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Stream Processor Implementation Plan

Phase 1: Core Foundation (Week 1-2)

1.1 Core Module

  • core/event.rs - ProcessorEvent wrapper
  • core/timestamp.rs - Timestamp utilities
  • core/key.rs - GroupingKey implementation
  • Unit tests for core types

1.2 Error Handling

  • error.rs - ProcessorError types
  • Error conversion implementations
  • Result type aliases

1.3 Configuration

  • config.rs - Configuration structures
  • Config validation
  • Config loading from YAML
  • Environment variable overrides

Deliverables: Core types, error handling, configuration loading

Phase 2: Window Management (Week 2-3)

2.1 Window Types

  • window/types.rs - Window, WindowSpec, WindowType
  • window/assigner.rs - WindowAssigner trait
  • window/tumbling.rs - TumblingWindowAssigner
  • window/sliding.rs - SlidingWindowAssigner
  • window/session.rs - SessionWindowAssigner
  • Unit tests for all window types

2.2 Window Triggers

  • window/trigger.rs - WindowTrigger trait
  • EventTimeTrigger implementation
  • ProcessingTimeTrigger implementation
  • CountTrigger implementation
  • Unit tests for triggers

Deliverables: Complete window management system with tests

Phase 3: Watermarking (Week 3-4)

3.1 Watermark Generation

  • watermark/generator.rs - Watermark and WatermarkGenerator trait
  • watermark/bounded.rs - BoundedOutOfOrdernessWatermark
  • watermark/periodic.rs - PeriodicWatermarkGenerator
  • watermark/alignment.rs - Timestamp alignment utilities
  • Unit tests for watermark generators

3.2 Late Event Handling

  • LateEventHandler implementation
  • LateEvent type
  • Side output mechanism
  • Integration tests for late events

Deliverables: Watermark system with late event handling

Phase 4: Aggregation Engine (Week 4-5)

4.1 Basic Aggregators

  • aggregation/aggregator.rs - Aggregator trait
  • aggregation/count.rs - CountAggregator
  • aggregation/sum.rs - SumAggregator
  • aggregation/average.rs - AverageAggregator
  • aggregation/minmax.rs - Min/MaxAggregators
  • Unit tests for basic aggregators

4.2 Advanced Aggregators

  • aggregation/percentile.rs - PercentileAggregator
  • aggregation/stddev.rs - StdDevAggregator
  • aggregation/composite.rs - CompositeAggregator
  • AggregationState structure
  • AggregationEngine coordinator
  • Unit tests for advanced aggregators

Deliverables: Complete aggregation system with all functions

Phase 5: State Management (Week 5-6)

5.1 State Backend Abstraction

  • state/backend.rs - StateBackend trait
  • state/manager.rs - WindowStateManager
  • Serialization/deserialization logic
  • Unit tests for state abstractions

5.2 Backend Implementations

  • state/memory.rs - MemoryStateBackend
  • state/rocksdb.rs - RocksDBStateBackend
  • Integration tests for backends

5.3 Checkpointing

  • state/checkpoint.rs - Checkpointing logic
  • state/recovery.rs - Recovery logic
  • End-to-end checkpoint/recovery tests

Deliverables: State management with persistence and recovery

Phase 6: Kafka Integration (Week 6-7)

6.1 Kafka Consumer

  • kafka/consumer.rs - KafkaEventSource
  • Offset management
  • Consumer group handling
  • Error handling and retries

6.2 Kafka Producer

  • kafka/producer.rs - KafkaAggregationSink
  • Batching and compression
  • Delivery guarantees
  • Error handling

6.3 Configuration

  • kafka/config.rs - Kafka configuration
  • Connection pooling
  • Integration tests with test Kafka

Deliverables: Complete Kafka integration

Phase 7: Processing Pipeline (Week 7-8)

7.1 Pipeline Builder

  • pipeline/builder.rs - StreamPipelineBuilder
  • Fluent API implementation
  • Validation logic
  • Builder tests

7.2 Pipeline Executor

  • pipeline/executor.rs - StreamPipeline
  • Event processing loop
  • Watermark propagation
  • Window triggering
  • Result emission

7.3 Operators

  • pipeline/operator.rs - Stream operators (map, filter, etc.)
  • pipeline/sink.rs - Output sink trait and implementations
  • Operator chaining
  • Unit tests for operators

Deliverables: Complete processing pipeline

Phase 8: Metrics and Observability (Week 8)

8.1 Metrics Collection

  • metrics/collector.rs - ProcessorMetrics
  • Throughput metrics
  • Latency metrics (p50, p95, p99)
  • State size metrics
  • Error rate metrics

8.2 Tracing

  • Structured logging integration
  • Span creation for operations
  • Distributed tracing support

8.3 Health Checks

  • Health check endpoint
  • Readiness checks
  • Liveness checks

Deliverables: Complete observability stack

Phase 9: Testing and Documentation (Week 9-10)

9.1 Unit Tests

  • Comprehensive unit test coverage (>80%)
  • Edge case testing
  • Property-based testing

9.2 Integration Tests

  • End-to-end processing tests
  • Late event handling tests
  • State recovery tests
  • Multi-window tests
  • Error handling tests

9.3 Performance Tests

  • Throughput benchmarks
  • Latency benchmarks
  • Memory usage tests
  • State backend performance tests

9.4 Documentation

  • API documentation
  • Usage examples
  • Configuration guide
  • Deployment guide
  • Performance tuning guide

