Search Results

Search found 6512 results on 261 pages for 'sqlauthority author visit'.

Page 229/261 | < Previous Page | 225 226 227 228 229 230 231 232 233 234 235 236  | Next Page >

  • Agile Development

    - by James Oloo Onyango
    Alot of literature has and is being written about agile developement and its surrounding philosophies. In my quest to find the best way to express the importance of agile methodologies, i have found Robert C. Martin's "A Satire Of Two Companies" to be both the most concise and thorough! Enjoy the read! Rufus Inc Project Kick Off Your name is Bob. The date is January 3, 2001, and your head still aches from the recent millennial revelry. You are sitting in a conference room with several managers and a group of your peers. You are a project team leader. Your boss is there, and he has brought along all of his team leaders. His boss called the meeting. "We have a new project to develop," says your boss's boss. Call him BB. The points in his hair are so long that they scrape the ceiling. Your boss's points are just starting to grow, but he eagerly awaits the day when he can leave Brylcream stains on the acoustic tiles. BB describes the essence of the new market they have identified and the product they want to develop to exploit this market. "We must have this new project up and working by fourth quarter October 1," BB demands. "Nothing is of higher priority, so we are cancelling your current project." The reaction in the room is stunned silence. Months of work are simply going to be thrown away. Slowly, a murmur of objection begins to circulate around the conference table.   His points give off an evil green glow as BB meets the eyes of everyone in the room. One by one, that insidious stare reduces each attendee to quivering lumps of protoplasm. It is clear that he will brook no discussion on this matter. Once silence has been restored, BB says, "We need to begin immediately. How long will it take you to do the analysis?" You raise your hand. Your boss tries to stop you, but his spitwad misses you and you are unaware of his efforts.   "Sir, we can't tell you how long the analysis will take until we have some requirements." "The requirements document won't be ready for 3 or 4 weeks," BB says, his points vibrating with frustration. "So, pretend that you have the requirements in front of you now. How long will you require for analysis?" No one breathes. Everyone looks around to see whether anyone has some idea. "If analysis goes beyond April 1, we have a problem. Can you finish the analysis by then?" Your boss visibly gathers his courage: "We'll find a way, sir!" His points grow 3 mm, and your headache increases by two Tylenol. "Good." BB smiles. "Now, how long will it take to do the design?" "Sir," you say. Your boss visibly pales. He is clearly worried that his 3 mms are at risk. "Without an analysis, it will not be possible to tell you how long design will take." BB's expression shifts beyond austere.   "PRETEND you have the analysis already!" he says, while fixing you with his vacant, beady little eyes. "How long will it take you to do the design?" Two Tylenol are not going to cut it. Your boss, in a desperate attempt to save his new growth, babbles: "Well, sir, with only six months left to complete the project, design had better take no longer than 3 months."   "I'm glad you agree, Smithers!" BB says, beaming. Your boss relaxes. He knows his points are secure. After a while, he starts lightly humming the Brylcream jingle. BB continues, "So, analysis will be complete by April 1, design will be complete by July 1, and that gives you 3 months to implement the project. This meeting is an example of how well our new consensus and empowerment policies are working. Now, get out there and start working. I'll expect to see TQM plans and QIT assignments on my desk by next week. Oh, and don't forget that your crossfunctional team meetings and reports will be needed for next month's quality audit." "Forget the Tylenol," you think to yourself as you return to your cubicle. "I need bourbon."   Visibly excited, your boss comes over to you and says, "Gosh, what a great meeting. I think we're really going to do some world shaking with this project." You nod in agreement, too disgusted to do anything else. "Oh," your boss continues, "I almost forgot." He hands you a 30-page document. "Remember that the SEI is coming to do an evaluation next week. This is the evaluation guide. You need to read through it, memorize it, and then shred it. It tells you how to answer any questions that the SEI auditors ask you. It also tells you what parts of the building you are allowed to take them to and what parts to avoid. We are determined to be a CMM level 3 organization by June!"   You and your peers start working on the analysis of the new project. This is difficult because you have no requirements. But from the 10-minute introduction given by BB on that fateful morning, you have some idea of what the product is supposed to do.   Corporate process demands that you begin by creating a use case document. You and your team begin enumerating use cases and drawing oval and stick diagrams. Philosophical debates break out among the team members. There is disagreement as to whether certain use cases should be connected with <<extends>> or <<includes>> relationships. Competing models are created, but nobody knows how to evaluate them. The debate continues, effectively paralyzing progress.   After a week, somebody finds the iceberg.com Web site, which recommends disposing entirely of <<extends>> and <<includes>> and replacing them with <<precedes>> and <<uses>>. The documents on this Web site, authored by Don Sengroiux, describes a method known as stalwart-analysis, which claims to be a step-by-step method for translating use cases into design diagrams. More competing use case models are created using this new scheme, but again, people can't agree on how to evaluate them. The thrashing continues. More and more, the use case meetings are driven by emotion rather than by reason. If it weren't for the fact that you don't have requirements, you'd be pretty upset by the lack of progress you are making. The requirements document arrives on February 15. And then again on February 20, 25, and every week thereafter. Each new version contradicts the previous one. Clearly, the marketing folks who are writing the requirements, empowered though they might be, are not finding consensus.   At the same time, several new competing use case templates have been proposed by the various team members. Each template presents its own particularly creative way of delaying progress. The debates rage on. On March 1, Prudence Putrigence, the process proctor, succeeds in integrating all the competing use case forms and templates into a single, all-encompassing form. Just the blank form is 15 pages long. She has managed to include every field that appeared on all the competing templates. She also presents a 159- page document describing how to fill out the use case form. All current use cases must be rewritten according to the new standard.   You marvel to yourself that it now requires 15 pages of fill-in-the-blank and essay questions to answer the question: What should the system do when the user presses Return? The corporate process (authored by L. E. Ott, famed author of "Holistic Analysis: A Progressive Dialectic for Software Engineers") insists that you discover all primary use cases, 87 percent of all secondary use cases, and 36.274 percent of all tertiary use cases before you can complete analysis and enter the design phase. You have no idea what a tertiary use case is. So in an attempt to meet this requirement, you try to get your use case document reviewed by the marketing department, which you hope will know what a tertiary use case is.   Unfortunately, the marketing folks are too busy with sales support to talk to you. Indeed, since the project started, you have not been able to get a single meeting with marketing, which has provided a never-ending stream of changing and contradictory requirements documents.   While one team has been spinning endlessly on the use case document, another team has been working out the domain model. Endless variations of UML documents are pouring out of this team. Every week, the model is reworked.   The team members can't decide whether to use <<interfaces>> or <<types>> in the model. A huge disagreement has been raging on the proper syntax and application of OCL. Others on the team just got back from a 5-day class on catabolism, and have been producing incredibly detailed and arcane diagrams that nobody else can fathom.   On March 27, with one week to go before analysis is to be complete, you have produced a sea of documents and diagrams but are no closer to a cogent analysis of the problem than you were on January 3. **** And then, a miracle happens.   **** On Saturday, April 1, you check your e-mail from home. You see a memo from your boss to BB. It states unequivocally that you are done with the analysis! You phone your boss and complain. "How could you have told BB that we were done with the analysis?" "Have you looked at a calendar lately?" he responds. "It's April 1!" The irony of that date does not escape you. "But we have so much more to think about. So much more to analyze! We haven't even decided whether to use <<extends>> or <<precedes>>!" "Where is your evidence that you are not done?" inquires your boss, impatiently. "Whaaa . . . ." But he cuts you off. "Analysis can go on forever; it has to be stopped at some point. And since this is the date it was scheduled to stop, it has been stopped. Now, on Monday, I want you to gather up all existing analysis materials and put them into a public folder. Release that folder to Prudence so that she can log it in the CM system by Monday afternoon. Then get busy and start designing."   As you hang up the phone, you begin to consider the benefits of keeping a bottle of bourbon in your bottom desk drawer. They threw a party to celebrate the on-time completion of the analysis phase. BB gave a colon-stirring speech on empowerment. And your boss, another 3 mm taller, congratulated his team on the incredible show of unity and teamwork. Finally, the CIO takes the stage to tell everyone that the SEI audit went very well and to thank everyone for studying and shredding the evaluation guides that were passed out. Level 3 now seems assured and will be awarded by June. (Scuttlebutt has it that managers at the level of BB and above are to receive significant bonuses once the SEI awards level 3.)   As the weeks flow by, you and your team work on the design of the system. Of course, you find that the analysis that the design is supposedly based on is flawedno, useless; no, worse than useless. But when you tell your boss that you need to go back and work some more on the analysis to shore up its weaker sections, he simply states, "The analysis phase is over. The only allowable activity is design. Now get back to it."   So, you and your team hack the design as best you can, unsure of whether the requirements have been properly analyzed. Of course, it really doesn't matter much, since the requirements document is still thrashing with weekly revisions, and the marketing department still refuses to meet with you.     The design is a nightmare. Your boss recently misread a book named The Finish Line in which the author, Mark DeThomaso, blithely suggested that design documents should be taken down to code-level detail. "If we are going to be working at that level of detail," you ask, "why don't we simply write the code instead?" "Because then you wouldn't be designing, of course. And the only allowable activity in the design phase is design!" "Besides," he continues, "we have just purchased a companywide license for Dandelion! This tool enables 'Round the Horn Engineering!' You are to transfer all design diagrams into this tool. It will automatically generate our code for us! It will also keep the design diagrams in sync with the code!" Your boss hands you a brightly colored shrinkwrapped box containing the Dandelion distribution. You accept it numbly and shuffle off to your cubicle. Twelve hours, eight crashes, one disk reformatting, and eight shots of 151 later, you finally have the tool installed on your server. You consider the week your team will lose while attending Dandelion training. Then you smile and think, "Any week I'm not here is a good week." Design diagram after design diagram is created by your team. Dandelion makes it very difficult to draw these diagrams. There are dozens and dozens of deeply nested dialog boxes with funny text fields and check boxes that must all be filled in correctly. And then there's the problem of moving classes between packages. At first, these diagram are driven from the use cases. But the requirements are changing so often that the use cases rapidly become meaningless. Debates rage about whether VISITOR or DECORATOR design patterns should be used. One developer refuses to use VISITOR in any form, claiming that it's not a properly object-oriented construct. Someone refuses to use multiple inheritance, since it is the spawn of the devil. Review meetings rapidly degenerate into debates about the meaning of object orientation, the definition of analysis versus design, or when to use aggregation versus association. Midway through the design cycle, the marketing folks announce that they have rethought the focus of the system. Their new requirements document is completely restructured. They have eliminated several major feature areas and replaced them with feature areas that they anticipate customer surveys will show to be more appropriate. You tell your boss that these changes mean that you need to reanalyze and redesign much of the system. But he says, "The analysis phase is system. But he says, "The analysis phase is over. The only allowable activity is design. Now get back to it."   You suggest that it might be better to create a simple prototype to show to the marketing folks and even some potential customers. But your boss says, "The analysis phase is over. The only allowable activity is design. Now get back to it." Hack, hack, hack, hack. You try to create some kind of a design document that might reflect the new requirements documents. However, the revolution of the requirements has not caused them to stop thrashing. Indeed, if anything, the wild oscillations of the requirements document have only increased in frequency and amplitude.   You slog your way through them.   On June 15, the Dandelion database gets corrupted. Apparently, the corruption has been progressive. Small errors in the DB accumulated over the months into bigger and bigger errors. Eventually, the CASE tool just stopped working. Of course, the slowly encroaching corruption is present on all the backups. Calls to the Dandelion technical support line go unanswered for several days. Finally, you receive a brief e-mail from Dandelion, informing you that this is a known problem and that the solution is to purchase the new version, which they promise will be ready some time next quarter, and then reenter all the diagrams by hand.   ****   Then, on July 1 another miracle happens! You are done with the design!   Rather than go to your boss and complain, you stock your middle desk drawer with some vodka.   **** They threw a party to celebrate the on-time completion of the design phase and their graduation to CMM level 3. This time, you find BB's speech so stirring that you have to use the restroom before it begins. New banners and plaques are all over your workplace. They show pictures of eagles and mountain climbers, and they talk about teamwork and empowerment. They read better after a few scotches. That reminds you that you need to clear out your file cabinet to make room for the brandy. You and your team begin to code. But you rapidly discover that the design is lacking in some significant areas. Actually, it's lacking any significance at all. You convene a design session in one of the conference rooms to try to work through some of the nastier problems. But your boss catches you at it and disbands the meeting, saying, "The design phase is over. The only allowable activity is coding. Now get back to it."   ****   The code generated by Dandelion is really hideous. It turns out that you and your team were using association and aggregation the wrong way, after all. All the generated code has to be edited to correct these flaws. Editing this code is extremely difficult because it has been instrumented with ugly comment blocks that have special syntax that Dandelion needs in order to keep the diagrams in sync with the code. If you accidentally alter one of these comments, the diagrams will be regenerated incorrectly. It turns out that "Round the Horn Engineering" requires an awful lot of effort. The more you try to keep the code compatible with Dandelion, the more errors Dandelion generates. In the end, you give up and decide to keep the diagrams up to date manually. A second later, you decide that there's no point in keeping the diagrams up to date at all. Besides, who has time?   Your boss hires a consultant to build tools to count the number of lines of code that are being produced. He puts a big thermometer graph on the wall with the number 1,000,000 on the top. Every day, he extends the red line to show how many lines have been added. Three days after the thermometer appears on the wall, your boss stops you in the hall. "That graph isn't growing quickly enough. We need to have a million lines done by October 1." "We aren't even sh-sh-sure that the proshect will require a m-million linezh," you blather. "We have to have a million lines done by October 1," your boss reiterates. His points have grown again, and the Grecian formula he uses on them creates an aura of authority and competence. "Are you sure your comment blocks are big enough?" Then, in a flash of managerial insight, he says, "I have it! I want you to institute a new policy among the engineers. No line of code is to be longer than 20 characters. Any such line must be split into two or more preferably more. All existing code needs to be reworked to this standard. That'll get our line count up!"   You decide not to tell him that this will require two unscheduled work months. You decide not to tell him anything at all. You decide that intravenous injections of pure ethanol are the only solution. You make the appropriate arrangements. Hack, hack, hack, and hack. You and your team madly code away. By August 1, your boss, frowning at the thermometer on the wall, institutes a mandatory 50-hour workweek.   Hack, hack, hack, and hack. By September 1st, the thermometer is at 1.2 million lines and your boss asks you to write a report describing why you exceeded the coding budget by 20 percent. He institutes mandatory Saturdays and demands that the project be brought back down to a million lines. You start a campaign of remerging lines. Hack, hack, hack, and hack. Tempers are flaring; people are quitting; QA is raining trouble reports down on you. Customers are demanding installation and user manuals; salespeople are demanding advance demonstrations for special customers; the requirements document is still thrashing, the marketing folks are complaining that the product isn't anything like they specified, and the liquor store won't accept your credit card anymore. Something has to give.    On September 15, BB calls a meeting. As he enters the room, his points are emitting clouds of steam. When he speaks, the bass overtones of his carefully manicured voice cause the pit of your stomach to roll over. "The QA manager has told me that this project has less than 50 percent of the required features implemented. He has also informed me that the system crashes all the time, yields wrong results, and is hideously slow. He has also complained that he cannot keep up with the continuous train of daily releases, each more buggy than the last!" He stops for a few seconds, visibly trying to compose himself. "The QA manager estimates that, at this rate of development, we won't be able to ship the product until December!" Actually, you think it's more like March, but you don't say anything. "December!" BB roars with such derision that people duck their heads as though he were pointing an assault rifle at them. "December is absolutely out of the question. Team leaders, I want new estimates on my desk in the morning. I am hereby mandating 65-hour work weeks until this project is complete. And it better be complete by November 1."   As he leaves the conference room, he is heard to mutter: "Empowermentbah!" * * * Your boss is bald; his points are mounted on BB's wall. The fluorescent lights reflecting off his pate momentarily dazzle you. "Do you have anything to drink?" he asks. Having just finished your last bottle of Boone's Farm, you pull a bottle of Thunderbird from your bookshelf and pour it into his coffee mug. "What's it going to take to get this project done? " he asks. "We need to freeze the requirements, analyze them, design them, and then implement them," you say callously. "By November 1?" your boss exclaims incredulously. "No way! Just get back to coding the damned thing." He storms out, scratching his vacant head.   A few days later, you find that your boss has been transferred to the corporate research division. Turnover has skyrocketed. Customers, informed at the last minute that their orders cannot be fulfilled on time, have begun to cancel their orders. Marketing is re-evaluating whether this product aligns with the overall goals of the company. Memos fly, heads roll, policies change, and things are, overall, pretty grim. Finally, by March, after far too many sixty-five hour weeks, a very shaky version of the software is ready. In the field, bug-discovery rates are high, and the technical support staff are at their wits' end, trying to cope with the complaints and demands of the irate customers. Nobody is happy.   In April, BB decides to buy his way out of the problem by licensing a product produced by Rupert Industries and redistributing it. The customers are mollified, the marketing folks are smug, and you are laid off.     Rupert Industries: Project Alpha   Your name is Robert. The date is January 3, 2001. The quiet hours spent with your family this holiday have left you refreshed and ready for work. You are sitting in a conference room with your team of professionals. The manager of the division called the meeting. "We have some ideas for a new project," says the division manager. Call him Russ. He is a high-strung British chap with more energy than a fusion reactor. He is ambitious and driven but understands the value of a team. Russ describes the essence of the new market opportunity the company has identified and introduces you to Jane, the marketing manager, who is responsible for defining the products that will address it. Addressing you, Jane says, "We'd like to start defining our first product offering as soon as possible. When can you and your team meet with me?" You reply, "We'll be done with the current iteration of our project this Friday. We can spare a few hours for you between now and then. After that, we'll take a few people from the team and dedicate them to you. We'll begin hiring their replacements and the new people for your team immediately." "Great," says Russ, "but I want you to understand that it is critical that we have something to exhibit at the trade show coming up this July. If we can't be there with something significant, we'll lose the opportunity."   "I understand," you reply. "I don't yet know what it is that you have in mind, but I'm sure we can have something by July. I just can't tell you what that something will be right now. In any case, you and Jane are going to have complete control over what we developers do, so you can rest assured that by July, you'll have the most important things that can be accomplished in that time ready to exhibit."   Russ nods in satisfaction. He knows how this works. Your team has always kept him advised and allowed him to steer their development. He has the utmost confidence that your team will work on the most important things first and will produce a high-quality product.   * * *   "So, Robert," says Jane at their first meeting, "How does your team feel about being split up?" "We'll miss working with each other," you answer, "but some of us were getting pretty tired of that last project and are looking forward to a change. So, what are you people cooking up?" Jane beams. "You know how much trouble our customers currently have . . ." And she spends a half hour or so describing the problem and possible solution. "OK, wait a second" you respond. "I need to be clear about this." And so you and Jane talk about how this system might work. Some of her ideas aren't fully formed. You suggest possible solutions. She likes some of them. You continue discussing.   During the discussion, as each new topic is addressed, Jane writes user story cards. Each card represents something that the new system has to do. The cards accumulate on the table and are spread out in front of you. Both you and Jane point at them, pick them up, and make notes on them as you discuss the stories. The cards are powerful mnemonic devices that you can use to represent complex ideas that are barely formed.   At the end of the meeting, you say, "OK, I've got a general idea of what you want. I'm going to talk to the team about it. I imagine they'll want to run some experiments with various database structures and presentation formats. Next time we meet, it'll be as a group, and we'll start identifying the most important features of the system."   A week later, your nascent team meets with Jane. They spread the existing user story cards out on the table and begin to get into some of the details of the system. The meeting is very dynamic. Jane presents the stories in the order of their importance. There is much discussion about each one. The developers are concerned about keeping the stories small enough to estimate and test. So they continually ask Jane to split one story into several smaller stories. Jane is concerned that each story have a clear business value and priority, so as she splits them, she makes sure that this stays true.   The stories accumulate on the table. Jane writes them, but the developers make notes on them as needed. Nobody tries to capture everything that is said; the cards are not meant to capture everything but are simply reminders of the conversation.   As the developers become more comfortable with the stories, they begin writing estimates on them. These estimates are crude and budgetary, but they give Jane an idea of what the story will cost.   At the end of the meeting, it is clear that many more stories could be discussed. It is also clear that the most important stories have been addressed and that they represent several months worth of work. Jane closes the meeting by taking the cards with her and promising to have a proposal for the first release in the morning.   * * *   The next morning, you reconvene the meeting. Jane chooses five cards and places them on the table. "According to your estimates, these cards represent about one perfect team-week's worth of work. The last iteration of the previous project managed to get one perfect team-week done in 3 real weeks. If we can get these five stories done in 3 weeks, we'll be able to demonstrate them to Russ. That will make him feel very comfortable about our progress." Jane is pushing it. The sheepish look on her face lets you know that she knows it too. You reply, "Jane, this is a new team, working on a new project. It's a bit presumptuous to expect that our velocity will be the same as the previous team's. However, I met with the team yesterday afternoon, and we all agreed that our initial velocity should, in fact, be set to one perfectweek for every 3 real-weeks. So you've lucked out on this one." "Just remember," you continue, "that the story estimates and the story velocity are very tentative at this point. We'll learn more when we plan the iteration and even more when we implement it."   Jane looks over her glasses at you as if to say "Who's the boss around here, anyway?" and then smiles and says, "Yeah, don't worry. I know the drill by now."Jane then puts 15 more cards on the table. She says, "If we can get all these cards done by the end of March, we can turn the system over to our beta test customers. And we'll get good feedback from them."   You reply, "OK, so we've got our first iteration defined, and we have the stories for the next three iterations after that. These four iterations will make our first release."   "So," says Jane, can you really do these five stories in the next 3 weeks?" "I don't know for sure, Jane," you reply. "Let's break them down into tasks and see what we get."   So Jane, you, and your team spend the next several hours taking each of the five stories that Jane chose for the first iteration and breaking them down into small tasks. The developers quickly realize that some of the tasks can be shared between stories and that other tasks have commonalities that can probably be taken advantage of. It is clear that potential designs are popping into the developers' heads. From time to time, they form little discussion knots and scribble UML diagrams on some cards.   Soon, the whiteboard is filled with the tasks that, once completed, will implement the five stories for this iteration. You start the sign-up process by saying, "OK, let's sign up for these tasks." "I'll take the initial database generation." Says Pete. "That's what I did on the last project, and this doesn't look very different. I estimate it at two of my perfect workdays." "OK, well, then, I'll take the login screen," says Joe. "Aw, darn," says Elaine, the junior member of the team, "I've never done a GUI, and kinda wanted to try that one."   "Ah, the impatience of youth," Joe says sagely, with a wink in your direction. "You can assist me with it, young Jedi." To Jane: "I think it'll take me about three of my perfect workdays."   One by one, the developers sign up for tasks and estimate them in terms of their own perfect workdays. Both you and Jane know that it is best to let the developers volunteer for tasks than to assign the tasks to them. You also know full well that you daren't challenge any of the developers' estimates. You know these people, and you trust them. You know that they are going to do the very best they can.   The developers know that they can't sign up for more perfect workdays than they finished in the last iteration they worked on. Once each developer has filled his or her schedule for the iteration, they stop signing up for tasks.   Eventually, all the developers have stopped signing up for tasks. But, of course, tasks are still left on the board.   "I was worried that that might happen," you say, "OK, there's only one thing to do, Jane. We've got too much to do in this iteration. What stories or tasks can we remove?" Jane sighs. She knows that this is the only option. Working overtime at the beginning of a project is insane, and projects where she's tried it have not fared well.   So Jane starts to remove the least-important functionality. "Well, we really don't need the login screen just yet. We can simply start the system in the logged-in state." "Rats!" cries Elaine. "I really wanted to do that." "Patience, grasshopper." says Joe. "Those who wait for the bees to leave the hive will not have lips too swollen to relish the honey." Elaine looks confused. Everyone looks confused. "So . . .," Jane continues, "I think we can also do away with . . ." And so, bit by bit, the list of tasks shrinks. Developers who lose a task sign up for one of the remaining ones.   The negotiation is not painless. Several times, Jane exhibits obvious frustration and impatience. Once, when tensions are especially high, Elaine volunteers, "I'll work extra hard to make up some of the missing time." You are about to correct her when, fortunately, Joe looks her in the eye and says, "When once you proceed down the dark path, forever will it dominate your destiny."   In the end, an iteration acceptable to Jane is reached. It's not what Jane wanted. Indeed, it is significantly less. But it's something the team feels that can be achieved in the next 3 weeks.   And, after all, it still addresses the most important things that Jane wanted in the iteration. "So, Jane," you say when things had quieted down a bit, "when can we expect acceptance tests from you?" Jane sighs. This is the other side of the coin. For every story the development team implements,   Jane must supply a suite of acceptance tests that prove that it works. And the team needs these long before the end of the iteration, since they will certainly point out differences in the way Jane and the developers imagine the system's behaviour.   "I'll get you some example test scripts today," Jane promises. "I'll add to them every day after that. You'll have the entire suite by the middle of the iteration."   * * *   The iteration begins on Monday morning with a flurry of Class, Responsibilities, Collaborators sessions. By midmorning, all the developers have assembled into pairs and are rapidly coding away. "And now, my young apprentice," Joe says to Elaine, "you shall learn the mysteries of test-first design!"   "Wow, that sounds pretty rad," Elaine replies. "How do you do it?" Joe beams. It's clear that he has been anticipating this moment. "OK, what does the code do right now?" "Huh?" replied Elaine, "It doesn't do anything at all; there is no code."   "So, consider our task; can you think of something the code should do?" "Sure," Elaine said with youthful assurance, "First, it should connect to the database." "And thereupon, what must needs be required to connecteth the database?" "You sure talk weird," laughed Elaine. "I think we'd have to get the database object from some registry and call the Connect() method. "Ah, astute young wizard. Thou perceives correctly that we requireth an object within which we can cacheth the database object." "Is 'cacheth' really a word?" "It is when I say it! So, what test can we write that we know the database registry should pass?" Elaine sighs. She knows she'll just have to play along. "We should be able to create a database object and pass it to the registry in a Store() method. And then we should be able to pull it out of the registry with a Get() method and make sure it's the same object." "Oh, well said, my prepubescent sprite!" "Hay!" "So, now, let's write a test function that proves your case." "But shouldn't we write the database object and registry object first?" "Ah, you've much to learn, my young impatient one. Just write the test first." "But it won't even compile!" "Are you sure? What if it did?" "Uh . . ." "Just write the test, Elaine. Trust me." And so Joe, Elaine, and all the other developers began to code their tasks, one test case at a time. The room in which they worked was abuzz with the conversations between the pairs. The murmur was punctuated by an occasional high five when a pair managed to finish a task or a difficult test case.   As development proceeded, the developers changed partners once or twice a day. Each developer got to see what all the others were doing, and so knowledge of the code spread generally throughout the team.   Whenever a pair finished something significant whether a whole task or simply an important part of a task they integrated what they had with the rest of the system. Thus, the code base grew daily, and integration difficulties were minimized.   The developers communicated with Jane on a daily basis. They'd go to her whenever they had a question about the functionality of the system or the interpretation of an acceptance test case.   Jane, good as her word, supplied the team with a steady stream of acceptance test scripts. The team read these carefully and thereby gained a much better understanding of what Jane expected the system to do. By the beginning of the second week, there was enough functionality to demonstrate to Jane. She watched eagerly as the demonstration passed test case after test case. "This is really cool," Jane said as the demonstration finally ended. "But this doesn't seem like one-third of the tasks. Is your velocity slower than anticipated?"   You grimace. You'd been waiting for a good time to mention this to Jane but now she was forcing the issue. "Yes, unfortunately, we are going more slowly than we had expected. The new application server we are using is turning out to be a pain to configure. Also, it takes forever to reboot, and we have to reboot it whenever we make even the slightest change to its configuration."   Jane eyes you with suspicion. The stress of last Monday's negotiations had still not entirely dissipated. She says, "And what does this mean to our schedule? We can't slip it again, we just can't. Russ will have a fit! He'll haul us all into the woodshed and ream us some new ones."   You look Jane right in the eyes. There's no pleasant way to give someone news like this. So you just blurt out, "Look, if things keep going like they're going, we're not going to be done with everything by next Friday. Now it's possible that we'll figure out a way to go faster. But, frankly, I wouldn't depend on that. You should start thinking about one or two tasks that could be removed from the iteration without ruining the demonstration for Russ. Come hell or high water, we are going to give that demonstration on Friday, and I don't think you want us to choose which tasks to omit."   "Aw forchrisakes!" Jane barely manages to stifle yelling that last word as she stalks away, shaking her head. Not for the first time, you say to yourself, "Nobody ever promised me project management would be easy." You are pretty sure it won't be the last time, either.   Actually, things went a bit better than you had hoped. The team did, in fact, have to drop one task from the iteration, but Jane had chosen wisely, and the demonstration for Russ went without a hitch. Russ was not impressed with the progress, but neither was he dismayed. He simply said, "This is pretty good. But remember, we have to be able to demonstrate this system at the trade show in July, and at this rate, it doesn't look like you'll have all that much to show." Jane, whose attitude had improved dramatically with the completion of the iteration, responded to Russ by saying, "Russ, this team is working hard, and well. When July comes around, I am confident that we'll have something significant to demonstrate. It won't be everything, and some of it may be smoke and mirrors, but we'll have something."   Painful though the last iteration was, it had calibrated your velocity numbers. The next iteration went much better. Not because your team got more done than in the last iteration but simply because the team didn't have to remove any tasks or stories in the middle of the iteration.   By the start of the fourth iteration, a natural rhythm has been established. Jane, you, and the team know exactly what to expect from one another. The team is running hard, but the pace is sustainable. You are confident that the team can keep up this pace for a year or more.   The number of surprises in the schedule diminishes to near zero; however, the number of surprises in the requirements does not. Jane and Russ frequently look over the growing system and make recommendations or changes to the existing functionality. But all parties realize that these changes take time and must be scheduled. So the changes do not cause anyone's expectations to be violated. In March, there is a major demonstration of the system to the board of directors. The system is very limited and is not yet in a form good enough to take to the trade show, but progress is steady, and the board is reasonably impressed.   The second release goes even more smoothly than the first. By now, the team has figured out a way to automate Jane's acceptance test scripts. The team has also refactored the design of the system to the point that it is really easy to add new features and change old ones. The second release was done by the end of June and was taken to the trade show. It had less in it than Jane and Russ would have liked, but it did demonstrate the most important features of the system. Although customers at the trade show noticed that certain features were missing, they were very impressed overall. You, Russ, and Jane all returned from the trade show with smiles on your faces. You all felt as though this project was a winner.   Indeed, many months later, you are contacted by Rufus Inc. That company had been working on a system like this for its internal operations. Rufus has canceled the development of that system after a death-march project and is negotiating to license your technology for its environment.   Indeed, things are looking up!

    Read the article

  • SQL SERVER – Developer Training Resources and Summary Roundup

    - by pinaldave
    It is always pleasure for any author when other renowned authors in the industry write about you. Earlier I wrote a five part blog series on Developer Training and I have received a phenomenal response to the series. I have received plenty of comments, questions and feedback. I thought it would be nice to sum up the whole series as well answer a few of the questions received. Quick Recap Developer Training - Importance and Significance - Part 1 In this part we discussed the importance of training in the real world. The most important and valuable resource any company is its employee. Employees who have been well-trained will be better at their jobs and produce a better product.  An employee who is well trained obviously knows more about their job and all the technical aspects. I have a very high opinion about training employees and it is the most important task. Developer Training – Employee Morals and Ethics – Part 2 In this part we discussed the most crucial components of training. Often employees are expecting the company to pay for their training and the company expresses no interest in training the employee. Quite often training expenses are the real issue for both the employee and employer. There are companies that pay for 100% of the expenses and there are employees who opt for training on their own expense during their personal time. Training is often looked at as vacation by employee and employers and we need to change this mind-set. One of the ways is to report back the learning to your manager and implement newly learned knowledge in day-to-day work. Developer Training – Difficult Questions and Alternative Perspective - Part 3 This part was the most difficult to write as I tried to address a few difficult questions and answers. Training is such a sensitive issue that many developers when not receiving chance for training think about leaving the organization. The manager often feels pressure to accommodate every single employee for training even though his training budget is limited. It is indeed the responsibility of the developer to get maximum advantage from the training. Training immediately helps organizations but stays as a part of an employee’s knowledge forever. Developer Training – Various Options for Developer Training – Part 4 In this part I tried to explore a few methods and options for training. The generic feedback I received on this blog post was short and I should have explored each of the subject of the training in details. I believe there are two big buckets of training 1) Instructor Lead Training and 2) Self Lead Training. The common element between both the methods is “learning material”. Learning material can be of any format – videos, books, paper notes or just a plain black board. Instructor-led training is a very effective mode but not possible every single time. During the course of the developer’s career, one has to learn lots of new technology and it is almost impossible to have a quality trainer available on that subject at that time. Books are most effective and proven methods, however, it always helps if someone explains the concepts of the book with a demonstration. In recent times I have started to believe in online trainings which leads to a hybrid experience. Online trainings take the best part of the books and the best part of the instructor-led training and gives effective training in a matter of hours. Developer Training – A Conclusive Summary- Part 5 In this part, I shared what I was continuously thinking about developer training. There is no better teacher than oneself. There is no better motivation than a personal desire to learn new technology. Honestly there is nothing more personal learning. That “change is the only constant” and “adapt & overcome” are the essential lessons of life. One cannot stop the learning and resist the change. In the IT industry “ego of knowing all” and the “resistance to change” are the most challenging issues. Once someone overcomes them, life is much easier. I believe that proper and appropriate high quality training can help to address the burning issues. Opinion of Friends I invited a few of my friends to express their opinion about developer training and here are their opinions. I am listing them here in the order of the blog post publishing date. Nakul Vachhrajani - Developer Trainings-Importance, Benefits, Tips and follow-up Nakul’s sums of many of the concepts which are complementary to my blog posts. Nakul addresses the burning question of developer training with different angles. I am personally very impressed by his following statement - “Being skilled does not mean having just a stack of certifications, but it also means having an understanding about the internals of the products that you are working on – and using that knowledge to improve the efficiency & productivity at the workplace in turn resulting in better products, better consulting abilities and a happier self.” Nakul also suggests the online training options of Pluralsight. Vinod Kumar - Training–a necessity or bonus Vinod Kumar comes up with excellent follow up on developer training. Vinod is known for his inspirational writing about SQL Server. Vinod starts with a story of a student who is extremely eager to learn the wisdom of life from a monk but the monk does not accept him as a disciple for a long time. The conversation between student and monk is indeed an essence of all learning. We all want to learn quickly and be successful but the most important thing in life is to have the right attitude towards learning and more so towards life. The blog post end with a very important thought about how to avoid the famous excuse – “I don’t have enough time.” Ritesh Shah - Training – useful or useless? Ritesh brings up very important concept related to training. Ritesh in his meticulous style explains why training is an important and lifelong process. Training must not stop at any age but should continue forever. The moment training stops, progress stops along with. Paras Doshi - Professional Development Resource Paras is known for his to–the-point writing, and has summarized the five part series very precisely. He read the five part series and created a digest summary of the blog post. If you are in a rush and have no time to read my five series – I suggest you read his blog post. Training Resources I am often asked what the best resources for learning new technology are. This is the most difficult question EVER. There are plenty of good training resources available. When it is about training our needs are different, our preference of learning is different and we all have an opinion. Additionally, we all are located in different geographic locations worldwide and there is no way one solution will fit all. However, let me list a few of the training resources which I have built so far and you can consume them if you find it relevant to your need. SQL Server Books SQL Server Interview Questions and Answers SQL Wait Stats SQL Programming Joes 2 Pros SQL Server Video Tutorials SQL Server Questions and Answers SQL Server Performance: Indexing Basics SQL Server Performance: Introduction to Query Tuning SQL in Sixty Seconds Series of Sixty Seconds Learning Video on YouTube Trust me worldwide web is very big and there are plenty of high quality learning materials available worldwide – trainer-led as well online. I suggest you explore various options and make the best choice for yourself. Remember, training is your personal journey and it should never stop. Are you ready? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Developer Training, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

    Read the article

  • SQL SERVER – How to Recover SQL Database Data Deleted by Accident

    - by Pinal Dave
    In Repair a SQL Server database using a transaction log explorer, I showed how to use ApexSQL Log, a SQL Server transaction log viewer, to recover a SQL Server database after a disaster. In this blog, I’ll show you how to use another SQL Server disaster recovery tool from ApexSQL in a situation when data is accidentally deleted. You can download ApexSQL Recover here, install, and play along. With a good SQL Server disaster recovery strategy, data recovery is not a problem. You have a reliable full database backup with valid data, a full database backup and subsequent differential database backups, or a full database backup and a chain of transaction log backups. But not all situations are ideal. Here we’ll address some sub-optimal scenarios, where you can still successfully recover data. If you have only a full database backup This is the least optimal SQL Server disaster recovery strategy, as it doesn’t ensure minimal data loss. For example, data was deleted on Wednesday. Your last full database backup was created on Sunday, three days before the records were deleted. By using the full database backup created on Sunday, you will be able to recover SQL database records that existed in the table on Sunday. If there were any records inserted into the table on Monday or Tuesday, they will be lost forever. The same goes for records modified in this period. This method will not bring back modified records, only the old records that existed on Sunday. If you restore this full database backup, all your changes (intentional and accidental) will be lost and the database will be reverted to the state it had on Sunday. What you have to do is compare the records that were in the table on Sunday to the records on Wednesday, create a synchronization script, and execute it against the Wednesday database. If you have a full database backup followed by differential database backups Let’s say the situation is the same as in the example above, only you create a differential database backup every night. Use the full database backup created on Sunday, and the last differential database backup (created on Tuesday). In this scenario, you will lose only the data inserted and updated after the differential backup created on Tuesday. If you have a full database backup and a chain of transaction log backups This is the SQL Server disaster recovery strategy that provides minimal data loss. With a full chain of transaction logs, you can recover the SQL database to an exact point in time. To provide optimal results, you have to know exactly when the records were deleted, because restoring to a later point will not bring back the records. This method requires restoring the full database backup first. If you have any differential log backup created after the last full database backup, restore the most recent one. Then, restore transaction log backups, one by one, it the order they were created starting with the first created after the restored differential database backup. Now, the table will be in the state before the records were deleted. You have to identify the deleted records, script them and run the script against the original database. Although this method is reliable, it is time-consuming and requires a lot of space on disk. How to easily recover deleted records? The following solution enables you to recover SQL database records even if you have no full or differential database backups and no transaction log backups. To understand how ApexSQL Recover works, I’ll explain what happens when table data is deleted. Table data is stored in data pages. When you delete table records, they are not immediately deleted from the data pages, but marked to be overwritten by new records. Such records are not shown as existing anymore, but ApexSQL Recover can read them and create undo script for them. How long will deleted records stay in the MDF file? It depends on many factors, as time passes it’s less likely that the records will not be overwritten. The more transactions occur after the deletion, the more chances the records will be overwritten and permanently lost. Therefore, it’s recommended to create a copy of the database MDF and LDF files immediately (if you cannot take your database offline until the issue is solved) and run ApexSQL Recover on them. Note that a full database backup will not help here, as the records marked for overwriting are not included in the backup. First, I’ll delete some records from the Person.EmailAddress table in the AdventureWorks database.   I can delete these records in SQL Server Management Studio, or execute a script such as DELETE FROM Person.EmailAddress WHERE BusinessEntityID BETWEEN 70 AND 80 Then, I’ll start ApexSQL Recover and select From DELETE operation in the Recovery tab.   In the Select the database to recover step, first select the SQL Server instance. If it’s not shown in the drop-down list, click the Server icon right to the Server drop-down list and browse for the SQL Server instance, or type the instance name manually. Specify the authentication type and select the database in the Database drop-down list.   In the next step, you’re prompted to add additional data sources. As this can be a tricky step, especially for new users, ApexSQL Recover offers help via the Help me decide option.   The Help me decide option guides you through a series of questions about the database transaction log and advises what files to add. If you know that you have no transaction log backups or detached transaction logs, or the online transaction log file has been truncated after the data was deleted, select No additional transaction logs are available. If you know that you have transaction log backups that contain the delete transactions you want to recover, click Add transaction logs. The online transaction log is listed and selected automatically.   Click Add if to add transaction log backups. It would be best if you have a full transaction log chain, as explained above. The next step for this option is to specify the time range.   Selecting a small time range for the time of deletion will create the recovery script just for the accidentally deleted records. A wide time range might script the records deleted on purpose, and you don’t want that. If needed, you can check the script generated and manually remove such records. After that, for all data sources options, the next step is to select the tables. Be careful here, if you deleted some data from other tables on purpose, and don’t want to recover them, don’t select all tables, as ApexSQL Recover will create the INSERT script for them too.   The next step offers two options: to create a recovery script that will insert the deleted records back into the Person.EmailAddress table, or to create a new database, create the Person.EmailAddress table in it, and insert the deleted records. I’ll select the first one.   The recovery process is completed and 11 records are found and scripted, as expected.   To see the script, click View script. ApexSQL Recover has its own script editor, where you can review, modify, and execute the recovery script. The insert into statements look like: INSERT INTO Person.EmailAddress( BusinessEntityID, EmailAddressID, EmailAddress, rowguid, ModifiedDate) VALUES( 70, 70, N'[email protected]' COLLATE SQL_Latin1_General_CP1_CI_AS, 'd62c5b4e-c91f-403f-b630-7b7e0fda70ce', '20030109 00:00:00.000' ); To execute the script, click Execute in the menu.   If you want to check whether the records are really back, execute SELECT * FROM Person.EmailAddress WHERE BusinessEntityID BETWEEN 70 AND 80 As shown, ApexSQL Recover recovers SQL database data after accidental deletes even without the database backup that contains the deleted data and relevant transaction log backups. ApexSQL Recover reads the deleted data from the database data file, so this method can be used even for databases in the Simple recovery model. Besides recovering SQL database records from a DELETE statement, ApexSQL Recover can help when the records are lost due to a DROP TABLE, or TRUNCATE statement, as well as repair a corrupted MDF file that cannot be attached to as SQL Server instance. You can find more information about how to recover SQL database lost data and repair a SQL Server database on ApexSQL Solution center. There are solutions for various situations when data needs to be recovered. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Backup and Restore, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

