#SQL Server MAXDOP

[thedbahub]

Navigating MAXDOP in SQL Server 2022: Best Practices for Multi-Database Environments

In the realm of SQL Server management, one of the pivotal settings that database administrators must judiciously configure is the Maximum Degree of Parallelism (MAXDOP). This setting determines the number of processors that SQL Server can use for the execution of a query. Proper configuration of MAXDOP is essential, especially in environments with multiple databases and a limited number of CPU…

[sandeep2363]

How to resolve CXPACKET wait in SQL Server

CXPACKET:o Happens when a parallel query runs and some threads are slower than others.o This wait type is common in highly parallel environments. How to resolve CXPACKET wait in SQL Server Adjust the MAXDOP Setting:o The MAXDOP (Maximum Degree of Parallelism) setting controls the number ofprocessors used for parallel query execution. Reducing the MAXDOP value can helpreduce CXPACKET waits.o You…

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Best practices for configuring performance parameters for Amazon RDS for SQL Server

With Amazon RDS for SQL Server, you can quickly launch a database instance in just a few clicks and start serving your application requirements. Although Amazon RDS for SQL Server doesn’t typically require configuration changes, you may want to customize certain parameters based on your workload. This post discusses some parameters you can tune to enhance performance. For example, you can change server-level parameters available under sp_configure using a custom parameter group. You customize database-level settings using SQL Server Management Studio (SSMS) GUI or T-SQL queries. In Amazon RDS, parameters can be either static or dynamic. A static parameter change needs an instance restart to take effect. Dynamic parameter changes take effect online without any restart and therefore can be changed on the fly. In this post, we discuss the following configuration options to fine-tune performance: Maximum server memory Maximum degree of parallelism Cost threshold for parallelism Optimize for ad hoc workloads Configuring Tempdb Enabling autogrowth Updating statistics We also discuss the steps to make these configuration changes in a custom parameter group. Maximum server memory SQL Server manages memory dynamically, freeing and adding memory as needed. Starting with SQL Server 2012 SingleMultipage allocations, CLR were all combined under Any Page Allocator and the maximum memory allocated to these is controlled by max server memory. After SQL Server is started, it slowly takes the memory specified under min server memory (MB) and continues to grow until it reaches the value specified in max server memory (MB). SQL Server memory is divided into two parts: buffer pool and non-buffer pool, or Mem To Leave (MTL). The value of max server memory determines the size of the SQL Server buffer pool. A buffer pool consists of various caches such as buffer cache, procedure cache, and plan cache. Starting with SQL Server 2012, max server memory accounts for all memory allocations for all caches (such as SQLGENERAL, SQLBUFFERPOOL, SQLQUERYCOMPILE, SQLQUERYPLAN, SQLQUERYEXEC, SQLOPTIMIZER, and SQLCLR). For a complete list of memory clerks under max server memory, see sys.dm_os_memory_clerks. You can calculate the total memory SQL Server 2012 or above uses as follows: Total memory used by SQL Server = max server memory + DLLs loaded into SQL Server memory space) + (2 MB (for 64 bit) * max worker threads) The objective behind a buffer pool is to minimize the disk I/O. You use a buffer pool as the cache, and max_server_memory controls its size. The target of buffer pool is not to become so big that the entire system runs low on memory and minimize disk I/O. The non-buffer pool or MTL comprises mainly of thread stacks, third-party drivers, and DLLs. SQL Server (on 64 bit) takes 2 MB of stack memory for each thread it creates. This thread stack memory is placed outside of max server memory or buffer pool and is part of non-buffer pool. To find the total server memory, use the below query: SELECT total_physical_memory_kb / 1024 AS MemoryMb FROM sys.dm_os_sys_memory To change the maximum server memory in Amazon RDS for SQL Server, you can use a custom parameter group. In the following screenshot, I change the maximum server memory to 100 GB. The idea is to cap max server memory to a value that doesn’t cause system-wide memory pressure. However, there’s no universal formula that applies to all the environments. You can use the following guidelines as a starting point: Max server memory = Total RAM on the system – ((1 – 4 GB for the Operating System) + (MTL (includes stack size (2 MB) * max worker threads)) Note: Some of the exceptions to the above method of calculation will be t2/t3 kind of lower sized instances, be cautious when configuring max server memory on the same. For further details please refer to Server memory configuration options. After initial configuration, monitor the freeable memory over a typical workload duration to determine if you need to increase or decrease the memory allocated to SQL Server. When using SSIS, SSAS, or SSRS, you should also consider the memory usage by those components when configuring max server memory in SQL Server. You can configure the value under a custom parameter group. To check the current value, use the below query: # sp_configure 'max_server_memory' Monitoring When using the Amazon RDS Performance Insights dashboard, you can monitor the following: physAvailKb – The amount of physical memory available in KB sqlServerTotKb – The amount of memory committed to SQL Server in KB For more information, see Performance Insights is Generally Available on Amazon RDS for SQL Server. When to change the configuration You should change the configuration based on monitoring in your environment. Select the metrics to monitor on the Performance Insights dashboard, under OS metrics. Maximum degree of parallelism (MAXDOP) In an OLTP environment, with high core, hyperthreaded machines being a norm these days, you should pay special attention to max degree of parallelism. Running with the default configuration can lead to severe parallelism-related wait time, severely impair performance, and in extreme cases, bring the server down. A runaway query can lead to server-wide blocking due to parallelism-related wait times. A runaway query example here could be a query going for a parallel plan and spending a lot of time waiting on operations of parallel threads to complete. Such queries typically spend a long time waiting on CXPACKET. A maximum degree of parallelism controls the number of processors used to run a single statement that has a parallel plan for running. The default value is set to 0, which allows you to use the maximum available processors on the machine. With SQL Server 2016 and above, if more than eight physical cores per NUMA node or socket are detected at startup, soft NUMA nodes are created automatically. Starting with SQL Server 2016 (13.x), use the following guidelines when you configure the maximum degree of parallelism server value: Single NUMA node: < = 8 logical processors, keep MAXDOP <= actual number of cores Single NUMA node: > 8 logical processors, keep MAXDOP = 8 Multiple NUMA nodes: < =16 logical processors, keep MAXDOP <= actual number of cores Multiple NUMA nodes: > 16 logical processors, keep MAXDOP = 16 (SQL Server 2016 and above), keep