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  • ??????Oracle Enterprise Manager???????

    - by Yusuke.Yamamoto
    ????? ??:2010/10/19 ??:???? ?????????????????????????????????????????????????? Oracle Enterprise Manager(EM)????????????4?????EM ???????????????????????? ?1? ???????????????/ ???????????? Oracle Database????????????????EM ??????????????????2? EM ??????????/ ????????????? EM?????????????????????3? ????????????·???/ ????????????????Enterprise Edition ?????????Standard Edition ?????????????????????????????????·???????4? ?????????????????/ Oracle Database ???????? EM ?????????????????????????????????????????·????·?????? ????????? ????????????????? http://oracletech.jp/products/pickup/000028.html

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  • How can I speed up queries against tables I cannot add indexes to?

    - by RenderIn
    I access several tables remotely via DB Link. They are very normalized and the data in each is effective-dated. Of the millions of records in each table, only a subset of ~50k are current records. The tables are internally managed by a commercial product that will throw a huge fit if I add indexes or make alterations to its tables in any way. What are my options for speeding up access to these tables?

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  • SQL Server Database In Single User Mode after Failover

    - by jlichauc
    Here is a weird situation we experienced with a SQL Server 2008 Database Mirroring Failover. We have a pair of mirrored databases running in high-availability mode and both the principal and mirror showed as synchronized. As part of some maintenance I triggered a manual failover of the principal to the mirror. However after the failover the principal was now in single-user mode instead of the expected "Principal/Synchronized" state we usually get. The database had been in multi-user mode on the previous principal before this had happened. We ended up stopping all applications, restarting the SQL Server instances, and executing "ALTER DATABASE ... SET MULTI_USER" to bring the database back to the expected "Principal/Synchronized" state in a multi-user mode. Question. Does anyone know where SQL Server stores information about whether a database should be in single-user mode or not? I'm wondering if there is some system database or table that has this setting recorded somewhere. In particular we had an incident once with the database on the original principal (the one I was failing over to) where when trying to detach the database it was put into single-user mode. I'm wondering if that setting is cached somewhere and is the reason that SQL Server put it back into single-user mode after a failover.

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  • 4GB limitation on these embedded/express DBs good enough? what's next if limitation is reached?

    - by edwin.nathaniel
    I'm wondering how long a (theoretically) desktop-app can consume the full 4GB limitation of these express/embedded database products (SQL-Server Express, Oracle Express, SQLite3, etc) provided that big blobs will be stored in filesystem. Also what would be your strategy when it hits the 4GB? Archive the old DB Copy 1-3 months of data to the new DB (consider this as cache strategy?) Start using the new DB from this point onward (How do you access the old data?) I understand that the answer might varies depending on how much data you stored in the table/column. But please describe based on your experience (what kind of desktop-app, write/read heavy, how long will it reach according to your guess).

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  • "Cannot perform a differential backup for database "myDb", because a current database backup does no

    - by krimerd
    Hi there, I have what seems to be a pretty common problem when trying to take a differential backup. We have a SQL Server 2008 Standard (64bit) and we use Litespeed v 5.0.2.0 to take our backups. We take full backups once a week and a differential on a daily basis. The problem is, every time I try to take a diff backup I get the following error: "VDI open failed due to requested abort. BACKUP DATABASE is terminating abnormally. Cannot perform a differential backup for database "myDb", because a current database backup does not exist. Perform a full database backup by reissuing BACKUP DATABASE, omitting the WITH DIFFERENTIAL option." The problem is that I know 100% I have a full backup because I just double checked. Only once I was able to take a diff backup and that was when I took it immediately after I took a full backup. I have searched around and noticed that this is pretty common (although mostly with SQL 2005) and a solution that a lot of ppl suggest and that I haven't tried yet is to disable the SQL Server VSS Writer service. The problem with this is #1 I think I might need this service since I am using a third party backup software and #2 I am not sure exactly what the service does and don't want to disable it just like that. Has any of you ever experienced this problem and how did you go about fixing it? Thank you,

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  • database replication for new user signup

    - by Jeff Storey
    I have a database that stores the users of my application. When a new user signs up, a record is inserted into the database for that user. I have a replicated version (slave) of this database (using mysql for now). What I'm concerned about is this scenario: step 1: user signs up and user record is inserted into the database step 2: user then tries to login, and the login process queries the database for the user. however, this query hits the slave database, but the user record has not yet been replicated in the slave and it returns an error that the user does not exist. This is a pretty trivial example, but I can see how it can apply to a lot of cases. Is there a strategy for configuring replicated databases to help prevent this situation?

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  • Copy Database Wizard fails on creation of view into another not-yet-copied database

    - by user22037
    Update - I found that doing a manual detach/reattach using MSDN article "How to: Move a Database Using Detach and Attach (Transact-SQL)" got around this issue. I'll just be creating a script to dettach and reattach but do the file copies manually. Any info on how to overcome the problems with the wizard would be helpful in the future. I am in the process of moving around 20 databases from our current server to a new one. When performing the copies however I have found that some databases can not copy if they have views into other databases that have not yet been copied to the target system. The log file generated says "failed with the following error: "Invalid object name" in reference to the database in the view. If I first copy just the database referenced in the view and then in a separate step copy the database over containing the view it is successful. However some other database have views into each other so can't just adjust the order in which the copy occurs. Is there any way to ignore this error and just allow everything to copy?

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  • I cannot connect to database from Drupal

    - by Patrick
    hi, I've uploaded my drupal website (and related database) to my new server. The database info is: host: localhost user: user pass: pass databaseName = database_name I've set the following line in settings.php file: $db_url = 'mysqli://user:password@localhost/database_name'; but what I get is this: If you are the maintainer of this site, please check your database settings in the settings.php file and ensure that your hosting provider's database server is running. For more help, see the handbook, or contact your hosting provider. I guess the database is running, it always run and I can access with phpmyadmin so I think the problem is not there. The database and website files upload have also been succesfull.. so I dunno what to do to fix this issue. It is mysql on IIS Server thanks

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  • Oracle 10g Failover Database - How to fail back?

    - by rrkwells
    I want to know how the failover database concept works after recovery. We have defined our application to connect to a backup database in case the production database fails. If this happens, then all the transactions will be happening on that backup database. Once the production db server is running again, then how do we make sure the changes made in the backup database will be reflected on the production database? We want to make sure that any changes made while failed over are not lost. We are using Oracle 10g.

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  • Event on SQL Server 2008 Disk IO and the new Complex Event Processing (StreamInsight) feature in R2

    - by tonyrogerson
    Allan Mitchell and myself are doing a double act, Allan is becoming one of the leading guys in the UK on StreamInsight and will give an introduction to this new exciting technology; on top of that I'll being talking about SQL Server Disk IO - well, "Disk" might not be relevant anymore because I'll be talking about SSD and IOFusion - basically I'll be talking about the underpinnings - making sure you understand and get it right, how to monitor etc... If you've any specific problems or questions just ping me an email [email protected]. To register for the event see: http://sqlserverfaq.com/events/217/SQL-Server-and-Disk-IO-File-GroupsFiles-SSDs-FusionIO-InRAM-DBs-Fragmentation-Tony-Rogerson-Complex-Event-Processing-Allan-Mitchell.aspx 18:15 SQL Server and Disk IOTony Rogerson, SQL Server MVPTony's Blog; Tony on TwitterIn this session Tony will talk about RAID levels, how SQL server writes to and reads from disk, the effect SSD has and will talk about other options for throughput enhancement like Fusion IO. He will look at the effect fragmentation has and how to minimise the impact, he will look at the File structure of a database and talk about what benefits multiple files and file groups bring. We will also touch on Database Mirroring and the effect that has on throughput, how to get a feeling for the throughput you should expect.19:15 Break19:45 Complex Event Processing (CEP)Allan Mitchell, SQL Server MVPhttp://sqlis.com/sqlisStreamInsight is Microsoft’s first foray into the world of Complex Event Processing (CEP) and Event Stream Processing (ESP).  In this session I want to show an introduction to this technology.  I will show how and why it is useful.  I will get us used to some new terminology but best of all I will show just how easy it is to start building your first CEP/ESP application.

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  • SQL SERVER – Cleaning Up SQL Server Indexes – Defragmentation, Fillfactor – Video

    - by pinaldave
    Storing data non-contiguously on disk is known as fragmentation. Before learning to eliminate fragmentation, you should have a clear understanding of the types of fragmentation. When records are stored non-contiguously inside the page, then it is called internal fragmentation. When on disk, the physical storage of pages and extents is not contiguous. We can get both types of fragmentation using the DMV: sys.dm_db_index_physical_stats. Here is the generic advice for reducing the fragmentation. If avg_fragmentation_in_percent > 5% and < 30%, then use ALTER INDEX REORGANIZE: This statement is replacement for DBCC INDEXDEFRAG to reorder the leaf level pages of the index in a logical order. As this is an online operation, the index is available while the statement is running. If avg_fragmentation_in_percent > 30%, then use ALTER INDEX REBUILD: This is replacement for DBCC DBREINDEX to rebuild the index online or offline. In such case, we can also use the drop and re-create index method.(Ref: MSDN) Here is quick video which covers many of the above mentioned topics. While Vinod and I were planning about Indexing course, we had plenty of fun and learning. We often recording few of our statement and just left it aside. Afterwords we thought it will be really funny Here is funny video shot by Vinod and Myself on the same subject: Here is the link to the SQL Server Performance:  Indexing Basics. Here is the additional reading material on the same subject: SQL SERVER – Fragmentation – Detect Fragmentation and Eliminate Fragmentation SQL SERVER – 2005 – Display Fragmentation Information of Data and Indexes of Database Table SQL SERVER – De-fragmentation of Database at Operating System to Improve Performance Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Index, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology, Video

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  • 10gR2 Transportable Tablespaces Certified for EBS 11i

    - by Steven Chan
    Database migration across platforms of different "endian" (byte ordering) formats using the Cross Platform Transportable Tablespaces (XTTS) process is now certified for Oracle E-Business Suite Release 11i (11.5.10.2) with Oracle Database 10g Release 2.  This process is sometimes also referred to as transportable tablespaces (TTS).What is the Cross-Platform Transportable Tablespace Feature?The Cross-Platform Transportable Tablespace feature allows users to move a user tablespace across Oracle databases. It's an efficient way to move bulk data between databases. If the source platform and the target platform are of different endianness, then an additional conversion step must be done on either the source or target platform to convert the tablespace being transported to the target format. If they are of the same endianness, then no conversion is necessary and tablespaces can be transported as if they were on the same platform.Moving data using transportable tablespaces can be much faster than performing either an export/import or unload/load of the same data. This is because transporting a tablespace only requires the copying of datafiles from source to the destination and then integrating the tablespace structural information. You can also use transportable tablespaces to move both table and index data, thereby avoiding the index rebuilds you would have to perform when importing or loading table data.

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  • How Mature is Your Database Change Management Process?

