How to Complete a Core Service Language Migration Without a Senior Expert Team

Background: When Resource Cost Becomes a Planning Input

A high-traffic API service had long run on a monolithic architecture written in a dynamic language. As traffic grew, problems became more visible: CPU utilization was low, and computational efficiency was limited by runtime overhead and the concurrency model. In the past, when resources were tight, the team could temporarily add machines on demand. Now CPU capacity must be planned in advance, and resource cost has become a direct input to architecture decisions.

There are three common solutions to this kind of problem: optimize the existing code, split the service and migrate it piece by piece, or rewrite it entirely. The cost and risk of the three paths differ significantly. The case discussed here chose the third path and completed it even though the team lacked experience in the target language. The choice itself is debatable, but its methodology is worth analyzing.

1. First Decide Whether a Full Rewrite Is Reasonable

A full rewrite is usually considered a high-risk option. Conventional wisdom holds that such projects take a long time and require splitting the system into multiple modules, migrating them one by one, and reducing the impact scope of each change.

Several preconditions enabled this case to adopt a full rewrite:

1. Clear interface boundaries: The external protocol was stable, and inputs and outputs could be fully described.
2. Comparable behavior: The same request could produce results in the old and new systems that could be compared.
3. Strictly limited migration scope: Only functionally equivalent porting was allowed; no new features were introduced.
4. A usable traffic replication mechanism: The new system could receive real requests without affecting user-facing results.

If these preconditions do not hold, the risk of a full rewrite increases rapidly. Teams should check these four conditions first before deciding whether to take this path.

2. Team Structure: Humans Own Architecture, AI Owns Implementation

The division of labor in this case was clear. AI coding tools handled most of the code writing, while human engineers were responsible for system design, implementation planning, and result validation.

The logic behind this division is that code generation is labor that can be replaced at scale, while architecture judgment for distributed systems, failure-boundary definition, and migration pacing control still require humans. Once a system spans multiple services and storage layers, local correctness does not mean overall correctness.

The project lead pointed out that the two most important things in the entire process were creating the implementation plan and validating the final result. Writing code was not the decisive factor for success. This has direct reference value for teams planning to use AI-assisted development: put human effort into planning and validation first, rather than trying to let AI handle everything.

3. A Six-Step Migration Method

The following six steps distill the approach used in this case. Each step corresponds to a checkable deliverable.

Step 1: Freeze Functional Scope

During migration, the new system only ports functionality equivalently. Every behavior of the old system must be replicated, but no new interfaces or parameters are added.

The reason is simple. When functional changes and implementation changes are mixed together, it is impossible to determine the cause when differences appear. After the scope is frozen, any difference can only be attributed to implementation issues.

Deliverable: A feature list covering all external interfaces, parameters, error codes, and boundary behaviors.

Step 2: Handle Feature Drift from Parallel Development

During migration, the original system continues to be developed. New features are constantly added to the old system, and if the new system does not keep pace, the migration will never catch up.

In this case, the migration team used static analysis and other methods to continuously inspect code changes in the old system and confirm that every new feature had a corresponding implementation in the new system. This is an easily overlooked workload that requires dedicated staffing and should be included as a milestone in the migration plan.

Deliverable: A change-tracking mechanism and feature-alignment reports.

Step 3: Build the New Service Completely

In this step, the team directly built the complete service in the target language rather than first extracting a small pilot. The advantage is that the system’s internal data structures, concurrency model, and error handling are designed according to the target language’s conventions from the start, avoiding line-by-line translation inside the old code’s framework.

Note that a complete build does not mean launching everything at once. The new service should first complete compilation, unit tests, and basic integration tests before entering the comparison phase.

Deliverable: A deployable new service and automated tests covering the main interfaces.

Step 4: Compare Shadow Traffic

Shadow traffic means sending real requests to both the old and new systems at the same time. The old system’s results are still returned to users as usual, while the new system’s results are only recorded for comparison.

Before comparison, expected differences must be excluded. For example, timestamps, randomly generated IDs, and the order of fields in responses can cause inconsistent results without indicating a functional problem. The comparison logic should filter out this noise first, then count real differences.

