I develop software that stores a lot of data in one of its database tables (SQL Server version 8, 9 or 10). Let's say, about 100,000 records are inserted into that table per day. This is about 36 million records per year. For fear that I would lose on performance, I decided to create a new table everyday (a table with current date in its name) to lower the number of records per table.
Could you please tell me, whether it was a good idea? Is there a record limit for SQL server tables? Or do you know how many records (more or less) can be stored in a table before performance is lowered significantly?
Row Size Limits. The maximum row size for a given table is determined by several factors: The internal representation of a MySQL table has a maximum row size limit of 65,535 bytes, even if the storage engine is capable of supporting larger rows.
1 byte is used to store the slot number, a single page can have at most 255 slots/rows. The maximum number of rows in a table or fragment is 4,278,189,825. These are all theoretical limits.
Millions of rows is not a problem, this is what SQL databases are designed to handle, if you have a well designed schema and good indexes.
SQL Server does have a wide-table feature that allows a table to have up to 30,000 columns instead of 1024. SQL Server 2008 and up have a Sparse Columns feature that can optimize storage when you have many columns with NULL values in the rows of the table. You are still limited to 8060 bytes of data per row.
These are some of the Maximum Capacity Specifications for SQL Server 2008 R2
I have a three column table with just over 6 Billion rows in SQL Server 2008 R2.
We query it every day to create minute-by-minute system analysis charts for our customers. I have not noticed any database performance hits (though the fact that it grows ~1 GB every day does make managing backups a bit more involved than I would like).
Update July 2016
We made it to ~24.5 billion rows before backups became large enough for us to decide to truncate records older than two years (~700 GB stored in multiple backups, including on expensive tapes). It's worth noting that performance was not a significant motivator in this decision (i.e., it was still working great).
For anyone who finds themselves trying to delete 20 billion rows from SQL Server, I highly recommend this article. Relevant code in case the link dies (read the article for a full explanation):
ALTER DATABASE DeleteRecord SET RECOVERY SIMPLE; GO BEGIN TRY BEGIN TRANSACTION -- Bulk logged SELECT * INTO dbo.bigtable_intermediate FROM dbo.bigtable WHERE Id % 2 = 0; -- minimal logged because DDL-Operation TRUNCATE TABLE dbo.bigtable; -- Bulk logged because target table is exclusivly locked! SET IDENTITY_INSERT dbo.bigTable ON; INSERT INTO dbo.bigtable WITH (TABLOCK) (Id, c1, c2, c3) SELECT Id, c1, c2, c3 FROM dbo.bigtable_intermediate ORDER BY Id; SET IDENTITY_INSERT dbo.bigtable OFF; COMMIT END TRY BEGIN CATCH IF @@TRANCOUNT > 0 ROLLBACK END CATCH ALTER DATABASE DeleteRecord SET RECOVERY FULL; GO
Update November 2016
If you plan on storing this much data in a single table: don't. I highly recommend you consider table partitioning (either manually or with the built-in features if you're running Enterprise edition). This makes dropping old data as easy as truncating a table once a (week/month/etc.). If you don't have Enterprise (which we don't), you can simply write a script which runs once a month, drops tables older than 2 years, creates next month's table, and regenerates a dynamic view that joins all of the partition tables together for easy querying. Obviously "once a month" and "older than 2 years" should be defined by you based on what makes sense for your use-case. Deleting directly from a table with tens of billions of rows of data will a) take a HUGE amount of time and b) fill up the transaction log hundreds or thousands of times over.
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