Search Results

Search found 848 results on 34 pages for 'sqlauthority'.

Page 32/34 | < Previous Page | 28 29 30 31 32 33 34  | Next Page >

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

  - by Pinal Dave

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

  Read the article

• SQL SERVER – PAGELATCH_DT, PAGELATCH_EX, PAGELATCH_KP, PAGELATCH_SH, PAGELATCH_UP – Wait Type – Day 12 of 28

  - by pinaldave

  This is another common wait type. However, I still frequently see people getting confused with PAGEIOLATCH_X and PAGELATCH_X wait types. Actually, there is a big difference between the two. PAGEIOLATCH is related to IO issues, while PAGELATCH is not related to IO issues but is oftentimes linked to a buffer issue. Before we delve deeper in this interesting topic, first let us understand what Latch is. Latches are internal SQL Server locks which can be described as very lightweight and short-term synchronization objects. Latches are not primarily to protect pages being read from disk into memory. It’s a synchronization object for any in-memory access to any portion of a log or data file.[Updated based on comment of Paul Randal] The difference between locks and latches is that locks seal all the involved resources throughout the duration of the transactions (and other processes will have no access to the object), whereas latches locks the resources during the time when the data is changed. This way, a latch is able to maintain the integrity of the data between storage engine and data cache. A latch is a short-living lock that is put on resources on buffer cache and in the physical disk when data is moved in either directions. As soon as the data is moved, the latch is released. Now, let us understand the wait stat type  related to latches. From Book On-Line: PAGELATCH_DT Occurs when a task is waiting on a latch for a buffer that is not in an I/O request. The latch request is in Destroy mode. PAGELATCH_EX Occurs when a task is waiting on a latch for a buffer that is not in an I/O request. The latch request is in Exclusive mode. PAGELATCH_KP Occurs when a task is waiting on a latch for a buffer that is not in an I/O request. The latch request is in Keep mode. PAGELATCH_SH Occurs when a task is waiting on a latch for a buffer that is not in an I/O request. The latch request is in Shared mode. PAGELATCH_UP Occurs when a task is waiting on a latch for a buffer that is not in an I/O request. The latch request is in Update mode. PAGELATCH_X Explanation: When there is a contention of access of the in-memory pages, this wait type shows up. It is quite possible that some of the pages in the memory are of very high demand. For the SQL Server to access them and put a latch on the pages, it will have to wait. This wait type is usually created at the same time. Additionally, it is commonly visible when the TempDB has higher contention as well. If there are indexes that are heavily used, contention can be created as well, leading to this wait type. Reducing PAGELATCH_X wait: The following counters are useful to understand the status of the PAGELATCH: Average Latch Wait Time (ms): The wait time for latch requests that have to wait. Latch Waits/sec: This is the number of latch requests that could not be granted immediately. Total Latch Wait Time (ms): This is the total latch wait time for latch requests in the last second. If there is TempDB contention, I suggest that you read the blog post of Robert Davis right away. He has written an excellent blog post regarding how to find out TempDB contention. The same blog post explains the terms in the allocation of GAM, SGAM and PFS. If there was a TempDB contention, Paul Randal explains the optimal settings for the TempDB in his misconceptions series. Trace Flag 1118 can be useful but use it very carefully. I totally understand that this blog post is not as clear as my other blog posts. I suggest if this wait stats is on one of your higher wait type. Do leave a comment or send me an email and I will get back to you with my solution for your situation. May the looking at all other wait stats and types together become effective as this wait type can help suggest proper bottleneck in your system. Read all the post in the Wait Types and Queue series. Note: The information presented here is from my experience and there is no way that I claim it to be accurate. I suggest reading Book OnLine for further clarification. All the discussions of Wait Stats in this blog are generic and vary from system to system. It is recommended that you test this on a development server before implementing it to a production server. 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, SQL Wait Stats, SQL Wait Types, T SQL, Technology

  Read the article

• SQL SERVER – Solution – Puzzle – Statistics are not Updated but are Created Once

  - by pinaldave

  Earlier I asked puzzle why statistics are not updated. Read the complete details over here: Statistics are not Updated but are Created Once In the question I have demonstrated even though statistics should have been updated after lots of insert in the table are not updated.(Read the details SQL SERVER – When are Statistics Updated – What triggers Statistics to Update) 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? I have many answers – here is the how I fixed it which has resolved the issue for me. NOTE: There are multiple answers to this problem and I will do my best to list all. Solution: Create nonclustered Index on column City Here is the working example for the same. Let us understand this script and there is added explanation at the end. -- Execution Plans Difference -- Estimated Execution Plan Vs Actual Execution Plan -- Create Sample Database CREATE DATABASE SampleDB GO USE SampleDB GO -- Create Table CREATE TABLE ExecTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO CREATE NONCLUSTERED INDEX IX_ExecTable1 ON ExecTable (City); GO -- Insert One Thousand Records -- INSERT 1 INSERT INTO ExecTable (ID,FirstName,LastName,City) SELECT TOP 1000 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)%20 = 1 THEN 'New York' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 5 THEN 'San Marino' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 3 THEN 'Los Angeles' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 7 THEN 'La Cinega' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 13 THEN 'San Diego' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 17 THEN 'Las Vegas' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Display statistics of the table sp_helpstats N'ExecTable', 'ALL' GO -- Select Statement SELECT FirstName, LastName, City FROM ExecTable WHERE City  = 'New York' GO -- Display statistics of the table sp_helpstats N'ExecTable', 'ALL' GO -- Replace your Statistics over here DBCC SHOW_STATISTICS('ExecTable', IX_ExecTable1); GO -------------------------------------------------------------- -- Round 2 -- Insert One Thousand Records -- INSERT 2 INSERT INTO ExecTable (ID,FirstName,LastName,City) SELECT TOP 1000 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)%20 = 1 THEN 'New York' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 5 THEN 'San Marino' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 3 THEN 'Los Angeles' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 7 THEN 'La Cinega' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 13 THEN 'San Diego' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 17 THEN 'Las Vegas' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Select Statement SELECT FirstName, LastName, City FROM ExecTable WHERE City  = 'New York' GO -- Display statistics of the table sp_helpstats N'ExecTable', 'ALL' GO -- Replace your Statistics over here DBCC SHOW_STATISTICS('ExecTable', IX_ExecTable1); GO -- Clean up Database DROP TABLE ExecTable GO When I created non clustered index on the column city, it also created statistics on the same column with same name as index. When we populate the data in the column the index is update – resulting execution plan to be invalided – this leads to the statistics to be updated in next execution of SELECT. This behavior does not happen on Heap or column where index is auto created. If you explicitly update the index, often you can see the statistics are updated as well. You can see this is for sure happening if you follow the tell of John Sansom. John Sansom‘s suggestion: That was fun! Although the column statistics are invalidated by the time the second select statement is executed, the query is not compiled/recompiled but instead the existing query plan is reused. It is the “next” compiled query against the column statistics that will see that they are out of date and will then in turn instantiate the action of updating statistics. You can see this in action by forcing the second statement to recompile. SELECT FirstName, LastName, City FROM ExecTable WHERE City = ‘New York’ option(RECOMPILE) GO Kevin Cross also have another suggestion: I agree with John. It is reusing the Execution Plan. Aside from OPTION(RECOMPILE), clearing the Execution Plan Cache before the subsequent tests will also work. i.e., run this before round 2: ————————————————————– – Clear execution plan cache before next test DBCC FREEPROCCACHE WITH NO_INFOMSGS; ————————————————————– Nice puzzle! Kevin As this was puzzle John and Kevin both got the correct answer, there was no condition for answer to be part of best practices. I know John and he is finest DBA around – his tremendous knowledge has always impressed me. John and Kevin both will agree that clearing cache either using DBCC FREEPROCCACHE and recompiling each query every time is for sure not good advice on production server. It is correct answer but not best practice. By the way, if you have better solution or have better suggestion please advise. I am open to change my answer and publish further improvement to this solution. On very separate note, I like to have clustered index on my Primary Key, which I have not mentioned here as it is out of the scope of this puzzle. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, Readers Contribution, Readers Question, SQL, SQL Authority, SQL Index, SQL Puzzle, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Statistics

  Read the article

• SQL SERVER – Example of Performance Tuning for Advanced Users with DB Optimizer

  - by Pinal Dave

  Performance tuning is such a subject that everyone wants to master it. In beginning everybody is at a novice level and spend lots of time learning how to master the art of performance tuning. However, as we progress further the tuning of the system keeps on getting very difficult. I have understood in my early career there should be no need of ego in the technology field. There are always better solutions and better ideas out there and we should not resist them. Instead of resisting the change and new wave I personally adopt it. Here is a similar example, as I personally progress to the master level of performance tuning, I face that it is getting harder to come up with optimal solutions. In such scenarios I rely on various tools to teach me how I can do things better. Once I learn about tools, I am often able to come up with better solutions when I face the similar situation next time. A few days ago I had received a query where the user wanted to tune it further to get the maximum out of the performance. I have re-written the similar query with the help of AdventureWorks sample database. SELECT * FROM HumanResources.Employee e INNER JOIN HumanResources.EmployeeDepartmentHistory edh ON e.BusinessEntityID = edh.BusinessEntityID INNER JOIN HumanResources.Shift s ON edh.ShiftID = s.ShiftID; User had similar query to above query was used in very critical report and wanted to get best out of the query. When I looked at the query – here were my initial thoughts Use only column in the select statements as much as you want in the application Let us look at the query pattern and data workload and find out the optimal index for it Before I give further solutions I was told by the user that they need all the columns from all the tables and creating index was not allowed in their system. He can only re-write queries or use hints to further tune this query. Now I was in the constraint box – I believe * was not a great idea but if they wanted all the columns, I believe we can’t do much besides using *. Additionally, if I cannot create a further index, I must come up with some creative way to write this query. I personally do not like to use hints in my application but there are cases when hints work out magically and gives optimal solutions. Finally, I decided to use Embarcadero’s DB Optimizer. It is a fantastic tool and very helpful when it is about performance tuning. I have previously explained how it works over here. First open DBOptimizer and open Tuning Job from File >> New >> Tuning Job. Once you open DBOptimizer Tuning Job follow the various steps indicates in the following diagram. Essentially we will take our original script and will paste that into Step 1: New SQL Text and right after that we will enable Step 2 for Generating Various cases, Step 3 for Detailed Analysis and Step 4 for Executing each generated case. Finally we will click on Analysis in Step 5 which will generate the report detailed analysis in the result pan. The detailed pan looks like. It generates various cases of T-SQL based on the original query. It applies various hints and available hints to the query and generate various execution plans of the query and displays them in the resultant. You can clearly notice that original query had a cost of 0.0841 and logical reads about 607 pages. Whereas various options which are just following it has different execution cost as well logical read. There are few cases where we have higher logical read and there are few cases where as we have very low logical read. If we pay attention the very next row to original query have Merge_Join_Query in description and have lowest execution cost value of 0.044 and have lowest Logical Reads of 29. This row contains the query which is the most optimal re-write of the original query. Let us double click over it. Here is the query: SELECT * FROM HumanResources.Employee e INNER JOIN HumanResources.EmployeeDepartmentHistory edh ON e.BusinessEntityID = edh.BusinessEntityID INNER JOIN HumanResources.Shift s ON edh.ShiftID = s.ShiftID OPTION (MERGE JOIN) If you notice above query have additional hint of Merge Join. With the help of this Merge Join query hint this query is now performing much better than before. The entire process takes less than 60 seconds. Please note that it the join hint Merge Join was optimal for this query but it is not necessary that the same hint will be helpful in all the queries. Additionally, if the workload or data pattern changes the query hint of merge join may be no more optimal join. In that case, we will have to redo the entire exercise once again. This is the reason I do not like to use hints in my queries and I discourage all of my users to use the same. However, if you look at this example, this is a great case where hints are optimizing the performance of the query. It is humanly not possible to test out various query hints and index options with the query to figure out which is the most optimal solution. Sometimes, we need to depend on the efficiency tools like DB Optimizer to guide us the way and select the best option from the suggestion provided. Let me know what you think of this article as well your experience with DB Optimizer. Please leave a comment. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Joins, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