MAXDOP = 8 (prior to SQL Server 2016) For more information, see Configure the max degree of parallelism Server Configuration Option. SQL Server estimates how costly a query is when run. If this cost exceeds the cost threshold of parallelism, SQL Server considers parallel plan for this query. The number of processors it can use is defined by the instance-level maximum degree of parallelism, which is superseded by the database-level maximum degree of parallelism, which in turn is superseded by the query hint for maximum degree of parallelism at the query level. To gather the current NUMA configuration for SQL Server 2016 and higher, run the following query: select @@SERVERNAME, SERVERPROPERTY('ComputerNamePhysicalNetBIOS'), cpu_count, /*the number of logical CPUs on the system*/ hyperthread_ratio, /*the ratio of the number of logical or physical cores that are exposed by one physical processor package*/ softnuma_configuration, /* 0 = OFF indicates hardware default, 1 = Automated soft-NUMA, 2 = Manual soft-NUMA via registry*/ softnuma_configuration_desc, /*OFF = Soft-NUMA feature is OFF, ON = SQL Server automatically determines the NUMA node sizes for Soft-NUMA, MANUAL = Manually configured soft-NUMA */ socket_count, /*number of processor sockets available on the system*/ numa_node_count /*the number of numa nodes available on the system. This column includes physical numa nodes as well as soft numa nodes*/ from sys.dm_os_sys_info You can configure the max_degree_of_parallelism value under a custom parameter group. In the following screenshot, I change the value to 4. You can check the current value using the following query: # sp_configure 'max_degree_of_parallelism' Monitoring You can use the sys.dm_os_wait_stats DMV to capture details on the most common wait types encountered in your environment. On the Performance Insights dashboard, you can slice by waitypes to get details on top wait types as shown below: If you see an increase in these metrics and parallelism-related wait types (such as CXPACKET), you might want to revisit the max degree of parallelism setting. When to change the configuration When the server has more than eight cores and you observe parallelism-related wait types, you should change this value according to best practices, monitor the wait types, and adjust further if needed. You can monitor the wait types using the methods outlined earlier in this section. Typically, for several short-lived, repetitive queries (OLTP), a lower MAXDOP setting works well because you can lose a lot of time with higher MAXDOP for synchronization of threads running subtasks. For OLAP workloads (longer and fewer queries), a higher maximum degree of parallelism can give better results because the query can use more cores to complete the work quickly. You can also set max degree of parallelism at the database level, starting at SQL Server 2014 SP2. The database-level setting overwrites the server-level configuration. Similarly, you can use a query hint specifying MAXDOP to override both the preceding settings. Cost threshold for parallelism The cost threshold for parallelism parameter determines the times at which SQL Server creates and runs parallel plans for queries. A parallel plan for a query only runs when the estimated cost of the serial plan for that query exceeds the value specified in the cost threshold for parallelism. The default value for this parameter is 5. Historically, the default value was 5 because processors had exorbitant price tags and processing power was low, therefore query processing was slower. Processors today are much faster. Comparatively smaller queries (for example, the cost of 32) don’t see much improvement with a parallel run, not to mention the overhead with coordination of a parallel run. With several queries going for a parallel plan, you may end up in a scenario with wait types like scheduler yield, threadpool, and parallelism related. You can configure the cost threshold for parallelism value under a custom parameter group. In the following screenshot, I change the value to 50 for 64 core environment. You can change this parameter using custom parameter group. To check the current value, use the below query: # sp_configure 'cost_threshold_for_parallelism' For more details on this configuration please refer to Configure the cost threshold for parallelism Server Configuration Option. Monitoring In the Performance Insights monitor CXPACKET wait events. If this is on higher side you may want to increase cost threshold of parallelism as described above. You may refer the Performance Insights screenshot under the section “maximum degree of parallelism.” When to change the configuration On modern machines, 50 is an acceptable value to start with. Optimize for ad hoc workloads To improve plan cache efficiency, configure optimize for ad hoc workloads. This works by only caching a compiled plan stub instead of a complete run plan on the first time you run an ad hoc query, thereby saving space in the plan cache. If the ad hoc batch runs again, the compile plan stub helps recognize the same and replaces the compiled plan stub with the full compiled plan in the plan cache. To find the number of single-use cached plans, enter the following query: SELECT objtype, cacheobjtype, SUM(refcounts), AVG(usecounts), SUM(CAST(size_in_bytes AS bigint))/1024/1024 AS Size_MB FROM sys.dm_exec_cached_plans WHERE usecounts = 1 AND objtype = 'Adhoc' GROUP BY cacheobjtype, objtype You can check the size of a stub and the plan of a query by running a query at least twice and checking the size in plan cache using a query similar to the following query: select * from sys.dm_exec_cached_plans cross apply sys.dm_exec_sql_text(plan_handle) where text like '%%' You can configure the optimize_for_ad_hoc_workloads value under a custom parameter group. In the following screenshot, I set the value to 1. You can change this value in custom parameter group. To check the current value, run the below query: # sp_configure 'optimize for ad hoc workloads' For more details please refer optimize for ad hoc workloads Server Configuration Option. Monitoring In addition to the preceding query, you can check the number of ad hoc queries on the Performance Insights dashboard by comparing the following: Batch requests – Number of Transact-SQL command batches received per second. SQL compilations – Number of SQL compilations per second. This indicates the number of times the compile code path is entered. It includes compiles caused by statement-level recompilations in SQL Server. When to change the configuration If your workload has many single-use ad hoc queries, it’s recommended to enable this parameter. Configuring tempdb On a busy database server that frequently uses tempdb, you may notice severe blocking when the server is experiencing a heavy load. You may sometimes notice the tasks are waiting for tempdb resources. The wait resources are pages in tempdb. These pages might be of the format 2:x:x, and therefore on the PFS and SGAM pages in tempdb. To improve the concurrency of tempdb, increase the number of data files to maximize disk bandwidth and reduce contention in allocation structures. You can start with the following guidelines: If the number of logical processors <=8, use the same number of data files as logical processors If the number of logical processors > 8, use eight data files On RDS for SQL Server 2017 or below we have a single tempdb file by default. If contention