    - by Ben Rees
    .dbd-banner p{ font-size:0.75em; padding:0 0 10px; margin:0 } .dbd-banner p span{ color:#675C6D; } .dbd-banner p:last-child{ padding:0; } @media ALL and (max-width:640px){ .dbd-banner{ background:#f0f0f0; padding:5px; color:#333; margin-top: 5px; } } -- Database Delivery Patterns & Practices Further Reading Organization and team processes How do you get your database schema changes live, on to your production system? As your team of developers and DBAs are working on the changes to the database to support your business-critical applications, how do these updates wend their way through from dev environments, possibly to QA, hopefully through pre-production and eventually to production in a controlled, reliable and repeatable way? In this article, I describe a model we use to try and understand the different stages that customers go through as their database change management processes mature, from the very basic and manual, through to advanced continuous delivery practices. I also provide a simple chart that will help you determine “How mature is our database change management process?” This process of managing changes to the database – which all of us who have worked in application/database development have had to deal with in one form or another – is sometimes known as Database Change Management (even if we’ve never used the term ourselves). And it’s a difficult process, often painfully so. Some developers take the approach of “I’ve no idea how my changes get live – I just write the stored procedures and add columns to the tables. It’s someone else’s problem to get this stuff live. I think we’ve got a DBA somewhere who deals with it – I don’t know, I’ve never met him/her”. I know I used to work that way. I worked that way because I assumed that making the updates to production was a trivial task – how hard can it be? Pause the application for half an hour in the middle of the night, copy over the changes to the app and the database, and switch it back on again? Voila! But somehow it never seemed that easy. And it certainly was never that easy for database changes. Why? Because you can’t just overwrite the old database with the new version. Databases have a state – more specifically 4Tb of critical data built up over the last 12 years of running your business, and if your quick hotfix happened to accidentally delete that 4Tb of data, then you’re “Looking for a new role” pretty quickly after the failed release. There are a lot of other reasons why a managed database change management process is important for organisations, besides job security, not least: Frequency of releases. Many business managers are feeling the pressure to get functionality out to their users sooner, quicker and more reliably. The new book (which I highly recommend) Lean Enterprise by Jez Humble, Barry O’Reilly and Joanne Molesky provides a great discussion on how many enterprises are having to move towards a leaner, more frequent release cycle to maintain their competitive advantage. It’s no longer acceptable to release once per year, leaving your customers waiting all year for changes they desperately need (and expect) Auditing and compliance. SOX, HIPAA and other compliance frameworks have demanded that companies implement proper processes for managing changes to their databases, whether managing schema changes, making sure that the data itself is being looked after correctly or other mechanisms that provide an audit trail of changes. We’ve found, at Red Gate that we have a very wide range of customers using every possible form of database change management imaginable. Everything from “Nothing – I just fix the schema on production from my laptop when things go wrong, and write it down in my notebook” to “A full Continuous Delivery process – any change made by a dev gets checked in and recorded, fully tested (including performance tests) before a (tested) release is made available to our Release Management system, ready for live deployment!”. And everything in between of course. Because of the vast number of customers using so many different approaches we found ourselves struggling to keep on top of what everyone was doing – struggling to identify patterns in customers’ behavior. This is useful for us, because we want to try and fit the products we have to different needs – different products are relevant to different customers and we waste everyone’s time (most notably, our customers’) if we’re suggesting products that aren’t appropriate for them. If someone visited a sports store, looking to embark on a new fitness program, and the store assistant suggested the latest $10,000 multi-gym, complete with multiple weights mechanisms, dumb-bells, pull-up bars and so on, then he’s likely to lose that customer. All he needed was a pair of running shoes! To solve this issue – in an attempt to simplify how we understand our customers and our offerings – we built a model. This is a an attempt at trying to classify our customers in to some sort of model or “Customer Maturity Framework” as we rather grandly term it, which somehow simplifies our understanding of what our customers are doing. The great statistician, George Box (amongst other things, the “Box” in the Box-Jenkins time series model) gave us the famous quote: “Essentially all models are wrong, but some are useful” We’ve taken this quote to heart – we know it’s a gross over-simplification of the real world of how users work with complex legacy and new database developments. Almost nobody precisely fits in to one of our categories. But we hope it’s useful and interesting. There are actually a number of similar models that exist for more general application delivery. We’ve found these from ThoughtWorks/Forrester, from InfoQ and others, and initially we tried just taking these models and replacing the word “application” for “database”. However, we hit a problem. From talking to our customers we know that users are far less further down the road of mature database change management than they are for application development. As a simple example, no application developer, who wants to keep his/her job would develop an application for an organisation without source controlling that code. Sure, he/she might not be using an advanced Gitflow branching methodology but they’ll certainly be making sure their code gets managed in a repo somewhere with all the benefits of history, auditing and so on. But this certainly isn’t the case (yet) for the database – a very large segment of the people we speak to have no source control set up for their databases whatsoever, even at the most basic level (for example, keeping change scripts in a source control system somewhere). By the way, if this is you, Red Gate has a great whitepaper here, on the barriers people face getting a source control process implemented at their organisations. This difference in maturity is the same as you move in to areas such as continuous integration (common amongst app developers, relatively rare for database developers) and automated release management (growing amongst app developers, very rare for the database). So, when we created the model we started from scratch and biased the levels of maturity towards what we actually see amongst our customers. But, what are these stages? And what level are you? The table below describes our definitions for four levels of maturity – Baseline, Beginner, Intermediate and Advanced. As I say, this is a model – you won’t fit any of these categories perfectly, but hopefully one will ring true more than others. We’ve also created a PDF with a flow chart to help you find which of these groups most closely matches your team:  Download the Database Delivery Maturity Framework PDF here   Level D1 – Baseline Work directly on live databases Sometimes work directly in production Generate manual scripts for releases. Sometimes use a product like SQL Compare or similar to do this Any tests that we might have are run manually Level D2 – Beginner Have some ad-hoc DB version control such as manually adding upgrade scripts to a version control system Attempt is made to keep production in sync with development environments There is some documentation and planning of manual deployments Some basic automated DB testing in process Level D3 – Intermediate The database is fully version-controlled with a product like Red Gate SQL Source Control or SSDT Database environments are managed Production environment schema is reproducible from the source control system There are some automated tests Have looked at using migration scripts for difficult database refactoring cases Level D4 – Advanced Using continuous integration for database changes Build, testing and deployment of DB changes carried out through a proper database release process Fully automated tests Production system is monitored for fast feedback to developers   Does this model reflect your team at all? Where are you on this journey? We’d be very interested in knowing how you get on. We’re doing a lot of work at the moment, at Red Gate, trying to help people progress through these stages. For example, if you’re currently not source controlling your database, then this is a natural next step. If you are already source controlling your database, what about the next stage – continuous integration and automated release management? To help understand these issues, there’s a summary of the Red Gate Database Delivery learning program on our site, alongside a Patterns and Practices library here on Simple-Talk and a Training Academy section on our documentation site to help you get up and running with the tools you need to progress. All feedback is welcome and it would be great to hear where you find yourself on this journey! This article is part of our database delivery patterns & practices series on Simple Talk. Find more articles for version control, automated testing, continuous integration & deployment.

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  • Best approach to accessing multiple data source in a web application

    - by ced
    I've a base web application developed with .net technologies (asp.net) used into our LAN by 30 users simultanousley. From this web application I've developed two verticalization used from online users. In future i expect hundreds users simultanousley. Our company has different locations. Each site use its own database. The web application needs to retrieve information from all existing databases. Currently there are 3 database, but it's not excluded in the future expansion of new offices. My question then is: What is the best strategy for a web application to retrieve information from different databases (which have the same schema) whereas the main objective performance data access and high fault tolerance? There are case studies in the literature that I can take as an example? Do you know some good documents to study? Do you have any tips to implement this task so efficient? Intuitively I would say that two possible strategy are: perform queries from different sources in real time and aggregate data on the fly; create a repository that contains the union of the entities of interest and perform queries directly on repository;

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  • Oracle Exadata?????????? ??1

    - by takashi.hitomi
    2009?7?~2010?5????Oracle Exadata???????????????? 2010?4?15? ???? ???????????? ? ??????Exadata V2??????????????????????? 2010?4?13? ???? ??????????? ? ?????????????????????????? 2010?4?6? ?????·???????·??????? ? T???????????????????????????????????? 2010?3?1? ?????? ????? ?????????????????????????????????????? 2010?2?2? ???? ????????? ? ??????????????Intel???????????????Sun Oracle Database Machine??????? 2010?1?26? ???? ????(????·???) ? ?Oracle Exadata??????????·??????????????????????? 2009?7?14? ?????????? ? ???????????HP Oracle Database Machine????

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  • Conflict resolution for two-way sync

    - by K.Steff
    How do you manage two-way synchronization between a 'main' database server and many 'secondary' servers, in particular conflict resolution, assuming a connection is not always available? For example, I have an mobile app that uses CoreData as the 'database' on the iOS and I'd like to allow users to edit the contents without Internet connection. In the same time, this information is available on a website the devices will connect to. What do I do if/when the data on the two DB servers is in conflict? (I refer to CoreData as a DB server, though I am aware it is something slightly different.) Are there any general strategies for dealing with this sort of issue? These are the options I can think of: 1. Always use the client-side data as higher-priority 2. Same for server-side 3. Try to resolve conflicts by marking each field's edit timestamp and taking the latest edit Though I'm certain the 3rd option will open room for some devastating data corruption. I'm aware that the CAP theorem concerns this, but I only want eventual consistency, so it doesn't rule it out completely, right? Related question: Best practice patterns for two-way data synchronization. The second answer to this question says it probably can't be done.