  Read the article

• SQL SERVER – Guest Post by Sandip Pani – SQL Server Statistics Name and Index Creation

  - by pinaldave

  Sometimes something very small or a common error which we observe in daily life teaches us new things. SQL Server Expert Sandip Pani (winner of Joes 2 Pros Contests) has come across similar experience. Sandip has written a guest post on an error he faced in his daily work. Sandip is working for QSI Healthcare as an Associate Technical Specialist and have more than 5 years of total experience. He blogs at SQLcommitted.com and contribute in various forums. His social media hands are LinkedIn, Facebook and Twitter. Once I faced following error when I was working on performance tuning project and attempt to create an Index. Mug 1913, Level 16, State 1, Line 1 The operation failed because an index or statistics with name ‘Ix_Table1_1′ already exists on table ‘Table1′. The immediate reaction to the error was that I might have created that index earlier and when I researched it further I found the same as the index was indeed created two times. This totally makes sense. This can happen due to many reasons for example if the user is careless and executes the same code two times as well, when he attempts to create index without checking if there was index already on the object. However when I paid attention to the details of the error, I realize that error message also talks about statistics along with the index. I got curious if the same would happen if I attempt to create indexes with the same name as statistics already created. There are a few other questions also prompted in my mind. I decided to do a small demonstration of the subject and build following demonstration script. The goal of my experiment is to find out the relation between statistics and the index. Statistics is one of the important input parameter for the optimizer during query optimization process. If the query is nontrivial then only optimizer uses statistics to perform a cost based optimization to select a plan. For accuracy and further learning I suggest to read MSDN. Now let’s find out the relationship between index and statistics. We will do the experiment in two parts. i) Creating Index ii) Creating Statistics We will be using the following T-SQL script for our example. IF (OBJECT_ID('Table1') IS NOT NULL) DROP TABLE Table1 GO CREATE TABLE Table1 (Col1 INT NOT NULL, Col2 VARCHAR(20) NOT NULL) GO We will be using following two queries to check if there are any index or statistics on our sample table Table1. -- Details of Index SELECT OBJECT_NAME(OBJECT_ID) AS TableName, Name AS IndexName, type_desc FROM sys.indexes WHERE OBJECT_NAME(OBJECT_ID) = 'table1' GO -- Details of Statistics SELECT OBJECT_NAME(OBJECT_ID) TableName, Name AS StatisticsName FROM sys.stats WHERE OBJECT_NAME(OBJECT_ID) = 'table1' GO When I ran above two scripts on the table right after it was created it did not give us any result which was expected. Now let us begin our test. 1) Create an index on the table Create following index on the table. CREATE NONCLUSTERED INDEX Ix_Table1_1 ON Table1(Col1) GO Now let us use above two scripts and see their results. We can see that when we created index at the same time it created statistics also with the same name. Before continuing to next set of demo – drop the table using following script and re-create the table using a script provided at the beginning of the table. DROP TABLE table1 GO 2) Create a statistic on the table Create following statistics on the table. CREATE STATISTICS Ix_table1_1 ON Table1 (Col1) GO Now let us use above two scripts and see their results. We can see that when we created statistics Index is not created. The behavior of this experiment is different from the earlier experiment. Clean up the table setup using the following script: DROP TABLE table1 GO Above two experiments teach us very valuable lesson that when we create indexes, SQL Server generates the index and statistics (with the same name as the index name) together. Now due to the reason if we have already had statistics with the same name but not the index, it is quite possible that we will face the error to create the index even though there is no index with the same name. A Quick Check To validate that if we create statistics first and then index after that with the same name, it will throw an error let us run following script in SSMS. Make sure to drop the table and clean up our sample table at the end of the experiment. -- Create sample table CREATE TABLE TestTable (Col1 INT NOT NULL, Col2 VARCHAR(20) NOT NULL) GO -- Create Statistics CREATE STATISTICS IX_TestTable_1 ON TestTable (Col1) GO -- Create Index CREATE NONCLUSTERED INDEX IX_TestTable_1 ON TestTable(Col1) GO -- Check error /*Msg 1913, Level 16, State 1, Line 2 The operation failed because an index or statistics with name 'IX_TestTable_1' already exists on table 'TestTable'. */ -- Clean up DROP TABLE TestTable GO While creating index it will throw the following error as statistics with the same name is already created. In simple words – when we create index the name of the index should be different from any of the existing indexes and statistics. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Error Messages, SQL Index, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: SQL Statistics

  Read the article

• SQL – Quick Start with Explorer Sections of NuoDB – Query NuoDB Database

  - by Pinal Dave

  This is the third post in the series of the blog posts I am writing about NuoDB. NuoDB is very innovative and easy-to-use product. I can clearly see how one can scale-out NuoDB with so much ease and confidence. In my very first blog post we discussed how we can install NuoDB (link), and in my second post I discussed how we can manage the NuoDB database transaction engines and storage managers with a few clicks (link). Note: You can Download NuoDB from here. In this post, we will learn how we can use the Explorer feature of NuoDB to do various SQL operations. NuoDB has a browser-based Explorer, which is very powerful and has many of the features any IDE would normally have. Let us see how it works in the following step-by-step tutorial. Let us go to the NuoDBNuoDB Console by typing the following URL in your browser: http://localhost:8080/ It will bring you to the QuickStart screen. Make sure that you have created the sample database. If you have not created sample database, click on Create Database and create it successfully. Now go to the NuoDB Explorer by clicking on the main tab, and it will ask you for your domain username and password. Enter the username as a domain and password as a bird. Alternatively you can also enter username as a quickstart and password as a quickstart. Once you enter the password you will be able to see the databases. In our example we have installed the Sample Database hence you will see the Test database in our Database Hierarchy screen. When you click on database it will ask for the database login. Note that Database Login is different from Domain login and you will have to enter your database login over here. In our case the database username is dba and password is goalie. Once you enter a valid username and password it will display your database. Further expand your database and you will notice various objects in your database. Once you explore various objects, select any database and click on Open. When you click on execute, it will display the SQL script to select the data from the table. The autogenerated script displays entire result set from the database. The NuoDB Explorer is very powerful and makes the life of developers very easy. If you click on List SQL Statements it will list all the available SQL statements right away in Query Editor. You can see the popup window in following image. Here is the cool thing for geeks. You can even click on Query Plan and it will display the text based query plan as well. In case of a SELECT, the query plan will be much simpler, however, when we write complex queries it will be very interesting. We can use the query plan tab for performance tuning of the database. Here is another feature, when we click on List Tables in NuoDB Explorer.  It lists all the available tables in the query editor. This is very helpful when we are writing a long complex query. Here is a relatively complex example I have built using Inner Join syntax. Right below I have displayed the Query Plan. The query plan displays all the little details related to the query. Well, we just wrote multi-table query and executed it against the NuoDB database. You can use the NuoDB Admin section and do various analyses of the query and its performance. NuoDB is a distributed database built on a patented emergent architecture with full support for SQL and ACID guarantees.  It allows you to add Transaction Engine processes to a running system to improve the performance of your system.  You can also add a second Storage Engine to your running system for redundancy purposes.  Conversely, you can shut down processes when you don’t need the extra database resources. NuoDB also provides developers and administrators with a single intuitive interface for centrally monitoring deployments. If you have read my blog posts and have not tried out NuoDB, I strongly suggest that you download it today and catch up with the learnings with me. Trust me though the product is very powerful, it is extremely easy to learn and use. Reference: Pinal Dave (http://blog.sqlauthority.com)   Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: NuoDB

  Read the article

• Big Data – Role of Cloud Computing in Big Data – Day 11 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the NewSQL. In this article we will understand the role of Cloud in Big Data Story What is Cloud? Cloud is the biggest buzzword around from last few years. Everyone knows about the Cloud and it is extremely well defined online. In this article we will discuss cloud in the context of the Big Data. Cloud computing is a method of providing a shared computing resources to the application which requires dynamic resources. These resources include applications, computing, storage, networking, development and various deployment platforms. The fundamentals of the cloud computing are that it shares pretty much share all the resources and deliver to end users as a service.  Examples of the Cloud Computing and Big Data are Google and Amazon.com. Both have fantastic Big Data offering with the help of the cloud. We will discuss this later in this blog post. There are two different Cloud Deployment Models: 1) The Public Cloud and 2) The Private Cloud Public Cloud Public Cloud is the cloud infrastructure build by commercial providers (Amazon, Rackspace etc.) creates a highly scalable data center that hides the complex infrastructure from the consumer and provides various services. Private Cloud Private Cloud is the cloud infrastructure build by a single organization where they are managing highly scalable data center internally. Here is the quick comparison between Public Cloud and Private Cloud from Wikipedia:   Public Cloud Private Cloud Initial cost Typically zero Typically high Running cost Unpredictable Unpredictable Customization Impossible Possible Privacy No (Host has access to the data Yes Single sign-on Impossible Possible Scaling up Easy while within defined limits Laborious but no limits Hybrid Cloud Hybrid Cloud is the cloud infrastructure build with the composition of two or more clouds like public and private cloud. Hybrid cloud gives best of the both the world as it combines multiple cloud deployment models together. Cloud and Big Data – Common Characteristics There are many characteristics of the Cloud Architecture and Cloud Computing which are also essentially important for Big Data as well. They highly overlap and at many places it just makes sense to use the power of both the architecture and build a highly scalable framework. Here is the list of all the characteristics of cloud computing important in Big Data Scalability Elasticity Ad-hoc Resource Pooling Low Cost to Setup Infastructure Pay on Use or Pay as you Go Highly Available Leading Big Data Cloud Providers There are many players in Big Data Cloud but we will list a few of the known players in this list. Amazon Amazon is arguably the most popular Infrastructure as a Service (IaaS) provider. The history of how Amazon started in this business is very interesting. They started out with a massive infrastructure to support their own business. Gradually they figured out that their own resources are underutilized most of the time. They decided to get the maximum out of the resources they have and hence  they launched their Amazon Elastic Compute Cloud (Amazon EC2) service in 2006. Their products have evolved a lot recently and now it is one of their primary business besides their retail selling. Amazon also offers Big Data services understand Amazon Web Services. Here is the list of the included services: Amazon Elastic MapReduce – It processes very high volumes of data Amazon DynammoDB – It is fully managed NoSQL (Not Only SQL) database service Amazon Simple Storage Services (S3) – A web-scale service designed to store and accommodate any amount of data Amazon High Performance Computing – It provides low-tenancy tuned high performance computing cluster Amazon RedShift – It is petabyte scale data warehousing service Google Though Google is known for Search Engine, we all know that it is much more than that. Google Compute Engine – It offers secure, flexible computing from energy efficient data centers Google Big Query – It allows SQL-like queries to run against large datasets Google Prediction API – It is a cloud based machine learning tool Other Players Besides Amazon and Google we also have other players in the Big Data market as well. Microsoft is also attempting Big Data with the Cloud with Microsoft Azure. Additionally Rackspace and NASA together have initiated OpenStack. The goal of Openstack is to provide a massively scaled, multitenant cloud that can run on any hardware. Thing to Watch The cloud based solutions provides a great integration with the Big Data’s story as well it is very economical to implement as well. However, there are few things one should be very careful when deploying Big Data on cloud solutions. Here is a list of a few things to watch: Data Integrity Initial Cost Recurring Cost Performance Data Access Security Location Compliance Every company have different approaches to Big Data and have different rules and regulations. Based on various factors, one can implement their own custom Big Data solution on a cloud. Tomorrow In tomorrow’s blog post we will discuss about various Operational Databases supporting Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

  Read the article

• Big Data – What is Big Data – 3 Vs of Big Data – Volume, Velocity and Variety – Day 2 of 21

