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It includes all the functionality of the Enterprise Edition, but is licensed as a development and test system, not as a production server. I write about tech trends, new tools and software, and rapidly emerging technologies.
Thank you for making these available. I am learning SQLServer and the book I have is based on versions and and this was the only site I can download without paying an arm and a leg. Thank you! Previous Previous. Next Continue. Leave a Reply Cancel reply You have to agree to the comment policy. User resource. When a thread is waiting for a resource that is potentially controlled by a user application, the resource is considered to be an external or user resource and is treated like a lock.
Session mutex. The tasks running in one session are interleaved, meaning that only one task can run under the session at a given time.
Before the task can run, it must have exclusive access to the session mutex. Transaction mutex. All tasks running in one transaction are interleaved, meaning that only one task can run under the transaction at a given time.
Before the task can run, it must have exclusive access to the transaction mutex. In order for a task to run under MARS, it must acquire the session mutex. If the task is running under a transaction, it must then acquire the transaction mutex. This guarantees that only one task is active at one time in a given session and a given transaction. Once the required mutexes have been acquired, the task can execute.
When the task finishes, or yields in the middle of the request, it will first release transaction mutex followed by the session mutex in reverse order of acquisition. However, deadlocks can occur with these resources. In the following code example, two tasks, user request U1 and user request U2, are running in the same session.
The stored procedure executing from user request U1 has acquired the session mutex. If the stored procedure takes a long time to execute, it is assumed by the SQL Server Database Engine that the stored procedure is waiting for input from the user. User request U2 is waiting for the session mutex while the user is waiting for the result set from U2, and U1 is waiting for a user resource. This is deadlock state logically illustrated as:. All of the resources listed in the section above participate in the SQL Server Database Engine deadlock detection scheme.
Deadlock detection is performed by a lock monitor thread that periodically initiates a search through all of the tasks in an instance of the SQL Server Database Engine. The following points describe the search process:. Because the number of deadlocks encountered in the system is usually small, periodic deadlock detection helps to reduce the overhead of deadlock detection in the system.
When the lock monitor initiates deadlock search for a particular thread, it identifies the resource on which the thread is waiting. The lock monitor then finds the owner s for that particular resource and recursively continues the deadlock search for those threads until it finds a cycle. A cycle identified in this manner forms a deadlock. After a deadlock is detected, the SQL Server Database Engine ends a deadlock by choosing one of the threads as a deadlock victim.
The SQL Server Database Engine terminates the current batch being executed for the thread, rolls back the transaction of the deadlock victim, and returns a error to the application. Rolling back the transaction for the deadlock victim releases all locks held by the transaction.
This allows the transactions of the other threads to become unblocked and continue. The deadlock victim error records information about the threads and resources involved in a deadlock in the error log. By default, the SQL Server Database Engine chooses as the deadlock victim the session running the transaction that is least expensive to roll back. If two sessions have different deadlock priorities, the session with the lower priority is chosen as the deadlock victim. If both sessions have the same deadlock priority, the session with the transaction that is least expensive to roll back is chosen.
If sessions involved in the deadlock cycle have the same deadlock priority and the same cost, a victim is chosen randomly. However, the deadlock is resolved by throwing an exception in the procedure that was selected to be the deadlock victim. It is important to understand that the exception does not automatically release resources currently owned by the victim; the resources must be explicitly released.
Consistent with exception behavior, the exception used to identify a deadlock victim can be caught and dismissed. Starting with SQL Server Also starting with SQL Server When deadlocks occur, trace flag and trace flag return information that is captured in the SQL Server error log.
Trace flag reports deadlock information formatted by each node involved in the deadlock. Trace flag formats deadlock information, first by processes and then by resources. It is possible to enable both trace flags to obtain two representations of the same deadlock event.
Avoid using trace flag and on workload-intensive systems that are causing deadlocks. Using these trace flags may introduce performance issues. Instead, use the Deadlock Extended Event. In addition to defining the properties of trace flag and , the following table also shows the similarities and differences. The following example shows the output when trace flag is turned on. In this case, the table in Node 1 is a heap with no indexes, and the table in Node 2 is a heap with a nonclustered index.
