Why Waiting for Database Optimization Until Problems Shows Up Is A Bad Idea [2021]
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Dealing with databases can be a complex or a simple task, depending on the person in charge. But, irrespective of the way an individual sees it, database management needs time and effort to ensure the smooth and optimal functioning of the system.

This means that constant database monitoring should be on the to-do list for all organizations. This also implies that constant database monitoring is essential to a successfully running database. This ensures that no outages or issues go unnoticed thus impacting the operations of the business.  

Proactive v/s Reactive Optimization: Which one is better?

As a decision-maker, database optimization strategy needs to be the top priority for any business. This is ensured through a combined effort of proactive and reactive monitoring of the systems. But before we move any further, let us find out what is the difference between proactive and reactive monitoring?

Reactive Database Optimization

In cases where the action is taken after an issue is identified and reported, a reactive approach is followed. This is done for troubleshooting performance problems, any incidents of a security breach, or reporting of any major incidents. Reactive tuning uses the bottom-up approach and is used to work within the existing architecture of the database, clearing any bottlenecks and optimizing performance.

Proactive Database Optimization 

A more preferred approach for database optimization is proactive SQL tuning, which uses certain metrics and performance markers to notify users before the problem arises. Any abnormalities in numbers are reported so that issues can be resolved before they become major problems and hinder the working of the database.

Among the two approaches, proactively maintaining a database is advisable as it not only keeps a check on all major metrics but also helps prevent any uncalled-for issues and expenses related to them. Moreover, any DBA who knows the value of high-performing databases knows that ‘Database optimization isn’t for when a problem shows up.’ So let us look at a few reasons why Proactive is the way to go.

Why is being proactive the way to go?

 Let’s look at some concrete reasons that favor DB optimization techniques being applied in a proactive manner.

1. Reduce the occurrences of emergency troubleshooting

When a DBA deploys a proactive approach to database monitoring, they save the time it takes to find the root cause of the problem every time an issue shows up. Keeping the system online is always a priority. But addressing constant notifications and alerts leaves one with less time to get to the root of the problem.

By using a proactive approach all the large-scale data gathered over time will give the DBA required metrics and patterns to understand the problem. Once the problem is identified, addressing it will prevent the constant occurrence of errors and help DBAs save time by reducing the downtime of the system. It is imperative to note that a data structure that is initially designed might not be sufficient as the volume of data grows. Thus, taking proactive measures ensures that these potential threats can be caught early on. 

2. Identification of growth opportunities

Certain types of performance issues such as slow queries, longer wait times, and excessive resource usage can be addressed using the performance metrics of the database.

Using these performance tracking statistics one can work on the process of MySQL tuning and not only improve these metrics but also find out areas of improvement. Once the problems have been identified and solved, the same can be worked on for system optimization and to improve the user experience. This may also include creating a unique index for various columns in the DB.

3 Notification of resource usage changes and availability

To ensure that one is utilizing the output of constant monitoring of the system, the use of alarm options is very important.

Using customized alarms will help you receive alerts based on certain thresholds and conditions input by the user. It also allows one to decide the severity of the incident that triggers an alarm, disable an alarm, add manual actions to disable the alarms and create exceptions where the alarm is not needed.

Because of the advancements in the system, smart alarms can also be used in certain monitoring tools. These smart alarms will not only notify the users of any issues but also offer intelligent solutions for the same. The smart alarms will help the user with the diagnosis of the problem as well as facilitate the solution to the concerned issue.

These alarms histories also create a pattern that can further be used to identify major problems that might occur in the system and take care of them before the water crosses the bridge. 

4. Makes way for more projects 

DBAs are not only responsible for solving the issues associated with a database but are also aligned to work on certain aspects critical to a business’ growth.

By deploying a proactive technique, one gets more time to focus on other key projects associated with the business including maintenance of database security and integrity. Not only this, it allows the DBA to use automation on repetitive tasks and save valuable time that can be utilized to complete other tasks.

5. Create a throughput baseline

Tracking performance over time can help DBAs create a baseline. This baseline can be further used to track all other performance metrics and see whether the system is performing as per the expectations or not.

