Optimizing queries by using observability: The latest techniques for optimizing database queries
Hi, Jon here. Have you ever thought about how much a process called a "query," which retrieves information from a database, affects the speed of a system? A query is a command that requests a database to "give me this data." However, if a query is slow, the entire website or app will lag. So, in this article, I'll explain in an easy-to-understand way, even for beginners, how to optimize queries using "observability," a hot topic in 2025. Observability is a method for observing and understanding the internal state of a system, using logs, metrics, and traces to quickly identify problems. In this article, I'll share actual use cases and trends based on the latest InfoWorld article.
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What is Observability? A Key Concept for Query Optimization
First, a quick rundown of observability basics: Observability means the "ability to see" a system, going beyond traditional monitoring (which simply detects anomalies) to allow deeper analysis of why something is wrong. For example, if a database query is slow, you don't just know it's slow; you can identify where the bottleneck is. Now, in 2025, this technique is essential for cloud-based databases and DevOps (an integrated approach to development and operations).
According to an InfoWorld article (published October 5, 2025), observability allows engineers to tune query performance before users notice. Specifically, they monitor real-time metrics and improve query execution times. For example, in databases like PostgreSQL, they analyze query execution plans and add indexes (markers for quickly finding data). This improves the efficiency of the entire system.
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The three pillars of observability
The foundation of observability is built on three main components, and understanding these will help you optimize your queries:
- Metrics: Measures the state of the system using numerical data, such as query execution time or CPU usage, and graphs this to see trends.
- log: Textual data recording system events that can provide detailed information about why a query failed.
- trace: Tracks the execution path of a query, visualizing which steps are taking time.
By using these tools in an integrated manner, you can quickly identify bottlenecks in your database. For example, tools like Datadog (a monitoring tool often mentioned in X's posts) allow you to create dashboards with drag and drop and monitor in real time.
Trends for 2025: Observability Integrated with AI
Observability in 2025 will be further advanced by advances in AI. According to a blog post by the Cloud Native Computing Foundation (CNCF) (published March 5, 2025), observability will be combined with AI and automation to enable predictive monitoring. In other words, it will detect signs of problems before they occur and automatically optimize queries. For example, an article on dbsnOOp (published August 8, 2025) lists predictive analytics and strategic use as five trends in cloud databases.
Secoda's blog (published April 9, 2025) points out AI-driven insights and tool consolidation as trends in data observability. Tool consolidation means centralizing multiple monitoring tools and optimizing costs. InfluxData's document (published July 1, 2025) also emphasizes scalability of log monitoring as a trend across the ecosystem.
A report by news site Dynatrace (published around October 7, 2025) points out that while AI adoption is becoming more commonplace, transitioning it to production environments is a challenge. It proposes a 90-day action plan for how to utilize observability data for business purposes. Furthermore, a blog post by Hydrolix (published September 5, 2025) states that a cost-effective approach to log monitoring is key for 2025.
Actual use cases
Let's take a look at some examples of query optimization that take advantage of these trends.
- Application in PostgreSQLAs mentioned in this InfoWorld article, use observability tools to analyze query execution plans, identify slow queries, and add indexes to improve their speed.
- AI IntegrationdbsnOOp Trends: AI predicts query anomalies, for example, adjusting resources before traffic increases.
- Cost Optimization: As Secoda points out, when combined with FinOps (financial operations), it cuts down on unnecessary queries and saves on cloud fees.
Posts on X (formerly Twitter) also show that tools like Datadog are popular for monitoring database performance, and that indexing is often discussed as an optimization strategy for SQL queries. For example, a 2023 post shared that indexes can significantly reduce query times. A more recent post (around October 2025) also discussed the importance of monitoring latency and error rates as observability metrics.
Key points to consider when implementing observability
Finally, we will summarize some points to consider when incorporating observability into query optimization. If you are a beginner, we recommend starting with a free tool.
- Tool selection: Choose something like Datadog or New Relic (mentioned in the Observability Platform Market report, due to be published around October 3, 2025). They offer free trials, making it easy to try them out.
- Data collection: Balance metrics, logs, and traces.
- Team collaboration: DevOps teams share data and respond quickly.
- Security: Integrate security to protect your data, as pointed out in the CNCF trends.
With this in mind, observability will become a standard tool for databases in 2025. With the help of AI, optimization will become easy for everyone.
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Jon's Summary
Observability is a powerful tool that makes database queries faster and smarter. Its integration with AI is a trend for 2025, enabling predictive optimization. Try incorporating it into your own systems to improve efficiency. The technology is constantly evolving, so keep learning by checking reliable sources.
Reference sources
- Optimizing queries by using observability | InfoWorld – https://www.infoworld.com/article/4068167/optimizing-queries-by-using-observability.html (Published October 5, 2025)
- Observability in 2025: Trends That Will Impact Databases and DevOps – dbsnOOp – https://dbsnoop.com/observability-in-2025-trends-that-impact-databases/ (Published August 8, 2025)
- Observability Trends in 2025 – What's Driving Change? | CNCF – https://www.cncf.io/blog/2025/03/05/observability-trends-in-2025-whats-driving-change/ (Published March 5, 2025)
- Key Data Observability Trends in 2025 | Secoda – https://www.secoda.co/blog/key-data-observability-trends (Published April 9, 2025)
- Observability trends in 2025 | InfluxData – https://www.influxdata.com/glossary/observability-trends-2025/ (Published July 1, 2025)
- The State of Observability 2025: Business impact, key trends, and a 90-day plan for decision-makers – https://dynatrace.com/news/blog/ai-observability-business-impact-2025 (Published around October 7, 2025)
- Observability Platform Market Projections 2025-2032: Key Trends, Opportunities, and Growth Factors in New Report – https://openpr.com/news/4207354/observability-platform-market-projections-2025-2032-key (Published around October 3, 2025)
- Observability in 2025: Key Trends Driving Log Monitoring – Hydrolix – https://hydrolix.io/blog/observability-in-2025 (Published September 5, 2025)
- Related posts from X (formerly Twitter) (Datadog, Inc., Peter Zaitsev, InfoWorld, etc., from 2020 to 2025)