Your database is one of the most crucial components of your application. When your application grows in scale and complexity, it becomes essential to ensure your database performs optimally. You can speed up the rendering of your web pages or refactor your APIs for a faster response, but any performance bottlenecks in your database will instill roadblocks in your application’s overall performance.
There can be many causes for a database’s slow performance. It may be that your queries or your schema need restructuring. Or it could simply be that you missed implementing some vital optimization techniques. Not only does your database performance directly affect your application, but it indirectly impacts your product, users, and business growth.
So, in this post, we’ll discuss the eight best database optimization techniques you can adopt and implement immediately. We’ll also discuss some useful tools that can help you go beyond these techniques to enhance the performance of your database systems. By the end of this post, you’ll gain valuable insights that will enable you to optimize your system’s database performance, and in turn, keep up with users’ expectations of your business.
8 Optimization Techniques
We’ll go over the following eight techniques for optimizing a database:
- Optimize Queries
- Create and Manage Indexes
- Minimize Redundant Data Retrieval
- Use Object-Relation Mapping (ORM)
- Use Identity Fields as Primary Keys
- Run Queries in Unbuffered Mode
- Use Static Tables
- Allocate More Resources
Let’s now look at each of these techniques in detail and learn how they can improve your database’s performance.
1. Optimize Queries
In most scenarios, the performance of your database can be directly linked to the structure of your queries. In an SQL database, the way you structure or write your queries affects the database’s response time, that is, how long it takes to retrieve data. Below, you can read about some of the methods and techniques commonly used to optimize database queries.
Use JOINS vs. Subqueries
A lot of times, database administrators (DBAs) and developers casually use subqueries to retrieve the required data from multiple tables for a certain condition. If you’re unaware of the trade-offs of using a subquery, it can cause a lot of performance issues for your database.
Subqueries run against parent queries. This implies that each subquery will wait to execute until the execution of a parent query completes. Moreover, each subquery runs the same number of times a parent query runs on a single row of a table in your database. This doesn’t happen in the case of a JOIN, since the two queries in a JOIN run like a single query.
Therefore, you should try to use JOINS in place of subqueries whenever and wherever possible for faster query response times.
Use PROCEDURE ANALYSE()
MySQL has a built-in PROCEDURE ANALYSE() command that you can include in your queries. This command helps analyze your queries and automatically recommends better data types and sizes for columns in your database. This, in turn, helps you understand what changes you can make to your columns to reduce overall table size in terms of memory consumed. You can use PROCEDURE ANALYSE() with any query and use the information to restructure the tables, columns, and fields in your database.
A single read, write, delete, or update query works the fastest when you have a single read, write, delete, or update operation to perform. However, in a real-life application, batch operations will be a common requirement.
A high-performant and efficient database can allow multiple queries to execute simultaneously without blocking the execution of another query. This gives more concurrency control to your database system. However, running individual queries takes a lot of time. Running each of these queries one by one to do the same update or delete operation decreases the overall concurrency of the database system. Instead, you can run your update and delete queries in batches and run them together to increase the concurrency of your database. This leads to faster execution, leaving room for other queries to execute, which gives your database more concurrency.
Use EXPLAIN in Queries
MySQL also provides an EXPLAIN. You can use the EXPLAIN keyword next to any query. This will give you valuable insight into how your query performs, and you can use this information to improve its performance. It also indicates if your query is slow and finds potential ways to optimize it. You can use EXPLAIN with any of the following commands:
Use NOT NULL vs. NULL
Developers often check for NULL fields in a column. However, a NULL field by default takes up more memory than a NOT NULL field. Hence, in cases when you have to run a query to validate if data exists in a column, you can simply invert the query and use the NOT NULL check instead. Not only does the NOT NULL column take up less memory, it’s also faster due to more effective indexing over the NOT NULL columns compared to the NULL columns.
2. Create and Manage Indexes
Indexes are a built-in way for your database to look through tables and columns more quickly using an enhanced data structure, like a lookup table. For most read queries, having an index will speed up the process of data retrieval when that query executes. In turn, you’ll get a faster response time to your query. You can apply an index to a table or on a column using a unique index, a composite index, and so on.
