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SQL 2014 Hosting - ASPHostPortal :: Make Your SSAS Works Like a Private Jet!

clock December 10, 2015 19:54 by author Jervis

SQL Server Analysis Services (SSAS) Tabular is a popular choice as an analytical engine for many customers. With its state-of-the-art compression algorithms, multi-threaded query processor and in-memory capabilities, SSAS Tabular can provide super quick access to data by reporting client applications. However, as a consultant, I have been called by many clients to resolve slow query performance when accessing data from SSAS Tabular models. My experiences have taught me most, if not all, of the performance issues can be resolved by taking care of the following five subject areas. 

Estimate Current Size and Growth Carefully

Tabular models compress data really well and on an average, you can expect to see 10x the compression rates (though it can be much more or less depending on the cardinality of your data). However, when you are estimating the size of your model as well as future growth, a rough figure like this is not going to be optimal. If you already have data sitting in a data warehouse, import a subset of that data — say a month — to find the model size for that and then extrapolate the value based on the required number of rows / years as well as the number of columns. If not, try to at least get a subset of the data from the source to find the model size. There are tools like BISM Memory Report and Vertipaq Analyzer that can further help in this process.

It is also important to record the number of users who will be accessing the system currently as well as the estimated growth for the number of users.

Select or Upgrade Hardware Appropriately

Everyone knows SSAS Tabular is a memory intensive application, and one major issue I have seen is only the RAM is considered when hardware selections are made. However, just focusing on the RAM is not enough and there are a lot of other variables. Suppose all the other variables are constant and there is an unlimited budget, these are the recommendations:

CPU Speed – The faster, the better, will help in computing results faster especially when there is a bottleneck on the single-threaded formula engine.

CPU Cores – In theory, the more the better as it helps in managing concurrent user loads. However, in reality, a rise in the number of cores usually corresponds to a decrease in the CPU speed due to management overload. So a balanced approach has to be taken when determining the CPU Cores and Speed. Also, licensing cost increases with the number of cores for many software.

CPU Sockets – The lesser, the better as SSAS Tabular is not NUMA aware till SQL 2014. However, this is expected to change in SQL 2016 where some NUMA optimization has been made. For large tabular models, it might be a challenge to go single socket as the amount of RAM that can be supported on a system will depend on the CPU sockets.

CPU Cache – The more, the better. Retrieving data from CPU caches are 10-100x faster than retrieving data from RAM.

CPU Architecture – The newer, the better due to the hardware performance optimizations. For eg, Intel Xeon processors with Haswell architecture is always going to be faster than Sandy architecture keeping all other variables constant.

Amount of RAM – Should have at least 2.5x the model size, if the model is going to be processed on the same server. The amount of RAM can be lesser in cases of certain scale out architectures where the model is processed in a separate server.

RAM Speed – The faster, the better (yes, RAMs have speed too!) This is very important for a memory-bound application like Tabular and should always go for the faster speeds, if budget allows.

Storage – Not important at all as it does not have any effect on query performance. However, if budget allows, it might not be a bad idea to get faster storage like SSDs, as that will help in maintenance related activities like backup, storage or even getting the tabular model online faster when the service is restarted. Apart from this, there are other factors also like network latency, server architecture (scale out), etc that have to be considered, but depending on the budget and specific customer requirements, a balanced approach will have to be made.

Design the Data Model Properly

Tabular is really good at performance and in the case of small models, is extremely forgiving in terms of bad design. However, when the amount of data grows, performance problems begin to show up. In theory, you will get the best performance in SSAS tabular if the entire data is flattened into a single table. However, in reality, this would translate to an extremely bad user experience as well as a lengthy and expensive ETL process. So the best practice is to have a star schema, generally. Also, it is recommended to only include the relevant columns from the source tables, as increasing the columns will result in an increase in model size which in turn will result in slower query performances. Increase in number of rows might still be ok as long as the cardinality of the columns don’t change much.

Depending on the specific customer requirements, there could be deviations from the best practices. For e.g., we built custom aggregate tables along with the detailed fact table in the case of a very large production model for a client. The resultant measure had a conditional statement to retrieve data from the aggregate table if the detailed level dimension data was not used in the report. Since the aggregate table was only 1/10 the size of the detailed fact table, the query came out 10x times faster whenever the details were not used, which was almost 90% of the times.

Optimize the DAX Calculations

In case of small models, Tabular is extremely forgiving in terms of bad DAX code also. However, just like in the case of bad design, performance takes a hit for the worse as you increase the data, add more users, or run complex queries. DAX performance tuning is the most difficult to tune from the current list, and it is important to have a strategy for maintaining and tuning the performance. A good place to start would be the Performance Tuning of Tabular models in SSAS 2012 whitepaper.

Monitor User Query Patterns and Train Users

Once your model is in production, it is important to keep monitoring the user query patterns as well as the resources to see potential bottlenecks. Through this, you can find whether the performance issues are being caused due to inefficient DAX, bad design, insufficient resources or most importantly, whether it is just because a user is using the model inefficiently. For e.g., in one of the cases, we found out the slow performance for all users was due to a single user dumping the entire 100 GB model into spreadsheets so he could perform custom calculations on top of it. This blocked the queries for all the other users and made things really slow for them. With appropriate requirement gatherings, we ensured all the required calculations for that user were there in the model and then trained the user to use the model for his analytics.