  - by Pinal Dave

  Data is forever. Think about it – it is indeed true. Are you using any application as it is which was built 10 years ago? Are you using any piece of hardware which was built 10 years ago? The answer is most certainly No. However, if I ask you – are you using any data which were captured 50 years ago, the answer is most certainly Yes. For example, look at the history of our nation. I am from India and we have documented history which goes back as over 1000s of year. Well, just look at our birthday data – atleast we are using it till today. Data never gets old and it is going to stay there forever.  Application which interprets and analysis data got changed but the data remained in its purest format in most cases. As organizations have grown the data associated with them also grew exponentially and today there are lots of complexity to their data. Most of the big organizations have data in multiple applications and in different formats. The data is also spread out so much that it is hard to categorize with a single algorithm or logic. The mobile revolution which we are experimenting right now has completely changed how we capture the data and build intelligent systems.  Big organizations are indeed facing challenges to keep all the data on a platform which give them a  single consistent view of their data. This unique challenge to make sense of all the data coming in from different sources and deriving the useful actionable information out of is the revolution Big Data world is facing. Defining Big Data The 3Vs that define Big Data are Variety, Velocity and Volume. Volume We currently see the exponential growth in the data storage as the data is now more than text data. We can find data in the format of videos, musics and large images on our social media channels. It is very common to have Terabytes and Petabytes of the storage system for enterprises. As the database grows the applications and architecture built to support the data needs to be reevaluated quite often. Sometimes the same data is re-evaluated with multiple angles and even though the original data is the same the new found intelligence creates explosion of the data. The big volume indeed represents Big Data. Velocity The data growth and social media explosion have changed how we look at the data. There was a time when we used to believe that data of yesterday is recent. The matter of the fact newspapers is still following that logic. However, news channels and radios have changed how fast we receive the news. Today, people reply on social media to update them with the latest happening. On social media sometimes a few seconds old messages (a tweet, status updates etc.) is not something interests users. They often discard old messages and pay attention to recent updates. The data movement is now almost real time and the update window has reduced to fractions of the seconds. This high velocity data represent Big Data. Variety Data can be stored in multiple format. For example database, excel, csv, access or for the matter of the fact, it can be stored in a simple text file. Sometimes the data is not even in the traditional format as we assume, it may be in the form of video, SMS, pdf or something we might have not thought about it. It is the need of the organization to arrange it and make it meaningful. It will be easy to do so if we have data in the same format, however it is not the case most of the time. The real world have data in many different formats and that is the challenge we need to overcome with the Big Data. This variety of the data represent  represent Big Data. Big Data in Simple Words Big Data is not just about lots of data, it is actually a concept providing an opportunity to find new insight into your existing data as well guidelines to capture and analysis your future data. It makes any business more agile and robust so it can adapt and overcome business challenges. Tomorrow In tomorrow’s blog post we will try to answer discuss Evolution of Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

  Read the article

• Developer Training – Difficult Questions and Alternative Perspective – Part 3

  - by pinaldave

  Developer Training - Importance and Significance - Part 1 Developer Training – Employee Morals and Ethics – Part 2 Developer Training – Difficult Questions and Alternative Perspective - Part 3 Developer Training – Various Options for Developer Training – Part 4 Developer Training – A Conclusive Summary- Part 5 Congratulations!  You are now a fully trained developer!  You spent hours in a classroom, watching webinars, and reading materials.  You are now more educated and more prepared than ever before.  Now what? Stay or Quit The simple answer is that you now have two options – stay where you are or move on to a new job.  Even though you might now be smarter than you have ever felt before, this can still be a tough decision to make.  You feel extra trained and ready for a promotion or a raise, but you and your employer might not see eye to eye on this issue.  The logical conclusion is to go on a job hunt, but that might not be the most ethical thing to do. Click Image to Enlarge Manager’s Perspective Click Image to Enlarge Try to see the issue from your manager’s perspective.  You feel that you have just spent a lot of time and energy getting trained, and you should be rewarded.  But they have invested their time and energy in you.  They might see the training as a way to help you complete the goals they require from you, or as a way to help you complete tasks that will ultimately end in a reward or promotion. Moral Compass As in most cases, honesty is the best policy.  Be open with your manager about your expectations, and ask them to explain their goals.  When there is open and honest communication, everyone can walk away happy.  If you’re unable to discuss with your manager for one reason or another, just try to keep the company policy in mind and follow your own moral compass.  If all else fails, and your company is unwilling to make allowances for your new value, offer to pay the company back for the training before moving on your way. Whether you stay at your old job or move on to a new one, you are still faced with the question of what you’re going to do with all your new knowledge.  If you feel comfortable, offer to train others around you who are interested in the same subject.  This can look very good on your resume, and if you are working in a team environment it is sure to help you in the long run! What Next? You can even offer to train other trainers at the company – managers, those above you, or even report back to your original trainer about how your education is helping you in the work place.  Obviously this should be completely voluntary on the trainer’s part.  Taking advice from a “newbie” may not be their favorite idea, but it could also show the company that you are open to expanding your horizons and being helpful to everyone around you. Last in Line for Opportunity Click Image to Enlarge At this time, let us address a subject related to training and what to do with it – what if you are always overlooked for training?  This can as thorny a problem as receiving training in the first place.  The best advice is to let your supervisors know that you are always open to training and very interested in certain topics.  If you are consistently passed over, be patient.  Your turn will probably come, but the company as a whole has to focus on other problems at the moment.  If you feel that there are more personal issues at play, be sure to bring this up with your supervisor in a calm and professional manager so that everything can be worked out best for both parties. You, Yourself and Your Future! If all else fails, offer to pay for training yourself.  Perhaps money problems are at the root of being passed over.  Even if there are other reasons, offering to pay your own way shows your dedication and could work out well for you in the long run.  Always remember – in life you have to go out and make your own way, you cannot always sit and wait for things to land in your lap. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Developer Training, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

  Read the article

• SQL SERVER – Identifying guest User using Policy Based Management

  - by pinaldave

  If you are following my recent blog posts, you may have noticed that I’ve been writing a lot about Guest User in SQL Server. Here are all the blog posts which I have written on this subject: SQL SERVER – Disable Guest Account – Serious Security Issue SQL SERVER – Force Removing User from Database – Fix: Error: Could not drop login ‘test’ as the user is currently logged in SQL SERVER – Detecting guest User Permissions – guest User Access Status SQL SERVER – guest User and MSDB Database – Enable guest User on MSDB Database One of the requests I received was whether we could create a policy that would prevent users unable guest user in user databases. Well, here is a quick tutorial to answer this. Let us see how quickly we can do it. Requirements Check if the guest user is disabled in all the user-created databases. Exclude master, tempdb and msdb database for guest user validation. We will create the following conditions based on the above two requirements: If the name of the user is ‘guest’ If the user has connect (@hasDBAccess) permission in the database Check in All user databases, except: master, tempDB and msdb Once we create two conditions, we will create a policy which will validate the conditions. Condition 1: Is the User Guest? Expand the Database >> Management >> Policy Management >> Conditions Right click on the Conditions, and click on “New Condition…”. First we will create a condition where we will validate if the user name is ‘guest’, and if it’s so, then we will further validate if it has DB access. Check the image for the necessary configuration for condition: Facet: User Expression: @Name = ‘guest’ Condition 2: Does the User have DBAccess? Expand the Database >> Management >> Policy Management >> Conditions Right click on Conditions and click on “New Condition…”. Now we will validate if the user has DB access. Check the image for necessary configuration for condition: Facet: User Expression: @hasDBAccess = False Condition 3: Exclude Databases Expand the Database >> Management >> Policy Management >> Conditions Write click on Conditions and click on “New Condition…” Now we will create condition where we will validate if database name is master, tempdb or msdb and if database name is any of them, we will not validate our first one condition with them. Check the image for necessary configuration for condition: Facet: Database Expression: @Name != ‘msdb’ AND @Name != ‘tempdb’ AND @Name != ‘master’ The next step will be creating a policy which will enforce these conditions. Creating a Policy Right click on Policies and click “New Policy…” Here, we justify what condition we want to validate against what the target is. Condition: Has User DBAccess Target Database: Every Database except (master, tempdb and MSDB) Target User: Every User in Target Database with name ‘guest’ Now we have options for two evaluation modes: 1) On Demand and 2) On Schedule We will select On Demand in this example; however, you can change the mode to On Schedule through the drop down menu, and select the interval of the evaluation of the policy. Evaluate the Policies We have selected OnDemand as our policy evaluation mode. We will now evaluate by means of executing Evaluate policy. Click on Evaluate and it will give the following result: The result demonstrates that one of the databases has a policy violation. Username guest is enabled in AdventureWorks database. You can disable the guest user by running the following code in AdventureWorks database. USE AdventureWorks; REVOKE CONNECT FROM guest; Once you run above query, you can already evaluate the policy again. Notice that the policy violation is fixed now. You can change the method of the evaluation policy to On Schedule and validate policy on interval. You can check the history of the policy and detect the violation. Quiz I have created three conditions to check if the guest user has database access or not. Now I want to ask you: Is it possible to do the same with 2 conditions? If yes, HOW? If no, WHY NOT? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Best Practices, CodeProject, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology Tagged: Policy Management

  Read the article

• SQL SERVER – Solution – Puzzle – SELECT * vs SELECT COUNT(*)

  - by pinaldave

  Earlier I have published Puzzle Why SELECT * throws an error but SELECT COUNT(*) does not. This question have received many interesting comments. Let us go over few of the answers, which are valid. Before I start the same, let me acknowledge Rob Farley who has not only answered correctly very first but also started interesting conversation in the same thread. The usual question will be what is the right answer. I would like to point to official Microsoft Connect Items which discusses the same. RGarvao https://connect.microsoft.com/SQLServer/feedback/details/671475/select-test-where-exists-select tiberiu utan http://connect.microsoft.com/SQLServer/feedback/details/338532/count-returns-a-value-1 Rob Farley count(*) is about counting rows, not a particular column. It doesn’t even look to see what columns are available, it’ll just count the rows, which in the case of a missing FROM clause, is 1. “select *” is designed to return columns, and therefore barfs if there are none available. Even more odd is this one: select ‘blah’ where exists (select *) You might be surprised at the results… Koushik The engine performs a “Constant scan” for Count(*) where as in the case of “SELECT *” the engine is trying to perform either Index/Cluster/Table scans. amikolaj When you query ‘select * from sometable’, SQL replaces * with the current schema of that table. With out a source for the schema, SQL throws an error. so when you query ‘select count(*)’, you are counting the one row. * is just a constant to SQL here. Check out the execution plan. Like the description states – ‘Scan an internal table of constants.’ You could do ‘select COUNT(‘my name is adam and this is my answer’)’ and get the same answer. Netra Acharya SELECT * Here, * represents all columns from a table. So it always looks for a table (As we know, there should be FROM clause before specifying table name). So, it throws an error whenever this condition is not satisfied. SELECT COUNT(*) Here, COUNT is a Function. So it is not mandetory to provide a table. Check it out this: DECLARE @cnt INT SET @cnt = COUNT(*) SELECT @cnt SET @cnt = COUNT(‘x’) SELECT @cnt Naveen Select 1 / Select ‘*’ will return 1/* as expected. Select Count(1)/Count(*) will return the count of result set of select statement. Count(1)/Count(*) will have one 1/* for each row in the result set of select statement. Select 1 or Select ‘*’ result set will contain only 1 result. so count is 1. Where as “Select *” is a sysntax which expects the table or equauivalent to table (table functions, etc..). It is like compilation error for that query. Ramesh Hi Friends, Count is an aggregate function and it expects the rows (list of records) for a specified single column or whole rows for *. So, when we use ‘select *’ it definitely give and error because ‘*’ is meant to have all the fields but there is not any table and without table it can only raise an error. So, in the case of ‘Select Count(*)’, there will be an error as a record in the count function so you will get the result as ’1'. Try using : Select COUNT(‘RAMESH’) and think there is an error ‘Must specify table to select from.’ in place of ‘RAMESH’ Pinal : If i am wrong then please clarify this. Sachin Nandanwar Any aggregate function expects a constant or a column name as an expression. DO NOT be confused with * in an aggregate function.The aggregate function does not treat it as a column name or a set of column names but a constant value, as * is a key word in SQL. You can replace any value instead of * for the COUNT function.Ex Select COUNT(5) will result as 1. The error resulting from select * is obvious it expects an object where it can extract the result set. I sincerely thank you all for wonderful conversation, I personally enjoyed it and I am sure all of you have the same feeling. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: CodeProject, Pinal Dave, PostADay, Readers Contribution, Readers Question, SQL, SQL Authority, SQL Puzzle, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

