What techniques can optimize SQL Server queries in high-traffic databases?

Create indexes strategically based on query patterns. Use covering indexes to minimize key lookups. Regularly monitor index fragmentation and rebuild/reorganize indexes as needed.

Indexes are powerful tools for optimizing queries. However, a "set it and forget it" approach can lead to performance problems. Over time, data changes require adjustments to existing indexes. Too many or poorly maintained indexes can hinder query execution. A proactive approach to index management ensures optimal performance.

In my experience, most current systems tend to have redundant or inefficient indexes. In my projects, I often optimize by evaluating and removing these ineffective indexes. To identify unused indexes, we can use various methods. For example, enabling the 'Monitor Index' parameter in the database

In high-traffic databases, it is very essential to optimize the sql server queries else you can face common issues of blocking, deadlocks and slow performance. There are few techniques that you can utilize to reduce these, like having proper database maintenance plans for index maintenance, stored procedure recompilations & stats update, setting appropriate isolation level in queries, use of proper query hints, running the queries in small batches, use of the stored procedures & views instead of querying base table directly. Keeping tempdb data files in separate SSD drives for avoiding tempdb contention. Drop unused, duplicate and hypothetical indexes that uses storage & impacts writes & updates to database.

One overlooked aspect of indexing is that the indexes likely need to change over time. As Developers write new code for new features, they build new tables and add columns to existing ones. These changes mean that index paths change to accommodate new queries. When the workload changes, it is likely that indexes also need to change. So, you can't create indexes one time and walk away.

The performance of SQL statements is a major factor affecting the overall performance of the database. To optimize these statements, here are some points I often apply in projects: 1. Before executing on the system, check the execution plan of the SQL statement using EXPLAIN to assess how it may impact performance. Any statements involving a FULL TABLE SCAN in the execution plan need to be reviewed, especially when dealing with large data tables. 2. Eliminate the use of regular data tables for temporary data storage, and prioritize using temporary tables instead. 3. Avoid using overly complex SQL statement

Refining queries can significantly reduce execution times. Breaking down complex queries into simpler parts can assist the SQL Server optimizer in generating better execution plans. Utilizing temporary tables to store intermediate results can also be beneficial for complex queries. This method allows for better management of query components, leading to improved overall performance and reduced execution times.

What helps the most for query tuning is having a thorough of understanding of the query optimizer which often means you need to have a thorough understanding of statistics as well. While there are lots of tactics you can take such as breaking queries into multiple pieces to "guide" the optimizer to a better plan... knowing what tactics to use when can be guided by understanding the optimizer. Lastly, understand your data and design! Dramatic skew in data can cause signficant swings in query plans (parameter sniffing, etc.) may take significant time to tune or develop around depending on severity of the performance degradation seen.

Relational databases store information in tables, with columns that are analogous to elements in a data structure and rows which are one instance of that data structure. SQL Select retrieves those rows that satisfy the search criteria.

One of my experiences in optimization projects: In databases like SQL Server and Oracle, the system can become extremely slow when statements cannot reuse execution plans. The main reason is that developers do not use parameters but directly pass values in the statements. For example: SELECT * FROM EMPLOYEES WHERE SALARY = 100; SELECT * FROM EMPLOYEES WHERE SALARY = 200; Although the execution plans for the two statements above are the same, the system has to re-analyze the execution plan because the user directly passed the values (100 and 200). You can learn more about this issue by looking up 'parameter sniffing' in SQL Server or 'bind variables' in Oracle.

I come from the old school and didn’t use cache as such; instead, I used temporary tables, which serve a similar purpose. Running queries on queries is not an optimal way to "attack" a database. Creating temporary tables is more efficient because a CREATE TABLE operation is faster than a SELECT * FROM (SELECT ... ). Store results in cache, use temporary tables, and leverage pre-calculated fields (often real-time queries are not necessary for every situation). There are many similar techniques to accelerate complex queries and optimize performance times, such as indexing, partitioning, and utilizing indexed views. These methods help streamline data retrieval and reduce the load on the database during high-traffic periods.

Plan application level caching techniques like Redis or Memcashed to cache frequently accessed data by queries and reduce load on database

The execution plan cache in SQL Server is a treasure trove of information about queries that have recently executed. In addition to query text and execution plan details, a wide variety of statistics, options, and parameters are available to research, if needed. This information may not always be needed for troubleshooting a performance challenge, but when it is, knowing where to go and how to use it can be a massive time saver.

Try to use stored procedures more. Their plan are becoming better over the time with looking at statistics. We should consider dbms’s objects and feature as much as we can. Don’t be conservative and just follow the same path all the time.

In my projects, the issue of lock escalation can have a significant impact, even causing the database to hang. To address locks, there are a few special considerations to keep in mind: Do not modify or compile stored procedures while the system is running. Do not rebuild indexes while the system is active. Do not modify the DDL (Data Definition Language) of tables while the system is running

In short, give attetion to Key Lookups that bring more than 25 lines. This type of Lookups that obviously uses the Nested Loop can start a behavior called Prefetch. This will change the isolation level of a portion of the query to Repeatable Read, wich can hold more lines and trigger Lock Escalation

I've encountered a situation where the application sends many individual updates instead of batching them. It seems that consideration should be given to row sets, not just single rows.

