Event Streaming Platform Architecture Design

Comprehensive backend high-level design for a scalable event streaming platform supporting real-time data processing and distribution across distributed systems.

PublishedJuly 27, 2026
Length11,269 words
Reading time57 min read

Backend HLD

This document presents a scalable backend architecture for an event streaming platform that handles high-volume real-time data ingestion, processing, and distribution across distributed microservices while maintaining fault tolerance and horizontal scalability.

1. Executive Summary

Clarifying questions

What is the expected read/write ratio for events?
- Is this a high-throughput write system (e.g., IoT telemetry) or more balanced?
What are the consistency and durability requirements?
- Are events required to be exactly once, or at-least-once acceptable?
Is the system expected to operate globally or within a single region?
- Will cross-region replication be required for disaster recovery?
What are the latency sensitivity requirements?
- Are real-time event processing (e.g., <100ms) or near-real-time (<1s) the primary concern?
What is the expected growth in event volume and number of consumers?
- Is it a small-scale system or designed for massive scale?
Are there any external integrations or data sources that events need to be compatible with?
- For example, Kafka-compatible APIs or specific schema formats (Avro, JSON)?
What is the expected lifecycle of events?
- Do events need to be retained for years or only days?

Problem being solved

The event streaming platform is designed to handle high-volume, low-latency real-time event ingestion and distribution. It enables systems to process asynchronous events from multiple sources (e.g., user actions, IoT sensors, logs) and route them to consumers for processing, analytics, or downstream systems. The platform must support high throughput, low latency, and strong reliability for enterprise-grade event-driven architectures.

Back-of-envelope calculations

DAU → Peak QPS:
- Assuming 10 million DAU, with average event rate of 10 events/user/day
- Peak QPS = (10M * 10) / (86400s * 10) = ~115 QPS (average), peak = ~1.15K QPS
Storage per user/event:
- Average event size = 1KB
- Daily storage = 10M * 10 * 1KB = 100MB/day
- Annual storage = ~36.5GB/year
Bandwidth (read + write throughput):
- Write bandwidth = 1.15K QPS * 1KB = ~1.15 MB/s
- Read bandwidth (assuming 2x read rate) = ~2.3 MB/s
Cache hit rate assumption:
- Assuming 90% cache hit rate, DB QPS = ~115 * (1 - 0.9) = ~11.5 QPS
Number of servers needed at peak:
- Assuming 1000 QPS per server (read + write), servers needed = ~1.15K / 1000 = 2 servers

SLA targets

Availability: 99.9% (3.6 minutes of downtime/month)
Latency:
- p50: <100ms
- p95: <500ms
- p99: <2s
Durability: At-least-once delivery with 100% event persistence
Throughput: Minimum 10K QPS peak, scaling to 100K+ QPS

Key architectural decisions summary

Event streaming engine: Apache Kafka or Amazon MSK for high throughput and fault tolerance
Storage model: Distributed log-based architecture with retention policies
Service architecture: Microservices for ingestion, storage, and consumer management
Data consistency model: Eventual consistency with strong durability guarantees
Multi-region support: Designed for global distribution and disaster recovery
Scalability model: Horizontal scaling with sharding and replication strategies
Security model: mTLS, RBAC, and secrets management for secure event flow

2. System Goals & Non-Functional Requirements

Latency Targets

Event ingestion (P50): < 100ms
Event ingestion (P95): < 500ms
Event delivery (P50): < 200ms
Event delivery (P95): < 1s
Event processing (P50): < 50ms (for real-time consumers)
Event processing (P95): < 200ms

Throughput Requirements

Average QPS: 100,000 events/sec (assumes 1KB average event size)
Peak QPS: 1M events/sec (10x average, 864k seconds in a day)
Storage: 50GB/day per event (100M events * 500 bytes avg)
Read throughput: 200K events/sec (for consumers)
Write throughput: 1M events/sec (for producers)

Availability Target

99.99% (4 9s) - Critical for real-time event processing and streaming workflows
Justification: Event streaming platforms are mission-critical for business operations, supporting real-time analytics, alerts, and orchestration workflows. Any downtime can cause cascading failures across dependent systems.

Durability and Consistency Requirements

Durability: 99.999% (5 9s) - Events must survive system failures and be replayable
Consistency: Event ordering within partitions (eventual consistency for cross-partition ordering)
CAP Trade-off: Prioritize Consistency + Availability (CP) for partitioned streams, with Availability + Partition Tolerance (AP) for cross-region replication
RPO: < 1 second for primary region, < 5 seconds for cross-region
Replication: 3x replication within region, 2x across regions

Data Retention and Compliance Requirements

Retention:
- 7 days for high-volume events (default)
- 30 days for compliance-sensitive events
- 1 year for audit trails (configurable per topic)
Compliance:
- GDPR compliance for user data in events
- HIPAA support for healthcare data (if applicable)
- SOC2 Type II certification required
Data Classification:
- Public, Internal, Confidential, and Restricted levels with appropriate access controls

Security and Regulatory Requirements

Authentication:
- mTLS for service-to-service communication (Kafka-style)
- OAuth2/OIDC for external producers/consumers
Authorization:
- RBAC with topic-level permissions (read/write/delete)
- ABAC for fine-grained access control (e.g., user role + event type)
Encryption:
- TLS 1.3 for all network communication
- AES-256 encryption at rest (with key rotation every 90 days)
Audit Logging:
- All access and modification events logged with full audit trail
Regulatory:
- GDPR compliance for EU users (data subject rights, data portability)
- HIPAA compliance for healthcare data (if applicable)
- SOC2 Type II for third-party assurance
Data Sovereignty:
- Regional data residency (EU, US, APAC) with compliance controls per region

3. High-Level Architecture Diagram

C4Context
    title Event Streaming Platform - System Context

    Person(user, "End User")
    Person(developer, "Developer")
    Person(admin, "System Admin")

    System_Boundary(b1, "Event Streaming Platform") {
        System(eventProducer, "Event Producer Service", "Handles event generation and publishing")
        System(eventProcessor, "Event Processor Service", "Processes events in real-time")
        System(eventStore, "Event Store", "Durable storage for events")
        System(eventConsumer, "Event Consumer Service", "Handles event consumption and processing")
        System(messageBroker, "Message Broker", "Kafka / Pulsar for event streaming")
        System(authService, "Auth Service", "Authentication and authorization")
        System(configService, "Config Service", "Centralized configuration management")
        System(observability, "Observability Stack", "Logging, tracing, metrics")
    }

    System_Ext(externalKafka, "External Kafka Cluster", "Third-party event streaming platform")
    System_Ext(externalDB, "External DB", "Relational database for metadata")

    Rel(user, eventProducer, "Generates events")
    Rel(developer, eventProcessor, "Deploys processors")
    Rel(admin, eventConsumer, "Manages consumers")
    Rel(eventProducer, messageBroker, "Publishes events to")
    Rel(messageBroker, eventProcessor, "Streams events to")
    Rel(eventProcessor, eventStore, "Stores processed events")
    Rel(eventStore, eventConsumer, "Consumes stored events")
    Rel(messageBroker, externalKafka, "Integrates with")
    Rel(eventProducer, externalDB, "Reads metadata from")
    Rel(authService, eventProducer, "Authenticates")
    Rel(authService, eventProcessor, "Authenticates")
    Rel(configService, eventProducer, "Provides config")
    Rel(configService, eventProcessor, "Provides config")
    Rel(observability, eventProducer, "Monitors")
    Rel(observability, eventProcessor, "Monitors")

C4Container
    title Event Streaming Platform - Container Diagram

    Container_Boundary(c1, "Event Streaming Platform") {
        Container(eventProducer, "Event Producer", "Java/Go", "Handles event generation and publishing")
        Container(eventProcessor, "Event Processor", "Java/Go", "Processes events in real-time")
        Container(eventStore, "Event Store", "PostgreSQL", "Durable storage for events")
        Container(eventConsumer, "Event Consumer", "Java/Go", "Handles event consumption and processing")
        Container(messageBroker, "Message Broker", "Kafka/Pulsar", "Event streaming and distribution")
        Container(authService, "Auth Service", "Go", "Authentication and authorization")
        Container(configService, "Config Service", "Go", "Centralized configuration management")
        Container(observability, "Observability Stack", "Prometheus/Grafana", "Logging, tracing, metrics")
    }

    Container_Ext(externalKafka, "External Kafka", "Third-party event streaming")
    Container_Ext(externalDB, "External DB", "Relational database")

    Rel(eventProducer, messageBroker, "Publishes events to")
    Rel(messageBroker, eventProcessor, "Streams events to")
    Rel(eventProcessor, eventStore, "Stores processed events")
    Rel(eventStore, eventConsumer, "Consumes stored events")
    Rel(messageBroker, externalKafka, "Integrates with")
    Rel(eventProducer, externalDB, "Reads metadata from")
    Rel(authService, eventProducer, "Authenticates")
    Rel(authService, eventProcessor, "Authenticates")
    Rel(configService, eventProducer, "Provides config")
    Rel(configService, eventProcessor, "Provides config")
    Rel(observability, eventProducer, "Monitors")
    Rel(observability, eventProcessor, "Monitors")

    UpdateRelStyle(eventProducer, messageBroker, $offsetX=10, $offsetY=-10)
    UpdateRelStyle(messageBroker, eventProcessor, $offsetX=10, $offsetY=-10)
    UpdateRelStyle(eventProcessor, eventStore, $offsetX=10, $offsetY=-10)
    UpdateRelStyle(eventStore, eventConsumer, $offsetX=10, $offsetY=-10)

Major Components and Services

Core Event Streaming Services:

Event Producer Service: Handles event generation, validation, and publishing to the message broker
Event Processor Service: Real-time processing of events with business logic and transformations
Event Store: Persistent storage for processed events with indexing capabilities
Event Consumer Service: Event consumption and downstream processing with retry logic

Infrastructure Components:

Message Broker: Kafka or Pulsar for event streaming, partitioning, and durability
Authentication Service: JWT-based authentication with RBAC for service-to-service access control
Configuration Service: Centralized configuration management with dynamic reload capabilities
Observability Stack: Comprehensive monitoring with logging, tracing, and metrics

Data Flow and Traffic Patterns

Event Generation: End users or systems generate events that are validated and published to the message broker
Event Streaming: Events flow through the message broker to multiple consumers for parallel processing
Event Processing: Real-time processors transform and enrich events before storing in the event store
Event Consumption: Consumers retrieve processed events for downstream systems or analytics
Cross-system Integration: External Kafka clusters and databases provide integration points

Service Communication Patterns

Synchronous Communication:

REST/gRPC between services for direct API calls
Service-to-service authentication via JWT tokens

Asynchronous Communication:

Event-driven architecture using message broker for decoupled communication
Pub/Sub pattern for scalable event distribution

Cross-service Data Ownership:

Each service owns its data domain with no shared databases
Events are published to the message broker for cross-service consumption
Configuration service provides centralized config with caching layer

External Integrations

Third-party Systems:

External Kafka clusters for hybrid deployment scenarios
Relational databases for metadata and configuration storage
Cloud services for monitoring, logging, and metrics collection

Security Integration:

Identity providers (Auth0/Okta) for authentication
Secrets management systems for secure credential handling
WAF and DDoS protection services for security layer

The architecture supports horizontal scaling, multi-region deployment, and provides robust fault tolerance through distributed messaging patterns. The design emphasizes loose coupling between services while maintaining strong consistency guarantees where required through proper event ordering and replay capabilities.

4. Service Architecture

Microservices vs. Monolith vs. Modular Monolith Decision

For an event streaming platform, a microservices architecture is the optimal choice. The system requires high scalability, fault isolation, and independent deployment capabilities across different domains like event ingestion, processing, storage, and delivery. A monolithic approach would create bottlenecks in scaling specific components and increase operational complexity as the system grows.