    Read the article

  • SQL SERVER – Interview Questions and Answers – Frequently Asked Questions – Introduction – Day 1 of 31

    - by pinaldave
    List of all the Interview Questions and Answers Series blogs Posts covering interview questions and answers always make for interesting reading.  Some people like the subject for their helpful hints and thought provoking subject, and others dislike these posts because they feel it is nothing more than cheating.  I’d like to discuss the pros and cons of a Question and Answer format here. Interview Questions and Answers are Helpful Just like blog posts, books, and articles, interview Question and Answer discussions are learning material.  The popular Dummy’s books or Idiots Guides are not only for “dummies,” but can help everyone relearn the fundamentals.  Question and Answer discussions can serve the same purpose.  You could call this SQL Server Fundamentals or SQL Server 101. I have administrated hundreds of interviews during my career and I have noticed that sometimes an interviewee with several years of experience lacks an understanding of the fundamentals.  These individuals have been in the industry for so long, usually working on a very specific project, that the ABCs of the business have slipped their mind. Or, when a college graduate is looking to get into the industry, he is not expected to have experience since he is just graduated. However, the new grad is expected to have an understanding of fundamentals and theory.  Sometimes after the stress of final exams and graduation, it can be difficult to remember the correct answers to interview questions, though. An interview Question and Answer discussion can be very helpful to both these individuals.  It is simply a way to go back over the building blocks of a topic.  Many times a simple review like this will help “jog” your memory, and all those previously-memorized facts will come flooding back to you.  It is not a way to re-learn a topic, but a way to remind yourself of what you already know. A Question and Answer discussion can also be a way to go over old topics in a more interesting manner.  Especially if you have been working in the industry, or taking lots of classes on the topic, everything you read can sound like a repeat of what you already know.  Going over a topic in a new format can make the material seem fresh and interesting.  And an interested mind will be more engaged and remember more in the end. Interview Questions and Answers are Harmful A common argument against a Question and Answer discussion is that it will give someone a “cheat sheet.” A new guy with relatively little experience can read the interview questions and answers, and then memorize them. When an interviewer asks him the same questions, he will repeat the answers and get the job. Honestly, is he good hire because he memorized the interview questions? Wouldn’t it be better for the interviewer to hire someone with actual experience?  The answer is not as easy as it seems – there are many different factors to be considered. If the interviewer is asking fundamentals-related questions only, he gets the answers he wants to hear, and then hires this first candidate – there is a good chance that he is hiring based on personality rather than experience.  If the interviewer is smart he will ask deeper questions, have more than one person on the interview team, and interview a variety of candidates.  If one interviewee happens to memorize some answers, it usually doesn’t mean he will automatically get the job at the expense of more qualified candidates. Another argument against interview Question and Answers is that it will give candidates a false sense of confidence, and that they will appear more qualified than they are. Well, if that is true, it will not last after the first interview when the candidate is asked difficult questions and he cannot find the answers in the list of interview Questions and Answers.  Besides, confidence is one of the best things to walk into an interview with! In today’s competitive job market, there are often hundreds of candidates applying for the same position.  With so many applicants to choose from, interviewers must make decisions about who to call back and who to hire based on their gut feeling.  One drawback to reading an interview Question and Answer article is that you might sound very boring in your interview – saying the same thing as every single candidate, and parroting answers that sound like someone else wrote them for you – because they did.  However, it is definitely better to go to an interview prepared, just make sure that you give a lot of thought to your answers to make them sound like your own voice.  Remember that you will be hired based on your skills as well as your personality, so don’t think that having all the right answers will make get you hired.  A good interviewee will be prepared, confident, and know how to stand out. My Opinion A list of interview Questions and Answers is really helpful as a refresher or for beginners. To really ace an interview, one needs to have real-world, hands-on experience with SQL Server as well. Interview questions just serve as a starter or easy read for experienced professionals. When I have to learn new technology, I often search online for interview questions and get an idea about the breadth and depth of the technology. Next Action I am going to write about interview Questions and Answers for next 30 days. I have previously written a series of interview questions and answers; now I have re-written them keeping the latest version of SQL Server and current industry progress in mind. If you have faced interesting interview questions or situations, please write to me and I will publish them as a guest post. If you want me to add few more details, leave a comment and I will make sure that I do my best to accommodate. Tomorrow we will start the interview Questions and Answers series, with a few interesting stories, best practices and guest posts. We will have a prize give-away and other awards when the series ends. List of all the Interview Questions and Answers Series blogs Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Interview Questions and Answers, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

    Read the article

  • SQL SERVER – Introduction to SQL Server 2014 In-Memory OLTP

    - by Pinal Dave
    In SQL Server 2014 Microsoft has introduced a new database engine component called In-Memory OLTP aka project “Hekaton” which is fully integrated into the SQL Server Database Engine. It is optimized for OLTP workloads accessing memory resident data. In-memory OLTP helps us create memory optimized tables which in turn offer significant performance improvement for our typical OLTP workload. The main objective of memory optimized table is to ensure that highly transactional tables could live in memory and remain in memory forever without even losing out a single record. The most significant part is that it still supports majority of our Transact-SQL statement. Transact-SQL stored procedures can be compiled to machine code for further performance improvements on memory-optimized tables. This engine is designed to ensure higher concurrency and minimal blocking. In-Memory OLTP alleviates the issue of locking, using a new type of multi-version optimistic concurrency control. It also substantially reduces waiting for log writes by generating far less log data and needing fewer log writes. Points to remember Memory-optimized tables refer to tables using the new data structures and key words added as part of In-Memory OLTP. Disk-based tables refer to your normal tables which we used to create in SQL Server since its inception. These tables use a fixed size 8 KB pages that need to be read from and written to disk as a unit. Natively compiled stored procedures refer to an object Type which is new and is supported by in-memory OLTP engine which convert it into machine code, which can further improve the data access performance for memory –optimized tables. Natively compiled stored procedures can only reference memory-optimized tables, they can’t be used to reference any disk –based table. Interpreted Transact-SQL stored procedures, which is what SQL Server has always used. Cross-container transactions refer to transactions that reference both memory-optimized tables and disk-based tables. Interop refers to interpreted Transact-SQL that references memory-optimized tables. Using In-Memory OLTP In-Memory OLTP engine has been available as part of SQL Server 2014 since June 2013 CTPs. Installation of In-Memory OLTP is part of the SQL Server setup application. The In-Memory OLTP components can only be installed with a 64-bit edition of SQL Server 2014 hence they are not available with 32-bit editions. Creating Databases Any database that will store memory-optimized tables must have a MEMORY_OPTIMIZED_DATA filegroup. This filegroup is specifically designed to store the checkpoint files needed by SQL Server to recover the memory-optimized tables, and although the syntax for creating the filegroup is almost the same as for creating a regular filestream filegroup, it must also specify the option CONTAINS MEMORY_OPTIMIZED_DATA. Here is an example of a CREATE DATABASE statement for a database that can support memory-optimized tables: CREATE DATABASE InMemoryDB ON PRIMARY(NAME = [InMemoryDB_data], FILENAME = 'D:\data\InMemoryDB_data.mdf', size=500MB), FILEGROUP [SampleDB_mod_fg] CONTAINS MEMORY_OPTIMIZED_DATA (NAME = [InMemoryDB_mod_dir], FILENAME = 'S:\data\InMemoryDB_mod_dir'), (NAME = [InMemoryDB_mod_dir], FILENAME = 'R:\data\InMemoryDB_mod_dir') LOG ON (name = [SampleDB_log], Filename='L:\log\InMemoryDB_log.ldf', size=500MB) COLLATE Latin1_General_100_BIN2; Above example code creates files on three different drives (D:  S: and R:) for the data files and in memory storage so if you would like to run this code kindly change the drive and folder locations as per your convenience. Also notice that binary collation was specified as Windows (non-SQL). BIN2 collation is the only collation support at this point for any indexes on memory optimized tables. It is also possible to add a MEMORY_OPTIMIZED_DATA file group to an existing database, use the below command to achieve the same. ALTER DATABASE AdventureWorks2012 ADD FILEGROUP hekaton_mod CONTAINS MEMORY_OPTIMIZED_DATA; GO ALTER DATABASE AdventureWorks2012 ADD FILE (NAME='hekaton_mod', FILENAME='S:\data\hekaton_mod') TO FILEGROUP hekaton_mod; GO Creating Tables There is no major syntactical difference between creating a disk based table or a memory –optimized table but yes there are a few restrictions and a few new essential extensions. Essentially any memory-optimized table should use the MEMORY_OPTIMIZED = ON clause as shown in the Create Table query example. DURABILITY clause (SCHEMA_AND_DATA or SCHEMA_ONLY) Memory-optimized table should always be defined with a DURABILITY value which can be either SCHEMA_AND_DATA or  SCHEMA_ONLY the former being the default. A memory-optimized table defined with DURABILITY=SCHEMA_ONLY will not persist the data to disk which means the data durability is compromised whereas DURABILITY= SCHEMA_AND_DATA ensures that data is also persisted along with the schema. Indexing Memory Optimized Table A memory-optimized table must always have an index for all tables created with DURABILITY= SCHEMA_AND_DATA and this can be achieved by declaring a PRIMARY KEY Constraint at the time of creating a table. The following example shows a PRIMARY KEY index created as a HASH index, for which a bucket count must also be specified. CREATE TABLE Mem_Table ( [Name] VARCHAR(32) NOT NULL PRIMARY KEY NONCLUSTERED HASH WITH (BUCKET_COUNT = 100000), [City] VARCHAR(32) NULL, [State_Province] VARCHAR(32) NULL, [LastModified] DATETIME NOT NULL, ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA); Now as you can see in the above query example we have used the clause MEMORY_OPTIMIZED = ON to make sure that it is considered as a memory optimized table and not just a normal table and also used the DURABILITY Clause= SCHEMA_AND_DATA which means it will persist data along with metadata and also you can notice this table has a PRIMARY KEY mentioned upfront which is also a mandatory clause for memory-optimized tables. We will talk more about HASH Indexes and BUCKET_COUNT in later articles on this topic which will be focusing more on Row and Index storage on Memory-Optimized tables. So stay tuned for that as well. Now as we covered the basics of Memory Optimized tables and understood the key things to remember while using memory optimized tables, let’s explore more using examples to understand the Performance gains using memory-optimized tables. I will be using the database which i created earlier in this article i.e. InMemoryDB in the below Demo Exercise. USE InMemoryDB GO -- Creating a disk based table CREATE TABLE dbo.Disktable ( Id INT IDENTITY, Name CHAR(40) ) GO CREATE NONCLUSTERED INDEX IX_ID ON dbo.Disktable (Id) GO -- Creating a memory optimized table with similar structure and DURABILITY = SCHEMA_AND_DATA CREATE TABLE dbo.Memorytable_durable ( Id INT NOT NULL PRIMARY KEY NONCLUSTERED Hash WITH (bucket_count =1000000), Name CHAR(40) ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA) GO -- Creating an another memory optimized table with similar structure but DURABILITY = SCHEMA_Only CREATE TABLE dbo.Memorytable_nondurable ( Id INT NOT NULL PRIMARY KEY NONCLUSTERED Hash WITH (bucket_count =1000000), Name CHAR(40) ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_only) GO -- Now insert 100000 records in dbo.Disktable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Disktable(Name) VALUES('sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END -- Do the same inserts for Memory table dbo.Memorytable_durable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Memorytable_durable VALUES(@i_t, 'sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END -- Now finally do the same inserts for Memory table dbo.Memorytable_nondurable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Memorytable_nondurable VALUES(@i_t, 'sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END The above 3 Inserts took 1.20 minutes, 54 secs, and 2 secs respectively to insert 100000 records on my machine with 8 Gb RAM. This proves the point that memory-optimized tables can definitely help businesses achieve better performance for their highly transactional business table and memory- optimized tables with Durability SCHEMA_ONLY is even faster as it does not bother persisting its data to disk which makes it supremely fast. Koenig Solutions is one of the few organizations which offer IT training on SQL Server 2014 and all its updates. Now, I leave the decision on using memory_Optimized tables on you, I hope you like this article and it helped you understand  the fundamentals of IN-Memory OLTP . 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 Tagged: Koenig

    Read the article

  • SQL SERVER – Shrinking Database is Bad – Increases Fragmentation – Reduces Performance

    - by pinaldave
    Earlier, I had written two articles related to Shrinking Database. I wrote about why Shrinking Database is not good. SQL SERVER – SHRINKDATABASE For Every Database in the SQL Server SQL SERVER – What the Business Says Is Not What the Business Wants I received many comments on Why Database Shrinking is bad. Today we will go over a very interesting example that I have created for the same. Here are the quick steps of the example. Create a test database Create two tables and populate with data Check the size of both the tables Size of database is very low Check the Fragmentation of one table Fragmentation will be very low Truncate another table Check the size of the table Check the fragmentation of the one table Fragmentation will be very low SHRINK Database Check the size of the table Check the fragmentation of the one table Fragmentation will be very HIGH REBUILD index on one table Check the size of the table Size of database is very HIGH Check the fragmentation of the one table Fragmentation will be very low Here is the script for the same. USE MASTER GO CREATE DATABASE ShrinkIsBed GO USE ShrinkIsBed GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Create FirstTable CREATE TABLE FirstTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_FirstTable_ID] ON FirstTable ( [ID] ASC ) ON [PRIMARY] GO -- Create SecondTable CREATE TABLE SecondTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_SecondTable_ID] ON SecondTable ( [ID] ASC ) ON [PRIMARY] GO -- Insert One Hundred Thousand Records INSERT INTO FirstTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Insert One Hundred Thousand Records INSERT INTO SecondTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO Let us check the table size and fragmentation. Now let us TRUNCATE the table and check the size and Fragmentation. USE MASTER GO CREATE DATABASE ShrinkIsBed GO USE ShrinkIsBed GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Create FirstTable CREATE TABLE FirstTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_FirstTable_ID] ON FirstTable ( [ID] ASC ) ON [PRIMARY] GO -- Create SecondTable CREATE TABLE SecondTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_SecondTable_ID] ON SecondTable ( [ID] ASC ) ON [PRIMARY] GO -- Insert One Hundred Thousand Records INSERT INTO FirstTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Insert One Hundred Thousand Records INSERT INTO SecondTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO You can clearly see that after TRUNCATE, the size of the database is not reduced and it is still the same as before TRUNCATE operation. After the Shrinking database operation, we were able to reduce the size of the database. If you notice the fragmentation, it is considerably high. The major problem with the Shrink operation is that it increases fragmentation of the database to very high value. Higher fragmentation reduces the performance of the database as reading from that particular table becomes very expensive. One of the ways to reduce the fragmentation is to rebuild index on the database. Let us rebuild the index and observe fragmentation and database size. -- Rebuild Index on FirstTable ALTER INDEX IX_SecondTable_ID ON SecondTable REBUILD GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO You can notice that after rebuilding, Fragmentation reduces to a very low value (almost same to original value); however the database size increases way higher than the original. Before rebuilding, the size of the database was 5 MB, and after rebuilding, it is around 20 MB. Regular rebuilding the index is rebuild in the same user database where the index is placed. This usually increases the size of the database. Look at irony of the Shrinking database. One person shrinks the database to gain space (thinking it will help performance), which leads to increase in fragmentation (reducing performance). To reduce the fragmentation, one rebuilds index, which leads to size of the database to increase way more than the original size of the database (before shrinking). Well, by Shrinking, one did not gain what he was looking for usually. Rebuild indexing is not the best suggestion as that will create database grow again. I have always remembered the excellent post from Paul Randal regarding Shrinking the database is bad. I suggest every one to read that for accuracy and interesting conversation. Let us run following script where we Shrink the database and REORGANIZE. -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO -- Shrink the Database DBCC SHRINKDATABASE (ShrinkIsBed); GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO -- Rebuild Index on FirstTable ALTER INDEX IX_SecondTable_ID ON SecondTable REORGANIZE GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO You can see that REORGANIZE does not increase the size of the database or remove the fragmentation. Again, I no way suggest that REORGANIZE is the solution over here. This is purely observation using demo. Read the blog post of Paul Randal. Following script will clean up the database -- Clean up USE MASTER GO ALTER DATABASE ShrinkIsBed SET SINGLE_USER WITH ROLLBACK IMMEDIATE GO DROP DATABASE ShrinkIsBed GO There are few valid cases of the Shrinking database as well, but that is not covered in this blog post. We will cover that area some other time in future. Additionally, one can rebuild index in the tempdb as well, and we will also talk about the same in future. Brent has written a good summary blog post as well. Are you Shrinking your database? Well, when are you going to stop Shrinking it? Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Index, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

    Read the article

  • Big Data – Is Big Data Relevant to me? – Big Data Questionnaires – Guest Post by Vinod Kumar

