Database performance allows developers or database administrators to enhance the system resources for lasting performance improvements. Databases are like the central nervous system of an application. They are responsible for the organization and function of critical processes. The minor database-related performance issues have the ability to impact the entire operation.
Locating issues in the databases will be very helpful for applications to stay healthy and accessible. When querying a production database, optimization is very essential. An inefficient query will drain the production database’s resources, and cause slow performance or loss of service for other users if the query contains errors. It’s vital to optimize the database to enjoy maximum performance.
Data scientists must be equipped to handle the entire modeling process as well as knowledge of data storage and infrastructure so they can build new applications, and monetize the massive data volume at a faster speed.
In this article, let’s understand the important ways to optimize the database for performance.
Analyzing the server thoroughly
As database servers are the host for all the processes and the driving force behind application performance, it is imperative that they have sufficient hardware and resources at all times. Ensuring that the host of the database processes has sufficient resources is an important step to solve the performance issues. If one experiences a longer than normal response time then they must start with checking CPU, memory, and server disk space which will be helpful to identify potential problems.
Always keep an eye on the CPU ready times, as it will give a notion of how many times the system attempted to use the CPU. This will be useful to understand the utilization of the CPU as well as if there is any requirement to upgrade it to a larger CPU. Because only a powerful CPU will be able to handle the multiple applications and requests, which leads to the advancement of database performance.
If the database underperforms on a regular basis, it may be necessary to upgrade to a higher class CPU unit. Due to the continuous baseload database servers induce, a minimum of two CPU cores may be necessary to keep the server responsive. Upgrading to a more powerful CPU can reduce the strain introduced by multiple applications and requests, improving the database, performance speed, and efficiency.
If the servers are lacking available memory, there is a huge chance of failure of the database. An effective evaluation of the memory involves assessing two different metrics: memory usage and page faults per second. A page faults number in the thousands signals that the hosts are running out of available memory space, and an increase is necessary.
Having more available memory can improve the efficiency and performance of the system. If the numbers of faults are high, it means the servers are running low or sometimes can be completely out of available memory. Allocating more memory to the servers will certainly help to optimize database performance. Increasing the amount of memory used by MySQL to allocate 70 percent of the total memory is another option, as long as the database is the only application on that server.
Server Disk Space
To have a lot of storage available for the database server is essential due to the fact that indexes and other performance improvements cause databases to consume more disk space than is usually necessary. Running the database on its own hard drives will minimize the disk fragmentation that occurs as a result of other processes. Also, dedicating a set of drives for data files, log files, backup files, and tempdb not only enhances the performance but also serves as a convenient backup in the event of a recovery disaster.
While setting up a new database server, it is best practice to store the data, log, and backup files onto separate disks. The types of disks in the server must be given consideration as well. Millions of I/O operations may be used to access or return the necessary data for a single query.
Choosing solid-state disks (SSDs) model designed for database usage for optimal results and also can offer the power to the SQL server, Oracle database, or other Relational Database Management System (RDBMS) needs for optimal performance. A prevalent issue that results in decreased database performance is an increase in disk latency. Monitor metrics related to disk latency closely. The fastest and most cost-efficient way to mitigate latency issues involves utilizing the available caching mechanisms.
Many performance issues are related to query performance. A query is a real-time request for data from a database. To boost the database performance, it is a good idea to optimize the most frequent queries the database server receives. To begin the query optimization process, one must target specific queries that have a significant impact on query execution times, such as queries that are occasionally or consistently slow or have red flags.
A subquery can make coding more convenient, it can also hinder database performance. Coding loops can also contribute to thousands of unnecessary requests which may weigh down your database. Avoid adding cursors, which are used for looping in SQL servers, in favor of SQL statements whenever possible. Streamline the coding for maximum efficiency by using a query optimizer to help guide coding choices to improve SQL query performance and overall database performance. It is very tough and time-consuming to manually optimize queries, so leveraging a query optimizer or outsourcing the optimization efforts can help to enhance the database performance.
Network Performance Management (NPM)
In addition to queries, indexes or Network Performance Management (NPM) is another important element of a database. Indexing creates a “structure” that helps to keep the data organized as it is helpful to make it easier to locate. Proper indexing can improve the database performance as it maximizes the efficiency of the data retrieval process, saving system time and effort.
Usually, it is overlooked during the development stage. The indexing can optimize the query execution and improve database performance. A strategic setup of the indexes can organize data structures in a way that facilitates more efficient data retrieval and improves response time. Thoroughly researching the best practices for structuring queries will help to optimize indexing strategies and also to improve performance.
Another approach was batching. Split the overall execution from serial processing to parallel processing, creating threads. Improve the execution time of each thread. One more solution is to reduce network delay because of data volume. Data integrity is absolutely paramount in relational databases. RDBMS satisfy the demands of Atomicity, Consistency, Isolation, and Durability (or ACID-compliant) by imposing a number of constraints to ensure that the stored data is reliable and accurate, making them ideal for tracking and storing things like account numbers, orders, and payments. But these constraints come with costly tradeoffs. Data scientists must set up an RDBMS as it requires users to have specific use cases in advance; any changes to the schema are usually difficult and time-consuming.
Connection capacity evaluation
If connection acquisition absorbs a large portion of the database’s response time, one may need to reconfigure the connection pool. Correctly configuring a connection pool involves knowing how many connections the database can realistically accommodate. Determine capacity by monitoring the server’s metrics as they gradually increase the load and number of connections until the CPU, memory, or disk performance reaches its limit. If they need additional connections, a hardware upgrade may be necessary to meet the needs of the application.
Defragmenting the data is one of the most effective approaches to increasing database performance. With data constantly being written to and removed from the database, it will inevitably become fragmented, which can slow down the data retrieval process or interfere with a query execution plan. When defragmenting the data, relevant data can be grouped together, allowing for I/O-related operations to run faster and more efficiently.
Optimizing the database for performance and following best practices begins with choosing ways that are simplified. The main goal of databases is accessing information, so the priority must be simply to have a well-organized system. Keeping the above-mentioned ways will help to minimize the number of issues and enhances the top-notch database performance.