Below is a simplified response-comparison example that recursively finds differences between two JSON structures:

use serde_json::Value;
/// Compare responses from the legacy and candidate systems and return all difference paths
fn compare_responses(legacy: &Value, candidate: &Value) -> Vec<String> {
    let mut diffs = Vec::new();
    diff_value("", legacy, candidate, &mut diffs);
    diffs
}
fn diff_value(path: &str, a: &Value, b: &Value, diffs: &mut Vec<String>) {
    match (a, b) {
        (Value::Object(ma), Value::Object(mb)) => {
            for (k, va) in ma {
                let child = format!("{path}.{k}");
                match mb.get(k) {
                    Some(vb) => diff_value(&child, va, vb, diffs),
                    None => diffs.push(format!("{child}: missing field in candidate")),
                }
            }
            for k in mb.keys() {
                if !ma.contains_key(k) {
                    diffs.push(format!("{path}.{k}: extra field in candidate"));
                }
            }
        }
        (Value::Array(xa), Value::Array(xb)) => {
            if xa.len() != xb.len() {
                diffs.push(format!("{path}: array length differs {} vs {}", xa.len(), xb.len()));
            }
            for (i, (va, vb)) in xa.iter().zip(xb.iter()).enumerate() {
                diff_value(&format!("{path}[{i}]"), va, vb, diffs);
            }
        }
        _ if a != b => diffs.push(format!("{path}: value differs {a} vs {b}")),
        _ => {}
    }
}

The example only shows the core logic. A real system also needs to handle ignored-field lists, floating-point tolerance, chunked comparison for streaming responses, and sampling and aggregation of differences.

Deliverable: A difference statistics dashboard categorized by interface and difference type, with acceptable difference thresholds.

Step 5: Gradually Shift Real Traffic

Once the comparison data is stable, gradually shift real traffic from the old system to the new system. A common approach is percentage-based canary releases, for example starting at one percent and increasing step by step, observing error rates, latency, and comparison differences at each level.

A rollback path must be retained during the switch. The old system cannot be taken offline before the switch is complete, and the traffic allocation ratio should be adjustable within minutes.

Deliverable: A traffic-shifting plan, acceptance metrics for each percentage level, and rollback drill records.

Step 6: Wrap Up and Capture Lessons

After the full traffic switch, the old system still needs to be retained for an observation period. During this time, the team should organize issues found during migration, including interface ambiguities, implicit contracts, and performance bottlenecks, and write them into the new system’s documentation and test cases.

The migration team’s capabilities also need to be retained. Even if the project had no target-language experts at the beginning, team members gradually learned the language features and engineering conventions during migration. In this case, team members generally believed afterward that they had the relevant capabilities, and more experienced engineers were added as needed. This means “starting from zero experience” is better described as a starting point, not the state throughout the project.

Deliverable: A migration retrospective report, an operations manual for the new system, and an expanded set of test cases.

4. Several Pitfalls to Watch For

Treating verbal estimates as baseline data. The numbers in this case regarding resource savings and latency improvements are interview estimates, not formal benchmark results. Readers evaluating their own project benefits should rely on load-test data from their own environment and keep a sufficient margin of error.

Treating the speed of a full rewrite as a universal conclusion. The success of this case depended on the combination of scope freeze, traffic replication, and a comparison system. Without any one of these, the risk of a full rewrite rises substantially. Traditional piecewise migration takes longer, but each step has a smaller impact scope and remains the safer choice in many scenarios.

Ignoring the cost of feature drift. While the old system continues to be developed, the migration team’s workload does not stop. Without a dedicated alignment mechanism, the gap between the old and new systems will grow over time, ultimately preventing the migration from converging.

Using AI-generated code as a substitute for validation. Code generation can be fast, but whether the generated code is correct can only be confirmed through tests, comparison, and canary releases. Investment in validation should be comparable to, or even greater than, investment in implementation.

5. Conclusion

The core of this kind of project is controlling variables. Scope freeze ensures that differences can be attributed, shadow traffic ensures that comparison uses real data, and canary switching ensures that failures can be rolled back. With these three conditions in place, AI coding tools can significantly reduce implementation cost, but planning and result validation still need to be owned by engineers.

For teams considering a similar migration, start with three questions: Are interface boundaries clear? Can reproducible real traffic be obtained? Can functionality be frozen during migration? If the answer to all three is yes, a full rewrite is worth evaluating. If any answer is no, prioritize closing the gap or choose a piecewise migration path.