    - by Pinal Dave
    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 of 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. I think the series from Pinal is a good one for anyone planning to start on Big Data journey from the basics. In my daily customer interactions this buzz of “Big Data” always comes up, I react generally saying – “Sir, do you really have a ‘Big Data’ problem or do you have a big Data problem?” Generally, there is a silence in the air when I ask this question. Data is everywhere in organizations – be it big data, small data, all data and for few it is bad data which is same as no data :). Wow, don’t discount me as someone who opposes “Big Data”, I am a big supporter as much as I am a critic of the abuse of this term by the people. In this post, I wanted to let my mind flow so that you can also think in the direction I want you to see these concepts. In any case, this is not an exhaustive dump of what is in my mind – but you will surely get the drift how I am going to question Big Data terms from customers!!! Is Big Data Relevant to me? Many of my customers talk to me like blank whiteboard with no idea – “why Big Data”. They want to jump into the bandwagon of technology and they want to decipher insights from their unexplored data a.k.a. unstructured data with structured data. So what are these industry scenario’s that come to mind? Here are some of them: Financials Fraud detection: Banks and Credit cards are monitoring your spending habits on real-time basis. Customer Segmentation: applies in every industry from Banking to Retail to Aviation to Utility and others where they deal with end customer who consume their products and services. Customer Sentiment Analysis: Responding to negative brand perception on social or amplify the positive perception. Sales and Marketing Campaign: Understand the impact and get closer to customer delight. Call Center Analysis: attempt to take unstructured voice recordings and analyze them for content and sentiment. Medical Reduce Re-admissions: How to build a proactive follow-up engagements with patients. Patient Monitoring: How to track Inpatient, Out-Patient, Emergency Visits, Intensive Care Units etc. Preventive Care: Disease identification and Risk stratification is a very crucial business function for medical. Claims fraud detection: There is no precise dollars that one can put here, but this is a big thing for the medical field. Retail Customer Sentiment Analysis, Customer Care Centers, Campaign Management. Supply Chain Analysis: Every sensors and RFID data can be tracked for warehouse space optimization. Location based marketing: Based on where a check-in happens retail stores can be optimize their marketing. Telecom Price optimization and Plans, Finding Customer churn, Customer loyalty programs Call Detail Record (CDR) Analysis, Network optimizations, User Location analysis Customer Behavior Analysis Insurance Fraud Detection & Analysis, Pricing based on customer Sentiment Analysis, Loyalty Management Agents Analysis, Customer Value Management This list can go on to other areas like Utility, Manufacturing, Travel, ITES etc. So as you can see, there are obviously interesting use cases for each of these industry verticals. These are just representative list. Where to start? A lot of times I try to quiz customers on a number of dimensions before starting a Big Data conversation. Are you getting the data you need the way you want it and in a timely manner? Can you get in and analyze the data you need? How quickly is IT to respond to your BI Requests? How easily can you get at the data that you need to run your business/department/project? How are you currently measuring your business? Can you get the data you need to react WITHIN THE QUARTER to impact behaviors to meet your numbers or is it always “rear-view mirror?” How are you measuring: The Brand Customer Sentiment Your Competition Your Pricing Your performance Supply Chain Efficiencies Predictive product / service positioning What are your key challenges of driving collaboration across your global business?  What the challenges in innovation? What challenges are you facing in getting more information out of your data? Note: Garbage-in is Garbage-out. Hold good for all reporting / analytics requirements Big Data POCs? A number of customers get into the realm of setting a small team to work on Big Data – well it is a great start from an understanding point of view, but I tend to ask a number of other questions to such customers. Some of these common questions are: To what degree is your advanced analytics (natural language processing, sentiment analysis, predictive analytics and classification) paired with your Big Data’s efforts? Do you have dedicated resources exploring the possibilities of advanced analytics in Big Data for your business line? Do you plan to employ machine learning technology while doing Advanced Analytics? How is Social Media being monitored in your organization? What is your ability to scale in terms of storage and processing power? Do you have a system in place to sort incoming data in near real time by potential value, data quality, and use frequency? Do you use event-driven architecture to manage incoming data? Do you have specialized data services that can accommodate different formats, security, and the management requirements of multiple data sources? Is your organization currently using or considering in-memory analytics? To what degree are you able to correlate data from your Big Data infrastructure with that from your enterprise data warehouse? Have you extended the role of Data Stewards to include ownership of big data components? Do you prioritize data quality based on the source system (that is Facebook/Twitter data has lower quality thresholds than radio frequency identification (RFID) for a tracking system)? Do your retention policies consider the different legal responsibilities for storing Big Data for a specific amount of time? Do Data Scientists work in close collaboration with Data Stewards to ensure data quality? How is access to attributes of Big Data being given out in the organization? Are roles related to Big Data (Advanced Analyst, Data Scientist) clearly defined? How involved is risk management in the Big Data governance process? Is there a set of documented policies regarding Big Data governance? Is there an enforcement mechanism or approach to ensure that policies are followed? Who is the key sponsor for your Big Data governance program? (The CIO is best) Do you have defined policies surrounding the use of social media data for potential employees and customers, as well as the use of customer Geo-location data? How accessible are complex analytic routines to your user base? What is the level of involvement with outside vendors and third parties in regard to the planning and execution of Big Data projects? What programming technologies are utilized by your data warehouse/BI staff when working with Big Data? These are some of the important questions I ask each customer who is actively evaluating Big Data trends for their organizations. These questions give you a sense of direction where to start, what to use, how to secure, how to analyze and more. Sign off Any Big data is analysis is incomplete without a compelling story. The best way to understand this is to watch Hans Rosling – Gapminder (2:17 to 6:06) videos about the third world myths. Don’t get overwhelmed with the Big Data buzz word, the destination to what your data speaks is important. In this blog post, we did not particularly look at any Big Data technologies. This is a set of questionnaire one needs to keep in mind as they embark their journey of Big Data. I did write some of the basics in my blog: Big Data – Big Hype yet Big Opportunity. Do let me know if these questions make sense?  Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

    Read the article

  • SQL SERVER – 5 Tips for Improving Your Data with expressor Studio

    - by pinaldave
    It’s no secret that bad data leads to bad decisions and poor results.  However, how do you prevent dirty data from taking up residency in your data store?  Some might argue that it’s the responsibility of the person sending you the data.  While that may be true, in practice that will rarely hold up.  It doesn’t matter how many times you ask, you will get the data however they decide to provide it. So now you have bad data.  What constitutes bad data?  There are quite a few valid answers, for example: Invalid date values Inappropriate characters Wrong data Values that exceed a pre-set threshold While it is certainly possible to write your own scripts and custom SQL to identify and deal with these data anomalies, that effort often takes too long and becomes difficult to maintain.  Instead, leveraging an ETL tool like expressor Studio makes the data cleansing process much easier and faster.  Below are some tips for leveraging expressor to get your data into tip-top shape. Tip 1:     Build reusable data objects with embedded cleansing rules One of the new features in expressor Studio 3.2 is the ability to define constraints at the metadata level.  Using expressor’s concept of Semantic Types, you can define reusable data objects that have embedded logic such as constraints for dealing with dirty data.  Once defined, they can be saved as a shared atomic type and then re-applied to other data attributes in other schemas. As you can see in the figure above, I’ve defined a constraint on zip code.  I can then save the constraint rules I defined for zip code as a shared atomic type called zip_type for example.   The next time I get a different data source with a schema that also contains a zip code field, I can simply apply the shared atomic type (shown below) and the previously defined constraints will be automatically applied. Tip 2:     Unlock the power of regular expressions in Semantic Types Another powerful feature introduced in expressor Studio 3.2 is the option to use regular expressions as a constraint.   A regular expression is used to identify patterns within data.   The patterns could be something as simple as a date format or something much more complex such as a street address.  For example, I could define that a valid IP address should be made up of 4 numbers, each 0 to 255, and separated by a period.  So 192.168.23.123 might be a valid IP address whereas 888.777.0.123 would not be.   How can I account for this using regular expressions? A very simple regular expression that would look for any 4 sets of 3 digits separated by a period would be:  ^[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}$ Alternatively, the following would be the exact check for truly valid IP addresses as we had defined above:  ^(25[0-5]|2[0-4][0-9]|1[0-9]{2}|[1-9]?[0-9])\.(25[0-5]|2[0-4][0-9]|1[0-9]{2}|[1-9]?[0-9])\.(25[0-5]|2[0-4][0-9]|1[0-9]{2}|[1-9]?[0-9])\.(25[0-5]|2[0-4][0-9]|1[0-9]{2}|[1-9]?[0-9])$ .  In expressor, we would enter this regular expression as a constraint like this: Here we select the corrective action to be ‘Escalate’, meaning that the expressor Dataflow operator will decide what to do.  Some of the options include rejecting the offending record, skipping it, or aborting the dataflow. Tip 3:     Email pattern expressions that might come in handy In the example schema that I am using, there’s a field for email.  Email addresses are often entered incorrectly because people are trying to avoid spam.  While there are a lot of different ways to define what constitutes a valid email address, a quick search online yields a couple of really useful regular expressions for validating email addresses: This one is short and sweet:  \b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,4}\b (Source: http://www.regular-expressions.info/) This one is more specific about which characters are allowed:  ^([a-zA-Z0-9_\-\.]+)@((\[[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.)|(([a-zA-Z0-9\-]+\.)+))([a-zA-Z]{2,4}|[0-9]{1,3})(\]?)$ (Source: http://regexlib.com/REDetails.aspx?regexp_id=26 ) Tip 4:     Reject “dirty data” for analysis or further processing Yet another feature introduced in expressor Studio 3.2 is the ability to reject records based on constraint violations.  To capture reject records on input, simply specify Reject Record in the Error Handling setting for the Read File operator.  Then attach a Write File operator to the reject port of the Read File operator as such: Next, in the Write File operator, you can configure the expressor operator in a similar way to the Read File.  The key difference would be that the schema needs to be derived from the upstream operator as shown below: Once configured, expressor will output rejected records to the file you specified.  In addition to the rejected records, expressor also captures some diagnostic information that will be helpful towards identifying why the record was rejected.  This makes diagnosing errors much easier! Tip 5:    Use a Filter or Transform after the initial cleansing to finish the job Sometimes you may want to predicate the data cleansing on a more complex set of conditions.  For example, I may only be interested in processing data containing males over the age of 25 in certain zip codes.  Using an expressor Filter operator, you can define the conditional logic which isolates the records of importance away from the others. Alternatively, the expressor Transform operator can be used to alter the input value via a user defined algorithm or transformation.  It also supports the use of conditional logic and data can be rejected based on constraint violations. However, the best tip I can leave you with is to not constrain your solution design approach – expressor operators can be combined in many different ways to achieve the desired results.  For example, in the expressor Dataflow below, I can post-process the reject data from the Filter which did not meet my pre-defined criteria and, if successful, Funnel it back into the flow so that it gets written to the target table. I continue to be impressed that expressor offers all this functionality as part of their FREE expressor Studio desktop ETL tool, which you can download from here.  Their Studio ETL tool is absolutely free and they are very open about saying that if you want to deploy their software on a dedicated Windows Server, you need to purchase their server software, whose pricing is posted on their website. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

    Read the article

  • SQL SERVER – Extending SQL Azure with Azure worker role – Guest Post by Paras Doshi

    - by pinaldave
    This is guest post by Paras Doshi. Paras Doshi is a research Intern at SolidQ.com and a Microsoft student partner. He is currently working in the domain of SQL Azure. SQL Azure is nothing but a SQL server in the cloud. SQL Azure provides benefits such as on demand rapid provisioning, cost-effective scalability, high availability and reduced management overhead. To see an introduction on SQL Azure, check out the post by Pinal here In this article, we are going to discuss how to extend SQL Azure with the Azure worker role. In other words, we will attempt to write a custom code and host it in the Azure worker role; the aim is to add some features that are not available with SQL Azure currently or features that need to be customized for flexibility. This way we extend the SQL Azure capability by building some solutions that run on Azure as worker roles. To understand Azure worker role, think of it as a windows service in cloud. Azure worker role can perform background processes, and to handle processes such as synchronization and backup, it becomes our ideal tool. First, we will focus on writing a worker role code that synchronizes SQL Azure databases. Before we do so, let’s see some scenarios in which synchronization between SQL Azure databases is beneficial: scaling out access over multiple databases enables us to handle workload efficiently As of now, SQL Azure database can be hosted in one of any six datacenters. By synchronizing databases located in different data centers, one can extend the data by enabling access to geographically distributed data Let us see some scenarios in which SQL server to SQL Azure database synchronization is beneficial To backup SQL Azure database on local infrastructure Rather than investing in local infrastructure for increased workloads, such workloads could be handled by cloud Ability to extend data to different datacenters located across the world to enable efficient data access from remote locations Now, let us develop cloud-based app that synchronizes SQL Azure databases. For an Introduction to developing cloud based apps, click here Now, in this article, I aim to provide a bird’s eye view of how a code that synchronizes SQL Azure databases look like and then list resources that can help you develop the solution from scratch. Now, if you newly add a worker role to the cloud-based project, this is how the code will look like. (Note: I have added comments to the skeleton code to point out the modifications that will be required in the code to carry out the SQL Azure synchronization. Note the placement of Setup() and Sync() function.) Click here (http://parasdoshi1989.files.wordpress.com/2011/06/code-snippet-1-for-extending-sql-azure-with-azure-worker-role1.pdf ) Enabling SQL Azure databases synchronization through sync framework is a two-step process. In the first step, the database is provisioned and sync framework creates tracking tables, stored procedures, triggers, and tables to store metadata to enable synchronization. This is one time step. The code for the same is put in the setup() function which is called once when the worker role starts. Now, the second step is continuous (or on demand) synchronization of SQL Azure databases by propagating changes between databases. This is done on a continuous basis by calling the sync() function in the while loop. The code logic to synchronize changes between SQL Azure databases should be put in the sync() function. Discussing the coding part step by step is out of the scope of this article. Therefore, let me suggest you a resource, which is given here. Also, note that before you start developing the code, you will need to install SYNC framework 2.1 SDK (download here). Further, you will reference some libraries before you start coding. Details regarding the same are available in the article that I just pointed to. You will be charged for data transfers if the databases are not in the same datacenter. For pricing information, go here Currently, a tool named DATA SYNC, which is built on top of sync framework, is available in CTP that allows SQL Azure <-> SQL server and SQL Azure <-> SQL Azure synchronization (without writing single line of code); however, in some cases, the custom code shown in this blogpost provides flexibility that is not available with Data SYNC. For instance, filtering is not supported in the SQL Azure DATA SYNC CTP2; if you wish to have such a functionality now, then you have the option of developing a custom code using SYNC Framework. Now, this code can be easily extended to synchronize at some schedule. Let us say we want the databases to get synchronized every day at 10:00 pm. This is what the code will look like now: (http://parasdoshi1989.files.wordpress.com/2011/06/code-snippet-2-for-extending-sql-azure-with-azure-worker-role.pdf) Don’t you think that by writing such a code, we are imitating the functionality provided by the SQL server agent for a SQL server? Think about it. We are scheduling our administrative task by writing custom code – in other words, we have developed a “Light weight SQL server agent for SQL Azure!” Since the SQL server agent is not currently available in cloud, we have developed a solution that enables us to schedule tasks, and thus we have extended SQL Azure with the Azure worker role! Now if you wish to track jobs, you can do so by storing this data in SQL Azure (or Azure tables). The reason is that Windows Azure is a stateless platform, and we will need to store the state of the job ourselves and the choice that you have is SQL Azure or Azure tables. Note that this solution requires custom code and also it is not UI driven; however, for now, it can act as a temporary solution until SQL server agent is made available in the cloud. Moreover, this solution does not encompass functionalities that a SQL server agent provides, but it does open up an interesting avenue to schedule some of the tasks such as backup and synchronization of SQL Azure databases by writing some custom code in the Azure worker role. Now, let us see one more possibility – i.e., running BCP through a worker role in Azure-hosted services and then uploading the backup files either locally or on blobs. If you upload it locally, then consider the data transfer cost. If you upload it to blobs residing in the same datacenter, then no transfer cost applies but the cost on blob size applies. So, before choosing the option, you need to evaluate your preferences keeping the cost associated with each option in mind. In this article, I have shown that Azure worker role solution could be developed to synchronize SQL Azure databases. Moreover, a light-weight SQL server agent for SQL Azure can be developed. Also we discussed the possibility of running BCP through a worker role in Azure-hosted services for backing up our precious SQL Azure data. Thus, we can extend SQL Azure with the Azure worker role. But remember: you will be charged for running Azure worker roles. So at the end of the day, you need to ask – am I willing to build a custom code and pay money to achieve this functionality? I hope you found this blog post interesting. If you have any questions/feedback, you can comment below or you can mail me at Paras[at]student-partners[dot]com Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Azure, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

    Read the article

  • SQL SERVER – Step by Step Guide to Beginning Data Quality Services in SQL Server 2012 – Introduction to DQS

    - by pinaldave
    Data Quality Services is a very important concept of SQL Server. I have recently started to explore the same and I am really learning some good concepts. Here are two very important blog posts which one should go over before continuing this blog post. Installing Data Quality Services (DQS) on SQL Server 2012 Connecting Error to Data Quality Services (DQS) on SQL Server 2012 This article is introduction to Data Quality Services for beginners. We will be using an Excel file Click on the image to enlarge the it. In the first article we learned to install DQS. In this article we will see how we can learn about building Knowledge Base and using it to help us identify the quality of the data as well help correct the bad quality of the data. Here are the two very important steps we will be learning in this tutorial. Building a New Knowledge Base  Creating a New Data Quality Project Let us start the building the Knowledge Base. Click on New Knowledge Base. In our project we will be using the Excel as a knowledge base. Here is the Excel which we will be using. There are two columns. One is Colors and another is Shade. They are independent columns and not related to each other. The point which I am trying to show is that in Column A there are unique data and in Column B there are duplicate records. Clicking on New Knowledge Base will bring up the following screen. Enter the name of the new knowledge base. Clicking NEXT will bring up following screen where it will allow to select the EXCE file and it will also let users select the source column. I have selected Colors and Shade both as a source column. Creating a domain is very important. Here you can create a unique domain or domain which is compositely build from Colors and Shade. As this is the first example, I will create unique domain – for Colors I will create domain Colors and for Shade I will create domain Shade. Here is the screen which will demonstrate how the screen will look after creating domains. Clicking NEXT it will bring you to following screen where you can do the data discovery. Clicking on the START will start the processing of the source data provided. Pre-processed data will show various information related to the source data. In our case it shows that Colors column have unique data whereas Shade have non-unique data and unique data rows are only two. In the next screen you can actually add more rows as well see the frequency of the data as the values are listed unique. Clicking next will publish the knowledge base which is just created. Now the knowledge base is created. We will try to take any random data and attempt to do DQS implementation over it. I am using another excel sheet here for simplicity purpose. In reality you can easily use SQL Server table for the same. Click on New Data Quality Project to see start DQS Project. In the next screen it will ask which knowledge base to use. We will be using our Colors knowledge base which we have recently created. In the Colors knowledge base we had two columns – 1) Colors and 2) Shade. In our case we will be using both of the mappings here. User can select one or multiple column mapping over here. Now the most important phase of the complete project. Click on Start and it will make the cleaning process and shows various results. In our case there were two columns to be processed and it completed the task with necessary information. It demonstrated that in Colors columns it has not corrected any value by itself but in Shade value there is a suggestion it has. We can train the DQS to correct values but let us keep that subject for future blog posts. Now click next and keep the domain Colors selected left side. It will demonstrate that there are two incorrect columns which it needs to be corrected. Here is the place where once corrected value will be auto-corrected in future. I manually corrected the value here and clicked on Approve radio buttons. As soon as I click on Approve buttons the rows will be disappeared from this tab and will move to Corrected Tab. If I had rejected tab it would have moved the rows to Invalid tab as well. In this screen you can see how the corrected 2 rows are demonstrated. You can click on Correct tab and see previously validated 6 rows which passed the DQS process. Now let us click on the Shade domain on the left side of the screen. This domain shows very interesting details as there DQS system guessed the correct answer as Dark with the confidence level of 77%. It is quite a high confidence level and manual observation also demonstrate that Dark is the correct answer. I clicked on Approve and the row moved to corrected tab. On the next screen DQS shows the summary of all the activities. It also demonstrates how the correction of the quality of the data was performed. The user can explore their data to a SQL Server Table, CSV file or Excel. The user also has an option to either explore data and all the associated cleansing info or data only. I will select Data only for demonstration purpose. Clicking explore will generate the files. Let us open the generated file. It will look as following and it looks pretty complete and corrected. Well, we have successfully completed DQS Process. The process is indeed very easy. I suggest you try this out yourself and you will find it very easy to learn. In future we will go over advanced concepts. Are you using this feature on your production server? If yes, would you please leave a comment with your environment and business need. It will be indeed interesting to see where it is implemented. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Business Intelligence, Data Warehousing, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Data Quality Services, DQS