  Read the article

• SQL SERVER – Fundamentals of Columnstore Index

  - by pinaldave

  There are two kind of storage in database. Row Store and Column Store. Row store does exactly as the name suggests – stores rows of data on a page – and column store stores all the data in a column on the same page. These columns are much easier to search – instead of a query searching all the data in an entire row whether the data is relevant or not, column store queries need only to search much lesser number of the columns. This means major increases in search speed and hard drive use. Additionally, the column store indexes are heavily compressed, which translates to even greater memory and faster searches. I am sure this looks very exciting and it does not mean that you convert every single index from row store to column store index. One has to understand the proper places where to use row store or column store indexes. Let us understand in this article what is the difference in Columnstore type of index. Column store indexes are run by Microsoft’s VertiPaq technology. However, all you really need to know is that this method of storing data is columns on a single page is much faster and more efficient. Creating a column store index is very easy, and you don’t have to learn new syntax to create them. You just need to specify the keyword “COLUMNSTORE” and enter the data as you normally would. Keep in mind that once you add a column store to a table, though, you cannot delete, insert or update the data – it is READ ONLY. However, since column store will be mainly used for data warehousing, this should not be a big problem. You can always use partitioning to avoid rebuilding the index. A columnstore index stores each column in a separate set of disk pages, rather than storing multiple rows per page as data traditionally has been stored. The difference between column store and row store approaches is illustrated below: In case of the row store indexes multiple pages will contain multiple rows of the columns spanning across multiple pages. In case of column store indexes multiple pages will contain multiple single columns. This will lead only the columns needed to solve a query will be fetched from disk. Additionally there is good chance that there will be redundant data in a single column which will further help to compress the data, this will have positive effect on buffer hit rate as most of the data will be in memory and due to same it will not need to be retrieved. Let us see small example of how columnstore index improves the performance of the query on a large table. As a first step let us create databaseset which is large enough to show performance impact of columnstore index. The time taken to create sample database may vary on different computer based on the resources. USE AdventureWorks GO -- Create New Table CREATE TABLE [dbo].[MySalesOrderDetail]( [SalesOrderID] [int] NOT NULL, [SalesOrderDetailID] [int] NOT NULL, [CarrierTrackingNumber] [nvarchar](25) NULL, [OrderQty] [smallint] NOT NULL, [ProductID] [int] NOT NULL, [SpecialOfferID] [int] NOT NULL, [UnitPrice] [money] NOT NULL, [UnitPriceDiscount] [money] NOT NULL, [LineTotal] [numeric](38, 6) NOT NULL, [rowguid] [uniqueidentifier] NOT NULL, [ModifiedDate] [datetime] NOT NULL ) ON [PRIMARY] GO -- Create clustered index CREATE CLUSTERED INDEX [CL_MySalesOrderDetail] ON [dbo].[MySalesOrderDetail] ( [SalesOrderDetailID]) GO -- Create Sample Data Table -- WARNING: This Query may run upto 2-10 minutes based on your systems resources INSERT INTO [dbo].[MySalesOrderDetail] SELECT S1.* FROM Sales.SalesOrderDetail S1 GO 100 Now let us do quick performance test. I have kept STATISTICS IO ON for measuring how much IO following queries take. In my test first I will run query which will use regular index. We will note the IO usage of the query. After that we will create columnstore index and will measure the IO of the same. -- Performance Test -- Comparing Regular Index with ColumnStore Index USE AdventureWorks GO SET STATISTICS IO ON GO -- Select Table with regular Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID GO -- Table 'MySalesOrderDetail'. Scan count 1, logical reads 342261, physical reads 0, read-ahead reads 0. -- Create ColumnStore Index CREATE NONCLUSTERED COLUMNSTORE INDEX [IX_MySalesOrderDetail_ColumnStore] ON [MySalesOrderDetail] (UnitPrice, OrderQty, ProductID) GO -- Select Table with Columnstore Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID GO It is very clear from the results that query is performance extremely fast after creating ColumnStore Index. The amount of the pages it has to read to run query is drastically reduced as the column which are needed in the query are stored in the same page and query does not have to go through every single page to read those columns. If we enable execution plan and compare we can see that column store index performance way better than regular index in this case. Let us clean up the database. -- Cleanup DROP INDEX [IX_MySalesOrderDetail_ColumnStore] ON [dbo].[MySalesOrderDetail] GO TRUNCATE TABLE dbo.MySalesOrderDetail GO DROP TABLE dbo.MySalesOrderDetail GO In future posts we will see cases where Columnstore index is not appropriate solution as well few other tricks and tips of the columnstore index. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Index, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

  Read the article

• SQL SERVER – CXPACKET – Parallelism – Usual Solution – Wait Type – Day 6 of 28

  - by pinaldave

  CXPACKET has to be most popular one of all wait stats. I have commonly seen this wait stat as one of the top 5 wait stats in most of the systems with more than one CPU. Books On-Line: Occurs when trying to synchronize the query processor exchange iterator. You may consider lowering the degree of parallelism if contention on this wait type becomes a problem. CXPACKET Explanation: 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. There is an organizer/coordinator thread (thread 0), which takes waits for all the threads to complete and gathers result together to present on the client’s side. The organizer thread has to wait for the all the threads to finish before it can move ahead. The Wait by this organizer thread for slow threads to complete is called CXPACKET wait. Note that not all the CXPACKET wait types are bad. You might experience a case when it totally makes sense. There might also be cases when this is unavoidable. If you remove this particular wait type for any query, then that query may run slower because the parallel operations are disabled for the query. Reducing CXPACKET wait: We cannot discuss about reducing the CXPACKET wait without talking about the server workload type. OLTP: On Pure OLTP system, where the transactions are smaller and queries are not long but very quick usually, set the “Maximum Degree of Parallelism” to 1 (one). This way it makes sure that the query never goes for parallelism and does not incur more engine overhead. EXEC sys.sp_configure N'cost threshold for parallelism', N'1' GO RECONFIGURE WITH OVERRIDE GO Data-warehousing / Reporting server: As queries will be running for long time, it is advised to set the “Maximum Degree of Parallelism” to 0 (zero). This way most of the queries will utilize the parallel processor, and long running queries get a boost in their performance due to multiple processors. EXEC sys.sp_configure N'cost threshold for parallelism', N'0' GO RECONFIGURE WITH OVERRIDE GO Mixed System (OLTP & OLAP): Here is the challenge. The right balance has to be found. I have taken a very simple approach. I set the “Maximum Degree of Parallelism” to 2, which means the query still uses parallelism but only on 2 CPUs. However, I keep the “Cost Threshold for Parallelism” very high. This way, not all the queries will qualify for parallelism but only the query with higher cost will go for parallelism. I have found this to work best for a system that has OLTP queries and also where the reporting server is set up. Here, I am setting ‘Cost Threshold for Parallelism’ to 25 values (which is just for illustration); you can choose any value, and you can find it out by experimenting with the system only. In the following script, I am setting the ‘Max Degree of Parallelism’ to 2, which indicates that the query that will have a higher cost (here, more than 25) will qualify for parallel query to run on 2 CPUs. This implies that regardless of the number of CPUs, the query will select any two CPUs to execute itself. EXEC sys.sp_configure N'cost threshold for parallelism', N'25' GO EXEC sys.sp_configure N'max degree of parallelism', N'2' GO RECONFIGURE WITH OVERRIDE GO Read all the post in the Wait Types and Queue series. Additionally a must read comment of Jonathan Kehayias. Note: The information presented here is from my experience and I no way claim it to be accurate. I suggest you all to read the online book for further clarification. All the discussion of Wait Stats over here is generic and it varies from system to system. It is recommended that you test this on the development server before implementing on the production server. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: DMV, Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

  Read the article

• SQL SERVER – Simple Example to Configure Resource Governor – Introduction to Resource Governor

  - by pinaldave

  Let us jump right away with question and answer mode. What is resource governor? Resource Governor is a feature which can manage SQL Server Workload and System Resource Consumption. We can limit the amount of CPU and memory consumption by limiting /governing /throttling on the SQL Server. Why is resource governor required? If there are different workloads running on SQL Server and each of the workload needs different resources or when workloads are competing for resources with each other and affecting the performance of the whole server resource governor is a very important task. What will be the real world example of need of resource governor? Here are two simple scenarios where the resource governor can be very useful. Scenario 1: A server which is running OLTP workload and various resource intensive reports on the same server. The ideal situation is where there are two servers which are data synced with each other and one server runs OLTP transactions and the second server runs all the resource intensive reports. However, not everybody has the luxury to set up this kind of environment. In case of the situation where reports and OLTP transactions are running on the same server, limiting the resources to the reporting workload it can be ensured that OTLP’s critical transaction is not throttled. Scenario 2: There are two DBAs in one organization. One DBA A runs critical queries for business and another DBA B is doing maintenance of the database. At any point in time the DBA A’s work should not be affected but at the same time DBA B should be allowed to work as well. The ideal situation is that when DBA B starts working he get some resources but he can’t get more than defined resources. Does SQL Server have any default resource governor component? Yes, SQL Server have two by default created resource governor component. 1) Internal –This is used by database engine exclusives and user have no control. 2) Default – This is used by all the workloads which are not assigned to any other group. What are the major components of the resource governor? Resource Pools Workload Groups Classification In simple words here is what the process of resource governor is. Create resource pool Create a workload group Create classification function based on the criteria specified Enable Resource Governor with classification function Let me further explain you the same with graphical image. Is it possible to configure resource governor with T-SQL? Yes, here is the code for it with explanation in between. Step 0: Here we are assuming that there are separate login accounts for Reporting server and OLTP server. /*----------------------------------------------- Step 0: (Optional and for Demo Purpose) Create Two User Logins 1) ReportUser, 2) PrimaryUser Use ReportUser login for Reports workload Use PrimaryUser login for OLTP workload -----------------------------------------------*/ Step 1: Creating Resource Pool We are creating two resource pools. 1) Report Server and 2) Primary OLTP Server. We are giving only a few resources to the Report Server Pool as described in the scenario 1 the other server is mission critical and not the report server. ----------------------------------------------- -- Step 1: Create Resource Pool ----------------------------------------------- -- Creating Resource Pool for Report Server CREATE RESOURCE POOL ReportServerPool WITH ( MIN_CPU_PERCENT=0, MAX_CPU_PERCENT=30, MIN_MEMORY_PERCENT=0, MAX_MEMORY_PERCENT=30) GO -- Creating Resource Pool for OLTP Primary Server CREATE RESOURCE POOL PrimaryServerPool WITH ( MIN_CPU_PERCENT=50, MAX_CPU_PERCENT=100, MIN_MEMORY_PERCENT=50, MAX_MEMORY_PERCENT=100) GO Step 2: Creating Workload Group We are creating two workloads each mapping to each of the resource pool which we have just created. ----------------------------------------------- -- Step 2: Create Workload Group ----------------------------------------------- -- Creating Workload Group for Report Server CREATE WORKLOAD GROUP ReportServerGroup USING ReportServerPool ; GO -- Creating Workload Group for OLTP Primary Server CREATE WORKLOAD GROUP PrimaryServerGroup USING PrimaryServerPool ; GO Step 3: Creating user defiled function which routes the workload to the appropriate workload group. In this example we are checking SUSER_NAME() and making the decision of Workgroup selection. We can use other functions such as HOST_NAME(), APP_NAME(), IS_MEMBER() etc. ----------------------------------------------- -- Step 3: Create UDF to Route Workload Group ----------------------------------------------- CREATE FUNCTION dbo.UDFClassifier() RETURNS SYSNAME WITH SCHEMABINDING AS BEGIN DECLARE @WorkloadGroup AS SYSNAME IF(SUSER_NAME() = 'ReportUser') SET @WorkloadGroup = 'ReportServerGroup' ELSE IF (SUSER_NAME() = 'PrimaryServerPool') SET @WorkloadGroup = 'PrimaryServerGroup' ELSE SET @WorkloadGroup = 'default' RETURN @WorkloadGroup END GO Step 4: In this final step we enable the resource governor with the classifier function created in earlier step 3. ----------------------------------------------- -- Step 4: Enable Resource Governer -- with UDFClassifier ----------------------------------------------- ALTER RESOURCE GOVERNOR WITH (CLASSIFIER_FUNCTION=dbo.UDFClassifier); GO ALTER RESOURCE GOVERNOR RECONFIGURE GO Step 5: If you are following this demo and want to clean up your example, you should run following script. Running them will disable your resource governor as well delete all the objects created so far. ----------------------------------------------- -- Step 5: Clean Up -- Run only if you want to clean up everything ----------------------------------------------- ALTER RESOURCE GOVERNOR WITH (CLASSIFIER_FUNCTION = NULL) GO ALTER RESOURCE GOVERNOR DISABLE GO DROP FUNCTION dbo.UDFClassifier GO DROP WORKLOAD GROUP ReportServerGroup GO DROP WORKLOAD GROUP PrimaryServerGroup GO DROP RESOURCE POOL ReportServerPool GO DROP RESOURCE POOL PrimaryServerPool GO ALTER RESOURCE GOVERNOR RECONFIGURE GO I hope this introductory example give enough light on the subject of Resource Governor. In future posts we will take this same example and learn a few more details. 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: Resource Governor