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  • Storing revisions of a document

    - by dev.e.loper
    This is a follow up question to my original question. I'm thinking of going with generating diffs and storing those diffs in the database 'History' table. I'm using diff-match-patch library to generate what is called a 'patch'. On every save, I compare previous and new version and generate this patch. The patch could be used to generate a document at specific point in time. My dilemma is how to store this data. Should I: a Insert a new database record for every patch? b. Store these patches in javascript array and store that array in history table. So there is only one db History record for document with an array of all the patches. Concerns with: a. Too many db records generated. Will be slow and CPU intensive to query. b. Only one record. If record is somehow corrupted/deleted. Entire revision history is gone. I'm looking for suggestions, concerns with either approach.

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  • Simple Select Statement on MySQL Database Hanging

    - by AlishahNovin
    I have a very simple sql select statement on a very large table, that is non-normalized. (Not my design at all, I'm just trying to optimize while simultaneously trying to convince the owners of a redesign) Basically, the statement is like this: SELECT FirstName, LastName, FullName, State FROM Activity Where (FirstName=@name OR LastName=@name OR FullName=@name) AND State=@state; Now, FirstName, LastName, FullName and State are all indexed as BTrees, but without prefix - the whole column is indexed. State column is a 2 letter state code. What I'm finding is this: When @name = 'John Smith', and @state = '%' the search is really fast and yields results immediately. When @name = 'John Smith', and @state = 'FL' the search takes 5 minutes (and usually this means the web service times out...) When I remove the FirstName and LastName comparisons, and only use the FullName and State, both cases above work very quickly. When I replace FirstName, LastName, FullName, and State searches, but use LIKE for each search, it works fast for @name='John Smith%' and @state='%', but slow for @name='John Smith%' and @state='FL' When I search against 'John Sm%' and @state='FL' the search finds results immediately When I search against 'John Smi%' and @state='FL' the search takes 5 minutes. Now, just to reiterate - the table is not normalized. The John Smith appears many many times, as do many other users, because there is no reference to some form of users/people table. I'm not sure how many times a single user may appear, but the table itself has 90 Million records. Again, not my design... What I'm wondering is - though there are many many problems with this design, what is causing this specific problem. My guess is that the index trees are just too large that it just takes a very long time traversing the them. (FirstName, LastName, FullName) Anyway, I appreciate anyone's help with this. Like I said, I'm working on convincing them of a redesign, but in the meantime, if I someone could help me figure out what the exact problem is, that'd be fantastic.

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  • Big Data – Buzz Words: Importance of Relational Database in Big Data World – Day 9 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned what is HDFS. In this article we will take a quick look at the importance of the Relational Database in Big Data world. A Big Question? Here are a few questions I often received since the beginning of the Big Data Series - Does the relational database have no space in the story of the Big Data? Does relational database is no longer relevant as Big Data is evolving? Is relational database not capable to handle Big Data? Is it true that one no longer has to learn about relational data if Big Data is the final destination? Well, every single time when I hear that one person wants to learn about Big Data and is no longer interested in learning about relational database, I find it as a bit far stretched. I am not here to give ambiguous answers of It Depends. I am personally very clear that one who is aspiring to become Big Data Scientist or Big Data Expert they should learn about relational database. NoSQL Movement The reason for the NoSQL Movement in recent time was because of the two important advantages of the NoSQL databases. Performance Flexible Schema In personal experience I have found that when I use NoSQL I have found both of the above listed advantages when I use NoSQL database. There are instances when I found relational database too much restrictive when my data is unstructured as well as they have in the datatype which my Relational Database does not support. It is the same case when I have found that NoSQL solution performing much better than relational databases. I must say that I am a big fan of NoSQL solutions in the recent times but I have also seen occasions and situations where relational database is still perfect fit even though the database is growing increasingly as well have all the symptoms of the big data. Situations in Relational Database Outperforms Adhoc reporting is the one of the most common scenarios where NoSQL is does not have optimal solution. For example reporting queries often needs to aggregate based on the columns which are not indexed as well are built while the report is running, in this kind of scenario NoSQL databases (document database stores, distributed key value stores) database often does not perform well. In the case of the ad-hoc reporting I have often found it is much easier to work with relational databases. SQL is the most popular computer language of all the time. I have been using it for almost over 10 years and I feel that I will be using it for a long time in future. There are plenty of the tools, connectors and awareness of the SQL language in the industry. Pretty much every programming language has a written drivers for the SQL language and most of the developers have learned this language during their school/college time. In many cases, writing query based on SQL is much easier than writing queries in NoSQL supported languages. I believe this is the current situation but in the future this situation can reverse when No SQL query languages are equally popular. ACID (Atomicity Consistency Isolation Durability) – Not all the NoSQL solutions offers ACID compliant language. There are always situations (for example banking transactions, eCommerce shopping carts etc.) where if there is no ACID the operations can be invalid as well database integrity can be at risk. Even though the data volume indeed qualify as a Big Data there are always operations in the application which absolutely needs ACID compliance matured language. The Mixed Bag I have often heard argument that all the big social media sites now a days have moved away from Relational Database. Actually this is not entirely true. While researching about Big Data and Relational Database, I have found that many of the popular social media sites uses Big Data solutions along with Relational Database. Many are using relational databases to deliver the results to end user on the run time and many still uses a relational database as their major backbone. Here are a few examples: Facebook uses MySQL to display the timeline. (Reference Link) Twitter uses MySQL. (Reference Link) Tumblr uses Sharded MySQL (Reference Link) Wikipedia uses MySQL for data storage. (Reference Link) There are many for prominent organizations which are running large scale applications uses relational database along with various Big Data frameworks to satisfy their various business needs. Summary I believe that RDBMS is like a vanilla ice cream. Everybody loves it and everybody has it. NoSQL and other solutions are like chocolate ice cream or custom ice cream – there is a huge base which loves them and wants them but not every ice cream maker can make it just right  for everyone’s taste. No matter how fancy an ice cream store is there is always plain vanilla ice cream available there. Just like the same, there are always cases and situations in the Big Data’s story where traditional relational database is the part of the whole story. In the real world scenarios there will be always the case when there will be need of the relational database concepts and its ideology. It is extremely important to accept relational database as one of the key components of the Big Data instead of treating it as a substandard technology. Ray of Hope – NewSQL In this module we discussed that there are places where we need ACID compliance from our Big Data application and NoSQL will not support that out of box. There is a new termed coined for the application/tool which supports most of the properties of the traditional RDBMS and supports Big Data infrastructure – NewSQL. Tomorrow In tomorrow’s blog post we will discuss about NewSQL. 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

