Oct 2, 2012 - Some of the benefits on database performance I can see are. Using smarter SQL for field INSERT or UPDATE is really hard to achieve since. An 'in-place update' (ie by changing a columns value to a new in the original row) or as a 'not-in-place UPDATE' (DELETE followed by an INSERT). In place updates are. I think there was a link to an Aaron Bertrand post about MERGE issues. Do a web search for 'sql server merge concurrency problem' and you will get a number of relevant links. How to update table using SQL on windows visual studio. Ask Question. Up vote-1 down vote favorite. SQL update query using joins. How can I do an UPDATE statement with JOIN in SQL? Update a table using JOIN in SQL Server? How do I UPDATE from a SELECT in SQL Server? The update performance, just like insert and delete, also depends on the number of. In a development environment to verify the generated SQL statements.
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When faced with having to import data directly into SQL Server from a client application, you have a number of choices. You can use bulk copy (bcp), insert the data with a series of INSERT statements, use a parameterized prepared statement from the client, or call a stored procedure with the data passed as a parameter. From that stored procedure, you’ll probably want to insert the data into a table by using statements that insert several rows at a time. I wanted to find the best way to import data into SQL Server, so I decided to experiment with a couple of different data load methods to compare the results. I had to address some questions to make sure the experiments would produce the quickest times. What’s the best way to use statements that insert several rows at a time, and how many rows should I try to pass at a time?
For performance, should they be wrapped in explicit transactions or should I use auto-commit? How do I pass the parameters to the stored procedure? Which is the best, and fastest, approach?
In this article I’ll describe how I experimented and adjusted my methods to find the best way to insert a million of rows of data into SQL Server. The background The more data you’re dealing with, the more important it is to find the quickest way to import large quantities of data. Last year, I participated in an Extract, Transform, Load (ETL) project. My task was to load data from a large comma-delimited file. This file had 220,000 rows, each of which had 840 delimited values and it had to be turned into 70 million rows for a target table. In all, about 184 million rows had to be processed. This file type was the largest in the project.
![Insert vs update sql performance studio club Insert vs update sql performance studio club](https://i.ytimg.com/vi/VpJyzd5BY3Y/maxresdefault.jpg)
Sql Delete Insert Vs Update
The ETL project task was to create a payment projection for a period of 70 to 100 years. A line in the file consisted of an object identifier and its payments for decades.
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The sizes of other file types were 30 times smaller, and we successfully used Informatica PowerCenter 9 to parse, validate, transform, and load the files into a staging table in SQL Server. Once the files were loaded, we were able to run sophisticated queries to calculate statistics and then load them into relational tables. After developing a prototype based on Informatica, we decided to use PowerCenter 9 for workflow control and supplemented this with SQL Server stored procedures for parsing, validating and inserting the data. We wanted to reduce the time spent on the loading process (16 hours), and by using stored procedures, we could now calculate some statistics with incrementing counters in the loop. Also, we were able to implement a business rule which stated that the number of values in a file line was a variable between 840 (12 months * 70 years) and 1200 (12 months * 100 years). The first prototype powered by Informatica had 840 fixed mappings (Informatica objects).
![Insert Insert](https://i.stack.imgur.com/QyLtH.jpg)
By using stored procedures with a parameter, we were able to insert variable numbers of values from each row as required. First steps I started evolving my solution by loading data into a staging table that had two columns: row number and delimited text.