In my point of view, this change affects the basic rules so be careful and just change it when you are sure how it behave in your DBMS.

If one finds oneself having to manipulate locking using query level hints, the first thing is to step back and review the design of the database. Locking manipulation is always a plaster and never the cure!

As per my experience we need to carefully examine the Query Execution Plan and identify bottleneck resources once you know the problem then you can find out appropriate solutions.

Unless dealing with a multi-tenant system with under-provisioned resources, Resource Governance is not too helpful. Think of it as a carpet on the floor for muffling the noisy neighbours.

Optimizing SQL Server queries in high-traffic databases involves creating appropriate indexes, avoiding SELECT *, using proper JOINs, and filtering early. Regularly update statistics, rebuild indexes, and analyze execution plans to identify bottlenecks. Ensure the database is properly configured, use temp tables, minimize lock contention, and monitor performance using tools like SQL Server Profiler. Training and adherence to best practices are also crucial.

Use Resource Governor to manage SQL Server workload and system resource consumption. Workload Management: Classify and manage workloads to ensure critical tasks get priority.

Database resource governance can take many forms, one of which is using read replicas. By isolating processes that only need to read specific records, read replicas offload the main database, which handles both read and write operations. This separation allows the primary database to focus on write-heavy tasks while read replicas efficiently handle data retrieval. Additionally, multiple read replicas can be created to further distribute the load, enhancing overall performance and scalability.

Performance monitoring is just a tool; the most important thing is to know what to monitor. In different databases, you can use different tools, but the mindset is the same. This is how I monitor performance in my projects: Step 1: Monitor for unusual waits. Waits are a key factor in performance. You can view these waits through dynamic performance views or tools such as: For Oracle, view in the AWR Report For SQL Server, view in the SQL Query Store Step 2: Identify which statements are causing the waits. Step 3: Identify which objects are being processed the most.

SQL Server Profiler and Extended Events: Use these tools to monitor and analyze query performance. Dynamic Management Views (DMVs): Use DMVs to get insights into query performance, indexing issues, and server health. Query Store: Utilize Query Store to track query performance over time and identify performance regressions.

Continuous monitoring is key to keeping a SQL Server optimized. We can use SQL Server Management Studio (SSMS) and Dynamic Management Views (DMV) to monitor query performance and identify bottlenecks. We can automate our task using SSMS and DMVs. If you have the ability to purchase a third party tool, you would have time to focus on other tasks, however, you could implement in a small company a query monitoring tool using SSMS (T-SQL) and DMVs without problem.

Dpending on your organization there's a lot of directions this can go. From my experience what works best is leveraging third party tools to handle the vast majority of your monitoring and building any specific monitoring you need to handle domain/product issues (and prioritizing that according to business or operational needs). Using a third party tool means accepting that some numnber of the features of a given tool will fit the needs of your team, business unit, or organization and other may be useless and/or cumbersome. Take your time to evaluate monitoring tools thoroughly as the switching costs in terms of financial, switching, and time can be quite high.

A good first step is to gain a detailed understanding of the specific environment and query characteristics. As part of maintenance, keeping statistics up to date ensures that the query optimizer can make informed decisions and appropriately use arithmetic corrections to improve performance estimates. cardinality.

With the millions of databases and database use cases out there, do not jump to the conclusion that your immediate problem is unique, the chances are that there are helpful professionals out there that have forgotten more than you will ever learn when it comes to solving performance problems.

At times following best practices might not cut it. It is absolutely essential to be thorough in the system design phase. More often than not performance limitations are implicitly imposed by the design of the database layer. In my experience with such cases, a denormalized architecture where one reduces the number of joins/lookups have much better scalability factors when compared to a normalised database.

Un ejemplo de la utilización de SQL en el manejo de la administración financiera es la complementación con power bi, lo cual permite transformar datos en visualizaciones comprensibles facilitando la toma de desiciones, basados en datos en tiempo real mejorando la eficiencia operativa y la gobernanza de la información, lo cual maximiza la productividad.

N?o se faz desempenho sozinho. Deve-se considerar um estreitamento entre DBA e DEVs, de maneira que ambos entendam os detalhes das atividades de ambos. O DEV deve estar ciente das responsabilidades do seu próprio código fonte, para evitar que o back envie "sujeiras" ao banco. Por outro lado, se necessário, o DBA deve procurar compartilhar com a equipe DEV técnicas de tuning para que, desde o envio até o recebimento/processamento da instru??o no banco, o desempenho permane?a otimizado. Outra coisa importante, ainda nas atribui??es dos DEVs, é que cada transa??o aberta, enviada e aberta no banco precisa ser fechada, ou o banco vai acumular transa??es abertas e isso, é uma das coisas que mais "mata" processamento, memória e HD no servidor.