June 10, 2024

Database performance monitoring: Going beyond essential metrics with observability

Table of contents

How is the database performing? Start by defining performance. 

A database’s performance includes how efficiently it handles the operations and queries thrown at it by individuals, applications, and pipelines. Good database performance means quick response times, minimal latency, and optimal resource usage, all of which are crucial for maintaining the reliability and speed of applications that rely on the database. When database performance is on track and improving, it supports optimal user experiences, lower operating costs, and rapid scalability. 

Database performance monitoring involves tracking, visualizing, and analyzing critical metrics. While database administrators and others throughout the data pipeline can do this manually, a database performance monitoring tool typically handles it to varying degrees. 

Database performance monitoring KPIs

Key database performance metrics fall into the following categories, with common examples included.

Query performance

How effectively is the database executing SQL queries (speed and efficiency)? 

  • Execution time: time taken to execute SQL queries
  • Number of slow queries: count of queries that exceed a predefined execution time threshold
  • Query latency: delay between query submission and completion
  • Query throughput: number of queries processed per unit time

Resource utilization

How much of the system’s resources is the database using?

  • CPU usage: percentage of CPU resources used by the database
  • Memory usage: amount of RAM consumed by database processes
  • Disk I/O: rate of read and write operations on disk storage
  • Network I/O: volume of data transferred over the network

Connections

What is the state of database connections?

  • Active connections: number of current connections to the database
  • Connection pool usage: utilization rate of connection pools
  • Connection wait times: time spent waiting for a database connection to be established

Throughput and latency

How is the database handling transaction volume and processing delays?

  • Transactions per second (TPS): number of transactions completed per second
  • Average response time: average time taken to respond to transactions and queries
  • Peak load handling: performance under maximum load conditions
  • Latency: time delay in processing and responding to database requests

All of these metrics and more can give a detailed understanding of the database’s ability to perform its core functions and serve applications, data pipelines, and the rest of the organization’s needs. 

NoSQL database performance monitoring

While the focus has been on traditional SQL databases, performance monitoring is equally critical for NoSQL databases. While SQL databases focus on query performance, resource utilization, connections, and throughput/latency, NoSQL databases require different approaches due to unique characteristics. 

First of all, NoSQL databases, such as document, key-value, column, and graph databases, have diverse data models that require monitoring tools to accommodate their unique structures and access patterns. These databases are designed for horizontal scalability, so monitoring tools should track data distribution across shards or nodes, replication latency, and the performance impact of scaling operations.

Unlike traditional SQL databases that prioritize ACID transactions, NoSQL databases often prioritize high availability and partition tolerance. So while both types require tracking of CPU, memory, disk I/O, and network I/O, NoSQL monitoring must focus more on read/write throughput, consistency levels, and latency.  Effective monitoring must also provide insights into indexing strategies, query performance, and the health of clusters, including node availability, fault tolerance, and automatic failover processes.

Benefits of database performance monitoring

Database performance monitoring helps teams ensure critical aspects of their database environments:

  • Availability and reliability
  • Issue detection
  • Query optimization
  • Latency reduction
  • Efficient resource utilization
  • Security

It also helps them detect unusual patterns including and beyond security issues to address procedural and organizational elements. Database performance monitoring also creates activity logs, which support auditing and compliance. Monitoring also facilitates easier capacity planning and proactive disaster recovery by verifying backup integrity and ensuring effective failover mechanisms. 

However, performance monitoring can’t necessarily or adequately inform about the health of the database — database observability answers those kinds of questions. 

Database performance monitoring vs. database observability

Database performance monitoring and database observability are closely related but serve distinct purposes in database management. Performance monitoring focuses on tracking and analyzing specific metrics such as query performance, resource utilization, and connection metrics to ensure efficient database operations. It involves setting up predefined alerts to proactively address issues and maintain optimal performance.

In contrast, database observability provides a holistic view of the database’s health by integrating and standardizing multiple types of logs (network, OS, security, backups, etc.), plus metrics and traces, from change management pipelines. This approach goes beyond monitoring by enabling deep root cause analysis and understanding the context of performance issues. 

Observability supports proactive problem resolution, enhances security, ensures compliance, and improves operational efficiency. It facilitates continuous improvement and strategic decision-making through comprehensive insights. 

Essentially, while performance monitoring is about ensuring efficient operation by addressing specific metrics, observability offers a broader, integrated understanding of the entire database environment, promoting deeper analysis and proactive management. These tools and capabilities complement one another for a complete view and control of database environments and pipelines. 

Database performance monitoring tools

Database performance monitoring tools detect and alert teams to concerning measurements when they hit the platform – enabling database managers to act quickly in protecting their data stores from a security breach or restoring service after a faulty update (or any number of other problems). These tools aren’t just reactive alert systems, though. They continuously track and analyze database metrics to give a dashboard of live performance and provide snapshots of historical states. 

Some common favorites for observability and monitoring, plus the best-reviewed (via TrustRadius) “database performance monitoring tools” include:

  • LogicMonitor
  • SolarWinds
  • Splunk
  • Datadog
  • dbForge
  • Percona
  • dbWatch
  • New Relic
  • Dynatrace
  • Prometheus

These tools help database, development, and IT teams understand resource usage and detect issues before they impact performance. The goals are to reduce downtime and improve problem resolution times. By allowing database managers, data analysts, and others to easily understand database performance and impacts, these tools minimize the amount of manual work required to understand performance across various perspectives and database environments. Primary capabilities and considerations for these tools include the following. 

