5 Ways SolarWinds Observability SaaS Can Help with Database Optimization

peter.distefano 23 days ago 9 minute read time

Database performance is a critical concern for IT administrators and CTOs. A slow or inefficient database can lead to sluggish applications, frustrated users, and increased operational costs. Whether you're running a high-traffic web application or managing large-scale enterprise data, optimizing database performance ensures smooth operations and long-term scalability.

Why does database optimization matter?

Performance impact: Slow queries and inefficient data retrieval degrade application responsiveness, leading to poor user experience.

Cost efficiency: Poorly optimized databases consume unnecessary CPU, memory, and storage, increasing infrastructure costs.

Operational reliability: Performance bottlenecks and resource contention can lead to unexpected downtime and system instability, disrupting business operations.

Proactive monitoring and optimization are essential to maintain an efficient and reliable database. In this post, we’ll explore five ways to optimize database performance and how SolarWinds® Observability SaaS can help you monitor, analyze, and improve your database efficiency.

#1: Identifying and Optimizing Slow Queries

A sluggish application can frustrate users and negatively impact business operations. When performance issues arise, one of the first questions to ask is: Is the database slowing things down?

Poorly optimized queries are one of the biggest culprits behind slow database performance. A single inefficient query can consume excessive CPU, memory, and I/O, dragging down response times and affecting application performance. Identifying and optimizing these queries is the first step toward improving overall database efficiency.

Consider this example: You manage an e-commerce platform, and users report that product searches are slow. Pages that should load instantly take several seconds. You suspect the database might be the issue, but where do you start? You would probably take the following steps:

Check database metrics. Begin by monitoring query execution times, response latency, and resource utilization (CPU, memory, and I/O).
Identify slow queries. Look for queries that take significantly longer to execute than others.
Analyze execution plans. Understand how the database processes these queries to find inefficiencies.
Optimize the query. Rewrite the query, possibly restructuring data to improve efficiency.

With SolarWinds Observability SaaS, you can monitor database query times and alert on inordinately slow queries, so you don’t have to guess where the problem is.

SolarWinds Observability SaaS helps you pinpoint query bottlenecks. It tracks performance over time, giving you historical insights to see if query optimizations are effective. In addition, you can quickly confirm database-related problems, see query performance details, and get insights to optimize database efficiency. SolarWinds Observability SaaS helps you restore fast, reliable application performance.

#2: Creating and Maintaining Proper Indexes

One of the most effective ways to speed up database queries is by using indexes. Without them, the database must scan entire tables to find matching records, leading to slow performance—especially as data volume grows.

Reviewing metrics and execution plans in SolarWinds Observability SaaS can help you determine whether queries are scanning the entire table instead of leveraging indexes.

Consider a query such as this:

Because this query uses a wildcard text search (%coffee%), a database like PostgreSQL cannot efficiently use its standard B-tree index. As a result, the database will execute a full table scan—every row in the products table is checked individually—making the query slower as more products are added. In this case, a Generalized Inverted Index (GIN) with trigram matching is preferable:

With this index in place, searches using ILIKE perform much faster, as the database can efficiently filter results instead of scanning the entire table.

Instead of guessing which queries need indexing, SolarWinds Observability SaaS helps by:

Detecting queries that could benefit from indexing by highlighting full table scans and suggesting indexing improvements.

Providing index usage insights to determine whether existing indexes are being utilized efficiently or if adjustments are needed.

Tracking index performance over time, helping you monitor the impact of indexes on query execution speed and resource usage.

With SolarWinds Observability SaaS, you have access to actionable insights into indexing problems and solutions, helping you keep your database running at peak efficiency.

#3: Monitoring Database Resource Utilization

Even if your queries are optimized and indexed properly, database performance can degrade due to resource limitations. High CPU, memory, or disk I/O usage can increase latency and sluggish performance. Even the best query optimizations won’t matter if your database lacks the necessary resources.

Consider a scenario where you notice that queries are taking longer to execute than usual. There haven't been major code changes recently. Yet, performance issues have been increasing. With SolarWinds Observability SaaS, you have clear visibility into database resource metrics.

By monitoring database metrics and setting up automatic alerts, you can know immediately if:

CPU utilization is consistently high, even during off-peak hours.