persists, increase the number of data files in multiples of 4 until the contention is remediated, maximum up to the number of logical processors on the server. You may refer the below article for more details Recommendations to reduce allocation contention in SQL Server tempdb database You add multiple tempdb files because the Amazon RDS primary account has been granted the control permission on tempdb. The following query creates and modifies four files with parameters SIZE = 8MB, FILEGROWTH = 10% (you should choose parameters best suited for your environment): ALTER DATABASE tempdb MODIFY FILE ( NAME = N'tempdev', SIZE = 8MB, FILEGROWTH = 10%) ALTER DATABASE tempdb ADD FILE ( NAME = N'tempdb2', FILENAME = N'D:RDSDBDATADatatempdb2.ndf' , SIZE = 8MB , FILEGROWTH = 10%) ALTER DATABASE tempdb ADD FILE ( NAME = N'tempdb3', FILENAME = N'D:RDSDBDATADatatempdb3.ndf' , SIZE = 8MB , FILEGROWTH = 10%) ALTER DATABASE tempdb ADD FILE ( NAME = N'tempdb4', FILENAME = N'D:RDSDBDATADatatempdb4.ndf' , SIZE = 8MB , FILEGROWTH = 10%) You can use sp_helpdb 'tempdb' to verify the changes. Note: For Multi AZ setup, please remember to make this change on the DR as well. When you create multiple files, you may still want to maintain the total size of the tempdb equal to what it was with a single file. In such cases, you need to shrink a tempdb file to achieve the desired size. To shrink the tempdev file, enter the following code: exec msdb..rds_shrink_tempdbfile @temp_filename='tempdev', @target_size =10; To shrink a templog file, enter the following code: exec msdb..rds_shrink_tempdbfile @temp_filename='templog', @target_size =10; Following the tempdev shrink command, you can alter the tempdev file and set the size as per your requirement. When initial pages are created for a table or index, the MIXED_PAGE_ALLOCATION setting controls whether mixed extent can be used for a database or not. When set to OFF it forces page allocations on uniform extents instead of mixed extents, reducing contention on the SGAM page. Starting with SQL Server 2016 (13.x) this behavior is controlled by the SET MIXED_PAGE_ALLOCATION option of ALTER DATABASE. For example, use the following query to turn it off: alter database MODIFY FILEGROUP [PRIMARY] AUTOGROW_ALL_FILES AUTOGROW_ALL_FILES determines that, when a file needs to grow in a file group, all the files in the file group grow with the same increment size. Starting with SQL Server 2016 (13.x), this behavior is controlled by the AUTOGROW_SINGLE_FILE and AUTOGROW_ALL_FILES option of ALTER DATABASE, you may use the following query to enable AUTOGROW_ALL_FILES: alter database set MIXED_PAGE_ALLOCATION OFF Monitoring You want to monitor for wait types on tempdb, such as PAGELATCH. You may monitor this via Performance Insights (PI), as per the screenshot above, under the section “Maximum degree of parallelism.” When to change the configuration When wait resources are like 2:x:x, you want to revisit the tempdb configuration. To check the wait resource in tempdb, use the following query: # select db_name(2) as db,* from master..sysprocesses where waitresource like '2%' Updating the statistics If the optimizer doesn’t have up-to-date information about the distribution of key values (statistics) of table columns, it can’t generate optimal run plans. Update the statistics for all the tables regularly; the frequency of the update statistics depends on the rate at which the database handles DML operations. For more information, see UPDATE STATISTICS. Please note that the update statistics works at one table at a time. sp_updatestats which is a database level command is not available in RDS. You may either write a cursor using update statistics to update statistics on all the objects in a database or you may build a wrapper around sp_updatestats. Please refer the below workaround to use a wrapper around sp_updatestats: create procedure myRDS_updatestats with execute as ‘dbo’ as exec sp_updatestats go Now, grant we will grant execute on our newly created procedure to an user grant execute on myRDS_updatestats to go Creating a custom parameter group in Amazon RDS for SQL Server To make these configuration changes, first determine the custom DB parameter group you want to use. You can create a new DB parameter group or use an existing one. If you want to use an existing custom parameter group, skip to the next step. Creating a new parameter group To create a new parameter group, complete the following steps: On the Amazon RDS console, choose Parameter groups. Choose Create parameter group. For the parameter group family, choose the applicable family from the drop-down menu (for example, for SQL Server 2012 Standard Edition, choose sqlserver-se-11.0). Enter a name and description. Choose Create. For more information, see Creating a DB Parameter Group. Modifying the parameter group To modify your parameter group, complete the following steps: On the Amazon RDS console, choose Parameter Groups. Choose the parameter group you created (or an existing one). Choose Edit parameters. Search for the parameter you want to modify (for example, max_server_memory, max_degree_of_parallelism, or optimize_for_ad_hoc_workloads). Change the value as needed. Choose Save. Repeat these steps for each parameter you want to change. For more information, see Modifying Parameters in a DB Parameter Group. Attaching the custom parameter group to your instance To attach the parameter group to your instance, complete the following steps: On the Amazon RDS console, choose the instance you want to attach the DB parameter group to. On the Instance Actions tab, choose Modify. On the Modify instance page, under Database Options, from the DB parameter group drop-down menu, choose your custom parameter group. Choose Continue. On the next page, select Apply immediately. Choose Continue. Choose Modify DB instance. Restarting the DB instance For the changes to take effect, you need to restart the DB instance. On the Amazon RDS console, choose Instance. Choose your instance. Under Instance details, you should see the parameter group you’re applying. When the status changes to Pending reboot (this may take a few minutes), under Instance actions, choose Reboot. Checking the parameter group is attached To confirm that the parameter group is attached to your instance, complete the following steps: On the Amazon RDS console, choose the instance you want to check the parameter group for. On the Details tab, look at the value for Parameter Group. Verifying the configuration changes To verify the configuration changes, complete the following steps: Connect to your Amazon RDS for SQL Server instance using your primary user account. Run the following to verify the configuration changes: # sp_configure Conclusion This post discussed how to fine-tune some parameters in Amazon RDS for SQL Server to improve the performance of critical database systems. The recommended values are applicable to most environments; however, you can tune them further to fit your specific workloads. We recommend changing one or two parameters at a time and monitoring them to see the impact.   About the Author Abhishek Soni is a Partner Solutions Architect at AWS. He works with the customers to provide technical guidance for best outcome of workloads on AWS. He is passionate about Databases and Analytics.         https://aws.amazon.com/blogs/database/best-practices-for-configuring-performance-parameters-for-amazon-rds-for-sql-server/

