Foliotek Developer Blog

Checking Integer Identity Columns in SQL Server for Overflow Risk

A very common practice is to use a column of type integer for the primary key on a table with auto increment. This works great in most cases. However, there is a limitation to this that should be kept in mind. The largest value that an integer column can hold is 2,147,483,647 (2.1 billion). So, if you have a database that will contain the names of everyone living in Wyoming and Montana, you’ll be fine. However, if your database will contain the names of everyone living in China and India, you will definitely have an overflow issue.

So, how can you tell what your risk level is? You could go through each table and select the max ID field, but that would take too long unless your database is very small. Yes, I did start doing it this way myself, but quickly realized this was not going to be very fun. So, I came up with this query that provides the Table Name, the Last Value used in the identity column, the Data Type of the column, and the percentage of valid integers that have been used (for the integer data type).

Running this on my database, I was able to see that we do have one table that has an identity column that has reached 50 million. This is only about 2.3% of the way to the largest integer, so we have some time to deal with it. Still, it is nice to know that we are safe for a while.

 SELECT AS [Table Name],  
 last_value AS [Last Value],  
 END AS DataType,  
 CAST(cast(last_value as int) / 2147483647.0 * 100.0 AS DECIMAL(5,2))  
 AS [Percentage of ID's Used]  
 FROM sys.identity_columns  
 INNER JOIN sys.tables  
 ON sys.identity_columns.object_id = sys.tables.object_id  
 ORDER BY last_value DESC  

For more tips on managing a SQL Server Database, see Managing a SQL Server Database: Tips for Beginners

Index Recommendations from the Database Engine Tuning Adviser should not be accepted blindly

The Problem

I ran a SQL Profiler session on one of our SQL databases toda to catch any expensive queries and see if they could be optimized. One query that showed up was one that selected the top 30 rows from one table where a certain bit column was set to true, sorted by oldest first. The table being queried had close to 100,000 rows in it. Each query took about 6,000 reads.

The query looked something like this:

 SELECT TOP (30) *  
 FROM TheTable  
 WHERE BitColumn = 1  
 ORDER BY DateColumn  

The Suggested Solution

My inclination was that all I needed to do was add a very small index on the column of type BIT being used in the WHERE clause. However, I went ahead and ran the query through the Database Engine Tuning Adviser, just to see what it came up with.

The index recommendation suggested an index on three columns, one of which was, in fact, the one in the WHERE clause. The Date column was also included along with the table’s Primary ID. The estimated improvement was 99%, which was great and what I had expected.

However, the suggested index also had every other table column in its INCLUDE statement. The result was that the new index was projected to take up 68.5 MB of space. That’s quite a bit of space for an index on a bit column.

The Employed Solution

Consequently, I manually went in to SQL Server Management Studio and just added a simple index on the column used in the WHERE clause. Then I checked the size of the new index and found that it was only 1.75 MB–quite significantly less than 68.5 MB.

The query now requires only 14 reads, compared with the nearly 6,000 it used before, and I haven’t added 68 MB of index storage to the database.

Final Remark

So, there are certainly times where the recommendations from the Database Tuning Adviser would probably be fine. Each case is different, of course. In this case, adding 68 MB of index storage just was not worth it. Adding a simple index manually gave the optimization we needed without really impacting the size of the database. Of course, 68 MB is not really a lot, but adding that 10 or 20 or 50 times when it is not necessary would certainly begin to unnecessarily impact it.

So, the Database Tuning Adviser is definitely helpful, but should probably be used more as a guide rather than as something not to be questioned.

Identifying Unused Indexes in a SQL Server Database

One of our databases began timing out during our weekly index rebuild job, so I began to look into what indexes were getting rebuilt. After looking at the Index Usage Report, I noticed that some of them were not even being used. Some of them were very large indexes as well.

Obviously, an index that is never used doesn’t really need to be rebuilt to improve performance, so I removed a few of them. Then I began to look more closely at how I could identify other indexes that were not being used, aside from looking through every index on the SQL Report.

