Sql server optimizing update

DBAs must ensure the production server continuously runs smoothly and serves the customers without any delay. To achieve this, separate databases should be maintained for different environments like production, development, testing, and analytics. Database performance largely depends on the currently deployed version of the database server. Installing the latest version of the database server can significantly impact the overall performance of the database. An ideal SQL Server monitoring tool will not only generate timely notifications but also provide deep insight into the root cause of issues and suggest solutions to troubleshoot them quickly.

Many commercial solutions are available in the market for SQL performance optimization. We recommend trying out these solutions as they offer a host of features to help simplify your work by automating routine tasks. The tool provides the flexibility to customize the dashboard for better visibility of applications, servers, storage, and infrastructure health.

DPA database management solutions can easily be integrated with other SolarWinds tools. A dedicated dashboard gives a quick overview of actionable metrics, helping you get to the root cause of problems. The tool helps in identifying problematic SQL queries and bottlenecks. So that we can easily see how many times the scalar-valued function is invoked. As the last step, we click the Aggregation menu item and select the SUM aggregate type for the duration column.

So we can analyze the total time it takes to invoke the scalar-valued function for this SQL update statement. Another problem with this query is that the query optimizer cannot use a parallel plan due to scalar-valued function usage. When we look at the estimated cost of the query is higher than the server Cost threshold for Parallesim. However, the optimizer can not generate a parallel plan.

As we analyzed, this SQL update statement performance problem is related to using scalar-valued function usage. How we can get rid of this function:. Use variable: We can assign the function result into a variable and then we can update the table to use this variable. Through this feature, the scalar function is converted into an expression or subquery and then adopted into the query automatically. The above query takes seconds to execute. When updating in batches, even if the update fails or it needs to be stopped, only rows from the current batch are rolled back.

Triggers with cursors can extremely slow down the performance of a delete query. Disabling After delete triggers will considerably increase the query performance. An update statement is a fully logged operation and thus it will certainly take considerable amount of time if millions of rows are to be updated. The fastest way to speed up the update query is to replace it with a bulk-insert operation.

It is a minimally logged operation in simple and Bulk-logged recovery model. This can be done easily by doing a bulk-insert in a new table and then rename the table to original one. The required indexes and constraint can be created on a new table as required. The code below shows how the update can be converted to a bulk-insert operation.

It takes 4 seconds to execute. May 4, May 4, April 27, April 27, April 22, April 22, This way you do not have to backup the defination. The execution plan can also show you missing indexes that could improve performance and thus optimize the SQL query. It would look like this:. In real life, the actual performance improvement might be significantly less. Quite often, it suggests indexes that can only slightly improve query performance and only if used with very specific parameters.

Keep in mind that extra indexes lead to slower data insertion and editing. Once you detect what parts need performance improvements, you can move to the query optimization process. In the next section, we explore how to optimize a query using an execution plan in SQL Server. Creating more indexes in a table can speed up the reading process, but it will slow down the writing of data to a table. Also, you have to pay attention to the order of columns when creating a composite index an index that covers more than one column.

But how to determine this order? To do this, you can use index selectivity. This coefficient shows how many records, as compared to the general number of records, are selected with a condition that uses an indexed column. The fewer records we get, the faster the query will be processed. Therefore, primary keys and unique fields have the best index selectivity coefficients. This is why the clustered index is created for primary keys by default. Say you have the following query and need to understand which column in this script is the most efficient:.

First, calculate the number of records returned as a result of executing the query with each condition separately. Then, calculate the total number of lines in the Employee table:. Since the [Type] field has the least number of rows, it is the column with the best index selectivity.

As a result of this query, you can form the index [Type], [State], [expiration date].