The success of any tabular project depends on the adoption by the end users and it is needless to say the adoption would be much better if the system is fast. These 5 tips will ensure you already have a jumpstart on that journey.

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SQL 2014 Hosting - ASPHostPortal :: How to Optimize Your SQL Query

clock October 6, 2015 08:59 by author Jervis

Modern day software applications have millions of concurrent users. Development of an efficiently serviceable application requires a huge amount of effort and requires many tools and techniques. Software developers always try to improve the performance of the application by improving design, coding and database development. For database development, query optimization and evaluation techniques are playing vital parts.

Selection of required field only.

It is very important to avoid unnecessary data selection of the query. We should select a data field that we need but not all fields of the table.

SELECT login_id, pawwsord FROM tbluser  


Properly created Indexes help to optimize search results. You need to better understand the databases before the selection of a better performing index. The selection of a highly used field as an index is very important.

CREATE clustered INDEX ind_login_id ON tbluser(login_id)  

Primary Key

The Primary Key is the most important index of the table. The most important thing about a Primary Key is the selection of a short and unique field. This will lead to easy access to the data records.

  id INT,  
  name VARCHAR(150),  
  email VARCHAR(100),  
  login_id VARCHAR(100),  
  password VARCHAR(10),  

Index unique column

The indexing of a unique column will improve searching and increase the efficiency of the database. You must have a better understanding of the data field and their utilization before indexing a unique column. The indexing of a less used column does not help improve the efficiency of the database.

CREATE INDEX ind_email ON tbluser(email)  

Select limited records

None of the user interfaces can visualize thousands of records at once. Hence there is no way to select all the records at once, so always limit the selection when you have a large number of records. Select the required data only.

SELECT id, name, email, login_id,password FROM tbluser WHERE 1 limite 10  

Selection of correct data type and length

Use the most appropriate data type and correct length of the data. The bad selection of a data type will produce bulky databases and poor performance. This will improve resource utilization of the database server.

CREATE TABLE tbluser(id INT,  
   name VARCHAR(150),  
   email VARCHAR(100),  
   login_id VARCHAR(100),  
   password VARCHAR(10)  

Avoid in sub query

Always avoid use of IN sub-queries in your applications. An In sub-query will evaluate all the records of table A with table B (product of records) before selecting the required data.

SELECT login_id,name, email FROM tbluser WHERE login_id IN ( SELECT login_id FROM tbllogin_details)

One of the correct ways is to use an inner join as in the following:  

SELECT login_id,name, email FROM tbluser INNER JOIN tbllogin_details ON tbluser.login_id =tbllogin_details.login_id 

Avoid NOT operator

Please avoid the usage of the NOT operator situation that the number of qualifying records are lower than unqualified records. Always use a positive operator such as LIKE, EXIST than NOT LIKE, NOT EXIST.

SELECT * FROM tbluser WHERE email NOT LIKE '%gmail%'  

The prefered way is:

SELECT * FROM tbluser WHERE email LIKE '%yahoo%'  

SQL Hosting with ASPHostPortal :: Using SQLBulkCopy and C# to Upload File

clock August 12, 2015 08:17 by author Jervis

In this article I am going to write about SQLBulkCopy and its major properties and methods. This article will give you the code for high performance transfer of rows from XML file to SQL server with SQLBulkCopy and C#.

SQLBulkCopy introduced as part of .Net framework 2.0. It is simple and easy tool to transfer complicated or simple data from one data source to other. You can read data from any data source as long as that data can be load to DataTable or read by IDataReader and transfer the data with high performance to SQL Server using SQLBulkCopy.

In real time applications every day millions of records get transferred from one data store to other. There are multiple ways to transfer the data like command prompt bcp utility of SQL Server, creating INSERT statements, creating SSIS packages and SQLBulkCopy. SQLBulkCopy gives you significant performance gain over other tools.

SQLBulkCopy Constructor

SQLBulkCopy initializes instance in four different way.

1. Accepts already open SqlConnection for destination.
2. Accepts connection string of SQLConnection. This constructor actually opens and initializes new instance of SQLConnection for destination.
3. Accepts connection string of SQLconnection and enum value of SqlBulkCopyOptions. This constructor actually opens and initializes new instance of SQLConnection for destination.
4. Accepts already opened SQLConnection and enum value of SqlBulkCopyOptions.

SqlBulkCopy bulkCopy =
            new SqlBulkCopy(destinationConnection.ConnectionString, 


SQLBulkCopy BatchSize is integer property with default value of 0. It decides how many rows need to be send to the server in one batch. If you do not set any value for this property or set it as 0, all the records will be send in single batch.

Following example sets BatchSize property as 50.

bulkCopy.BatchSize = 50;


SQLBulkCopy ColumnMappings is a collection of columns which needs to be map from source table to destination table's columns. You do not need to map the columns if column names are same. However it is very important to map the columns if column names are different. If matching SQLBulkCopy does not found the matching column it throws System.InvalidOperationException.

