datason Product Roadmap¶

Mission: Make ML/data workflows reliably portable, readable, and structurally type-safe using human-friendly JSON.

🎯 Core Principles (Non-Negotiable)¶

✅ Minimal Dependencies¶

Zero required dependencies for core functionality
Optional dependencies only for specific integrations (pandas, torch, etc.)
Never add dependencies that duplicate Python stdlib functionality

✅ Performance First¶

Maintain <3x stdlib JSON overhead for simple types
Benchmark-driven development with regression prevention
Memory efficiency through configurable limits and smart defaults

✅ Comprehensive Test Coverage¶

Maintain >90% test coverage across all features
Test all edge cases and failure modes
Performance regression testing for every release

🎯 Current State (v0.1.4)¶

✅ Foundation Complete¶

Core Serialization: 20+ data types, circular reference detection, security limits
Configuration System: 4 preset configs + 13+ configurable options
Advanced Type Handling: Complex numbers, decimals, UUIDs, paths, enums, collections
ML/AI Integration: PyTorch, TensorFlow, scikit-learn, NumPy, JAX, PIL
Pandas Deep Integration: 6 DataFrame orientations, Series, Categorical, NaN handling
Performance Optimizations: Early detection, memory streaming, configurable limits
Comprehensive Testing: 83% coverage, 300+ tests, benchmark suite

📊 Performance Baseline¶

Simple JSON: 1.6x overhead vs stdlib (excellent for added functionality)
Complex types: Only option for UUIDs/datetime/ML objects in pure JSON
Advanced configs: 15-40% performance improvement over default

🚀 Focused Roadmap¶

Philosophy: Deepen what datason uniquely does well rather than expanding scope

v0.3.0 - Pickle Bridge¶

"Convert legacy ML pickle files to portable JSON - solve a real workflow pain point"

🎯 Unique Value Proposition¶

No other JSON serializer handles the ML community's massive pickle legacy. This bridges the gap safely.

# Convert pickle to datason JSON (unique capability)
import datason

# Safe conversion with class whitelisting  
json_data = datason.from_pickle("model.pkl",
                                safe_classes=["sklearn", "numpy", "pandas"])

# Bulk migration tools for ML teams
datason.convert_pickle_directory("old_models/", "json_models/")

🔧 Implementation Goals¶

Zero new dependencies - use stdlib pickle module only
Security-first - whitelist approach for safe class loading
Leverage existing type handlers - reuse 100% of current ML object support
Maintain performance - streaming conversion for large pickle files

📈 Success Metrics¶

Support 95%+ of sklearn/torch/pandas pickle files
Zero security vulnerabilities in pickle processing
<5% performance overhead vs direct pickle loading
No new dependencies added

v0.3.5 - Advanced ML Types¶

"Handle more ML framework objects that competitors can't serialize"

🎯 Unique Value Proposition¶

Extend datason's unique strength - serializing ML objects that other JSON libraries simply can't handle.

# New ML object support (competitors can't do this)
import xarray as xr
import dask.dataframe as dd

data = {
    "xarray_dataset": xr.Dataset({"temp": (["x", "y"], np.random.random((3, 4)))}),
    "dask_dataframe": dd.from_pandas(large_df, npartitions=4),
    "pytorch_dataset": torchvision.datasets.MNIST(root="./data"),
    "huggingface_tokenizer": AutoTokenizer.from_pretrained("bert-base-uncased")
}

# Only datason can serialize this to readable JSON
result = datason.serialize(data, config=get_ml_config())

🔧 Implementation Goals¶

Extend type handler system - no architectural changes needed
Optional dependencies only - graceful fallbacks when libs unavailable
Consistent with existing patterns - reuse configuration system
Maintain performance - efficient handling of large scientific objects

📈 Success Metrics¶

Support 10+ additional ML/scientific libraries
Zero new required dependencies
Maintain existing performance characteristics
100% backward compatibility

v0.4.0 - Performance & Memory Optimization¶

"Make datason the fastest option for ML object serialization"

🎯 Unique Value Proposition¶

Other JSON libraries either can't handle ML objects or are slow. Make datason both capable AND fast.

