Enterprise Feature Store System

System Architecture

A scalable feature store system that provides unified feature management, serving, and computation for machine learning applications.

Core Components

1. Feature Registry

class FeatureRegistry:
    def __init__(self, config: Dict[str, Any]):
        self.metadata_store = MetadataStore(config['metadata_config'])
        self.validator = FeatureValidator(config['validation_rules'])
        self.versioner = FeatureVersioner()
        
    async def register_feature(
        self,
        feature_def: FeatureDefinition
    ) -> str:
        # Validate feature definition
        await self.validator.validate(feature_def)
        
        # Generate feature version
        version = self.versioner.generate_version(feature_def)
        
        # Store metadata
        feature_id = await self.metadata_store.store_feature(
            feature_def,
            version
        )
        
        return feature_id
        
    async def get_feature_info(
        self,
        feature_id: str,
        version: Optional[str] = None
    ) -> FeatureInfo:
        return await self.metadata_store.get_feature(
            feature_id,
            version
        )

class FeatureDefinition:
    def __init__(
        self,
        name: str,
        entity: str,
        value_type: str,
        transformation: Transform,
        dependencies: List[str],
        ttl: Optional[timedelta] = None
    ):
        self.name = name
        self.entity = entity
        self.value_type = value_type
        self.transformation = transformation
        self.dependencies = dependencies
        self.ttl = ttl

2. Feature Computation Engine

class FeatureComputer:
    def __init__(self, config: Dict[str, Any]):
        self.executor = SparkExecutor(config['spark_config'])
        self.cache = FeatureCache(config['cache_config'])
        self.scheduler = ComputationScheduler(
            config['schedule_config']
        )
        
    async def compute_features(
        self,
        feature_ids: List[str],
        entity_keys: List[str]
    ) -> pd.DataFrame:
        # Get computation graph
        graph = await self._build_computation_graph(feature_ids)
        
        # Check cache
        cached_features = await self.cache.get_features(
            feature_ids,
            entity_keys
        )
        
        # Compute missing features
        missing_features = await self._compute_missing_features(
            graph,
            cached_features,
            entity_keys
        )
        
        # Merge and return results
        return pd.concat([cached_features, missing_features])
        
    async def _compute_missing_features(
        self,
        graph: ComputationGraph,
        cached: pd.DataFrame,
        keys: List[str]
    ) -> pd.DataFrame:
        # Generate Spark job
        job = self.executor.create_job(graph)
        
        # Execute computation
        result = await job.execute(keys)
        
        # Cache results
        await self.cache.store_features(result)
        
        return result

3. Feature Serving System

class FeatureServer:
    def __init__(self, config: Dict[str, Any]):
        self.online_store = RedisFeatureStore(
            config['online_store_config']
        )
        self.offline_store = S3FeatureStore(
            config['offline_store_config']
        )
        self.router = FeatureRouter(config['routing_config'])
        
    async def get_online_features(
        self,
        feature_ids: List[str],
        entity_key: str
    ) -> Dict[str, Any]:
        # Route request
        store = self.router.route_request(feature_ids)
        
        if store == 'online':
            return await self.online_store.get_features(
                feature_ids,
                entity_key
            )
        else:
            return await self.offline_store.get_features(
                feature_ids,
                entity_key
            )
            
    async def get_batch_features(
        self,
        feature_ids: List[str],
        entity_keys: List[str]
    ) -> pd.DataFrame:
        return await self.offline_store.get_batch_features(
            feature_ids,
            entity_keys
        )

class RedisFeatureStore:
    def __init__(self, config: Dict[str, Any]):
        self.redis = aioredis.Redis(
            host=config['host'],
            port=config['port']
        )
        self.serializer = FeatureSerializer()
        
    async def get_features(
        self,
        feature_ids: List[str],
        entity_key: str
    ) -> Dict[str, Any]:
        keys = [
            f"{entity_key}:{feature_id}"
            for feature_id in feature_ids
        ]
        
        values = await self.redis.mget(keys)
        return {
            feature_id: self.serializer.deserialize(value)
            for feature_id, value in zip(feature_ids, values)
            if value is not None
        }

4. Feature Pipeline Manager

class FeaturePipelineManager:
    def __init__(self, config: Dict[str, Any]):
        self.dag_generator = DAGGenerator()
        self.airflow_client = AirflowClient(
            config['airflow_config']
        )
        self.monitor = PipelineMonitor(
            config['monitoring_config']
        )
        
    async def create_pipeline(
        self,
        feature_ids: List[str]
    ) -> str:
        # Generate computation DAG
        dag = await self.dag_generator.generate_dag(feature_ids)
        
        # Deploy to Airflow
        pipeline_id = await self.airflow_client.deploy_dag(dag)
        
        # Setup monitoring
        await self.monitor.setup_pipeline_monitoring(
            pipeline_id,
            feature_ids
        )
        
        return pipeline_id
        
    async def get_pipeline_status(
        self,
        pipeline_id: str
    ) -> PipelineStatus:
        status = await self.airflow_client.get_dag_status(
            pipeline_id
        )
        metrics = await self.monitor.get_pipeline_metrics(
            pipeline_id
        )
        return PipelineStatus(status, metrics)

Usage Example

# Initialize feature store
config = {
    'metadata_config': {
        'database_url': 'postgresql://localhost:5432/feature_store'
    },
    'online_store_config': {
        'host': 'localhost',
        'port': 6379
    },
    'offline_store_config': {
        'bucket': 'feature-store',
        'region': 'us-west-2'
    },
    'compute_config': {
        'spark_master': 'spark://localhost:7077',
        'executor_memory': '4g'
    }
}

feature_store = FeatureStore(config)

# Register new feature
feature_def = FeatureDefinition(
    name='user_purchase_30d',
    entity='user',
    value_type='float',
    transformation=WindowedAggregation(
        agg_func='sum',
        window='30d'
    ),
    dependencies=['purchase_amount']
)

feature_id = await feature_store.register_feature(feature_def)

# Create feature pipeline
pipeline_id = await feature_store.create_pipeline([feature_id])

# Get online features
features = await feature_store.get_online_features(
    [feature_id],
    'user_123'
)

# Get batch features
batch_features = await feature_store.get_batch_features(
    [feature_id],
    ['user_123', 'user_456']
)

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