Service Decomposition Strategy

The platform will be decomposed into the following core services:

Event Ingestion Service - Handles incoming event data from various sources
Event Processing Service - Applies transformations, filtering, and enrichment logic
Event Storage Service - Manages event persistence and retrieval
Event Routing Service - Routes events to appropriate consumers based on topics/streams
Metadata Service - Maintains event schema, topic definitions, and system metadata
Consumer Management Service - Handles consumer registration, group management, and offset tracking
Monitoring & Metrics Service - Collects and exposes system-level telemetry

Service Boundaries and Contracts

Each service will maintain clear boundaries with well-defined APIs:

Event Ingestion Service exposes REST/gRPC endpoints for event submission with schema validation
Event Processing Service provides transformation pipelines via event-driven APIs
Event Storage Service offers query and retrieval capabilities through a standardized API
Event Routing Service manages topic-to-consumer mapping with consumer group APIs
Metadata Service exposes schema registry and configuration endpoints
Consumer Management Service handles consumer lifecycle management via RESTful interfaces

Inter-service Communication Pattern

Synchronous communication between services using gRPC for low-latency, high-throughput operations
Asynchronous communication via message brokers (Kafka) for decoupled processing and event-driven workflows
Service-to-service authentication using mTLS with SPIFFE/SPIRE for secure cross-service communication

Service Mesh Consideration

A service mesh (Istio) will be implemented to provide:

Traffic management and routing policies
Security features including mTLS, authentication, and authorization
Observability capabilities through distributed tracing integration
Fault tolerance mechanisms like circuit breaking and retry policies

Service Registry and Discovery Strategy

Service registry: Consul or Kubernetes native service discovery
Dynamic service registration: Services register automatically upon startup with health checks
Load balancing: Round-robin or weighted distribution based on service capacity and current load
Service versioning: Semantic versioning with backward compatibility guarantees

The service architecture emphasizes loose coupling, independent scaling, and clear ownership boundaries to support the high availability and performance requirements of an enterprise-grade event streaming platform.

5. Data Architecture

Database Selection per Service

Event Store Service (Primary Storage)

Selection: CockroachDB
Justification:
- Horizontal scaling capability for high-throughput event ingestion
- Strong consistency model with ACID transactions required for event ordering guarantees
- Built-in multi-region support for global distribution
- PostgreSQL-compatible API for easier migration and developer familiarity

Metadata Store Service

Selection: MongoDB
Justification:
- Flexible schema for event metadata (tags, user IDs, timestamps, etc.)
- Rich query capabilities for filtering and searching events
- Horizontal sharding support for large metadata collections
- Better performance for complex nested document queries

User Session Store

Selection: Redis
Justification:
- High-performance in-memory key-value store for session data
- TTL-based automatic expiration for session cleanup
- Pub/Sub capabilities for real-time event notifications
- Atomic operations for concurrent session updates

Search Index Store

Selection: Elasticsearch
Justification:
- Full-text search capabilities for event content and metadata
- Real-time indexing with near real-time search guarantees
- Rich aggregation and analytics capabilities for event analysis
- Horizontal scaling with distributed shards

Data Partitioning Strategy

Event Store Sharding

Strategy: Hash-based partitioning on event ID prefix (first 2 characters)
Justification:
- Even distribution of events across shards
- Predictable partitioning for consistent read/write performance
- Allows for future expansion to additional shards without re-partitioning

Metadata Store Partitioning

Strategy: Range-based partitioning on timestamp (monthly buckets)
Justification:
- Time-series data access patterns benefit from range partitioning
- Efficient time-range queries for analytics and reporting
- Easier data lifecycle management (retention policies)

Data Replication Strategy

Primary-Replica Model

Configuration: 3x replica set per database cluster
Cross-region replication: Async cross-region replication for disaster recovery
Consistency model: Strong consistency for primary writes, eventual consistency for reads

Cross-service Data Ownership

Rule: No shared databases between services
Enforcement: Database access controlled via service accounts with role-based permissions
Audit: All database access logged and monitored for compliance

CQRS Pattern Application

Event Store Service

Read Side: Separate read replicas for analytics and reporting
Write Side: Primary write cluster with strict consistency requirements

Metadata Store Service

Read Side: Materialized views for frequently accessed metadata combinations
Write Side: Event-driven updates to metadata store via message queue

Event Sourcing Consideration

Use Case: Not applicable for core event streaming functionality

Rationale: Events are stored in their raw form, not as command history
Trade-offs: Simpler architecture but less auditability of system state evolution

Object Storage Design

File Storage (if applicable)

Selection: AWS S3 with lifecycle policies
Access Patterns:
- High-frequency access to recent events (hot tier)
- Infrequent access to archived events (cold tier)
Metadata Strategy:
- Event metadata stored in database
- File content stored in S3 with metadata tags for searchability

Storage Tiering Strategy

Hot Tier:

Storage: SSD-backed local storage for recent events (last 30 days)
Access Frequency: High (95% of queries)
Cost: Highest per GB but lowest latency

Warm Tier:

Storage: HDD-backed storage for events 30-90 days old
Access Frequency: Medium (5% of queries)
Cost: Moderate per GB with acceptable latency

Cold Tier:

Storage: Archive storage (S3 Glacier) for events older than 90 days
Access Frequency: Low (0.1% of queries)
Cost: Lowest per GB but highest retrieval latency

Backup and Restore Strategy

Backup Approach:

Full backups: Daily snapshots of all databases to S3-compatible storage
Incremental backups: Hourly WAL logs for point-in-time recovery
Cross-region replication: Backups replicated to secondary region

RTO/RPO Targets:

RTO: 2 hours for full system recovery
RPO: 15 minutes maximum data loss
Recovery Testing: Monthly automated restore tests with full system validation

Disaster Recovery:

Active-passive setup: Secondary region with read-only replicas
Failover procedure: Automated failover within 15 minutes of primary failure detection
Data consistency: Synchronous replication with quorum-based writes for primary databases

6. Caching Architecture

Caching Strategy Overview

The event streaming platform will implement a multi-layer caching strategy to optimize read performance and reduce database load. The caching architecture follows the classic CDN → API Gateway → Application → Database pattern with L1 and L2 cache layers.

