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Tuning Basics – Updating an Update

You are out doing your scheduled sweeps of the environment and you notice a huge I/O consumer is one of your update procedures. Your initial thought may be to panic because how can you test data that is changing? You have several methods to tuning queries that adjust data that allow data to revert between tests.

NOTE: We always recommend tuning queries in non-production until we have our final tested solution to implement in production.

Tuning with Rollback Transaction

One of the more basic methods for executing queries without changing data is to wrap the statements are you running in a BEGIN TRANSACTION and ROLLBACK TRANSACTION. This allows everything to execute and give you the performance statistics you are looking for, while leaving the data intact to allow for repeat testing.

Using rollback to revert a change for tuning
Using rollback to revert a change made to data

Tuning using Database Backups

You have a couple options with your standard SQL Server backups to assist in reverting changes made to the testing environment and the same concepts are applied to your basic DR strategy. If the test is short, you can take a full backup before making a change and restore that backup to restart a test.

Using a full backup to revert a change for tuning
Using a full backup to revert a data change

If you are making a series of changes over a period of time and don’t want to completely restart your test, you can utilize point in time recovery. Once your database is in full recovery, you can start with a full backup and take log backups at set intervals or before different pieces of your change. This allows you to restore to a specific point in your test.

Point in time recovery with full and log backups for tuning
Using a full backup with transaction log backups for Point in Time Recovery

Tuning Database Snapshot

Database Snapshots are a great way to quickly revert large databases to an initial state. Database Snapshots keep a record of database pages as they change and the copy of the page so those extents can be reverted instead of restoring the whole database. Therefore, they require the associated source database as it is not a full copy of the database. Snapshots contain the original version of files of the source database and a pointer file that shows the changes since the snapshot was taken, called a sparse file. The source database must be available for snapshots to be usable. This makes them not an ideal DR solution, but great for analysis in auditing and in tuning situations.

Using snapshots to revert a data change for tuning
Using 2016 Database Snapshots to revert a change


There are many ways to quickly get back to your original state to allow for quick repetitive testing for queries that change data. A huge benefit of the methods I listed today allow you to also document where transactions and backups occur so you can open a .sql file and have all the needed steps in one document. Do you like other ways to revert changes? Maybe temporal tables? I’d be happy to cover that in a post in the near future. Tell me all about your methods in the comments below!


Clustered Index vs Heap

What is a heap?

Heap was actually named pretty accurately. It is a “pile” of unsorted/unorganized data. A heap will exist on any table that lacks a clustered index. Heaps can exist even if you have a non clustered indexes. The main advantage to a heap is the speed at which you can insert data into your tables, as it doesn’t need to follow a logical order like inserting identity values into the last page of a table.

What is a clustered index?

A clustered index is used as the main method of logically sorting data in a table. It can be done on a key value defined in the table, but it doesn’t require an actual primary key to exist. Clustered indexes should be created on the most common column(s) used in the predicates of your most frequently executed statements. All data will be sorted to match the index and you can only have 1 clustered index per table. The main advantage to having a clustered index is increased speed on data reads.

How should I identify when a clustered index should be used instead of a heap?

As noted above, the main advantage of a clustered index is an increase in the speed of your reads. To get an idea of where you might need to increase read speed, you should first identify all of your tables that do not have a clustered index. I found it very helpful to also place lower limits on the number of rows in a table and the number of reads against that table (by checking dynamic system views). The below query is a great start into looking at those things.

DECLARE @type_of_index INT = 0 /*0 = heap, 1 = clustered, 2 = non clustered*/ 
	, @min_num_of_reads INT = 10
	, @min_num_of_rows INT = 100

