The Problem
When tables use an identity column as a clustered primary key, high-volume concurrent insert operations often create a performance bottleneck known as last page insert contention. This occurs because all insert threads compete to write to the same last page of the index, causing PAGELATCH_EX wait types to dominate your wait statistics.
In production environments with heavy insert workloads, this contention can cause significant performance degradation. Testing shows that in extreme cases, PAGELATCH_EX waits can account for 40% or more of total wait time, with wait times reaching thousands of seconds across concurrent threads.
Understanding the Root Cause
The issue stems from the sequential nature of identity values. When you have a clustered index on an identity column combined with multiple concurrent insert operations and high transaction volume, every new row must be inserted at the physical end of the table. This creates a hot spot where all threads queue up waiting for exclusive access to the same data page, resulting in severe PAGELATCH_EX contention.
Think of it like a single checkout line at a busy store where everyone must wait in sequence, even though there's plenty of space elsewhere in the store. The physical constraint of sequential ordering forces all activity through one bottleneck point.
Solution 1: Use OPTIMIZE_FOR_SEQUENTIAL_KEY (SQL Server 2019+)
SQL Server 2019 introduced a specific feature to address this exact problem: the OPTIMIZE_FOR_SEQUENTIAL_KEY index option. When creating your table, you would define your primary key constraint with this option enabled by adding "WITH (OPTIMIZE_FOR_SEQUENTIAL_KEY = ON)" to the primary key definition on your identity column.
This option introduces a new B-tree insertion flow control mechanism that intelligently manages concurrent inserts. Instead of all threads competing equally for page access, the system prioritizes threads that can complete their operations quickly with minimal delay. This reduces the queue buildup at the last page by essentially creating a smarter traffic management system that lets faster operations proceed while briefly holding back those that would take longer.
The performance impact is substantial. Testing with 100 parallel threads inserting 10,000 rows each showed dramatic improvements. Without optimization, the operation took 1 minute 54 seconds with 3,503 seconds of PAGELATCH_EX wait time accumulated across all threads. With OPTIMIZE_FOR_SEQUENTIAL_KEY enabled, the same operation completed in 1 minute 31 seconds with only 520 seconds of PAGELATCH_EX wait time. This represents a 23 second improvement in total execution time (roughly 20% faster) and an 85% reduction in PAGELATCH_EX waits.
There's an important consideration when using this feature. A new wait type called BTREE_INSERT_FLOW_CONTROL will appear when this feature is enabled. This wait represents the improved flow control logic managing insertions. While this is expected behavior and indicates the feature is working as designed, you should monitor it to ensure it's not becoming excessive. In healthy implementations, this new wait type should remain modest compared to the eliminated PAGELATCH_EX waits.
A critical warning: this feature is most effective on tables with heavy concurrent insert activity. On tables with minimal insert workload, you may see no improvement or, in rare cases, slight performance degradation. The flow control mechanism adds overhead that only pays off when there's actual contention to manage. Always test in your specific environment before implementing in production.
Solution 2: Convert Clustered Primary Key to Non-Clustered
For SQL Server versions prior to 2019, or when OPTIMIZE_FOR_SEQUENTIAL_KEY doesn't provide sufficient improvement, converting the primary key to non-clustered can eliminate the contention. You would define your primary key constraint as "PRIMARY KEY NONCLUSTERED" instead of the default clustered option.
By making the primary key non-clustered, you eliminate the physical ordering requirement. Insert operations no longer compete for the same last physical page because the heap or alternative clustered index doesn't enforce sequential physical storage based on the identity column. It's like switching from a single checkout line to multiple cashiers working simultaneously, each handling customers independently without coordination overhead.
Testing with the same workload (100 parallel threads, 10,000 rows each) showed even more dramatic improvements than the first solution. With a clustered primary key, execution took 1 minute 54 seconds with 3,503 seconds of PAGELATCH_EX wait time. With a non-clustered primary key, the operation completed in just 1 minute 6 seconds with 2,234 seconds of PAGELATCH_EX wait time. This represents a 48 second improvement (42% reduction in total time) and a 36% reduction in PAGELATCH_EX waits.
However, there's a critical caveat with this approach. While this solution effectively reduces contention, it creates a heap table, which is a table without a clustered index. Heaps have their own performance implications that can be severe. Search operations become slower because the database must scan the entire table structure for many queries instead of using an efficient index seek. Fragmentation becomes more problematic, and updates can create forward pointers that further degrade performance over time.
The best practice is clear: don't leave the table as a heap. After removing the clustered index from the identity column, you must create a new clustered index on a different column. Choose a column that is frequently used in WHERE clauses, has good selectivity (meaning it has many distinct values), doesn't create a similar sequential insert pattern, and supports your most common query patterns. This way, you get the benefit of reduced contention on inserts while maintaining efficient query performance.
Choosing the Right Solution
The decision between these two approaches depends on your specific circumstances. Use OPTIMIZE_FOR_SEQUENTIAL_KEY when you're running SQL Server 2019 or later, when the identity column must remain the clustered index for query performance reasons, when you need the simplest implementation with minimal schema changes, and when your insert volume is consistently high.
Use the non-clustered primary key approach when you're running SQL Server versions prior to 2019, when you can identify a better column for the clustered index, when your query patterns don't require clustering on the identity column, or when OPTIMIZE_FOR_SEQUENTIAL_KEY didn't provide sufficient improvement in your testing.
Regardless of which solution you choose, certain practices remain essential. Always test thoroughly in a development environment first, as production issues with index changes can be catastrophic. Monitor wait statistics both before and after implementation to validate that the change actually improved performance. Consider the impact on existing queries and application code, since changing index structure can affect query plans and execution paths. Finally, evaluate your changes during peak load conditions, as performance characteristics under light load may not reflect real-world behavior.
Testing Methodology
To identify and validate these issues in your environment, follow a systematic approach. Start by capturing baseline wait statistics before making any changes, so you have a clear picture of the current state. Use load testing tools to simulate concurrent insert operations that mirror your production workload patterns. Monitor PAGELATCH_EX waits specifically during high insert periods, as this is the key metric for this particular problem. After implementing your chosen solution, compare both total execution time and wait statistics to ensure the change delivered the expected benefit. Most importantly, test with production-like workloads that accurately reflect the complexity and volume of your real system, since simplified test cases may not reveal all performance characteristics.
Conclusion
Last page insert contention on identity columns is a well-understood performance problem with proven solutions. SQL Server 2019's OPTIMIZE_FOR_SEQUENTIAL_KEY provides an elegant fix with minimal schema changes, while converting to non-clustered primary keys offers a viable alternative for earlier versions, provided you establish an appropriate clustered index on another column to avoid heap-related performance issues.
The key to success is identifying the problem through wait statistics analysis and choosing the solution that best fits your SQL Server version, schema design, and query patterns. In production environments with heavy concurrent inserts, either solution can provide substantial performance improvements ranging from 20% to 40% reduction in execution time. The investment in proper testing and implementation pays dividends in both system performance and user experience, making this one of the more straightforward performance optimizations with clear, measurable results.
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