An In-Depth Examination of SQL Server’s Query Hash and Query Plan Hash
Performance tuning in SQL Server is a critical activity for database administrators and developers alike. One common challenge is identifying and optimizing expensive queries that contribute to server load and decreased performance. This is where the concepts of Query Hash and Query Plan Hash come into play. In this article, we’ll dive deeply into these two features to better understand how they can be leveraged to monitor and improve the performance of your SQL Server instances.
Introduction to Query Hash and Query Plan Hash in SQL Server
The complexity of managing databases requires sophisticated tools and techniques for diagnosing performance issues. SQL Server provides a plethora of dynamic management views (DMVs) and functions that give insights into the workings of the server. Among these instruments, Query Hash and Query Plan Hash are vital for tracking and comparing the performance of different queries.
Query Hash is a computed value that represents the logical characteristics of a query. It is SQL Server’s way of identifying queries that are similar in nature, if not identical. This means that queries that differ slightly in their literals or conjoin with different parameters can still be considered ‘the same’ when their structural components, like the JOIN and WHERE clauses, are identical.
Query Plan Hash, on the other hand, is another computed value used specifically to identify query plans that are structurally similar. A query plan lays out how SQL Server executes a query, which includes the order of joins, index usages, and other relevant execution details. Query Plan Hash helps in understanding how changes in the query or the data can cause SQL Server to generate a different plan for the same query hash.
Benefits of Tracking Query Hash and Query Plan Hash
Understanding and analyzing Query Hash and Query Plan Hash offers several benefits:
- Provides a unique fingerprint for similar queries, allowing performance tuning to focus on patterns rather than isolated queries.
- Helps in identifying query regressions. When a query’s performance deteriorates over time, a different query plan might be a cause.
- Facilitates the detection of duplicated or redundant queries which could be optimized or consolidated.
- Lets you correlate different queries that may not have originally appeared similar at first glance.
- Makes performance monitoring more scalable by tracking groups of queries rather than individual statements.
- Assists in index tuning by allowing you to see which queries are using which execution plans, aligning with possible missing indexes.
How SQL Server Generates Query Hash and Query Plan Hash
When a query is executed in SQL Server, it undergoes a series of steps that include parsing, compilation, and eventual execution. During the compilation phase, SQL Server generates the execution plan for the query. At this point, the Query Hash and Query Plan Hash are calculated.
The Query Hash is generated based on the normalized form of the query. This involves removing aspects that do not affect the logical equivalence of queries, like comments, different whitespace characters, and the values of literals.
For the Query Plan Hash, the calculation is based on the shape of the execution plan generated. The number and type of operations included in the plan, their sequence, and the tree structure all contribute to this hash.
Gathering Data with Query Hash and Query Plan Hash
To utilize Query Hash and Query Plan Hash information, you will delve into SQL Server’s dynamic management views (DMVs). Specifically, sys.dm_exec_query_stats, a DMV that returns aggregate performance statistics for cached query plans, contains columns query_hash and query_plan_hash. By querying this DMV, you can unearth rich details on similar queries and their corresponding execution plan patterns.
SELECT
qs.execution_count,
qs.total_worker_time,
qs.total_elapsed_time,
qs.total_logical_reads,
qs.total_logical_writes,
qs.query_hash,
qs.query_plan_hash
FROM sys.dm_exec_query_stats AS qs
This SQL script retrieves the number of times a query has been executed, the total worker time, the elapsed time, the total logical reads and writes, and both hash values from the cached plans. Combining this data helps you understand and evaluate the resource usage patterns of similar queries.
Identifying and Resolving Performance Issues
Armed with data gathered via these hash values, you can start resolving performance issues.
First, identify queries with high-resource usage. These queries can then be isolated and analyzed by using their Query Hash value. After isolating the high-impact queries, examine their Query Plan Hash to review their execution plans.
In the instance where various execution plans exist for queries with identical query hashes, examine the plans to determine the most efficient one and investigate what causes the generation of less optimal plans. This could lead to actions such as query refactoring, index redesign, or updates on statistics. Additionally, if the plans are deemed equivalent, investigate environmental factors such as parameter sniffing, which can influence plan selection.
Query Store: The Game-Changer for Query Analysis
While DMVs are phenomenal tools, they are limited to in-memory data which can be lost in a server restart or after execution plan cache eviction. SQL Server 2016 introduced the Query Store, which persists query performance data across restarts and flushes. Query Store has details on query text, runtime statistics, and execution plans, making it extremely useful for historical analysis and tracking query performance over time.
By utilizing Query Store, you can retain long-term insights on Query Hash and Query Plan Hash pairs. This information can be invaluable for tracking changes in the environment that may impact query performance.