Important maintenance activities and updates require monitoring to ensure nothing is affected in the system. The baseline can help give a clear reflection of the before v/s after picture and help identify any new issues that might have come up in the system.

Based on the above observations, it is pretty clear why being proactive is the favorite option for a majority of the DBAs in the industry. But how exactly can we be proactive in monitoring and optimizing our databases? 

Well, there are a lot of different techniques which apply to different database systems but that doesn’t mean MariaDB optimization will not follow the principles of MySQL tuning

Now let’s have a look at some of the best practices we can use to ensure a proactive approach towards database management and optimization.

Why optimization is important? and what are the best practices?

1. Monitor resource availability and consumption

As previously discussed, monitoring the processes is a crucial part of optimizing how your system performs. A running database server will not give warnings before it fails. However, monitoring it will give you insights and patterns crucial to the identification of any issues and increase in demand for resources. 

Carrying it out during business hours as well as after them is essential to the business. Being a basic test, it will help you plan the entire work of your database. But constant checks do not require manual intervention every day. Rather, installing a monitoring tool and creating alarms should suffice the whole practice.

After keeping a check on the working of the database, one should keep an eye out for resource consumption. Resources include CPU, memory, disk, and network among other components. If any of these functions are being over-utilized or being overburdened, chances are the system might fail. Monitoring can help create alerts and whenever the system is low on disk space or low on memory, the triggered alert can help identify and replace the component at fault. 

When optimizing MySQL or in MariaDB optimization, these resource modifications can also lead to improved performance

2. Optimize your SQL queries

SQL query optimization is often ignored in the process of database optimization but as basic as it might sound, it can give great results. The basis of any SQL query optimization attempt is to clearly define the business requirements. In situations where adding missing index doesn’t improve performance, it’s another indication that the SQL statements need to be optimized.

Giving the SQL statements a defined identity, expectations, and requirements can narrow down its use and lead to optimal functioning of the same. Certain such examples include:

 a. Using SELECT instead of SELECT*: Read as “Select all”, it queries all available data from a table, and sometimes the irrelevant data is also called. Instead using SELECT will only query the required data and get results quicker.

b. Use INNER JOINS instead of using WHERE: Using WHERE creates a Cartesian join and thus calls for all possible combinations of the variables that are created and then choose the qualifying conditions. Instead, using an INNER JOIN would only generate the desired results.

 c. Running the query during off-peak hours: Analytical queries can have a major impact on the production database. Running queries during hours when the number of concurrent users is at its lowest can help one minimize the impact of these queries. The usual hours for this practice are between 3 AM to 5 AM.

3. Monitor the expense of each query: 

A major factor to be considered during query optimization is the cost of each query i.e. the amount of time it takes to execute a query. There can be multiple values for this number. After considering each of the possible numbers, select the one that uses minimum resources and takes minimum time. This process is known as cost-based query optimization in DBMS and can massively impact performance.

This is also applicable to the troubleshooting process where certain queries that are already running can prove to be expensive. Finding out these expensive queries and modifying them can help filter out issues within the system.

Minimizing such slow queries can be achieved through a proper audit of all unused large tables and checking the temporary tables that are being created, tombstone records, orphan connections, and bloated indexes.

4. Track all changes to the database: 

App developments often introduce new features and functionalities. But in the process, it might affect the database performance. It might add, drop, or modify database objects leading to slow or failed queries. Keeping a record of all such major changes can help track down the root cause of the problem in case any error occurs.

A simple way to achieve this and monitor the process is by creating a throughput baseline right after the change. Using this baseline to compare it to the before baseline can help keep a check. Another method is to constantly monitor and track any changes to the database schema.

Conclusion

If you are someone who has been ignoring the process of proactive monitoring of the databases or you are unsure whether you are following all the right methods, now is the time to reach out to a professional and get your system analyzed. With optimizDBA, you can get the required guidance from our industry experts with decades of experience working with enterprise-scale databases in database performance tuning. Do reach out to us and get all your queries clarified

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