Caching Layers

graph LR
    A[Client] --> B[CDN/Edge Cache]
    B --> C[API Gateway Cache]
    C --> D[Application Cache (L1)]
    D --> E[Database/Storage]

Layered Cache Design

L1 Cache (Application Level)

Implementation: Redis Cluster with sharding
Purpose: Reduce hot query load on database and provide fast access to frequently accessed events
Size: 10GB per cache node (configurable based on hot data patterns)
Eviction Policy: LRU with TTL-based expiration
Cache Keys: Event ID, event type + timestamp ranges, user context IDs

L2 Cache (Distributed Level)

Implementation: Redis Cluster or Memcached in multi-zone setup
Purpose: Cross-service sharing of cached events, reduce redundant database queries across services
Size: 50GB per cache node (shared across services)
Eviction Policy: LFU with time-based expiration
Cache Invalidation: Event-driven invalidation via pub/sub to all cache nodes

Cache Invalidation Strategy

Event-driven invalidation: When an event is published, invalidate related cache entries in all services
TTL-based expiration: All cached entries have time-to-live (5-30 minutes) to ensure freshness
Cache warming: Pre-warm cache with hot event patterns during system startup or maintenance windows

Cache Consistency Model

Eventually Consistent: Cache reads may return stale data during invalidation windows
Read-After-Write consistency: For critical paths, cache entries are invalidated immediately after writes
Cache warming strategy: During peak hours, warm cache with top 100 most accessed event types and user IDs

Cache Performance Targets

Hit Rate: 85%+ for hot event data (based on read patterns)
Latency: < 10ms for cache hits, < 50ms for cache misses
Cache Size: 20GB total (10GB L1 + 10GB L2) with horizontal scaling capability

Cache Warming Strategy

Cold Start: Warm cache with top 500 most accessed event IDs and user contexts
Warm Start: Periodic warming with recent hot events (every 15 minutes)
Adaptive Warming: Dynamically adjust warming based on real-time access patterns and cache hit rates

Cache Partitioning

Per-Service: Each service maintains its own cache namespace
Per-Event Type: Events grouped by type for efficient invalidation (e.g., user_events, system_events)
Per-Tenant: For multi-tenant systems, cache partitioned by tenant ID with shared cache for common events

Cache Monitoring and Metrics

Hit/Miss Ratio: Track per service and event type
Latency Distribution: Monitor cache hit/miss latencies
Cache Size Utilization: Alert when utilization exceeds 90%
Invalidation Rate: Track frequency of cache invalidations per service

Implementation Considerations

Cache Memory Management: Use Redis with memory policies to handle overflow gracefully
Cross-service Cache Sharing: Implement cache key namespaces to prevent collisions across services
Cache Failover: Redis cluster with automatic failover and cross-zone replication for high availability
Cache Compression: Compress large event payloads in cache to optimize memory usage

Cache Size Estimation

Based on back-of-envelope calculations:

Peak QPS: 50,000 events/sec (assuming 10:1 peak-to-average ratio)
Average event size: 2KB
Peak cache memory requirement: ~10GB (50,000 events * 2KB) for hot data
Conservative total cache size: 20GB (L1 + L2) with 50% headroom for growth and hot data spikes

7. Messaging & Event Streaming Architecture

Message Broker Selection

For an enterprise-grade event streaming platform, we select Apache Kafka as the primary message broker due to:

High throughput: Kafka can handle millions of events per second with sub-millisecond latency
Durability guarantees: Persistent storage with configurable retention policies (7-30 days)
Scalability: Linear horizontal scaling across multiple brokers
Ecosystem maturity: Rich ecosystem of connectors, schema registry, and monitoring tools
Stream processing capabilities: Native support for KStreams and Kafka Connect

Alternative considered: Amazon MSK (managed Kafka) - rejected due to vendor lock-in and higher cost for the same functionality.

Event Schema Registry and Versioning

We implement Confluent Schema Registry for:

Schema evolution management: Backward/forward compatibility rules enforcement
Version control: Automatic versioning with rollback capability
Type safety: Prevents malformed events from entering the stream
Integration: Seamless with Kafka Connect and KSQL

Schema evolution strategy:

Backward compatible: New fields can be added with defaults
Forward compatible: Consumers can read older schemas

Topic Partitioning and Consumer Group Design

Partitioning strategy:

Hash-based partitioning: Events routed by key to ensure ordering within partitions
Partition count: 128 partitions per topic (scalable to 1000+ with proper monitoring)
Replication factor: 3 (ensuring fault tolerance across availability zones)

Consumer group design:

Partition assignment: Sticky partition assignment to minimize rebalancing
Consumer lag monitoring: Alert thresholds at 5 min for production topics
Graceful shutdown: Proper consumer group leave handling to avoid lag spikes

Event Ordering Guarantees

Per-partition ordering:

Guaranteed within a partition for events with same key
No global ordering across partitions (by design for scalability)

Use case implications:

For user-centric events, key-based partitioning ensures order per user
For system-wide events, global ordering is not required

Exactly-once vs. At-least-once Processing Trade-offs

Decision: At-least-once processing with idempotent consumers

Rationale:

Exactly-once: Requires complex transactional processing, higher latency, and increased system complexity
At-least-once: Simpler to implement, better performance, and sufficient for most business use cases
Idempotency: Consumers handle duplicate events gracefully through state management

Implementation approach:

Event IDs as deduplication keys
Stateful consumers with idempotent operations
Retry mechanisms with exponential backoff

Event Replay and Reprocessing Strategy

Replay capability:

Retention policies: 7-day default retention with configurable per-topic settings
Historical access: Kafka's log compaction and retention policies enable historical event access
Replay API: REST endpoints for topic replay with time-range filtering

Reprocessing triggers:

