A Useful Guide Of Improving The Read Performance Of A Database

It is no secret that a well-performing database is essential to the success of any business. In order to ensure that your database is running at its peak potential, you need to be aware of the factors that can affect its read performance. In this article, we will discuss some of the most common causes of poor read performance and offer tips on how you can remedy them.

1. Insufficient Disk I/O Subsystem Performance

The disk is responsible for storing and retrieving data from the database, therefore, it is understandable that the insufficient performance of this subsystem will result in poor read performance. As a rule of thumb, you should ensure that the I/O subsystem has enough disk space as well as write as well as read throughput to support all your demands.

While it may be difficult to predict exactly how much space and throughput your database needs, you can start with a particular type of system and upgrade if needed. For example, on a physical server with 16 cores and 32Gb RAM, you can use a dual-core configuration with 4Gb each for CPUs and memory respectively on the database server itself.

However, if you are using more than one physical server, you can assign each system with a single CPU and 2Gb RAM.

2. Indexes That Are Not Being Used

One of the best ways to avoid this problem is by periodically reviewing user queries against the indexes you have created on columns where there is an index hint. This will ensure that your indexes are being used properly and not wasting resources on reading requests that could be better served by simply reading data from disk without taking up additional resources for an index lookup.

If the query doesn’t need to use any of your existing indexes, then it would make sense to create new ones for these types of requests specifically. By ensuring that only relevant data is, you will achieve a higher read performance.

3. Denormalizing the data

Not all denormalization is bad, but it does mean that you will need to compromise between faster writes and slow reads. If your writes are the main concern, then database denormalization may be a good solution for your data warehouse type of workloads.

However, if higher read performance is more important than write speeds, then you should consider normalizing your tables and using views to present unified result sets. You can also create materialized views over some of the frequently asked queries to further improve read times from those requests.

4. Improperly Designed Indexes

The keys of an index can be used to identify rows in a particular table. Therefore, it is important that the key columns are defined properly and contain data relevant for all expected requests. You should always remember that indexes can be created on multiple columns. This means you can include several columns as part of the definition instead of relying on only one column for identification purposes.

For example, if your application users often search by both name and department then creating an index using (name, dept) would allow quicker lookups than (dept, name). For best performance you should avoid adding unnecessary data types to indexes as these will increase their size hence reading performance negatively.

5. Poor Statistics

The MySQL server keeps track of how much data has been added or changed in each table and uses this information to determine the most efficient way to execute a request. When you create an index, MySQL stores statistics on how well that index is performing and if it determines that a particular access path (i.e., table or secondary index) takes too long for reads then it will automatically change its behavior in order to speed up processing.

Whenever you make changes such as adding columns to tables, dropping indexes, etc, MySQL updates the appropriate statistics. However, sometimes these values can become out of date causing inefficient execution. You can manually update the statistics by running ANALYZE TABLE tablename, a command which updates all appropriate counters for your tables.

6. Network Congestion

In today’s world where businesses have expanded beyond their regional boundaries – and sometimes beyond their national boundaries, database requests can originate from different geographical locales.

If your application servers are located in one location and the database server is somewhere else then it may be possible that the round-trip time between these two systems is more than what can support efficient read operations. It is always a good idea to keep your network utilization low as this causes issues with not only read performance but also all other types of interactions with MySQL on the database side.

7. Expensive Operations

While large data sets are great for business, they do require more resources to operate against effectively which will affect how quickly MySQL returns results after executing a request. While some queries may take longer because they must fetch information from several tables at once (which is normal) – anything that causes MySQL to repeatedly flush buffers or change storage engines will slow down performance.

If your read operations are taking a long time it may be helpful to run the EXPLAIN command against them. This provides you with additional information about how MySQL is reading data from tables and where it falls in the access path.

8. Too Many Queries

This issue could probably fall under many different categories but we have selected it as a potential problem because sometimes developers can feel inclined to retrieve more data than they really need due to being so close to the codebase.

For example, if a developer decides to iterate through all of the rows returned by a SELECT statement instead of using WHERE clauses then their application becomes responsible for iterating through all of the returned rows which can be a huge issue when thousands or even millions of items are being processed.

Even if you need to access every item in a table it is usually a good idea to only load in the minimum amount required and then use WHERE clauses in your SQL queries in order to reduce overall resource utilization.

There are many potential reasons why your database may be performing poorly when it comes to reading data. By understanding some of the most common causes, you can take steps to correct them and improve overall performance.

Keep in mind that these are general tips and sometimes more specific analysis is required in order to pinpoint the root cause of a problem. If you need help getting started or would like us to take a look at your specific situation, don’t hesitate to contact us.