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  • Beware Sneaky Reads with Unique Indexes

    - by Paul White NZ
    A few days ago, Sandra Mueller (twitter | blog) asked a question using twitter’s #sqlhelp hash tag: “Might SQL Server retrieve (out-of-row) LOB data from a table, even if the column isn’t referenced in the query?” Leaving aside trivial cases (like selecting a computed column that does reference the LOB data), one might be tempted to say that no, SQL Server does not read data you haven’t asked for.  In general, that’s quite correct; however there are cases where SQL Server might sneakily retrieve a LOB column… Example Table Here’s a T-SQL script to create that table and populate it with 1,000 rows: CREATE TABLE dbo.LOBtest ( pk INTEGER IDENTITY NOT NULL, some_value INTEGER NULL, lob_data VARCHAR(MAX) NULL, another_column CHAR(5) NULL, CONSTRAINT [PK dbo.LOBtest pk] PRIMARY KEY CLUSTERED (pk ASC) ); GO DECLARE @Data VARCHAR(MAX); SET @Data = REPLICATE(CONVERT(VARCHAR(MAX), 'x'), 65540);   WITH Numbers (n) AS ( SELECT ROW_NUMBER() OVER (ORDER BY (SELECT 0)) FROM master.sys.columns C1, master.sys.columns C2 ) INSERT LOBtest WITH (TABLOCKX) ( some_value, lob_data ) SELECT TOP (1000) N.n, @Data FROM Numbers N WHERE N.n <= 1000; Test 1: A Simple Update Let’s run a query to subtract one from every value in the some_value column: UPDATE dbo.LOBtest WITH (TABLOCKX) SET some_value = some_value - 1; As you might expect, modifying this integer column in 1,000 rows doesn’t take very long, or use many resources.  The STATITICS IO and TIME output shows a total of 9 logical reads, and 25ms elapsed time.  The query plan is also very simple: Looking at the Clustered Index Scan, we can see that SQL Server only retrieves the pk and some_value columns during the scan: The pk column is needed by the Clustered Index Update operator to uniquely identify the row that is being changed.  The some_value column is used by the Compute Scalar to calculate the new value.  (In case you are wondering what the Top operator is for, it is used to enforce SET ROWCOUNT). Test 2: Simple Update with an Index Now let’s create a nonclustered index keyed on the some_value column, with lob_data as an included column: CREATE NONCLUSTERED INDEX [IX dbo.LOBtest some_value (lob_data)] ON dbo.LOBtest (some_value) INCLUDE ( lob_data ) WITH ( FILLFACTOR = 100, MAXDOP = 1, SORT_IN_TEMPDB = ON ); This is not a useful index for our simple update query; imagine that someone else created it for a different purpose.  Let’s run our update query again: UPDATE dbo.LOBtest WITH (TABLOCKX) SET some_value = some_value - 1; We find that it now requires 4,014 logical reads and the elapsed query time has increased to around 100ms.  The extra logical reads (4 per row) are an expected consequence of maintaining the nonclustered index. The query plan is very similar to before (click to enlarge): The Clustered Index Update operator picks up the extra work of maintaining the nonclustered index. The new Compute Scalar operators detect whether the value in the some_value column has actually been changed by the update.  SQL Server may be able to skip maintaining the nonclustered index if the value hasn’t changed (see my previous post on non-updating updates for details).  Our simple query does change the value of some_data in every row, so this optimization doesn’t add any value in this specific case. The output list of columns from the Clustered Index Scan hasn’t changed from the one shown previously: SQL Server still just reads the pk and some_data columns.  Cool. Overall then, adding the nonclustered index hasn’t had any startling effects, and the LOB column data still isn’t being read from the table.  Let’s see what happens if we make the nonclustered index unique. Test 3: Simple Update with a Unique Index Here’s the script to create a new unique index, and drop the old one: CREATE UNIQUE NONCLUSTERED INDEX [UQ dbo.LOBtest some_value (lob_data)] ON dbo.LOBtest (some_value) INCLUDE ( lob_data ) WITH ( FILLFACTOR = 100, MAXDOP = 1, SORT_IN_TEMPDB = ON ); GO DROP INDEX [IX dbo.LOBtest some_value (lob_data)] ON dbo.LOBtest; Remember that SQL Server only enforces uniqueness on index keys (the some_data column).  The lob_data column is simply stored at the leaf-level of the non-clustered index.  With that in mind, we might expect this change to make very little difference.  Let’s see: UPDATE dbo.LOBtest WITH (TABLOCKX) SET some_value = some_value - 1; Whoa!  Now look at the elapsed time and logical reads: Scan count 1, logical reads 2016, physical reads 0, read-ahead reads 0, lob logical reads 36015, lob physical reads 0, lob read-ahead reads 15992.   CPU time = 172 ms, elapsed time = 16172 ms. Even with all the data and index pages in memory, the query took over 16 seconds to update just 1,000 rows, performing over 52,000 LOB logical reads (nearly 16,000 of those using read-ahead). Why on earth is SQL Server reading LOB data in a query that only updates a single integer column? The Query Plan The query plan for test 3 looks a bit more complex than before: In fact, the bottom level is exactly the same as we saw with the non-unique index.  The top level has heaps of new stuff though, which I’ll come to in a moment. You might be expecting to find that the Clustered Index Scan is now reading the lob_data column (for some reason).  After all, we need to explain where all the LOB logical reads are coming from.  Sadly, when we look at the properties of the Clustered Index Scan, we see exactly the same as before: SQL Server is still only reading the pk and some_value columns – so what’s doing the LOB reads? Updates that Sneakily Read Data We have to go as far as the Clustered Index Update operator before we see LOB data in the output list: [Expr1020] is a bit flag added by an earlier Compute Scalar.  It is set true if the some_value column has not been changed (part of the non-updating updates optimization I mentioned earlier). The Clustered Index Update operator adds two new columns: the lob_data column, and some_value_OLD.  The some_value_OLD column, as the name suggests, is the pre-update value of the some_value column.  At this point, the clustered index has already been updated with the new value, but we haven’t touched the nonclustered index yet. An interesting observation here is that the Clustered Index Update operator can read a column into the data flow as part of its update operation.  SQL Server could have read the LOB data as part of the initial Clustered Index Scan, but that would mean carrying the data through all the operations that occur prior to the Clustered Index Update.  The server knows it will have to go back to the clustered index row to update it, so it delays reading the LOB data until then.  Sneaky! Why the LOB Data Is Needed This is all very interesting (I hope), but why is SQL Server reading the LOB data?  