You can map the columns in different ways, giving both column names is easy and readable method.

Below code match the column OrderID from source table with columnNewOrderID of destination column.

bulkCopy.ColumnMappings.Add("OrderID", "NewOrderID");  

Data Type issue while mapping the column

SqlBulkCopy is particular about matching column DataType. Both the columns has to be of same DataType. If you have nullable columns, you explicitly have to convert such columns into desired DataType.

Below code converts Null to varchar(2) and can be mapped to any varchar(2) column of destination table.

SELECT  CAST(ISNULL(ShipRegion,'') as varchar(2))
            as ShipRegion FROM Orders

Quick note: If you are having computed columns like SUM, AVG etc. make sure it returns in expected DataType. If your destination table expects columns with decimal(15,7) you will have to explicitly convert the source column as decimal(15,7) because SUM will by default return decimal(38,7).


It sets the name of destination table. The method WriteToServer will copy the source rows to this particular table.

Below code will set the destination table as "TopOrders".

bulkCopy.DestinationTableName = "TopOrders";   

NotifyAfter and SqlRowsCopied

NotifyAfter is an integer property with default value of 0 and SqlRowsCopied is an event. The value of NotifyAfter indicates when to raise eventSqlRowsCopied.

The below code shows after processing 100 rows, event SqlRowsCopied will be executed.

bulkCopy.SqlRowsCopied +=
    new SqlRowsCopiedEventHandler(OnSqlRowsTransfer);
bulkCopy.NotifyAfter = 100;

private static void
    OnSqlRowsTransfer(object sender, SqlRowsCopiedEventArgs e)
        Console.WriteLine("Copied {0} so far...", e.RowsCopied);


WriteToServer is a method which actually processes your source table data to destination table. It accepts array of DataRows or DataTable or IDataReader. With DataTable you can also specify the state of the rows that needs to be processed.

The following code will process rows from sourceData DataTable which has RowState as Added to DestinationTable.

bulkCopy.WriteToServer(sourceData, DataRowState.Added);

SQL 2014 Hosting - :: Handling Divide BY Zero Exception

clock March 16, 2015 07:24 by author Ben

Some instances whilst undertaking Calculation within your query you got an error or exception "Divide by Zero" and some time you will have Output value Like NULL So how you can manage these Problem and Exception.

Use NULLIF -To Manage Divide by zero Exception
Use ISNULL -To Show some worth rather of NULL in your output

Below will be the total explanation
select 10/0

in case you run the above query it is going to throw an error

Msg 8134, Level 16, State 1, Line 1
Divide by zero error encountered.

Now to be able to Resolve this we'll use Nullif function

 select nullif (10/ nullif (0,0),0)

Output NULL

Now it's going to not throw an error and your output will likely be null. Explanation: NULLIF function takes two arguments verify wheather they're equal or not if they are equal it'll return NULL as output
IF both expressions are not equal it is going to return the very first expression with identical data kind. Now inside the denominator it checked wheather 0 is equal to 0 The conditon was true it returned Null in denominator. now the expression was like

select Nullif(10/null,0)

now again it will check wheather 10/Null is equal to zero condition fails and you will get.

Output  NULL

Note : AnyNumber divide multiplied,added subtracted with NULL will Resultant to NULL only. Now how to use NULLIF and ISNULL in your Code or in TSQL to avoid exception and NULL Values. In the below code i have used nullif with ISNULL function. For this we will  Create a table

   col1            int   IDENTITY,
   col2      int   NULL,
   col3   int   NULL
INSERT Test_NULLIF1 VALUES(300000, 21);

If you will divide column 3 by column 2 for Row 3 it will throw Divide By zero Exception Run this query to generate error.. select col3/col2  from Test_NULLIF1  where col1 =3

Msg 8134, Level 16, State 1, Line 1
Divide by zero error encountered.

In order to handle this Use nullif function as explained in above example

select nullif(col3/nullif(col2,0),0)  from Test_NULLIF1  where col1 =3

Output NULL

Now You can Use ISNULL function after handling divide by zero exception to give a value when you have output like NULL
for eg like instead of NULL you want to show value like 0 or 1 or anything

select ISNULL(col3/nullif(col2,0),1)  from Test_NULLIF1  where col1 =3

Output 1

Explanation: ISNULL Function takes two argument check wheather first expression is NULL if null provide a replacement for that NULL in second expression it can be any value.
IN query ISNULL checked the first expression it was NULL so it replaced the NULL Value with 1,
and return output  as 1.

IN case if first expression is not null it willl return the first expression value only. .

Test IsNULL function with below queries

Select ISNULL(null,2)

output 2

select isnull(3,1)--as first expression is not null so it returns first value i.e. 3

output 3

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SQL 2014 Hosting - :: Recovering SQL Server Database from Suspect Mode

clock November 13, 2014 06:49 by author Mark

How Recovering SQL Server Database from Suspect Mode ??