Disk I/O is spiking, especially during complex queries.

Memory usage is nearing its limit, reducing available cache space.

These conditions might indicate that your database may be under-provisioned and struggling to keep up with demand. From there, tackling your database resource problem might involve the following steps:

Analyzing workload patterns to find correlations between resource spikes and specific queries or peak usage times.

Adjusting memory allocation, cache settings, and connection limits to maximize efficiency.

Scaling infrastructure vertically (increasing CPU or memory) or horizontally (distributing the workload across replicas or clusters).

As an example, consider a database that’s frequently hitting memory limits. In this case, increasing the PostgreSQL shared buffers can allow for better caching and reduce disk I/O:

SolarWinds Observability SaaS gives you quick access to real-time and historical monitoring of database resource utilization. You can track CPU, memory, and disk I/O usage over time, and correlate resource spikes with query performance. Access to these data-driven insights helps you identify if your database is under stress or overprovisioned, enabling you to fine-tune database settings or adjust infrastructure capacity.

#4: Managing Database Connections Efficiently

A well-optimized database can still be overloaded and perform poorly if connection management isn’t handled properly. If your database has too many open connections, inefficient connection pooling, or improperly configured limits, your end users will feel it. Queries that usually execute quickly may become delayed or even time out entirely.

Tracking active connections and correlating these metrics with performance dips is the first step toward detecting and managing database connection issues. Next, consider taking the following steps:

Use a connection pooler to limit the number of simultaneous database connections.

Adjust settings like max_connections in PostgreSQL to balance availability and resource usage. For example:

Identify and terminate long-lived or abandoned connections to free up resources.

Rather than manually tracking and tuning database connections, you can leverage SolarWinds Observability SaaS for:

Real-time monitoring of active connections: See how many connections are open and when spikes occur.

Alerts for excessive connections: Get notified when the database approaches connection limits to prevent outages.

Correlation with performance metrics: Identify whether slow queries or CPU spikes are tied to connection overloads.

Insights into connection behavior: Detect inefficient usage patterns, such as applications opening and not closing connections properly.

#5: Detecting and Resolving Locking and Contention Issues

When queries are optimized and resources are well-managed, databases can still suffer from locking and contention issues. When multiple transactions compete for the same data, the resulting delays can bring about slow performance or application failures. How might this issue present itself in your systems?

Queries get stuck waiting for locks: Some transactions are blocked and unable to proceed until another finishes.

Deadlocks occur: Two or more transactions are waiting on resources held by each other, causing a standstill.

Throughput drops: Even simple queries slow down due to excessive locking and contention.

Transaction conflicts create performance bottlenecks, preventing the database from executing queries efficiently. Generally speaking, resolving locking and contention issues begins by monitoring locking behavior to identify long-running transactions and queries that frequently cause locks. Techniques for addressing contention issues include:

Optimize transaction design: Keep transactions short to minimize lock duration and reduce contention.

Adjust isolation levels: Lower isolation levels where possible to reduce locking overhead while maintaining data consistency.

Detect and resolve deadlocks: Review deadlock logs and restructure queries or indexing strategies to avoid circular dependencies.

In PostgreSQL, you can enable logging to capture detailed deadlock information by running the following commands:

This allows you to analyze and troubleshoot locking problems before they significantly impact performance.

SolarWinds Observability SaaS provides:

Deadlock detection and analysis: Identify which queries and transactions are causing conflicts.

Lock wait-time monitoring: See how long transactions are waiting on locks, helping pinpoint bottlenecks.

Blocking query identification: Detect queries that are preventing others from executing.

Historical trends and alerts: Track contention over time and receive alerts when lock waits exceed safe thresholds.

Conclusion

Optimizing database performance isn’t just about writing efficient queries—it requires a holistic approach that includes indexing, resource management, connection handling, and resolving contention issues. By adopting these techniques, you can significantly improve database efficiency and application performance.

SolarWinds Observability SaaS simplifies these processes by providing real-time monitoring, performance insights, and automated alerts, helping IT teams detect and resolve database issues before they impact users. With proactive database optimization, you can help ensure reliability, scalability, and cost efficiency—keeping your systems running smoothly.