Your database is running slow and you don't know why. What's the best way to diagnose the problem?

Database performance can slow down for innumerable number of reasons. The main categories to look for is 1) Are the SQL's performing slow than earlier, diagnoise those. 2) Are the stored procedure performing Slow than earlier, look for the individual SQL's performance inside of it. 3) Look the sqls in the package cache which has both dynamic and statis SQL's and identify different categories with monreport.pkgcache monreport module. 4) Also get the database summary details with the monreport.dbsummary() procedure to get overall details where the things are going wrong. 5) Also look at the wait times and processing times at the database level metrics

first thing here is to understand the root cause of the performance issue. Many databases provide various reports option to understand and analyse the current state of database. As performance issue could span across hardware ( cores, RAM allocated etc) , network ( bandwidth , latency etc),software & app ( including OS, database configuration and feature bugs etc), it is important to get a complete view ( via logs & diagnostics )across all these components . If there is a need to investigate further, sometimes we may also need to collect the driver logs between database client applications and database server to understand the SQL 'time spend' analysis and focus on the required component ( db client , network or driver) as well.

Check the database logs for errors messages, if found the issue then solve it. 2ndly check OS reaources (CPU, Mwmory), if there is spike check the process who is eating the reourses. 3. Check for deadlock. 4. Check space, mount point on OS level and DB level.

When analyzing logs (if possible) look for execution times and start with the longest running queries first and see if anything seems off. This might be a query that you expect to run quickly and is suddenly taking much longer than expected. Also, if you see spikes, starting a certain time, in certain queries ... it would be a good idea to pass this information onto tech leads and developers to see if any recent code changes might be responsible.

Si la base de datos y la instancia presentan una buena salud y el cambio se evidencia de un día para otro, la seguridad sería lo mas importante a revisar. Si encontramos patrones en la data o los registros podríamos creer que tal vez puede ser un tema de escalabilidad. Para tal caso a nivel de servidor y de aplicación una herramienta de monitoreo constante y con practicas adecuadas nos indicaría una solución acertada.

It’s always important to establish baselines for your production and mission critical databases and database servers. Several tools are available. Set up frequent monitoring and alerting for baseline data monitoring, so anything under/over needs a DBA’s immediate attention.

Every database has got its way of diagnosing performance issues, but they generally show parameters like io wait times, cpu, memory, top SQL Statements by elapsed time, and Wait Events stats. Example, oracle uses Awr (Automatic Workload Repository) report and MS SQL uses Database Engine Tuning Advisor, it's more graphic than the oracle but all do the same thing. Once you get these indicators then the next step is to focus on the top issues individually. E.g. Identify high io by tables and further check if the queries need new index, rebuilding or reorganizing. E.g. In SQL if the there are lots of wait times for queries then you can enable parallel execution of queries by increasing query parallelism parameter.

For PostgreSQL sse tools like top, htop, iostat, and vmstat to monitor CPU, memory, and disk usage. Check if the PostgreSQL server has enough RAM and whether it's configured to use it effectively with the shared_buffers and work_mem settings.

Para otimizar a performance do banco de dados, é vital monitorar métricas essenciais. Comece avaliando a taxa de utilização da CPU, identificando possíveis gargalos. Monitore também o desempenho do disco, verificando a taxa de leitura e gravação. Acompanhe a eficiência do cache, ajustando configurações conforme necessário. Utilize ferramentas de profiling para identificar consultas ineficientes e ajustar índices para aprimorar a velocidade de recuperação de dados. Além disso, implemente alertas para notificar sobre eventos críticos. Manter uma vigilância constante dessas métricas permite uma abordagem proativa na otimização do banco de dados, garantindo um desempenho rápido e eficiente.

Além das métricas mencionadas, é crucial monitorar a taxa de transferência e identificar erros no sistema de banco de dados. A análise da taxa de transferência fornece insights sobre a eficiência na transferência de dados, enquanto a identificação de erros permite corrigir problemas potenciais. Utilizar ferramentas avançadas de monitoramento, como o Oracle Enterprise Manager ou o SQL Server Management Studio, pode aprimorar ainda mais a capacidade de diagnosticar e solucionar problemas de desempenho. Essas ferramentas oferecem interfaces gráficas intuitivas para visualizar e interpretar as métricas, facilitando a tomada de decisões informadas para otimizar o banco de dados.

