Introduction
Database optimization and performance tuning within SQL Server are crucial for achieving smooth and responsive application experiences. An often overlooked but crucial component in this quest for performance is the SQL Server Procedure Cache. This article delves into its significance, its operation, and provides guidance on how to manage it effectively.
The Procedure Cache is essentially a dedicated memory space within the SQL Server buffer pool. Its primary role is storing the execution plans for T-SQL code. Execution plans, often likened to “road maps”, guide SQL Server on data retrieval. The benefit to this is that SQL Server avoids the overhead of recompiling queries or stored procedures on every run. Instead, it looks to reuse previously compiled plans from this cache, streamlining operations and conserving resources.
Operational Mechanics of the Procedure Cache
So, how does SQL Server decide when to cache and when to reuse? The process is layered:
- Plan Generation: Here, the optimizer crafts an efficient execution plan tailored for the query.
- Plan Storage: Once optimized, the plan finds its resting place in the procedure cache.
- Plan Reuse: On subsequent runs of similar queries, SQL Server scouts the procedure cache for a fitting plan. It will reuse a matching plan, but in its absence, a fresh one gets generated.
Why the Emphasis on Procedure Cache?
Three primary reasons underscore its importance:
- Performance Boost: Reusing execution plans sidesteps the need for SQL Server to invest time in crafting a new plan from scratch.
- Resource Conservation: Plan compilations are resource-guzzlers. Reusing shrinks CPU and memory consumption.
- Predictability: A consistent cache means consistent execution plans, translating to consistent performance.
Monitoring the Cache Using DMVs
DMVs, or Dynamic Management Views, are SQL Server’s internal surveillance cameras. They provide a lens into the procedure cache’s health and state. A particularly handy DMV is sys.dm_exec_cached_plans.
To get an overview of your procedure cache:
SELECT * FROM sys.dm_exec_cached_plans;
For a detailed perspective on specific plans, combine it with other DMVs:
SELECT cp.*, q.text, p.query_plan
FROM sys.dm_exec_cached_plans AS cp
CROSS APPLY sys.dm_exec_query_plan(cp.plan_handle) AS p
CROSS APPLY sys.dm_exec_sql_text(cp.plan_handle) AS q;
Clearing the Procedure Cache
At times, manually clearing the procedure cache becomes necessary, especially during performance testing. But tread with caution β clearing it prompts SQL Server to recompile SQL statements, which can spike CPU usage.
For a complete cache reset:
DBCC FREEPROCCACHE;
To remove a specific plan (using its unique identifier or ‘plan handle’):
DBCC FREEPROCCACHE(plan_handle);
Clearing cache specific to a database:
DBCC FLUSHPROCINDB(db_id);
Best Practices for Procedure Cache Management
- Limit Frequent Clearing: Avoid clearing cache in production unless it’s absolutely necessary.
- Post-clearing Monitoring: Watch out for CPU spikes or performance dips after a reset.
- Optimal Cache Size: Ensure your cache isn’t overflowing with outdated plans. Revisit memory allocations or scrutinize application queries if needed.
- Beware of Ad Hoc Queries: They can congest your cache. Lean towards parameterized queries or stored procedures.
- Update Statistics: They guide the creation of execution plans. Keep them current.
- Evaluate Query Performance: Cached doesn’t always mean optimal. If certain queries underperform consistently, reconsider their design or associated indexing.
- Allocate Adequate Memory: Ensure SQL Server gets sufficient memory, balancing the needs of the procedure cache with other operations.
Conclusion
Mastering the SQL Server Procedure Cache is a blend of understanding its architecture, keeping a watchful eye through DMVs, and following best practices. It’s an invaluable asset for every database professional intent on harnessing SQL Server’s full performance potential. Proper procedure cache monitoring and maintenance will allow SQL Server to work smarter, not harder, and increase overall query performance.
There have been a few times where SQL Server has generated a new query plan, that is orders of magnitude less performant than another version that it also has in the cache, but seems to stick to the newly generate plan. The “old” query plan would have an average execution time of under 1 second, and the newly generated one would have an average time of > 30 seconds. We went through this enough times that we got pretty good at being able to identify the problem pretty efficiently, and force the old plan. This hasn’t happened in quite some time now, but I never understood what caused this to happen in the first place. Maybe you could provide some insight on a couple of things.
1. What are the most common reasons SQL Server decides that it should generate a new plan for something that it already has a plan for in the cache?
2. In the case where it generates a new plan, but performance is significantly worse than the prior plan, why wouldn’t it revert back to the old plan after some period of time?
Thanks again for another informative and well written post!
Hi Patrick! Thanks for the insightful reply. This isnβt an easy straightforward answer, but I will give it my best shot. π
The query optimizer tries to find the most efficient execution plan for a given query, but it can at times produces a less optimal plan because of things related to the environment. One of the most common reasons is stats updates, where SQL uses stats to anticipate data distribution in tables. If there’s a significant shift in data distribution, due to large data modifications like inserts, deletes, or updates, the stats are refreshed, potentially leading to a new plan. Plans can also be recompiled due to changes in set options, schema changes related to the tables referenced by the query, or even minor changes in the query itself.
Other factors include eviction from the plan cache, typically due to memory constraints, and circumstances like database restarts, manual flushing of cache, or plan aging, which can push plans out of the cache. The next execution of a query would then trigger the compilation of a new plan. Iβm sure I am forgetting other possible causes, but these are the more common ones I believe.
Once SQL generates a new plan, it doesn’t compare its efficiency against the previous one. It lacks a mechanism to remember performance history of past plans for a particular query and doesn’t revert to an older plan even if a new one proves less efficient. This makes the system stateless in that regard. Even if a plan is a result of updated statistics, SQL Server presumes that the newer stats and resulting plan better represent the current data distribution. When a plan gets cached and reused, it can become sticky, and even if less than ideal, the system chooses it to avoid the overhead of recompiling.
There are new Automatic Tuning and Automatic Plan Correction features that were added in 2019 and 2022 that allow SQL to identify queries that have been executed multiple times with different plans. It then enforces the best performing plan. It can help alleviate the plan regression issues in a lot of instances. In situations where the plan constantly degrades over time, manual interventions are sometimes the only way, doing things like deploying plan guides, forcing plans with query store, or using hints to force a stable performing plan.
Did I answer your question? Please let me know if I can help further.
Thank your for the thorough response!
We experienced this due to statistics updates as you indicated. We had automatic statistics updates enabled, and when a couple of our larger tables would get enough churn on them to trip the threshold, stats would update. The stats update itself could be resource intensive. If it also caused a new bad query plan to be generated, we found ourselves in a bad spot. We ended up disabling automatic statistic updates and instead started running it nightly during off hours.
When we encountered this problem, we were running in SQL Azure with the automatic query regression feature enabled. It was probably working 99% of the time, but as you indicated, sometimes manual intervention is the only way. We would typically force a specific plan to stabilize the system immediately, then make sure we had the correct indexes in place so that if the same query plan was generated again, we wouldn’t have the same issue.
We have discussed plan guides and hints in the past, but have been able to avoid having to rely on them. While I am sure they are needed in some instances, it always seemed like the type of thing to avoid using, if possible.
Thanks again for another helpful article and reply!