01
Deployment gridlock
Many developers blocked on one shared deployment queue, with frequent rollbacks.
Blocked deployments and monolith friction
Strangler fig decomposition: monolith to microservices
Break a monolith apart without a big-bang rewrite: extract one bounded context at a time behind a routing layer, verify it in shadow mode, then shift traffic in steps.
Symptoms
Signs your platform has this problem
If several of these sound familiar, the plan below is where we would start.
02
Cascading outages
A memory leak in a minor feature taking down checkout and billing.
03
Scaling inefficiency
Scaling the whole application cluster to handle one background worker.
Remediation plan
How we fix it, step by step
Each phase ends with a measurement, so you can see what changed before the next one starts.
01
Domain discovery and event storming
Mapping subdomains and data dependencies to find clean bounded contexts.
02
Edge routing
A reverse proxy (Envoy, Cloudflare or your gateway) that routes traffic between the monolith and new services.
03
Change data capture
Streaming database changes with Debezium and Kafka to verify the new service in shadow mode.
04
Traffic cutover and pruning
Shifting production traffic in steps (for example 1%, 10%, then all of it) and deleting the legacy code.
Technical checklist
Remediation checklist
What we check before a change goes to production:
- Map bounded contexts with event storming
- Put a reverse proxy in front of the monolith to route each domain's traffic
- Set up change data capture with Debezium and Kafka
- Use the saga pattern for transactions that span services
What we measure
We take a baseline first and report the same measurements after each change, from your own monitoring — evidence, not promised results.
- Deploy frequency
- Per service, from CI history, before and after
- Error rate
- Old vs new path at every traffic step
- Compute cost
- Monolith cluster vs extracted services for the same load
Related service
IT consulting
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Questions about this remediation
With the saga pattern: each step has a compensating action, orchestrated by Temporal or by events, instead of distributed two-phase locking.
It depends on how tangled the module's data is. The first extraction takes longest because it also builds the routing, data sync and observability groundwork that later ones reuse. We estimate per service after the domain mapping.
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Want an engineer to look at this with you?
Send us the symptoms and any metrics you have. We'll reply within one business day, set up a call and agree what to measure before anything changes.