The sending device depends on other devices on the network to forward the data packages to the destination devices in order to complete the transmission. As you can tell this type of transmission is less resource intensive because not connection polling is needed,  but should not be used for transmitting data with speed or reliability requirements. This is due to the fact that the sending device can not ensure that the transmission is received.  An example of this type of communication can be seen when a teacher tells a student that they would like to speak with their parents. The teacher is relying on the student to complete the transmission to the parents, and the teacher has no guarantee that the student will actually inform the parents about the request. Both TCP and UPD are invaluable when attempting to send data across a network, but depending on the situation one protocol may be better than the other. Before deciding on which protocol to use an evaluation for transmission speed, reliability, latency, and overhead must be completed in order to define the best protocol for the situation.  

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  • SQL SERVER – Concurrency Basics – Guest Post by Vinod Kumar

    - by pinaldave
    This guest post is by Vinod Kumar. Vinod Kumar has worked with SQL Server extensively since joining the industry over a decade ago. Working on various versions from SQL Server 7.0, Oracle 7.3 and other database technologies – he now works with the Microsoft Technology Center (MTC) as a Technology Architect. Let us read the blog post in Vinod’s own voice. Learning is always fun when it comes to SQL Server and learning the basics again can be more fun. I did write about Transaction Logs and recovery over my blogs and the concept of simplifying the basics is a challenge. In the real world we always see checks and queues for a process – say railway reservation, banks, customer supports etc there is a process of line and queue to facilitate everyone. Shorter the queue higher is the efficiency of system (a.k.a higher is the concurrency). Every database does implement this using checks like locking, blocking mechanisms and they implement the standards in a way to facilitate higher concurrency. In this post, let us talk about the topic of Concurrency and what are the various aspects that one needs to know about concurrency inside SQL Server. Let us learn the concepts as one-liners: Concurrency can be defined as the ability of multiple processes to access or change shared data at the same time. The greater the number of concurrent user processes that can be active without interfering with each other, the greater the concurrency of the database system. Concurrency is reduced when a process that is changing data prevents other processes from reading that data or when a process that is reading data prevents other processes from changing that data. Concurrency is also affected when multiple processes are attempting to change the same data simultaneously. Two approaches to managing concurrent data access: Optimistic Concurrency Model Pessimistic Concurrency Model Concurrency Models Pessimistic Concurrency Default behavior: acquire locks to block access to data that another process is using. Assumes that enough data modification operations are in the system that any given read operation is likely affected by a data modification made by another user (assumes conflicts will occur). Avoids conflicts by acquiring a lock on data being read so no other processes can modify that data. Also acquires locks on data being modified so no other processes can access the data for either reading or modifying. Readers block writer, writers block readers and writers. Optimistic Concurrency Assumes that there are sufficiently few conflicting data modification operations in the system that any single transaction is unlikely to modify data that another transaction is modifying. Default behavior of optimistic concurrency is to use row versioning to allow data readers to see the state of the data before the modification occurs. Older versions of the data are saved so a process reading data can see the data as it was when the process started reading and not affected by any changes being made to that data. Processes modifying the data is unaffected by processes reading the data because the reader is accessing a saved version of the data rows. Readers do not block writers and writers do not block readers, but, writers can and will block writers. Transaction Processing A transaction is the basic unit of work in SQL Server. Transaction consists of SQL commands that read and update the database but the update is not considered final until a COMMIT command is issued (at least for an explicit transaction: marked with a BEGIN TRAN and the end is marked by a COMMIT TRAN or ROLLBACK TRAN). Transactions must exhibit all the ACID properties of a transaction. ACID Properties Transaction processing must guarantee the consistency and recoverability of SQL Server databases. Ensures all transactions are performed as a single unit of work regardless of hardware or system failure. A – Atomicity C – Consistency I – Isolation D- Durability Atomicity: Each transaction is treated as all or nothing – it either commits or aborts. Consistency: ensures that a transaction won’t allow the system to arrive at an incorrect logical state – the data must always be logically correct.  