For that matter, why does it need to pass the pre-update value of the some_value column out of the Clustered Index Update? The answer relates to the top row of the query plan for test 3.  I’ll reproduce it here for convenience: Notice that this is a wide (per-index) update plan.  SQL Server used a narrow (per-row) update plan in test 2, where the Clustered Index Update took care of maintaining the nonclustered index too.  I’ll talk more about this difference shortly. The Split/Sort/Collapse combination is an optimization, which aims to make per-index update plans more efficient.  It does this by breaking each update into a delete/insert pair, reordering the operations, removing any redundant operations, and finally applying the net effect of all the changes to the nonclustered index. Imagine we had a unique index which currently holds three rows with the values 1, 2, and 3.  If we run a query that adds 1 to each row value, we would end up with values 2, 3, and 4.  The net effect of all the changes is the same as if we simply deleted the value 1, and added a new value 4. By applying net changes, SQL Server can also avoid false unique-key violations.  If we tried to immediately update the value 1 to a 2, it would conflict with the existing value 2 (which would soon be updated to 3 of course) and the query would fail.  You might argue that SQL Server could avoid the uniqueness violation by starting with the highest value (3) and working down.  That’s fine, but it’s not possible to generalize this logic to work with every possible update query. SQL Server has to use a wide update plan if it sees any risk of false uniqueness violations.  It’s worth noting that the logic SQL Server uses to detect whether these violations are possible has definite limits.  As a result, you will often receive a wide update plan, even when you can see that no violations are possible. Another benefit of this optimization is that it includes a sort on the index key as part of its work.  Processing the index changes in index key order promotes sequential I/O against the nonclustered index. A side-effect of all this is that the net changes might include one or more inserts.  In order to insert a new row in the index, SQL Server obviously needs all the columns – the key column and the included LOB column.  This is the reason SQL Server reads the LOB data as part of the Clustered Index Update. In addition, the some_value_OLD column is required by the Split operator (it turns updates into delete/insert pairs).  In order to generate the correct index key delete operation, it needs the old key value. The irony is that in this case the Split/Sort/Collapse optimization is anything but.  Reading all that LOB data is extremely expensive, so it is sad that the current version of SQL Server has no way to avoid it. Finally, for completeness, I should mention that the Filter operator is there to filter out the non-updating updates. Beating the Set-Based Update with a Cursor One situation where SQL Server can see that false unique-key violations aren’t possible is where it can guarantee that only one row is being updated.  Armed with this knowledge, we can write a cursor (or the WHILE-loop equivalent) that updates one row at a time, and so avoids reading the LOB data: SET NOCOUNT ON; SET STATISTICS XML, IO, TIME OFF;   DECLARE @PK INTEGER, @StartTime DATETIME; SET @StartTime = GETUTCDATE();   DECLARE curUpdate CURSOR LOCAL FORWARD_ONLY KEYSET SCROLL_LOCKS FOR SELECT L.pk FROM LOBtest L ORDER BY L.pk ASC;   OPEN curUpdate;   WHILE (1 = 1) BEGIN FETCH NEXT FROM curUpdate INTO @PK;   IF @@FETCH_STATUS = -1 BREAK; IF @@FETCH_STATUS = -2 CONTINUE;   UPDATE dbo.LOBtest SET some_value = some_value - 1 WHERE CURRENT OF curUpdate; END;   CLOSE curUpdate; DEALLOCATE curUpdate;   SELECT DATEDIFF(MILLISECOND, @StartTime, GETUTCDATE()); That completes the update in 1280 milliseconds (remember test 3 took over 16 seconds!) I used the WHERE CURRENT OF syntax there and a KEYSET cursor, just for the fun of it.  One could just as well use a WHERE clause that specified the primary key value instead. Clustered Indexes A clustered index is the ultimate index with included columns: all non-key columns are included columns in a clustered index.  Let’s re-create the test table and data with an updatable primary key, and without any non-clustered indexes: IF OBJECT_ID(N'dbo.LOBtest', N'U') IS NOT NULL DROP TABLE dbo.LOBtest; GO CREATE TABLE dbo.LOBtest ( pk INTEGER NOT NULL, some_value INTEGER NULL, lob_data VARCHAR(MAX) NULL, another_column CHAR(5) NULL, CONSTRAINT [PK dbo.LOBtest pk] PRIMARY KEY CLUSTERED (pk ASC) ); GO DECLARE @Data VARCHAR(MAX); SET @Data = REPLICATE(CONVERT(VARCHAR(MAX), 'x'), 65540);   WITH Numbers (n) AS ( SELECT ROW_NUMBER() OVER (ORDER BY (SELECT 0)) FROM master.sys.columns C1, master.sys.columns C2 ) INSERT LOBtest WITH (TABLOCKX) ( pk, some_value, lob_data ) SELECT TOP (1000) N.n, N.n, @Data FROM Numbers N WHERE N.n <= 1000; Now here’s a query to modify the cluster keys: UPDATE dbo.LOBtest SET pk = pk + 1; The query plan is: As you can see, the Split/Sort/Collapse optimization is present, and we also gain an Eager Table Spool, for Halloween protection.  In addition, SQL Server now has no choice but to read the LOB data in the Clustered Index Scan: The performance is not great, as you might expect (even though there is no non-clustered index to maintain): Table 'LOBtest'. Scan count 1, logical reads 2011, physical reads 0, read-ahead reads 0, lob logical reads 36015, lob physical reads 0, lob read-ahead reads 15992.   Table 'Worktable'. Scan count 1, logical reads 2040, physical reads 0, read-ahead reads 0, lob logical reads 34000, lob physical reads 0, lob read-ahead reads 8000.   SQL Server Execution Times: CPU time = 483 ms, elapsed time = 17884 ms. Notice how the LOB data is read twice: once from the Clustered Index Scan, and again from the work table in tempdb used by the Eager Spool. If you try the same test with a non-unique clustered index (rather than a primary key), you’ll get a much more efficient plan that just passes the cluster key (including uniqueifier) around (no LOB data or other non-key columns): A unique non-clustered index (on a heap) works well too: Both those queries complete in a few tens of milliseconds, with no LOB reads, and just a few thousand logical reads.  (In fact the heap is rather more efficient). There are lots more fun combinations to try that I don’t have space for here. Final Thoughts The behaviour shown in this post is not limited to LOB data by any means.  If the conditions are met, any unique index that has included columns can produce similar behaviour – something to bear in mind when adding large INCLUDE columns to achieve covering queries, perhaps. Paul White Email: [email protected] Twitter: @PaulWhiteNZ

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  • MERGE Bug with Filtered Indexes