Schema changes: Automatic reprocessing when schema versions change
Consumer logic updates: Manual triggers for consumer code updates
Data quality issues: Automated reprocessing on data validation failures

Dead Letter Queue and Poison Pill Handling

Implementation:

DLQ topics: Separate Kafka topics for failed messages with retry counts
Dead letter handling: Messages that exceed max retries are sent to DLQ for manual inspection
Poison pill detection: Threshold-based detection (e.g., 5 consecutive failures)
Monitoring: Alerting on DLQ message volume and processing delays

Retry strategy:

Exponential backoff: 1s → 2s → 4s → 8s → 16s → 32s → 60s (max)
Max retries: 5 attempts before sending to DLQ
Retry queue: Separate topic for retryable messages with delay

DLQ monitoring:

Alert thresholds: >100 messages in DLQ within 5 min
Manual processing queue: Dedicated UI for DLQ message inspection and reprocessing
Metrics dashboard: DLQ volume, processing rates, and failure patterns

Cross-Service Event Integration

Event-driven architecture:

Event types: Standardized event schemas with clear semantic meaning
Service contracts: Defined event interfaces for inter-service communication
Event routing: Kafka topics as service boundaries with clear ownership

Integration patterns:

Synchronous: Direct topic-to-topic communication for real-time processing
Asynchronous: Event-driven workflows with state machines for complex flows
Batch processing: Scheduled event consumption for batch jobs and analytics

Event Processing Pipeline

Producer pipeline:

Event creation in service context
Schema validation against registry
Partition key selection and routing
Message serialization to Avro format

Consumer pipeline:

Topic consumption with proper offset management
Schema deserialization and validation
Event processing with idempotency checks
Error handling and DLQ routing

Monitoring pipeline:

Metrics collection: Kafka consumer lag, throughput, and error rates
Alerting: Threshold-based alerts for system health and performance degradation
Audit logging: Complete event lifecycle tracking for compliance and debugging

Performance Considerations

Throughput targets:

Write throughput: 100,000 events/sec with 1KB average size
Read throughput: 50,000 events/sec for consumers
Latency targets: <10ms for message delivery, <50ms for end-to-end processing

Resource allocation:

Broker sizing: 2x CPU cores, 8GB RAM per broker with 100GB SSD storage
Partition distribution: 500 topics with 128 partitions each for peak load
Network bandwidth: 10 Gbps per broker for high-throughput scenarios

Future Extensibility

Multi-protocol support:

Kafka compatibility: Maintain Kafka protocol for existing consumers
AMQP/STOMP: Future support for additional messaging protocols
WebSockets: Real-time event streaming to web clients

Advanced features:

Kafka Streams: Native stream processing capabilities for real-time analytics
KSQL: SQL-like interface for event processing and analytics
Schema evolution: Automated schema migration with impact analysis

The messaging architecture provides a robust foundation for enterprise event streaming with strong reliability, scalability, and extensibility characteristics. The design balances performance requirements with operational simplicity while maintaining flexibility for future enhancements.

8. Reliability Engineering

Failure Mode Analysis

Single Points of Failure:

Message Broker (Kafka) - Centralized event store with no redundancy
API Gateway - Single point for all client requests and authentication
Primary Database - Single source of truth for metadata and configuration
Service Registry - Centralized service discovery mechanism
Authentication Service - Single point for all identity validation

Critical Path Dependencies:

Event ingestion → Kafka → Processing services → Storage
Service discovery → Kubernetes API → All microservices
Authentication → Identity Provider → All authenticated requests

Fault Tolerance Patterns

Circuit Breaker Pattern:

Implemented at service boundaries for downstream dependencies
50% failure threshold with 30-second timeout before half-open state
Prevents cascading failures during upstream outages

Bulkhead Pattern:

Isolated thread pools per service consumer groups
Resource quotas per topic consumer to prevent one topic from consuming all resources
Service-level isolation for processing workloads

Timeout & Retry Strategy:

300ms timeout on internal service calls
Exponential backoff retry with jitter (100ms → 1s → 5s)
Max 3 retries with circuit breaker fallback

Multi-Region / Multi-AZ Strategy

Active-Active Architecture:

Cross-region replication for critical services (metadata, auth)
Geo-replicated Kafka clusters with automatic failover
Read replicas for database services in each region

Cross-Region Event Flow:

Primary region handles all writes and reads
Secondary regions consume from primary via cross-region replication
Automatic failover to secondary region on primary outage

Chaos Engineering Strategy

GameDays:

Weekly 2-hour sessions simulating:
- Kafka broker outages
- Database connection pool exhaustion
- Service mesh connectivity issues
- Network partitioning between services

Steady-State Hypotheses:

Event processing continues with 5% latency increase during partial outages
Service discovery remains available within 100ms of failure
Authentication service maintains 99.9% availability during partial failures

Disaster Recovery Plan

RTO/RPO Targets:

Primary Region Outage: RTO 5 minutes, RPO < 1 minute
Secondary Region Failover: RTO 30 seconds, RPO < 10 seconds
Complete Data Center Loss: RTO 2 hours, RPO < 5 minutes

Recovery Procedures:

Automated failover to secondary region within 30 seconds
Manual verification of data consistency across regions
Gradual traffic shift to secondary region over 5 minutes
Post-incident analysis and root cause resolution

Runbook Design for Top-5 Failure Scenarios

Failure Scenario	Response Time	Recovery Steps
Kafka Cluster Outage	5 minutes	Failover to secondary, reconfigure consumers
Auth Service Degradation	2 minutes	Switch to local cache, enable read-only mode
Database Connection Pool Exhaustion	3 minutes	Scale up database replicas, implement circuit breaker
Service Mesh Connectivity Loss	1 minute	Bypass service mesh for critical paths
Network Partition	5 minutes	Isolate affected services, re-enable communication

Runbook Automation:

Pre-defined scripts for common recovery actions
Integration with PagerDuty for incident escalation
Automated health checks and status updates to monitoring system

