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• JQuery hide div based on day

  - by Ryan

  I'm trying to hide a div based on the day of week? <script type="text/javascript" src="http://code.jquery.com/jquery-latest.js"></script> <script type="text/javascript"> var rightNow = new Date(); var day = rightNow.getUTCDay(); if (day = 5;) { $('#friday').hide(); } </script> <div id='friday'> friday </div> <br> <div id='saturday'> saturday </div> <br> <div id='sunday'> sunday </div> <br> <div id='monday'> monday </div>

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• Google Web Toolkit or Microsoft Technology (Silverlight, ASP.NET)

  - by NativeByte

  We have a large code base in MFC and VB. A few applications are in .NET. All these applications interoperate with each other on the user's machine and also connect with Unix servers via sockets. Recently we have started discussing a re-write of our applications and possibility of moving a lot of these desktop applications to web (they would run in intranet). A straight forward way is rewritting them in one of the .NET technologies. But a suggestion about using Google Web tookit has popped up and the argument is that it would help creating applications that would run in a browser on both desktop and mobile devices. One of the key problem that I see is that GWT is a large abstraction over Javascript. This will require the team to learn GWT, Javascript, IDEs etc as their experience has been primarily Microsoft technologies and not Java. It would be easier for them to learn .NET technologies instead of GWT. I do not have a depth of GWT and its drawback pittfalls and do not know about a parallel Microsoft Technology that I should investigate. So I would appreciate if people here can share their views or experiences using GWT or equivalent Microsoft technology.

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• 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

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• 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

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• 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

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• 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

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• 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

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• 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

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• 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

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• SPARC T3-1 Record Results Running JD Edwards EnterpriseOne Day in the Life Benchmark with Added Batch Component

  - by Brian

  Using Oracle's SPARC T3-1 server for the application tier and Oracle's SPARC Enterprise M3000 server for the database tier, a world record result was produced running the Oracle's JD Edwards EnterpriseOne applications Day in the Life benchmark run concurrently with a batch workload. The SPARC T3-1 server based result has 25% better performance than the IBM Power 750 POWER7 server even though the IBM result did not include running a batch component. The SPARC T3-1 server based result has 25% better space/performance than the IBM Power 750 POWER7 server as measured by the online component. The SPARC T3-1 server based result is 5x faster than the x86-based IBM x3650 M2 server system when executing the online component of the JD Edwards EnterpriseOne 9.0.1 Day in the Life benchmark. The IBM result did not include a batch component. The SPARC T3-1 server based result has 2.5x better space/performance than the x86-based IBM x3650 M2 server as measured by the online component. The combination of SPARC T3-1 and SPARC Enterprise M3000 servers delivered a Day in the Life benchmark result of 5000 online users with 0.875 seconds of average transaction response time running concurrently with 19 Universal Batch Engine (UBE) processes at 10 UBEs/minute. The solution exercises various JD Edwards EnterpriseOne applications while running Oracle WebLogic Server 11g Release 1 and Oracle Web Tier Utilities 11g HTTP server in Oracle Solaris Containers, together with the Oracle Database 11g Release 2. The SPARC T3-1 server showed that it could handle the additional workload of batch processing while maintaining the same number of online users for the JD Edwards EnterpriseOne Day in the Life benchmark. This was accomplished with minimal loss in response time. JD Edwards EnterpriseOne 9.0.1 takes advantage of the large number of compute threads available in the SPARC T3-1 server at the application tier and achieves excellent response times. The SPARC T3-1 server consolidates the application/web tier of the JD Edwards EnterpriseOne 9.0.1 application using Oracle Solaris Containers. Containers provide flexibility, easier maintenance and better CPU utilization of the server leaving processing capacity for additional growth. A number of Oracle advanced technology and features were used to obtain this result: Oracle Solaris 10, Oracle Solaris Containers, Oracle Java Hotspot Server VM, Oracle WebLogic Server 11g Release 1, Oracle Web Tier Utilities 11g, Oracle Database 11g Release 2, the SPARC T3 and SPARC64 VII+ based servers. This is the first published result running both online and batch workload concurrently on the JD Enterprise Application server. No published results are available from IBM running the online component together with a batch workload. The 9.0.1 version of the benchmark saw some minor performance improvements relative to 9.0. When comparing between 9.0.1 and 9.0 results, the reader should take this into account when the difference between results is small. Performance Landscape JD Edwards EnterpriseOne Day in the Life Benchmark Online with Batch Workload This is the first publication on the Day in the Life benchmark run concurrently with batch jobs. The batch workload was provided by Oracle's Universal Batch Engine. System RackUnits Online Users Resp Time (sec) BatchConcur(# of UBEs) BatchRate(UBEs/m) Version SPARC T3-1, 1xSPARC T3 (1.65 GHz), Solaris 10 M3000, 1xSPARC64 VII+ (2.86 GHz), Solaris 10 4 5000 0.88 19 10 9.0.1 Resp Time (sec) — Response time of online jobs reported in seconds Batch Concur (# of UBEs) — Batch concurrency presented in the number of UBEs Batch Rate (UBEs/m) — Batch transaction rate in UBEs/minute. JD Edwards EnterpriseOne Day in the Life Benchmark Online Workload Only These results are for the Day in the Life benchmark. They are run without any batch workload. System RackUnits Online Users ResponseTime (sec) Version SPARC T3-1, 1xSPARC T3 (1.65 GHz), Solaris 10 M3000, 1xSPARC64 VII (2.75 GHz), Solaris 10 4 5000 0.52 9.0.1 IBM Power 750, 1xPOWER7 (3.55 GHz), IBM i7.1 4 4000 0.61 9.0 IBM x3650M2, 2xIntel X5570 (2.93 GHz), OVM 2 1000 0.29 9.0 IBM result from http://www-03.ibm.com/systems/i/advantages/oracle/, IBM used WebSphere Configuration Summary Hardware Configuration: 1 x SPARC T3-1 server 1 x 1.65 GHz SPARC T3 128 GB memory 16 x 300 GB 10000 RPM SAS 1 x Sun Flash Accelerator F20 PCIe Card, 92 GB 1 x 10 GbE NIC 1 x SPARC Enterprise M3000 server 1 x 2.86 SPARC64 VII+ 64 GB memory 1 x 10 GbE NIC 2 x StorageTek 2540 + 2501 Software Configuration: JD Edwards EnterpriseOne 9.0.1 with Tools 8.98.3.3 Oracle Database 11g Release 2 Oracle 11g WebLogic server 11g Release 1 version 10.3.2 Oracle Web Tier Utilities 11g Oracle Solaris 10 9/10 Mercury LoadRunner 9.10 with Oracle Day in the Life kit for JD Edwards EnterpriseOne 9.0.1 Oracle’s Universal Batch Engine - Short UBEs and Long UBEs Benchmark Description JD Edwards EnterpriseOne is an integrated applications suite of Enterprise Resource Planning (ERP) software. Oracle offers 70 JD Edwards EnterpriseOne application modules to support a diverse set of business operations. Oracle's Day in the Life (DIL) kit is a suite of scripts that exercises most common transactions of JD Edwards EnterpriseOne applications, including business processes such as payroll, sales order, purchase order, work order, and other manufacturing processes, such as ship confirmation. These are labeled by industry acronyms such as SCM, CRM, HCM, SRM and FMS. The kit's scripts execute transactions typical of a mid-sized manufacturing company. The workload consists of online transactions and the UBE workload of 15 short and 4 long UBEs. LoadRunner runs the DIL workload, collects the user’s transactions response times and reports the key metric of Combined Weighted Average Transaction Response time. The UBE processes workload runs from the JD Enterprise Application server. Oracle's UBE processes come as three flavors: Short UBEs < 1 minute engage in Business Report and Summary Analysis, Mid UBEs > 1 minute create a large report of Account, Balance, and Full Address, Long UBEs > 2 minutes simulate Payroll, Sales Order, night only jobs. The UBE workload generates large numbers of PDF files reports and log files. The UBE Queues are categorized as the QBATCHD, a single threaded queue for large UBEs, and the QPROCESS queue for short UBEs run concurrently. One of the Oracle Solaris Containers ran 4 Long UBEs, while another Container ran 15 short UBEs concurrently. The mixed size UBEs ran concurrently from the SPARC T3-1 server with the 5000 online users driven by the LoadRunner. Oracle’s UBE process performance metric is Number of Maximum Concurrent UBE processes at transaction rate, UBEs/minute. Key Points and Best Practices Two JD Edwards EnterpriseOne Application Servers and two Oracle Fusion Middleware WebLogic Servers 11g R1 coupled with two Oracle Fusion Middleware 11g Web Tier HTTP Server instances on the SPARC T3-1 server were hosted in four separate Oracle Solaris Containers to demonstrate consolidation of multiple application and web servers. See Also SPARC T3-1 oracle.com SPARC Enterprise M3000 oracle.com Oracle Solaris oracle.com JD Edwards EnterpriseOne oracle.com Oracle Database 11g Release 2 Enterprise Edition oracle.com Disclosure Statement Copyright 2011, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 6/27/2011.