Sometimes we have to face a critical situation when SQL Server database going to Suspect Mode. In that moment no work can be done on database. Database may go into suspect mode because the primary file group is damaged and the database cannot be recovered during the startup of the SQL Server

Reason for database to go into suspect mode:

  • Data files or log files are corrupt.
  • Database server was shut down improperly
  • Lack of Disk Space
  • SQL cannot complete a rollback or roll forward operation

How to recover database from suspect mode:

  • Change the status of your database. Suppose database name is “BluechipDB”?

EXEC sp_resetstatus '';
EXEC sp_resetstatus 'BlueChipDB'

  • Set the database in “Emergency” mode


  • Check the database for any inconsistency

DBCC checkdb('BlueChipDB')

If you get any error after executing DBCC CHECKDB then immediately bring the database in SINGLE USER MODE by running following query. If no error found then you need not execute the following query.


  • For safety, take the backup of the database.
  • Run the following query as next step.Remember while using the below query, that uses REPAIR_ALLOW_DATA_LOSS, is a one way operation that is once the database is repaired all the actions performed by these queries can’t be undone.
  • There is no way to go back to the previous state of the database.
  • So as a precautionary step you should take backup of your database in step 5 mentioned above.


  • Finally, bring the database in MULTI USER mode


  •  Refresh your database server and verify the connectivity of your database. Now users should be able to connect to the database properly. If any data loss, you can restore database – backup taken in step 5.


SQL 2014 Hosting Tutorial - :: SQL Server 2014 Analysis, Migrate, and Report Tool

clock November 10, 2014 11:28 by author Mark

Determine which tables and stored procedures would benefit from In-Memory OLTP

With SQL Server 2014 new In-Memory OLTP engine, you can load tables and stored procedures in memory, which provides very fast response times. The goal isn't to load all the database tables and stored procedures in memory, but rather just those tables that are crucial to performance and those stored procedures that have complex logical calculations.

To help you identify which tables and stored procedures will give you the best performance gain after being migrated to In-Memory OLTP, SQL Server 2014 provides the new Analysis, Migrate, and Report (AMR) tool. Built into SQL Server Management Studio (SSMS), the AMR tool consists of the:

  • Transaction performance collector (which collects data about existing tables and stored procedures in order to analyze workloads) and transaction performance analysis reports (which gives recommendations about the tables and stored procedures to migrate to In-Memory OLTP based on the collected data)
  • Memory Optimization Advisor (which guides you through the process of migrating a table to a memory-optimized table)
  • Native Compilation Advisor (which helps you identify T-SQL elements that need to be changed before migrating a stored procedure to a natively compiled stored procedure)

The AMR tool leverages the new Transaction Performance Collection Sets for gathering information about workloads and the Management Data Warehouse (a relational database) to store the collected data. The Transaction Performance Collection Sets includes the:

  • Stored Procedure Usage Analysis collection set (which captures information about stored procedures for a future migration to natively compiled stored procedures)
  • Table Usage Analysis collection set (which captures information about disk-based tables for a future migration to memory optimized tables)

Before you can use the AMR tool, you need to configure the Management Data Warehouse and the data collection process. After showing you how to do so, I'll demonstrate how to run the transaction performance analysis reports and how to use the two advisors.

Configuring the Management Data Warehouse

To configure the Management Data Warehouse, go to Object Explorer in SSMS. Expand the Management folder, right-click Data Collection, select Tasks, and click Configure Management Data Warehouse. This will launch the Configure Management Data Warehouse Wizard.

After the Welcome page, you'll find yourself on the Select Configuration Task page. On this page, select the option to configure a Management Data Warehouse.

  • On the Configure Management Data Warehouse Storage page, you need to specify the name of database that will host the Management Data Warehouse and the name of the server on which that database resides. If you need to create the database, click the New button to create one.
  • On the Map Logins and Users page, you'll find the existing logins allowed for the server that will host the Management Data Warehouse. If needed, you can edit the logins or map users to the administrator, reader, and writer roles for the Management Data Warehouse.
  • On the Complete the Wizard page, you need to verify the Management Data Warehouse configuration. If it's OK, click Finish. When the configuration of the Management Data Warehouse has successfully completed, you should see a page like that in Figure 1.

Figure 1: Verifying Configuration of the Management Data Warehouse

The Management Data Warehouse setup is now finished.

Configuring the Data Collection Process

To configure the data collection process, go to Object Explorer in SSMS. Expand the Management folder, right-click Data Collection, select Tasks, and click Configure Data Collection. This will launch the Configure Data Collection Wizard.

After the Welcome page, you'll find the Setup Data Collection Sets page shown in Figure 2. Besides needing to specify the server and database that will host the Management Data Warehouse, you need to specify the data collector sets. In the list of collection sets, select the Transaction Performance Collection Sets check box so that the data collector will collect statistics for transaction performance issues.

Figure 2: Specifying the Data Collector Sets

If the Management Data Warehouse is located on a different SQL Server instance from the data collector and if SQL Server Agent isn't running under a domain account that has dc_admin permissions on the remote instance, you have to use a SQL Server Agent proxy. If that's the case, be sure to select the Use a SQL Server Agent proxy for remote uploads check box.

Once you're done configuring the Setup Data Collection Sets page, click Finish. When the wizard completes the configuration, you'll have an enabled data collection process that will collect information about all user databases. Note that SQL Server Agent must be started on the instance that will collect the data.