The index key in Node 2 is being updated when the deadlock occurs. In this case, one table is a heap with no indexes, and the other table is a heap with a nonclustered index. In the second table, the index key is being updated when the deadlock occurs. This is an event in SQL Profiler that presents a graphical depiction of the tasks and resources involved in a deadlock.
The following example shows the output from SQL Profiler when the deadlock graph event is turned on. For more information about the deadlock event, see Lock:Deadlock Event Class. When an instance of the SQL Server Database Engine chooses a transaction as a deadlock victim, it terminates the current batch, rolls back the transaction, and returns error message to the application.
Rerun your transaction. Because any application submitting Transact-SQL queries can be chosen as the deadlock victim, applications should have an error handler that can trap error message If an application does not trap the error, the application can proceed unaware that its transaction has been rolled back and errors can occur.
Implementing an error handler that traps error message allows an application to handle the deadlock situation and take remedial action for example, automatically resubmitting the query that was involved in the deadlock. By resubmitting the query automatically, the user does not need to know that a deadlock occurred. The application should pause briefly before resubmitting its query. This gives the other transaction involved in the deadlock a chance to complete and release its locks that formed part of the deadlock cycle.
This minimizes the likelihood of the deadlock reoccurring when the resubmitted query requests its locks. Although deadlocks cannot be completely avoided, following certain coding conventions can minimize the chance of generating a deadlock.
Minimizing deadlocks can increase transaction throughput and reduce system overhead because fewer transactions are:. If all concurrent transactions access objects in the same order, deadlocks are less likely to occur. For example, if two concurrent transactions obtain a lock on the Supplier table and then on the Part table, one transaction is blocked on the Supplier table until the other transaction is completed.
After the first transaction commits or rolls back, the second continues, and a deadlock does not occur. Using stored procedures for all data modifications can standardize the order of accessing objects. Avoid writing transactions that include user interaction, because the speed of batches running without user intervention is much faster than the speed at which a user must manually respond to queries, such as replying to a prompt for a parameter requested by an application.
For example, if a transaction is waiting for user input and the user goes to lunch or even home for the weekend, the user delays the transaction from completing. This degrades system throughput because any locks held by the transaction are released only when the transaction is committed or rolled back.
Even if a deadlock situation does not arise, other transactions accessing the same resources are blocked while waiting for the transaction to complete. A deadlock typically occurs when several long-running transactions execute concurrently in the same database.
The longer the transaction, the longer the exclusive or update locks are held, blocking other activity and leading to possible deadlock situations. Keeping transactions in one batch minimizes network roundtrips during a transaction, reducing possible delays in completing the transaction and releasing locks.
Determine whether a transaction can run at a lower isolation level. Implementing read committed allows a transaction to read data previously read not modified by another transaction without waiting for the first transaction to complete. Using a lower isolation level, such as read committed, holds shared locks for a shorter duration than a higher isolation level, such as serializable.
This reduces locking contention. Some applications rely upon locking and blocking behavior of read committed isolation. For these applications, some change is required before this option can be enabled. Snapshot isolation also uses row versioning, which does not use shared locks during read operations. Implement these isolation levels to minimize deadlocks that can occur between read and write operations.
Using bound connections, two or more connections opened by the same application can cooperate with each other. Any locks acquired by the secondary connections are held as if they were acquired by the primary connection, and vice versa. Therefore they do not block each other. For large computer systems, locks on frequently referenced objects can become a performance bottleneck as acquiring and releasing locks place contention on internal locking resources.
Lock partitioning enhances locking performance by splitting a single lock resource into multiple lock resources. This feature is only available for systems with 16 or more CPUs, and is automatically enabled and cannot be disabled.
