You're overwhelmed with database optimization tasks. How do you decide where to focus your limited resources?

I am sharing my real time experience. I would like to start with knowing the complete env before getting into any assessments. If you have any knowledge database about the env digest that first and if any stale information update with the latest info so that others also will get benefited. Once you know the base env then learn what type of workloads application will handle like whether it is OLTP/OLAP etc. Once you know some information about the workloads then dig into frequently used objects / packages / routines etc. If system is in good shape then also dig for improvements this should starts from lower envs to prod envs. Keep update the knowledge db about the high priority tasks so that every one will aware of these loads.

When faced with the challenge of optimizing a database, it’s crucial not to jump directly into making changes without knowing where the problems lie. Optimization refers to the process of making a database run more efficiently, reducing query execution times, improving resource utilization, and ensuring that the system is scalable and performance under various loads.

I would begin by identifying and addressing the most impactful areas for improvement. Prioritizing these critical tasks can lead to significant performance enhancements and more efficient resource management in the database. Often, once these key issues are resolved, other problems naturally start to diminish or become easier to manage.

You MUST determine where you will get the biggest benefit from your time. I use the 'WORST AGGREGATE TIME' method. In other words, you find the process that is wasting the MOST time during some timeframe (day/week/month). How many times is the process executed and how inefficient it is will tell you where to start. Work on that ONE process. Make that process BETTER using iterative improvements. It doesn't have to be perfect. Once the worst offender is better, move to the 'new' worst offender and repeat.

Start with the users, not the database. The database doesn't care if it is slow or fast; people do. It sounds counter-intuitive, but the slowest query or process is rarely an issue, but an important query in a process that people really care about is always an issue. If you don't know what is important, ask, and the users will tell you exactly what needs to be optimized and why. Find that 3-minute critical process and make it run in 3 seconds or less, and they will love you. Don't waste time optimizing that 3-hour job that nobody cares about.

Your top priority isn't usually made unilaterally. You are part of a team. In the unlikely event of a production outage that will always be the top priority but below that is there a release that needs to be migrated to production, a database that needs to be created or performance tuning that needs to be done. This is usually made clear while speaking with your teammates. What would have the highest payoff. What project is the most important the the highest number of people.

3 point analysis : 1. Infrastructural analysis: Ex. Available resources and utilizations, CPU core and assigned ram for each core, disk i/o R/W , version. 2. Configuration Analysis. Ex. my.ini, Change the default storage engine to InnoDB. 3. Executional analysis. Ex. Slow query idetification and rewrite with optimized query, indexing, normalization.

In order to isolate the problematic areas start with low hanging fruits like setting the database and system parameters for optimum performance. Then look for easily fixable queries using plan fixing and lastly fix the rest using query tuning or rewriting. Then we can switch to other areas like application tuning,backup optimization,etc.

I would begin by identifying key issues to be resolved, other problems naturally start to diminish or become easier to manage. Database optimization is an evolving process which keeps changing based on the amount of data in the system as well as the number of users or load in other words connecting to the system and query for the data. When facing overwhelming database optimization tasks, start by pinpointing critical issues impacting performance. Assess their effects, estimate the resources needed, and prioritize based on impact and effort. Align tasks with business goals and continuously monitor results to reassess priorities. This strategic approach ensures efficient use of limited resources for maximum impact.

Optimizing queries is essential for performance improvement ??. Use tools like Explain Plan to analyze slow-running queries ???♂?. Rewrite queries for efficiency, adjust indexes ???, and avoid unnecessary joins or subqueries. Streamlining queries reduces load times and enhances user satisfaction ??.

Since I work with SAP Hana database, the usual process that I follow is to make sure that the expensive statements trace is active so it can capture all the statements beyond the threshold we have set while configuring the trace. Then, we can sort them and identify which statements are occupying the most memory and CPU along with other details, e.g application and DB user and the runtime. This information will help us coordinate with the respective teams so those queries can be optimised. Eventually this will significantly help in improving the overall performance of the database.

3. Optimize Queries Optimizing queries is often the quickest way to boost database performance. Utilize tools like Explain Plan to analyze slow queries and identify areas for improvement. Focus on rewriting inefficient queries, adjusting indexes, and eliminating unnecessary joins or subqueries. Streamlined queries can lead to significant reductions in load times, enhancing both performance and user satisfaction.

Once the bottlenecks are identified, check why the bottleneck is occurring. Bad written query? Wrong or non-existent index? Unnecessary joins? Too much data overloading the server? Once you get these answers you will know what to do.

Indexes are crucial for quick data retrieval but must be managed efficiently ??. Review your indexing strategy to ensure appropriate indexes are in place and eliminate unnecessary or duplicate ones ??. Create new indexes that support current query patterns to cut search times and boost overall performance.