In the SQL Server Agent's Jobs folder, you'll see the jobs used to collect data from your workloads and the jobs used to upload the collected data into the Management Data Warehouse. The data collection jobs use the naming convention collection_set_N_collection, where N is a number. The upload jobs use the naming convention collection_set_N_upload, where N is a number.

By default, the AMR tool collects data from three dynamic management views every 15 minutes for both the Stored Procedure Usage Analysis and Table Usage Analysis collection sets. The upload job runs every 30 minutes for the Stored Procedure Usage Analysis collection set and every 15 minutes for the Table Usage Analysis collection set. If you want to speed your upload, you can execute these jobs manually. Uploading the data has a minimal impact on performance.

Running the Transaction Performance Analysis Reports

To access the recommendations based on the information collected about all your user databases on the workload server, you need to run the transaction performance analysis reports. To access them, right-click your Management Data Warehouse database, select Reports, choose Management Data Warehouse, and click Transaction Performance Analysis. From the Transaction Performance Analysis Overview page, you can choose to run three reports, depending on what type of information you need:

  • Recommended Tables Based on Usage
  • Recommended Tables Based on Contention
  • Recommended Stored Procedures Based on Usage

Recommended Tables Based on Usage. This report tells you which tables are the best candidates for migration to In-Memory OLTP based on their usage. Figure 3 shows a sample report. On the left side, you can select the database and how many tables you'd like to see from that database. The chart will then show the selected tables. The horizontal axis represents the amount of work needed to migrate a table to In-Memory OLTP. The vertical axis represents the gains you'll achieve after migrating the table. The best candidates for In-Memory OLTP are located in the top right corner. As you can see, they can be easily migrated and will give you the best performance gain.

Figure 3: Determining Which Tables Are the Best Candidates for Migration Based on Usage

You can access a detailed report for a table by clicking its name in the chart. As Figure 4 shows, this report provides the table's access statistics (e.g., lookups, range scan) and contention statistics (e.g., latches, locks), as well as when this information was captured.

Figure 4: Reviewing the Detailed Performance Statistics for a Table

Recommended Tables Based on Contention. This report tells you which tables are the best candidates for migration to In-Memory OLTP based on their contention. If you compare the contention analysis report in Figure 5 with the usage analysis report in Figure 3, you'll see that they're very similar.

Figure 5: Determining Which Tables Are the Best Candidates for Migration Based on Contention

You can select the database and how many tables you'd like to see from that database. The resulting chart shows the amount of work needed to migrate the tables (horizontal axis) and the gains you'll achieve after migrating them (vertical axis). In the top right corner, you'll find the best candidates for migration based on contention. You can click a table name in the chart to access a detailed report showing the table's statistics. This report provides the table's access and contention statistics.

Recommended Stored Procedures Based on Usage. This report shows you which stored procedures are the top candidates for an In-Memory OLTP migration based on their usage (i.e., total CPU time). After selecting the database and how many stored procedures you'd like to see from that database, the resulting chart shows the top candidates for migration, as Figure 6 shows.

Figure 6: Seeing Which Stored Procedures Are the Top Candidates for Migration Based on Usage

If you want to see the detailed usage statistics for a specific stored procedure, you can click its blue bar. Figure 7 shows an example of the report you'll receive.

Figure 7: Reviewing the Detailed Usage Statistics for a Stored Procedure

Using the Memory Optimization Advisor

  • After you know which tables you want to migrate to In-Memory OLTP, you can use the AMR tool's Memory Optimization Advisor to help you with the migration process. To access this advisor, open Object Explorer in SSMS and navigate to the table you want to migrate. Right-click the table and choose Memory Optimization Advisor.
  • The advisor will launch with the Introduction page, which you can read or skip. Clicking Next brings you to the Migration Optimization Checklist page, where the advisor will check to see if your table can be migrated. If one or more validation items fail, the migration process will stop. If needed, you can generate a report for this analysis. If all you see are green checkmarks, your table doesn't have any features that could prevent the migration process, in which case you can proceed to the next page.
  • On the Migration Optimization Warnings page, you'll find important information about what isn't supported in memory-optimized tables and other types of issues. The issues listed won't prevent the table from being migrated, but they might cause other objects to fail or behave in an unexpected manner.

If a warning applies to the table you selected for migration, an exclamation point in a yellow triangle will appear next to the warning, as shown in Figure 8.

Figure 8: Reviewing the Migration Optimization Warnings

In this case, the selected table has an unsupported French_CI_AS collation on the indexed column named Person_OnDisk_Name. (Only BIN2 collations are supported for indexes in memory-optimized tables.) Thus, the index collation will need to be changed later in the migration process.

Figure 9: Reviewing the Optimization Options

On the Review Optimization Options page, which Figure 9 shows, you have the option to change the defaults listed for the:

  • Name of memory-optimized file group (only one memory-optimized file group is allowed per instance)
  • Logical filename
  • Path where the logical file will be saved
  • New name given to the original table (the original table is renamed to prevent naming conflicts)

You can also choose to copy data from the original table to the new memory-optimized table during the migration process, and you can change the durability of the memory-optimized table. By default, its DURABILITY option will be set to schema_and_data, but you can change it to schema_only by selecting the Check this box to migrate this table to a memory-optimized table with no data durability option. If you do so, the data will be lost after the SQL Server service is restarted. In other words, just the table's schema is persistent. Finally, the Review Optimization Options page shows the estimated current memory cost for the memory-optimized table. If there isn't sufficient memory, the migration process might fail.

Once you're done with the Review Optimization Options page, you can click Next to go to the Review Primary Key Conversion page. When the migration process begins, it will start by converting the primary key. You can convert it to:

  • A nonclustered hash index, which gives the best performance for point lookups. If you select this option, you also need to specify the bucket count, which should be twice the expected number of rows.
  • A nonclustered index, which gives the best performance for range predicates.

For each index you have in the table being migrated, you'll be presented with a Review Index Conversion page that has been populated with the columns and data types for that index. The options you can configure in the Review Index Conversion page are similar to those in the Review Primary Key Conversion page. In this case, for the indexed column Person_OnDisk_Name with the unsupported French_CI_AS collation, you'd have to select BIN2 collation as the Char data type.

On the Verify Migration Actions page, you'll see all operations that will be performed to migrate your table to In-Memory OLTP. You have the option to script those operations by clicking the Script button. After verifying all the options, you can click the Migrate button to start the migration process.

Figure 10 shows how the new memory-optimized table appears in SSMS. If you view its properties, you'll see that the Memory optimized property is set to True and that the schema and data are durable for this table.

Figure 10: New Memory-Optimized Table in SSMS

In Figure 10, you can also see how the original table has been renamed.

Using the Native Compilation Advisor

After you know which stored procedures you want to migrate to In-Memory OLTP, you can use the AMR tool's Native Compilation Advisor to help you with their migration. To access this advisor, open Object Explorer in SSMS and navigate to the stored procedure you want to migrate. Right-click the stored procedure and choose Native Compilation Advisor.

After clicking through the Welcome page, you'll be presented with the Stored Procedure Validation page, which will give you warnings if your stored procedure contains some T-SQL elements that aren't supported by native compilation. If the stored procedure is valid, it can become a natively compiled stored procedure without modification. However, the Native Compilation Advisor doesn't migrate stored procedures like the Memory Optimization Advisor migrates tables. You have to do the migration on your own.

If the stored procedure has unsupported T-SQL elements, the validation will fail. To see the details about the unsupported elements, you need to click Next to go to the Stored Procedure Validation Result page, which Figure 11 shows.

Figure 11: Reviewing the Unsupported T-SQL Elements

You have to modify the unsupported elements before you can migrate your stored procedure to a natively compiled stored procedure.

Eliminate the Guesswork

The AMR tool is useful because it eliminates the guesswork in determining which tables and stored procedures would benefit from In-Memory OLTP. After identifying which tables to migrate, you can use the Memory Optimization Advisor to quickly migrate them. Although the Native Compilation Advisor can help you identify the T-SQL elements you need to change before migrating your stored procedure to a natively compiled one, it unfortunately doesn't guide you through the migration process.


SQL 2014 Hosting Tutorial - :: How to Add Data in SQL Server 2014

clock October 31, 2014 06:03 by author Ben

In the earlier publish, we all know about The Best Way to Create a Table in SQL Server 2014. We now possess a databases, and table, but no data.

There are lots of techniques of obtaining data into your database in SQL Server 2014. Listed here are the primary ones that come to mind:

  • Manually: Type data directly into your table rows.
  • Copy/Paste: Similar to the earlier alternative, but this 1 is in which you duplicate data from an additional source, then paste it into a table within your databases.
  • Import: You are able to utilize the Import and Export Wizard to import info from yet another source.
  • SQL Scripts: You can operate a SQL script which contains all info to insert.
  • Application/Website: Consumers update the database via an application or site.

Here's much more element on each of these methods.


We will make use of the Edit Top 200 Rows option to manually kind info directly in to the table rows.

Manually getting into data is Okay should you have only just a little little bit of data to enter. But it's a little bit clunky and might impractical if you have lots of information. Furthermore it doesn't actually match most company requirements, in which non-technical customers need to be capable of update the database.

In any case, here's how to manually enter data directly into the table :

  1. In the Object Explorer, right click on the table you wish to open, and select Edit Top 200 Rows:

  2. You can now start entering the data directly into your table.


You could use a similar method towards the above by copying from an additional datasource and pasting into your databases table. Of course, this may require which the resource table has the same columns since the destination table. Comparable towards the manual method above, this really is Okay to get a small quantity of information but not for any good deal of data.

Here's how to copy/paste into your table:

  1. Select all required records from the datasource
  2. In the destination database (i.e. the one you want to populate with data), right-click on the destination table and select Edit Top 200 Rows
  3. Select an empty row by right-clicking in the left-most column (it's more of a button to the left of your left-most column that allows you to select the whole row) and select Paste from the contextual menu:

    If you need to paste more than 200 rows, click the Show SQL Pane icon from the toolbar to display the SQL statement behind the 200 rows being displayed. Simply change the 200 to a larger number of rows as required.