    Read the article

  • SQL SERVER – Guest Post – Architecting Data Warehouse – Niraj Bhatt

    - by pinaldave
    Niraj Bhatt works as an Enterprise Architect for a Fortune 500 company and has an innate passion for building / studying software systems. He is a top rated speaker at various technical forums including Tech·Ed, MCT Summit, Developer Summit, and Virtual Tech Days, among others. Having run a successful startup for four years Niraj enjoys working on – IT innovations that can impact an enterprise bottom line, streamlining IT budgets through IT consolidation, architecture and integration of systems, performance tuning, and review of enterprise applications. He has received Microsoft MVP award for ASP.NET, Connected Systems and most recently on Windows Azure. When he is away from his laptop, you will find him taking deep dives in automobiles, pottery, rafting, photography, cooking and financial statements though not necessarily in that order. He is also a manager/speaker at BDOTNET, Asia’s largest .NET user group. Here is the guest post by Niraj Bhatt. As data in your applications grows it’s the database that usually becomes a bottleneck. It’s hard to scale a relational DB and the preferred approach for large scale applications is to create separate databases for writes and reads. These databases are referred as transactional database and reporting database. Though there are tools / techniques which can allow you to create snapshot of your transactional database for reporting purpose, sometimes they don’t quite fit the reporting requirements of an enterprise. These requirements typically are data analytics, effective schema (for an Information worker to self-service herself), historical data, better performance (flat data, no joins) etc. This is where a need for data warehouse or an OLAP system arises. A Key point to remember is a data warehouse is mostly a relational database. It’s built on top of same concepts like Tables, Rows, Columns, Primary keys, Foreign Keys, etc. Before we talk about how data warehouses are typically structured let’s understand key components that can create a data flow between OLTP systems and OLAP systems. There are 3 major areas to it: a) OLTP system should be capable of tracking its changes as all these changes should go back to data warehouse for historical recording. For e.g. if an OLTP transaction moves a customer from silver to gold category, OLTP system needs to ensure that this change is tracked and send to data warehouse for reporting purpose. A report in context could be how many customers divided by geographies moved from sliver to gold category. In data warehouse terminology this process is called Change Data Capture. There are quite a few systems that leverage database triggers to move these changes to corresponding tracking tables. There are also out of box features provided by some databases e.g. SQL Server 2008 offers Change Data Capture and Change Tracking for addressing such requirements. b) After we make the OLTP system capable of tracking its changes we need to provision a batch process that can run periodically and takes these changes from OLTP system and dump them into data warehouse. There are many tools out there that can help you fill this gap – SQL Server Integration Services happens to be one of them. c) So we have an OLTP system that knows how to track its changes, we have jobs that run periodically to move these changes to warehouse. The question though remains is how warehouse will record these changes? This structural change in data warehouse arena is often covered under something called Slowly Changing Dimension (SCD). While we will talk about dimensions in a while, SCD can be applied to pure relational tables too. SCD enables a database structure to capture historical data. This would create multiple records for a given entity in relational database and data warehouses prefer having their own primary key, often known as surrogate key. As I mentioned a data warehouse is just a relational database but industry often attributes a specific schema style to data warehouses. These styles are Star Schema or Snowflake Schema. The motivation behind these styles is to create a flat database structure (as opposed to normalized one), which is easy to understand / use, easy to query and easy to slice / dice. Star schema is a database structure made up of dimensions and facts. Facts are generally the numbers (sales, quantity, etc.) that you want to slice and dice. Fact tables have these numbers and have references (foreign keys) to set of tables that provide context around those facts. E.g. if you have recorded 10,000 USD as sales that number would go in a sales fact table and could have foreign keys attached to it that refers to the sales agent responsible for sale and to time table which contains the dates between which that sale was made. These agent and time tables are called dimensions which provide context to the numbers stored in fact tables. This schema structure of fact being at center surrounded by dimensions is called Star schema. A similar structure with difference of dimension tables being normalized is called a Snowflake schema. This relational structure of facts and dimensions serves as an input for another analysis structure called Cube. Though physically Cube is a special structure supported by commercial databases like SQL Server Analysis Services, logically it’s a multidimensional structure where dimensions define the sides of cube and facts define the content. Facts are often called as Measures inside a cube. Dimensions often tend to form a hierarchy. E.g. Product may be broken into categories and categories in turn to individual items. Category and Items are often referred as Levels and their constituents as Members with their overall structure called as Hierarchy. Measures are rolled up as per dimensional hierarchy. These rolled up measures are called Aggregates. Now this may seem like an overwhelming vocabulary to deal with but don’t worry it will sink in as you start working with Cubes and others. Let’s see few other terms that we would run into while talking about data warehouses. ODS or an Operational Data Store is a frequently misused term. There would be few users in your organization that want to report on most current data and can’t afford to miss a single transaction for their report. Then there is another set of users that typically don’t care how current the data is. Mostly senior level executives who are interesting in trending, mining, forecasting, strategizing, etc. don’t care for that one specific transaction. This is where an ODS can come in handy. ODS can use the same star schema and the OLAP cubes we saw earlier. The only difference is that the data inside an ODS would be short lived, i.e. for few months and ODS would sync with OLTP system every few minutes. Data warehouse can periodically sync with ODS either daily or weekly depending on business drivers. Data marts are another frequently talked about topic in data warehousing. They are subject-specific data warehouse. Data warehouses that try to span over an enterprise are normally too big to scope, build, manage, track, etc. Hence they are often scaled down to something called Data mart that supports a specific segment of business like sales, marketing, or support. Data marts too, are often designed using star schema model discussed earlier. Industry is divided when it comes to use of data marts. Some experts prefer having data marts along with a central data warehouse. Data warehouse here acts as information staging and distribution hub with spokes being data marts connected via data feeds serving summarized data. Others eliminate the need for a centralized data warehouse citing that most users want to report on detailed data. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Best Practices, Business Intelligence, Data Warehousing, Database, Pinal Dave, PostADay, Readers Contribution, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

    Read the article

  • Microsoft TechEd 2010 - Day 3 @ Bangalore

    - by sathya
    Microsoft TechEd 2010 - Day 3 @ Bangalore Sorry for my delayed post on day 3 because I had to travel from Blore to Chennai So I couldnt write for the past two days. On day 3 as usual we had lot of simultaneous tracks on various sessions. This day I choose the Your Data, Our Platform Track. It had sessions on the following 5 topics :   Developing Data-tier Applications in Visual Studio 2010 - by Sanjay Nagamangalam SQL Server Query Optimization, Execution and Debugging Query Performance - by Vinod Kumar M SQL Server Utility - Its about more than 1 SQL Server - by Vinod Kumar Jagannathan Data Recovery / Consistency with CheckDB - by Vinod Kumar M Developing with SQL Server Spatial and Deep dive into Spatial Indexing - by Pinal Dave Developing Data-tier Applications in Visual Studio 2010 - by Sanjay Nagamangalam This was one of the superb sessions i have attended. He explained all the concepts in detail with a demo. The important thing in this is there is something called Data-Tier application project which is newly introduced in this VS2010 with which we can manage all our data along with our application inside our VS itself. We can create DB,Tables,Procs,Views etc. here itself and once we deploy it creates a compressed file called .dacpac which stores all the changes in Table Schema,Created procs, etc. on to that single file which reduces our (developer's) effort in preparing the deployment scripts and giving it to the DBA. It also has some policy configurations which can be managed easily by checking some rules like in outlook. For Ex : IF the SQL Server Version > 10 then deploy else dont. This rule specifies that even if we try to deploy on SQL Server DB with version less than 10 It will not do it. And if we deploy some .dacpac to SQL server production db with the option upgrade DB with this dacpac once everything completes successfully it will say success else it rollsback to the prior version. Even if it gets deployed successfully and later @ a point of time you wish to revert it back to the prior version, you can go ahead and delete the existing dacpac version so that it reverts to the older version of the db changes. And for the good questions that were asked in the session T-Shirts were given. SQL Server Query Optimization, Execution and Debugging Query Performance - by Vinod Kumar M This one too was the best session. The speaker Vinod explained everything very much clearly. This was really useful session and you dont believe, as per my knowledge, in the total 3 days in the TechEd except the Keynote, for this session seats were full (House FULL)  People were even standing out to attend this session. Such a great one it was. The speaker did a deep dive in to the Query Plan section and showed which actually causes the problem. Its all about the thing that we need to understand about the execution of SQL server Queries. We think in a way and SQL Server never executes in that way. We need to understand that first. He also told about there might be two plans generated for a single query at a point of time because of parallel processors in the system. The Key is here in every query. There is something called Estimated Row Count and Actual Row Count in the query plan. If the estimated row count by SQL server tallies with the actual row count your performance will be awesome. He said some tweaks to achieve the same. After this as usual we had lunch SQL Server Utility - Its about more than 1 SQL Server - by Vinod Kumar Jagannathan This was more of a DBA's session. Am really sorry I was totally blank and I was not interested to attend this session and walked out to attend Migrating to the cloud by Harish Ranganathan (My favorite Speaker) but unfortunately that was some other persons session. There the speaker was telling about how to configure the connection strings in such a way that we can connect to the SQL Azure platform from our VS and also showed us how to deploy the same in to Windows Azure. In between there were lot of technical problems like laptop hang, user locked and he was switching between systems, also i came in the half so i wasnt able to listen that fully. In between, Since I got an MCTS certification they gave me T-Shirt with the lines 'Iam Certified. Are you?' and they asked me to wear that. If we wear that we might get spotted and they would give us some goodies  So on the 3rd day I was wearing that T-Shirt. I got spotted by the person Tarun who was coordinating things about the certification, and he was accompanied with a cameraman and they interviewed me about the certification and I was shown live in the Teched and was seen by 60000 live viewers of the TechEd. I was really happy on that. Data Recovery / Consistency with CheckDB - by Vinod Kumar M This was one of the best sessions too in the TechEd. This guy is really amazing. In front of us he crashed a DB and showed how to recover the same in 6 different ways for different no of failures. Showed about Different types of error msgs like : 823,824,825 msdb..suspect_pages DBCC CheckDB (different parameters to it) I am really waiting for his session to get uploaded live in the Teched Website. Here is his contact info If you wish to connect to him : Twitter : @vinodk_sql Website : www.ExtremeExperts.com Blog : http://blogs.sqlxml.org/vinodkumar Developing with SQL Server Spatial and Deep dive into Spatial Indexing - by Pinal Dave Pinal Dave is a King in SQL and he is a SQL MVP and he is the owner of SQLAuthority.com He took the session on Spatial Databases from the start. Showed about the different types of Spatial : Geometric and Geographic Geometric : x and y axis its a planar surface Geographic : Spherical surface with 3600  as the maximum which is used to represent the geographic points on the earth and easy to draw maps of different kinds. He had a lot of obstacles during his session like rain coming inside the hall, mic wires got bursted due to rain, Videos off on the display screens. In spite of that he asked the audience to come in the front rows and managed to take a good session without ppts and finally we got the displays on and he was showing demos on the same what he explained orally. That was really a fun filled informative session. He gave some books for the persons who asked good questions and answered well for his questions and I got one too  (It was a book on Data Mining - Wrox Publishers) And finally after all these things there was Keynote session for close of the TechEd. and we all assembled in a big hall where Mr.Ashok Soota, a man of age around 70  co-founder of Mindtree was called to give some lecture on his successes. He was explaining about his past and what all companies he switched and for what reasons and what are all his successes and what are all his failures and the learnings of him from his past failures. and his success and failures on his partnerships with the other concern. And there were some questions for him like What is your suggestion on young entrepreneur? How did you learn from past failures? What is reiterating your success? What is your suggestion on partnerships? How to choose partnerships? etc. And they said @ 7.30 Pm there would be a party night, but unfortunately i was not able to attend that because I had to catch my train and before that i had to pack things, so I started @ 7 itself. Thats it about the TechED!!! Stay tuned for further Technology updates.

    Read the article

  • MySQL – Scalability on Amazon RDS: Scale out to multiple RDS instances

    - by Pinal Dave
    Today, I’d like to discuss getting better MySQL scalability on Amazon RDS. The question of the day: “What can you do when a MySQL database needs to scale write-intensive workloads beyond the capabilities of the largest available machine on Amazon RDS?” Let’s take a look. In a typical EC2/RDS set-up, users connect to app servers from their mobile devices and tablets, computers, browsers, etc.  Then app servers connect to an RDS instance (web/cloud services) and in some cases they might leverage some read-only replicas.   Figure 1. A typical RDS instance is a single-instance database, with read replicas.  This is not very good at handling high write-based throughput. As your application becomes more popular you can expect an increasing number of users, more transactions, and more accumulated data.  User interactions can become more challenging as the application adds more sophisticated capabilities. The result of all this positive activity: your MySQL database will inevitably begin to experience scalability pressures. What can you do? Broadly speaking, there are four options available to improve MySQL scalability on RDS. 1. Larger RDS Instances – If you’re not already using the maximum available RDS instance, you can always scale up – to larger hardware.  Bigger CPUs, more compute power, more memory et cetera. But the largest available RDS instance is still limited.  And they get expensive. “High-Memory Quadruple Extra Large DB Instance”: 68 GB of memory 26 ECUs (8 virtual cores with 3.25 ECUs each) 64-bit platform High I/O Capacity Provisioned IOPS Optimized: 1000Mbps 2. Provisioned IOPs – You can get provisioned IOPs and higher throughput on the I/O level. However, there is a hard limit with a maximum instance size and maximum number of provisioned IOPs you can buy from Amazon and you simply cannot scale beyond these hardware specifications. 3. Leverage Read Replicas – If your application permits, you can leverage read replicas to offload some reads from the master databases. But there are a limited number of replicas you can utilize and Amazon generally requires some modifications to your existing application. And read-replicas don’t help with write-intensive applications. 4. Multiple Database Instances – Amazon offers a fourth option: “You can implement partitioning,thereby spreading your data across multiple database Instances” (Link) However, Amazon does not offer any guidance or facilities to help you with this. “Multiple database instances” is not an RDS feature.  And Amazon doesn’t explain how to implement this idea. In fact, when asked, this is the response on an Amazon forum: Q: Is there any documents that describe the partition DB across multiple RDS? I need to use DB with more 1TB but exist a limitation during the create process, but I read in the any FAQ that you need to partition database, but I don’t find any documents that describe it. A: “DB partitioning/sharding is not an official feature of Amazon RDS or MySQL, but a technique to scale out database by using multiple database instances. The appropriate way to split data depends on the characteristics of the application or data set. Therefore, there is no concrete and specific guidance.” So now what? The answer is to scale out with ScaleBase. Amazon RDS with ScaleBase: What you get – MySQL Scalability! ScaleBase is specifically designed to scale out a single MySQL RDS instance into multiple MySQL instances. Critically, this is accomplished with no changes to your application code.  Your application continues to “see” one database.   ScaleBase does all the work of managing and enforcing an optimized data distribution policy to create multiple MySQL instances. With ScaleBase, data distribution, transactions, concurrency control, and two-phase commit are all 100% transparent and 100% ACID-compliant, so applications, services and tooling continue to interact with your distributed RDS as if it were a single MySQL instance. The result: now you can cost-effectively leverage multiple MySQL RDS instance to scale out write-intensive workloads to an unlimited number of users, transactions, and data. Amazon RDS with ScaleBase: What you keep – Everything! And how does this change your Amazon environment? 1. Keep your application, unchanged – There is no change your application development life-cycle at all.  You still use your existing development tools, frameworks and libraries.  Application quality assurance and testing cycles stay the same. And, critically, you stay with an ACID-compliant MySQL environment. 2. Keep your RDS value-added services – The value-added services that you rely on are all still available. Amazon will continue to handle database maintenance and updates for you. You can still leverage High Availability via Multi A-Z.  And, if it benefits youra application throughput, you can still use read replicas. 3. Keep your RDS administration – Finally the RDS monitoring and provisioning tools you rely on still work as they did before. With your one large MySQL instance, now split into multiple instances, you can actually use less expensive, smallersmaller available RDS hardware and continue to see better database performance. Conclusion Amazon RDS is a tremendous service, but it doesn’t offer solutions to scale beyond a single MySQL instance. Larger RDS instances get more expensive.  And when you max-out on the available hardware, you’re stuck.  Amazon recommends scaling out your single instance into multiple instances for transaction-intensive apps, but offers no services or guidance to help you. This is where ScaleBase comes in to save the day. It gives you a simple and effective way to create multiple MySQL RDS instances, while removing all the complexities typically caused by “DIY” sharding andwith no changes to your applications . With ScaleBase you continue to leverage the AWS/RDS ecosystem: commodity hardware and value added services like read replicas, multi A-Z, maintenance/updates and administration with monitoring tools and provisioning. SCALEBASE ON AMAZON If you’re curious to try ScaleBase on Amazon, it can be found here – Download NOW. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: MySQL, PostADay, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

    Read the article

  • SQL SERVER – A Quick Look at Logging and Ideas around Logging

    - by pinaldave
    This blog post is written in response to the T-SQL Tuesday post on Logging. When someone talks about logging, personally I get lots of ideas about it. I have seen logging as a very generic term. Let me ask you this question first before I continue writing about logging. What is the first thing comes to your mind when you hear word “Logging”? Now ask the same question to the guy standing next to you. I am pretty confident that you will get  a different answer from different people. I decided to do this activity and asked 5 SQL Server person the same question. Question: What is the first thing comes to your mind when you hear the word “Logging”? Strange enough I got a different answer every single time. Let me just list what answer I got from my friends. Let us go over them one by one. Output Clause The very first person replied output clause. Pretty interesting answer to start with. I see what exactly he was thinking. SQL Server 2005 has introduced a new OUTPUT clause. OUTPUT clause has access to inserted and deleted tables (virtual tables) just like triggers. OUTPUT clause can be used to return values to client clause. OUTPUT clause can be used with INSERT, UPDATE, or DELETE to identify the actual rows affected by these statements. Here are some references for Output Clause: OUTPUT Clause Example and Explanation with INSERT, UPDATE, DELETE Reasons for Using Output Clause – Quiz Tips from the SQL Joes 2 Pros Development Series – Output Clause in Simple Examples Error Logs I was expecting someone to mention Error logs when it is about logging. The error log is the most looked place when there is any error either with the application or there is an error with the operating system. I have kept the policy to check my server’s error log every day. The reason is simple – enough time in my career I have figured out that when I am looking at error logs I find something which I was not expecting. There are cases, when I noticed errors in the error log and I fixed them before end user notices it. Other common practices I always tell my DBA friends to do is that when any error happens they should find relevant entries in the error logs and document the same. It is quite possible that they will see the same error in the error log  and able to fix the error based on the knowledge base which they have created. There can be many different kinds of error log files exists in SQL Server as well – 1) SQL Server Error Logs 2) Windows Event Log 3) SQL Server Agent Log 4) SQL Server Profile Log 5) SQL Server Setup Log etc. Here are some references for Error Logs: Recycle Error Log – Create New Log file without Server Restart SQL Error Messages Change Data Capture I got surprised with this answer. I think more than the answer I was surprised by the person who had answered me this one. I always thought he was expert in HTML, JavaScript but I guess, one should never assume about others. Indeed one of the cool logging feature is Change Data Capture. Change Data Capture records INSERTs, UPDATEs, and DELETEs applied to SQL Server tables, and makes a record available of what changed, where, and when, in simple relational ‘change tables’ rather than in an esoteric chopped salad of XML. These change tables contain columns that reflect the column structure of the source table you have chosen to track, along with the metadata needed to understand the changes that have been made. Here are some references for Change Data Capture: Introduction to Change Data Capture (CDC) in SQL Server 2008 Tuning the Performance of Change Data Capture in SQL Server 2008 Download Script of Change Data Capture (CDC) CDC and TRUNCATE – Cannot truncate table because it is published for replication or enabled for Change Data Capture Dynamic Management View (DMV) I like this answer. If asked I would have not come up with DMV right away but in the spirit of the original question, I think DMV does log the data. DMV logs or stores or records the various data and activity on the SQL Server. Dynamic management views return server state information that can be used to monitor the health of a server instance, diagnose problems, and tune performance. One can get plethero of information from DMVs – High Availability Status, Query Executions Details, SQL Server Resources Status etc. Here are some references for Dynamic Management View (DMV): SQL SERVER – Denali – DMV Enhancement – sys.dm_exec_query_stats – New Columns DMV – sys.dm_os_windows_info – Information about Operating System DMV – sys.dm_os_wait_stats Explanation – Wait Type – Day 3 of 28 DMV sys.dm_exec_describe_first_result_set_for_object – Describes the First Result Metadata for the Module Transaction Log Impact Detection Using DMV – dm_tran_database_transactions Log Files I almost flipped with this final answer from my friend. This should be probably the first answer. Yes, indeed log file logs the SQL Server activities. One can write infinite things about log file. SQL Server uses log file with the extension .ldf to manage transactions and maintain database integrity. Log file ensures that valid data is written out to database and system is in a consistent state. Log files are extremely useful in case of the database failures as with the help of full backup file database can be brought in the desired state (point in time recovery is also possible). SQL Server database has three recovery models – 1) Simple, 2) Full and 3) Bulk Logged. Each of the model uses the .ldf file for performing various activities. It is very important to take the backup of the log files (along with full backup) as one never knows when backup of the log file come into the action and save the day! How to Stop Growing Log File Too Big Reduce the Virtual Log Files (VLFs) from LDF file Log File Growing for Model Database – model Database Log File Grew Too Big master Database Log File Grew Too Big SHRINKFILE and TRUNCATE Log File in SQL Server 2008 Can I just say I loved this month’s T-SQL Tuesday Question. It really provoked very interesting conversation around me. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Optimization, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