# Optimized for large ML workflows
import datason

# Memory-efficient streaming for large objects
with datason.stream_serialize("large_experiment.json") as stream:
    stream.write({"model": huge_model})
    stream.write({"data": massive_dataset})
    # Memory usage stays bounded

# Parallel serialization for multi-object workflows
results = datason.serialize_parallel([model1, model2, model3])

🔧 Implementation Goals¶

Zero new dependencies - optimize existing algorithms
Memory streaming - handle objects larger than RAM
Parallel processing - utilize multiple cores efficiently
Profile-driven optimization - target real-world ML bottlenecks

📈 Success Metrics¶

50%+ performance improvement for large ML objects
Handle 10GB+ objects with <2GB RAM usage
Maintain <2x stdlib overhead for simple JSON
Zero new dependencies

v0.4.5 - Typed Deserialization & Round-Trip Support¶

"Complete the portability story with safe data reconstruction"

🎯 Unique Value Proposition¶

Enable truly portable ML workflows by safely reconstructing Python objects from datason JSON.

# Template-based deserialization with type safety
import datason

# Infer template from existing objects
template = datason.infer_template({"features": np.array([[1,2,3]]), "timestamp": datetime.now()})
# Result: {"features": Array(dtype="float64", shape=(-1, 3)), "timestamp": DateTime()}

# Type-safe reconstruction
json_data = '{"features": [[1.0, 2.0, 3.0]], "timestamp": "2024-01-15T10:30:00"}'
reconstructed = datason.cast_to_template(json_data, template)
assert reconstructed["features"].dtype == np.float64  # Type guaranteed

🔧 Implementation Goals¶

Leverage existing type handlers - reuse serialization logic in reverse
Zero new dependencies - use stdlib and existing optional deps
Template inference - automatically generate cast templates from examples
Type safety - prevent runtime type errors in ML pipelines

📈 Success Metrics¶

99%+ fidelity for numpy array round-trips (dtype, shape, values)
Support for 15+ cast types (arrays, datetime, ML objects)
<20% overhead vs naive JSON parsing
Zero runtime type errors with proper templates

"Perfect the configuration system based on real-world usage"

🎯 Unique Value Proposition¶

No other JSON serializer offers ML-specific configuration presets. Refine this unique advantage.

# Enhanced presets based on user feedback
import datason
from datason.config import get_inference_config, get_research_config

# New specialized configurations
inference_config = get_inference_config()  # Optimized for model serving
research_config = get_research_config()    # Preserve maximum information
logging_config = get_logging_config()      # Safe for production logs
training_config = get_training_config()    # Balance speed and fidelity

# Environment-based auto-configuration
datason.auto_configure()  # Detects ML environment and optimizes

🔧 Implementation Goals¶

Refine existing system - no architectural changes
User feedback driven - address real pain points
Maintain simplicity - keep API surface small
Performance tuning - optimize configuration combinations

📈 Success Metrics¶

90%+ user satisfaction with default configurations
25%+ performance improvement for common workflows
Zero breaking changes to existing API
Maintain zero required dependencies

v0.5.5 - Production Safety & Redaction¶

"Make datason safe for production ML logging and compliance"

🎯 Unique Value Proposition¶

ML workflows often contain sensitive data. Provide built-in redaction without breaking serialization fidelity.

# Safe logging configuration for production ML
import datason
from datason.config import SerializationConfig

# Field-level redaction with smart patterns
config = SerializationConfig(
    redact_fields=["password", "api_key", "*.secret", "user.email"],
    redact_large_objects=True,  # Auto-redact >10MB objects
    redact_patterns=[r"\b\d{4}-\d{4}-\d{4}-\d{4}\b"],  # Credit cards
    redaction_replacement="<REDACTED>",
    include_redaction_summary=True
)

# Safe for production logs
result = datason.serialize(ml_experiment_data, config=config)

🔧 Implementation Goals¶

Extend configuration system - no new dependencies
Pattern-based redaction - regex and field path matching
Audit trails - track what was redacted and why
Performance conscious - minimal overhead when not redacting

📈 Success Metrics¶

99.9%+ sensitive data detection for common patterns
<5% false positive rate for redaction
<10% performance overhead for redaction processing
Zero new dependencies

v0.6.0 - Snapshot Testing & ML DevX¶

"Turn datason's readable JSON into powerful ML testing infrastructure"

🎯 Unique Value Proposition¶

Leverage datason's human-readable JSON to create the best ML testing experience available.

