Introduction
High concurrency can expose subtle performance bottlenecks in SQL Server, particularly those stemming from spinlocks and latch contention. Both mechanisms exist to synchronize access to shared data structures, yet they operate differently and require distinct troubleshooting approaches. By recognizing how they work and knowing what causes them to overload a system, DBAs can reduce CPU spikes, timeouts, and overall application slowdowns.
Spinlocks: A Lightweight Synchronization Mechanism
Spinlocks are internal synchronization primitives designed to protect in-memory data structures for very short durations. Instead of immediately giving up the CPU to the operating system, a spinlock allows a thread to “spin” briefly, checking repeatedly if a resource becomes free. This approach avoids the overhead of context switching when lock waits are expected to be minimal.
However, in high-concurrency environments, too many threads spinning for the same resource can trigger steep CPU usage and block other operations from using the scheduler. The system appears busy, yet throughput may plummet because excessive spinning delays access to critical structures.
Diagnosing Spinlock Contention
Spinlock contention typically manifests as high CPU consumption without a corresponding increase in database workload. To pinpoint problems, DBAs can use sys.dm_os_spinlock_stats
to check back-off counts for each spinlock type. Extended Events that capture spinlock_backoff
activity offer further clarity by highlighting when and where spinlock collisions occur.
Excess spinlock back-offs often relate to plan cache management (for example, SOS_CACHESTORE spinlocks), worker scheduling, or memory-optimized table activity. In such cases, improving parameterization, reusing plans, or batching large DML operations can ease the burden on these in-memory structures.
Latch Contention: Coordinating Access to Shared Resources
While spinlocks protect small in-memory structures, latches serve as locks on pages, index structures, and other shared resources. Latches can be taken in different modes—such as shared (SH), exclusive (EX), or update (UP)—depending on the type of access requested. Contention arises when multiple sessions require incompatible latch modes on the same resource.
Frequent latched page splits, high-volume inserts in tempdb, or large update operations can lead to long-running PAGELATCH_EX
waits, which block sessions until the resource becomes free.
Diagnosing Latch Contention
Latch contention frequently shows up as PAGELATCH_xx
or BUFFERLATCH_xx
waits in sys.dm_os_wait_stats
, and DBAs can also employ Extended Events to log latch acquisition and release. By correlating latch waits with particular tables or indexes, it becomes easier to identify whether heavy DML, page splitting, or intense tempdb usage is the underlying cause.
Mitigation Strategies
Addressing spinlock or latch contention often involves a balance of performance tuning and structural changes:
- Optimize Plan Caching: Improve parameterization or reduce ad-hoc query generation to alleviate SOS_CACHESTORE spinlocks.
- Batch Large Transactions: Group or throttle bulk DML operations to shorten lock durations on heavily contended structures.
- Distribute I/O: Spread tempdb and heavily accessed data files across multiple drives to avoid page-level hotspots.
- Adjust Index Maintenance: Lower fill factors for frequently updated indexes to mitigate page splits.
- Partition Large Tables: Segment large tables to isolate hotspots and reduce latch contention on heavily accessed pages.
- Leverage Extended Events: Capture
latch_acquired
orspinlock_backoff
events to pinpoint which queries, tables, or indexes drive contention.
Conclusion
Spinlocks and latch contention do not typically dominate everyday DBA tasks until high concurrency or specialized workloads bring them to light. When contention does become problematic, systematic investigation of spinlock statistics, latch waits, and Extended Events can reveal hot spots that tax CPU time and reduce overall throughput. Addressing the issues usually requires a combination of architectural changes—such as partitioning or distributing tempdb—and careful tuning of queries and indexing strategies. By proactively monitoring these underlying synchronization mechanisms, DBAs can ensure SQL Server scales gracefully and remains responsive even as workload demands grow.
Further Reading: