In my last article “Converting Comma Separated Value to Rows and Vice Versa in SQL Server”, I talked about how you can convert comma separated (or separated with some other character) values in a single column into rows and vice versa. In this article, I demonstrate how you can convert rows values into columns values (PIVOT) and columns values into rows values (UNPIVOT) in SQL Server. For more information click here.
Friday, October 16, 2015
Often while reporting you will encounter a situation where you will have comma separated (or separated with some other character) values in a single column but you want to report them in rows whereas in some other cases you might have values in multiple rows and want them to be a single value separated by comma or some other character. In this article, I am going to demonstrate how you can write queries in SQL Server to handle these scenarios quickly. For more information, click here.
We often need to combine two or more string values to use this combined string value in reporting. Although there was a way to do that in earlier versions of SQL Server, starting with SQL Server 2012 we have CONCAT function for this specific situation. This T-SQL function also takes care of data type conversion and handling NULLs appropriately. Apart from that, there has been a STUFF T-SQL function in SQL Server, which you can use to insert\replace a string value into another string value. The difference lies in the fact that CONCAT allows you to append a string value at the end of another string value whereas STUFF allows you insert or replace a string value into or in between another string value. I am going to demonstrate these functions and their real life usages in this article. For more information, click here.
There are different techniques to optimize the performance of SQL Server queries, like keeping required and updated statistics, creating required indexes, partitioning tables, etc., but wouldn’t it be great if we had some recommendations before we started planning or optimizing queries so that we didn’t have to start from the scratch every time and in every scenario? This is where you can use the Database Engine Tuning Advisor utility to get recommendations based on your workload. I will be talking about Database Engine Tuning Advisor, how it works and its different interfaces, in this article series. For more information click here for Part 1 and here for Part 2.
Wednesday, October 14, 2015
Statistics refers to the statistical information about the distribution of values in one or more columns of a table or an index. The SQL Server Query Optimizer uses this statistical information to estimate the cardinality, or number of rows, in the query result to be returned, which enables the SQL Server Query Optimizer to create a high-quality query execution plan. For example, based on these statistical information SQL Server Query Optimizer might decide whether to use the index seek operator or a more resource-intensive index scan operator in order to provide optimal query performance. In this article series, I am going to talk about statistics in detail. For information click here for Part 1 and here for Part 2.
Have you ever been in a situation where you needed to write a query that needed to do comparisons or access data from the subsequent rows along with the data from the current row? This article discusses different ways to write these types of queries and more specifically examines LEAD and LAG analytics functions, which were introduced with SQL Server 2012, and helps you understand how leveraging these functions can aid you in such situations. For more information click here.
There are several high availability solutions that can be used with SQL Server, like AlwaysOn, Fail-over clustering, or Database mirroring. While these high availability solutions ensure maximum uptime for your databases, you need to setup backup and restore strategies to recover the data or minimize the risk of data loss in case a failure happens. In this article series, I am going to discuss backup and restore strategies in SQL Server in detail. For more information click here for Part 1 and here for Part 2.
Have you ever wondered, especially in the case of a data warehousing scenario, why the transaction log file grows bigger and bigger and sometimes even much bigger than your actual database's data files? What caused it to happen? How do you control it? How does the recovery model of a database control the growing size of the transaction log? These are some of the questions I am going to explain to you in this article. For information click here.
In my most recent articles, I’ve talked about encryption in detail and demonstrated its usage at the entire database level with Transparent Data Encryption and at the column level with granular\cell level encryption. In this article, I am going to discuss hashing in SQL Server and how it is different from encryption. For more information click here.