Radixor/docs/quality-and-operations.md

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
• CLI compilation
• Programmatic usage

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.