[gregdodd]

Query Hints

New blog post: Should I use Query Hints?

SQL Server has a couple of keywords that can be added to a query to give the query optimizer some ideas on how to best compile your SQL statements. Hints like FORCE ORDER, MAXDOP and NOEXPAND can be used as a way to tell the optimizer “Hey, I’ve got this idea, you should try it.”

And while these Query Hints can certainly help in some scenarios, I think it should be the exception rather than…

[johnattaway]

Will Free Hosting Javascript

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What Vps Security Tax

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[robertbryantblog]

Will Free Hosting Javascript

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Which Ssh Host Vcenter

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[my-codeme-blog]

Defrag Those Indexes - Maintenance

This article was written back before I was looking into Sql Server 2005. The underlying idea is the same, in order to keep your database running healthy you will need to maintain and administer the underlying architecture. In this short but subtle refresh I've separated the 2000 concepts and implementations from those used today in Sql Server 2005. If you have comments or corrections do feel free to email me or leave a comment. SQL Server 2000 ===================================================== It is imperative you maintenance your Database. One way to check up on the indexes per table is to run the DBCC SHOWCONTIG command as DBCC SHOWCONTIG ('tbl_YourTableName') with fast,ALL_INDEXES You'll end up with a very similar display like the following... DBCC SHOWCONTIG scanning 'tbl_YourTableName' table... Table: 'tbl_YourTableName' (1113627606); index ID: 1, database ID: 8 TABLE level scan performed. - Pages Scanned................................: 1680 - Extent Switches..............................: 217 - Scan Density .......: 96.33% - Logical Scan Fragmentation ..................: 0.18% DBCC SHOWCONTIG scanning 'tbl_YourTableName' table... Table: 'tbl_YourTableName' (1113627606); index ID: 2, database ID: 8 LEAF level scan performed. - Pages Scanned................................: 480 - Extent Switches..............................: 64 - Scan Density .......: 92.31% - Logical Scan Fragmentation ..................: 0.83% DBCC SHOWCONTIG scanning 'tbl_YourTableName' table... Table: 'tbl_YourTableName' (1113627606); index ID: 5, database ID: 8 LEAF level scan performed. - Pages Scanned................................: 696 - Extent Switches..............................: 95 - Scan Density .......: 90.63% - Logical Scan Fragmentation ..................: 0.72% DBCC execution completed. If DBCC printed error messages, contact your system administrator. What you're really looking for is the Scan fragmentation to be as low as possible. I've also read on other sites that you want the Scan Desity to be as close to each other as possible like for example 87:96 is fairly close and gives you a density over 90%. From that you can easily run a defrag on each index as follows DBCC INDEXDEFRAG (8, 1113627606, 5) DBCC INDEXDEFRAG (8, 1113627606, 2) DBCC INDEXDEFRAG (8, 1113627606, 1) More Information can be obtained via: Sql Server Performancedbcc commands And sql-server-performance.com/rebuilding_indexes and here, SQL Server Index Fragmentation and Its Resolution If you just wish to defrangment the entire database this little script will put you on your way: (from the link above sql server performance site) SET DatabaseName --Enter the name of the database you want to reindex DECLARE @TableName varchar(255) DECLARE TableCursor CURSOR FOR SELECT table_name FROM information_schema.tables WHERE table_type = 'base table' OPEN TableCursor FETCH NEXT FROM TableCursor INTO @TableName WHILE @@FETCH_STATUS = 0 BEGIN DBCC DBREINDEX(@TableName,' ',90) FETCH NEXT FROM TableCursor INTO @TableName END CLOSE TableCursor DEALLOCATE TableCursor ------END SCRIPT SQL Server 2005 =================================================== Fastforward to today, IndexDefrag still works in 2005, but if you read around the 'net you'll find many more references to choose to rebuild your indexes via the ALTER INDEX command, which is new for SQL Server 2005. Before you go out and begin de-fragmenting tables like crazy, it's best to have a plan, finding out how much fragmentation is in your table in 2005 is easier too with the newer function that provides this information. below I've expanded on what you can find around the internet, the below script will allow you to identify rows over 100k and with more than 10% fragmentation, I also handled the commonly seen error for databases that are of the standard dictionary order (80), and the common "Error near '(' ". /script/ SET NOCOUNT ON DECLARE @db_id SMALLINT SET @db_id = DB_ID() SELECT OBJECT_NAME(i.object_id) AS TableName, i.name AS TableIndexName, phystat.avg_fragmentation_in_percent, rows FROM sys.dm_db_index_physical_stats(@db_id, NULL, NULL, NULL, 'DETAILED') phystat inner JOIN sys.indexes i WITH(NOLOCK) ON i.object_id = phystat.object_id AND i.index_id = phystat.index_id INNER JOIN sys.partitions p WITH(NOLOCK) ON p.OBJECT_ID = i.object_id WHERE phystat.avg_fragmentation_in_percent > 10 AND ROWS > 100000 /end script/ More information on this system function is availble here: http://technet.microsoft.com/en-us/library/ms188917.aspx This link will even give you a cool little script you can use to find out what tables in your database are fragmented over 40%, which is very useful if you are planning a weekend of maintenance. http://www.sql-server-performance.com/articles/per/detect_fragmentation_sql2000_sql2005_p1.aspx Now that you have the needed information on how fragmented the indexes are you can begin defragmenting using the Alter Index command ALTER INDEX ALL ON TableName REBUILD What is especially cool about 2005 is that you can choose to throttle the amount of CPUs you use for the defragging process, with the MAXDOP option note, with the following command you can restrict the number of processors down to 2 CPUs: ALTER INDEX ALL ON TableName REBUILD WITH(MAXDOP=2) You can also choose to rebuild your INDEX and continue to make it available for your users as it defrags, I suspect this has performance implications but none the less the command would be: ALTER INDEX ALL ON TableName REBUILD WITH(MAXDOP=2, ONLINE=ON) More information on the ALTER INDEX command is available on the MSDN here: http://msdn2.microsoft.com/en-us/library/ms188388.aspx Happy Defragging!