Deliverables: Production-ready with comprehensive tests and docs

Phase 10: Production Hardening (Week 10-11)

10.1 Performance Optimization

  • Profile hot paths
  • Optimize memory allocations
  • Reduce lock contention
  • Batch state updates

10.2 Reliability

  • Circuit breakers
  • Graceful shutdown
  • Backpressure handling
  • Resource limits

10.3 Operations

  • Docker image
  • Kubernetes manifests
  • Monitoring dashboards
  • Alerting rules
  • Runbooks

Deliverables: Production-ready deployment artifacts

Dependencies and Prerequisites

External Dependencies

[dependencies]
# Core
tokio = { version = "1.40", features = ["full"] }
tokio-stream = "0.1"
async-trait = "0.1"
futures = "0.3"

# Kafka
rdkafka = { version = "0.36", features = ["tokio"] }

# Serialization
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
bincode = "1.3"

# State backends
rocksdb = "0.22"
sled = "0.34"

# Statistics
statrs = "0.17"

# Utilities
chrono = { version = "0.4", features = ["serde"] }
uuid = { version = "1.10", features = ["v4", "serde"] }
thiserror = "1.0"
anyhow = "1.0"
dashmap = "6.0"

# Observability
tracing = "0.1"
tracing-subscriber = { version = "0.3", features = ["env-filter", "json"] }

# Testing
mockall = "0.13"
criterion = { version = "0.5", features = ["async_tokio"] }
tempfile = "3.8"

Internal Dependencies

  • llm-optimizer-types - Core types and events
  • llm-optimizer-config - Configuration management

Infrastructure Prerequisites

  • Kafka cluster (for integration tests and production)
  • PostgreSQL (for integration with other components)
  • Prometheus (for metrics)
  • Grafana (for dashboards)

Success Metrics

Functional Requirements

  • Process 10,000+ events/sec
  • p99 processing latency < 100ms
  • Support all three window types
  • Support all aggregation functions
  • Handle late events up to configured lateness
  • Persistent state with recovery
  • Zero data loss

Non-Functional Requirements

  • 99.9% uptime
  • Graceful degradation under load
  • Memory usage < 2GB for 100k windows
  • State recovery < 30 seconds
  • Checkpoint creation < 5 seconds
  • Comprehensive error handling
  • Full observability

Code Quality

  • Unit test coverage > 80%
  • Integration test coverage > 70%
  • All public APIs documented
  • Zero clippy warnings
  • Zero unsafe code (except FFI)

Risk Mitigation

Technical Risks

Risk: RocksDB performance degradation with large state

  • Mitigation: Implement state TTL and compaction strategies
  • Fallback: Use Sled or Redis as alternative backend

Risk: Memory growth from unbounded percentile calculation

  • Mitigation: Implement T-Digest or HdrHistogram for streaming percentiles
  • Fallback: Sample values or use fixed-size reservoir

Risk: Kafka consumer lag under high load

  • Mitigation: Implement backpressure and load shedding
  • Fallback: Scale horizontally with consumer groups

Operational Risks

Risk: State corruption during checkpoint

  • Mitigation: Atomic checkpointing with versioning
  • Fallback: Multiple checkpoint retention

Risk: Resource exhaustion

  • Mitigation: Memory limits, circuit breakers, rate limiting
  • Fallback: Graceful degradation mode

Deployment Strategy

Development Environment

# Run with in-memory state
cargo run --bin processor -- --config config/processor-dev.yaml

Testing Environment

# Run with RocksDB state
docker-compose up -d kafka
cargo run --bin processor -- --config config/processor-test.yaml

Production Environment

# Kubernetes deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: llm-optimizer-processor
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: processor
        image: llm-optimizer-processor:latest
        resources:
          limits:
            memory: "4Gi"
            cpu: "2000m"
          requests:
            memory: "2Gi"
            cpu: "1000m"
        volumeMounts:
        - name: state
          mountPath: /var/lib/optimizer/state
      volumes:
      - name: state
        persistentVolumeClaim:
          claimName: processor-state

Monitoring and Alerting

Key Metrics to Monitor

  • Events processed per second
  • Processing latency (p50, p95, p99)
  • Kafka consumer lag
  • State size (MB)
  • Checkpoint duration
  • Error rate
  • Memory usage
  • CPU usage

Alert Conditions

  • Consumer lag > 10,000 messages
  • p99 latency > 1 second
  • Error rate > 1%
  • State size > 10GB
  • Memory usage > 80%
  • Checkpoint failures

Timeline Summary

Phase Duration Deliverable
Phase 1 Week 1-2 Core foundation
Phase 2 Week 2-3 Window management
Phase 3 Week 3-4 Watermarking
Phase 4 Week 4-5 Aggregation engine
Phase 5 Week 5-6 State management
Phase 6 Week 6-7 Kafka integration
Phase 7 Week 7-8 Processing pipeline
Phase 8 Week 8 Metrics and observability
Phase 9 Week 9-10 Testing and documentation
Phase 10 Week 10-11 Production hardening

Total Duration: 11 weeks (2.5 months)

Next Steps

  1. Review and Approve: Stakeholder review of architecture and plan
  2. Setup Environment: Development environment and infrastructure
  3. Phase 1 Kickoff: Begin core foundation implementation
  4. Weekly Checkpoints: Progress reviews and adjustments
  5. Integration Planning: Coordinate with Analyzer and Collector teams