Unused Indexes with no reads in the Index Usage Stats Table

From research I did online, I saw that the system table sys.dmdbindexusagestats contains usage statistics for indexes. There are two key columns on this table that are of particular interest, namely userscans **and **userseeks. If both of these values are zero, then the system probably isn’t using the index.

It is probably a good idea to at least look at a few other columns as well, including userlookups, systemseeks, systemscans, and systemlookups.

I created a query that identifies unused indexes based on this criteria and outputs them in a pretty nice format. I was unable to find any other queries online that output everything in as nice of a format as this, so I hope you find this helpful.

 DECLARE @MinimumPageCount int  
 SET @MinimumPageCount = 500

SELECT AS [Database],  
 object_name(Indexes.object_id) AS [Table], AS [Index],  
 PhysicalStats.page_count as [Page_Count],  
 CONVERT(decimal(18,2), PhysicalStats.page_count * 8 / 1024.0) AS [Total Size (MB)],  
 CONVERT(decimal(18,2), PhysicalStats.avg_fragmentation_in_percent) AS [Frag %],  
 ParititionStats.row_count AS [Row Count],  
 CONVERT(decimal(18,2), (PhysicalStats.page_count * 8.0 * 1024)  
 / ParititionStats.row_count) AS [Index Size/Row (Bytes)]  
 FROM sys.dm_db_index_usage_stats UsageStats  
 INNER JOIN sys.indexes Indexes  
 ON Indexes.index_id = UsageStats.index_id  
 AND Indexes.object_id = UsageStats.object_id  
 INNER JOIN SYS.databases Databases  
 ON Databases.database_id = UsageStats.database_id  
 INNER JOIN sys.dm_db_index_physical_stats (DB_ID(),NULL,NULL,NULL,NULL)  
 AS PhysicalStats  
 ON PhysicalStats.index_id = UsageStats.Index_id  
 and PhysicalStats.object_id = UsageStats.object_id  
 INNER JOIN SYS.dm_db_partition_stats ParititionStats  
 ON ParititionStats.index_id = UsageStats.index_id  
 and ParititionStats.object_id = UsageStats.object_id  
 WHERE UsageStats.user_scans = 0  
 AND UsageStats.user_seeks = 0  
 -- ignore indexes with less than a certain number of pages of memory  
 AND PhysicalStats.page_count > @MinimumPageCount  
 -- Exclude primary keys, which should not be removed  
 AND Indexes.type_desc != ‘CLUSTERED’  
 ORDER BY [Page_Count] DESC

This query includes the following helpful information on the unused indexes:

  • Table Name
  • Index Name
  • Page Count
  • Total Index Size (MB)
  • Fragmentation (%)
  • Row Count
  • Index Size/Row (Bytes)

Unused Indexes with no entry in the Index Usage Stats Table

For part of my research on other sites, I noticed that there are some indexes that somehow never get listed in the dmdbindexusagestats table. So, running this first query is not really enough. In fact, the SQL Index Usage Report doesn’t list all the indexes either. So, there is another query that I created to identify these types of indexes. I joined on several tables again to make the output more useful.

 DECLARE @dbid INT  
 SELECT @dbid = DB_ID(DB_NAME())

SELECT Databases.Name AS [Database],  
 Objects.NAME AS [Table],  
 Indexes.NAME AS [Index],  
 PhysicalStats.page_count as [Page Count],  
 CONVERT(decimal(18,2), PhysicalStats.page_count * 8 / 1024.0) AS [Total Index Size (MB)],  
 CONVERT(decimal(18,2), PhysicalStats.avg_fragmentation_in_percent) AS [Fragmentation (%)]  
 LEFT JOIN sys.dm_db_index_physical_stats(@dbid, null, null, null, null) PhysicalStats  
 ON PhysicalStats.object_id = Indexes.object_id  
 AND PhysicalStats.index_id = indexes.index_id  
 INNER JOIN sys.databases Databases  
 ON Databases.database_id = PhysicalStats.database_id  
 WHERE Objects.type = ‘U’ — Is User Table  
 AND Indexes.is_primary_key = 0  
 AND Indexes.INDEX_ID NOT IN (  
 WHERE UsageStats.OBJECT_ID = Indexes.OBJECT_ID  
 AND Indexes.INDEX_ID = UsageStats.INDEX_ID  
 AND DATABASE_ID = @dbid)  
 ORDER BY PhysicalStats.page_count DESC,  
 Indexes.NAME ASC