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• SQL SERVER – Weekly Series – Memory Lane – #048

  - by Pinal Dave

  Here is the list of selected articles of SQLAuthority.com across all these years. Instead of just listing all the articles I have selected a few of my most favorite articles and have listed them here with additional notes below it. Let me know which one of the following is your favorite article from memory lane. 2007 Order of Result Set of SELECT Statement on Clustered Indexed Table When ORDER BY is Not Used Above theory is true in most of the cases. However SQL Server does not use that logic when returning the resultset. SQL Server always returns the resultset which it can return fastest.In most of the cases the resultset which can be returned fastest is the resultset which is returned using clustered index. Effect of TRANSACTION on Local Variable – After ROLLBACK and After COMMIT One of the Jr. Developer asked me this question (What will be the Effect of TRANSACTION on Local Variable – After ROLLBACK and After COMMIT?) while I was rushing to an important meeting. I was getting late so I asked him to talk with his Application Tech Lead. When I came back from meeting both of them were looking for me. They said they are confused. I quickly wrote down following example for them. 2008 SQL SERVER – Guidelines and Coding Standards Complete List Download Coding standards and guidelines are very important for any developer on the path of a successful career. A coding standard is a set of guidelines, rules and regulations on how to write code. Coding standards should be flexible enough or should take care of the situation where they should not prevent best practices for coding. They are basically the guidelines that one should follow for better understanding. Download Guidelines and Coding Standards complete List Download Get Answer in Float When Dividing of Two Integer Many times we have requirements of some calculations amongst different fields in Tables. One of the software developers here was trying to calculate some fields having integer values and divide it which gave incorrect results in integer where accurate results including decimals was expected. Puzzle – Computed Columns Datatype Explanation SQL Server automatically does a cast to the data type having the highest precedence. So the result of INT and INT will be INT, but INT and FLOAT will be FLOAT because FLOAT has a higher precedence. If you want a different data type, you need to do an EXPLICIT cast. Renaming SP is Not Good Idea – Renaming Stored Procedure Does Not Update sys.procedures I have written many articles about renaming a tables, columns and procedures SQL SERVER – How to Rename a Column Name or Table Name, here I found something interesting about renaming the stored procedures and felt like sharing it with you all. The interesting fact is that when we rename a stored procedure using SP_Rename command, the Stored Procedure is successfully renamed. But when we try to test the procedure using sp_helptext, the procedure will be having the old name instead of new names. 2009 Insert Values of Stored Procedure in Table – Use Table Valued Function It is clear from the result set that , where I have converted stored procedure logic into the table valued function, is much better in terms of logic as it saves a large number of operations. However, this option should be used carefully. The performance of the stored procedure is “usually” better than that of functions. Interesting Observation – Index on Index View Used in Similar Query Recently, I was working on an optimization project for one of the largest organizations. While working on one of the queries, we came across a very interesting observation. We found that there was a query on the base table and when the query was run, it used the index, which did not exist in the base table. On careful examination, we found that the query was using the index that was on another view. This was very interesting as I have personally never experienced a scenario like this. In simple words, “Query on the base table can use the index created on the indexed view of the same base table.” Interesting Observation – Execution Plan and Results of Aggregate Concatenation Queries Working with SQL Server has never seemed to be monotonous – no matter how long one has worked with it. Quite often, I come across some excellent comments that I feel like acknowledging them as blog posts. Recently, I wrote an article on SQL SERVER – Execution Plan and Results of Aggregate Concatenation Queries Depend Upon Expression Location, which is well received in the community. 2010 I encourage all of you to go through complete series and write your own on the subject. If you write an article and send it to me, I will publish it on this blog with due credit to you. If you write on your own blog, I will update this blog post pointing to your blog post. SQL SERVER – ORDER BY Does Not Work – Limitation of the View 1 SQL SERVER – Adding Column is Expensive by Joining Table Outside View – Limitation of the View 2 SQL SERVER – Index Created on View not Used Often – Limitation of the View 3 SQL SERVER – SELECT * and Adding Column Issue in View – Limitation of the View 4 SQL SERVER – COUNT(*) Not Allowed but COUNT_BIG(*) Allowed – Limitation of the View 5 SQL SERVER – UNION Not Allowed but OR Allowed in Index View – Limitation of the View 6 SQL SERVER – Cross Database Queries Not Allowed in Indexed View – Limitation of the View 7 SQL SERVER – Outer Join Not Allowed in Indexed Views – Limitation of the View 8 SQL SERVER – SELF JOIN Not Allowed in Indexed View – Limitation of the View 9 SQL SERVER – Keywords View Definition Must Not Contain for Indexed View – Limitation of the View 10 SQL SERVER – View Over the View Not Possible with Index View – Limitations of the View 11 2011 Startup Parameters Easy to Configure If you are a regular reader of this blog, you must be aware that I have written about SQL Server Denali recently. Here is the quickest way to reach into the screen where we can change the startup parameters. Go to SQL Server Configuration Manager >> SQL Server Services >> Right Click on the Server >> Properties >> Startup Parameters 2012 Validating Unique Columnname Across Whole Database I sometimes come across very strange requirements and often I do not receive a proper explanation of the same. Here is the one of those examples. For example “Our business requirement is when we add new column we want it unique across current database.” Read the solution to this strange request in this blog post. Excel Losing Decimal Values When Value Pasted from SSMS ResultSet It is very common when users are coping the resultset to Excel, the floating point or decimals are missed. The solution is very much simple and it requires a small adjustment in the Excel. By default Excel is very smart and when it detects the value which is getting pasted is numeric it changes the column format to accommodate that. Basic Calculation and PEMDAS Order of Operation Read this interesting blog post for fantastic conversation about the subject. Copy Column Headers from Resultset – SQL in Sixty Seconds #027 – Video http://www.youtube.com/watch?v=x_-3tLqTRv0 Delete From Multiple Table – Update Multiple Table in Single Statement There are two questions which I get every single day multiple times. In my gmail, I have created standard canned reply for them. Let us see the questions here. I want to delete from multiple table in a single statement how will I do it? I want to update multiple table in a single statement how will I do it? Read the answer in the blog post. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Memory Lane, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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• SQL SERVER – Thinking about Deprecated, Discontinued Features and Breaking Changes while Upgrading to SQL Server 2012 – Guest Post by Nakul Vachhrajani