  Read the article

• SQL SERVER – An Efficiency Tool to Compare and Synchronize SQL Server Databases

  - by Pinal Dave

  There is no need to reinvent the wheel if it is already invented and if the wheel is already available at ease, there is no need to wait to grab it. Here is the similar situation. I came across a very interesting situation and I had to look for an efficient tool which can make my life easier and solve my business problem. Here is the scenario. One of the developers had deleted few rows from the very important mapping table of our development server (thankfully, it was not the production server). Though it was a development server, the entire development team had to stop working as the application started to crash on every page. Think about the lost of manpower and efficiency which we started to loose.  Pretty much every department had to stop working as our internal development application stopped working. Thankfully, we even take a backup of our development server and we had access to full backup of the entire database at 6 AM morning. We do not take as a frequent backup of development server as production server (naturally!). Even though we had a full backup, the solution was not to restore the database. Think about it, there were plenty of the other operations since the last good full backup and if we restore a full backup, we will pretty much overwrite on the top of the work done by developers since morning. Now, as restoring the full backup was not an option we decided to restore the same database on another server. Once we had restored our database to another server, the challenge was to compare the table from where the database was deleted. The mapping table from where the data were deleted contained over 5000 rows and it was humanly impossible to compare both the tables manually. Finally we decided to use efficiency tool dbForge Data Compare for SQL Server from DevArt. dbForge Data Compare for SQL Server is a powerful, fast and easy to use SQL compare tool, capable of using native SQL Server backups as metadata source. (FYI we Downloaded dbForge Data Compare) Once we discovered the product, we immediately downloaded the product and installed on our development server. After we installed the product, we were greeted with the following screen. We clicked on the New Data Comparision to start our new comparison project. It brought up following screen. Here is the best part of the product, we just had to enter our database connection username and password along with source and destination details and we are done. The entire process is very simple and self intuiting. The best part was that for the source, we can either select database or even backup. This was indeed fantastic feature. Think about this, if you have a very big database, it will take long time to restore on the server. Once it is restored, you will be able to work with it. However, when you are working with dbForge Data Compare it will accept database backup as your source or destination. Once I click on the execute it brought up following screen where it displayed an excellent summary of the data compare. It has dedicated tabs for the what is changing in what table as well had details of the changed data. The best part is that, once we had reviewed the change. We click on the Synchronize button in the menu bar and it brought up following screen. You can see that the screen has very simple straight forward but very powerful features. You can generate a script to synchronize from target to source or even from source to target. Additionally, the database is a very complicated world and there are extensive options to configure various database options on the next screen. We also have the option to either generate script or directly execute the script to target server. I like to play on the safe side and I generated the script for my synchronization and later on after review I deployed the scripts on the server. Well, my team and we were able to get going from our disaster in less than 10 minutes. There were few people in our team were indeed disappointed as they were thinking of going home early that day but in less than 10 minutes they had to get back to work. There are so many other features in  dbForge Data Compare for SQL Server, I am already planning to make this product company wide recommended product for Data Compare tool. Hats off to the team who have build this product. Here are few of the features salient features of the dbForge Data Compare for SQL Server Perform SQL Server database comparison to detect changes Compare SQL Server backups with live databases Analyze data differences between two databases Synchronize two databases that went out of sync Restore data of a particular table from the backup Generate data comparison reports in Excel and HTML formats Copy look-up data from development database to production Automate routine data synchronization tasks with command-line interface Go Ahead and Download the dbForge Data Compare for SQL Server right away. It is always a good idea to get familiar with the important tools before hand instead of learning it under pressure of disaster. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology

  Read the article

• SQL SERVER – Auditing and Profiling Database Made Easy with SQL Audit and Comply

  - by Pinal Dave

  Do you like auditing your database, or can you think of about a million other things you’d rather do?  Unfortunately, auditing is incredibly important.  As with tax audits, it is important to audit databases to ensure they are following all the rules, but they are also important for troubleshooting and security. There are several ways to audit SQL Server.  There is manual auditing, which is going through your database “by hand,” and obviously takes a long time and is quite inefficient.  SQL Server also provides programs to help you audit your systems.  Different administrators will have different opinions about best practices and which tools to use, and each one will be perfected for certain systems and certain users. Today, though, I would like to talk about Apex SQL Audit.  It is an auditing tool that acts like “track changes” in a word processing document.  It will log what has changed on the database, who made the changes, and what effects these changes have had (i.e. what objects were affected down the line).  All this information is logged, and can be easily viewed or printed for easy access. One of the best features of Apex is that it is so customizable (and easy to use!).  First, start Apex.  Then you can connect to the database you would like to monitor. Once you select your database, you can select which table you want to audit. You can customize right down to the field you’d like to audit, and then select which types of actions you’d like tracked – insert, delete, or update.  Repeat these steps for every database you want monitored. To create the logs, choose “Create triggers” in the menu.  The script written here will be what logs each insert, delete, and update function.  Press F5 to execute.  All this tracking information will be stored in AUDIT_LOG_DATA and AUDIT_LOG_TRANSACTIONS tables.  View these tables using ApexSQL Audit reports. These transaction logs can be extremely detailed – especially on very busy servers, where every move it traced.  Reading them can be overwhelming, to say the least.  Apex has tried to make things easier for the average DBA, though. You can read these tracking logs in Apex, and it will display data and objects that affect your server – even things that were happening on your server before you installed Apex! To read these logs, open Apex, and connect to that database you want to audit. Go to the Transaction Logs tab, and add the logs you want to read. To narrow down what results you want to see, you can use the Filter tab to choose time, operation type, name, users, and more. Click Open, and you can see the results in a grid (as shown below).  You can export these results to CSV, HTML, XML or SQL files and save on the hard disk. One of the advantages is that since there are no triggers here, there are no other processes that will affect SQL Server performance.  Using this method is also how to view history from your database that occurred before Apex was installed.  This type of tracking does require storage space for the data sources, as the database must be fully running, and the transaction logs must exist (things not stored in the transactions logs will not be recoverable). Apex can also replace SQL Server Profiler and SQL Server Traces – which are much more complex and error-prone – with its ApexSQL Comply.  It can do fault tolerant auditing, centralized reporting, and “who saw what” information in an easy-to-use interface.  The tracking settings can be altered by the user, or the default options will provide solutions to the most common auditing problems. To get started: open ApexSQL Comply, and selected Database Filter Settings to choose which database you’d like to audit.  You can select which tracking you’re like in Operation Types – DML, DDL, queries executed, execute statements, and more.  To get started, click Start Auditing. After this, every action will be stored in the central repository database (ApexSQLCrd).  You can view the audit and create a report (or view the standard default report) using a wizard. You can see how easy it is to use ApexSQL Comply.  You can easily set audits, including the type and time, and create customized reports.  Remote users can easily access the reports through the user interface (available online, as well), and security concerns are all taken care of by the program.  Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology

  Read the article

• SQL SERVER – Simple Example of Incremental Statistics – Performance improvements in SQL Server 2014 – Part 2

  - by Pinal Dave

  This is the second part of the series Incremental Statistics. Here is the index of the complete series. What is Incremental Statistics? – Performance improvements in SQL Server 2014 – Part 1 Simple Example of Incremental Statistics – Performance improvements in SQL Server 2014 – Part 2 DMV to Identify Incremental Statistics – Performance improvements in SQL Server 2014 – Part 3 In part 1 we have understood what is incremental statistics and now in this second part we will see a simple example of incremental statistics. This blog post is heavily inspired from my friend Balmukund’s must read blog post. If you have partitioned table and lots of data, this feature can be specifically very useful. Prerequisite Here are two things you must know before you start with the demonstrations. AdventureWorks – For the demonstration purpose I have installed AdventureWorks 2012 as an AdventureWorks 2014 in this demonstration. Partitions – You should know how partition works with databases. Setup Script Here is the setup script for creating Partition Function, Scheme, and the Table. We will populate the table based on the SalesOrderDetails table from AdventureWorks. -- Use Database USE AdventureWorks2014 GO -- Create Partition Function CREATE PARTITION FUNCTION IncrStatFn (INT) AS RANGE LEFT FOR VALUES (44000, 54000, 64000, 74000) GO -- Create Partition Scheme CREATE PARTITION SCHEME IncrStatSch AS PARTITION [IncrStatFn] TO ([PRIMARY], [PRIMARY], [PRIMARY], [PRIMARY], [PRIMARY]) GO -- Create Table Incremental_Statistics CREATE TABLE [IncrStatTab]( [SalesOrderID] [int] NOT NULL, [SalesOrderDetailID] [int] NOT NULL, [CarrierTrackingNumber] [nvarchar](25) NULL, [OrderQty] [smallint] NOT NULL, [ProductID] [int] NOT NULL, [SpecialOfferID] [int] NOT NULL, [UnitPrice] [money] NOT NULL, [UnitPriceDiscount] [money] NOT NULL, [ModifiedDate] [datetime] NOT NULL) ON IncrStatSch(SalesOrderID) GO -- Populate Table INSERT INTO [IncrStatTab]([SalesOrderID], [SalesOrderDetailID], [CarrierTrackingNumber], [OrderQty], [ProductID], [SpecialOfferID], [UnitPrice],   [UnitPriceDiscount], [ModifiedDate]) SELECT     [SalesOrderID], [SalesOrderDetailID], [CarrierTrackingNumber], [OrderQty], [ProductID], [SpecialOfferID], [UnitPrice],   [UnitPriceDiscount], [ModifiedDate] FROM       [Sales].[SalesOrderDetail] WHERE      SalesOrderID < 54000 GO Check Details Now we will check details in the partition table IncrStatSch. -- Check the partition SELECT * FROM sys.partitions WHERE OBJECT_ID = OBJECT_ID('IncrStatTab') GO You will notice that only a few of the partition are filled up with data and remaining all the partitions are empty. Now we will create statistics on the Table on the column SalesOrderID. However, here we will keep adding one more keyword which is INCREMENTAL = ON. Please note this is the new keyword and feature added in SQL Server 2014. It did not exist in earlier versions. -- Create Statistics CREATE STATISTICS IncrStat ON [IncrStatTab] (SalesOrderID) WITH FULLSCAN, INCREMENTAL = ON GO Now we have successfully created statistics let us check the statistical histogram of the table. Now let us once again populate the table with more data. This time the data are entered into a different partition than earlier populated partition. -- Populate Table INSERT INTO [IncrStatTab]([SalesOrderID], [SalesOrderDetailID], [CarrierTrackingNumber], [OrderQty], [ProductID], [SpecialOfferID], [UnitPrice],   [UnitPriceDiscount], [ModifiedDate]) SELECT     [SalesOrderID], [SalesOrderDetailID], [CarrierTrackingNumber], [OrderQty], [ProductID], [SpecialOfferID], [UnitPrice],   [UnitPriceDiscount], [ModifiedDate] FROM       [Sales].[SalesOrderDetail] WHERE      SalesOrderID > 54000 GO Let us check the status of the partition once again with following script. -- Check the partition SELECT * FROM sys.partitions WHERE OBJECT_ID = OBJECT_ID('IncrStatTab') GO Statistics Update Now here has the new feature come into action. Previously, if we have to update the statistics, we will have to FULLSCAN the entire table irrespective of which partition got the data. However, in SQL Server 2014 we can just specify which partition we want to update in terms of Statistics. Here is the script for the same. -- Update Statistics Manually UPDATE STATISTICS IncrStatTab (IncrStat) WITH RESAMPLE ON PARTITIONS(3, 4) GO Now let us check the statistics once again. -- Show Statistics DBCC SHOW_STATISTICS('IncrStatTab', IncrStat) WITH HISTOGRAM GO Upon examining statistics histogram, you will notice that now the distribution has changed and there is way more rows in the histogram. Summary The new feature of Incremental Statistics is indeed a boon for the scenario where there are partitions and statistics needs to be updated frequently on the partitions. In earlier version to update statistics one has to do FULLSCAN on the entire table which was wasting too many resources. With the new feature in SQL Server 2014, now only those partitions which are significantly changed can be specified in the script to update statistics. Cleanup You can clean up the database by executing following scripts. -- Clean up DROP TABLE [IncrStatTab] DROP PARTITION SCHEME [IncrStatSch] DROP PARTITION FUNCTION [IncrStatFn] GO Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: SQL Statistics, Statistics

