We spent years improving our monitoring solutions. We can now capture metrics nearly automatically thanks to solutions like OpenTelemetry. We can visualize data points easily with open-source solutions. Finally, we can easily measure our applications' performance. And yet, there are still more things to incorporate to make observability even better. Let's see 4 hot trends in database observability that modern companies are chasing now.
Integration With Developers’ Environments
The first thing is integration with developers’ environments to capture how the software evolves over time. We want to extend our observability solutions to include data points from all stages of SDLC, including private environments and CI/CD pipelines.
This gives us the following:
- We can analyze the changes much earlier in the pipeline which shortens the feedback loop and lets developers iterate faster
- We can see how individual changes affect the production database because we see every little detail
- We can easily troubleshoot issues since we know exactly what’s happening across our pipelines
Integration with developers’ environments is yet another way of pushing to the left and is now a hot trend across the software industry.
Being Proactive Instead of Reactive
Modern observability solutions should warn us about issues before they actually appear. This includes detecting incorrect code changes, faulty schema migrations, or invalid configurations. The state-of-the-art solutions can reason about how a single line of code will affect the whole system and prevent risky changes from being pushed to production.
This brings many benefits:
- Developers feel confident that their changes are safe to be deployed and any issues are identified early
- Managers can plan their roadmaps reliably as their teams avoid expensive rollbacks and lengthy troubleshooting
- All DORA metrics are improved as teams move faster and face fewer errors
- Velocity increases and MTTR goes down
Proactivity brings significant improvements across the whole SDLC and is now a must-have for every observability solution.
Solutions Instead of Data Points
Good observability systems avoid swamping users with data points. They don’t show all possible metrics in every possible way. Instead, they reason about captured data points and provide comprehensive explanations. Thanks to that, developers don’t need to troubleshoot things manually. Instead, they get well-crafted guides for fixing the issues that they can just follow to get the problems resolved.
This unblocks the teams in the following ways:
- They need lower working knowledge about the system to keep it in shape and maintain it over time
- Junior developers can be as effective as senior developers in troubleshooting issues
- Teams can work independently as they don’t need help from other teams (like DBAs) to debug the problems
- They don’t need to spend time on troubleshooting and can apply the solutions immediately
There is no need to slice and dice metrics anymore. World-class observability solutions are now giving guides that developers can follow mechanically to solve the issues.
Hypotheses Verification
Last but not least, observability solutions can now verify many hypotheses and provide answers. They can analyze queries running in the database, generate a list of possible improvements, and then verify them automatically to see if they actually help. This can be as simple as changing the order of filers in an SQL query, but can also incorporate adding indexes, partitioning tables, or changing column types.
This gives the following:
- Observability tools can confirm what will work and what will not bring any improvements
- Developers don’t need to try things out. They just come and immediately see which things are effective
- MTTR is greatly reduced as many solutions are tested at once
- Developers are sure improvement suggestions will work in their production systems and are not only theoretically valid
This makes achieving database reliability much easier as developers can simply approve the suggestions provided by the observability tools. There is no lengthy troubleshooting anymore. We can improve our velocity and focus on important things.
How Metis Stays on Top of Observability Trends
Metis plugs into all the steps of the SDLC, analyzes your developers’ queries, and captures execution plans. It can then provide actionable insights into how to fix these queries and how to change the database to make things faster. Metis can analyze production databases and find anomalies or issues with live queries. It can capture extensions, schemas, configurations, and live transactions to explain why things are slow. Metis uses an AI engine to suggest improvements and instructions on how to rewrite queries to make them faster.
Say goodbye to old monitoring solutions that swamp your developers with raw data points. Use Metis and get all of that automated and reliable, improve your velocity and reduce MTTR, and focus on doing your business instead of maintaining databases.