  - by pinaldave

  Nakul Vachhrajani is a Technical Specialist and systems development professional with iGATE having a total IT experience of more than 7 years. Nakul is an active blogger with BeyondRelational.com (150+ blogs), and can also be found on forums at SQLServerCentral and BeyondRelational.com. Nakul has also been a guest columnist for SQLAuthority.com and SQLServerCentral.com. Nakul presented a webcast on the “Underappreciated Features of Microsoft SQL Server” at the Microsoft Virtual Tech Days Exclusive Webcast series (May 02-06, 2011) on May 06, 2011. He is also the author of a research paper on Database upgrade methodologies, which was published in a CSI journal, published nationwide. In addition to his passion about SQL Server, Nakul also contributes to the academia out of personal interest. He visits various colleges and universities as an external faculty to judge project activities being carried out by the students. Disclaimer: The opinions expressed herein are his own personal opinions and do not represent his employer’s view in anyway. Blog | LinkedIn | Twitter | Google+ Let us hear the thoughts of Nakul in first person - Those who have been following my blogs would be aware that I am recently running a series on the database engine features that have been deprecated in Microsoft SQL Server 2012. Based on the response that I have received, I was quite surprised to know that most of the audience found these to be breaking changes, when in fact, they were not! It was then that I decided to write a little piece on how to plan your database upgrade such that it works with the next version of Microsoft SQL Server. Please note that the recommendations made in this article are high-level markers and are intended to help you think over the specific steps that you would need to take to upgrade your database. Refer the documentation – Understand the terms Change is the only constant in this world. Therefore, whenever customer requirements, newer architectures and designs require software vendors to make a change to the keywords, functions, etc; they ensure that they provide their end users sufficient time to migrate over to the new standards before dropping off the old ones. Microsoft does that too with it’s Microsoft SQL Server product. Whenever a new SQL Server release is announced, it comes with a list of the following features: Breaking changes These are changes that would break your currently running applications, scripts or functionalities that are based on earlier version of Microsoft SQL Server These are mostly features whose behavior has been changed keeping in mind the newer architectures and designs Lesson: These are the changes that you need to be most worried about! Discontinued features These features are no longer available in the associated version of Microsoft SQL Server These features used to be “deprecated” in the prior release Lesson: Without these changes, your database would not be compliant/may not work with the version of Microsoft SQL Server under consideration Deprecated features These features are those that are still available in the current version of Microsoft SQL Server, but are scheduled for removal in a future version. These may be removed in either the next version or any other future version of Microsoft SQL Server The features listed for deprecation will compose the list of discontinued features in the next version of SQL Server Lesson: Plan to make necessary changes required to remove/replace usage of the deprecated features with the latest recommended replacements Once a feature appears on the list, it moves from bottom to the top, i.e. it is first marked as “Deprecated” and then “Discontinued”. We know of “Breaking change” comes later on in the product life cycle. What this means is that if you want to know what features would not work with SQL Server 2012 (and you are currently using SQL Server 2008 R2), you need to refer the list of breaking changes and discontinued features in SQL Server 2012. Use the tools! There are a lot of tools and technologies around us, but it is rarely that I find teams using these tools religiously and to the best of their potential. Below are the top two tools, from Microsoft, that I use every time I plan a database upgrade. The SQL Server Upgrade Advisor Ever since SQL Server 2005 was announced, Microsoft provides a small, very light-weight tool called the “SQL Server upgrade advisor”. The upgrade advisor analyzes installed components from earlier versions of SQL Server, and then generates a report that identifies issues to fix either before or after you upgrade. The analysis examines objects that can be accessed, such as scripts, stored procedures, triggers, and trace files. Upgrade Advisor cannot analyze desktop applications or encrypted stored procedures. Refer the links towards the end of the post to know how to get the Upgrade Advisor. The SQL Server Profiler Another great tool that you can use is the one most SQL Server developers & administrators use often – the SQL Server profiler. SQL Server Profiler provides functionality to monitor the “Deprecation” event, which contains: Deprecation announcement – equivalent to features to be deprecated in a future release of SQL Server Deprecation final support – equivalent to features to be deprecated in the next release of SQL Server You can learn more using the links towards the end of the post. A basic checklist There are a lot of finer points that need to be taken care of when upgrading your database. But, it would be worth-while to identify a few basic steps in order to make your database compliant with the next version of SQL Server: Monitor the current application workload (on a test bed) via the Profiler in order to identify usage of features marked as Deprecated If none appear, you are all set! (This almost never happens) Note down all the offending queries and feature usages Run analysis sessions using the SQL Server upgrade advisor on your database Based on the inputs from the analysis report and Profiler trace sessions, Incorporate solutions for the breaking changes first Next, incorporate solutions for the discontinued features Revisit and document the upgrade strategy for your deployment scenarios Revisit the fall-back, i.e. rollback strategies in case the upgrades fail Because some programming changes are dependent upon the SQL server version, this may need to be done in consultation with the development teams Before any other enhancements are incorporated by the development team, send out the database changes into QA QA strategy should involve a comparison between an environment running the old version of SQL Server against the new one Because minimal application changes have gone in (essential changes for SQL Server version compliance only), this would be possible As an ongoing activity, keep incorporating changes recommended as per the deprecated features list As a DBA, update your coding standards to ensure that the developers are using ANSI compliant code – this code will require a change only if the ANSI standard changes Remember this: Change management is a continuous process. Keep revisiting the product release notes and incorporate recommended changes to stay prepared for the next release of SQL Server. May the power of SQL Server be with you! Links Referenced in this post Breaking changes in SQL Server 2012: Link Discontinued features in SQL Server 2012: Link Get the upgrade advisor from the Microsoft Download Center at: Link Upgrade Advisor page on MSDN: Link Profiler: Review T-SQL code to identify objects no longer supported by Microsoft: Link Upgrading to SQL Server 2012 by Vinod Kumar: Link 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: Upgrade

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• 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

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• SQL SERVER – Weekly Series – Memory Lane – #034

  - by Pinal Dave

  Here is the list of selected articles of SQLAuthority.com across all these years. Instead of just listing all the articles I have selected a few of my most favorite articles and have listed them here with additional notes below it. Let me know which one of the following is your favorite article from memory lane. 2007 UDF – User Defined Function to Strip HTML – Parse HTML – No Regular Expression The UDF used in the blog does fantastic task – it scans entire HTML text and removes all the HTML tags. It keeps only valid text data without HTML task. This is one of the quite commonly requested tasks many developers have to face everyday. De-fragmentation of Database at Operating System to Improve Performance Operating system skips MDF file while defragging the entire filesystem of the operating system. It is absolutely fine and there is no impact of the same on performance. Read the entire blog post for my conversation with our network engineers. Delay Function – WAITFOR clause – Delay Execution of Commands How do you delay execution of the commands in SQL Server – ofcourse by using WAITFOR keyword. In this blog post, I explain the same with the help of T-SQL script. Find Length of Text Field To measure the length of TEXT fields the function is DATALENGTH(textfield). Len will not work for text field. As of SQL Server 2005, developers should migrate all the text fields to VARCHAR(MAX) as that is the way forward. Retrieve Current Date Time in SQL Server CURRENT_TIMESTAMP, GETDATE(), {fn NOW()} There are three ways to retrieve the current datetime in SQL SERVER. CURRENT_TIMESTAMP, GETDATE(), {fn NOW()} Explanation and Comparison of NULLIF and ISNULL An interesting observation is NULLIF returns null if it comparison is successful, whereas ISNULL returns not null if its comparison is successful. In one way they are opposite to each other. Here is my question to you - How to create infinite loop using NULLIF and ISNULL? If this is even possible? 2008 Introduction to SERVERPROPERTY and example SERVERPROPERTY is a very interesting system function. It returns many of the system values. I use it very frequently to get different server values like Server Collation, Server Name etc. SQL Server Start Time We can use DMV to find out what is the start time of SQL Server in 2008 and later version. In this blog you can see how you can do the same. Find Current Identity of Table Many times we need to know what is the current identity of the column. I have found one of my developers using aggregated function MAX () to find the current identity. However, I prefer following DBCC command to figure out current identity. Create Check Constraint on Column Some time we just need to create a simple constraint over the table but I have noticed that developers do many different things to make table column follow rules than just creating constraint. I suggest constraint is a very useful concept and every SQL Developer should pay good attention to this subject. 2009 List Schema Name and Table Name for Database This is one of the blog post where I straight forward display script. One of the kind of blog posts, which I still love to read and write. Clustered Index on Separate Drive From Table Location A table devoid of primary key index is called heap, and here data is not arranged in a particular order, which gives rise to issues that adversely affect performance. Data must be stored in some kind of order. If we put clustered index on it then the order will be forced by that index and the data will be stored in that particular order. Understanding Table Hints with Examples Hints are options and strong suggestions specified for enforcement by the SQL Server query processor on DML statements. The hints override any execution plan the query optimizer might select for a query. 2010 Data Pages in Buffer Pool – Data Stored in Memory Cache One of my earlier year article, which I still read it many times and point developers to read it again. It is clear from the Resultset that when more than one index is used, datapages related to both or all of the indexes are stored in Memory Cache separately. TRANSACTION, DML and Schema Locks Can you create a situation where you can see Schema Lock? Well, this is a very simple question, however during the interview I notice over 50 candidates failed to come up with the scenario. In this blog post, I have demonstrated the situation where we can see the schema lock in database. 2011 Solution – Puzzle – Statistics are not updated but are Created Once In this example I have created following situation: Create Table Insert 1000 Records Check the Statistics Now insert 10 times more 10,000 indexes Check the Statistics – it will be NOT updated Auto Update Statistics and Auto Create Statistics for database is TRUE Now I have requested two things in the example 1) Why this is happening? 2) How to fix this issue? Selecting Domain from Email Address This is a straight to script blog post where I explain how to select only domain name from entire email address. Solution – Generating Zero Without using Any Numbers in T-SQL How to get zero digit without using any digit? This is indeed a very interesting question and the answer is even interesting. Try to come up with answer in next 10 minutes and if you can’t come up with the answer the blog post read this post for solution. 2012 Simple Explanation and Puzzle with SOUNDEX Function and DIFFERENCE Function In simple words - SOUNDEX converts an alphanumeric string to a four-character code to find similar-sounding words or names. DIFFERENCE function returns an integer value. The  integer returned is the number of characters in the SOUNDEX values that are the same. Read Only Files and SQL Server Management Studio (SSMS) I have come across a very interesting feature in SSMS related to “Read Only” files. I believe it is a little unknown feature as well so decided to write a blog about the same. Identifying Column Data Type of uniqueidentifier without Querying System Tables How do I know if any table has a uniqueidentifier column and what is its value without using any DMV or System Catalogues? Only information you know is the table name and you are allowed to return any kind of error if the table does not have uniqueidentifier column. Read the blog post to find the answer. Solution – User Not Able to See Any User Created Object in Tables – Security and Permissions Issue Interesting question – “When I try to connect to SQL Server, it lets me connect just fine as well let me open and explore the database. I noticed that I do not see any user created instances but when my colleague attempts to connect to the server, he is able to explore the database as well see all the user created tables and other objects. Can you help me fix it?” Importing CSV File Into Database – SQL in Sixty Seconds #018 – Video Here is interesting small 60 second video on how to import CSV file into Database. ColumnStore Index – Batch Mode vs Row Mode Here is the logic behind when Columnstore Index uses Batch Mode and when it uses Row Mode. A batch typically represents about 1000 rows of data. Batch mode processing also uses algorithms that are optimized for the multicore CPUs and increased memory throughput. Follow up – Usage of $rowguid and $IDENTITY This is an excellent follow up blog post of my earlier blog post where I explain where to use $rowguid and $identity.  If you do not know the difference between them, this is a blog with a script example. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Memory Lane, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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• 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

