Tackling Large Tables to Improve MySQL Database Performance

improve MySQL database performance

Oftentimes, database professionals make the mistake of jumping to conclusions when trying to improve MySQL database performance. They assume that the database must be the reason why the application has slowed down. 

In most cases, they may be right- which is why it’s important to start looking for possible bottlenecks and removing them to reduce lag. However, make sure you consider multiple forms of diagnostic data when attempting to uncover the root cause behind poor MySQL database performance. Don’t stick to just monitoring CPU usage or disk IO as relying on a single metric has greater chances of leading you to an incorrect diagnosis.

We need to look at the full picture to understand the complex interdependencies among CPU, memory, and IO. It is important to do so before making reactive changes, such as increasing disk capacity or memory. In this blog, we will take a look at one such reason behind performance bottlenecks- large data volumes.

How Large Data Volumes Affect MySQL Database Performance

Statements that cover a wide scope of data or are unrefined may fetch unreasonably large quantities of information from the database. This doesn’t seem like a problem at first when the database is new and has minimal data.

The true issue emerges as it grows in size, gradually leading to the requirement of Database Server. This is because when a statement fetches data, the data must be scanned into memory. The bigger the size of the data that needs scanning, the greater the load on the CPU, resulting in the need for burst mode due to sudden CPU spikes. This kind of usage increases the chances of your database server crashing.

Additionally, in case the data does make it from the database server, your app server may not be sufficiently provisioned to handle it. Known as over-fetching, you can overcome this problem by limiting the scope of data selection to relevant records. One way to do that is to opt for the WHERE clause in such queries- after you find them, of course.

The key to locating them is by searching through the database logs and metrics for tell-tale signs of large-scale data fetching. Although you might be able to spot CPU spikes or burst credit utilization from these metrics, it might not be easy to tell which statements are causing this specifically.

Things You Can Do to Improve MySQL Database Performance

Query optimization is one of the best places to begin when you have to improve MySQL database performance. But it differs from case to case and is far from a one-size-fits-all endeavor. That said, there are certain tasks that help in a lot of cases:

  • As mentioned above, you can prevent large result sets and decrease data volume by limiting the search to relevant records using the WHERE clause.
  • Go through the database schema to uncover ways that decrease complexity. For instance, keep an eye out on queries that contain a lot of joins since they take more time than most queries. You can make them run faster by reducing their relationships.
  • A large number of queries also fetch unnecessary fields from tables. You can set them to return only those fields that are important to keep from over-fetching again.
  • Views can help in some, but not all cases. A view is similar to a table that you can create beforehand by executing a statement to predetermine values that may require on-the-spot calculation otherwise.
  • Change the syntax of the SQL to influence database SQL optimizer to generate a better query plan.

Conclusion

If your application is performing poorly, the problem often lies with the database, with inefficient queries. While there isn’t any solution that works for every single query out there, database experts can hone in on the ones that require optimization using diligent analysis and monitoring, along with the right SQL optimizer tool for sql server.

After they successfully find the queries behind slow database performance, all they have to do is take the right steps to resolve this issue. These include optimization techniques, such as adding indexes, editing out unnecessary fields, and inserting the WHERE clause wherever necessary.

The Importance of Disk Operations in Query Performance Tuning

Query Performance Tuning

DBAs can’t ignore disk operations when working on query performance tuning. When talking about databases, ‘disk’ may be called by one of its many names, such as ‘storage’, ‘I\O’, ‘Reads’, or disk operations.

Although database professionals know all these terms mean the same thing, these might confuse those outside this field. When referring to one of these terms, they usually mean the number of disk operations required to fetch the data from the Disk resource.

Why You Can’t Ignore Disk Operations During Query Performance Tuning

The fact remains, however, that an overwhelming majority of SQL Server databases face the bottleneck issue when it comes to the disk resource. This doesn’t change, regardless of whether you have old-school hard drives or the latest flash storage arrays. Given below are some major reasons behind this, and how these can be affected with MySQL query optimization:

  1. Most slow queries are slow because they have to scan a large amount of data. A lot of the time, this is unnecessary and it’s making your SQL Server perform a lot of unneeded and really sluggish read operations.
  2. When the database reads data, it requires a place to store that information- which it does in the RAM. However, since the RAM has a limited capacity, older information starts getting removed with newer data coming in.
  3. Because RAM is never enough, it is often unable to store all the data that SQL Server fetches. Therefore, the remaining data has to be kept on the disk, which is far slower than the RAM. The information that isn’t present in the RAM has to be fetched from the disk- an operation that is known as the slowest in all of the database operations. Some DBAs even compare data fetching from the RAM and the disk to sprinting and tip-toeing.
  4. So, if we tune a query to read less data than it did before, such as twenty rows instead of twenty thousand, it will help in two ways. Not only will it reduce the workload on the database in terms of disk operations, but it will also require far fewer resources, including CPU and RAM, to process all the data. That said, the end-user is unaware of all these operations- all they know and appreciate is the speed or the time it takes for the query to fetch information. To put it simply, they just want the screen on the app to return as quickly as possible. This is why query performance tuning focuses on decreasing disk reads.
  5. DBAs also perform tuning to lower the other resources, such as CPU or RAM usage. But they only do this in certain special situations where such resources are consistently being overused at dangerous levels. For instance, if the CPU is in constant use of 90% or above, then the DBA will consider CPU tuning.
  6. Tuning queries that fetch large volumes of data to fetch much smaller volumes instead improves SQL Server capacity. This is because when a query takes up fewer resources, it leaves room for more users and queries. This allows the same server to take greater loads than it could. Performing MySQL query optimization also improves the lifespan of the same server, delaying the requirement for a hardware upgrade.

Summing Up

The above-mentioned reasons shed light on the fact that disk operations play a major role in enabling efficient database query performance. You can’t always blame the CPU; in fact, you can rarely do so since 95% of bottlenecks occur on the disk resource.

The CPU, on the other hand, is only a lagging indicator whose use can decrease if the storage reads differ.