When to Remove an Index

Just because an index is not being used does not necessarily mean it should be removed. The queries above ignore the clustered indexes on the tables, since there are very few times, if any, when a table should not have any index at all. There may be other reasons to keep an index around as well. Those decisions are best made by those that have a knowledge of how the data is used and will be used in the future.


I was able to use these two queries to get rid of a lot of dead weight in several of our databases. In one of them, in particular, I was able to remove almost 1 GB of space. That’s 1 GB less for every backup, so it adds up over time.

One last note… After removing indexes, it’s probably a good idea to run SQL Profiler, just to be safe, on the database to catch any queries that may be expensive and somehow used an index that should not have been deleted. See my other post on SQL Optimization – A Beginner’s Guide

For more tips on managing a SQL Server Database, see Managing a SQL Server Database: Tips for Beginners

SQL Server Query Optimization - CPU Impact of Datatype Mismatching

I’ve been identifying “expensive” queries in our database lately and came across one that was particularly interesting. One query showed 1400 CPU, which is certainly high. Why in the world would a query require that much processing. It was a pretty simple query as well:

SELECT [t0].[ItemID], [t0].[UserID], [t0].[ItemTypeID], etc.  
 FROM [dbo].[Items] AS [t0]  
 WHERE (CONVERT(NVarChar, [t0].[ItemID])) IN (@p0, @p1, @p2, @p3)  
 ORDER BY [t0].[Name]  

This just takes a list of Item ID’s and returns the Items. The “Order By” clause doesn’t take much. Notice the CONVERT statement in the WHERE clause. The CONVERT statement does not convert the input data to that of the database column, but rather converts the datatype of the database column to match the datatype of the input variables.

The problem was a datatype mismatch. Item ID is an integer, but, through carelessness, I had passed in a list of strings (in, C#). This query (generated by Linq) appears to have converted every Item ID in the index to an nvarchar–all to retrieve, in this case, four rows from the Item table. This was a very small thing to correct–simply convert the list of strings to a list of integers in C# prior to using that in a Linq query. Failing to match types in the database had huge consequences. A little carelessness resulted in a very substantial CPU load.

After the simple correction, this query didn’t even show up in the SQL Profiler results, even when I reduced the threshold substantially. So, 1400+ CPU to 0 CPU.

For more, information on how I identified this query, see A Beginner’s Guide to SQL Server Query Optimization.

SQL Server Database Optimization - a Beginner's Guide

Over the past month, I've been analyzing our SQL Server Database activity to identify and resolve any significantly inefficient queries. Through the process I've learned a few things that may help other people who are doing the same work. Let me know what you think.

Optimization Strategy

  • Focus on Reads and CPU.**

If a query has a high number of reads, then it is likely that either a table is not indexed properly or a query is written in such a way that appropriate indexes cannot be used. If the CPU is particularly high, a variety of factors could be at issue.

  • Identify BOTH expensive individual queries AND expensive aggregate queries.

When I started, I just focused on identifying the queries with the highest read counts and CPU. That's certainly helpful if very expensive queries can be significantly optimized. However, an expensive query that is rarely runs is probably not as problematic as a moderately expensive query that runs very often.

For instance, one query may run about once an hour and have 100,000 reads and 500 CPU. Yet, another query may only have 25,000 reads and 125 CPU, but runs 100 times an hour. The cumulative hourly expense would therefore be 2,500,000 reads and 12,500 CPU; a much higher expense than the query that only runs once an hour.