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• MySQL replication not working on leap day

  - by danneth

  Though out of my "core" knowledge I maintain a two-way replicated MySQL database (primary and backup). It's been working fine mostly. All changes are almost instantly replicated between the two servers. But now I've noticed something strange: I have a couple of cases where there are no replication on feb 29th. Admittedly I have not yet confirmed that all replication is lost. But all cases I've found so far have had this issue. Not too long ago I changed timezone from UTC to CET on the backup, it has been CET on the primary all along. Am I fixating on this because it happened on the leap day, or could there be something to it? The servers are both CentOS 5.4 with MySQL 5.0

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• iptables: limiting bytes downloaded per IP per day?

  - by Miles

  On a public-facing web server, I'd like to limit the total bytes downloaded per IP address per day. For example, after a visitor downloaded 100MB, any additional requests would be dropped or rejected for the next 24 hours. Is it possible to accomplish this using iptables alone? The connbytes, connlimit, hashlimit, quota, and recent options all look promising, but the man page plays its cards close to the vest (e.g., "quota - Implements network quotas by decrementing a byte counter with each packet. --quota bytes The quota in bytes."). Would like to avoid using a proxy (like Squid) if possible.

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• Recurring network issues the same time every day.

  - by Peter Turner

  Something has been happening on my company's network at 9:30 every day. I'm not the sysadmin but he's not a ServerFault guy so I'm not privy to every aspect of the network but I can ask questions if follow up is needed. The symptoms are the following : Sluggish network and download speed (I don't notice it, but others do) 3Com phones start ringing without having people on the other end. We've got the following ports exposed to the public for a web server, a few other ports for communicating with our clients for tech support and a VPN. We've got a Cisco ASA blocking everything else. We've got a smallish network (less than 50 computers/vms on at any time). An Active Directory server and a few VM servers. We host our own mail server too. I'm thinking the problem is internal, but what's a good way to figure out where it's coming from?

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• SQL SERVER – Shrinking NDF and MDF Files – Readers’ Opinion

  - by pinaldave

  Previously, I had written a blog post about SQL SERVER – Shrinking NDF and MDF Files – A Safe Operation. After that, I have written the following blog post that talks about the advantage and disadvantage of Shrinking and why one should not be Shrinking a file SQL SERVER – SHRINKFILE and TRUNCATE Log File in SQL Server 2008. On this subject, SQL Server Expert Imran Mohammed left an excellent comment. I just feel that his comment is worth a big article itself. For everybody to read his wonderful explanation, I am posting this blog post here. Thanks Imran! Shrinking Database always creates performance degradation and increases fragmentation in the database. I suggest that you keep that in mind before you start reading the following comment. If you are going to say Shrinking Database is bad and evil, here I am saying it first and loud. Now, the comment of Imran is written while keeping in mind only the process showing how the Shrinking Database Operation works. Imran has already explained his understanding and requests further explanation. I have removed the Best Practices section from Imran’s comments, as there are a few corrections. Comments from Imran - Before I explain to you the concept of Shrink Database, let us understand the concept of Database Files. When we create a new database inside the SQL Server, it is typical that SQl Server creates two physical files in the Operating System: one with .MDF Extension, and another with .LDF Extension. .MDF is called as Primary Data File. .LDF is called as Transactional Log file. If you add one or more data files to a database, the physical file that will be created in the Operating System will have an extension of .NDF, which is called as Secondary Data File; whereas, when you add one or more log files to a database, the physical file that will be created in the Operating System will have the same extension as .LDF. The questions now are, “Why does a new data file have a different extension (.NDF)?”, “Why is it called as a secondary data file?” and, “Why is .MDF file called as a primary data file?” Answers: Note: The following explanation is based on my limited knowledge of SQL Server, so experts please do comment. A data file with a .MDF extension is called a Primary Data File, and the reason behind it is that it contains Database Catalogs. Catalogs mean Meta Data. Meta Data is “Data about Data”. An example for Meta Data includes system objects that store information about other objects, except the data stored by the users. sysobjects stores information about all objects in that database. sysindexes stores information about all indexes and rows of every table in that database. syscolumns stores information about all columns that each table has in that database. sysusers stores how many users that database has. Although Meta Data stores information about other objects, it is not the transactional data that a user enters; rather, it’s a system data about the data. Because Primary Data File (.MDF) contains important information about the database, it is treated as a special file. It is given the name Primary Data file because it contains the Database Catalogs. This file is present in the Primary File Group. You can always create additional objects (Tables, indexes etc.) in the Primary data file (This file is present in the Primary File group), by mentioning that you want to create this object under the Primary File Group. Any additional data file that you add to the database will have only transactional data but no Meta Data, so that’s why it is called as the Secondary Data File. It is given the extension name .NDF so that the user can easily identify whether a specific data file is a Primary Data File or a Secondary Data File(s). There are many advantages of storing data in different files that are under different file groups. You can put your read only in the tables in one file (file group) and read-write tables in another file (file group) and take a backup of only the file group that has read the write data, so that you can avoid taking the backup of a read-only data that cannot be altered. Creating additional files in different physical hard disks also improves I/O performance. A real-time scenario where we use Files could be this one: Let’s say you have created a database called MYDB in the D-Drive which has a 50 GB space. You also have 1 Database File (.MDF) and 1 Log File on D-Drive and suppose that all of that 50 GB space has been used up and you do not have any free space left but you still want to add an additional space to the database. One easy option would be to add one more physical hard disk to the server, add new data file to MYDB database and create this new data file in a new hard disk then move some of the objects from one file to another, and put the file group under which you added new file as default File group, so that any new object that is created gets into the new files, unless specified. Now that we got a basic idea of what data files are, what type of data they store and why they are named the way they are, let’s move on to the next topic, Shrinking. First of all, I disagree with the Microsoft terminology for naming this feature as “Shrinking”. Shrinking, in regular terms, means to reduce the size of a file by means of compressing it. BUT in SQL Server, Shrinking DOES NOT mean compressing. Shrinking in SQL Server means to remove an empty space from database files and release the empty space either to the Operating System or to SQL Server. Let’s examine this through an example. Let’s say you have a database “MYDB” with a size of 50 GB that has a free space of about 20 GB, which means 30GB in the database is filled with data and the 20 GB of space is free in the database because it is not currently utilized by the SQL Server (Database); it is reserved and not yet in use. If you choose to shrink the database and to release an empty space to Operating System, and MIND YOU, you can only shrink the database size to 30 GB (in our example). You cannot shrink the database to a size less than what is filled with data. So, if you have a database that is full and has no empty space in the data file and log file (you don’t have an extra disk space to set Auto growth option ON), YOU CANNOT issue the SHRINK Database/File command, because of two reasons: There is no empty space to be released because the Shrink command does not compress the database; it only removes the empty space from the database files and there is no empty space. Remember, the Shrink command is a logged operation. When we perform the Shrink operation, this information is logged in the log file. If there is no empty space in the log file, SQL Server cannot write to the log file and you cannot shrink a database. Now answering your questions: (1) Q: What are the USEDPAGES & ESTIMATEDPAGES that appear on the Results Pane after using the DBCC SHRINKDATABASE (NorthWind, 10) ? A: According to Books Online (For SQL Server 2000): UsedPages: the number of 8-KB pages currently used by the file. EstimatedPages: the number of 8-KB pages that SQL Server estimates the file could be shrunk down to. Important Note: Before asking any question, make sure you go through Books Online or search on the Google once. The reasons for doing so have many advantages: 1. If someone else already has had this question before, chances that it is already answered are more than 50 %. 2. This reduces your waiting time for the answer. (2) Q: What is the difference between Shrinking the Database using DBCC command like the one above & shrinking it from the Enterprise Manager Console by Right-Clicking the database, going to TASKS & then selecting SHRINK Option, on a SQL Server 2000 environment? A: As far as my knowledge goes, there is no difference, both will work the same way, one advantage of using this command from query analyzer is, your console won’t be freezed. You can do perform your regular activities using Enterprise Manager. (3) Q: What is this .NDF file that is discussed above? I have never heard of it. What is it used for? Is it used by end-users, DBAs or the SERVER/SYSTEM itself? A: .NDF File is a secondary data file. You never heard of it because when database is created, SQL Server creates database by default with only 1 data file (.MDF) and 1 log file (.LDF) or however your model database has been setup, because a model database is a template used every time you create a new database using the CREATE DATABASE Command. Unless you have added an extra data file, you will not see it. This file is used by the SQL Server to store data which are saved by the users. Hope this information helps. I would like to as the experts to please comment if what I understand is not what the Microsoft guys meant. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Readers Contribution, Readers Question, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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• SQL – NuoDB and Third Party Explorer – SQuirreL SQL Client, SQL Workbench/J and DbVisualizer

  - by Pinal Dave

  I recently wrote a four-part series on how I started to learn about and begin my journey with NuoDB. Big Data is indeed a big world and the learning of the Big Data is like spaghetti – no one knows in reality where to start, so I decided to learn it with the help of NuoDB. You can download NuoDB and continue your journey with me as well. Part 1 – Install NuoDB in 90 Seconds Part 2 – Manage NuoDB Installation Part 3 – Explore NuoDB Database Part 4 – Migrate from SQL Server to NuoDB …and in this blog post we will try to answer the most asked question about NuoDB. “I like the NuoDB Explorer but can I connect to NuoDB from my preferred Graphical User Interface?” Honestly, I did not expect this question to be asked of me so many times but from the question it is clear that we developers absolutely want to learn new things and along with that we do want to continue to use our most efficient developer tools. Now here is the answer to the question: “Absolutely, you can continue to use any of the following most popular SQL clients.” NuoDB supports the three most popular 3rd-party SQL clients. In all the leading development environments there are always more than one database installed and managing each of them with a different tool is often a very difficult task. Developers like to use one tool, which can control most of the databases. Once developers are familiar with one database tool it is very difficult for them to switch to another tool. This is particularly difficult when we developers find that tool to be the key reason for our efficiency. Let us see how to install each of the NuoDB supported 3rd party tools along with a quick tutorial on how to go about using them. SQuirreL SQL Client First download SQuirreL Universal SQL client. On the Windows platform you can double-click on the file and it will install the SQuirrel client. Once it is installed, open the application and it will bring up the following screen. Now go to the Drivers tab on the left side and scroll it down. You will find NuoDB mentioned there. Now right click over it and click on Modify Driver. Now here is where you need to make sure that you make proper entries or your client will not work with the database. Enter following values: Name: NuoDB Example URL: jdbc:com:nuodb://localhost:48004/test Website URL: http://www.nuodb.com Now click on the Extra Class Path tab and Add the location of the nuodbjdbc.jar file. If you are following my blog posts and have installed NuoDB in the default location, you will find the default path as C:\Program Files\NuoDB\jar\nuodbjdbc.jar. The class name of the driver is automatically populated. Once you click OK you will see that there is a small icon displayed to the left of NuoDB, which shows that you have successfully configured and installed the NuoDB driver. Now click on the tab of Alias tab and you can notice that it is empty. Now click on the big Plus icon and it will open screen of adding an alias. “Alias” means nothing more than adding a database to your system. The database name of the original installation can be anything and, if you wish, you can register the database with any other alternative name. Here are the details you should fill into the Alias screen below. Name: Test (or your preferred alias) Driver: NuoDB URL: jdbc:com:nuodb://localhost:48004/test (This is for test database) User Name: dba (This is the username which I entered for test Database) Password: goalie (This is the password which I entered for test Database) Check Auto Logon and Connect at Startup and click on OK. That’s it! You are done. On the right side you will see a table name and on the left side you will see various tabs with all the relevant details from respective table. You can see various metadata, schemas, data types and other information in the table. In addition, you can also generate script and do various important tasks related to database. You can see how easy it is to configure NuoDB with the SQuirreL Client and get going with it immediately. SQL Workbench/J This is another wonderful client tool, which works very well with NuoDB. The best part is that in the Driver dropdown you will see NuoDB being mentioned there. Click here to download  SQL Workbench/J Universal SQL client. The download process is straight forward and the installation is a very easy process for SQL Workbench/J. As soon as you open the client, you will notice on following screen the NuoDB driver when selecting a New Connection Profile. Select NuoDB from the drop down and click on OK. In the driver information, enter following details: Driver: NuoDB (com.nuodb.jdbc.Driver) URL: jdbc:com.nuodb://localhost/test Username: dba Password: goalie While clicking on OK, it will bring up the following pop-up. Click Yes to edit the driver information. Click on OK and it will bring you to following screen. This is the screen where you can perform various tasks. You can write any SQL query you want and it will instantly show you the results. Now click on the database icon, which you see right on the left side of the word User=dba.  Once you click on Database Explorer, you can perform various database related tasks. As a developer, one of my favorite tasks is to look at the source of the table as it gives me a proper view of the structure of the database. I find SQL Workbench/J very efficient in doing the same. DbVisualizer DBVisualizer is another great tool, which helps you to connect to NuoDB and retrieve database information in your desired format. A developer who is familiar with DBVisualizer will find this client to be very easy to work with. The installation of the DBVisualizer is very pretty straight forward. When we open the client, it will bring us to the following screen. As a first step we need to set up the driver. Go to Tools >> Driver Manager. It will bring up following screen where we set up the diver. Click on Create Driver and it will open up the driver settings on the right side. On the right side of the area where it displays Driver Settings please enter the following values- Name: NuoDB URL Format: jdbc:com.nuodb://localhost:48004/test Now under the driver path, click on the folder icon and it will ask for the location of the jar file. Provide the path as a C:\Program Files\NuoDB\jar\nuodbjdbc.jar and click OK. You will notice there is a green button displayed at the bottom right corner. This means the driver is configured properly. Once driver is configured properly, we can go to Create Database Connection and create a database. If the pop up show up for the Wizard. Click on No Wizard and continue to enter the settings manually. Here is the Database Connection screen. This screen can be bit tricky. Here are the settings you need to remember to enter. Name: NuoDB Database Type: Generic Driver: NuoDB Database URL: jdbc:com.nuodb://localhost:48004/test Database Userid: dba Database Password: goalie Once you enter the values, click on Connect. Once Connect is pressed, it will change the button value to Reconnect if the connection is successfully established and it will show the connection details on lthe eft side. When we further explore the NuoDB, we can see various tables created in our test application. We can further click on the right side screen and see various details on the table. If you click on the Data Tab, it will display the entire data of the table. The Tools menu also has some very interesting and cool features like Driver Manager, Data Monitor and SQL History. Summary Well, this was a relatively long post but I find it is extremely essential to cover all the three important clients, which we developers use in our daily database development. Here is my question to you? Which one of the following is your favorite NuoDB 3rd-Party Database Client? (Pick One) SQuirreL SQL Client SQL Workbench/J DbVisualizer I will be very much eager to read your experience about NuoDB. You can download NuoDB from here. 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

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• SQL SERVER – Import CSV into Database – Transferring File Content into a Database Table using CSVexpress

