The recent migration from SQL Server 2016 SP3 to SQL Server 2022 CU16 has introduced significant performance challenges for our client. Despite a well-executed log-shipping migration and the strategic decision to retain compatibility mode 2012, average CPU utilization has increased from 30% to 50% during business hours. A key observation is that many queries, while retaining the same execution plan shape, are now taking significantly longer to execute. This detailed analysis explores the root causes, from the new Query Optimizer and operating system to specific code patterns, and provides a clear path forward for optimization.

Migration Comparison Summary

Interactive Performance Metrics

Select a metric below to view its simulated trend based on our analysis.

Detailed Analysis: The 'Silent Saboteurs'

The core issue is a paradox: the execution plans appear stable, yet queries are running slower. This points to subtle, underlying changes in the SQL Server 2022 engine itself. The new Cardinality Estimator (CE), even in compatibility mode 2012, can influence runtime decisions, leading to suboptimal memory grants and increased I/O.

Furthermore, the use of the new Windows Server 2025 is an unquantified risk. The operating system's kernel and memory management can have a profound impact on SQL Server's performance, and this new platform may not be fully optimized for the SQL Server 2022 workload, potentially contributing to memory allocation and CPU scheduling inefficiencies.

Deep Dive: Root Cause Analysis

The performance degradation is not a single issue, but a combination of factors related to the new SQL Server 2022 engine and the operating system.

Query Optimizer and Cardinality Estimator Behavior

A key part of SQL Server 2022 is the new Cardinality Estimator (CE160). Even in compatibility mode 2012 (level 110), the underlying SQL Server engine can still be influenced by its native CE, leading to different, and sometimes less accurate, estimates about intermediate result sets. This can cause:

• Suboptimal memory grants for queries.
• Inefficient join orders, even with the same plan shape.
• Increased I/O operations due to inaccurate row estimates.

This is the "same plan, slower execution" paradox, where the plan looks identical but the internal execution is less efficient, directly causing higher CPU usage and longer query times.

TempDB Configuration and NVMe vs. RAM Disk

The migration of TempDB from a RAM disk to a high-speed NVMe disk should, in theory, improve performance. RAM disks can lead to "double RAM caching," which reduces the memory available for the SQL Server buffer pool. The NVMe disk eliminates this issue and provides excellent I/O performance.

However, SQL Server 2022 also has enhanced TempDB caching and latch-free allocation. The fact that recompilations are *increasing* for a temp table query suggests the existing code is not properly utilizing these new features, likely by violating temp table caching rules like explicitly dropping tables or altering schemas. This behavior can overwhelm the improved TempDB subsystem.

Windows Server 2025: The Bleeding Edge Risk

The new environment runs on the very recent Windows Server 2025. This is a critical, unquantified variable. The interaction between SQL Server 2022 and this new OS is not well-documented and could be a source of performance issues. External reports have noted similar slowness on newer Windows Server versions, often linked to memory allocation (`MEMORY_ALLOCATION_EXT`) and CPU scheduling inefficiencies (`SOS_SCHEDULER_YIELD`).

Deep Dive: Temp Table Recompilations

The high rate of recompilations for a stored procedure using a temporary table is a critical symptom and a direct contributor to increased CPU. Each recompile consumes CPU and adds latency.

Why This is Happening

SQL Server 2022 has enhanced temp table caching to prevent recompiles. However, this caching mechanism is sensitive to certain coding patterns. If the stored procedure is:

• Explicitly using a `DROP TABLE` statement for the temp table.
• Altering the temp table's schema after its initial creation (e.g., adding an index in a separate statement).

The new engine is likely more sensitive to these violations, triggering a recompile each time the procedure runs, which was not the case on SQL Server 2016. The client's test of converting to a table variable is a valid workaround, as table variables are not subject to these same recompilation triggers.

Analysis of Monitoring Metrics and Wait Statistics

The monitoring dashboards reveal key metrics that provide direct evidence of the performance degradation. The following table summarizes the most critical observations.

The most critical metrics are the spikes in Lock Wait Time and Latch Waits/sec. The Lock Wait Time graph shows that queries are frequently being blocked, which directly contributes to their longer execution times. The high Latch Waits indicate contention on SQL Server's internal memory structures, suggesting that the underlying engine is struggling to manage resources under the increased load. This combination of waits, alongside the CPU increase, strongly points to a cascade of issues where less efficient query execution (caused by the new CE and OS) is leading to increased resource contention.