# Snapshot testing for ML workflows
import datason

# Generate readable test snapshots
@datason.snapshot_test("test_model_prediction")
def test_model_output():
    model = load_trained_model()
    prediction = model.predict(test_data)

    # Auto-generates human-readable JSON snapshot
    datason.assert_snapshot(prediction, normalize_floats=True)

# Update snapshots when behavior intentionally changes
datason.update_snapshots("test_model_*", reason="Added new features")

# Compare model outputs semantically
datason.assert_equivalent(old_predictions, new_predictions,
                         tolerance=1e-6, ignore_fields=["timestamp"])

🔧 Implementation Goals¶

Leverage existing serialization - build on proven JSON generation
Git-friendly diffs - human-readable changes in model outputs
ML-specific normalization - handle float precision, timestamps, etc.
Integration friendly - work with pytest, unittest, CI/CD

📈 Success Metrics¶

50%+ reduction in ML test maintenance overhead
Support for 95%+ of ML output types
<10s snapshot update time for large test suites
Zero false positive failures from irrelevant changes

v0.6.5 - Test Infrastructure & Quality¶

"Achieve industry-leading reliability for production ML workflows"

🎯 Unique Value Proposition¶

ML workflows need bulletproof reliability. Make datason the most tested serialization library.

# Enhanced testing and validation tools
import datason

# Built-in validation for ML workflows
result = datason.serialize(model, validate=True)  # Catches issues early

# Property-based testing for edge cases
datason.test_roundtrip(my_custom_object)  # Ensures perfect fidelity

# Performance monitoring
with datason.monitor() as m:
    result = datason.serialize(large_data)
    print(f"Serialized {m.size_mb}MB in {m.time_ms}ms")

🔧 Implementation Goals¶

Expand test coverage to 95%+ - test all edge cases
Property-based testing - use hypothesis for edge case discovery
Performance regression tests - prevent performance degradation
ML-specific test utilities - help users test their integrations

📈 Success Metrics¶

95%+ test coverage across all modules
Zero critical bugs in production deployments
Comprehensive property-based test suite
Built-in performance monitoring tools

v0.7.0 - Delta Serialization & Efficiency¶

"Optimize storage and improve traceability for evolving ML objects"

🎯 Unique Value Proposition¶

Make ML experiment tracking and model versioning storage-efficient with structural diffs.

# Delta-aware serialization for efficient storage
import datason

# Only serialize what changed
baseline_model = load_model("v1.0")
updated_model = train_incremental(baseline_model, new_data)

delta = datason.serialize_delta(updated_model, baseline=baseline_model)
# Result: {"changed_params": {"layer_3.weight": [...]}, "metadata": {...}}

# Reconstruct from baseline + delta
reconstructed = datason.apply_delta(baseline_model, delta)

# Great for Git-friendly experiment tracking
datason.save_experiment_delta("experiment_v2.json", new_experiment,
                             baseline="experiment_v1.json")

🔧 Implementation Goals¶

Structural diff algorithms - efficient comparison of deep objects
Configurable sensitivity - control what constitutes a "change"
Storage optimization - significant space savings for incremental updates
Git integration - meaningful diffs for version control

📈 Success Metrics¶

80%+ storage reduction for incremental model updates
<100ms delta computation for typical ML models
Support delta chains of 50+ steps without degradation
Human-readable diffs in version control

v0.8.0 - Documentation & Ecosystem¶

"Make datason the easiest ML serialization library to adopt"

🎯 Unique Value Proposition¶

Complex ML serialization made simple through excellent documentation and examples.

# Comprehensive examples for every ML use case
import datason

# Interactive documentation with runnable examples
datason.examples.pytorch_model_serialization()
datason.examples.pandas_workflow()
datason.examples.sklearn_pipeline()

# Migration guides from other solutions
datason.migrate_from_pickle(existing_workflow)
datason.migrate_from_joblib(sklearn_artifacts)

🔧 Implementation Goals¶

Comprehensive documentation - cover every use case
Interactive examples - runnable code for all features
Migration guides - help users switch from pickle/joblib
Performance guides - help users optimize for their workloads

📈 Success Metrics¶

100% API documentation coverage
Migration guides for 5+ popular alternatives
Interactive examples for all major ML frameworks
<5 minute time-to-first-success for new users

🚫 What We Won't Build¶

Schema Validation¶

Why Not: Covered excellently by Pydantic, marshmallow, cerberus
Instead: Focus on serializing whatever users already validate