  - by pinaldave

  One of the most common data integration tasks I run into is a desire to move data from a file into a database table.  Generally the user is familiar with his data, the structure of the file, and the database table, but is unfamiliar with data integration tools and therefore views this task as something that is difficult.  What these users really need is a point and click approach that minimizes the learning curve for the data integration tool.  This is what CSVexpress (www.CSVexpress.com) is all about!  It is based on expressor Studio, a data integration tool I’ve been reviewing over the last several months. With CSVexpress, moving data between data sources can be as simple as providing the database connection details, describing the structure of the incoming and outgoing data and then connecting two pre-programmed operators.   There’s no need to learn the intricacies of the data integration tool or to write code.  Let’s look at an example. Suppose I have a comma separated value data file with data similar to the following, which is a listing of terminated employees that includes their hiring and termination date, department, job description, and final salary. EMP_ID,STRT_DATE,END_DATE,JOB_ID,DEPT_ID,SALARY 102,13-JAN-93,24-JUL-98 17:00,Programmer,60,"$85,000" 101,21-SEP-89,27-OCT-93 17:00,Account Representative,110,"$65,000" 103,28-OCT-93,15-MAR-97 17:00,Account Manager,110,"$75,000" 304,17-FEB-96,19-DEC-99 17:00,Marketing,20,"$45,000" 333,24-MAR-98,31-DEC-99 17:00,Data Entry Clerk,50,"$35,000" 100,17-SEP-87,17-JUN-93 17:00,Administrative Assistant,90,"$40,000" 334,24-MAR-98,31-DEC-98 17:00,Sales Representative,80,"$40,000" 400,01-JAN-99,31-DEC-99 17:00,Sales Manager,80,"$55,000" Notice the concise format used for the date values, the fact that the termination date includes both date and time information, and that the salary is clearly identified as money by the dollar sign and digit grouping.  In moving this data to a database table I want to express the dates using a format that includes the century since it’s obvious that this listing could include employees who left the company in both the 20th and 21st centuries, and I want the salary to be stored as a decimal value without the currency symbol and grouping character.  Most data integration tools would require coding within a transformation operation to effect these changes, but not expressor Studio.  Directives for these modifications are included in the description of the incoming data. Besides starting the expressor Studio tool and opening a project, the first step is to create connection artifacts, which describe to expressor where data is stored.  For this example, two connection artifacts are required: a file connection, which encapsulates the file system location of my file; and a database connection, which encapsulates the database connection information.  With expressor Studio, I use wizards to create these artifacts. First click New Connection > File Connection in the Home tab of expressor Studio’s ribbon bar, which starts the File Connection wizard.  In the first window, I enter the path to the directory that contains the input file.  Note that the file connection artifact only specifies the file system location, not the name of the file. Then I click Next and enter a meaningful name for this connection artifact; clicking Finish closes the wizard and saves the artifact. To create the Database Connection artifact, I must know the location of, or instance name, of the target database and have the credentials of an account with sufficient privileges to write to the target table.  To use expressor Studio’s features to the fullest, this account should also have the authority to create a table. I click the New Connection > Database Connection in the Home tab of expressor Studio’s ribbon bar, which starts the Database Connection wizard.  expressor Studio includes high-performance drivers for many relational database management systems, so I can simply make a selection from the “Supplied database drivers” drop down control.  If my desired RDBMS isn’t listed, I can optionally use an existing ODBC DSN by selecting the “Existing DSN” radio button. In the following window, I enter the connection details.  With Microsoft SQL Server, I may choose to use Windows Authentication rather than rather than account credentials.  After clicking Next, I enter a meaningful name for this connection artifact and clicking Finish closes the wizard and saves the artifact. Now I create a schema artifact, which describes the structure of the file data.  When expressor reads a file, all data fields are typed as strings.  In some use cases this may be exactly what is needed and there is no need to edit the schema artifact.  But in this example, editing the schema artifact will be used to specify how the data should be transformed; that is, reformat the dates to include century designations, change the employee and job ID’s to integers, and convert the salary to a decimal value. Again a wizard is used to create the schema artifact.  I click New Schema > Delimited Schema in the Home tab of expressor Studio’s ribbon bar, which starts the Database Connection wizard.  In the first window, I click Get Data from File, which then displays a listing of the file connections in the project.  When I click on the file connection I previously created, a browse window opens to this file system location; I then select the file and click Open, which imports 10 lines from the file into the wizard. I now view the file’s content and confirm that the appropriate delimiter characters are selected in the “Field Delimiter” and “Record Delimiter” drop down controls; then I click Next. Since the input file includes a header row, I can easily indicate that fields in the file should be identified through the corresponding header value by clicking “Set All Names from Selected Row. “ Alternatively, I could enter a different identifier into the Field Details > Name text box.  I click Next and enter a meaningful name for this schema artifact; clicking Finish closes the wizard and saves the artifact. Now I open the schema artifact in the schema editor.  When I first view the schema’s content, I note that the types of all attributes in the Semantic Type (the right-hand panel) are strings and that the attribute names are the same as the field names in the data file.  To change an attribute’s name and type, I highlight the attribute and click Edit in the Attributes grouping on the Schema > Edit tab of the editor’s ribbon bar.  This opens the Edit Attribute window; I can change the attribute name and select the desired type from the “Data type” drop down control.  In this example, I change the name of each attribute to the name of the corresponding database table column (EmployeeID, StartingDate, TerminationDate, JobDescription, DepartmentID, and FinalSalary).  Then for the EmployeeID and DepartmentID attributes, I select Integer as the data type, for the StartingDate and TerminationDate attributes, I select Datetime as the data type, and for the FinalSalary attribute, I select the Decimal type. But I can do much more in the schema editor.  For the datetime attributes, I can set a constraint that ensures that the data adheres to some predetermined specifications; a starting date must be later than January 1, 1980 (the date on which the company began operations) and a termination date must be earlier than 11:59 PM on December 31, 1999.  I simply select the appropriate constraint and enter the value (1980-01-01 00:00 as the starting date and 1999-12-31 11:59 as the termination date). As a last step in setting up these datetime conversions, I edit the mapping, describing the format of each datetime type in the source file. I highlight the mapping line for the StartingDate attribute and click Edit Mapping in the Mappings grouping on the Schema > Edit tab of the editor’s ribbon bar.  This opens the Edit Mapping window in which I either enter, or select, a format that describes how the datetime values are represented in the file.  Note the use of Y01 as the syntax for the year.  This syntax is the indicator to expressor Studio to derive the century by setting any year later than 01 to the 20th century and any year before 01 to the 21st century.  As each datetime value is read from the file, the year values are transformed into century and year values. For the TerminationDate attribute, my format also indicates that the datetime value includes hours and minutes. And now to the Salary attribute. I open its mapping and in the Edit Mapping window select the Currency tab and the “Use currency” check box.  This indicates that the file data will include the dollar sign (or in Europe the Pound or Euro sign), which should be removed. And on the Grouping tab, I select the “Use grouping” checkbox and enter 3 into the “Group size” text box, a comma into the “Grouping character” text box, and a decimal point into the “Decimal separator” character text box. These entries allow the string to be properly converted into a decimal value. By making these entries into the schema that describes my input file, I’ve specified how I want the data transformed prior to writing to the database table and completely removed the requirement for coding within the data integration application itself. Assembling the data integration application is simple.  Onto the canvas I drag the Read File and Write Table operators, connecting the output of the Read File operator to the input of the Write Table operator. Next, I select the Read File operator and its Properties panel opens on the right-hand side of expressor Studio.  For each property, I can select an appropriate entry from the corresponding drop down control.  Clicking on the button to the right of the “File name” text box opens the file system location specified in the file connection artifact, allowing me to select the appropriate input file.  I indicate also that the first row in the file, the header row, should be skipped, and that any record that fails one of the datetime constraints should be skipped. I then select the Write Table operator and in its Properties panel specify the database connection, normal for the “Mode,” and the “Truncate” and “Create Missing Table” options.  If my target table does not yet exist, expressor will create the table using the information encapsulated in the schema artifact assigned to the operator. The last task needed to complete the application is to create the schema artifact used by the Write Table operator.  This is extremely easy as another wizard is capable of using the schema artifact assigned to the Read Table operator to create a schema artifact for the Write Table operator.  In the Write Table Properties panel, I click the drop down control to the right of the “Schema” property and select “New Table Schema from Upstream Output…” from the drop down menu. The wizard first displays the table description and in its second screen asks me to select the database connection artifact that specifies the RDBMS in which the target table will exist.  The wizard then connects to the RDBMS and retrieves a list of database schemas from which I make a selection.  The fourth screen gives me the opportunity to fine tune the table’s description.  In this example, I set the width of the JobDescription column to a maximum of 40 characters and select money as the type of the LastSalary column.  I also provide the name for the table. This completes development of the application.  The entire application was created through the use of wizards and the required data transformations specified through simple constraints and specifications rather than through coding.  To develop this application, I only needed a basic understanding of expressor Studio, a level of expertise that can be gained by working through a few introductory tutorials.  expressor Studio is as close to a point and click data integration tool as one could want and I urge you to try this product if you have a need to move data between files or from files to database tables. Check out CSVexpress in more detail.  It offers a few basic video tutorials and a preview of expressor Studio 3.5, which will support the reading and writing of data into Salesforce.com. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Documentation, SQL Download, SQL Query, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