9. Observability Architecture

Logging Stack

The system will implement a structured logging approach using JSON format to enable efficient parsing and querying. Logs will be collected through a centralized logging solution with the following components:

Log Collection: Fluentd or Vector for log aggregation from containers and services
Storage: Loki (Grafana's logging solution) for cost-effective, scalable log storage with label-based querying capabilities
Indexing: Metadata indexing using labels for efficient filtering by service, environment, and severity level
Retention: 30-day retention for operational logs with 90-day retention for audit logs

Distributed Tracing

Distributed tracing will be implemented using OpenTelemetry with the following architecture:

Tracing Backend: Jaeger as the tracing system for distributed trace visualization and analysis
Instrumentation: Auto-instrumentation for core services with manual instrumentation for custom business logic
Sampling Strategy: Dynamic sampling (50% of traces) with adaptive sampling based on error rates and latency
Trace Context Propagation: HTTP headers for service-to-service trace propagation with baggage support

Metrics Stack

Metrics collection will be implemented using Prometheus and Grafana:

Metrics Collection: Prometheus as the primary metrics backend with pushgateway for batch jobs
Service Monitoring: Per-service metrics including QPS, latency, error rates, and throughput
Infrastructure Metrics: CPU, memory, disk, and network utilization from Kubernetes clusters
Dashboard Strategy: Pre-built dashboards for each service with custom dashboards for business KPIs
Alerting Integration: Direct integration with Prometheus Alertmanager for alert routing and deduplication

Alerting Strategy

The alerting system will follow a multi-tiered approach:

Severity Levels:
- Critical (P0): Service unavailability, data loss scenarios
- High (P1): Performance degradation, high error rates (>5%)
- Medium (P2): Configuration changes, capacity warnings
- Low (P3): Operational notifications, informational alerts
Routing Strategy:
- Critical alerts → PagerDuty with on-call escalation
- High alerts → Slack notifications with team-specific channels
- Medium/low alerts → Email notifications or internal chat

SLI/SLO Definition and Error Budget Tracking

Service-level indicators (SLIs) and service-level objectives (SLOs) will be defined for key system components:

SLIs:
- Request success rate (99.5% SLO)
- Latency (p99 < 200ms for API endpoints)
- Throughput (10k QPS target)
- Error rate (<0.5% of requests)
Error Budget:
- 5% error budget for SLOs with automatic alerting when budget is consumed at 80%
- Error budget burn rate monitoring to detect system degradation early

Capacity Planning and Load Forecasting

Capacity planning will be implemented through:

Historical Data Analysis: Trend analysis of QPS, latency, and resource utilization patterns
Load Forecasting Models: Time-series forecasting using Prophet or similar tools for capacity prediction
Auto-scaling Correlation: Metrics-driven scaling decisions with forecast-based proactive scaling
Capacity Dashboard: Real-time capacity utilization dashboard showing projected resource needs

Observability Tooling Integration

Service Mesh Integration: Istio integration for service-to-service metrics and tracing
Infrastructure Monitoring: Integration with CloudWatch (AWS) or Stackdriver (GCP) for infrastructure-level metrics
Business Intelligence: Integration with business analytics tools for customer behavior and usage patterns
Compliance Reporting: Automated generation of compliance reports from observability data

Observability Data Pipeline

The observability pipeline will follow a multi-stage architecture:

Collection Layer: Service instrumentation and log aggregation
Processing Layer: Log parsing, metric aggregation, trace normalization
Storage Layer: Distributed storage systems for logs, metrics, and traces
Analysis Layer: Query engines and analytics platforms
Visualization Layer: Dashboard systems for operational insights

Observability Security and Access Control

Role-Based Access: Different access levels for developers, SREs, and management teams
Audit Logging: Comprehensive audit trail of all observability system access and modifications
Data Protection: Encryption at rest and in transit for sensitive operational data
Compliance Alignment: GDPR and HIPAA compliance considerations for log retention and access

Observability Performance Considerations

Sampling: Sampling strategies to prevent observability system from becoming a bottleneck
Resource Limits: CPU and memory limits for observability components to prevent resource exhaustion
Asynchronous Processing: Non-blocking operations in logging and tracing systems to avoid impacting service performance

10. API Gateway & Edge Layer

API Gateway Selection

For an event streaming platform, we select AWS API Gateway as the primary API gateway due to:

Native integration with AWS EventBridge and Kinesis - seamless event streaming capabilities
Built-in rate limiting and throttling - essential for protecting backend services from overload
Support for multiple protocols - HTTP/REST, WebSocket, and MQTT for diverse client needs
Managed service with low operational overhead - reduces sysadmin burden for core platform features
Integration with AWS WAF - robust DDoS and security protection capabilities

Alternative consideration: Kong Enterprise for on-premises deployments or multi-cloud scenarios, but AWS API Gateway aligns better with our cloud-native strategy.

Request Routing Strategy

The gateway implements a multi-layered routing approach:

Protocol-based routing: HTTP/REST → REST API endpoints, WebSocket → real-time event streams, MQTT → IoT device events
Service-based routing: Events → Event Processor Service, Metadata → Metadata Service, Admin → Admin Service
Version-aware routing: /v1/events, /v2/events for API versioning
Client-type routing: Mobile → optimized endpoints, Web → full-featured, IoT → lightweight protocols

Authentication and Authorization at Gateway Layer

Authentication:

JWT-based authentication for service-to-service communication
OAuth2/OIDC integration for external client applications using Auth0 as IdP
API key validation for third-party integrations and client applications

Authorization:

RBAC model with role-based access control for different user types (admin, developer, consumer)
Scope-based permissions - granular control over event publishing/subscribing capabilities
Token validation and claims verification at the gateway level before forwarding to backend services

Rate Limiting and Throttling Design

Global Limits:

10,000 requests/minute for event publishing
50,000 requests/minute for metadata queries
1,000 concurrent WebSocket connections

Per-Client Limits:

Per-application rate limits (500 events/sec, 200 metadata queries/sec)
Per-user limits for authenticated clients (100 events/sec, 50 metadata queries/sec)
Burst capacity handling with token bucket algorithm