Optimization Process

  • Create a Baseline for your database

As you achieve performance improvements, you will want to compare them with how the system performed prior to the improvements being made. Having a baseline will allow you to quantify the improvements made in real numbers. Seeing significant improvements is very satisfying and well worth the time.

- Create a baseline copy of the database ? This will allow you to compare how new indexes impact performance.
- Store a run of SQL Profiler as a baseline ? Profiler allows you to write results to a database table. Name it ?Baseline? or something like that. I write to a test database to isolate that work from development. It may be good to do two runs?one that perhaps catches all queries with more than 1,000 reads and one for all queries with more than 100 CPU for a time period of 30 minutes. (These numbers certainly depend on the size and structure of your particular database. You may want to do some preliminary runs to figure out the best numbers for your database.)

1. From SQL Server Management Studio => Tools => SQL Server Profiler
2. Select database
3. Click "Save to Table" on General tab. Select development database. Table name = "Baseline". (or BaselineReads5000 and BaselineCPU100)
4. Events Selection Tab: TSQL ? SQL BatchCompleted and Stored Procedures ? RPC:Completed (if you use stored procedures)
5. Column Filters: - Run 1: Reads ? Greater than or Equal ? 5000 //Exclude rows that do not contain values
- Run 2: CPU ? Greater than or Equal ? 100 //Exclude rows that do not contain values

- Identify Expensive Queries

Query your baseline run of SQL Profiler. The following query could be of great help. It aggregates the total reads and total CPU, allowing you to sort by those columns. This allows you to see which queries are the most expensive from an aggregate perspective. Some queries that are rarely run still may show up in such sorts. This really emphasizes the importance of tackling such queries.

The key grouping is on ?SUBSTRING(Textdata, 1, 150)?. Each query that is run typically has variables appended to the end of the query. Comparing the first part of the query can allow identical queries with different parameters to be grouped together to provide aggregate results. You may need to play with the number of characters from time to time.

I also group by DatabaseName, since multiple databases may be stored on the same server.

 SELECT DatabaseName,  
 COUNT(*) AS [Query Count],  
 SUM(CPU) AS [CPU Query Total],  
 AVG(CPU) AS [CPU Per Query],  
 SUM(Reads) AS [Reads Query Total],  
 AVG(Reads) AS [Reads Per Query],  
 SUBSTRING(Textdata, 1, 150) AS Query  
 FROM My_Database  
 GROUP BY DatabaseName, SUBSTRING(Textdata, 1, 150)  
 ORDER BY [Reads, Query, Total] DESC  

This is another query that is a little more complex, but it adds some interesting information. Specifically, it gets the totals across all queries (that met the read or CPU threshold) for query counts, Reads, and CPU, then computes the percentages for each query.

 WITH [Total All Queries] AS  
 SELECT COUNT(*) AS [Query Count],  
 SUM(CPU) AS [Total CPU],  
 SUM(Reads) AS [Total Reads]  
 FROM My_Database  

SELECT DatabaseName,  
 COUNT(*) AS [Query Count],  
 CAST(COUNT(*) * 100.0 / (SELECT TOP 1 [Query Count] FROM [Total All Queries]) AS DECIMAL(4,2)) AS [Query Count Percentage],  
 SUM(CPU) AS [CPU Query Total],  
 AVG(CPU) AS [CPU Per Query],  
 CAST(SUM(CPU) * 100.0 / (SELECT TOP 1 [Total CPU] FROM [Total All Queries]) AS DECIMAL(4,2)) AS [CPU Query Percentage],  
 SUM(Reads) AS [Reads Query Total],  
 AVG(Reads) AS [Reads Per Query],  
 CAST(SUM(Reads) * 100.0 / (SELECT TOP 1 [Total Reads] FROM [Total All Queries]) AS DECIMAL(4,2)) AS [Reads Query Percentage],  
 SUBSTRING(Textdata, 1, 150) AS Query  
 FROM My_Database  
 GROUP BY DatabaseName, SUBSTRING(Textdata, 1, 150)  
 ORDER BY [Reads Query Total] DESC  

After you have run this and identified a query for optimization, you?ll need to query the database again so you can get the entire query at issue. Identify something unique in the query and use that as a comparison. (In my experience, I have had to compare on individual terms.)