Consistency is honored even in the event of a system failure. Isolation: separates concurrent transactions from the updates of other incomplete transactions. SQL Server accomplishes isolation among transactions by locking data or creating row versions. Durability: After a transaction commits, the durability property ensures that the effects of the transaction persist even if a system failure occurs. If a system failure occurs while a transaction is in progress, the transaction is completely undone, leaving no partial effects on data. Transaction Dependencies In addition to supporting all four ACID properties, a transaction might exhibit few other behaviors (known as dependency problems or consistency problems). Lost Updates: Occur when two processes read the same data and both manipulate the data, changing its value and then both try to update the original data to the new value. The second process might overwrite the first update completely. Dirty Reads: Occurs when a process reads uncommitted data. If one process has changed data but not yet committed the change, another process reading the data will read it in an inconsistent state. Non-repeatable Reads: A read is non-repeatable if a process might get different values when reading the same data in two reads within the same transaction. This can happen when another process changes the data in between the reads that the first process is doing. Phantoms: Occurs when membership in a set changes. It occurs if two SELECT operations using the same predicate in the same transaction return a different number of rows. Isolation Levels SQL Server supports 5 isolation levels that control the behavior of read operations. Read Uncommitted All behaviors except for lost updates are possible. Implemented by allowing the read operations to not take any locks, and because of this, it won’t be blocked by conflicting locks acquired by other processes. The process can read data that another process has modified but not yet committed. When using the read uncommitted isolation level and scanning an entire table, SQL Server can decide to do an allocation order scan (in page-number order) instead of a logical order scan (following page pointers). If another process doing concurrent operations changes data and move rows to a new location in the table, the allocation order scan can end up reading the same row twice. Also can happen if you have read a row before it is updated and then an update moves the row to a higher page number than your scan encounters later. Performing an allocation order scan under Read Uncommitted can cause you to miss a row completely – can happen when a row on a high page number that hasn’t been read yet is updated and moved to a lower page number that has already been read. Read Committed Two varieties of read committed isolation: optimistic and pessimistic (default). Ensures that a read never reads data that another application hasn’t committed. If another transaction is updating data and has exclusive locks on data, your transaction will have to wait for the locks to be released. Your transaction must put share locks on data that are visited, which means that data might be unavailable for others to use. A share lock doesn’t prevent others from reading but prevents them from updating. Read committed (snapshot) ensures that an operation never reads uncommitted data, but not by forcing other processes to wait. SQL Server generates a version of the changed row with its previous committed values. Data being changed is still locked but other processes can see the previous versions of the data as it was before the update operation began. Repeatable Read This is a Pessimistic isolation level. Ensures that if a transaction revisits data or a query is reissued the data doesn’t change. That is, issuing the same query twice within a transaction cannot pickup any changes to data values made by another user’s transaction because no changes can be made by other transactions. However, this does allow phantom rows to appear. Preventing non-repeatable read is a desirable safeguard but cost is that all shared locks in a transaction must be held until the completion of the transaction. Snapshot Snapshot Isolation (SI) is an optimistic isolation level. Allows for processes to read older versions of committed data if the current version is locked. Difference between snapshot and read committed has to do with how old the older versions have to be. It’s possible to have two transactions executing simultaneously that give us a result that is not possible in any serial execution. Serializable This is the strongest of the pessimistic isolation level. Adds to repeatable read isolation level by ensuring that if a query is reissued rows were not added in the interim, i.e, phantoms do not appear. Preventing phantoms is another desirable safeguard, but cost of this extra safeguard is similar to that of repeatable read – all shared locks in a transaction must be held until the transaction completes. In addition serializable isolation level requires that you lock data that has been read but also data that doesn’t exist. Ex: if a SELECT returned no rows, you want it to return no. rows when the query is reissued. This is implemented in SQL Server by a special kind of lock called the key-range lock. Key-range locks require that there be an index on the column that defines the range of values. If there is no index on the column, serializable isolation requires a table lock. Gets its name from the fact that running multiple serializable transactions at the same time is equivalent of running them one at a time. Now that we understand the basics of what concurrency is, the subsequent blog posts will try to bring out the basics around locking, blocking, deadlocks because they are the fundamental blocks that make concurrency possible. Now if you are with me – let us continue learning for SQL Server Locking Basics. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Concurrency

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