[marcosplavsczyk]

In aggregate, SQL Server Wait statistics are complex, comprising hundreds of wait types registered with every execution of the query. So, to better understand how and why the wait statistic, in general, is the most important for detecting and troubleshooting SQL Server performance issues, it is important to understand the mechanism of how a query executes on SQL Server, since SQL Server performance problems are the ones that most often affect end users.

SQL Server processing is based on query execution, and most of the performance problems are related to queries executed against the SQL Server.

The query execution flow graphic is simplified to a certain level for easier understanding of the basic steps in query execution. Every query execution starts with the actual T-SQL statement, and when the user runs the statement, it goes through the execution steps as elaborated below:

Parsing (Syntax check) – this is the first stage in the processing of the T-SQL statement. Once the application runs the statement, it issues a parse call to SQL Server. During the parse phase, SQL Server performs the following:

Validates the syntax of the executed T-SQL statement

Validates that the process that issued the statement has appropriate privileges to execute that statement

Reserves a SQL statement private area

In case that any error occurs here, the SQL Server parser engine returns the appropriate error information.

Algebrizer (Object verification) – Once the parser engine verifies the T-SQL statement as valid, it passes the statement to the algebrizer. The algebrizer performs the following tasks:

Verifies that objects and columns names provided in the query or referenced by the query exist in the target database. If an object referenced in the T-SQL script does not exist in the database, the algebrizer returns appropriate error information. For example, if a query uses the wrong name of a column, the algebrizer identifies that such a column does not exist in the target database and generates an error

Identifies and verifies all data types (i.e. uniqueidentifier, datetime, int…) defined in a T-SQL statement

Inspects and validates that columns in GROUP BY statements are properly placed. If not, the algebrizer returns an error

Optimizer – It is in charge of generating the most optimal execution plan for the given query by analyzing various data related to the query execution. The optimizer uses various data as input information to create a query plan, such as:

Hardware used for the query execution

Estimated row count that affects by the query

Analysis of the hints used in the query (when statements is forcing the query to use the specific index for example) that can force specific way of execution

Configuration parameters set (MAXDOP value for example)

Information about indexes that should be used during the query execution

Analysis of the use of partitioning, filegroups and files

Those are all factors that query optimizer gathers for analysis and for creating an appropriate execution plan. The SQL query optimizer uses all that information to create multiple execution plan candidates, and then it picks the one that is the most cost-effective. The SQL Server optimizer is cost-based, and it uses a cost estimation model to determine the final cost of different execution plan candidates. So, the query with the lowest cost will be selected and sent along to the SQL Server execution engine.

Execution – this is the step where SQL Server takes the execution plan, passed by the optimizer, and executes it. To perform the execution, it performs multiple functions, such as:

Dispatches all the commands contained in the execution plan

Iterates through all execution plan commands until it completes execution

Cooperates with the SQL Server storage engine to get and store/update data in tables and indexes

Wait statistics

SQL Server has the mechanisms to monitor query execution, and it logs not just the total time needed for a query to complete the execution, but also how long and on what the query is waiting in each step along its execution. Having such a granular data about each query execution helps to identify, more precisely, where the actual bottlenecks occurs during the query execution.

Based on this, it is evident that the ability of SQL Server to monitor wait statistics from a query execution (what query waits for and how long at each step of execution) is both a powerful and precise tool for determining the root causes of the slow query execution on SQL Server.