    Read the article

  • SQL – Migrate Database from SQL Server to NuoDB – A Quick Tutorial

    - by Pinal Dave
    Data is growing exponentially and every organization with growing data is thinking of next big innovation in the world of Big Data. Big data is a indeed a future for every organization at one point of the time. Just like every other next big thing, big data has its own challenges and issues. The biggest challenge associated with the big data is to find the ideal platform which supports the scalability and growth of the data. If you are a regular reader of this blog, you must be familiar with NuoDB. I have been working with NuoDB for a while and their recent release is the best thus far. NuoDB is an elastically scalable SQL database that can run on local host, datacenter and cloud-based resources. A key feature of the product is that it does not require sharding (read more here). Last week, I was able to install NuoDB in less than 90 seconds and have explored their Explorer and Admin sections. You can read about my experiences in these posts: SQL – Step by Step Guide to Download and Install NuoDB – Getting Started with NuoDB SQL – Quick Start with Admin Sections of NuoDB – Manage NuoDB Database SQL – Quick Start with Explorer Sections of NuoDB – Query NuoDB Database Many SQL Authority readers have been following me in my journey to evaluate NuoDB. One of the frequently asked questions I’ve received from you is if there is any way to migrate data from SQL Server to NuoDB. The fact is that there is indeed a way to do so and NuoDB provides a fantastic tool which can help users to do it. NuoDB Migrator is a command line utility that supports the migration of Microsoft SQL Server, MySQL, Oracle, and PostgreSQL schemas and data to NuoDB. The migration to NuoDB is a three-step process: NuoDB Migrator generates a schema for a target NuoDB database It loads data into the target NuoDB database It dumps data from the source database Let’s see how we can migrate our data from SQL Server to NuoDB using a simple three-step approach. But before we do that we will create a sample database in MSSQL and later we will migrate the same database to NuoDB: Setup Step 1: Build a sample data CREATE DATABASE [Test]; CREATE TABLE [Department]( [DepartmentID] [smallint] NOT NULL, [Name] VARCHAR(100) NOT NULL, [GroupName] VARCHAR(100) NOT NULL, [ModifiedDate] [datetime] NOT NULL, CONSTRAINT [PK_Department_DepartmentID] PRIMARY KEY CLUSTERED ( [DepartmentID] ASC ) ) ON [PRIMARY]; INSERT INTO Department SELECT * FROM AdventureWorks2012.HumanResources.Department; Note that I am using the SQL Server AdventureWorks database to build this sample table but you can build this sample table any way you prefer. Setup Step 2: Install Java 64 bit Before you can begin the migration process to NuoDB, make sure you have 64-bit Java installed on your computer. This is due to the fact that the NuoDB Migrator tool is built in Java. You can download 64-bit Java for Windows, Mac OSX, or Linux from the following link: http://java.com/en/download/manual.jsp. One more thing to remember is that you make sure that the path in your environment settings is set to your JAVA_HOME directory or else the tool will not work. Here is how you can do it: Go to My Computer >> Right Click >> Select Properties >> Click on Advanced System Settings >> Click on Environment Variables >> Click on New and enter the following values. Variable Name: JAVA_HOME Variable Value: C:\Program Files\Java\jre7 Make sure you enter your Java installation directory in the Variable Value field. Setup Step 3: Install JDBC driver for SQL Server. There are two JDBC drivers available for SQL Server.  Select the one you prefer to use by following one of the two links below: Microsoft JDBC Driver jTDS JDBC Driver In this example we will be using jTDS JDBC driver. Once you download the driver, move the driver to your NuoDB installation folder. In my case, I have moved the JAR file of the driver into the C:\Program Files\NuoDB\tools\migrator\jar folder as this is my NuoDB installation directory. Now we are all set to start the three-step migration process from SQL Server to NuoDB: Migration Step 1: NuoDB Schema Generation Here is the command I use to generate a schema of my SQL Server Database in NuoDB. First I go to the folder C:\Program Files\NuoDB\tools\migrator\bin and execute the nuodb-migrator.bat file. Note that my database name is ‘test’. Additionally my username and password is also ‘test’. You can see that my SQL Server database is running on my localhost on port 1433. Additionally, the schema of the table is ‘dbo’. nuodb-migrator schema –source.driver=net.sourceforge.jtds.jdbc.Driver –source.url=jdbc:jtds:sqlserver://localhost:1433/ –source.username=test –source.password=test –source.catalog=test –source.schema=dbo –output.path=/tmp/schema.sql The above script will generate a schema of all my SQL Server tables and will put it in the folder C:\tmp\schema.sql . You can open the schema.sql file and execute this file directly in your NuoDB instance. You can follow the link here to see how you can execute the SQL script in NuoDB. Please note that if you have not yet created the schema in the NuoDB database, you should create it before executing this step. Step 2: Generate the Dump File of the Data Once you have recreated your schema in NuoDB from SQL Server, the next step is very easy. Here we create a CSV format dump file, which will contain all the data from all the tables from the SQL Server database. The command to do so is very similar to the above command. Be aware that this step may take a bit of time based on your database size. nuodb-migrator dump –source.driver=net.sourceforge.jtds.jdbc.Driver –source.url=jdbc:jtds:sqlserver://localhost:1433/ –source.username=test –source.password=test –source.catalog=test –source.schema=dbo –output.type=csv –output.path=/tmp/dump.cat Once the above command is successfully executed you can find your CSV file in the C:\tmp\ folder. However, you do not have to do anything manually. The third and final step will take care of completing the migration process. Migration Step 3: Load the Data into NuoDB After building schema and taking a dump of the data, the very next step is essential and crucial. It will take the CSV file and load it into the NuoDB database. nuodb-migrator load –target.url=jdbc:com.nuodb://localhost:48004/mytest –target.schema=dbo –target.username=test –target.password=test –input.path=/tmp/dump.cat Please note that in the above script we are now targeting the NuoDB database, which we have already created with the name of “MyTest”. If the database does not exist, create it manually before executing the above script. I have kept the username and password as “test”, but please make sure that you create a more secure password for your database for security reasons. Voila!  You’re Done That’s it. You are done. It took 3 setup and 3 migration steps to migrate your SQL Server database to NuoDB.  You can now start exploring the database and build excellent, scale-out applications. In this blog post, I have done my best to come up with simple and easy process, which you can follow to migrate your app from SQL Server to NuoDB. Download NuoDB I strongly encourage you to download NuoDB and go through my 3-step migration tutorial from SQL Server to NuoDB. Additionally here are two very important blog post from NuoDB CTO Seth Proctor. He has written excellent blog posts on the concept of the Administrative Domains. NuoDB has this concept of an Administrative Domain, which is a collection of hosts that can run one or multiple databases.  Each database has its own TEs and SMs, but all are managed within the Admin Console for that particular domain. http://www.nuodb.com/techblog/2013/03/11/getting-started-provisioning-a-domain/ http://www.nuodb.com/techblog/2013/03/14/getting-started-running-a-database/ Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: NuoDB

    Read the article

  • Developer’s Life – Attitude and Communication – They Can Cause Problems – Notes from the Field #027

    - by Pinal Dave
    [Note from Pinal]: This is a 27th episode of Notes from the Field series. The biggest challenge for anyone is to understand human nature. We human have so many things on our mind at any moment of time. There are cases when what we say is not what we mean and there are cases where what we mean we do not say. We do say and things as per our mood and our agenda in mind. Sometimes there are incidents when our attitude creates confusion in the communication and we end up creating a situation which is absolutely not warranted. In this episode of the Notes from the Field series database expert Mike Walsh explains a very crucial issue we face in our career, which is not technical but more to relate to human nature. Read on this may be the best blog post you might read in recent times. In this week’s note from the field, I’m taking a slight departure from technical knowledge and concepts explained. We’ll be back to it next week, I’m sure. Pinal wanted us to explain some of the issues we bump into and how we see some of our customers arrive at problem situations and how we have helped get them back on the right track. Often it is a technical problem we are officially solving – but in a lot of cases as a consultant, we are really helping fix some communication difficulties. This is a technical blog post and not an “advice column” in a newspaper – but the longer I am a consultant, the more years I add to my experience in technology the more I learn that the vast majority of the problems we encounter have “soft skills” included in the chain of causes for the issue we are helping overcome. This is not going to be exhaustive but I hope that sharing four pieces of advice inspired by real issues starts a process of searching for places where we can be the cause of these challenges and look at fixing them in ourselves. Or perhaps we can begin looking at resolving them in teams that we manage. I’ll share three statements that I’ve either heard, read or said and talk about some of the communication or attitude challenges highlighted by the statement. 1 – “But that’s the SAN Administrator’s responsibility…” I heard that early on in my consulting career when talking with a customer who had serious corruption and no good recent backups – potentially no good backups at all. The statement doesn’t have to be this one exactly, but the attitude here is an attitude of “my job stops here, and I don’t care about the intent or principle of why I’m here.” It’s also a situation of having the attitude that as long as there is someone else to blame, I’m fine…  You see in this case, the DBA had a suspicion that the backups were not being handled right.  They were the DBA and they knew that they had responsibility to ensure SQL backups were good to go – it’s a basic requirement of a production DBA. In my “As A DBA Where Do I start?!” presentation, I argue that is job #1 of a DBA. But in this case, the thought was that there was someone else to blame. Rather than create extra work and take on responsibility it was decided to just let it be another team’s responsibility. This failed the company, the company’s customers and no one won. As technologists – we should strive to go the extra mile. If there is a lack of clarity around roles and responsibilities and we know it – we should push to get it resolved. Especially as the DBAs who should act as the advocates of the data contained in the databases we are responsible for. 2 – “We’ve always done it this way, it’s never caused a problem before!” Complacency. I have to say that many failures I’ve been paid good money to help recover from would have not happened had it been for an attitude of complacency. If any thoughts like this have entered your mind about your situation you may be suffering from it. If, while reading this, you get this sinking feeling in your stomach about that one thing you know should be fixed but haven’t done it.. Why don’t you stop and go fix it then come back.. “We should have better backups, but we’re on a SAN so we should be fine really.” “Technically speaking that could happen, but what are the chances?” “We’ll just clean that up as a fast follow” ..and so on. In the age of tightening IT budgets, increased expectations of up time, availability and performance there is no room for complacency. Our customers and business units expect – no demand – the best. Complacency says “we will give you second best or hopefully good enough and we accept the risk and know this may hurt us later. Sometimes an organization will opt for “good enough” and I agree with the concept that at times the perfect can be the enemy of the good. But when we make those decisions in a vacuum and are not reporting them up and discussing them as an organization that is different. That is us unilaterally choosing to do something less than the best and purposefully playing a game of chance. 3 – “This device must accept interference from other devices but not create any” I’ve paraphrased this one – but it’s something the Federal Communications Commission – a federal agency in the United States that regulates electronic communication – requires of all manufacturers of any device that could cause or receive interference electronically. I blogged in depth about this here (http://www.straightpathsql.com/archives/2011/07/relationship-advice-from-the-fcc/) so I won’t go into much detail other than to say this… If we all operated more on the premise that we should do our best to not be the cause of conflict, and to be less easily offended and less upset when we perceive offense life would be easier in many areas! This doesn’t always cause the issues we are called in to help out. Not directly. But where we see it is in unhealthy relationships between the various technology teams at a client. We’ll see teams hoarding knowledge, not sharing well with others and almost working against other teams instead of working with them. If you trace these problems back far enough it often stems from someone or some group of people violating this principle from the FCC. To Sum It Up Technology problems are easy to solve. At Linchpin People we help many customers get past the toughest technological challenge – and at the end of the day it is really just a repeatable process of pattern based troubleshooting, logical thinking and starting at the beginning and carefully stepping through to the end. It’s easy at the end of the day. The tough part of what we do as consultants is the people skills. Being able to help get teams working together, being able to help teams take responsibility, to improve team to team communication? That is the difficult part, and we get to use the soft skills on every engagement. Work on professional development (http://professionaldevelopment.sqlpass.org/) and see continuing improvement here, not just with technology. I can teach just about anyone how to be an excellent DBA and performance tuner, but some of these soft skills are much more difficult to teach. If you want to get started with performance analytics and triage of virtualized SQL Servers with the help of experts, read more over at Fix Your SQL Server. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Notes from the Field, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

    Read the article

  • SQL SERVER – 3 Online SQL Courses at Pluralsight and Free Learning Resources

    - by pinaldave
    Usain Bolt is an inspiration for all. He broke his own record multiple times because he wanted to do better! Read more about him on wikipedia. He is great and indeed fastest man on the planet. Usain Bolt – World’s Fastest Man “Can you teach me SQL Server Performance Tuning?” This is one of the most popular questions which I receive all the time. The answer is YES. I would love to do performance tuning training for anyone, anywhere.  It is my favorite thing to do, and it is my favorite thing to train others in.  If possible, I would love to do training 24 hours a day, 7 days a week, 365 days a year.  To me, it doesn’t feel like a job. Of course, as much as I would love to do performance tuning 24/7/365, obviously I am just one human being and can only be in one place t one time.  It is also very difficult to train more than one person at a time, and it is difficult to train two or more people at a time, especially when the two people are at different levels.  I am also limited by geography.  I live in India, and adjust to my own time zone.  Trying to teach a live course from India to someone whose time zone is 12 or more hours off of mine is very difficult.  If I am trying to teach at 2 am, I am sure I am not at my best! There was only one solution to scale – Online Trainings. I have built 3 different courses on SQL Server Performance Tuning with Pluralsight. Now I have no problem – I am 100% scalable and available 24/7 and 365. You can make me say the same things again and again till you find it right. I am in your mobile, PC as well as on XBOX. This is why I am such a big fan of online courses.  I have recorded many performance tuning classes and you can easily access them online, at your own time.  And don’t think that just because these aren’t live classes you won’t be able to get any feedback from me.  I encourage all my viewers to go ahead and ask me questions by e-mail, Twitter, Facebook, or whatever way you can get a hold of me. Here are details of three of my courses with Pluralsight. I suggest you go over the description of the course. As an author of the course, I have few FREE codes for watching the free courses. Please leave a comment with your valid email address, I will send a few of them to random winners. SQL Server Performance: Introduction to Query Tuning  SQL Server performance tuning is an art to master – for developers and DBAs alike. This course takes a systematic approach to planning, analyzing, debugging and troubleshooting common query-related performance problems. This includes an introduction to understanding execution plans inside SQL Server. In this almost four hour course we cover following important concepts. Introduction 10:22 Execution Plan Basics 45:59 Essential Indexing Techniques 20:19 Query Design for Performance 50:16 Performance Tuning Tools 01:15:14 Tips and Tricks 25:53 Checklist: Performance Tuning 07:13 The duration of each module is mentioned besides the name of the module. SQL Server Performance: Indexing Basics This course teaches you how to master the art of performance tuning SQL Server by better understanding indexes. In this almost two hour course we cover following important concepts. Introduction 02:03 Fundamentals of Indexing 22:21 Practical Indexing Implementation Techniques 37:25 Index Maintenance 16:33 Introduction to ColumnstoreIndex 08:06 Indexing Practical Performance Tips and Tricks 24:56 Checklist : Index and Performance 07:29 The duration of each module is mentioned besides the name of the module. SQL Server Questions and Answers This course is designed to help you better understand how to use SQL Server effectively. The course presents many of the common misconceptions about SQL Server, and then carefully debunks those misconceptions with clear explanations and short but compelling demos, showing you how SQL Server really works. In this almost 2 hours and 15 minutes course we cover following important concepts. Introduction 00:54 Retrieving IDENTITY value using @@IDENTITY 08:38 Concepts Related to Identity Values 04:15 Difference between WHERE and HAVING 05:52 Order in WHERE clause 07:29 Concepts Around Temporary Tables and Table Variables 09:03 Are stored procedures pre-compiled? 05:09 UNIQUE INDEX and NULLs problem 06:40 DELETE VS TRUNCATE 06:07 Locks and Duration of Transactions 15:11 Nested Transaction and Rollback 09:16 Understanding Date/Time Datatypes 07:40 Differences between VARCHAR and NVARCHAR datatypes 06:38 Precedence of DENY and GRANT security permissions 05:29 Identify Blocking Process 06:37 NULLS usage with Dynamic SQL 08:03 Appendix Tips and Tricks with Tools 20:44 The duration of each module is mentioned besides the name of the module. SQL in Sixty Seconds You will have to login and to get subscribed to the courses to view them. Here are my free video learning resources SQL in Sixty Seconds. These are 60 second video which I have built on various subjects related to SQL Server. Do let me know what you think about them? Here are three of my latest videos: Identify Most Resource Intensive Queries – SQL in Sixty Seconds #028 Copy Column Headers from Resultset – SQL in Sixty Seconds #027 Effect of Collation on Resultset – SQL in Sixty Seconds #026 You can watch and learn at your own pace.  Then you can easily ask me any questions you have.  E-mail is easiest, but for really tough questions I’m willing to talk on Skype, Gtalk, or even Facebook chat.  Please do watch and then talk with me, I am always available on the internet! Here is the video of the world’s fastest man.Usain St. Leo Bolt inspires us that we all do better than best. We can go the next level of our own record. We all can improve if we have a will and dedication.  Watch the video from 5:00 mark. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL in Sixty Seconds, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, SQL Training, SQLServer, T SQL, Technology, Video