A key tool for analyzing queries is looking at your execution plan, which you can find by using EXPLAIN. Generally EXPLAIN works by adding that keyword before your query like so: EXPLAIN SELECT name FROM individuals; This should give you plan, and let you know (for example) if the query is using an index or is needing to do a "table scan" which is much slower since it needs to look at each row in the table instead of using an index, which is much faster. You can also use explain with other databases such as MongoDB: db.individuals.find({ "name": "David" }).explain() This will return JSON object with a query plan and other information about your query such as if an index was used or if COLLSCAN was performed (similar to a table scan).

Best way to tackle this problem is try to analyze explain plan, see if it is full tablscan. If it is table scan then create required index on column/documents.

I want to add one more perspective to this discussion: Most of the queries out there are generated automatically by ORMs like Hibernate or Django. for companies with a large codebase, it can be very hard to implement any query improvement(which is quite easy to improve today using LLMs). Hence, I do recommend leveraging any DB proxy solution that can re-write your optimized query on the fly.

I start with the top down approach. Are there any bottlenecks at the OS level in terms of memory/ cpu/ io. That would dictate then what diagnostics to look at at the database level. If memory is the bottleneck then is db2sysc process consuming the max memory or some other 3rd party process using up more memory starving database of memory? If it is indeed the db then dig into the sub memory components of the db. In db2 the db2pd -dbptnmem to see if any abnormality seen. Is the instance memory fully utilized or not? Then drill down the component using the memory. Also look for locktimeouts/ latchwaits that can contribute to slow down as well.

For PostgreSQL use EXPLAIN and EXPLAIN ANALYZE to understand the execution plan of slow queries. Identify missing indexes or inefficient queries with pg_stat_statements extension.

- look at the tables which you are using in your complex queries (with multiple joins) - Check if those tables are large or small, if those are large tables then check if it has necessary indexing(clustered or non clustered) if not then create indexes on top of that table. - Try again those complex queries and check if performance improved or not.

-Check the database server resources such as CPU, Space. - use query analytics tool to analyze queries and find the ones that causes issues. - check for transactions that might locking data and slow database performance.

El diseño de una base de datos debe ser flexible, lógico y metódico asegurándose que cumpla con los requerimientos del SI para el cual se diseñó y que se adapte a futuros cambios . En DB Relacional se puede optar por utilizar las formas normales de Codd, de forma tal que de asegurar que todos los atributos o campos son atómicos, que no haya columnas repetidas, y que se hayan eliminado las dependencias parciales, funcionales y transitivas. El diagrama de la base de datos permite ver y corregir aquellas deficiencias utilizando sentencias SQL como CREATE, ALTER y DROP. Una vez optimizado el diseño se puede pensar en la necesidad crear otros objetos de base de datos como índices, restricciones, vistas, SP, etc.

You can configure your database management system to log the queries that are not using indexes so you can identify unoptimized queries faster! In MySQL for example, the variable name is log_queries_not_using_indexes.

Es la solución más recomendada para corregir alguna situación, sin embargo realizar algún cambio de estructura es algo que se tiene que analizar primero para tratar de prevenir alguna situación no deseada, por los que se tiene que realizar primero en alguna copia de la base de datos y validar que afectaciones tiene este cambio. Si las afectaciones son manejables se procede con los cambios. Pero mi pregunta sería: tiene el control de la aplicación que tiene esta base de datos?

Es una situación ideal el llevar este control. Se ocupa de tener dos copias de dos bases de datos, dos aplicaciones con acceso, personas dedicadas para realizar esta labor, tiempo para realizar estas pruebas, y la operación de los sistemas y las métricas de uso de recursos.

It seems so obvious as to not bear mentioning but it's often the obvious things that get overlooked. Don't forget to create a repeatable, atomic, simple benchmark which can be tested both before and after any changes are made. "Running slow" is a nebulous problem to resolve. Taking 25 milliseconds to run a _specific_ query can be bench marked and hence we can figure out if we're actually improving or not.

Ensure that proper indexes are in place. Missing or poorly designed indexes can significantly impact performance. Analyze the execution plans of slow queries to identify areas where indexing improvements can be made.

Collecting statistics will help .The demographic information/ statistics collected will help optimizer to create a efficient query plan.But is important to ensure that statistics are updated.Stale stats are worse than no stats

When troubleshooting a slow database, consider network latency, resource utilization, indexing, database configuration, and query execution plans. These factors complement options like checking logs, monitoring metrics, analyzing queries, optimizing design, and testing changes, enabling comprehensive diagnosis and resolution of performance issues.

Call in a consultant - like myself or one of the other experts who responded here. I have 40 years experience and can fix most data queries. Sometimes, though, the query has to be rewritten from scratch and sometimes the data has to be restructured.