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8 Proven Strategies to Improve Database Performance

Fast and efficient database operations are the key for a seamless and responsive user experience that keeps customers engaged and loyal. In this article we focus on eight strategies to improve database performance. We include both hardware and software, query optimizations, configuration, and data normalization aspects.
Published on
August 7, 2023
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Adam Furmanek
Dev Rel
Metis Team
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We need to take care of the performance of our databases. Slow and inefficient database operations can lead to frustrating delays, degraded application functionality, and ultimately drive users away. Conversely, a high-performing database ensures smooth, real-time access to critical information, providing a seamless and responsive user experience that keeps customers engaged and loyal.

By honing in on effective database optimization techniques, businesses can unlock a plethora of benefits. Improved database performance allows applications to process transactions swiftly, reducing the risk of bottlenecks during peak usage periods. Enhanced efficiency means databases can handle larger workloads and scale to meet the growing demands of expanding user bases without compromising performance.

What’s more, optimal database performance contributes to better resource utilization, ensuring that hardware and infrastructure investments are maximized. This translates to cost savings and a higher return on investment. Moreover, streamlined database operations free up valuable computational resources, enabling businesses to focus on innovation, product development, and gaining a competitive edge.

In this blog post, we will go through eight proven strategies for enhancing database performance. By implementing these techniques, businesses can accelerate their applications, reduce downtime, and provide their users with an unmatched experience that fosters loyalty and drives success in today's fiercely competitive market.

Key Factors Affecting Database Performance

Numerous factors can impact the speed, efficiency, and overall responsiveness of a database. Understanding and addressing these key factors is critical to ensuring optimal performance and delivering a seamless user experience.

Let’s start with hardware and infrastructure. The hardware and infrastructure on which a database operates can significantly influence its performance. Insufficient memory, slow storage systems, and underutilized CPU resources can lead to sluggish data retrieval and processing. Businesses must invest in robust hardware configurations tailored to their database workload to ensure smooth and swift operations. However, we as developers need to utilize the infrastructure efficiently and not let precious resources remain idle.

Next come queries. Queries are the heart of database interactions, and inefficiently written queries can hamper performance. Complex, poorly optimized, or unindexed queries may lead to increased response times, resulting in delays for end-users. Employing query optimization techniques, such as index utilization and query rewriting, can significantly boost query performance and reduce resource consumption.

We also need to implement applications so that they manage memory efficiently. Caching frequently accessed data in memory is a powerful technique to alleviate database workload. Proper caching mechanisms, such as query result caching and object caching, can enhance response times and reduce the need for repetitive data retrieval. Conversely, inadequate memory management can lead to memory leaks and contention, negatively impacting database performance.

Database performance issues have a direct and profound impact on the functionality of applications and services. Slow database operations can lead to delayed response times, causing frustration among users and adversely affecting user engagement. In an age where consumers expect real-time access to information, sluggish databases can result in missed opportunities and lost revenue.

Moreover, the interconnected nature of modern applications means that a slowdown in one part can have a cascading effect on other components, causing a domino effect of performance bottlenecks. For instance, a slow database can cause delays in loading web pages, affecting user experience and search engine rankings. In worst-case scenarios, it may lead to application crashes, resulting in customer dissatisfaction and potential reputational damage.

Identifying and resolving performance bottlenecks is a fundamental aspect of maintaining a high-performing database. Bottlenecks occur when specific components of the system, such as CPU, memory, or disk, become overloaded, restricting the overall performance. Without prompt identification and resolution, bottlenecks can lead to system instability and reduced productivity.

Monitoring database performance metrics and employing tools for performance analysis is crucial to identifying bottlenecks and their root causes. Performance testing and load balancing can help simulate high-stress scenarios and identify weak points in the system. By proactively addressing these bottlenecks, businesses can ensure consistent and smooth database operations, resulting in improved user experiences and increased operational efficiency.

The 8 Key Strategies for Improving Database Performance

Let’s go through thestrategies one by one.

Strategy One: Database Indexing

A database index is a data structure that helps DBMS to quickly find specific rows in a table. There are several types of database indexes. Most popular ones are:

  • B-tree Index: It is suitable for data with a low cardinality (few distinct values), such as names, dates, or status codes. It supports range queries and offers balanced search and insertion operations.
  • Bitmap Index: Bitmap indexes are effective for columns with a high cardinality (many distinct values). They use bitmap vectors to represent the presence or absence of a value in the indexed column. They are very good when multiple conditions are combined with AND or OR operators.
  • Hash Index: Hash indexes are best suited for handling point lookups on columns with high cardinality.
  • GIST/GIN Index: GIST/GIN indexes are great for complex data structures like JSONs or hashtables. They are well-suited for situations when we know something about the internal structure of the entities.
  • Trigram Index:Trigram indexes are well-suited for understanding the context of words appearing together. We can use them to improve performance of the LIKE operator with wildcards.