 SELECT CPU, Reads, DatabaseName, TextData AS Query,*  
 FROM Baseline_Reads_5000  
 WHERE Baseline_Reads_5000.Textdata like ‘%part of query%’  
 ORDER BY Baseline_Reads_5000.CPU DESC  
  • Analyze Expensive Queries in Database Engine Tuning Adviser

You can copy and paste queries from your Baseline database into a new query window, then select the query, right mouse button click, and select "Analyze Query in Database Engine Tuning Adviser". This will show any recommendations for new indexes or statistics that would help optimize the query. It also gives you a projected percentage of optimization that can be achieved.

If it recommends a new index and a few statistics that are pretty small that is projected to yield a high improvement, then you're probably in good shape. If it recommends very large indexes to achieve small results or if it doesn't offer any recommendations, then you have more work to do.

Sometimes a particular column is indexed on one table, but your query is looking for it on a different table. Changing the query to use an index that already exists on the column in a different table can give you substantial improvements without needing to create any new indexes.

Sometimes the query is just not well written. In those cases, you may need to break up the query or think of a different method to obtain the same results.

  • Profile the Results of the Optimizations

Sometimes you can see a very significant difference by just running a query in SQL Server Management Studio before and after, especially for very expensive queries. With moderately expensive queries, though, it may be helpful to obtain more precise numbers.

It is possible to run SQL Profiler and restrict the results to your machine. Alternatively, you can isolate a test database and run the query there. If you're using Linq, then you will need to get the new generated SQL to compare or just run it in a development environment that you can isolate.

For this run of SQL Profiler, you don't have to write your results to a database table, since the rows should be minimal enough to view onscreen. It can still be nice to have for later reference, though, so you can name the table something like "Optimize Registrations".

Still, you can see the difference your changes make right in the Profiler window. Sometimes the first run will show up, but the changes are so significant that the second run (with the new index or whatever) will be below the threshold you set. So, for these runs, it's good to set a lower threshold of reads and/or CPU.

Additionally, watching the impact of a specific event on the database can identify redundant queries–queries that are run multiple times to get the same data. Storing the query results the first time can provide very significant decreases in database usage. Also, every access of the database requires some overhead, so accessing the database a hundred times to get a small piece of information may be more expensive to accessing it once to get a hundred small pieces of information.

Final Notes

  • Read and CPU Thresholds

After you've optimized your most expensive queries, you may need to lower these to get more results. However, at some point the gains you achieve will not be worth the amount of effort it takes. At that point, set a reminder to come back in a few months and run another profile to see if any new queries have been introduced that are not properly optimized.

  • Complex Queries

Not all expensive queries can be resolved by adding indexes. Sometimes a query is just too complex and needs to be broken up into smaller queries. For instance, a long OR statement like ?(@value1 = -1 AND IndexedColumn2 = @value2) OR (@value1 > -1 AND IndexedColumn1 = @value1) can render the indexing of columns of no use.

  • Random Activity Profiling and Planned Activity Profiling

Random Activity Planning: When you first start out, it?s probably good to just profile the system as people are using it. This will measure random activity of your users. It may not be best to start profiling during your highest volume time, since you don?t want to overload the system, so pick an average load time at first. Eventually, though, it would be good to profile high activity times.

Planned Activity Profiling: During a less active time or in a development environment, it is a good idea to do some profiling on specific actions which may not randomly be performed by users in a particular window of profiling. They may, however, occur occasionally during your peak hours, and if they involve very expensive queries, they can really slow down everyone?s experience. So, profiling a period of time in which rare but possibly expensive queries are explicitly run can be very critical.

Good luck!

P.S. You may also find this post helpful that describes Highly Inefficient Linq Queries that Break Database Indexing

For more tips on managing a SQL Server Database, see Managing a SQL Server Database: Tips for Beginners