Only object locks can be partitioned. Object locks that have a subtype are not partitioned. For more information, see sys. Without lock partitioning, one spinlock manages all lock requests for a single lock resource. On systems that experience a large volume of activity, contention can occur as lock requests wait for the spinlock to become available. Under this situation, acquiring locks can become a bottleneck and can negatively impact performance. To reduce contention on a single lock resource, lock partitioning splits a single lock resource into multiple lock resources to distribute the load across multiple spinlocks.
Once the spinlock is acquired, lock structures are stored in memory and then accessed and possibly modified. Distributing lock access across multiple resources helps to eliminate the need to transfer memory blocks between CPUs, which will help to improve performance.
Lock partitioning is turned on by default for systems with 16 or more CPUs. When lock partitioning is enabled, an informational message is recorded in the SQL Server error log. These locks on a partitioned resource will use more memory than locks in the same mode on a non-partitioned resource since each partition is effectively a separate lock. The memory increase is determined by the number of partitions.
The SQL Server lock counters in the Windows Performance Monitor will display information about memory used by partitioned and non-partitioned locks. A transaction is assigned to a partition when the transaction starts. For the transaction, all lock requests that can be partitioned use the partition assigned to that transaction. By this method, access to lock resources of the same object by different transactions is distributed across different partitions. The following code examples illustrate lock partitioning.
In the examples, two transactions are executed in two different sessions in order to show lock partitioning behavior on a computer system with 16 CPUs. The IS lock will be acquired only on the partition assigned to the transaction. For this example, it is assumed that the IS lock is acquired on partition ID 7. A transaction is started, and the SELECT statement running under this transaction will acquire and retain a shared S lock on the table.
The S lock will be acquired on all partitions, which results in multiple table locks, one for each partition. For example, on a cpu system, 16 S locks will be issued across lock partition IDs Because the S lock is compatible with the IS lock being held on partition ID 7 by the transaction in session 1, there is no blocking between transactions. Because of the exclusive X table lock hint, the transaction will attempt to acquire an X lock on the table.
However, the S lock that is being held by the transaction in session 2 will block the X lock at partition ID 0. For this example, it is assumed that the IS lock is acquired on partition ID 6.
Remember that the X lock must be acquired on all partitions starting with partition ID 0. On partition IDs that the X lock has not yet reached, other transactions can continue to acquire locks. Starting with SQL Server 9. SQL Server Database Engine also offers a transaction isolation level, snapshot, that provides a transaction level snapshot also using row versioning.
Row versioning is a general framework in SQL Server that invokes a copy-on-write mechanism when a row is modified or deleted. This requires that while the transaction is running, the old version of the row must be available for transactions that require an earlier transactionally consistent state.
Row versioning is used to do the following:. The tempdb database must have enough space for the version store. When tempdb is full, update operations will stop generating versions and continue to succeed, but read operations might fail because a particular row version that is needed no longer exists.
This affects operations like triggers, MARS, and online indexing. The transaction sequence number is incremented by one each time it is assigned. Every time a row is modified by a specific transaction, the instance of the SQL Server Database Engine stores a version of the previously committed image of the row in tempdb. Each version is marked with the transaction sequence number of the transaction that made the change. The versions of modified rows are chained using a link list.
The newest row value is always stored in the current database and chained to the versioned rows stored in tempdb. For modification of large objects LOBs , only the changed fragment is copied to the version store in tempdb.
Row versions are held long enough to satisfy the requirements of transactions running under row versioning-based isolation levels. The SQL Server Database Engine tracks the earliest useful transaction sequence number and periodically deletes all row versions stamped with transaction sequence numbers that are lower than the earliest useful sequence number.
Those row versions are released when no longer needed. A background thread periodically executes to remove stale row versions. For short-running transactions, a version of a modified row may get cached in the buffer pool without getting written into the disk files of the tempdb database.
When transactions running under row versioning-based isolation read data, the read operations do not acquire shared S locks on the data being read, and therefore do not block transactions that are modifying data. Also, the overhead of locking resources is minimized as the number of locks acquired is reduced.
Read committed isolation using row versioning and snapshot isolation are designed to provide statement-level or transaction-level read consistencies of versioned data. All queries, including transactions running under row versioning-based isolation levels, acquire Sch-S schema stability locks during compilation and execution.