There are common techniques for identifying and creating indexes:

  • Identify Frequently Accessed Columns: Analyze the queries frequently executed in the application and identify the columns involved in those queries. Creating indexes on these columns can significantly improve performance.
  • Composite Indexes: For queries involving multiple columns in the WHERE clause, consider creating composite indexes that cover all the columns used in the query. This helps in reducing the number of indexes required and optimizing query performance.
  • Avoid Over-Indexing: Creating too many indexes can lead to increased overhead during data modifications (inserts, updates, and deletes).

Strategy Two: Query Optimization

We can optimize queries in multiple ways to increase the performance:

  • Use Indexes: Ensure that the columns used in the WHERE clause and JOIN conditions are indexed appropriately.
  • Rewrite Queries: Consider rewriting complex queries to simplify their structure and reduce the number of joins or subqueries to let the database find more efficient query execution plans.
  • Avoid SELECT *: Instead of selecting all columns from a table, specify only the required columns in the SELECT statement.
  • Filter Data Early: Use WHERE clauses to filter data as early as possible in the query execution process.
  • Limit Results: Use the LIMIT (or equivalent) clause to restrict the number of rows returned by the query.
  • Avoid Cursors: In situations where processing row by row is necessary, prefer using set-based operations instead of cursors.
  • Use JOINs Wisely: Be cautious when using JOINs, especially with large tables. Make use of appropriate indexes to optimize JOIN operations and prevent performance degradation due to excessive JOINs.

Strategy Three: Database Caching

Caching can help databases serve subsequent requests much faster, resulting in a significant boost in application responsiveness. There are multiple techniques for caching:

  • Query Result Caching: This involves storing the results of frequently executed queries in memory. When a similar query is requested, the database can retrieve the cached result instead of re-executing the query.
  • Object Caching: Object caching involves storing frequently accessed data objects, such as user profiles or product information, in memory.
  • Caching at Multiple Levels: Implementing caching at various levels, such as application-level caching and database-level caching, can further enhance performance.

There are two challenges with caching: invalidation and coherence. Cache invalidation is the process of removing or updating cached data when the corresponding data in the database is modified. When data in the database changes, the associated cached data becomes outdated and must be invalidated to avoid serving stale information. Cache coherence refers to the state where the data in the cache and the database remain consistent. We can use various techniques to maintain consistency, for instance time-based invalidation or event-based invalidation.

Strategy Four: Database Normalization

The goal of database normalization is to reduce data anomalies and ensure that each piece of data is stored in only one place, thereby avoiding duplication and inconsistencies.There are several normal forms:

  1. First Normal Form (1NF): In 1NF, data is organized into separate tables, and each attribute (column) contains only atomic (indivisible) values.
  2. Second Normal Form (2NF): A table is in 2NF if it is in 1NF and does not have non-prime attributes (attributes not part of the primary key) that are dependent on only part of the primary key..
  3. Third Normal Form (3NF): A table is in 3NF if it is in 2NF and has no non-prime attributes dependent on other non-prime attributes.
  4. Other Normal Forms like EKNF, BCNF, 4NF, ETNF, 5NF, DKNF, 6NF: They focus on more aspects that increase reliability and make design more robust, but they are not prevalent.

Normalization has many benefits.

  • Data Integrity: Normalization reduces data redundancy, minimizing the chances of data inconsistencies and errors.
  • Smaller Tables: Normalization leads to smaller, more focused tables, which results in faster query performance.
  • Improved Indexing: With normalized data, indexes become more effective and selective, leading to faster data retrieval and reduced disk I/O.

Unfortunately, they have drawbacks:

  • More Joins: Highly normalized databases often require more complex join operations to retrieve data from multiple related tables.
  • Data Integrity Constraints: Enforcing data integrity constraints can add some overhead during data modifications (inserts, updates, and deletes).

We can start by identifying key entities and their attributes to determine primary keys and foreign keys. Next, we can progress through the normalization forms step by step (1NF, 2NF, 3NF, and so on) to avoid unnecessary complexity. Always strive for a balance between normalization to maintain data integrity and reducing query complexity to improve performance.

Strategy Five: Hardware Optimization

Several key hardware components contribute to the efficiency and responsiveness of the database system:

  • Storage Systems: Faster storage technologies, such as Solid State Drives (SSDs), can drastically reduce data access times and improve read and write operations. Use SSDs over HDDS, and use NVMe where possible.
  • Memory Allocation: The more data that can be stored in memory, the fewer disk reads and writes are required, resulting in faster data retrieval and query execution. Sufficient memory also allows the database to cache frequently accessed data, further reducing disk I/O and improving overall system responsiveness.
  • CPU Utilization: A powerful CPU can handle complex calculations and data transformations more efficiently, reducing query execution times and enhancing the overall performance of the database system.
  • RAID Level Selection: RAID (Redundant Array of Independent Disks) configurations can enhance data availability and improve I/O performance. For example, RAID 10 (combining RAID 1 and RAID 0) offers both data redundancy and improved read and write performance.
  • Partitioning: Consider partitioning large tables and databases to distribute data across multiple physical disks.
  • Separate Data and Log Files: Store database data and log files on separate physical disks or RAID arrays.

Database scaling is crucial on the hardware and software levels. We can introduce database performance optimization depending on our needs and with increasing user base. Sometimes it’s worth focusing on the queries, but sometimes the bottleneck is the inefficient hardware, so we need to control both of these aspects.