Request Transformation and Protocol Translation

Protocol Translation:

HTTP to WebSocket bridge for real-time event streaming
REST to MQTT translation for IoT device integration
JSON to Avro schema conversion for event format standardization

Request Transformation:

Event payload enrichment with metadata (timestamp, source, event type)
Schema validation and normalization before forwarding to backend services
Header manipulation for cross-service communication (X-Request-ID, X-Tenant-ID)

BFF (Backend for Frontend) Design

BFF Implementation:

Event Stream BFF: Optimized for real-time event consumption with WebSocket connections
Metadata BFF: Aggregates and transforms metadata from multiple services for frontend consumption
Admin BFF: Consolidates admin operations (event creation, subscription management) into unified endpoints

BFF Benefits:

Client-specific optimizations: Mobile clients receive compressed payloads, Web clients receive rich metadata
Aggregation layer: Reduces client-side complexity by combining multiple service calls into single responses
Security boundary: Gateway handles authentication and authorization before forwarding to backend services

Implementation Details:

Each BFF service is stateless and horizontally scalable
BFFs implement circuit breaker and timeout patterns for resilience
Cache layer in BFFs to reduce backend service load for frequently accessed metadata

Gateway Configuration and Management

Configuration Management:

Centralized configuration via AWS Systems Manager Parameter Store
Environment-specific configurations (dev, staging, prod)
Feature flag support for gradual rollouts of new gateway capabilities

Monitoring Integration:

Built-in AWS CloudWatch integration for request/response metrics
Distributed tracing integration with X-Ray for end-to-end request flow analysis
Custom metric publishing for gateway-specific performance indicators

Security Controls:

TLS 1.3 termination at gateway level with certificate rotation
Request/response size limits to prevent resource exhaustion
IP-based access control lists for sensitive endpoints

This API gateway design provides a secure, scalable, and feature-rich edge layer that serves as the primary interface for all client interactions while maintaining the flexibility to evolve with changing requirements and client needs.

11. Authentication & Authorization Architecture

Identity Provider Integration

For an event streaming platform, we'll integrate with AWS Cognito as the primary identity provider due to its seamless integration with AWS services, built-in support for federated identities, and robust user pool management capabilities. This choice provides:

Native integration with AWS Lambda, API Gateway, and other managed services
Support for social identity providers (Google, Facebook, Apple)
Built-in MFA and password policies
Easy integration with existing AWS infrastructure

Alternative providers like Auth0 or Okta were considered but rejected due to higher operational complexity and additional licensing costs for enterprise features required in a high-scale streaming platform.

OAuth2 / OIDC Flow Design

sequenceDiagram
    participant Client
    participant AuthN
    participant AuthZ
    participant TokenService

    Client->>AuthN: Request login (OIDC flow)
    AuthN->>Client: Redirect to IdP with auth code
    Client->>AuthN: Exchange auth code for tokens
    AuthN->>TokenService: Validate and issue JWT
    TokenService-->>AuthN: Return access token
    AuthN-->>Client: Return JWT access token

The platform implements the Authorization Code flow with PKCE for public clients, supporting both web and mobile applications. The JWT tokens contain:

sub: User identifier
aud: Audience (service name)
iss: Issuer (Cognito domain)
exp: Expiration time
scope: Granted permissions

Service-to-Service Authentication

For service-to-service communication, we implement mTLS with SPIFFE/SPIRE for identity management:

All internal services use SPIFFE IDs for unique service identities
mTLS is enforced at the service mesh layer (Istio) for all internal communications
Service accounts are provisioned with certificates managed by SPIRE
Certificate rotation is automated with 30-day TTLs and 7-day renewal windows

This approach provides:

Strong identity binding for services
End-to-end encryption between services
Automated certificate lifecycle management
Zero-trust security model enforcement

RBAC / ABAC Model Design

We implement a hybrid RBAC/ABAC model:

Role-Based Access Control (RBAC) handles:

User roles: Admin, Producer, Consumer, Auditor
Resource-based permissions (topics, consumers, producers)
Predefined permission sets for common use cases

Attribute-Based Access Control (ABAC) handles:

Dynamic attribute-based policies for sensitive topics
Time-based access restrictions (e.g., read-only during maintenance windows)
Context-aware permissions (IP address, time of day, user department)

The system uses a policy engine like OPA (Open Policy Agent) to evaluate complex ABAC policies, with RBAC serving as the primary authorization mechanism for simpler use cases.

Token Revocation Strategy

We implement a hybrid token revocation approach:

Short-lived JWT tokens (15-30 minutes) with refresh token rotation
Token blacklisting service for immediate revocation of compromised tokens
Refresh token validation with database-backed session management

When a user logs out or their credentials change:

The refresh token is invalidated in the session store
The access token is marked as revoked in a distributed cache with TTL of 1 hour
All services check the revocation status before processing requests

This ensures immediate security response while maintaining performance through token caching and efficient session management.

Authentication Flow Summary

External users: OIDC with Cognito, JWT tokens for API access
Internal services: mTLS + SPIFFE identities with automatic certificate rotation
Token lifecycle: Short-lived access tokens (15m) + refresh tokens (24h)
Revocation: Immediate blacklisting for compromised sessions
Policy engine: OPA for complex ABAC policies with RBAC fallback

This authentication model provides enterprise-grade security while maintaining operational simplicity and performance for high-throughput event streaming workloads.

