#Sqlite count rows in response series

Instead of calling a single row insert, I sent five rows in the one statement. We had enough server resources for two parallel processes, so we just needed to add two parameters in a procedure to assign the starting row and the ending row of the staging table, and implement parallel calls of procedures in the Informatica workflow.īefore implementing parallel loading we wanted to find a simple way to speed up the INSERT performance of our procedure, something small that made a big difference – multiple rows insert. We were going to split the staging data into two parts and process each part in parallel. This first approach to the problem doubled the speed of the prototype powered by Informatica, but we were itching to make it fast, so we set up parallel loading. Get parsed data into variables into TargetTable(c1,c2.c7) explicit transaction control, each 10,000 inserts were wrapped into transaction I then created a procedure that built a loop that parsed the data and inserted it into the target table. The target table had a heap structure with seven columns without indexes or constraints. I started evolving my solution by loading data into a staging table that had two columns: row number and delimited text. By using stored procedures with a parameter, we were able to insert variable numbers of values from each row as required. The first prototype powered by Informatica had 840 fixed mappings (Informatica objects). 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).

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. Once the files were loaded, we were able to run sophisticated queries to calculate statistics and then load them into relational tables.Īfter 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. 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. A line in the file consisted of an object identifier and its payments for decades. The ETL project task was to create a payment projection for a period of 70 to 100 years. This file type was the largest in the project. In all, about 184 million rows had to be processed. 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. My task was to load data from a large comma-delimited file. Last year, I participated in an Extract, Transform, Load (ETL) project. The more data you’re dealing with, the more important it is to find the quickest way to import large quantities of data.

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. 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? I had to address some questions to make sure the experiments would produce the quickest times. 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. 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.

#Sqlite count rows in response series

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. When faced with having to import data directly into SQL Server from a client application, you have a number of choices.