  Read the article

• SQL SERVER – Parsing SSIS Catalog Messages – Notes from the Field #030

  - by Pinal Dave

  [Note from Pinal]: This is a new episode of Notes from the Field series. SQL Server Integration Service (SSIS) is one of the most key essential part of the entire Business Intelligence (BI) story. It is a platform for data integration and workflow applications. The tool may also be used to automate maintenance of SQL Server databases and updates to multidimensional cube data. In this episode of the Notes from the Field series I requested SSIS Expert Andy Leonard to discuss one of the most interesting concepts of SSIS Catalog Messages. There are plenty of interesting and useful information captured in the SSIS catalog and we will learn together how to explore the same. The SSIS Catalog captures a lot of cool information by default. Here’s a query I use to parse messages from the catalog.operation_messages table in the SSISDB database, where the logged messages are stored. This query is set up to parse a default message transmitted by the Lookup Transformation. It’s one of my favorite messages in the SSIS log because it gives me excellent information when I’m tuning SSIS data flows. The message reads similar to: Data Flow Task:Information: The Lookup processed 4485 rows in the cache. The processing time was 0.015 seconds. The cache used 1376895 bytes of memory. The query: USE SSISDB GO DECLARE @MessageSourceType INT = 60 DECLARE @StartOfIDString VARCHAR(100) = 'The Lookup processed ' DECLARE @ProcessingTimeString VARCHAR(100) = 'The processing time was ' DECLARE @CacheUsedString VARCHAR(100) = 'The cache used ' DECLARE @StartOfIDSearchString VARCHAR(100) = '%' + @StartOfIDString + '%' DECLARE @ProcessingTimeSearchString VARCHAR(100) = '%' + @ProcessingTimeString + '%' DECLARE @CacheUsedSearchString VARCHAR(100) = '%' + @CacheUsedString + '%' SELECT operation_id , SUBSTRING(MESSAGE, (PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1)) - (PATINDEX(@StartOfIDSearchString, MESSAGE) + LEN(@StartOfIDString) + 1))) AS LookupRowsCount , SUBSTRING(MESSAGE, (PATINDEX(@ProcessingTimeSearchString,MESSAGE) + LEN(@ProcessingTimeString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@ProcessingTimeSearchString,MESSAGE) + LEN(@ProcessingTimeString) + 1)) - (PATINDEX(@ProcessingTimeSearchString, MESSAGE) + LEN(@ProcessingTimeString) + 1))) AS LookupProcessingTime , CASE WHEN (CONVERT(numeric(3,3),SUBSTRING(MESSAGE, (PATINDEX(@ProcessingTimeSearchString,MESSAGE) + LEN(@ProcessingTimeString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@ProcessingTimeSearchString,MESSAGE) + LEN(@ProcessingTimeString) + 1)) - (PATINDEX(@ProcessingTimeSearchString, MESSAGE) + LEN(@ProcessingTimeString) + 1))))) = 0 THEN 0 ELSE CONVERT(bigint,SUBSTRING(MESSAGE, (PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1)) - (PATINDEX(@StartOfIDSearchString, MESSAGE) + LEN(@StartOfIDString) + 1)))) / CONVERT(numeric(3,3),SUBSTRING(MESSAGE, (PATINDEX(@ProcessingTimeSearchString,MESSAGE) + LEN(@ProcessingTimeString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@ProcessingTimeSearchString,MESSAGE) + LEN(@ProcessingTimeString) + 1)) - (PATINDEX(@ProcessingTimeSearchString, MESSAGE) + LEN(@ProcessingTimeString) + 1)))) END AS LookupRowsPerSecond , SUBSTRING(MESSAGE, (PATINDEX(@CacheUsedSearchString,MESSAGE) + LEN(@CacheUsedString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@CacheUsedSearchString,MESSAGE) + LEN(@CacheUsedString) + 1)) - (PATINDEX(@CacheUsedSearchString, MESSAGE) + LEN(@CacheUsedString) + 1))) AS LookupBytesUsed ,CASE WHEN (CONVERT(bigint,SUBSTRING(MESSAGE, (PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1)) - (PATINDEX(@StartOfIDSearchString, MESSAGE) + LEN(@StartOfIDString) + 1)))))= 0 THEN 0 ELSE CONVERT(bigint,SUBSTRING(MESSAGE, (PATINDEX(@CacheUsedSearchString,MESSAGE) + LEN(@CacheUsedString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@CacheUsedSearchString,MESSAGE) + LEN(@CacheUsedString) + 1)) - (PATINDEX(@CacheUsedSearchString, MESSAGE) + LEN(@CacheUsedString) + 1)))) / CONVERT(bigint,SUBSTRING(MESSAGE, (PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1)) - (PATINDEX(@StartOfIDSearchString, MESSAGE) + LEN(@StartOfIDString) + 1)))) END AS LookupBytesPerRow FROM [catalog].[operation_messages] WHERE message_source_type = @MessageSourceType AND MESSAGE LIKE @StartOfIDSearchString GO Note that you have to set some parameter values: @MessageSourceType [int] – represents the message source type value from the following results: Value     Description 10           Entry APIs, such as T-SQL and CLR Stored procedures 20           External process used to run package (ISServerExec.exe) 30           Package-level objects 40           Control Flow tasks 50           Control Flow containers 60           Data Flow task 70           Custom execution message Note: Taken from Reza Rad’s (excellent!) helper.MessageSourceType table found here. @StartOfIDString [VarChar(100)] – use this to uniquely identify the message field value you wish to parse. In this case, the string ‘The Lookup processed ‘ identifies all the Lookup Transformation messages I desire to parse. @ProcessingTimeString [VarChar(100)] – this parameter is message-specific. I use this parameter to specifically search the message field value for the beginning of the Lookup Processing Time value. For this execution, I use the string ‘The processing time was ‘. @CacheUsedString [VarChar(100)] – this parameter is also message-specific. I use this parameter to specifically search the message field value for the beginning of the Lookup Cache  Used value. It returns the memory used, in bytes. For this execution, I use the string ‘The cache used ‘. The other parameters are built from variations of the parameters listed above. The query parses the values into text. The string values are converted to numeric values for ratio calculations; LookupRowsPerSecond and LookupBytesPerRow. Since ratios involve division, CASE statements check for denominators that equal 0. Here are the results in an SSMS grid: This is not the only way to retrieve this information. And much of the code lends itself to conversion to functions. If there is interest, I will share the functions in an upcoming post. If you want to get started with SSIS with the help of experts, read more over at Fix Your SQL Server. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Notes from the Field, PostADay, SQL, SQL Authority, SQL Backup and Restore, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: SSIS

  Read the article

• SQL SERVER – SSMS: Top Object and Batch Execution Statistics Reports

  - by Pinal Dave

  The month of June till mid of July has been the fever of sports. First, it was Wimbledon Tennis and then the Soccer fever was all over. There is a huge number of fan followers and it is great to see the level at which people sometimes worship these sports. Being an Indian, I cannot forget to mention the India tour of England later part of July. Following these sports and as the events unfold to the finals, there are a number of ways the statisticians can slice and dice the numbers. Cue from soccer I can surely say there is a team performance against another team and then there is individual member fairs against a particular opponent. Such statistics give us a fair idea to how a team in the past or in the recent past has fared against each other, head-to-head stats during World cup and during other neutral venue games. All these statistics are just pointers. In reality, they don’t reflect the calibre of the current team because the individuals who performed in each of these games are totally different (Typical example being the Brazil Vs Germany semi-final match in FIFA 2014). So at times these numbers are misleading. It is worth investigating and get the next level information. Similar to these statistics, SQL Server Management studio is also equipped with a number of reports like a) Object Execution Statistics report and b) Batch Execution Statistics reports. As discussed in the example, the team scorecard is like the Batch Execution statistics and individual stats is like Object Level statistics. The analogy can be taken only this far, trust me there is no correlation between SQL Server functioning and playing sports – It is like I think about diet all the time except while I am eating. Performance – Batch Execution Statistics Let us view the first report which can be invoked from Server Node -> Reports -> Standard Reports -> Performance – Batch Execution Statistics. Most of the values that are displayed in this report come from the DMVs sys.dm_exec_query_stats and sys.dm_exec_sql_text(sql_handle). This report contains 3 distinctive sections as outline below.   Section 1: This is a graphical bar graph representation of Average CPU Time, Average Logical reads and Average Logical Writes for individual batches. The Batch numbers are indicative and the details of individual batch is available in section 3 (detailed below). Section 2: This represents a Pie chart of all the batches by Total CPU Time (%) and Total Logical IO (%) by batches. This graphical representation tells us which batch consumed the highest CPU and IO since the server started, provided plan is available in the cache. Section 3: This is the section where we can find the SQL statements associated with each of the batch Numbers. This also gives us the details of Average CPU / Average Logical Reads and Average Logical Writes in the system for the given batch with object details. Expanding the rows, I will also get the # Executions and # Plans Generated for each of the queries. Performance – Object Execution Statistics The second report worth a look is Object Execution statistics. This is a similar report as the previous but turned on its head by SQL Server Objects. The report has 3 areas to look as above. Section 1 gives the Average CPU, Average IO bar charts for specific objects. The section 2 is a graphical representation of Total CPU by objects and Total Logical IO by objects. The final section details the various objects in detail with the Avg. CPU, IO and other details which are self-explanatory. At a high-level both the reports are based on queries on two DMVs (sys.dm_exec_query_stats and sys.dm_exec_sql_text) and it builds values based on calculations using columns in them: SELECT * FROM    sys.dm_exec_query_stats s1 CROSS APPLY sys.dm_exec_sql_text(sql_handle) AS s2 WHERE   s2.objectid IS NOT NULL AND DB_NAME(s2.dbid) IS NOT NULL ORDER BY  s1.sql_handle; This is one of the simplest form of reports and in future blogs we will look at more complex reports. I truly hope that these reports can give DBAs and developers a hint about what is the possible performance tuning area. As a closing point I must emphasize that all above reports pick up data from the plan cache. If a particular query has consumed a lot of resources earlier, but plan is not available in the cache, none of the above reports would show that bad query. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: SQL, SQL Authority, SQL Query, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL Tagged: SQL Reports