Because of this, queries are blocked when a concurrent transaction holds a Sch-M schema modification lock on the table. For example, a data definition language DDL operation acquires a Sch-M lock before it modifies the schema information of the table.
Query transactions, including those running under a row versioning-based isolation level, are blocked when attempting to acquire a Sch-S lock. Conversely, a query holding a Sch-S lock blocks a concurrent transaction that attempts to acquire a Sch-M lock. When a transaction using the snapshot isolation level starts, the instance of the SQL Server Database Engine records all of the currently active transactions. When the snapshot transaction reads a row that has a version chain, the SQL Server Database Engine follows the chain and retrieves the row where the transaction sequence number is:.
Read operations performed by a snapshot transaction retrieve the last version of each row that had been committed at the time the snapshot transaction started. This provides a transactionally consistent snapshot of the data as it existed at the start of the transaction. Read-committed transactions using row versioning operate in much the same way. The difference is that the read-committed transaction does not use its own transaction sequence number when choosing row versions.
Each time a statement is started, the read-committed transaction reads the latest transaction sequence number issued for that instance of the SQL Server Database Engine.
This is the transaction sequence number used to select the correct row versions for that statement. This allows read-committed transactions to see a snapshot of the data as it exists at the start of each statement. Even though read-committed transactions using row versioning provides a transactionally consistent view of the data at a statement level, row versions generated or accessed by this type of transaction are maintained until the transaction completes.
In a read-committed transaction using row versioning, the selection of rows to update is done using a blocking scan where an update U lock is taken on the data row as data values are read. This is the same as a read-committed transaction that does not use row versioning. If the data row does not meet the update criteria, the update lock is released on that row and the next row is locked and scanned. Transactions running under snapshot isolation take an optimistic approach to data modification by acquiring locks on data before performing the modification only to enforce constraints.
Otherwise, locks are not acquired on data until the data is to be modified. When a data row meets the update criteria, the snapshot transaction verifies that the data row has not been modified by a concurrent transaction that committed after the snapshot transaction began. If the data row has been modified outside of the snapshot transaction, an update conflict occurs and the snapshot transaction is terminated.
The update conflict is handled by the SQL Server Database Engine and there is no way to disable the update conflict detection. Update operations running under snapshot isolation internally execute under read committed isolation when the snapshot transaction accesses any of the following:.
However, even under these conditions the update operation will continue to verify that the data has not been modified by another transaction. If data has been modified by another transaction, the snapshot transaction encounters an update conflict and is terminated.
The following table summarizes the differences between snapshot isolation and read committed isolation using row versioning. The row versioning framework also supports the following row versioning-based transaction isolation levels, which are not enabled by default:. Row versioning-based isolation levels reduce the number of locks acquired by transaction by eliminating the use of shared locks on read operations. This increases system performance by reducing the resources used to manage locks.
Performance is also increased by reducing the number of times a transaction is blocked by locks acquired by other transactions. Row versioning-based isolation levels increase the resources needed by data modifications. Enabling these options causes all data modifications for the database to be versioned. A copy of the data before modification is stored in tempdb even when there are no active transactions using row versioning-based isolation.
The data after modification includes a pointer to the versioned data stored in tempdb. For large objects, only part of the object that changed is copied to tempdb. For each instance of the SQL Server Database Engine, tempdb must have enough space to hold the row versions generated for every database in the instance.
The database administrator must ensure that tempdb has ample space to support the version store. There are two version stores in tempdb:. Row versions must be stored for as long as an active transaction needs to access it. Once every minute, a background thread removes row versions that are no longer needed and frees up the version space in tempdb. A long-running transaction prevents space in the version store from being released if it meets any of the following conditions:.
When a trigger is invoked inside a transaction, the row versions created by the trigger are maintained until the end of the transaction, even though the row versions are no longer needed after the trigger completes. This also applies to read-committed transactions that use row versioning. With this type of transaction, a transactionally consistent view of the database is needed only for each statement in the transaction. This means that the row versions created for a statement in the transaction are no longer needed after the statement completes.
