Driver 01
Deployment contention
Many engineers queued on one deployment pipeline, where any change can cause a regression somewhere else.
Migration playbook: Legacy monolith → Modular microservices
Monolith to microservices migration: a strangler fig playbook
Break a monolith into independently deployable services one bounded context at a time, without a big-bang rewrite.
Migration drivers
Why teams make this move
Driver 02
Scaling inefficiency
Scaling the whole application to serve one CPU-heavy endpoint or background job.
Driver 03
Large blast radius
An unhandled failure in a minor feature taking down the core checkout flow.
Execution sequence
How the migration runs
Each phase ends with a check you can verify — data parity, error rates, latency — and the rollback path is agreed before any traffic moves.
- 01Phase
Domain discovery and bounded contexts
Mapping subdomains and data dependencies with event storming and an audit of the code and data.
- 02Phase
Edge routing
Putting a reverse proxy (Envoy, Cloudflare or your API gateway) in front of the monolith so traffic can move route by route.
- 03Phase
Data sync and shadow mode
Replicating data with change data capture (Debezium) and comparing the new service's reads and writes with the monolith's.
- 04Phase
Traffic cutover and pruning
Shifting production traffic in steps — for example 1%, 10%, then 100% — and deleting the monolith code once the new path is stable.
Risk prevention
Pitfalls that derail this migration
Risk 01
Splitting data too early
Moving tables into separate services before the transactions that span them have been redesigned.
Risk 02
Synchronous call chains
Chaining synchronous REST calls between services, which recreates the monolith's coupling with network latency added.
Risk 03
No distributed tracing
Debugging latency without OpenTelemetry context propagated across service boundaries.
Before and after
What we measure
We take a baseline before any change and report the same numbers after cutover, from your own tools. They are the evidence of whether the migration worked — not figures promised in advance.
- Error rate
- Old and new paths compared at every traffic step
- p95 latency
- Per route, before extraction and after each cutover step
- Deploy frequency
- From your CI history: a baseline before, tracked after
Questions
Frequently asked migration questions
With the saga pattern: each step has a compensating action, orchestrated by a workflow engine such as Temporal or by events, instead of distributed two-phase commit.
When a small team ships from one pipeline without queuing and the codebase is already well modularized. A monolith is then cheaper to build and run, and the migration would add operational work without removing a real bottleneck.
Rehearse the cutover before the real one
Tell us about your data volume, traffic and timeline. An engineer will reply within one business day to set up a call about the migration plan and its rollback path.