    Note that this will work up to a certain extent, but you may encounter times where have so much data to transfer that you need to use another method.


You are able to import data from yet another datasource. The top end result is comparable towards the copy/paste method (i.e. information is copied across to the location databases), but importing the data is a lot more flexible and could be more appropriate on several occasions. For instance, you can choose info from several views and tables, and you can compose a question on the fly to import only the data you'll need.

To import data, right-click on the database and select Tasks > Import Data... and follow the Wizard from there.

The SQL Server Import and Export Wizard can copy data to and from any data source for which a managed .NET Framework data provider or a native OLE DB provider is available. These include:

  • SQL Server
  • Flat files
  • Microsoft Office Access
  • Microsoft Office Excel

You may also commence the SQL Server Import and Export Wizard in the Windows Commence menu, from inside of SQL Server Data Tools (SSDT), and through the command prompt (by running DTSWizard.exe which you will locate in both C:\Program Files\Microsoft SQL Server\100\DTS\Binn or in C:\Program Files\Microsoft SQL Server\120\DTS\Binn or other location depending on your configuration and push letter).

SQL Scripts

In many circumstances, you'll find it much more efficient to run a SQL script which contains the data you need to insert. You'll be able to make use of the SQL INSERT assertion to insert just the info you specify inside the statement.

SQL scripts are excellent for inserting static/reference data (like say, countries/regions). They are able to be saved and run once more any time it really is required (for example on another database). Scripts aren't generally so excellent for data that continually changes (like customer particulars). You almost certainly would not be maintaining a duplicate of outdated data within a SQL script. But you'll find often exceptions. For example, you may use this kind of a script to populate a client table inside your testing/development atmosphere.

More about SQL scripts arising.


Most SQL Server databases are the backend data storage for any front-end application. Customers from the application are responsible for including data towards the databases (as well as editing it). As a result, many of the tables in your database will likely be up-to-date via the application. Within this situation, the applying is updating the database utilizing SQL scripts.

The main difference between these scripts and the kinds we mentioned over is the fact that, the scripts being used within the software will probably be dynamic. They'll accept data as a parameter that is handed to the script. Hence the user can enter say, an e-mail tackle in to the application, and unbeknownst to him, the application runs a SQL script that requires his e-mail tackle, validates it, provides it to the script, and when it passes each of the business/validation principles, inserts it in to the database.

These SQL scripts may be placed directly into your site code (PHP, ColdFusion and so forth), or they're able to be stored within the databases as Saved Processes or Views and operate only when the application says so.

SQL 2014 Hosting Tutorial - :: The Best Way to Create a Table in SQL Server 2014

clock October 24, 2014 07:04 by author Ben

Seeing as our database is actually a task-tracker database, let us get in touch with our initial table "Tasks". This desk will hold all duties - regardless of their status (eg. done, to do, in progress, and so on). Then we are able to develop yet another table known as "Status". Then when our tables contain information, we'll be capable of operate queries towards these tables to discover what jobs have to be carried out and which of them are in a offered position, and so on.

But let's not get forward of ourselves. Let's develop our first table.

They are the measures to make a table in a SQL Server 2014 databases using SQL Server Administration Studio (SSMS).

  1. Ensuring you have the correct database expanded (in our case, the TaskTracker database), right click on the Tables icon and select Table... from the contextual menu:

  2. A new table will open in Design view. While you have this screen open, do the following:
    • Utilizing the values in the screenshot, full the details inside the Column Name column, data Type column, and allow Nulls column.
    • Make the TaskId column a Primary Key discipline by right-clicking the button close to TaskId (i.e. exactly the same area exactly where the true secret seems in the subsequent screenshot) and picking Set Primary Key.
    • Make the TaskId column an identity column by placing Is identity to yes (you can discover this feature underneath the Id Specification area in the bottom pane). Be aware that to set values in the base pane, you should pick the column name within the leading pane initial. We are environment this column to be an auto-number column - it's going to immediately produce a brand new amount for every document that is created.
    • Set the Default Price from the DateCreated column to (getdate()). (This will automatically insert the present date into that field for each new file).

    What we are carrying out is creating the column names, specifying the sort of data that may be entered into them, and setting default values. Restricting the data sort for each column is extremely crucial and aids maintain data integrity. For example, it may stop us from accidentally entering a task title right into a field for storing the existing day. SQL Server will avoid information from getting into tables in which the data does not adhere towards the rules that we have established for each column.

  3. Save the table by selecting File > Save Table_1 or by right-clicking on the Table's tab and selecting Save Table_1 from the contextual menu:

  4. When prompted, name your table (we'll call it Tasks):

Your New Table

Now that you've created a new table, it will appear under your database in the Tables section. If you don't see it immediately, try refreshing the Object Explorer. You can do this by right-clicking on Tables and clicking Refresh from the contextual menu:

Here's a screenshot of the table with the Columns folder expanded:

SQL 2014 Hosting Tutorial :: How to Create Histogram Chart in SQL

clock September 15, 2014 08:42 by author Ben

SQL Server 2014 will be an amazing release regarding all the various performance enhancements that are part of this new release. And know I wanna tell you about how to create Histogram Chart in SQL.