Strategy Six: Database Tuning

Database tuning is the systematic process of optimizing a database system to enhance its performance, efficiency, and overall responsiveness. We can use multiple techniques:

  • Redesigning Schema: Proper schema design can improve data retrieval efficiency and simplify query execution.
  • Rewriting Queries: We can reduce the number of joins, improve indexes, and avoid subqueries.
  • Reorganizing Indexes: Adding, removing, or adjusting indexes can significantly improve query performance.
  • Partitioning: For large tables, consider implementing table partitioning based on usage patterns. This can reduce contention and improve data retrieval efficiency.

Strategy Seven: Backup & Recovery

We should have reliable backup and recovery strategies to quickly and accurately restore the data in case of data loss. There are multiple techniques for backups:

  • Full Backups: Full backups create a complete copy of the entire database, including all data, tables, and configurations. They are typically performed at regular intervals and serve as a baseline for incremental backups.
  • Incremental Backups: Incremental backups capture only the changes made since the last backup, reducing the backup time and storage requirements compared to full backups.
  • Point-in-Time Recovery: Point-in-time recovery allows for the restoration of the database to a specific point in time, between the last full backup and the most recent incremental backup. This feature is crucial for recovering the database to a specific state just before data loss or corruption occurred.

We should follow the following best practices to configure proper backups:

  • Regular Backup Schedule: Define a regular backup schedule based on the criticality of the data and the frequency of changes. Full backups may be performed weekly or bi-weekly, while incremental backups can be scheduled more frequently, such as daily or hourly. Take backups automatically.
  • Offsite Backups: Keep at least one set of backups offsite or in a different location from the primary database. This provides an additional layer of protection against physical disasters or data center failures.
  • Regular Recovery Testing: Have a well-defined testing procedure for backups, and run the procedure periodically.

Strategy Eight: Partitioning for Performance

There are multiple strategies for partitioning:

  • Range Partitioning: Range partitioning involves dividing data based on a specified range of values from a column known as the partitioning key. For example, data can be partitioned based on date ranges or numerical ranges.
  • Hash Partitioning: In hash partitioning, data is distributed across partitions based on the hash value of the partitioning key.
  • List Partitioning: List partitioning involves defining explicit lists of values that determine data distribution into partitions. Each partition is associated with a specific set of values from the partitioning key column.

Partitioning can help in the following scenarios:

  • Time-Series Data: In scenarios with time-series data, range partitioning based on date or timestamp can significantly improve query performance. Queries that involve specific time intervals can target only the relevant partitions, reducing the query execution time.
  • Large Historical Data: In applications dealing with large historical datasets, range partitioning based on date or an identifier can make data retrieval and analysis more efficient. Users can focus on specific time periods or identifiers, reducing the volume of data to process.
  • Highly Concurrent Workloads: In environments with high concurrent access, partitioning allows multiple users to access different partitions concurrently, reducing contention and improving response times.
  • Archiving and Purging: Partitioning simplifies data archiving and purging processes. Older data can be easily moved to separate partitions, and archiving can be performed on a partition-by-partition basis, reducing the impact on day-to-day operations.

See our article about partitioning in PostgreSQL to get a deeper understanding of this function.


There are multiple strategies for improving the database performance. They focus on indexing the database, optimizing the queries, caching, normalizing the table schema, optimizing the hardware, tuning the database, configuring backups, and partitioning the data. We can greatly improve the performance by applying these strategies across all the layers of our systems. We need to remember that it’s not only about the performance, but also about the user experience that leads to improving our business. We don’t improve databases just for the sake of doing so. We want to find the right balance between our business and technical needs.

Use this guideline above and boost up your performance now. See also our other articles about partitioning, high CPU usage, or performance problems with JSONB.


Why is database performance important for businesses?

Database performance directly affects application responsiveness and user satisfaction, which, in turn, impacts customer loyalty and overall business success.

What are the common challenges faced in database performance?

Common challenges include poorly optimized queries, lack of indexing, inadequate hardware resources, and inefficient caching strategies.

How can indexing improve database performance?

Indexing enables faster data retrieval by creating data structures that expedite query execution.

What are the best practices for query optimization?

Best practices include using efficient SQL statements, creating appropriate indexes, and optimizing query execution plans.

What role does hardware play in enhancing database performance?

Hardware choices, such as storage systems, memory allocation, and CPU utilization, significantly influence database efficiency and speed.

How can caching techniques be used to improve database performance?

Caching reduces database workload by storing frequently accessed data in memory, resulting in faster response times.

What is the significance of database normalization in performance improvement?

Database normalization reduces data redundancy and ensures data integrity, leading to improved query performance and efficient storage.

How can database tuning enhance overall performance?

Database tuning involves optimizing the database schema and queries to improve performance and resource utilization.

What are the recommended backup and recovery strategies to ensure database performance?

Reliable backup and recovery strategies, including regular backups and testing recovery procedures, ensure data availability and maintain database performance.

How can partitioning be utilized to improve database performance?

Database partitioning can enhance performance by dividing large tables into smaller, manageable partitions, reducing query execution time and resource usage.

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