    Read the article

  • SQL SERVER – SSMS Automatically Generates TOP (100) PERCENT in Query Designer

    - by pinaldave
    Earlier this week, I was surfing various SQL forums to see what kind of help developer need in the SQL Server world. One of the question indeed caught my attention. I am here regenerating complete question as well scenario to illustrate the point in a precise manner. Additionally, I have added added second part of the question to give completeness. Question: I am trying to create a view in Query Designer (not in the New Query Window). Every time I am trying to create a view it always adds  TOP (100) PERCENT automatically on the T-SQL script. No matter what I do, it always automatically adds the TOP (100) PERCENT to the script. I have attempted to copy paste from notepad, build a query and a few other things – there is no success. I am really not sure what I am doing wrong with Query Designer. Here is my query script: (I use AdventureWorks as a sample database) SELECT Person.Address.AddressID FROM Person.Address INNER JOIN Person.AddressType ON Person.Address.AddressID = Person.AddressType.AddressTypeID ORDER BY Person.Address.AddressID This script automatically replaces by following query: SELECT TOP (100) PERCENT Person.Address.AddressID FROM Person.Address INNER JOIN Person.AddressType ON Person.Address.AddressID = Person.AddressType.AddressTypeID ORDER BY Person.Address.AddressID However, when I try to do the same from New Query Window it works totally fine. However, when I attempt to create a view of the same query it gives following error. Msg 1033, Level 15, State 1, Procedure myView, Line 6 The ORDER BY clause is invalid in views, inline functions, derived tables, subqueries, and common table expressions, unless TOP, OFFSET or FOR XML is also specified. It is pretty clear to me now that the script which I have written seems to need TOP (100) PERCENT, so Query . Why do I need it? Is there any work around to this issue. I particularly find this question pretty interesting as it really touches the fundamentals of the T-SQL query writing. Please note that the query which is automatically changed is not in New Query Editor but opened from SSMS using following way. Database >> Views >> Right Click >> New View (see the image below) Answer: The answer to the above question can be very long but I will keep it simple and to the point. There are three things to discuss in above script 1) Reason for Error 2) Reason for Auto generates TOP (100) PERCENT and 3) Potential solutions to the above error. Let us quickly see them in detail. 1) Reason for Error The reason for error is already given in the error. ORDER BY is invalid in the views and a few other objects. One has to use TOP or other keywords along with it. The way semantics of the query works where optimizer only follows(honors) the ORDER BY in the same scope or the same SELECT/UPDATE/DELETE statement. There is a possibility that one can order after the scope of the view again the efforts spend to order view will be wasted. The final resultset of the query always follows the final ORDER BY or outer query’s order and due to the same reason optimizer follows the final order of the query and not of the views (as view will be used in another query for further processing e.g. in SELECT statement). Due to same reason ORDER BY is now allowed in the view. For further accuracy and clear guidance I suggest you read this blog post by Query Optimizer Team. They have explained it very clear manner the same subject. 2) Reason for Auto Generated TOP (100) PERCENT One of the most popular workaround to above error is to use TOP (100) PERCENT in the view. Now TOP (100) PERCENT allows user to use ORDER BY in the query and allows user to overcome above error which we discussed. This gives the impression to the user that they have resolved the error and successfully able to use ORDER BY in the View. Well, this is incorrect as well. The way this works is when TOP (100) PERCENT is used the result is not guaranteed as well it is ignored in our the query where the view is used. Here is the blog post on this subject: Interesting Observation – TOP 100 PERCENT and ORDER BY. Now when you create a new view in the SSMS and build a query with ORDER BY to avoid the error automatically it adds the TOP 100 PERCENT. Here is the connect item for the same issue. I am sure there will be more connect items as well but I could not find them. 3) Potential Solutions If you are reading this post from the beginning in that case, it is clear by now that ORDER BY should not be used in the View as it does not serve any purpose unless there is a specific need of it. If you are going to use TOP 100 PERCENT with ORDER BY there is absolutely no need of using ORDER BY rather avoid using it all together. Here is another blog post of mine which describes the same subject ORDER BY Does Not Work – Limitation of the Views Part 1. It is valid to use ORDER BY in a view if there is a clear business need of using TOP with any other percentage lower than 100 (for example TOP 10 PERCENT or TOP 50 PERCENT etc). In most of the cases ORDER BY is not needed in the view and it should be used in the most outer query for present result in desired order. User can remove TOP 100 PERCENT and ORDER BY from the view before using the view in any query or procedure. In the most outer query there should be ORDER BY as per the business need. I think this sums up the concept in a few words. This is a very long topic and not easy to illustrate in one single blog post. I welcome your comments and suggestions. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, SQL View, T SQL, Technology

    Read the article

  • SQL SERVER – SSMS: Disk Usage Report

    - by Pinal Dave
    Let us start with humor!  I think we the series on various reports, we come to a logical point. We covered all the reports at server level. This means the reports we saw were targeted towards activities that are related to instance level operations. These are mostly like how a doctor diagnoses a patient. At this point I am reminded of a dialog which I read somewhere: Patient: Doc, It hurts when I touch my head. Doc: Ok, go on. What else have you experienced? Patient: It hurts even when I touch my eye, it hurts when I touch my arms, it even hurts when I touch my feet, etc. Doc: Hmmm … Patient: I feel it hurts when I touch anywhere in my body. Doc: Ahh … now I get it. You need a plaster to your finger John. Sometimes the server level gives an indicator to what is happening in the system, but we need to get to the root cause for a specific database. So, this is the first blog in series where we would start discussing about database level reports. To launch database level reports, expand selected server in Object Explorer, expand the Databases folder, and then right-click any database for which we want to look at reports. From the menu, select Reports, then Standard Reports, and then any of database level reports. In this blog, we would talk about four “disk” reports because they are similar: Disk Usage Disk Usage by Top Tables Disk Usage by Table Disk Usage by Partition Disk Usage This report shows multiple information about the database. Let us discuss them one by one.  We have divided the output into 5 different sections. Section 1 shows the high level summary of the database. It shows the space used by database files (mdf and ldf). Under the hood, the report uses, various DMVs and DBCC Commands, it is using sys.data_spaces and DBCC SHOWFILESTATS. Section 2 and 3 are pie charts. One for data file allocation and another for the transaction log file. Pie chart for “Data Files Space Usage (%)” shows space consumed data, indexes, allocated to the SQL Server database, and unallocated space which is allocated to the SQL Server database but not yet filled with anything. “Transaction Log Space Usage (%)” used DBCC SQLPERF (LOGSPACE) and shows how much empty space we have in the physical transaction log file. Section 4 shows the data from Default Trace and looks at Event IDs 92, 93, 94, 95 which are for “Data File Auto Grow”, “Log File Auto Grow”, “Data File Auto Shrink” and “Log File Auto Shrink” respectively. Here is an expanded view for that section. If default trace is not enabled, then this section would be replaced by the message “Trace Log is disabled” as highlighted below. Section 5 of the report uses DBCC SHOWFILESTATS to get information. Here is the enhanced version of that section. This shows the physical layout of the file. In case you have In-Memory Objects in the database (from SQL Server 2014), then report would show information about those as well. Here is the screenshot taken for a different database, which has In-Memory table. I have highlighted new things which are only shown for in-memory database. The new sections which are highlighted above are using sys.dm_db_xtp_checkpoint_files, sys.database_files and sys.data_spaces. The new type for in-memory OLTP is ‘FX’ in sys.data_space. The next set of reports is targeted to get information about a table and its storage. These reports can answer questions like: Which is the biggest table in the database? How many rows we have in table? Is there any table which has a lot of reserved space but its unused? Which partition of the table is having more data? Disk Usage by Top Tables This report provides detailed data on the utilization of disk space by top 1000 tables within the Database. The report does not provide data for memory optimized tables. Disk Usage by Table This report is same as earlier report with few difference. First Report shows only 1000 rows First Report does order by values in DMV sys.dm_db_partition_stats whereas second one does it based on name of the table. Both of the reports have interactive sort facility. We can click on any column header and change the sorting order of data. Disk Usage by Partition This report shows the distribution of the data in table based on partition in the table. This is so similar to previous output with the partition details now. Here is the query taken from profiler. SELECT row_number() OVER (ORDER BY a1.used_page_count DESC, a1.index_id) AS row_number ,      (dense_rank() OVER (ORDER BY a5.name, a2.name))%2 AS l1 ,      a1.OBJECT_ID ,      a5.name AS [schema] ,       a2.name ,       a1.index_id ,       a3.name AS index_name ,       a3.type_desc ,       a1.partition_number ,       a1.used_page_count * 8 AS total_used_pages ,       a1.reserved_page_count * 8 AS total_reserved_pages ,       a1.row_count FROM sys.dm_db_partition_stats a1 INNER JOIN sys.all_objects a2  ON ( a1.OBJECT_ID = a2.OBJECT_ID) AND a1.OBJECT_ID NOT IN (SELECT OBJECT_ID FROM sys.tables WHERE is_memory_optimized = 1) INNER JOIN sys.schemas a5 ON (a5.schema_id = a2.schema_id) LEFT OUTER JOIN  sys.indexes a3  ON ( (a1.OBJECT_ID = a3.OBJECT_ID) AND (a1.index_id = a3.index_id) ) WHERE (SELECT MAX(DISTINCT partition_number) FROM sys.dm_db_partition_stats a4 WHERE (a4.OBJECT_ID = a1.OBJECT_ID)) >= 1 AND a2.TYPE <> N'S' AND  a2.TYPE <> N'IT' ORDER BY a5.name ASC, a2.name ASC, a1.index_id, a1.used_page_count DESC, a1.partition_number Using all of the above reports, you should be able to get the usage of database files and also space used by tables. I think this is too much disk information for a single blog and I hope you have used them in the past to get data. Do let me know if you found anything interesting using these reports in your environments. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL Tagged: SQL Reports

    Read the article

  • SQL SERVER – Faster SQL Server Databases and Applications – Power and Control with SafePeak Caching Options

    - by Pinal Dave
    Update: This blog post is written based on the SafePeak, which is available for free download. Today, I’d like to examine more closely one of my preferred technologies for accelerating SQL Server databases, SafePeak. Safepeak’s software provides a variety of advanced data caching options, techniques and tools to accelerate the performance and scalability of SQL Server databases and applications. I’d like to look more closely at some of these options, as some of these capabilities could help you address lagging database and performance on your systems. To better understand the available options, it is best to start by understanding the difference between the usual “Basic Caching” vs. SafePeak’s “Dynamic Caching”. Basic Caching Basic Caching (or the stale and static cache) is an ability to put the results from a query into cache for a certain period of time. It is based on TTL, or Time-to-live, and is designed to stay in cache no matter what happens to the data. For example, although the actual data can be modified due to DML commands (update/insert/delete), the cache will still hold the same obsolete query data. Meaning that with the Basic Caching is really static / stale cache.  As you can tell, this approach has its limitations. Dynamic Caching Dynamic Caching (or the non-stale cache) is an ability to put the results from a query into cache while maintaining the cache transaction awareness looking for possible data modifications. The modifications can come as a result of: DML commands (update/insert/delete), indirect modifications due to triggers on other tables, executions of stored procedures with internal DML commands complex cases of stored procedures with multiple levels of internal stored procedures logic. When data modification commands arrive, the caching system identifies the related cache items and evicts them from cache immediately. In the dynamic caching option the TTL setting still exists, although its importance is reduced, since the main factor for cache invalidation (or cache eviction) become the actual data updates commands. Now that we have a basic understanding of the differences between “basic” and “dynamic” caching, let’s dive in deeper. SafePeak: A comprehensive and versatile caching platform SafePeak comes with a wide range of caching options. Some of SafePeak’s caching options are automated, while others require manual configuration. Together they provide a complete solution for IT and Data managers to reach excellent performance acceleration and application scalability for  a wide range of business cases and applications. Automated caching of SQL Queries: Fully/semi-automated caching of all “read” SQL queries, containing any types of data, including Blobs, XMLs, Texts as well as all other standard data types. SafePeak automatically analyzes the incoming queries, categorizes them into SQL Patterns, identifying directly and indirectly accessed tables, views, functions and stored procedures; Automated caching of Stored Procedures: Fully or semi-automated caching of all read” stored procedures, including procedures with complex sub-procedure logic as well as procedures with complex dynamic SQL code. All procedures are analyzed in advance by SafePeak’s  Metadata-Learning process, their SQL schemas are parsed – resulting with a full understanding of the underlying code, objects dependencies (tables, views, functions, sub-procedures) enabling automated or semi-automated (manually review and activate by a mouse-click) cache activation, with full understanding of the transaction logic for cache real-time invalidation; Transaction aware cache: Automated cache awareness for SQL transactions (SQL and in-procs); Dynamic SQL Caching: Procedures with dynamic SQL are pre-parsed, enabling easy cache configuration, eliminating SQL Server load for parsing time and delivering high response time value even in most complicated use-cases; Fully Automated Caching: SQL Patterns (including SQL queries and stored procedures) that are categorized by SafePeak as “read and deterministic” are automatically activated for caching; Semi-Automated Caching: SQL Patterns categorized as “Read and Non deterministic” are patterns of SQL queries and stored procedures that contain reference to non-deterministic functions, like getdate(). Such SQL Patterns are reviewed by the SafePeak administrator and in usually most of them are activated manually for caching (point and click activation); Fully Dynamic Caching: Automated detection of all dependent tables in each SQL Pattern, with automated real-time eviction of the relevant cache items in the event of “write” commands (a DML or a stored procedure) to one of relevant tables. A default setting; Semi Dynamic Caching: A manual cache configuration option enabling reducing the sensitivity of specific SQL Patterns to “write” commands to certain tables/views. An optimization technique relevant for cases when the query data is either known to be static (like archive order details), or when the application sensitivity to fresh data is not critical and can be stale for short period of time (gaining better performance and reduced load); Scheduled Cache Eviction: A manual cache configuration option enabling scheduling SQL Pattern cache eviction based on certain time(s) during a day. A very useful optimization technique when (for example) certain SQL Patterns can be cached but are time sensitive. Example: “select customers that today is their birthday”, an SQL with getdate() function, which can and should be cached, but the data stays relevant only until 00:00 (midnight); Parsing Exceptions Management: Stored procedures that were not fully parsed by SafePeak (due to too complex dynamic SQL or unfamiliar syntax), are signed as “Dynamic Objects” with highest transaction safety settings (such as: Full global cache eviction, DDL Check = lock cache and check for schema changes, and more). The SafePeak solution points the user to the Dynamic Objects that are important for cache effectiveness, provides easy configuration interface, allowing you to improve cache hits and reduce cache global evictions. Usually this is the first configuration in a deployment; Overriding Settings of Stored Procedures: Override the settings of stored procedures (or other object types) for cache optimization. For example, in case a stored procedure SP1 has an “insert” into table T1, it will not be allowed to be cached. However, it is possible that T1 is just a “logging or instrumentation” table left by developers. By overriding the settings a user can allow caching of the problematic stored procedure; Advanced Cache Warm-Up: Creating an XML-based list of queries and stored procedure (with lists of parameters) for periodically automated pre-fetching and caching. An advanced tool allowing you to handle more rare but very performance sensitive queries pre-fetch them into cache allowing high performance for users’ data access; Configuration Driven by Deep SQL Analytics: All SQL queries are continuously logged and analyzed, providing users with deep SQL Analytics and Performance Monitoring. Reduce troubleshooting from days to minutes with database objects and SQL Patterns heat-map. The performance driven configuration helps you to focus on the most important settings that bring you the highest performance gains. Use of SafePeak SQL Analytics allows continuous performance monitoring and analysis, easy identification of bottlenecks of both real-time and historical data; Cloud Ready: Available for instant deployment on Amazon Web Services (AWS). As you can see, there are many options to configure SafePeak’s SQL Server database and application acceleration caching technology to best fit a lot of situations. If you’re not familiar with their technology, they offer free-trial software you can download that comes with a free “help session” to help get you started. You can access the free trial here. Also, SafePeak is available to use on Amazon Cloud. 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

    Read the article

  • SQL SERVER – Core Concepts – Elasticity, Scalability and ACID Properties – Exploring NuoDB an Elastically Scalable Database System

    - by pinaldave
    I have been recently exploring Elasticity and Scalability attributes of databases. You can see that in my earlier blog posts about NuoDB where I wanted to look at Elasticity and Scalability concepts. The concepts are very interesting, and intriguing as well. I have discussed these concepts with my friend Joyti M and together we have come up with this interesting read. The goal of this article is to answer following simple questions What is Elasticity? What is Scalability? How ACID properties vary from NOSQL Concepts? What are the prevailing problems in the current database system architectures? Why is NuoDB  an innovative and welcome change in database paradigm? Elasticity This word’s original form is used in many different ways and honestly it does do a decent job in holding things together over the years as a person grows and contracts. Within the tech world, and specifically related to software systems (database, application servers), it has come to mean a few things - allow stretching of resources without reaching the breaking point (on demand). What are resources in this context? Resources are the usual suspects – RAM/CPU/IO/Bandwidth in the form of a container (a process or bunch of processes combined as modules). When it is about increasing resources the simplest idea which comes to mind is the addition of another container. Another container means adding a brand new physical node. When it is about adding a new node there are two questions which comes to mind. 1) Can we add another node to our software system? 2) If yes, does adding new node cause downtime for the system? Let us assume we have added new node, let us see what the new needs of the system are when a new node is added. Balancing incoming requests to multiple nodes Synchronization of a shared state across multiple nodes Identification of “downstate” and resolution action to bring it to “upstate” Well, adding a new node has its advantages as well. Here are few of the positive points Throughput can increase nearly horizontally across the node throughout the system Response times of application will increase as in-between layer interactions will be improved Now, Let us put the above concepts in the perspective of a Database. When we mention the term “running out of resources” or “application is bound to resources” the resources can be CPU, Memory or Bandwidth. The regular approach to “gain scalability” in the database is to look around for bottlenecks and increase the bottlenecked resource. When we have memory as a bottleneck we look at the data buffers, locks, query plans or indexes. After a point even this is not enough as there needs to be an efficient way of managing such large workload on a “single machine” across memory and CPU bound (right kind of scheduling)  workload. We next move on to either read/write separation of the workload or functionality-based sharing so that we still have control of the individual. But this requires lots of planning and change in client systems in terms of knowing where to go/update/read and for reporting applications to “aggregate the data” in an intelligent way. What we ideally need is an intelligent layer which allows us to do these things without us getting into managing, monitoring and distributing the workload. Scalability In the context of database/applications, scalability means three main things Ability to handle normal loads without pressure E.g. X users at the Y utilization of resources (CPU, Memory, Bandwidth) on the Z kind of hardware (4 processor, 32 GB machine with 15000 RPM SATA drives and 1 GHz Network switch) with T throughput Ability to scale up to expected peak load which is greater than normal load with acceptable response times Ability to provide acceptable response times across the system E.g. Response time in S milliseconds (or agreed upon unit of measure) – 90% of the time The Issue – Need of Scale In normal cases one can plan for the load testing to test out normal, peak, and stress scenarios to ensure specific hardware meets the needs. With help from Hardware and Software partners and best practices, bottlenecks can be identified and requisite resources added to the system. Unfortunately this vertical scale is expensive and difficult to achieve and most of the operational people need the ability to scale horizontally. This helps in getting better throughput as there are physical limits in terms of adding resources (Memory, CPU, Bandwidth and Storage) indefinitely. Today we have different options to achieve scalability: Read & Write Separation The idea here is to do actual writes to one store and configure slaves receiving the latest data with acceptable delays. Slaves can be used for balancing out reads. We can also explore functional separation or sharing as well. We can separate data operations by a specific identifier (e.g. region, year, month) and consolidate it for reporting purposes. For functional separation the major disadvantage is when schema changes or workload pattern changes. As the requirement grows one still needs to deal with scale need in manual ways by providing an abstraction in the middle tier code. Using NOSQL solutions The idea is to flatten out the structures in general to keep all values which are retrieved together at the same store and provide flexible schema. The issue with the stores is that they are compromising on mostly consistency (no ACID guarantees) and one has to use NON-SQL dialect to work with the store. The other major issue is about education with NOSQL solutions. Would one really want to make these compromises on the ability to connect and retrieve in simple SQL manner and learn other skill sets? Or for that matter give up on ACID guarantee and start dealing with consistency issues? Hybrid Deployment – Mac, Linux, Cloud, and Windows One of the challenges today that we see across On-premise vs Cloud infrastructure is a difference in abilities. Take for example SQL Azure – it is wonderful in its concepts of throttling (as it is shared deployment) of resources and ability to scale using federation. However, the same abilities are not available on premise. This is not a mistake, mind you – but a compromise of the sweet spot of workloads, customer requirements and operational SLAs which can be supported by the team. In today’s world it is imperative that databases are available across operating systems – which are a commodity and used by developers of all hues. An Ideal Database Ability List A system which allows a linear scale of the system (increase in throughput with reasonable response time) with the addition of resources A system which does not compromise on the ACID guarantees and require developers to learn new paradigms A system which does not force fit a new way interacting with database by learning Non-SQL dialect A system which does not force fit its mechanisms for providing availability across its various modules. Well NuoDB is the first database which has all of the above abilities and much more. In future articles I will cover my hands-on experience with it. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: NuoDB