Cloud Storage Integration¶

Why Not: Adds dependencies, covered by cloud SDKs
Instead: Focus on generating JSON that works with any storage

Cross-Format Support (Arrow, Protobuf)¶

Why Not: Violates "human-friendly JSON" mission
Instead: Perfect JSON serialization, let users convert if needed

Enterprise Features (Auth, Monitoring, etc.)¶

Why Not: Adds complexity and dependencies far beyond core mission
Instead: Integrate well with existing enterprise tools

Plugin System¶

Why Not: Adds architectural complexity, can revisit post-v1.0
Decision: Document for potential v1.0+ exploration, focus on core excellence first

🎯 Success Metrics¶

Technical Excellence¶

Performance: Always <3x stdlib JSON for simple types
Reliability: >99.9% uptime for critical ML workflows
Coverage: Support 95%+ of common ML objects without dependencies
Quality: 95%+ test coverage with zero critical production bugs

Adoption Goals¶

v0.3.0: 5,000+ monthly active users in ML community
v0.5.0: Standard tool in 3+ major ML frameworks' documentation
v0.7.0: 50,000+ downloads, referenced in ML courses/tutorials

Community Impact¶

Unique Value: Only JSON serializer that "just works" for ML objects
Reliability: Teams trust datason for production ML pipelines
Simplicity: Stays true to zero-dependency, minimal-complexity philosophy

🤝 Community & Feedback¶

Current Users¶

Share your ML serialization pain points
Report performance bottlenecks with real workloads
Suggest ML frameworks we should prioritize

ML Framework Authors¶

Partner on official integration examples
Provide feedback on type handler implementations
Help test edge cases in your frameworks

Enterprise Teams¶

Share production use cases and requirements
Provide feedback on configuration presets
Help validate reliability improvements

Roadmap Principles: Stay focused, stay fast, stay simple, solve real problems

Last updated: JUne 2025 | Next review: Q3 2025

datason Product Roadmap¶

🎯 Core Principles (Non-Negotiable)¶

✅ Minimal Dependencies¶

✅ Performance First¶

✅ Comprehensive Test Coverage¶

🎯 Current State (v0.1.4)¶

✅ Foundation Complete¶

📊 Performance Baseline¶

🚀 Focused Roadmap¶

v0.3.0 - Pickle Bridge¶

🎯 Unique Value Proposition¶

🔧 Implementation Goals¶

📈 Success Metrics¶

v0.3.5 - Advanced ML Types¶

🎯 Unique Value Proposition¶

🔧 Implementation Goals¶

📈 Success Metrics¶

v0.4.0 - Performance & Memory Optimization¶

🎯 Unique Value Proposition¶

🔧 Implementation Goals¶

📈 Success Metrics¶

v0.4.5 - Typed Deserialization & Round-Trip Support¶

🎯 Unique Value Proposition¶

🔧 Implementation Goals¶

📈 Success Metrics¶

v0.5.0 - Configuration Refinement¶

🎯 Unique Value Proposition¶

🔧 Implementation Goals¶

📈 Success Metrics¶

v0.5.5 - Production Safety & Redaction¶

🎯 Unique Value Proposition¶

🔧 Implementation Goals¶

📈 Success Metrics¶

v0.6.0 - Snapshot Testing & ML DevX¶

🎯 Unique Value Proposition¶

🔧 Implementation Goals¶

📈 Success Metrics¶

v0.6.5 - Test Infrastructure & Quality¶

🎯 Unique Value Proposition¶

🔧 Implementation Goals¶

📈 Success Metrics¶

v0.7.0 - Delta Serialization & Efficiency¶

🎯 Unique Value Proposition¶

🔧 Implementation Goals¶

📈 Success Metrics¶

v0.8.0 - Documentation & Ecosystem¶

🎯 Unique Value Proposition¶

🔧 Implementation Goals¶

📈 Success Metrics¶

🚫 What We Won't Build¶

Schema Validation¶

Cloud Storage Integration¶

Cross-Format Support (Arrow, Protobuf)¶

Enterprise Features (Auth, Monitoring, etc.)¶

Plugin System¶

🎯 Success Metrics¶

Technical Excellence¶

Adoption Goals¶

Community Impact¶

🤝 Community & Feedback¶

Current Users¶

ML Framework Authors¶

Enterprise Teams¶