For a DBA that has to maintain SQL Server and address performance issues, every single wait event time for all executed queries has to be measured to isolate queries with the most significant impact on the SQL Server performance.

To create a proper system that is capable of monitoring SQL Server waits statistic, the following propositions must be meet:

Every specific wait event that causes query to wait long has to be identified

Queries or a set of queries, which cause performance issues, have to be identified. Therefore, SQL Server wait statistics, at a cumulative and individual query level is important for identifying performance issues and their root cause

Each single event, between the query request and the query response, that causes the query to wait must be measured and logged

Recollecting query wait statistics, for some period, to ensure insight into performance and historical trends of queries over time must be accomplished. This allows for the ability to separate “false positives” that may resemble, at first glance, actual performance problems.

SQL Server monitoring using wait statistics

In general, SQL Server wait statistic monitoring can be split into two different approaches. One is collecting cumulative wait statistics, and another is collecting all queries whose wait times are larger than a pre-determined duration defined by the user (collecting all queries could be counter-productive by requiring voluminous amounts of information to be stored). SQL Server dynamic management views (DMV) allow the user to get details about each request that are executed within SQL Server, and DMVs can be used in both approaches for performance monitoring.

Proper collecting of cumulative wait statistic considers that the user should be able to see what wait times are accumulated for wait types of interest and in a given period of time. The SQL Server Operating System or SQLOS enables continuous monitoring of SQL Server wait statistics and logs information that about why and on what query the system has to wait during execution. That means that using information collected by SQL Server itself can help locate and understand the cause of performance problems.

SQL Server wait statistics comprise the so-called “waits” and “queues”. SQLOS itself tracks “waits”, and “queues” are info on the resources a query must wait for. SQL Server utilizes over 900 different wait types, and every wait type indicates a different resource for which query waited for during its execution.

The sys.dm_os_wait_stats Dynamic Management View is where SQL Server logs the wait statistic data.

SELECT * FROM sys.dm_os_wait_stats WHERE waiting_tasks_count > 0 ORDER BY wait_time_ms DESC GO

While it is easy to get information about the wait types listed with the highest wait time using the above query, the data isn’t presented in a manner that is particularly useful when it comes to identifying the causes of performance issues.

Data obtained by default tend to be too generic and doesn’t contain required additional details needed for troubleshooting, such as what wait time is accumulated in the specific time frame of interest and what wait types are present in that particular time frame. DMVs provide a list of wait types with the highest time accumulated since the last SQL Server startup, which can be several months or even longer. To get information sufficient for at least basic troubleshooting, collecting and storing wait statistic data periodically with deltas will do a better job at providing the necessary information.

Scheduling a query every 30 minutes, for example, and storing the data in a table allows going back into a specific period to identify a problem with acceptable precision. 30 minutes period is a good compromise between the amount of collected data and therefore storage resources and precision. Using shorter time intervals increases the accuracy at the expense of data storage while increasing the time reduces the precision and relax the need for storage space. So it is up to users to strike the appropriate balance and decide what suits their need the best.

Here is an example of how to set up our repository:

First, create the table should store the collected data.

CREATE TABLE [WaitStatsStorage]( [WaitTypeName] [nvarchar](MAX) NOT NULL, [Value] [float] NOT NULL, [Time] [datetime] NOT NULL ) GO

After that, the query that collects data should be executed periodically (30 minutes as suggested above).

INSERT INTO dbo.WaitStatsStorage ([WaitTypeName], [Value], [Time] ) (SELECT wait_type , wait_time_ms, GETDATE() FROM sys.dm_os_wait_stats WHERE wait_time_ms > 0)

The WHERE wait_time_ms > 0 is used to prevent unnecessary collecting and storing wait types which wait time is 0.

The query can be scheduled using the SQL Agent or any other method available to the user.

Once the collecting of data is established, the following query can be used for reading the data.

declare @StartTime DateTime declare @EndTime DateTime SET @StartTime = '2017-10-02 02:00:00.000' -- Set the end time for the period of interest SET @EndTime = '2017-10-02 02:30:00.000' -- Set the start time for the period of interest ;WITH ws AS ( SELECT [WaitTypeName], [Value], LAG ([Value],1) OVER (PARTITION BY [WaitTypeName] ORDER BY [Time] DESC) - [Value] AS val, [Time] FROM dbo.WaitStatsStorage --WHERE [WaitTypeName] IN (‘CXPACKET’,’PAGEIOLATCH_SH’, ‘ASYNC_NETWORK_IO’)) SELECT ws.[WaitTypeName], SUM(ws.val) as [WaitTime], MIN(Time) as [Start Time], MAX(Time) as [End Time] FROM ws WHERE ws.val IS NOT NULL AND ws.val > 0 AND @StartTime <= [Time] AND [Time] <= @EndTime GROUP BY ws.WaitTypeName ORDER BY [WaitTime] DESC

The yellow highlighted part of the query can be uncommented if there is a need to display only the results for the particular wait type or set of wait types to narrow down the results. The part in the query is just an example, and the user is free to customize to accommodate their own particular wait types instead.

Now that the method for collecting wait statistics has been established, the user can identify which wait types have accumulated the highest wait times, as those are potential candidates for further inspection. However, here it is important to note that not all wait types are a potential problem just because of a high wait time. Some typical examples are CXPACKET, SOS_SCHEDULER_YIELD, etc. For some wait types, a high wait time is normal and could indicate that SQL Server is, in fact, working optimally. Therefore, besides having that data, it is important to establish the method that prevents knee-jerk or superficial conclusions that can result in wasted time and effort investigating what is turns out to be a “false positive”.