   , AS SchemaName
   , AS Index_Name
   ,IX.type_desc AS Index_Type
   ,PS.row_count AS TableRowCount
   ,SUM(PS.[used_page_count]) * 8 IndexSizeKB
   ,IXUS.user_seeks AS NumOfSeeks
   ,IXUS.user_scans AS NumOfScans
   ,IXUS.user_seeks + user_scans AS NumOfReads
   ,IXUS.user_lookuPS AS NumOfLookuPS
   ,IXUS.user_updates AS NumOfUpdates
   ,IXUS.last_user_seek AS LastSeek
   ,IXUS.last_user_scan AS LastScan
   ,IXUS.last_user_lookup AS LastLookup
   ,IXUS.last_user_update AS LastUpdate
FROM sys.indexes IX
	INNER JOIN sys.objects O 
		ON ix.object_id = O.object_id
	INNER JOIN sys.schemas SCH 
		ON SCH.schema_id = O.schema_id
	INNER JOIN sys.dm_db_index_usage_stats IXUS 
		ON IXUS.index_id = IX.index_id 
	INNER JOIN sys.dm_db_partition_stats PS 
		ON PS.object_id = IX.object_id  
		AND PS.index_id = IX.index_id
	AND IX.index_id = @type_of_index 
	AND PS.index_id = @type_of_index 
	AND IXUS.index_id = @type_of_index
	AND IXUS.database_id = (DB_ID())
	AND IXUS.user_scans + IXUS.user_seeks > @min_num_of_reads
	AND PS.row_count > @min_num_of_rows
GROUP BY OBJECT_NAME(IX.OBJECT_ID),, IX.type_desc, IXUS.user_seeks, IXUS.user_scans, IXUS.user_lookuPS, IXUS.user_updates, last_user_seek, IXUS.last_user_scan, IXUS.last_user_lookup, IXUS.last_user_update,, PS.row_count
ORDER BY TableRowCount DESC, NumOfReads DESC

Now that you see what tables are on the larger side that also have a lot of reads, we can dive deeper into the MSSQL system dynamic management views to see how these tables are being used in the system by looking at query stats and plans. Enter your table name in the variable and you can see the number of times a plan has been used, the text associated to that plan, and the plan itself to validate if the query is using a table scan due to the heap or if there is a non clustered index that is being used instead. The 2nd result set will give you object names if the table is being used in a SQL object like a stored procedure or function

DECLARE @TableName NVARCHAR(50) = 'ExampleTableName'

	, dm_exec_sql_text.text AS TSQL_Text
	, dm_exec_query_stats.creation_time
	, dm_exec_query_stats.execution_count
	, dm_exec_query_stats.total_worker_time AS total_cpu_time
	, dm_exec_query_stats.total_elapsed_time
	, dm_exec_query_stats.total_elapsed_time / dm_exec_query_stats.execution_count AS avg_elapsed_time
	, dm_exec_query_stats.total_logical_reads
	, dm_exec_query_stats.total_logical_reads / dm_exec_query_stats.execution_count AS avg_logical_reads
	, dm_exec_query_stats.total_physical_reads
	, dm_exec_query_plan.query_plan
FROM sys.dm_exec_query_stats 
	CROSS APPLY sys.dm_exec_sql_text(dm_exec_query_stats.plan_handle)
	CROSS APPLY sys.dm_exec_query_plan(dm_exec_query_stats.plan_handle)
	INNER JOIN sys.databases
		ON dm_exec_sql_text.dbid = databases.database_id
WHERE dm_exec_sql_text.text LIKE '%'+@TableName+'%'

FROM sys.sql_modules M 
	INNER JOIN sys.objects O 
		ON M.object_id = O.object_id
WHERE M.definition LIKE '%'+@TableName+'%'

After reviewing use cases and their query plans, it’s time to decide if the table in question meets the above recommendations for a clustered index or if it’s better served as a heap, and what columns to include in the index. If the table has multiple use cases but they all typically share the same column/s, create the clustered index on that table with those column/s.

Now don’t go throwing clustered indexes around like Oprah to the studio audience

The clustered index is helpful in a lot of situations, but there are some instances where it is not necessary or can actually do some harm to your performance.

The main example of this would be logging tables. These tables typically have a lot of insert operations, but not necessarily a lot of reads or even updates as they are just logging everything that is happening and are referenced infrequently. Putting an index on a table like that could cause hot latches which are caused by data waiting to be inserted into the last available page as the key is typically sequential and other data is frequently being inserted into that page. The only exception to this rule would be if your key column of the index is a GUID, as the GUID random and not sequential.

Watch out for making your clustered index on columns/data that is typically not static, meaning you would update these key values frequently. Updating key values on an index will likely cause performance issues. The clustered index is being used as a lookup point for all non clustered indexes and by updating the key values, you can cause page splits which will require index maintenance to fix your problem and return performance to where it belongs.

Another example of common mistake is making the clustered index too specific with too many columns included in the index. A clustered index is defining your default sort of all data in the table. Too many columns in an index could further slow down data operations by requiring resorting for additional use cases and it will also increase the size of all non clustered indexes on that table as well (and there is a limit to how wide a key can be for an index).

Finally, never forget the most important part of adding indexes: TEST TEST and TEST.

Post a comment below if you have any other tips or tricks when it comes to fixing your heap woes below!