  Read the article

• SQL SERVER – Planned and Unplanned Availablity Group Failovers – Notes from the Field #031

  - by Pinal Dave

  [Note from Pinal]: This is a new episode of Notes from the Fields series. AlwaysOn is a very complex subject and not everyone knows many things about this. The matter of the fact is there is very little information available on this subject online and not everyone knows everything about this. This is why when a very common question related to AlwaysOn comes, people get confused. In this episode of the Notes from the Field series database expert John Sterrett (Group Principal at Linchpin People) explains a very common issue DBAs and Developer faces in their career and is related to Planned and Unplanned Availablity Group Failovers. Linchpin People are database coaches and wellness experts for a data driven world. Read the experience of John in his own words. Whenever a disaster occurs it will be a stressful scenario regardless of how small or big the disaster is. This gets multiplied when it is your first time working with newer technology or the first time you are going through a disaster without a proper run book. Today, were going to help you establish a run book for creating a planned failover with availability groups. To make today’s session simple were going to have two instances of SQL Server 2012 included in an availability group and walk through the steps of doing an unplanned failover.  We will focus on using the user interface and T-SQL to complete the failovers. We are going to use a two replica Availability Group where each replica is in another location. Therefore, we will be covering Asynchronous (non automatic failover) the following is a breakdown of our availability group utilized today. Seeing the following screen might be scary the first time you come across an unplanned failover.  It looks like our test database used in this Availability Group is not functional and it currently isn’t. The database status is not synchronizing which makes sense because the primary replica went down so it couldn’t synchronize. With that said, we can still failover and make it functional while we troubleshoot why we lost our primary replica. To start we are going to right click on the availability group that needs to be restarted and select failover. This will bring up the following wizard, which will walk you through several steps needed to complete the failover using the graphical user interface provided with SQL Server Management Studio (SSMS). You are going to see warning messages simply because we are in Asynchronous commit mode and can not guarantee ‘no data loss’ when we do failover. Just incase you missed it; you get another screen warning you about potential data loss because we are in Asynchronous mode. Next we get to connect to the specific replica we want to become the primary replica after the failover occurs. In our case, we only have two replicas so this is trivial. In order to failover, it’s required to connect to the replica that will become primary.  The following screen shows that the connection has been made successfully. Next, you will see the final summary screen. Once again, this reminds you that the failover action will cause data loss as were using Asynchronous commit mode due to the distance between instances used for disaster recovery. Finally, once the failover is completed you will see the following screen. If you followed along this long you might be wondering what T-SQL scripts are generated for clicking through all the sections of the wizard. If you have used Database Mirroring in the past you might be surprised.  It’s not too different, which makes sense because the data is being replicated via SQL Server endpoints just like the good old database mirroring. Now were going to take a look at how to do a failover with just T-SQL. First, were going to need to open a new query window and run our query in SQLCMD mode. Just incase you haven’t used SQLCMD mode before we will show you how to enable it below. Now you can run the following statement. Notice, we connect to the replica we want to become primary after failover and specify to force failover to allow data loss. We can use the following script to failback over when our primary instance comes back online. -- YOU MUST EXECUTE THE FOLLOWING SCRIPT IN SQLCMD MODE. :Connect SQL2012PROD1 ALTER AVAILABILITY GROUP [AGSQL2] FORCE_FAILOVER_ALLOW_DATA_LOSS; GO Are your servers running at optimal speed or are you facing any SQL Server Performance Problems? If you want to get started with the help of experts read more over here: Fix Your SQL Server. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Notes from the Field, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

  Read the article

• Big Data – Evolution of Big Data – Day 3 of 21

  - by Pinal Dave

  In yesterday’s blog post we answered what is the Big Data. Today we will understand why and how the evolution of Big Data has happened. Though the answer is very simple, I would like to tell it in the form of a history lesson. Data in Flat File In earlier days data was stored in the flat file and there was no structure in the flat file.  If any data has to be retrieved from the flat file it was a project by itself. There was no possibility of retrieving the data efficiently and data integrity has been just a term discussed without any modeling or structure around. Database residing in the flat file had more issues than we would like to discuss in today’s world. It was more like a nightmare when there was any data processing involved in the application. Though, applications developed at that time were also not that advanced the need of the data was always there and there was always need of proper data management. Edgar F Codd and 12 Rules Edgar Frank Codd was a British computer scientist who, while working for IBM, invented the relational model for database management, the theoretical basis for relational databases. He presented 12 rules for the Relational Database and suddenly the chaotic world of the database seems to see discipline in the rules. Relational Database was a promising land for all the unstructured database users. Relational Database brought into the relationship between data as well improved the performance of the data retrieval. Database world had immediately seen a major transformation and every single vendors and database users suddenly started to adopt the relational database models. Relational Database Management Systems Since Edgar F Codd proposed 12 rules for the RBDMS there were many different vendors who started them to build applications and tools to support the relationship between database. This was indeed a learning curve for many of the developer who had never worked before with the modeling of the database. However, as time passed by pretty much everybody accepted the relationship of the database and started to evolve product which performs its best with the boundaries of the RDBMS concepts. This was the best era for the databases and it gave the world extreme experts as well as some of the best products. The Entity Relationship model was also evolved at the same time. In software engineering, an Entity–relationship model (ER model) is a data model for describing a database in an abstract way. Enormous Data Growth Well, everything was going fine with the RDBMS in the database world. As there were no major challenges the adoption of the RDBMS applications and tools was pretty much universal. There was a race at times to make the developer’s life much easier with the RDBMS management tools. Due to the extreme popularity and easy to use system pretty much every data was stored in the RDBMS system. New age applications were built and social media took the world by the storm. Every organizations was feeling pressure to provide the best experience for their users based the data they had with them. While this was all going on at the same time data was growing pretty much every organization and application. Data Warehousing The enormous data growth now presented a big challenge for the organizations who wanted to build intelligent systems based on the data and provide near real time superior user experience to their customers. Various organizations immediately start building data warehousing solutions where the data was stored and processed. The trend of the business intelligence becomes the need of everyday. Data was received from the transaction system and overnight was processed to build intelligent reports from it. Though this is a great solution it has its own set of challenges. The relational database model and data warehousing concepts are all built with keeping traditional relational database modeling in the mind and it still has many challenges when unstructured data was present. Interesting Challenge Every organization had expertise to manage structured data but the world had already changed to unstructured data. There was intelligence in the videos, photos, SMS, text, social media messages and various other data sources. All of these needed to now bring to a single platform and build a uniform system which does what businesses need. The way we do business has also been changed. There was a time when user only got the features what technology supported, however, now users ask for the feature and technology is built to support the same. The need of the real time intelligence from the fast paced data flow is now becoming a necessity. Large amount (Volume) of difference (Variety) of high speed data (Velocity) is the properties of the data. The traditional database system has limits to resolve the challenges this new kind of the data presents. Hence the need of the Big Data Science. We need innovation in how we handle and manage data. We need creative ways to capture data and present to users. Big Data is Reality! Tomorrow In tomorrow’s blog post we will try to answer discuss Basics of Big Data Architecture. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

  Read the article

• SQL SERVER – SSIS Parameters in Parent-Child ETL Architectures – Notes from the Field #040

  - by Pinal Dave

  [Notes from Pinal]: SSIS is very well explored subject, however, there are so many interesting elements when we read, we learn something new. A similar concept has been Parent-Child ETL architecture’s relationship in SSIS. Linchpin People are database coaches and wellness experts for a data driven world. In this 40th episode of the Notes from the Fields series database expert Tim Mitchell (partner at Linchpin People) shares very interesting conversation related to how to understand SSIS Parameters in Parent-Child ETL Architectures. In this brief Notes from the Field post, I will review the use of SSIS parameters in parent-child ETL architectures. A very common design pattern used in SQL Server Integration Services is one I call the parent-child pattern.  Simply put, this is a pattern in which packages are executed by other packages.  An ETL infrastructure built using small, single-purpose packages is very often easier to develop, debug, and troubleshoot than large, monolithic packages.  For a more in-depth look at parent-child architectures, check out my earlier blog post on this topic. When using the parent-child design pattern, you will frequently need to pass values from the calling (parent) package to the called (child) package.  In older versions of SSIS, this process was possible but not necessarily simple.  When using SSIS 2005 or 2008, or even when using SSIS 2012 or 2014 in package deployment mode, you would have to create package configurations to pass values from parent to child packages.  Package configurations, while effective, were not the easiest tool to work with.  Fortunately, starting with SSIS in SQL Server 2012, you can now use package parameters for this purpose. In the example I will use for this demonstration, I’ll create two packages: one intended for use as a child package, and the other configured to execute said child package.  In the parent package I’m going to build a for each loop container in SSIS, and use package parameters to pass in a value – specifically, a ClientID – for each iteration of the loop.  The child package will be executed from within the for each loop, and will create one output file for each client, with the source query and filename dependent on the ClientID received from the parent package. Configuring the Child and Parent Packages When you create a new package, you’ll see the Parameters tab at the package level.  Clicking over to that tab allows you to add, edit, or delete package parameters. As shown above, the sample package has two parameters.  Note that I’ve set the name, data type, and default value for each of these.  Also note the column entitled Required: this allows me to specify whether the parameter value is optional (the default behavior) or required for package execution.  In this example, I have one parameter that is required, and the other is not. Let’s shift over to the parent package briefly, and demonstrate how to supply values to these parameters in the child package.  Using the execute package task, you can easily map variable values in the parent package to parameters in the child package. The execute package task in the parent package, shown above, has the variable vThisClient from the parent package mapped to the pClientID parameter shown earlier in the child package.  Note that there is no value mapped to the child package parameter named pOutputFolder.  Since this parameter has the Required property set to False, we don’t have to specify a value for it, which will cause that parameter to use the default value we supplied when designing the child pacakge. The last step in the parent package is to create the for each loop container I mentioned earlier, and place the execute package task inside it.  I’m using an object variable to store the distinct client ID values, and I use that as the iterator for the loop (I describe how to do this more in depth here).  For each iteration of the loop, a different client ID value will be passed into the child package parameter. The final step is to configure the child package to actually do something meaningful with the parameter values passed into it.  In this case, I’ve modified the OleDB source query to use the pClientID value in the WHERE clause of the query to restrict results for each iteration to a single client’s data.  Additionally, I’ll use both the pClientID and pOutputFolder parameters to dynamically build the output filename. As shown, the pClientID is used in the WHERE clause, so we only get the current client’s invoices for each iteration of the loop. For the flat file connection, I’m setting the Connection String property using an expression that engages both of the parameters for this package, as shown above. Parting Thoughts There are many uses for package parameters beyond a simple parent-child design pattern.  For example, you can create standalone packages (those not intended to be used as a child package) and still use parameters.  Parameter values may be supplied to a package directly at runtime by a SQL Server Agent job, through the command line (via dtexec.exe), or through T-SQL. Also, you can also have project parameters as well as package parameters.  Project parameters work in much the same way as package parameters, but the parameters apply to all packages in a project, not just a single package. Conclusion Of the numerous advantages of using catalog deployment model in SSIS 2012 and beyond, package parameters are near the top of the list.  Parameters allow you to easily share values from parent to child packages, enabling more dynamic behavior and better code encapsulation. If you want me to take a look at your server and its settings, or if your server is facing any issue we can Fix Your SQL Server. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Notes from the Field, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

  Read the article

• SQL SERVER – The Story of a Lesser Known Startup Parameter in SQL Server – Guest Post by Balmukund Lakhani