    - by Paul White
    A MERGE statement can fail, and incorrectly report a unique key violation when: The target table uses a unique filtered index; and No key column of the filtered index is updated; and A column from the filtering condition is updated; and Transient key violations are possible Example Tables Say we have two tables, one that is the target of a MERGE statement, and another that contains updates to be applied to the target.  The target table contains three columns, an integer primary key, a single character alternate key, and a status code column.  A filtered unique index exists on the alternate key, but is only enforced where the status code is ‘a’: CREATE TABLE #Target ( pk integer NOT NULL, ak character(1) NOT NULL, status_code character(1) NOT NULL,   PRIMARY KEY (pk) );   CREATE UNIQUE INDEX uq1 ON #Target (ak) INCLUDE (status_code) WHERE status_code = 'a'; The changes table contains just an integer primary key (to identify the target row to change) and the new status code: CREATE TABLE #Changes ( pk integer NOT NULL, status_code character(1) NOT NULL,   PRIMARY KEY (pk) ); Sample Data The sample data for the example is: INSERT #Target (pk, ak, status_code) VALUES (1, 'A', 'a'), (2, 'B', 'a'), (3, 'C', 'a'), (4, 'A', 'd');   INSERT #Changes (pk, status_code) VALUES (1, 'd'), (4, 'a');          Target                     Changes +-----------------------+    +------------------+ ¦ pk ¦ ak ¦ status_code ¦    ¦ pk ¦ status_code ¦ ¦----+----+-------------¦    ¦----+-------------¦ ¦  1 ¦ A  ¦ a           ¦    ¦  1 ¦ d           ¦ ¦  2 ¦ B  ¦ a           ¦    ¦  4 ¦ a           ¦ ¦  3 ¦ C  ¦ a           ¦    +------------------+ ¦  4 ¦ A  ¦ d           ¦ +-----------------------+ The target table’s alternate key (ak) column is unique, for rows where status_code = ‘a’.  Applying the changes to the target will change row 1 from status ‘a’ to status ‘d’, and row 4 from status ‘d’ to status ‘a’.  The result of applying all the changes will still satisfy the filtered unique index, because the ‘A’ in row 1 will be deleted from the index and the ‘A’ in row 4 will be added. Merge Test One Let’s now execute a MERGE statement to apply the changes: MERGE #Target AS t USING #Changes AS c ON c.pk = t.pk WHEN MATCHED AND c.status_code <> t.status_code THEN UPDATE SET status_code = c.status_code; The MERGE changes the two target rows as expected.  The updated target table now contains: +-----------------------+ ¦ pk ¦ ak ¦ status_code ¦ ¦----+----+-------------¦ ¦  1 ¦ A  ¦ d           ¦ <—changed from ‘a’ ¦  2 ¦ B  ¦ a           ¦ ¦  3 ¦ C  ¦ a           ¦ ¦  4 ¦ A  ¦ a           ¦ <—changed from ‘d’ +-----------------------+ Merge Test Two Now let’s repopulate the changes table to reverse the updates we just performed: TRUNCATE TABLE #Changes;   INSERT #Changes (pk, status_code) VALUES (1, 'a'), (4, 'd'); This will change row 1 back to status ‘a’ and row 4 back to status ‘d’.  As a reminder, the current state of the tables is:          Target                        Changes +-----------------------+    +------------------+ ¦ pk ¦ ak ¦ status_code ¦    ¦ pk ¦ status_code ¦ ¦----+----+-------------¦    ¦----+-------------¦ ¦  1 ¦ A  ¦ d           ¦    ¦  1 ¦ a           ¦ ¦  2 ¦ B  ¦ a           ¦    ¦  4 ¦ d           ¦ ¦  3 ¦ C  ¦ a           ¦    +------------------+ ¦  4 ¦ A  ¦ a           ¦ +-----------------------+ We execute the same MERGE statement: MERGE #Target AS t USING #Changes AS c ON c.pk = t.pk WHEN MATCHED AND c.status_code <> t.status_code THEN UPDATE SET status_code = c.status_code; However this time we receive the following message: Msg 2601, Level 14, State 1, Line 1 Cannot insert duplicate key row in object 'dbo.#Target' with unique index 'uq1'. The duplicate key value is (A). The statement has been terminated. Applying the changes using UPDATE Let’s now rewrite the MERGE to use UPDATE instead: UPDATE t SET status_code = c.status_code FROM #Target AS t JOIN #Changes AS c ON t.pk = c.pk WHERE c.status_code <> t.status_code; This query succeeds where the MERGE failed.  The two rows are updated as expected: +-----------------------+ ¦ pk ¦ ak ¦ status_code ¦ ¦----+----+-------------¦ ¦  1 ¦ A  ¦ a           ¦ <—changed back to ‘a’ ¦  2 ¦ B  ¦ a           ¦ ¦  3 ¦ C  ¦ a           ¦ ¦  4 ¦ A  ¦ d           ¦ <—changed back to ‘d’ +-----------------------+ What went wrong with the MERGE? In this test, the MERGE query execution happens to apply the changes in the order of the ‘pk’ column. In test one, this was not a problem: row 1 is removed from the unique filtered index by changing status_code from ‘a’ to ‘d’ before row 4 is added.  At no point does the table contain two rows where ak = ‘A’ and status_code = ‘a’. In test two, however, the first change was to change row 1 from status ‘d’ to status ‘a’.  This change means there would be two rows in the filtered unique index where ak = ‘A’ (both row 1 and row 4 meet the index filtering criteria ‘status_code = a’). The storage engine does not allow the query processor to violate a unique key (unless IGNORE_DUP_KEY is ON, but that is a different story, and doesn’t apply to MERGE in any case).  This strict rule applies regardless of the fact that if all changes were applied, there would be no unique key violation (row 4 would eventually be changed from ‘a’ to ‘d’, removing it from the filtered unique index, and resolving the key violation). Why it went wrong The query optimizer usually detects when this sort of temporary uniqueness violation could occur, and builds a plan that avoids the issue.  I wrote about this a couple of years ago in my post Beware Sneaky Reads with Unique Indexes (you can read more about the details on pages 495-497 of Microsoft SQL Server 2008 Internals or in Craig Freedman’s blog post on maintaining unique indexes).  To summarize though, the optimizer introduces Split, Filter, Sort, and Collapse operators into the query plan to: Split each row update into delete followed by an inserts Filter out rows that would not change the index (due to the filter on the index, or a non-updating update) Sort the resulting stream by index key, with deletes before inserts Collapse delete/insert pairs on the same index key back into an update The effect of all this is that only net changes are applied to an index (as one or more insert, update, and/or delete operations).  In this case, the net effect is a single update of the filtered unique index: changing the row for ak = ‘A’ from pk = 4 to pk = 1.  In case that is less than 100% clear, let’s look at the operation in test two again:          Target                     Changes                   Result +-----------------------+    +------------------+    +-----------------------+ ¦ pk ¦ ak ¦ status_code ¦    ¦ pk ¦ status_code ¦    ¦ pk ¦ ak ¦ status_code ¦ ¦----+----+-------------¦    ¦----+-------------¦    ¦----+----+-------------¦ ¦  1 ¦ A  ¦ d           ¦    ¦  1 ¦ d           ¦    ¦  1 ¦ A  ¦ a           ¦ ¦  2 ¦ B  ¦ a           ¦    ¦  4 ¦ a           ¦    ¦  2 ¦ B  ¦ a           ¦ ¦  3 ¦ C  ¦ a           ¦    +------------------+    ¦  3 ¦ C  ¦ a           ¦ ¦  4 ¦ A  ¦ a           ¦                            ¦  4 ¦ A  ¦ d           ¦ +-----------------------+                            +-----------------------+ From the filtered index’s point of view (filtered for status_code = ‘a’ and shown in nonclustered index key order) the overall effect of the query is:   Before           After +---------+    +---------+ ¦ pk ¦ ak ¦    ¦ pk ¦ ak ¦ ¦----+----¦    ¦----+----¦ ¦  4 ¦ A  ¦    ¦  1 ¦ A  ¦ ¦  2 ¦ B  ¦    ¦  2 ¦ B  ¦ ¦  3 ¦ C  ¦    ¦  3 ¦ C  ¦ +---------+    +---------+ The single net change there is a change of pk from 4 to 1 for the nonclustered index entry ak = ‘A’.  This is the magic performed by the split, sort, and collapse.  Notice in particular how the original changes to the index key (on the ‘ak’ column) have been transformed into an update of a non-key column (pk is included in the nonclustered index).  By not updating any nonclustered index keys, we are guaranteed to avoid transient key violations. The Execution Plans The estimated MERGE execution plan that produces the incorrect key-violation error looks like this (click to enlarge in a new window): The successful UPDATE execution plan is (click to enlarge in a new window): The MERGE execution plan is a narrow (per-row) update.  The single Clustered Index Merge operator maintains both the clustered index and the filtered nonclustered index.  The UPDATE plan is a wide (per-index) update.  The clustered index is maintained first, then the Split, Filter, Sort, Collapse sequence is applied before the nonclustered index is separately maintained. There is always a wide update plan for any query that modifies the database. The narrow form is a performance optimization where the number of rows is expected to be relatively small, and is not available for all operations.  One of the operations that should disallow a narrow plan is maintaining a unique index where intermediate key violations could occur. Workarounds The MERGE can be made to work (producing a wide update plan with split, sort, and collapse) by: Adding all columns referenced in the filtered index’s WHERE clause to the index key (INCLUDE is not sufficient); or Executing the query with trace flag 8790 set e.g. OPTION (QUERYTRACEON 8790). Undocumented trace flag 8790 forces a wide update plan for any data-changing query (remember that a wide update plan is always possible).  Either change will produce a successfully-executing wide update plan for the MERGE that failed previously. Conclusion The optimizer fails to spot the possibility of transient unique key violations with MERGE under the conditions listed at the start of this post.  It incorrectly chooses a narrow plan for the MERGE, which cannot provide the protection of a split/sort/collapse sequence for the nonclustered index maintenance. The MERGE plan may fail at execution time depending on the order in which rows are processed, and the distribution of data in the database.  Worse, a previously solid MERGE query may suddenly start to fail unpredictably if a filtered unique index is added to the merge target table at any point. Connect bug filed here Tests performed on SQL Server 2012 SP1 CUI (build 11.0.3321) x64 Developer Edition © 2012 Paul White – All Rights Reserved Twitter: @SQL_Kiwi Email: [email protected]