Not just indexes need to be reviewed. Sometime more impactful in reporting warehouses (which often DON"T USE INDEXES) it's more inprotant to understand how the DBMS handles storage and retrieval of data. Often, setting different partitioning, sorting, or replication amongst nodes (such as in Redshift) can highly impact query execution. I had a specific Redshift query at Amazon. It took 6+ hours to process daily clickstream data. By understanding access patterns, I cut that down to about 45 minutes, by changing how the data was partitioned. It actually saved me time to broadcast a smaller table to all 96 nodes, than to run the query as it was, because that was causing a larger table to be broadcast to all 96 nodes as originally designed.

Check the database structure (and compare with the current running database to see if it's the current scenario yet) and work on the indexes. Periodically review and maintain indexes to remove unused or redundant ones. Consider using database-specific maintenance tasks and tools to rebuild or reorganize indexes.

4. Review Indexes Indexes are essential for fast data retrieval, but they need careful management to avoid unnecessary overhead. Regularly review your indexing strategy to ensure that your indexes are still relevant and effective. Consider dropping any unused or duplicate indexes that may be dragging down performance and create new ones that align with your current query patterns. A well-optimized indexing strategy can significantly reduce search times and boost overall database performance, making it a crucial step in maintaining an efficient database system.

Developers tend to create an index for every query or report they design making their query/report run faster. However they tend to overlook the fact that each insert/delete will take longer due to each index being updated. Deletes will also make the indexes inflated requiring rebuilding of indexes. Optimal design review should be done to identify the indexes which can be removed.

While maintenance automation is definitely a must, proactive monitoring and alerting can save the day and avoid the problems before they reach high scale. When a new challenging workload is in the development phase, proactive monitoring can spot performance problems even before the load or concurrency testing is in place and catch issues in queries, functions and procedures before they are promoted to higher environments. Proactive monitoring also allows to detect patterns in performance that can be a result of an upgrade, new release or performance degradation caused by data volume growth.

Statistics are a DBA's best friend and they are supposed to be up to date. They are the parameters which are used to generate the best execution plan. Automating the stats collection job as well as index rebuilding job helps in minimizing outages and rules out chances of DB going haywire due to missing stats or inflated indexes. DBAs can focus more on identifying erring queries and try and tune them or send them back to developers for rewriting.

5. Automate Maintenance Automation is a powerful tool in database optimization. Implementing automated maintenance tasks like index rebuilding and statistics updates ensures that your database remains in top shape. Not only does automation save valuable time, but it also helps prevent gradual performance degradation. By setting up regular maintenance jobs, you ensure that routine optimizations are consistently handled, freeing you up to concentrate on more complex and strategic tasks. Embracing automation is key to maintaining an efficient and reliable database system.

Automation is key in database optimization ??. Implement automated tasks like index rebuilding ?? and statistics updates to maintain peak performance. Automation saves time ? and prevents performance degradation, allowing you to focus on more complex optimization tasks.

The most common tasks to automate should not only be maintenance. The space in the Disk should be monitored, depending on the engine that the filegroups have room to grow. alerts for queries with more than 10 minutes in execution. and the typical maintenance processes of rebuilding every other day, recreating every week if the transactional level is very high, and the statistics after the maintenance of the indexes.

Using best practices in schema design,using caching mechanism and to achieve better hit ratios, considering partitions in large tables and attention storage config on tables to handle pressure, use caching techniques on client side to decrease load in database, transfer some possible read on other type of databases that works as a proxy caching such Elastic search, Casandra, Times Ten,.. Using archiving technology to maintain old data in cheaper storages, and if possible migrated to Cloud databases to minimized cost maintenance, tuning queries and Tuning schemes is top of your decision to using minimal resources.

Avaliar o ambiente de dados existente; Avaliar o estado actual vs capacidade dos recursos existentes ( espa?o de armazenamento, memória, processamento e outros ); Fazer o diagnóstico se os recursos est?o a ser realocados de acordo a necessidade; Garantir que a aloca??o dos recursos seja atribuído mediante necessidade de consumo de forma a n?o haver desperdício; Monitoriza??o da estatística de performance e do consumo das Base de Dados.

Consider scaling resources ??? as a solution to performance issues. This could involve upgrading hardware, increasing memory ??, or expanding storage ??. Evaluate if the performance gains justify the costs ?? and consider if smaller tweaks might resolve bottlenecks without significant investment.

To effectively scale resources amidst database optimization tasks, prioritize high-impact areas that align with business goals. Leverage automated tools to handle routine optimizations, freeing up resources for critical issues. Assess and allocate resources based on performance data and stakeholder input. Implement solutions incrementally, balancing immediate needs with long-term improvements to ensure efficient and sustainable scaling.