What are effective ways to prevent common database performance issues?

Before selecting a database, it's important to understand the structure of your data and how it will be used. This includes understanding the relationships between different data entities, the types of queries that will be performed, and the expected read/write patterns.

Common database performance issues could be 1. DB performing slow. 2. Database queries getting timed out. 3. Database blocking and deadlocks. 4. Database long running queries 5. Inadequate resource allocation. Possible causes for db performance issues are: 1. Database design : It is important to understand and design your database as per recommended practices for it to perform optimally. 2. Creating DB maintenance plans : With time, the data inside database keeps growing and frequent DML operations could cause data fragmentations in database impacting its performance. 3.Resources : Any DB engine requires adequate resource allocation (CPU, Memory and Storage ). 4.Query design : Application's query is the main culprit for database blocking.

Optimize CPU Utilization, Enough Disk Space and Memory allocation. If these are not well configured, databases are going to run into performance issues.

Making sure we have good database design. Normalization, Making sure SQL Server is running on a domain account, Allocating proper resources to SAL Server, -TempDB configuration. -MAXDOP configuration. - proper indexing and handling statistics. - Tuning queries to avoid waits: blocking, deadlock. -parametizing frequently used queries

Choosing the right database is an important first step. The database technology should match the specific needs of the application, process, or service. For instance, highly transactional environments may benefit from an in-memory database like Redis or an in-memory cache system like Memcached for speed. A relational database like PostgreSQL, MySQL, MS SQL or Oracle Database might be more suitable for complex queries with strong consistency requirements. Be sure to thoroughly understand the costs associated with launching and maintaining each platform -- instance sizing, networking, clustering, redundancy, disaster recovery, backup, and data movement.

- Before designing your schema, it's important to understand the structure of your data and how it will be used. This includes understanding the relationships between different data entities, the types of queries that will be performed, and the expected read/write patterns. - Normalization is the process of organizing data into tables to minimize redundancy and dependency. This can help improve the efficiency of your database by reducing the amount of data that needs to be stored and retrieved.

Schema design will of course provide good performance because that way we will be able to design tables well and it will be easier to organize data and storage.

A well-designed database schema is normalized to reduce data redundancy and improve data integrity. However, over normalization requires excessive JOIN operations, which can slow down database performance. For read-heavy operations, denormalization can improve performance. An effective indexing strategy (primary keys, foreign keys, clusters, distribution keys, sort keys, etc.) will optimize query execution.

Schema needs to support data company's lifecycle. Not only collecting but also removing. Nowadays even relational database should be designed as flexible as possible. Choosing database engine which supports online schema changes is huge advantage. On the other hand 99% of performance problems is caused by not optimal schema design and bad SQL. Young developers do not want to learn 50 year old technology. They keep faith in ORM.

Optimizing SQL is time consuming. Nowadays time to market is very short. And we need to remember that development environment e.g. database size is not comparable with production. SQL flying with few records in dev can be very slow in production with billions of records. Rarely developer knows what execution plan is. And is willing to learn how to use it.

Database queries can have a significant impact on the performance of your database. Make sure to optimize your queries by using indexes, limiting the number of queries per request, and minimizing the amount of data retrieved.

This optimization query is part of the tuning required, of course if we have designed it from the start it will reduce one of the tuning options if one day we have to tune performance. So design your query well.

Slow and inefficient queries cause significant delays / increase wait times. Use EXPLAIN statements to understand the query execution plan and how the plan uses indexes. Avoid SELECT * statements, only select the columns and rows needed for an operation, minimize the use of subqueries, and write queries that take advantage of indexes.

monitoring plays an important part in knowing early and in when there is a performance issue, and when monitoring is done well performance issues can often be resolved more quickly

There are many monitoring tools available that can help you track the performance of your database. These tools can provide real-time metrics on CPU usage, memory utilization, disk I/O, and other key performance indicators. Some popular monitoring tools include Prometheus, Grafana, and New Relic.

Monitor system, host, and database metrics to identify performance issues early before they escalate. Tools installed with both the operating system and the DBMS are good starting points to monitor query performance, CPU usage, memory usage, and more. Third party monitoring & observability products provide faster and often more robust access to industry-standard best practices.

To monitor performance you need to set KPI. It needs careful planing and monitoring tools as well as procedures what to do when SLA is not met. On the other hand you need the application to provide runtime metrics. Application team needs to work closely with DBAs and monitoring team to agree what are the application key parts. What should be their performance and what to do when not met.

Adjust database configuration parameters according to the overall workload. This includes settings for memory allocation, connection pooling, and caching. It's not a one-size-fits-all solution. Tune each database instance on its specific needs and regularly review those settings as the needs change. Track the changes to the configuration in a secure location outside of the database instance, host, and cluster.

tune the parameters of the following section that we need to pay attention to in the initial design and we need to adjust this as the load of a database increases so that performance is maintained well

Very rarely performance issues are caused by database configuration parameters. Usually database engine provides a lot of metrics and is well known by monitoring tools. Also good practices on how to configure the database are widely available and known. Vendors provide specialists to help. The most problems come from application side. Especially homegrown ones.

- As your database grows in size, it can become slower and less efficient. Make sure to monitor the size of your database and consider partitioning or archiving old data to keep it lean and fast. - Keeping your database software up to date with the latest security patches and updates can help prevent security vulnerabilities and improve performance.

Usually it is very easy to add data and indexes. Without procedures and cooperation between application team, developers and DBAs database can become hard to manage and not optimal performing. E.g. audit data. How log should they should be kept in the database? Application logs? Some data lifecycle is regulated by government.

Don't always go for report recommendations,understand your environments and take calls. Avoid excessive hinting of queries,paralliesm.Tune your queries so that optimizer takes the best plans. Make changes to parameters considering any cascading effects. Make sure to patch regularly,inventory gets updated and a DB bounce cleans up any held resources eventually causing slowness in long term.

Procedures are crucial. Not only written but also regularly walked through. On regular basis best. E.g. database restore. You check quality of your backups and train DBAs in restoring in one shot. You need to remember that the moment backup restore is needed in production is usually very stressful.