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  • Sql Server 2005 database lost, How to recover all records. MDF/LDF size is same as it should be

    - by Shantanu Gupta
    Few months back, I installed a sql server 2005 on one of my client machine. I gave him a backup option to take backup timely but he never took any backup. Today he called me that "i m not able to see any record of mine." I visited at my clients system and saw that none of the record was present on the tables. There was not even a single row in any of the tables. Then I checked if he has any backup file which i found to be absent. I asked him the reason what could be the possible cause. He said it might be due to virus. After this I checked the size of mdf and ldf file and found it should be what it is. when i created his server mdf ldf file had 2MB of database now it is 83 MB and 193Mb mdf/ldf respectively. This shows the data is still present in it but it is not being displayed. What could be the possible cause and how can i restore all data back to my tables ?

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  • Is there any reason not to go directly from client-side Javascript to a database?

    - by Chris Smith
    So, let's say I'm going to build a Stack Exchange clone and I decide to use something like CouchDB as my backend store. If I use their built-in authentication and database-level authorization, is there any reason not to allow the client-side Javascript to write directly to the publicly available CouchDB server? Since this is basically a CRUD application and the business logic consists of "Only the author can edit their post" I don't see much of a need to have a layer between the client-side stuff and the database. I would simply use validation on the CouchDB side to make sure someone isn't putting in garbage data and make sure that permissions are set properly so that users can only read their own _user data. The rendering would be done client-side by something like AngularJS. In essence you could just have a CouchDB server and a bunch of "static" pages and you're good to go. You wouldn't need any kind of server-side processing, just something that could serve up the HTML pages. Opening my database up to the world seems wrong, but in this scenario I can't think of why as long as permissions are set properly. It goes against my instinct as a web developer, but I can't think of a good reason. So, why is this a bad idea? EDIT: Looks like there is a similar discussion here: Writing Web "server less" applications EDIT: Awesome discussion so far, and I appreciate everyone's feedback! I feel like I should add a few generic assumptions instead of calling out CouchDB and AngularJS specifically. So let's assume that: The database can authenticate users directly from its hidden store All database communication would happen over SSL Data validation can (but maybe shouldn't?) be handled by the database The only authorization we care about other than admin functions is someone only being allowed to edit their own post We're perfectly fine with everyone being able to read all data (EXCEPT user records which may contain password hashes) Administrative functions would be restricted by database authorization No one can add themselves to an administrator role The database is relatively easy to scale There is little to no true business logic; this is a basic CRUD app

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  • E-Business Suite Certified with DB 11.2.0.2 on HP-UX Itanium and IBM AIX on Power

    - by Steven Chan
    As a follow-on to our previous certification announcement, Oracle Database 11g Release 2 (11.2.0.2) s now certified with Oracle E-Business Suite Release 12 (12.0.x and 12.1.x) and 11i (11.5.10.2 + ATG PF.H RUP 6 and higher) on the following additional platforms:Oracle E-Business Suite Release 12HP-UX Itanium (11.31) IBM AIX on Power Systems (64-bit) (5.3, 6.1) Oracle E-Business Suite Release 11iIBM AIX on Power Systems (64-bit) (5.3, 6.1)

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