ClimsTech

E-commerce technology · Peak-readiness modernisation

Preparing a commerce platform for four times normal transaction volume

A focused modernisation programme across infrastructure, deployment, observability, rollback and capacity testing.

GKEJenkinsTerraformKubernetes

4.2×

normal transaction volume supported

99.98%

seasonal availability

72%

faster deployments

48%

fewer peak-period incidents

In brief

An e-commerce technology organisation needed to improve platform resilience before a major sales period without a full rewrite. ClimsTech prioritised transaction-critical services, containerised suitable workloads onto GKE, standardised infrastructure through Terraform, introduced reusable Jenkins pipelines, centralised logging and ran staged capacity tests — so teams could deploy, diagnose, recover and scale safely during the peak.

Working constraints

  • Fixed sales deadline
  • Multiple application owners
  • Existing production dependencies
  • Limited time for modernisation
  • High cost of transaction interruption
  • Different release methods by service
  • Need to preserve business continuity

The problem

What was actually going wrong

The organisation did not need a complete platform rewrite. It needed to reduce operational risk before a known period of elevated demand. The critical question was not simply whether the platform could handle more traffic — it was whether engineering teams could deploy, diagnose, recover, and scale safely during the peak.

What discovery surfaced

  1. 1Critical services did not share a release standard.
  2. 2Infrastructure changes were difficult to review.
  3. 3Rollback procedures varied by application.
  4. 4Logs could not be correlated across transaction flows.
  5. 5Capacity assumptions were not supported by repeatable tests.
  6. 6Scaling thresholds had not been calibrated against real workload behaviour.

The engineering

What we built and changed

1Workload prioritisation

Services were ranked according to customer impact, transaction criticality, and operational risk.

2Container and platform standardisation

Selected workloads were containerised and deployed to GKE with defined health, resource, and scaling policies.

3Delivery automation

Reusable Jenkins pipeline templates standardised build, validation, deployment, and rollback.

4Observability

Logs and service metrics were centralised around transaction paths, payment dependencies, queue depth, latency, and error rate.

5Peak-readiness testing

Load tests simulated campaign demand; results informed resource allocation, autoscaling, connection management, and recovery procedures.

The team entered the peak period with rehearsed procedures, shared dashboards, standardised releases, and a clearer escalation model.

The architecture

Before and after

Before
  • Independently developed applications
  • Inconsistent deployment workflows
  • Fragmented, uncorrelated logs
  • Manual infrastructure changes
  • Varied rollback procedures
  • Untested capacity assumptions
After
  • CDN and edge security
  • Load balancing
  • GKE platform
  • Web and API services
  • Order services
  • Workers and queues
  • Data layer
  • Logs, metrics and alerts

Judgement calls

Decisions that shaped the outcome

Why modernise only selected services?

The deadline required a risk-based approach. Transaction-critical and high-change services offered the greatest operational benefit.

Why reusable pipelines?

Standard templates reduced variation without forcing every team to redesign delivery independently.

Why test rollback explicitly?

Peak readiness is incomplete when deployment succeeds but recovery remains untested.

Verified outcomes

What changed for the business

  • Supported 4.2× normal transaction volume
  • Seasonal availability maintained at 99.98%
  • Deployment duration reduced by 72%
  • Rollback reduced from 45 minutes to under 10 minutes
  • Peak-period incidents reduced by 48%
  • Manual deployment steps reduced from 23 to 6

What this engagement proves

Peak readiness depends as much on operational discipline as raw infrastructure capacity.

Field notes on this class of problem

All field notes
Kubernetes & platform

Autoscaling for traffic spikes: beyond a single HPA

Layer pod, node and event-driven scaling — a lone HPA won't survive launch day.

21 min read

Cloud architecture

Caching that helps: CDN, Redis and the thundering herd

Every cache layer from browser to database, with the incidents that live there.

20 min read

DevOps & delivery

Zero-downtime deployments: rolling, blue-green and canary

Rolling, blue-green or canary — and the database problem that defeats all three.

18 min read

Preparing for a high-demand event?

See more engagements

Discuss peak readiness