# Quality and Operations

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This document describes quality, testing, and operational practices for **Radixor**.

It focuses on:

- reliability and determinism
- testing strategies
- deployment patterns
- performance considerations
- lifecycle management of stemmer data



## Overview

Radixor is designed to separate:

- **data preparation** (dictionary construction and compilation)
- **runtime execution** (lookup and patch application)

This separation enables:

- predictable runtime behavior
- reproducible builds
- controlled evolution of stemming data



## Determinism and reproducibility

Radixor emphasizes deterministic behavior.

### Deterministic outputs

Given:

- the same dictionary input
- the same reduction settings

Radixor guarantees:

- identical compiled trie structure
- identical value ordering
- identical lookup results

### Why this matters

- stable search behavior across deployments
- reproducible builds
- easier debugging and regression analysis



## Testing strategy

### Unit testing

Core components should be tested independently:

- patch encoding and decoding
- trie construction
- reduction behavior
- binary serialization and deserialization

### Dictionary validation tests

A recommended pattern:

1. load dictionary input
2. compile trie
3. re-apply all word → stem mappings
4. verify that:

- expected stem is present in `getAll()`
- preferred result (`get()`) is correct when deterministic

This ensures:

- no data loss during reduction
- correctness of patch encoding



## Regression testing

Maintain a stable test dataset:

- representative vocabulary
- edge cases (short words, long words, ambiguous forms)

Use it to:

- detect unintended changes
- verify behavior after refactoring
- validate reduction mode changes



## Performance testing

Performance should be evaluated in terms of:

### Throughput

- words processed per second

### Latency

- time per lookup

### Memory footprint

- size of compiled trie
- runtime memory usage

Benchmark with:

- realistic token streams
- production-like dictionaries



## Deployment model

### Recommended workflow

1. prepare dictionary data
2. compile using CLI
3. store `.radixor.gz` artifact
4. deploy artifact with application
5. load using `loadBinary(...)`

### Why this model

- avoids runtime compilation overhead
- reduces startup latency
- ensures consistent behavior across environments



## Artifact management

Compiled stemmers should be treated as versioned assets.

### Versioning

- include version in filename or metadata
- track dictionary source and reduction settings

Example:

```
english-v1.2-ranked.radixor.gz
```

### Storage

- store in repository or artifact storage
- ensure consistent distribution across environments



## Runtime usage

### Loading

- load once during application startup
- reuse `FrequencyTrie` instance

### Thread safety

- compiled trie is safe for concurrent access
- no synchronization required for reads

### Avoid repeated loading

Do not:

- load trie per request
- rebuild trie at runtime



## Memory considerations

- compiled tries are compact but not negligible
- size depends on:
  - dictionary size
  - reduction mode

Recommendations:

- monitor memory usage in production
- choose reduction mode appropriately



## Reduction mode in production

Default recommendation:

- use **ranked mode**

Switch to other modes only when:

- memory constraints are strict
- multiple candidate results are not required

Always validate behavior after changing reduction mode.



## Dictionary lifecycle

### Updating dictionaries

When dictionary data changes:

1. update source file
2. recompile
3. run validation tests
4. deploy new artifact

### Backward compatibility

- changes in dictionary may affect stemming results
- evaluate impact on search relevance



## Observability

Radixor itself does not provide observability features; integration should provide:

- logging for loading failures
- metrics for lookup throughput
- monitoring of memory usage

Optional:

- sampling of ambiguous results (`getAll()`)



## Error handling

### During compilation

Handle:

- invalid dictionary format
- I/O failures
- invalid arguments

### During runtime

Handle:

- missing dictionary files
- corrupted binary artifacts

Fail fast on initialization errors.



## Operational best practices

- compile dictionaries offline
- version compiled artifacts
- test before deployment
- load once and reuse
- monitor performance and memory
- document reduction settings used



## Security considerations

- treat dictionary input as trusted data
- validate external sources before compilation
- avoid loading unverified binary artifacts



## Integration checklist

Before production deployment:

- dictionary validated
- compiled artifact generated
- reduction mode documented
- performance tested
- memory usage verified
- regression tests passing



## Next steps

- [Quick start](quick-start.md)
- [CLI compilation](cli-compilation.md)
- [Programmatic usage](programmatic-usage.md)



## Summary

Radixor is designed for:

- deterministic behavior
- efficient runtime execution
- controlled data-driven evolution

By separating compilation from runtime and following proper operational practices, it can be reliably integrated into production-grade systems.