However, row versions created by each statement in the transaction are maintained until the transaction completes. During the shrink process, the longest running transactions that have not yet generated row versions are marked as victims.
A message is generated in the error log for each victim transaction. If a transaction is marked as a victim, it can no longer read the row versions in the version store. When it attempts to read row versions, message is generated and the transaction is rolled back. If the shrinking process succeeds, space becomes available in tempdb. Otherwise, tempdb runs out of space and the following occurs:. Write operations continue to execute but do not generate versions.
An information message appears in the error log, but the transaction that writes data is not affected. Transactions that attempt to access row versions that were not generated because of a tempdb full rollback terminate with an error Each database row may use up to 14 bytes at the end of the row for row versioning information.
The row versioning information contains the transaction sequence number of the transaction that committed the version and the pointer to the versioned row. These 14 bytes are added the first time the row is modified, or when a new row is inserted, under any of these conditions:.
These 14 bytes are removed from the database row the first time the row is modified under all of these conditions:. If you use any of the row versioning features, you might need to allocate additional disk space for the database to accommodate the 14 bytes per database row. Adding the row versioning information can cause index page splits or the allocation of a new data page if there is not enough space available on the current page.
For example, if the average row length is bytes, the additional 14 bytes cause an existing table to grow up to 14 percent.
Decreasing the fill factor might help to prevent or decrease fragmentation of index pages. To view fragmentation information for the data and indexes of a table or view, you can use sys. The SQL Server Database Engine supports six data types that can hold large strings up to 2 gigabytes GB in length: nvarchar max , varchar max , varbinary max , ntext , text , and image.
Large strings stored using these data types are stored in a series of data fragments that are linked to the data row. Row versioning information is stored in each fragment used to store these large strings. Data fragments are a collection of pages dedicated to large objects in a table. As new large values are added to a database, they are allocated using a maximum of bytes of data per fragment.
Earlier versions of the SQL Server Database Engine stored up to bytes of ntext , text , or image data per fragment. However, the first time the LOB data is modified, it is dynamically upgraded to enable storage of versioning information. This will happen even if row versions are not generated.
After the LOB data is upgraded, the maximum number of bytes stored per fragment is reduced from bytes to bytes. The upgrade process is equivalent to deleting the LOB value and reinserting the same value. The LOB data is upgraded even if only one byte is modified. It may also generate a large amount of logging activity if the modification is fully logged.
The nvarchar max , varchar max , and varbinary max data types are not available in earlier versions of SQL Server. Therefore, they have no upgrade issues. The following DMVs provide information about the current system state of tempdb and the version store, as well as transactions using row versioning. Returns space usage information for each file in the database.
Returns page allocation and deallocation activity by session for the database. Returns page allocation and deallocation activity by task for the database.
Returns a virtual table for the objects producing the most versions in the version store. Use this function to find the largest consumers of the version store. Returns a virtual table that displays all version records in the common version store. Returns a virtual table that displays the total space in tempdb used by version store records for each database. Returns a virtual table for all active transactions in all databases within the SQL Server instance that use row versioning.
System transactions do not appear in this DMV. Returns a virtual table that displays snapshots taken by each transaction. The snapshot contains the sequence number of the active transactions that use row versioning. Returns a single row that displays row versioning-related state information of the transaction in the current session. Returns a virtual table that displays all active transactions at the time the current snapshot isolation transaction starts.
If the current transaction is using snapshot isolation, this function returns no rows. The following performance counters monitor tempdb and the version store, as well as transactions using row versioning.
Free Space in tempdb KB. Monitors the amount, in kilobytes KB , of free space in the tempdb database. There must be enough free space in tempdb to handle the version store that supports snapshot isolation. The following formula provides a rough estimate of the size of the version store. For long-running transactions, it may be useful to monitor the generation and cleanup rate to estimate the maximum size of the version store. The longest running time of transactions should not include online index builds.