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• SQL SERVER – Weekly Series – Memory Lane – #049

  - by Pinal Dave

  Here is the list of selected articles of SQLAuthority.com across all these years. Instead of just listing all the articles I have selected a few of my most favorite articles and have listed them here with additional notes below it. Let me know which one of the following is your favorite article from memory lane. 2007 Two Connections Related Global Variables Explained – @@CONNECTIONS and @@MAX_CONNECTIONS @@CONNECTIONS Returns the number of attempted connections, either successful or unsuccessful since SQL Server was last started. @@MAX_CONNECTIONS Returns the maximum number of simultaneous user connections allowed on an instance of SQL Server. The number returned is not necessarily the number currently configured. Query Editor – Microsoft SQL Server Management Studio This post may be very simple for most of the users of SQL Server 2005. Earlier this year, I have received one question many times – Where is Query Analyzer in SQL Server 2005? I wrote small post about it and pointed many users to that post – SQL SERVER – 2005 Query Analyzer – Microsoft SQL SERVER Management Studio. Recently I have been receiving similar question. OUTPUT Clause Example and Explanation with INSERT, UPDATE, DELETE SQL Server 2005 has a new OUTPUT clause, which is quite useful. OUTPUT clause has access to insert 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. OUTPUT clause can generate a table variable, a permanent table, or temporary table. Even though, @@Identity will still work with SQL Server 2005, however I find the OUTPUT clause very easy and powerful to use. Let us understand the OUTPUT clause using an example. Find Name of The SQL Server Instance Based on database server stored procedures has to run different logic. We came up with two different solutions. 1) When database schema is very much changed, we wrote completely new stored procedure and deprecated older version once it was not needed. 2) When logic depended on Server Name we used global variable @@SERVERNAME. It was very convenient while writing migrating script which depended on the server name for the same database. Explanation of TRY…CATCH and ERROR Handling With RAISEERROR Function One of the developers at my company thought that we can not use the RAISEERROR function in new feature of SQL Server 2005 TRY… CATCH. When asked for an explanation he suggested SQL SERVER – 2005 Explanation of TRY… CATCH and ERROR Handling article as excuse suggesting that I did not give example of RAISEERROR with TRY…CATCH. We all thought it was funny. Just to keep records straight, TRY… CATCH can sure use RAISEERROR function. Different Types of Cache Objects Serveral kinds of objects can be stored in the procedure cache: Compiled Plans: When the query optimizer finishes compiling a query plan, the principal output is compiled plan. Execution contexts: While executing a compiled plan, SQL Server has to keep track of information about the state of execution. Cursors: Cursors track the execution state of server-side cursors, including the cursor’s current location within a resultset. Algebrizer trees: The Algebrizer’s job is to produce an algebrizer tree, which represents the logic structure of a query. Open SSMS From Command Prompt – sqlwb.exe Example This article is written by request and suggestion of Sr. Web Developer at my organization. Due to the nature of this article most of the content is referred from Book On-Line. sqlwbcommand prompt utility which opens SQL Server Management Studio. Squib command does not run queries from the command prompt. sqlcmd utility runs queries from command prompt, read for more information. 2008 Puzzle – Solution – Computed Columns Datatype Explanation Just a day before I wrote article SQL SERVER – Puzzle – Computed Columns Datatype Explanation which was inspired by SQL Server MVP Jacob Sebastian. I suggest that before continuing this article read the original puzzle question SQL SERVER – Puzzle – Computed Columns Datatype Explanation.The question was if the computed column was of datatype TINYINT how to create a Computed Column of datatype INT? 2008 – Find If Index is Being Used in Database It is very often I get a query that how to find if any index is being used in the database or not. If any database has many indexes and not all indexes are used it can adversely affect performance. If the number of indices are higher it reduces the INSERT / UPDATE / DELETE operation but increase the SELECT operation. It is recommended to drop any unused indexes from table to improve the performance. 2009 Interesting Observation – Execution Plan and Results of Aggregate Concatenation Queries If you want to see what’s going on here, I think you need to shift your point of view from an implementation-centric view to an ANSI point of view. ANSI does not guarantee processing the order. Figure 2 is interesting, but it will be potentially misleading if you don’t understand the ANSI rule-set SQL Server operates under in most cases. Implementation thinking can certainly be useful at times when you really need that multi-million row query to finish before the backup fire off, but in this case, it’s counterproductive to understanding what is going on. SQL Server Management Studio and Client Statistics Client Statistics are very important. Many a times, people relate queries execution plan to query cost. This is not a good comparison. Both parameters are different, and they are not always related. It is possible that the query cost of any statement is less, but the amount of the data returned is considerably larger, which is causing any query to run slow. How do we know if any query is retrieving a large amount data or very little data? 2010 I encourage all of you to go through complete series and write your own on the subject. If you write an article and send it to me, I will publish it on this blog with due credit to you. If you write on your own blog, I will update this blog post pointing to your blog post. SQL SERVER – ORDER BY Does Not Work – Limitation of the View 1 SQL SERVER – Adding Column is Expensive by Joining Table Outside View – Limitation of the View 2 SQL SERVER – Index Created on View not Used Often – Limitation of the View 3 SQL SERVER – SELECT * and Adding Column Issue in View – Limitation of the View 4 SQL SERVER – COUNT(*) Not Allowed but COUNT_BIG(*) Allowed – Limitation of the View 5 SQL SERVER – UNION Not Allowed but OR Allowed in Index View – Limitation of the View 6 SQL SERVER – Cross Database Queries Not Allowed in Indexed View – Limitation of the View 7 SQL SERVER – Outer Join Not Allowed in Indexed Views – Limitation of the View 8 SQL SERVER – SELF JOIN Not Allowed in Indexed View – Limitation of the View 9 SQL SERVER – Keywords View Definition Must Not Contain for Indexed View – Limitation of the View 10 SQL SERVER – View Over the View Not Possible with Index View – Limitations of the View 11 SQL SERVER – Get Query Running in Session I was recently looking for syntax where I needed a query running in any particular session. I always remembered the syntax and ha d actually written it down before, but somehow it was not coming to mind quickly this time. I searched online and I ended up on my own article written last year SQL SERVER – Get Last Running Query Based on SPID. I felt that I am getting old because I forgot this really simple syntax. Find Total Number of Transaction on Interval In one of my recent Performance Tuning assignments I was asked how do someone know how many transactions are happening on a server during certain interval. I had a handy script for the same. Following script displays transactions happened on the server at the interval of one minute. You can change the WAITFOR DELAY to any other interval and it should work. 2011 Here are two DMV’s which are newly introduced in SQL Server 2012 and provides vital information about SQL Server. DMV – sys.dm_os_volume_stats – Information about operating system volume DMV – sys.dm_os_windows_info – Information about Operating System SQL Backup and FTP – A Quick and Handy Tool I have used this tool extensively since 2009 at numerous occasion and found it to be very impressive. What separates it from the crowd the most – it is it’s apparent simplicity and speed. When I install SQLBackupAndFTP and configure backups – all in 1 or 2 minutes, my clients are always impressed. Quick Note about JOIN – Common Questions and Simple Answers In this blog post we are going to talk about join and lots of things related to the JOIN. I recently started office hours to answer questions and issues of the community. I receive so many questions that are related to JOIN. I will share a few of the same over here. Most of them are basic, but note that the basics are of great importance. 2012 Importance of User Without Login Question: “In recent version of SQL Server we can create user without login. What is the use of it?” Great question indeed. Let me first attempt to answer this question but after reading my answer I need your help. I want you to help him as well with adding more value to it. Preserve Leading Zero While Coping to Excel from SSMS Earlier I wrote two articles about how to efficiently copy data from SSMS to Excel. Since I wrote that post there are plenty of interest generated on this subject. There are a few questions I keep on getting over this subject. One of the question is how to get the leading zero preserved while copying the data from SSMS to Excel. Well it is almost the same way as my earlier post SQL SERVER – Excel Losing Decimal Values When Value Pasted from SSMS ResultSet. The key here is in EXCEL and not in SQL Server. Solution – 2 T-SQL Puzzles – Display Star and Shortest Code to Display 1 Earlier on this blog we had asked two puzzles. The response from all of you is nothing but Amazing. I have received 350+ responses. Many are valid and many were indeed something I had not thought about it. I strongly suggest you read all the puzzles and their answers here - trust me if you start reading the comments you will not stop till you read every single comment. Seriously trust me on it. Personally I have learned a lot from it. Identify Most Resource Intensive Queries – SQL in Sixty Seconds #028 – Video http://www.youtube.com/watch?v=TvlYy-TGaaA Importance of User Without Login – T-SQL Demo Script Earlier I wrote a blog post about SQL SERVER – Importance of User Without Login and my friend and SQL Expert Vinod Kumar has written excellent follow up blog post about Contained Databases inside SQL Server 2012. Now lots of people asked me if I can also explain the same concept again so here is the small demonstration for it. Let me show you how login without user can help. Before we continue on this subject I strongly recommend that you read my earlier blog post here. In following demo I am going to demonstrate following situation. Login using the System Admin account Create a user without login Checking Access Impersonate the user without login Checking Access Revert Impersonation Give Permission to user without login Impersonate the user without login Checking Access Revert Impersonation Clean up Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Memory Lane, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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• SQL SERVER – Weekly Series – Memory Lane – #007