The most reliable way is establishing the baseline for the collected wait statistics. Proper establishing of baselines for collected data and its presentation in an acceptable manner out of scope of this particular article but more on this topic can be found in the following articles: How to detect SQL Server performance issues using baselines – Part 1 – Introduction and How to detect SQL Server performance issues using baselines – Part 2 – Collecting metrics and reporting

SQL Server monitoring using query waits

Now that we have wait statistic data and we can identify the potentially problematic wait types with high wait times, the next step would be to identify queries executed contemporaneously, that contain the wait type that has to be inspected. To get the information about the currently running queries, use the sys.dm_exec_requests dynamic management view. Unfortunately, SQL Server uses this DMV to display details about queries that are active at the moment of reading data from the sys.dm_exec_requests DMV, and once the query completes execution, all related data to that query will be flushed. Therefore, it is necessary to establish the method for collecting and storing the queries and its data.

One way to get reasonably precise data is incrementally reading from the sys.dm_exec_requests DMV and collecting the data into the table designed to store collected query wait data. The more frequent reading from the DMV is, the better and more precise data will be collected. The maximum frequency of reading should not be higher than 1 second, but ultimately the frequency could depend on the required accuracy by the user. When the reading frequency is increased to 2, 5 10 or even more seconds, it is almost inevitable that some problematic or potentially problematic queries are going to be missed, as often it is not the wait time per query but high execution count for that query that generates the excessive wait time.

For collecting the query wait statistic, the following system can be established.

For storing the query waits data three tables must be created:

The table that temporarily stores the raw data obtained from the DMV.

CREATE TABLE [QueryWaitCurrentExecutionTemp] ( [WaitTypeName] [nvarchar](MAX) NOT NULL, [WaitTime] [bigint] NOT NULL, [SPID] [int] NOT NULL, [SqlHandle] [binary](20) NOT NULL, [StartTime] [datetime] NOT NULL, ) GO

Since the reading frequency is quite high, the raw data obtained from the DMV that have to be processed first are stored in this table.

The table that temporarily stores the data after processing. This table is used to store the data collected during the query execution.

CREATE TABLE [QueryWaitCurrentExecution]( [WaitTypeName] [nvarchar](MAX) NOT NULL, [WaitTime] [bigint] NOT NULL, [SPID] [int] NOT NULL, [SqlHandle] [binary](20) NOT NULL, [StartTime] [datetime] NOT NULL, [UpdateTime] [datetime] NOT NULL, ) GO

The data related to specific query will be stored in this table as long as the query is running. Once the query execution is completed, the data will be moved in the table that stores the final data.

The table that stores the final data processed in a way suitable for presenting to the final user.

CREATE TABLE [QueryWaitStorage]( [SqlHandle] [binary](20) NOT NULL, [WaitTypeName] [nvarchar](MAX) NOT NULL, [WaitTime] [bigint] NOT NULL, [StartTime] [datetime] NOT NULL, [SQLText] [nvarchar](MAX) NOT NULL, ) GO

Once the query execution completes, the data for that session will be moved from the temporary table into this permanent table. That is the table used by end-users for reading the data.

Now that required tables are created, the query that reads the data from the DMV processes the raw data from the temporary table and stores into the table that holds the data while the query is active and finally that moves the data for completed queries into a permanent table. Schedule the below query to executes at one-second interval.

-- Delete data from temp table DELETE FROM dbo.QueryWaitCurrentExecutionTemp -- Get data for currently running queries INSERT INTO dbo.QueryWaitCurrentExecutionTemp ([SPID], [SqlHandle], [WaitTypeName],[WaitTime], [StartTime]) (SELECT sprc.spid as [SPID], sprc.sql_handle as [SqlHandle], RTRIM(sprc.lastwaittype) as [WaitTypeName], sprc.waittime as [WaitTime], req.start_time as [StartTime] FROM master..sysprocesses AS sprc LEFT OUTER JOIN sys.dm_exec_requests req ON req.session_id = sprc.spid WHERE ( sprc.dbid <> 0 AND sprc.spid >= 51 AND sprc.spid <> @@SPID AND sprc.cmd <>'AWAITING COMMAND' AND sprc.cmd NOT LIKE '%BACKUP%' AND sprc.cmd NOT LIKE '%RESTORE%' AND sprc.hostprocess > '' )) -- Set current time Declare @currentUpdateTime DateTime SET @currentUpdateTime = (SELECT GETDATE() ) -- Merge Active queries MERGE dbo.QueryWaitCurrentExecution AS T USING (SELECT [SPID], [SqlHandle],[WaitTypeName],[WaitTime], [StartTime] FROM QueryWaitCurrentExecutionTemp) AS U ON U.[SqlHandle] = T.[SqlHandle] AND U.[SPID] = T.[SPID] AND U.[WaitTypeName] = T.[WaitTypeName] AND U.[StartTime] = T.[StartTime] WHEN MATCHED THEN UPDATE SET T.[WaitTime] = U.[WaitTime] WHEN NOT MATCHED THEN INSERT ( [SPID], [SqlHandle], [WaitTypeName], [WaitTime], [StartTime] , [UpdateTime]) VALUES (U.[SPID], U.[SqlHandle],U.[WaitTypeName],U.[WaitTime], U.[StartTime] , @currentUpdateTime); MERGE dbo.QueryWaitCurrentExecution AS T USING (SELECT [SPID], [SqlHandle], [StartTime] FROM QueryWaitCurrentExecutionTemp) AS U ON U.[SqlHandle] = T.[SqlHandle] AND U.[SPID] = T.[SPID] AND U.[StartTime] = T.[StartTime] WHEN MATCHED THEN UPDATE SET T.[UpdateTime] = @currentUpdateTime; -- Move data for completed queries from temporay table into a permanent table INSERT INTO dbo.QueryWaitStorage ([SqlHandle], [WaitTypeName], [WaitTime], [StartTime], [SQLText] ) (SELECT [SqlHandle] , [WaitTypeName], [WaitTime], [StartTime], (select text from sys.dm_exec_sql_text([SqlHandle])) FROM QueryWaitCurrentExecution WHERE [WaitTime] > 0 AND UpdateTime <> @currentUpdateTime) DELETE FROM dbo.QueryWaitCurrentExecution WHERE [UpdateTime] <> @currentUpdateTime