    Read the article

  • SQL SERVER – Data Sources and Data Sets in Reporting Services SSRS

    - by Pinal Dave
    This example is from the Beginning SSRS by Kathi Kellenberger. Supporting files are available with a free download from the www.Joes2Pros.com web site. This example is from the Beginning SSRS. Supporting files are available with a free download from the www.Joes2Pros.com web site. Connecting to Your Data? When I was a child, the telephone book was an important part of my life. Maybe I was just a nerd, but I enjoyed getting a new book every year to page through to learn about the businesses in my small town or to discover where some of my school acquaintances lived. It was also the source of maps to my town’s neighborhoods and the towns that surrounded me. To make a phone call, I would need a telephone number. In order to find a telephone number, I had to know how to use the telephone book. That seems pretty simple, but it resembles connecting to any data. You have to know where the data is and how to interact with it. A data source is the connection information that the report uses to connect to the database. You have two choices when creating a data source, whether to embed it in the report or to make it a shared resource usable by many reports. Data Sources and Data Sets A few basic terms will make the upcoming choses make more sense. What database on what server do you want to connect to? It would be better to just ask… “what is your data source?” The connection you need to make to get your reports data is called a data source. If you connected to a data source (like the JProCo database) there may be hundreds of tables. You probably only want data from just a few tables. This means you want to write a specific query against this data source. A query on a data source to get just the records you need for an SSRS report is called a Data Set. Creating a local Data Source You can connect embed a connection from your report directly to your JProCo database which (let’s say) is installed on a server named Reno. If you move JProCo to a new server named Tampa then you need to update the Data Set. If you have 10 reports in one project that were all pointing to the JProCo database on the Reno server then they would all need to be updated at once. It’s possible to make a project level Data Source and have each report use that. This means one change can fix all 10 reports at once. This would be called a Shared Data Source. Creating a Shared Data Source The best advice I can give you is to create shared data sources. The reason I recommend this is that if a database moves to a new server you will have just one place in Report Manager to make the server name change. That one change will update the connection information in all the reports that use that data source. To get started, you will start with a fresh project. Go to Start > All Programs > SQL Server 2012 > Microsoft SQL Server Data Tools to launch SSDT. Once SSDT is running, click New Project to create a new project. Once the New Project dialog box appears, fill in the form, as shown in. Be sure to select Report Server Project this time – not the wizard. Click OK to dismiss the New Project dialog box. You should now have an empty project, as shown in the Solution Explorer. A report is meant to show you data. Where is the data? The first task is to create a Shared Data Source. Right-click on the Shared Data Sources folder and choose Add New Data Source. The Shared Data Source Properties dialog box will launch where you can fill in a name for the data source. By default, it is named DataSource1. The best practice is to give the data source a more meaningful name. It is possible that you will have projects with more than one data source and, by naming them, you can tell one from another. Type the name JProCo for the data source name and click the Edit button to configure the database connection properties. If you take a look at the types of data sources you can choose, you will see that SSRS works with many data platforms including Oracle, XML, and Teradata. Make sure SQL Server is selected before continuing. For this post, I am assuming that you are using a local SQL Server and that you can use your Windows account to log in to the SQL Server. If, for some reason you must use SQL Server Authentication, choose that option and fill in your SQL Server account credentials. Otherwise, just accept Windows Authentication. If your database server was installed locally and with the default instance, just type in Localhost for the Server name. Select the JProCo database from the database list. At this point, the connection properties should look like. If you have installed a named instance of SQL Server, you will have to specify the server name like this: Localhost\InstanceName, replacing the InstanceName with whatever your instance name is. If you are not sure about the named instance, launch the SQL Server Configuration Manager found at Start > All Programs > Microsoft SQL Server 2012 > Configuration Tools. If you have a named instance, the name will be shown in parentheses. A default instance of SQL Server will display MSSQLSERVER; a named instance will display the name chosen during installation. Once you get the connection properties filled in, click OK to dismiss the Connection Properties dialog box and OK again to dismiss the Shared Data Source properties. You now have a data source in the Solution Explorer. What’s next I really need to thank Kathi Kellenberger and Rick Morelan for sharing this material for this 5 day series of posts on SSRS. To get really comfortable with SSRS you will get to know the different SSDT windows, Build reports on your own (without the wizards),  Add report headers and footers, Accept user input,  create levels, charts, or even maps for visual appeal. You might be surprise to know a small 230 page book starts from the very beginning and covers the steps to do all these items. Beginning SSRS 2012 is a small easy to follow book so you can learn SSRS for less than $20. See Joes2Pros.com for more on this and other books. If you want to learn SSRS in easy to simple words – I strongly recommend you to get Beginning SSRS book from Joes 2 Pros. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: Reporting Services, SSRS

    Read the article

  • SQL SERVER – Using expressor Composite Types to Enforce Business Rules

    - by pinaldave
    One of the features that distinguish the expressor Data Integration Platform from other products in the data integration space is its concept of composite types, which provide an effective and easily reusable way to clearly define the structure and characteristics of data within your application.  An important feature of the composite type approach is that it allows you to easily adjust the content of a record to its ultimate purpose.  For example, a record used to update a row in a database table is easily defined to include only the minimum set of columns, that is, a value for the key column and values for only those columns that need to be updated. Much like a class in higher level programming languages, you can also use the composite type as a way to enforce business rules onto your data by encapsulating a datum’s name, data type, and constraints (for example, maximum, minimum, or acceptable values) as a single entity, which ensures that your data can not assume an invalid value.  To what extent you use this functionality is a decision you make when designing your application; the expressor design paradigm does not force this approach on you. Let’s take a look at how these features are used.  Suppose you want to create a group of applications that maintain the employee table in your human resources database. Your table might have a structure similar to the HumanResources.Employee table in the AdventureWorks database.  This table includes two columns, EmployeID and rowguid, that are maintained by the relational database management system; you cannot provide values for these columns when inserting new rows into the table. Additionally, there are columns such as VacationHours and SickLeaveHours that you might choose to update for all employees on a monthly basis, which justifies creation of a dedicated application. By creating distinct composite types for the read, insert and update operations against this table, you can more easily manage this table’s content. When developing this application within expressor Studio, your first task is to create a schema artifact for the database table.  This process is completely driven by a wizard, only requiring that you select the desired database schema and table.  The resulting schema artifact defines the mapping of result set records to a record within the expressor data integration application.  The structure of the record within the expressor application is a composite type that is given the default name CompositeType1.  As you can see in the following figure, all columns from the table are included in the result set and mapped to an identically named attribute in the default composite type. If you are developing an application that needs to read this table, perhaps to prepare a year-end report of employees by department, you would probably not be interested in the data in the rowguid and ModifiedDate columns.  A typical approach would be to drop this unwanted data in a downstream operator.  But using an alternative composite type provides a better approach in which the unwanted data never enters your application. While working in expressor  Studio’s schema editor, simply create a second composite type within the same schema artifact, which you could name ReadTable, and remove the attributes corresponding to the unwanted columns. The value of an alternative composite type is even more apparent when you want to insert into or update the table.  In the composite type used to insert rows, remove the attributes corresponding to the EmployeeID primary key and rowguid uniqueidentifier columns since these values are provided by the relational database management system. And to update just the VacationHours and SickLeaveHours columns, use a composite type that includes only the attributes corresponding to the EmployeeID, VacationHours, SickLeaveHours and ModifiedDate columns. By specifying this schema artifact and composite type in a Write Table operator, your upstream application need only deal with the four required attributes and there is no risk of unintentionally overwriting a value in a column that does not need to be updated. Now, what about the option to use the composite type to enforce business rules?  If you review the composition of the default composite type CompositeType1, you will note that the constraints defined for many of the attributes mirror the table column specifications.  For example, the maximum number of characters in the NationaIDNumber, LoginID and Title attributes is equivalent to the maximum width of the target column, and the size of the MaritalStatus and Gender attributes is limited to a single character as required by the table column definition.  If your application code leads to a violation of these constraints, an error will be raised.  The expressor design paradigm then allows you to handle the error in a way suitable for your application.  For example, a string value could be truncated or a numeric value could be rounded. Moreover, you have the option of specifying additional constraints that support business rules unrelated to the table definition. Let’s assume that the only acceptable values for marital status are S, M, and D.  Within the schema editor, double-click on the MaritalStatus attribute to open the Edit Attribute window.  Then click the Allowed Values checkbox and enter the acceptable values into the Constraint Value text box. The schema editor is updated accordingly. There is one more option that the expressor semantic type paradigm supports.  Since the MaritalStatus attribute now clearly specifies how this type of information should be represented (a single character limited to S, M or D), you can convert this attribute definition into a shared type, which will allow you to quickly incorporate this definition into another composite type or into the description of an output record from a transform operator. Again, double-click on the MaritalStatus attribute and in the Edit Attribute window, click Convert, which opens the Share Local Semantic Type window that you use to name this shared type.  There’s no requirement that you give the shared type the same name as the attribute from which it was derived.  You should supply a name that makes it obvious what the shared type represents. In this posting, I’ve overviewed the expressor semantic type paradigm and shown how it can be used to make your application development process more productive.  The beauty of this feature is that you choose when and to what extent you utilize the functionality, but I’m certain that if you opt to follow this approach your efforts will become more efficient and your work will progress more quickly.  As always, I encourage you to download and evaluate expressor Studio for your current and future data integration needs. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: CodeProject, Pinal Dave, PostADay, SQL, SQL Authority, SQL Documentation, SQL Query, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

    Read the article

  • SQL SERVER – SSIS Look Up Component – Cache Mode – Notes from the Field #028

    - by Pinal Dave
    [Notes from Pinal]: Lots of people think that SSIS is all about arranging various operations together in one logical flow. Well, the understanding is absolutely correct, but the implementation of the same is not as easy as it seems. Similarly most of the people think lookup component is just component which does look up for additional information and does not pay much attention to it. Due to the same reason they do not pay attention to the same and eventually get very bad performance. Linchpin People are database coaches and wellness experts for a data driven world. In this 28th episode of the Notes from the Fields series database expert Tim Mitchell (partner at Linchpin People) shares very interesting conversation related to how to write a good lookup component with Cache Mode. In SQL Server Integration Services, the lookup component is one of the most frequently used tools for data validation and completion.  The lookup component is provided as a means to virtually join one set of data to another to validate and/or retrieve missing values.  Properly configured, it is reliable and reasonably fast. Among the many settings available on the lookup component, one of the most critical is the cache mode.  This selection will determine whether and how the distinct lookup values are cached during package execution.  It is critical to know how cache modes affect the result of the lookup and the performance of the package, as choosing the wrong setting can lead to poorly performing packages, and in some cases, incorrect results. Full Cache The full cache mode setting is the default cache mode selection in the SSIS lookup transformation.  Like the name implies, full cache mode will cause the lookup transformation to retrieve and store in SSIS cache the entire set of data from the specified lookup location.  As a result, the data flow in which the lookup transformation resides will not start processing any data buffers until all of the rows from the lookup query have been cached in SSIS. The most commonly used cache mode is the full cache setting, and for good reason.  The full cache setting has the most practical applications, and should be considered the go-to cache setting when dealing with an untested set of data. With a moderately sized set of reference data, a lookup transformation using full cache mode usually performs well.  Full cache mode does not require multiple round trips to the database, since the entire reference result set is cached prior to data flow execution. There are a few potential gotchas to be aware of when using full cache mode.  First, you can see some performance issues – memory pressure in particular – when using full cache mode against large sets of reference data.  If the table you use for the lookup is very large (either deep or wide, or perhaps both), there’s going to be a performance cost associated with retrieving and caching all of that data.  Also, keep in mind that when doing a lookup on character data, full cache mode will always do a case-sensitive (and in some cases, space-sensitive) string comparison even if your database is set to a case-insensitive collation.  This is because the in-memory lookup uses a .NET string comparison (which is case- and space-sensitive) as opposed to a database string comparison (which may be case sensitive, depending on collation).  There’s a relatively easy workaround in which you can use the UPPER() or LOWER() function in the pipeline data and the reference data to ensure that case differences do not impact the success of your lookup operation.  Again, neither of these present a reason to avoid full cache mode, but should be used to determine whether full cache mode should be used in a given situation. Full cache mode is ideally useful when one or all of the following conditions exist: The size of the reference data set is small to moderately sized The size of the pipeline data set (the data you are comparing to the lookup table) is large, is unknown at design time, or is unpredictable Each distinct key value(s) in the pipeline data set is expected to be found multiple times in that set of data Partial Cache When using the partial cache setting, lookup values will still be cached, but only as each distinct value is encountered in the data flow.  Initially, each distinct value will be retrieved individually from the specified source, and then cached.  To be clear, this is a row-by-row lookup for each distinct key value(s). This is a less frequently used cache setting because it addresses a narrower set of scenarios.  Because each distinct key value(s) combination requires a relational round trip to the lookup source, performance can be an issue, especially with a large pipeline data set to be compared to the lookup data set.  If you have, for example, a million records from your pipeline data source, you have the potential for doing a million lookup queries against your lookup data source (depending on the number of distinct values in the key column(s)).  Therefore, one has to be keenly aware of the expected row count and value distribution of the pipeline data to safely use partial cache mode. Using partial cache mode is ideally suited for the conditions below: The size of the data in the pipeline (more specifically, the number of distinct key column) is relatively small The size of the lookup data is too large to effectively store in cache The lookup source is well indexed to allow for fast retrieval of row-by-row values No Cache As you might guess, selecting no cache mode will not add any values to the lookup cache in SSIS.  As a result, every single row in the pipeline data set will require a query against the lookup source.  Since no data is cached, it is possible to save a small amount of overhead in SSIS memory in cases where key values are not reused.  In the real world, I don’t see a lot of use of the no cache setting, but I can imagine some edge cases where it might be useful. As such, it’s critical to know your data before choosing this option.  Obviously, performance will be an issue with anything other than small sets of data, as the no cache setting requires row-by-row processing of all of the data in the pipeline. I would recommend considering the no cache mode only when all of the below conditions are true: The reference data set is too large to reasonably be loaded into SSIS memory The pipeline data set is small and is not expected to grow There are expected to be very few or no duplicates of the key values(s) in the pipeline data set (i.e., there would be no benefit from caching these values) Conclusion The cache mode, an often-overlooked setting on the SSIS lookup component, represents an important design decision in your SSIS data flow.  Choosing the right lookup cache mode directly impacts the fidelity of your results and the performance of package execution.  Know how this selection impacts your ETL loads, and you’ll end up with more reliable, faster packages. If you want me to take a look at your server and its settings, or if your server is facing any issue we can Fix Your SQL Server. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Notes from the Field, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: SSIS

    Read the article

  • SQL SERVER – Windows File/Folder and Share Permissions – Notes from the Field #029

    - by Pinal Dave
    [Note from Pinal]: This is a 29th episode of Notes from the Field series. Security is the task which we should give it to the experts. If there is a small overlook or misstep, there are good chances that security of the organization is compromised. This is very true, but there are always devils’s advocates who believe everyone should know the security. As a DBA and Administrator, I often see people not taking interest in the Windows Security hiding behind the reason of not expert of Windows Server. We all often miss the important mission statement for the success of any organization – Teamwork. In this blog post Brian tells the story in very interesting lucid language. Read On! In this episode of the Notes from the Field series database expert Brian Kelley explains a very crucial issue DBAs and Developer faces on their production server. Linchpin People are database coaches and wellness experts for a data driven world. Read the experience of Brian in his own words. When I talk security among database professionals, I find that most have at least a working knowledge of how to apply security within a database. When I talk with DBAs in particular, I find that most have at least a working knowledge of security at the server level if we’re speaking of SQL Server. One area I see continually that is weak is in the area of Windows file/folder (NTFS) and share permissions. The typical response is, “I’m a database developer and the Windows system administrator is responsible for that.” That may very well be true – the system administrator may have the primary responsibility and accountability for file/folder and share security for the server. However, if you’re involved in the typical activities surrounding databases and moving data around, you should know these permissions, too. Otherwise, you could be setting yourself up where someone is able to get to data he or she shouldn’t, or you could be opening the door where human error puts bad data in your production system. File/Folder Permission Basics: I wrote about file/folder permissions a few years ago to give the basic permissions that are most often seen. Here’s what you must know as a minimum at the file/folder level: Read - Allows you to read the contents of the file or folder. Having read permissions allows you to copy the file or folder. Write  – Again, as the name implies, it allows you to write to the file or folder. This doesn’t include the ability to delete, however, nothing stops a person with this access from writing an empty file. Delete - Allows the file/folder to be deleted. If you overwrite files, you may need this permission. Modify - Allows read, write, and delete. Full Control - Same as modify + the ability to assign permissions. File/Folder permissions aggregate, unless there is a DENY (where it trumps, just like within SQL Server), meaning if a person is in one group that gives Read and antoher group that gives Write, that person has both Read and Write permissions. As you might expect me to say, always apply the Principle of Least Privilege. This likely means that any additional permission you might add does not need Full Control. Share Permission Basics: At the share level, here are the permissions. Read - Allows you to read the contents on the share. Change - Allows you to read, write, and delete contents on the share. Full control - Change + the ability to modify permissions. Like with file/folder permissions, these permissions aggregate, and DENY trumps. So What Access Does a Person / Process Have? Figuring out what someone or some process has depends on how the location is being accessed: Access comes through the share (\\ServerName\Share) – a combination of permissions is considered. Access is through a drive letter (C:\, E:\, S:\, etc.) – only the file/folder permissions are considered. The only complicated one here is access through the share. Here’s what Windows does: Figures out what the aggregated permissions are at the file/folder level. Figures out what the aggregated permissions are at the share level. Takes the most restrictive of the two sets of permissions. You can test this by granting Full Control over a folder (this is likely already in place for the Users local group) and then setting up a share. Give only Read access through the share, and that includes to Administrators (if you’re creating a share, likely you have membership in the Administrators group). Try to read a file through the share. Now try to modify it. The most restrictive permission is the Share level permissions. It’s set to only allow Read. Therefore, if you come through the share, it’s the most restrictive. Does This Knowledge Really Help Me? In my experience, it does. I’ve seen cases where sensitive files were accessible by every authenticated user through a share. Auditors, as you might expect, have a real problem with that. I’ve also seen cases where files to be imported as part of the nightly processing were overwritten by files intended from development. And I’ve seen cases where a process can’t get to the files it needs for a process because someone changed the permissions. If you know file/folder and share permissions, you can spot and correct these types of security flaws. Given that there are a lot of database professionals that don’t understand these permissions, if you know it, you set yourself apart. And if you’re able to help on critical processes, you begin to set yourself up as a linchpin (link to .pdf) for your organization. If you want to get started with performance tuning and database security with the help of experts, read more over at Fix Your SQL Server. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Notes from the Field, PostADay, SQL, SQL Authority, SQL Query, SQL Security, SQL Server, SQL Tips and Tricks, T SQL

    Read the article

< Previous Page | 225 226 227 228 229 230 231 232 233 234 235 236  | Next Page >