The strangler pattern is a strategy for incrementally refactoring a monolithic application into microservices by gradually replacing parts of the monolith with new services. This approach allows enterprises to transition their existing monolithic critical business applications into a modern architecture without disrupting the existing production system.
Architects and Engineering Managers have to effectively communicate the business value of the strangler pattern by demonstrating tangible benefits such as improved agility, scalability, cost savings, and resilience.
So, business applications teams and IT teams must monitor and measure specific Ops and Site Reliability Engineering (SRE) metrics of new microservices, and effectively communicate to the key stakeholders the business value of the strangler pattern.
Dashboard
A well-structured dashboard can provide a comprehensive view of these metrics. This should help business and IT teams helping Ops and SRE teams) to monitor and ensure the smooth transition from monolith to microservices
Above mentioned metrics ensure the new microservices are performing optimally and help in identifying any issues that might arise during the transition.
The metrics dashboard should include:
| Performance Section |
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Availability Section (Service health indicators) |
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| Deployments |
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| User Experience Section |
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| CPU usage |
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Critical metrics to track
Below are the critical metrics to track
| Performance Metrics |
Latency (Response Time), Throughput, Error Rates Tools: AWS CloudWatch, Datadog, New Relic. Prometheus, Grafana, Sentry |
| Availability and Reliability Metrics |
Up-Time (availability) Mean Time Between Failures (MTBF) – The average time between system failures Mean Time To Recover (MTTR) – The average time it takes to recover from a failure |
| Deployment and Change Metrics |
Deployment Frequency (The frequency at which new updates are deployed to production) Change Failure Rate (The percentage of deployments that result in a failure requiring remediation) |
| User Experience Metrics |
Service Level Indicators (SLI) ( SLI means specific metrics that indicate the performance of a service from the user’s perspective (e.g., latency, availability) – Directly tied to user satisfaction and Service Level Agreements (SLAs) Service Level Objectives (SLO) – Target values for SLIs that a service aims to achieve. Provides clear performance targets to ensure a good user experience |
| Resource Utilization Metrics |
CPU and Memory Usage – The amount of CPU and memory resources consumed by the new microservices Disk I/O – The rate of read and write operations on the disk |