The important thing is to make sure most of your read queries have an index associated with them. However, remember that on the flip side, indexes make your write queries slow because every time you perform a write on a table or column, the index itself must update. Therefore, you should not blindly use indexes everywhere. If your requirements or queries change according to your schema, you need to revisit the indexes and manage them so that they don’t end up hampering your database performance instead of enhancing it.
3. Minimize Redundant Data Retrieval
Often developers naively write SQL queries that retrieve more data than needed, taking up more time and memory. You should routinely revisit your SQL queries and ensure that you minimize redundant data retrieval wherever possible. Retrieving less data enables faster response times for your queries and less CPU usage for all your write operations.
4. Use Object-Relation Mapping (ORM)
ORM stands for object-relation mapping. Some developers find raw SQL queries hard to write, analyze, and maintain and that they leave room for errors and bugs. ORM takes away the heavy lifting of writing raw SQL queries by giving you a layer of abstraction to work with. It makes working with your database easier. It also makes your queries more robust and secure. A robust database will always perform better because it likely has fewer bottlenecks and errors.
5. Use Identity Fields as Primary Keys
Primary keys uniquely identify a record in a database table. You may have been creating primary keys for a long time, but are you doing it correctly?
A lot of times DBAs and developers assign field values that can be duplicated, and are bound to repeat, or are prone to typos and other errors. As a general rule, you should try to use an identity field as a primary key wherever possible. Identity fields are integers and thus, less prone to typos and errors. They also take up less memory, leaving more storage for your database operations and enhancing the overall performance of your database. Since identity fields are like user IDs or UUIDs, they’re also more likely to be unique.
6. Run Queries in Unbuffered Mode
When you run MySQL queries, by default they run in a buffered mode. In buffered mode, the system first stores query results in memory before sending them back to the caller. Thus, query executions have slow response times. However, you can change this default behavior and instead run your SQL queries in an unbuffered mode. Then, your SQL queries will be first executed, and stored in memory only when the system returns the result to you. This makes the query execution faster.
7. Use Static Tables
If most of your tables have columns with variable length fields, it can cause performance bottlenecks for your database. Variable-length columns are difficult to cache and slower to retrieve from their cached storage. You should try using fixed-length fields or static tables as much as possible for a faster and easier caching mechanism for your query results.
8. Allocate More Resources
On a server, the CPU and memory resources your database has access to can limit your database performance. As your database needs to expand, your schema becomes more complex and your tables grow in size and complexity. Thus, you should consider allocating more hardware resources for your production database servers. A high-performance database server will require greater CPU horsepower and more memory for fast processing.
Useful Tools for Database Optimization
You’ve learned about common techniques that can help optimize your database. Now let’s look at some useful tools that can help you automate the process of optimizing your database.
SolarWinds Database Performance Analyzer (DPA)
The SolarWinds® DPA monitors the response times of your SQL statements. It can send alerts and provide analytics and suggestions for optimizing your database. It identifies performance bottlenecks in your database and can monitor the database in any environment from testing, to staging, to production. Furthermore, you can visualize the performance through charts and graphs on a dashboard.
SQL Sentry®, by SolarWinds, performs automated monitoring of your database. It quickly diagnoses problems related to missing data to keep your application intact. It optimizes all your database environments efficiently to manage more data using the existing infrastructure.
Paessler helps you monitor your database queries, connections, responses, and more via a customizable dashboard. You can also use it to monitor specific datasets from your database and send alerts to advise you to take action to prevent database downtime.
Redgate instantly tells you the status of all your servers and your database. You can use it to diagnose the database and customize alerts about potential issues pertaining to your database server state.
We’ve gone through some of the most important strategies to optimize your database. However, you also need to understand the constraints that apply, with respect to your business and product, and not just blindly apply them. Remember to routinely measure and track the performance of your database. As a best practice, you should use a database monitoring tool that also helps you optimize it as it grows in complexity and size. You can choose any of the four tools we discussed in this post.