Because these operations may take a long time on very large tables, online index builds use a separate version store. The approximate size of the online index build version store equals the amount of data modified in the table, including all indexes, while the online index build is active. Version Store Size KB. Monitors the size in KB of all version stores. This information helps determine the amount of space needed in the tempdb database for the version store.
Monitoring this counter over a period of time provides a useful estimate of additional space needed for tempdb.
Monitors the version generation rate in KB per second in all version stores. Monitors the version cleanup rate in KB per second in all version stores.
Version Store unit creation. Monitors the total number of version store units created to store row versions since the instance was started.
Version Store unit truncation. Monitors the total number of version store units truncated since the instance was started. A version store unit is truncated when SQL Server determines that none of the version rows stored in the version store unit are needed to run active transactions.
Update conflict ratio. Monitors the ratio of update snapshot transaction that have update conflicts to the total number of update snapshot transactions. Longest Transaction Running Time. Monitors the longest running time in seconds of any transaction using row versioning. This can be used to determine if any transaction is running for an unreasonable amount of time.
Monitors the total number of active transactions. This does not include system transactions. Snapshot Transactions. Monitors the total number of active snapshot transactions. Update Snapshot Transactions. Monitors the total number of active snapshot transactions that perform update operations. NonSnapshot Version Transactions. Monitors the total number of active non-snapshot transactions that generate version records.
The sum of Update Snapshot Transactions and NonSnapshot Version Transactions represents the total number of transactions that participate in version generation. The difference of Snapshot Transactions and Update Snapshot Transactions reports the number of read-only snapshot transactions. The following examples show the differences in behavior between snapshot isolation transactions and read-committed transactions that use row versioning.
In this example, a transaction running under snapshot isolation reads data that is then modified by another transaction. When adding these volumes to Windows, there are four important volume configuration settings required to examine or discuss with your storage administrator.
Setting this to 64 KB for each volume can have a significant impact on storage efficiency and performance. The file unit allocation size is returned with the Bytes Per Cluster; thus the desired 64 KB would be displayed as 65, bytes.
If formatted as the default, this will display Correcting the file unit allocation size requires formatting the drive, so it is important to check this setting prior to installation. If you notice this on an existing SQL Server instance, your likely resolution steps are to create a new volume with the proper file unit allocation size and then move files to the new volume during an outage. Do not format or re-create the partition on volumes with existing data: you will of course lose the data.
Usually a DBA will not notice or even be aware of this difference. This cannot be resolved via a formatting decision, but can potentially be resolved via hardware-level storage or firmware settings. To avoid this, all storage that hosts the transaction log files of SQL Servers in an Availability Group or log shipping relationship should have the same Bytes per Physical Sector.
TF overrides disk default behavior and writes the transaction log in 4 KB sectors, resolving the issue. Check the Bytes per Physical Sector setting of a volume by using the same Fsutil command noted in the previous code sample. Aligning disk starting offset was far more important prior to Windows Server Still, it should be verified upon first use of a new storage system or the migration of disks to a new storage system.
To access the disk starting offset information, run the following from the Administrator: Prompt:. A KB starting offset is a Windows default, which is displayed as bytes for Disk 0 Partition 0. The following are brief descriptions for all of the editions in the SQL Server family, including past editions that you might recognize. This book is not intended to be a reference for licensing or sales-related documentation; rather, editions are a key piece of knowledge for SQL administrators to understand what features may or may not be available.
Enterprise edition. Appropriate for production environments. For these environments, instead use the free Developer edition. Developer edition. Appropriate for all preproduction environments, especially those under a production Enterprise edition.
Not allowed for production environments. This edition supports the same features and capacity as Enterprise edition and is free. Standard edition. Lacks the scale and compliance features of Enterprise edition that are required in some regulatory environments. Limited to the lesser of 4 sockets or 24 cores and also GB of buffer pool memory, whereas Enterprise edition is limited only by the OS for compute and memory.
Web edition. Appropriate for production environments but limited to low-cost server environments for web applications.