SQL query analysis, tuning, & reporting

By enabling SQL query analysis, database performance monitoring tools help teams understand their SQL query performance and identify inefficiencies. Basic query analysis includes evaluating execution times, identifying slow queries, and checking resource usage.  

These tools allow users to tune SQL queries for optimal database efficiency, performance, and operating costs. By optimizing queries for performance and generating detailed reports of granular performance metrics, they help not only identify inefficient queries but also suggest improvements. Over time, these reports can show changes to query execution times, resource usage, and overall performance. 

Some tools offer manual review, tuning, and reporting, while others automate the process with algorithms and machine learning. Advanced or automated capabilities go further by providing real-time insights, using machine learning to suggest optimizations, and automatically adjusting queries for better performance. These advanced tools can also provide in-depth execution plans, visualize query flows, and predict performance impacts. 

Automated tools can consistently enhance query performance, reduce response times, and free up database administrators to focus on more complex tasks. With the sheer number of cloud and on-prem systems in an organizational IT landscape, proper setup and fine-tuning of these alerts is crucial – false alerts can lead to DBAs ignoring the notifications, which negates their purpose. 

As with any pipeline automation, this amplifies efficiency, performance, and cost reductions. 

Change tracking, logs, & auditing

Change tracking involves continuously monitoring and recording all modifications to a database, including schema changes, data updates, configuration adjustments, and user activities. This helps identify the impact of changes on performance, ensures compliance, facilitates troubleshooting, enhances security, and supports audit processes. By keeping a detailed record of changes, administrators can quickly pinpoint and address issues, maintaining optimal database performance.

Database performance logs provide detailed records of database events and activities, forming a crucial part of performance monitoring. Auditing involves the systematic review and analysis of these logs to ensure database compliance, security, and performance. Typical logging categories include:

  • Error logs, for errors and warnings generated by database workflows
  • Transaction logs, documenting all transactions and changes
  • Access logs, tracking access, user activity, and security information

A database DevOps tool can also handle this element of database performance monitoring. For example, Liquibase produces structured JSON logs that enable observability for database change management. These logs can be ingested by aggregation, observability, and monitoring platforms to bring database deployment metrics into pipeline monitoring dashboards. Database change logs can also be audited directly. Liquibase’s DATABASECHANGELOGHISTORY table provides an easily discoverable list of every change that’s been made in the specified time range. 

Together, change tracking, logs, and auditing help maintain a robust, secure, and efficient database environment.

Alerts & notifications

Here’s where metrics meet action – performance monitoring tools feature alert and notification capabilities, so teams can jump into action when security, availability, or other issues arise. Threshold-based alerts are triggered when specifically selected metrics hit a certain limit (e.g., CPU usage or response time), indicating a potential problem with how the database handles its normal operations. 

Anomaly detection, on the other hand, looks for unusual patterns or deviations from how the database environments typically operate. For example, unexpected spikes in traffic could cause problems, even when the database is functioning as it should. 

These tools can link with messaging systems like Teams or Slack to get the right person or team alerted promptly. 

Integration and cloud vs. on-prem compatibility

When selecting database performance monitoring tools, it’s crucial to ensure they are compatible with your database environment, whether cloud-based or on-premises. Key considerations include:

  • Database types and versions – ensuring the tool supports your specific database systems (e.g., SQL Server, MySQL, Oracle) and their versions
  • Deployment flexibility – ensuring the tool is deployable in your specific environment, be it cloud, on-premises, or hybrid
  • Ease of configuration and use – ensuring the tool can get up and running quickly, provide positive user experiences, and change to suit evolving pipelines
  • Tech stack integration – ensuring compatibility with current monitoring, alerting, and logging systems (e.g., Prometheus, Grafana) to ensure seamless operation
  • APIs and extensibility – ensuring adaptation to unique workflows, requirements, and custom integrations
  • Support and updates – ensuring you have the right technical know-how, data protection, compatibility, and feature innovations to match evolving data stores

It’s also important to remember that cloud-based databases often charge based on the number of reads and writes. This pricing model influences not just the ongoing costs of monitoring the database but also affects the initial design of the database itself. Database administrators must consider these factors when designing and implementing cloud databases to optimize both performance and cost-efficiency.By considering these factors, database administrators can select tools that enhance monitoring capabilities without disrupting existing systems and workflows.

In addition to these considerations, database teams also need to ask themselves – is performance monitoring enough? Or, do we need a more holistic, DevOps-aligned approach to understanding and optimizing the health of our databases as they evolve, grow, and multiply?

As mentioned earlier, database observability takes visibility and analytics to the next level. 

Enhancing performance monitoring with database observability

While database performance monitoring focuses on tracking specific metrics to ensure efficient operations, database observability goes further by providing a comprehensive understanding of the database’s health and behavior. 

With observability, teams gain insights into the context of performance issues, which supports enhanced security, compliance, and operational efficiency. This comprehensive approach facilitates continuous improvement, better strategic decision-making, and aligns with DevOps practices for a more robust and resilient database environment.

Learn the top 5 benefits of database DevOps observability that help you unlock the future faster.

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