  - by Pinal Dave

  This is a fantastic blog post from my dear friend Balmukund ( blog | twitter | facebook ). He had presented a fantastic session in our last UG and there were lots of requests from attendees that he blogs about it. Well, here is the blog post about the same very popular UG session. Let us read the entire blog post in the voice of the Balmukund himself. During my last session in SQL Bangalore User Group (Facebook) meeting, I was lucky enough to deliver a session on SQL Server Startup issue. The name of the session was “SQL Engine Starting Trouble – How to start?” From the feedback, I realized that one of the “not well known” startup parameter is “-m”. Okay, you might say “I know that this is used to start the SQL in single user mode”. But what you might not know is that you can pass a string with -m which has special meaning and use. I have used this parameter in my blog here but looks like not many of you have seen that. It happens most of the time when we want to start SQL Server in single user mode, someone else makes connection before you can. The only choice you have is to repeat same process again till you succeed. Some smart DBAs may disable the remote network protocols (TCP/IP and Named Pipes) of SQL Instance and allow only local connections to SQL. Once the activity is complete, our dear smart DBA has to remember to re-enable network protocols. Sometimes, it may be a local service or application getting connection to SQL before we can. There is a better way to deal with it. Yes, you have guessed it correctly: -m parameter which a string. Since I work with SQL Product Support team, I may know little more undocumented commands and parameters, but this is not an undocumented stuff. It’s already documented in books online. So in this blog, I am going to show a demo of its usage. As documentation shows, “Do not use this option as a security feature.” So please read this blog as knowledge enhancer and troubleshooting issues not security feature. In my laptop, I have a default instance of SQL Server 2012 and here is what we would in the configuration manager. Now, I would go ahead and stop SQL Service by selecting SQL Server (MSSQLServer) > Right Click > Stop. There are multiple ways to start SQL with startup parameter. 1) Use Net Start Command from command prompt Net Start MSSQLServer /mSQLCMD The above command is the simplest way to add startup parameter to SQL. This parameter would be cleared once we stop and start SQL. 2) Add Startup Parameter via configuration manager. Step is already listed here. We need to add -mSQLCMD If we compare 1 and 2, it’s clear that unless we modify startup parameter and remove -m, it would be in effect. 3) Start SQL Service via command line SQLServr.exe –mSQLCMD –s<InstanceName> Wait, what does SQLCMD mean with /m? It’s the instruction to SQL that start SQL Server in Single User Mode and allow only the application which is SQLCMD. Any other application would fail with Login Failed for User Error message. It would be important to note that string is case sensitive. This value should be picked up from application_name column from sys.dm_exec_sessions. I have made a connection using SQLCMD and as we can see it comes as upper case “SQLCMD”. If we want only management studio query windows to connect then we need to give -m” Microsoft SQL Server Management Studio – Query” as startup parameter. In below example, I have given it as SQLCMd (lower case d at the end) and we would notice that we would not be able to connect to SQL Instance. Above proves that parameter works as expected and it’s case sensitive. Error Log would show below information. How to get error log location? I have already blogged about it. Hope you have learned something new. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL, Technology

  Read the article

• SQL SERVER – Replace a Column Name in Multiple Stored Procedure all together

  - by pinaldave

  I receive a lot of emails every day. I try to answer each and every email and comments on Facebook and Twitter. I prefer communication on social media as this gives opportunities to others to read the questions and participate along with me. There is always some question which everyone likes to read and remember. Here is one of the questions which I received in email. I believe the same question will be there any many developers who are beginning with SQL Server. I decided to blog about it so everyone can read it and participate. “I am beginner in SQL Server. I have a very interesting situation and need your help. I am beginner to SQL Server and that is why I do not have access to the production server and I work entirely on the development server. The project I am working on is also in the infant stage as well. In product I had to create a multiple tables and every table had few columns. Later on I have written Stored Procedures using those tables. During a code review my manager has requested to change one of the column which I have used in the table. As per him the naming convention was not accurate. Now changing the columname in the table is not a big issue. I figured out that I can do it very quickly either using T-SQL script or SQL Server Management Studio. The real problem is that I have used this column in nearly 50+ stored procedure. This looks like a very mechanical task. I believe I can go and change it in nearly 50+ stored procedure but is there a better solution I can use. Someone suggested that I should just go ahead and find the text in system table and update it there. Is that safe solution? If not, what is your solution. In simple words, How to replace a column name in multiple stored procedure efficiently and quickly? Please help me here with keeping my experience and non-production server in mind.” Well, I found this question very interesting. Honestly I would have preferred if this question was asked on my social media handles (Facebook and Twitter) as I am very active there and quite often before I reach there other experts have already answered this question. Anyway I am now answering the same question on the blog so all of us can participate here and come up with an appropriate answer. Here is my answer - “My Friend, I do not advice to touch system table. Please do not go that route. It can be dangerous and not appropriate. The issue which you faced today is what I used to face in early career as well I still face it often. There are two sets of argument I have observed – there are people who see no value in the name of the object and name objects like obj1, obj2 etc. There are sets of people who carefully chose the name of the object where object name is self-explanatory and almost tells a story. I am not here to take any side in this blog post – so let me go to a quick solution for your problem. Note: Following should not be directly practiced on Production Server. It should be properly tested on development server and once it is validated they should be pushed to your production server with your existing deployment practice. The answer is here assuming you have regular stored procedures and you are working on the Development NON Production Server. Go to Server Note >> Databases >> DatabaseName >> Programmability >> Stored Procedure Now make sure that Object Explorer Details are open (if not open it by clicking F7). You will see the list of all the stored procedures there. Now you will see a list of all the stored procedures on the right side list. Select either all of them or the one which you believe are relevant to your query. Now… Right click on the stored procedures >> SELECT DROP and CREATE to >> Now select New Query Editor Window or Clipboard. Paste the complete script to a new window if you have selected Clipboard option. Now press Control+H which will bring up the Find and Replace Screen. In this screen insert the column to be replaced in the “Find What”box and new column name into “Replace With” box. Now execute the whole script. As we have selected DROP and CREATE to, it will created drop the old procedure and create the new one. Another method would do all the same procedure but instead of DROP and CREATE manually replace the CREATE word with ALTER world. There is a small advantage in doing this is that if due to any reason the error comes up which prevents the new stored procedure to be created you will have your old stored procedure in the system as it is. “ Well, this was my answer to the question which I have received. Do you see any other workaround or solution? Reference : Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Server Management Studio, SQL Stored Procedure, SQL Tips and Tricks, T SQL, Technology

  Read the article

• SQL SERVER – SmallDateTime and Precision – A Continuous Confusion

  - by pinaldave

  Some kinds of confusion never go away. Here is one of the ancient confusing things in SQL. The precision of the SmallDateTime is one concept that confuses a lot of people, proven by the many messages I receive everyday relating to this subject. Let me start with the question: What is the precision of the SMALLDATETIME datatypes? What is your answer? Write it down on your notepad. Now if you do not want to continue reading the blog post, head to my previous blog post over here: SQL SERVER – Precision of SMALLDATETIME. A Social Media Question Since the increase of social media conversations, I noticed that the amount of the comments I receive on this blog is a bit staggering. I receive lots of questions on facebook, twitter or Google+. One of the very interesting questions yesterday was asked on Facebook by Raghavendra. I am re-organizing his script and asking all of the questions he has asked me. Let us see if we could help him with his question: CREATE TABLE #temp (name VARCHAR(100),registered smalldatetime) GO DECLARE @test smalldatetime SET @test=GETDATE() INSERT INTO #temp VALUES ('Value1',@test) INSERT INTO #temp VALUES ('Value2',@test) GO SELECT * FROM #temp ORDER BY registered DESC GO DROP TABLE #temp GO Now when the above script is ran, we will get the following result: Well, the expectation of the query was to have the following result. The row which was inserted last was expected to return as first row in result set as the ORDER BY descending. Side note: Because the requirement is to get the latest data, we can’t use any  column other than smalldatetime column in order by. If we use name column in the order by, we will get an incorrect result as it can be any name. My Initial Reaction My initial reaction was as follows: 1) DataType DateTime2: If file precision of the column is expected from the column which store date and time, it should not be smalldatetime. The precision of the column smalldatetime is One Minute (Read Here) for finer precision use DateTime or DateTime2 data type. Here is the code which includes above suggestion: CREATE TABLE #temp (name VARCHAR(100), registered datetime2) GO DECLARE @test datetime2 SET @test=GETDATE() INSERT INTO #temp VALUES ('Value1',@test) INSERT INTO #temp VALUES ('Value2',@test) GO SELECT * FROM #temp ORDER BY registered DESC GO DROP TABLE #temp GO 2) Tie Breaker Identity: There are always possibilities that two rows were inserted at the same time. In that case, you may need a tie breaker. If you have an increasing identity column, you can use that as a tie breaker as well. CREATE TABLE #temp (ID INT IDENTITY(1,1), name VARCHAR(100),registered datetime2) GO DECLARE @test datetime2 SET @test=GETDATE() INSERT INTO #temp VALUES ('Value1',@test) INSERT INTO #temp VALUES ('Value2',@test) GO SELECT * FROM #temp ORDER BY ID DESC GO DROP TABLE #temp GO Those two were the quick suggestions I provided. It is not necessary that you should use both advices. It is possible that one can use only DATETIME datatype or Identity column can have datatype of BIGINT or have another tie breaker. An Alternate NO Solution In the facebook thread this was also discussed as one of the solutions: CREATE TABLE #temp (name VARCHAR(100),registered smalldatetime) GO DECLARE @test smalldatetime SET @test=GETDATE() INSERT INTO #temp VALUES ('Value1',@test) INSERT INTO #temp VALUES ('Value2',@test) GO SELECT name, registered, ROW_NUMBER() OVER(ORDER BY registered DESC) AS "Row Number" FROM #temp ORDER BY 3 DESC GO DROP TABLE #temp GO However, I believe it is not the solution and can be further misleading if used in a production server. Here is the example of why it is not a good solution: CREATE TABLE #temp (name VARCHAR(100) NOT NULL,registered smalldatetime) GO DECLARE @test smalldatetime SET @test=GETDATE() INSERT INTO #temp VALUES ('Value1',@test) INSERT INTO #temp VALUES ('Value2',@test) GO -- Before Index SELECT name, registered, ROW_NUMBER() OVER(ORDER BY registered DESC) AS "Row Number" FROM #temp ORDER BY 3 DESC GO -- Create Index ALTER TABLE #temp ADD CONSTRAINT [PK_#temp] PRIMARY KEY CLUSTERED (name DESC) GO -- After Index SELECT name, registered, ROW_NUMBER() OVER(ORDER BY registered DESC) AS "Row Number" FROM #temp ORDER BY 3 DESC GO DROP TABLE #temp GO Now let us examine the resultset. You will notice that an index which is created on the base table which is (indeed) schema change the table but can affect the resultset. As you can see, an index can change the resultset, so this method is not yet perfect to get the latest inserted resultset. No Schema Change Requirement After giving these two suggestions, I was waiting for the feedback of the asker. However, the requirement of the asker was there can’t be any schema change because the application was used by many other applications. I validated again, and of course, the requirement is no schema change at all. No addition of the column of change of datatypes of any other columns. There is no further help as well. This is indeed an interesting question. I personally can’t think of any solution which I could provide him given the requirement of no schema change. Can you think of any other solution to this? Need of Database Designer This question once again brings up another ancient question:  “Do we need a database designer?” I often come across databases which are facing major performance problems or have redundant data. Normalization is often ignored when a database is built fast under a very tight deadline. Often I come across a database which has table with unnecessary columns and performance problems. While working as Developer Lead in my earlier jobs, I have seen developers adding columns to tables without anybody’s consent and retrieving them as SELECT *.  There is a lot to discuss on this subject in detail, but for now, let’s discuss the question first. Do you have any suggestions for the above question? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: CodeProject, Developer Training, PostADay, SQL, SQL Authority, SQL DateTime, SQL Query, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

  Read the article

< Previous Page | 28 29 30 31 32 33 34  | Next Page >