In this tips we will create a histogram chart in SQL 2014 to show how to aggregate data will very little effort.

Step 1
Let's create a sample table called Employee that has columns EmpID and EmpAge with the data as shown in the below screenshot.

Step 2
Create a new SSRS report and configure it to use the data from the table we just created. Add a bar chart to the report and configure it as shown in the below screenshot.

Step 3
Execute/Preview this report and your report should look like the below screenshot and you will see data listed for all fifteen employees.

In reality, an employee table can contain records for hundreds to thousands of employees and therefore if the requirement is to analyze age distribution this type of report won't serve the purpose. A typical approach to solve this issue would be to retrieve the count of employees falling into a few predefined age categories and show it in a distribution chart like a pie-chart. In the next few steps we will see how a histogram chart can be a much simpler approach.

Step 4
Select the EmpAge chart series and open the properties window. In the CustomAttribute category, select "ShowColumnAs" property and set the value as "Histogram" as shown in the below screenshot.

Step 5
Execute the report and you should find results similar to the below screenshot. This is a histogram chart, but the problem with this chart is that by default the chart has selected all fifteen distributions, so you get one employee for each age category which is not very helpful.

Step 6
Let's say we want to see the data in three age groups 20 - 25, 25 - 30 and 30 - 35 and the number of employees in each age group. In the CustomAttribute category, you will find a property "HistogramSegmentIntervalWidth" and the default value is zero. Change this value to "5", so that the age group we have is divided into 3 categories as desired. Execute the report and the report should look like the below screenshot. This chart shows 4 employees in the 20 - 25 group, 5 employees in the 25 - 30 group and 6 employees in the 30 - 35 group.

Also note that the "Percentage of Total" axis is generated and calculated automatically without any programming efforts and this axis is very useful in statistical analysis. This type of axis is not possible out-of-box in charts like pie-charts and hence charts such as histogram charts are preferred.

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SQL Server Hosting with :: How to Backup and Restore Your Database with PowerShell Commands

clock July 10, 2014 09:20 by author Ben

PowerShell is Microsoft’s new command-line shell and scripting language that promises to simplify automation and integration across different Microsoft applications and components. Database professionals can leverage PowerShell by utilizing its numerous built-in cmdlets, or using any of the readily available .NET classes, to automate database tasks, simplify integration, or just discover new ways to accomplish the job at hand.

Windows PowerShell commands can be a valuable addition to your SQL Server management tools. PowerShell is going to replace SQL Server Management Studio (SSMS) anytime soon, it can be used for a wide range of scripted management tasks. PowerShell can run T-SQL commands and also work with objects outside of the SQL Server database. You can use the SQL Server PowerShell Provider to navigate and manage SQL Server database objects, and PowerShell scripts can be run by SQL Agent.

Here is a working Windows PowerShell script to perform a FULL database backup against the Northwind database, storing the backup file in your file system.

[System.Reflection.Assembly]::LoadWithPartialName('Microsoft.SqlServer.SMO') | out-null
$s = New-Object ('Microsoft.SqlServer.Management.Smo.Server') "LOCALHOST\SQL2005_1"

#Create a Backup object instance with the Microsoft.SqlServer.Management.Smo.Backup namespace
$dbBackup = new-object ("Microsoft.SqlServer.Management.Smo.Backup")

#Set the Database property to Northwind
$dbBackup.Database = "Northwind"

#Add the backup file to the Devices collection and specify File as the backup type
$dbBackup.Devices.AddDevice("D:\PSScripts\backups\NWind_FULL.bak", "File")

#Specify the Action property to generate a FULL backup

#Call the SqlBackup method to generate the backup

Since you won't be performing backups of just a single database, it would be better if we loop the entire script in a For-Each cmdlet iterating thru the Databases collection of the Server object.

[System.Reflection.Assembly]::LoadWithPartialName("Microsoft.SqlServer.SMO") | out-null
$s = new-object ("Microsoft.SqlServer.Management.Smo.Server") $instance

$bkdir = "D:\PSScripts\backups" #We define the folder path as a variable
$dbs = $s.Databases
foreach ($db in $dbs)
     if($db.Name -ne "tempdb") #We don't want to backup the tempdb database
     $dbname = $db.Name
     $dt = get-date -format yyyyMMddHHmm #We use this to create a file name based on the timestamp
     $dbBackup = new-object ("Microsoft.SqlServer.Management.Smo.Backup")
     $dbBackup.Action = "Database"
     $dbBackup.Database = $dbname
     $dbBackup.Devices.AddDevice($bkdir + "\" + $dbname + "_db_" + $dt + ".bak", "File")

There are a lot of different reasons why we need to restore databases, so there are a lot more options with restores than there are with backups. The easiest way to demonstrate a restore is to simply restore a database from a full backup, setting the option to overwrite the existing database.

Restore-SqlDatabase -ServerInstance TESTSQL -Database Northwind`
-BackupFile "E:\Backup\Northwind_db_20130420153024.bak" -ReplaceDatabase

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