  - by pinaldave

  Here is the list of selected articles of SQLAuthority.com across all these years. Instead of just listing all the articles I have selected a few of my most favorite articles and have listed them here with additional notes below it. Let me know which one of the following is your favorite article from memory lane. 2006 Find Stored Procedure Related to Table in Database – Search in All Stored Procedure In 2006 I wrote a small script which will help user  find all the Stored Procedures (SP) which are related to one or more specific tables. This was quite a popular script however, in SQL Server 2012 the same can be achieved using new DMV sys.sql-expression_dependencies. I recently blogged about it over Find Referenced or Referencing Object in SQL Server using sys.sql_expression_dependencies. 2007 SQL SERVER – Versions, CodeNames, Year of Release 1993 – SQL Server 4.21 for Windows NT 1995 – SQL Server 6.0, codenamed SQL95 1996 – SQL Server 6.5, codenamed Hydra 1999 – SQL Server 7.0, codenamed Sphinx 1999 – SQL Server 7.0 OLAP, codenamed Plato 2000 – SQL Server 2000 32-bit, codenamed Shiloh (version 8.0) 2003 – SQL Server 2000 64-bit, codenamed Liberty 2005 – SQL Server 2005, codenamed Yukon (version 9.0) 2008 – SQL Server 2008, codenamed Katmai (version 10.0) 2011 – SQL Server 2008, codenamed Denali (version 11.0) Search String in Stored Procedure Searching sting in the stored procedure is one of the most frequent task developer do. They might be searching for a table, view or any other details. I have written a script to do the same in SQL Server 2000 and SQL Server 2005. This is worth bookmarking blog post. There is an alternative way to do the same as well here is the example. 2008 SQL SERVER – Refresh Database Using T-SQL NO! Some of the questions have a single answer NO! You may want to read the question in the original blog post. I had a great time saying No! SQL SERVER – Delete Backup History – Cleanup Backup History SQL Server stores history of all the taken backup forever. History of all the backup is stored in the msdb database. Many times older history is no more required. Following Stored Procedure can be executed with a parameter which takes days of history to keep. In the following example 30 is passed to keep a history of month. 2009 Stored Procedure are Compiled on First Run – SP taking Longer to Run First Time Is stored procedure pre-compiled? Why the Stored Procedure takes a long time to run for the first time?  This is a very common questions often discussed by developers and DBAs. There is an absolutely definite answer but the question has been discussed forever. There is a misconception that stored procedures are pre-compiled. They are not pre-compiled, but compiled only during the first run. For every subsequent runs, it is for sure pre-compiled. Read the entire article for example and demonstration. Removing Key Lookup – Seek Predicate – Predicate – An Interesting Observation Related to Datatypes This is one of the most important performance tuning lesson on my blog. I suggest this weekend you spend time reading them and let me know what you think about the concepts which I have demonstrated in the four part series. Part 1 | Part 2 | Part 3 | Part 4 Seek Predicate is the operation that describes the b-tree portion of the Seek. Predicate is the operation that describes the additional filter using non-key columns. Based on the description, it is very clear that Seek Predicate is better than Predicate as it searches indexes whereas in Predicate, the search is on non-key columns – which implies that the search is on the data in page files itself. Policy Based Management – Create, Evaluate and Fix Policies This article will cover the most spectacular feature of SQL Server – Policy-based management and how the configuration of SQL Server with policy-based management architecture can make a powerful difference. Policy based management is loaded with several advantages. It can help you implement various policies for reliable configuration of the system. It also provides additional administration assistance to DBAs and helps them effortlessly manage various tasks of SQL Server across the enterprise. 2010 Recycle Error Log – Create New Log file without Server Restart Once I observed a DBA to restaring the SQL Server when he needed new error log file. This was funny and sad both at the same time. There is no need to restart the server to create a new log file or recycle the log file. You can run sp_cycle_errorlog and achieve the same result. Get Database Backup History for a Single Database Simple but effective script! Reducing CXPACKET Wait Stats for High Transactional Database The subject is very complex and I have done my best to simplify the concept. In simpler words, when a parallel operation is created for SQL Query, there are multiple threads for a single query. Each query deals with a different set of the data (or rows). Due to some reasons, one or more of the threads lag behind, creating the CXPACKET Wait Stat. Threads which came first have to wait for the slower thread to finish. The Wait by a specific completed thread is called CXPACKET Wait Stat. Information Related to DATETIME and DATETIME2 There are quite a lot of confusion with DATETIME and DATETIME2. DATETIME2 is also one of the underutilized datatype of SQL Server.  In this blog post I have written a follow up of the my earlier datetime series where I clarify a few of the concepts related to datetime. Difference Between GETDATE and SYSDATETIME Difference Between DATETIME and DATETIME2 – WITH GETDATE Difference Between DATETIME and DATETIME2 2011 Introduction to CUME_DIST – Analytic Functions Introduced in SQL Server 2012 SQL Server 2012 introduces new analytical function CUME_DIST(). This function provides cumulative distribution value. It will be very difficult to explain this in words so I will attempt small example to explain you this function. Instead of creating new table, I will be using AdventureWorks sample database as most of the developer uses that for experiment. Introduction to FIRST _VALUE and LAST_VALUE – Analytic Functions Introduced in SQL Server 2012 SQL Server 2012 introduces new analytical functions FIRST_VALUE() and LAST_VALUE(). This function returns first and last value from the list. It will be very difficult to explain this in words so I’d like to attempt to explain its function through a brief example. Instead of creating a new table, I will be using the AdventureWorks sample database as most developers use that for experiment purposes. OVER clause with FIRST _VALUE and LAST_VALUE – Analytic Functions Introduced in SQL Server 2012 – ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING “Don’t you think there is bug in your first example where FIRST_VALUE is remain same but the LAST_VALUE is changing every line. I think the LAST_VALUE should be the highest value in the windows or set of result.” Puzzle – Functions FIRST_VALUE and LAST_VALUE with OVER clause and ORDER BY You can see that row number 2, 3, 4, and 5 has same SalesOrderID = 43667. The FIRST_VALUE is 78 and LAST_VALUE is 77. Now if these function was working on maximum and minimum value they should have given answer as 77 and 80 respectively instead of 78 and 77. Also the value of FIRST_VALUE is greater than LAST_VALUE 77. Why? Explain in detail. Introduction to LEAD and LAG – Analytic Functions Introduced in SQL Server 2012 SQL Server 2012 introduces new analytical function LEAD() and LAG(). This functions accesses data from a subsequent row (for lead) and previous row (for lag) in the same result set without the use of a self-join . It will be very difficult to explain this in words so I will attempt small example to explain you this function. Instead of creating new table, I will be using AdventureWorks sample database as most of the developer uses that for experiment. A Real Story of Book Getting ‘Out of Stock’ to A 25% Discount Story Available Our book was out of stock in 48 hours of it was arrived in stock! We got call from the online store with a request for more copies within 12 hours. But we had printed only as many as we had sent them. There were no extra copies. We finally talked to the printer to get more copies. However, due to festivals and holidays the copies could not be shipped to the online retailer for two days. We knew for sure that they were going to be out of the book for 48 hours. This is the story of how we overcame that situation! Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Memory Lane, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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• Julian day of the year in Java

  - by Mark

  I have seen the "solution" at http://www.rgagnon.com/javadetails/java-0506.html, but it doesn't work correctly. E.g. yesterday (June 8) should have been 159, but it said it was 245. So, does someone have a solution in Java for getting the current date's three digit Julian day (not Julian date - I need the day this year)? Thanks! Mark

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• multiple columns per day with fullcalendar

  - by Emanuel Schwarz

  I want to show a scheduling plan for a couple of seminar-rooms, each of them in a seperate column, within a fullcalendar day view. So it schould look like a current fullcalendar week view but for every single day. Is there an obvious solution for this? And if not, where in the source should i start, what to go for, what to avoid? Some suggestions would be nice. Thanks Emanuel

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