Now that the Query waits collecting system is established, the needed data for the analysis can be retrieved. After the user identified, upon reviewing the collected wait stats results, that the specific wait type for example, SOS_SCHEDULER_YIELD is excessive, it can now can be further inspected by checking what queries executed in the same period when excessive wait type is identified.

Note that this query might not perform optimally and results could be incorrect for the queries that execute using parallelism. The parallelism problem could be resolved as well, but any potential resolution is beyond the scope of this article, so if used, the query must be accepted with this limitation.

Use the following query to read the necessary information.

declare @StartTime DateTime declare @EndTime DateTime SET @StartTime = '2017-10-02 02:00:00.000' -- Set the end time for the period of interest SET @EndTime = '2017-10-02 02:30:00.000' -- Set the start time for the period of interest SELECT * FROM dbo.QueryWaitStorage WHERE [WaitTypeName] = ' SOS_SCHEDULER_YIELD' – Replace with the name of the wait type to be displayed -- AND [WaitTime] > 4000 – Uncomment to display queries with wait type time larger than specified here AND @StartTime < [StartTime] AND [StartTime] < @EndTime

The query will display all queries that were waited for the SOS_SCHEDULER_YIELD wait type between the 2:00 and 2:30 in this particular example.

In this example, it is evident that some excessive wait times for SOS-SCHEDULER_YIELD wait type are associated with four queries executed in that period, and analyzing those queries to understand why it executes slowly is paramount.

We’ve accomplished a lot with our manual solution, but as it can be seen, using this approach to detect excessive wait types and then to drill down for queries that are the cause is quite complex, a potentially time-consuming and expensive effort.

SQL Server monitoring using ApexSQL Monitor

Another approach to collect this kind of performance data and identifying the problematic SQL query is with a 3rd party tool, in this case a SQL Server performance monitoring tool that is focused on wait statistic, like ApexSQL Monitor. ApexSQL Monitor collects the wait statistic data on 30 minutes (this is also a configurable option, and the frequency of reading can be decreased or increased) and can graphically convey sorted result in a chart, to allow the user to process the provided information with a minimal effort.

To inspect the wait stats, navigate to the Wait stats details page via the instance dashboard of the SQL Server instance that has to be inspected. In the instance dashboard, click on the Details link from the Waits section.

The user has an option to chose the specific period that wants to analyze by simply selecting the start or end time from the drop-down date time picker and selecting one of the predefined time frames.

The wait types in the chart legend are sorted in descending order according to Wait stats time accumulated, so Wait stats with the highest wait time value are displayed first. In our case, the excessive SOS_SCHEDULER_YIELD is visibly excessive in the chart.

Besides allowing the user to identify the excessive wait types quick and easy just by quick look at the wait stats chart, ApexSQL Monitor can also be configured to trigger an alert and notify the user when wait stats time exceeds the alert thresholds predefined by the user via the metrics configuration page.

Moreover, ApexSQL Monitor can baseline the wait statistics and set up alerts to be triggered according to calculated baseline thresholds, which ensures in most cases the most reliable alerting with a minimal number of false positive alerts. More about baselining in ApexSQL Monitor and how to properly interpret and use baseline can be found in How to detect SQL Server performance issues using baselines – Part 3

Now when the potentially problematic wait type is identified, it is important to find the queries that were caused excessive wait time for that wait type. To do that navigate to the Query waits page via the instance dashboard by selecting the Waits link in the Query section.

After opening the query wait page, select the desired time frame and choose the Single execution radio button. Selecting this radio button ensures that each query execution will be displayed. Now enter the name of the wait type, in this particular example SOS_SCHEDULER_YIELD, in the search box. As a result, queries that have waited for the SOS_SCHEDULER_YIELD wait type will be displayed in the grid, along with information about the wait time and time when the query has executed.

By expanding the displayed query by clicking on the next to the query name, displays additional details about the particular query execution.

Click on the wait type name displays the helper with details about the specific wait type, troubleshooting tips and links to additional resources.

Useful resources:

sys.dm_os_wait_stats (Transact-SQL) | Microsoft Docs

sys.dm_exec_requests (Transact-SQL)

How to analyse SQL Server performance

Introduction to Wait Statistics in SQL Server

SQL Server wait types

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[sandeep2363]

Checklist for performance tuning in MS SQL Server

Database & Server Configuration ✅ Ensure SQL Server is running on an optimized hardware setup.✅ Configure max server memory to avoid excessive OS paging.✅ Set max degree of parallelism (MAXDOP) based on CPU cores.✅ Optimize cost threshold for parallelism (default 5 is often too low).✅ Enable Instant File Initialization for faster data file growth.✅ Keep TempDB on fast storage & configure…

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Optimizing MAXDOP and Cost Threshold for Parallelism in SQL Server with Multiple Instances

Introduction Hey there, fellow SQL Server enthusiasts! Today, I want to dive into a topic that can be a bit confusing, especially when dealing with servers running multiple SQL Server instances. We’ll explore how to handle MAXDOP (Maximum Degree of Parallelism) and Cost Threshold for Parallelism settings in such environments. By the end of this article, you’ll have a clearer understanding of…

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