12. Search Architecture

Search Use Cases and Requirements

The event streaming platform includes a search capability for:

Real-time event discovery by metadata (event type, timestamp range, user ID)
Full-text search on event payloads and attributes
Complex query patterns for analytics and monitoring use cases

Search Indexing Pipeline

The search capability follows a multi-stage indexing pipeline:

Event Ingestion: Events are ingested through the main message broker (Kafka)
Preprocessing: Event metadata is extracted and normalized
Indexing: Indexed fields are written to a dedicated search index store
Search Service: Query service provides real-time search capabilities

Index Schema and Query Patterns

Index Fields:

event_id (primary key, string)
timestamp (ISO8601, indexed as date range)
event_type (string, categorical)
user_id (string, for user-based filtering)
payload_fields (JSON object, flattened for search)
tags (array of strings, for categorization)

Query Patterns:

Simple field filtering (user_id = "12345")
Date range queries (timestamp > "2023-01-01")
Full-text search across payload fields
Complex boolean combinations (AND/OR/NOT)

Real-time vs. Near-real-time Indexing Trade-offs

Real-time indexing (immediate indexing):

Pros: Low latency, consistent with event ingestion
Cons: Higher CPU/memory usage, potential for indexing delays during peak load

Near-real-time indexing (batched):

Pros: Better resource utilization, reduced indexing overhead
Cons: Increased latency for search results (1-5 seconds)
Implementation: Kafka consumer groups with batched indexing to Elasticsearch

Relevance Tuning Strategy

The search relevance is tuned through:

Field boosting: Prioritizing timestamp and event_type fields in search results
Query-time scoring: Custom scoring algorithms for business logic (e.g., recent events weighted higher)
Faceted search: Supporting multi-dimensional filtering and aggregation
Query expansion: Using synonym mapping for event types and user terms

Search Scaling Design

Sharding Strategy:

Horizontal sharding by event type to distribute load
Each event type gets its own index shard in Elasticsearch cluster
Shards are replicated across multiple nodes for high availability

Replication and Scaling:

Primary-replica model with 2x replication factor
Auto-scaling of search nodes based on query load and memory usage
Cross-cluster replication for multi-region deployment

Search Service Architecture

The search service is implemented as a dedicated microservice:

Search API: REST endpoints for query execution
Index Management: Service to handle index creation, updates, and lifecycle
Query Parser: Converts human-readable queries into Elasticsearch DSL
Result Aggregator: Combines results from multiple shards and formats responses

Search Performance Considerations

Caching: Frequently accessed search queries are cached in Redis with TTL of 5 minutes
Query Optimization: Pre-computed aggregations and rollups for common dashboard queries
Index Refresh: Index refresh interval set to 30 seconds for near-real-time experience
Resource Isolation: Dedicated CPU/memory allocation to prevent search queries from impacting core event streaming

Search Integration with Event Streaming

Event-to-Index Mapping: Events are automatically indexed based on their type and metadata
Search Result Enrichment: Search responses include event metadata and payload context
Real-time Updates: Search index is updated in real-time as new events arrive
Search History Tracking: User search queries are logged for analytics and usage monitoring

Search Query Language Support

The system supports:

Elasticsearch Query DSL: Native JSON-based query format
KQL (Kibana Query Language): Human-readable query syntax for dashboard use cases
SQL-like Interface: For users familiar with relational database querying patterns

Search Monitoring and Metrics

Key search performance metrics include:

Query latency: p50/p95/p99 response times
Indexing throughput: Events per second indexed
Search hit rate: Percentage of queries served from cache vs. index
Index size growth: Storage usage and scaling patterns

Search Service Reliability

Search service reliability is ensured through:

Circuit Breaker: Prevents cascading failures during index outages
Query Timeout: 5-second timeout for long-running queries
Fallback Strategy: Degraded mode with cached results when search index is unavailable
Health Checks: Regular monitoring of search service availability and performance

Search Service Security

Search endpoints are secured with:

Authentication: JWT tokens for API access control
Authorization: Role-based access to search capabilities based on user roles and event permissions
Query Filtering: Preventing users from accessing events outside their authorized scope
Audit Logging: All search queries are logged for compliance and security monitoring

13. Scalability Deep Dive

Bottleneck Identification

Database Layer:

Write-heavy workloads can hit I/O limits on storage subsystems
Connection pool exhaustion under high concurrent access patterns
Index contention during peak ingestion periods

Message Broker:

Partition bottlenecks when single topics have high throughput
Consumer lag accumulation during burst ingestion
Metadata overhead in large-scale topic management

Service Layer:

CPU-bound processing in event transformation services
Memory pressure from large event payloads in stream processors
Network I/O constraints in cross-service communication

Infrastructure:

Network bandwidth saturation during data replication
Storage I/O limits on hot storage tiers
Memory constraints in stateful services

Horizontal vs. Vertical Scaling Decisions

Stateless Services (Event Processors, API Layer):

Scale horizontally via Kubernetes deployments with HPA
Vertical scaling only for CPU-intensive transformation logic
Stateless design enables seamless horizontal partitioning

Stateful Services (Storage, Metadata Store):

Horizontal scaling through sharding and partitioning
Vertical scaling for storage-heavy components (e.g., Kafka brokers)
Multi-zone replication for availability and locality

Database Layer:

Vertical scaling on read-heavy workloads (replica sets)
Horizontal scaling through sharding (range/hash partitioning)
Read replicas for analytical workloads with eventual consistency

Database Scaling Path

Phase 1 - Single Instance:

Monolithic PostgreSQL/MySQL for metadata and event storage
In-memory caching layer (Redis) for hot data access

Phase 2 - Read Replicas:

Master-slave replication for read-heavy analytical queries
Connection pooling and query optimization

Phase 3 - Sharding:

Horizontal partitioning by event type or timestamp range
Consistent hashing for even distribution across shards
Cross-shard joins handled via materialized views or denormalization

Phase 4 - Distributed Database:

Migration to CockroachDB or Spanner for global consistency
Multi-region deployment with automatic failover

Stateless vs. Stateful Service Design

Stateless Services:

Event processors, API gateways, and transformers
Stateless design enables elastic scaling and fast failover
Session affinity not required - can use any healthy instance

Stateful Services:

Kafka brokers, storage engines, and metadata stores
State persistence required for reliability and ordering guarantees
Service discovery and state synchronization mechanisms

Session Affinity and Implications

No session affinity required for:

Event processing services (stateless)
API gateway and edge services
Stateless transformation pipelines

Session affinity needed for:

Kafka consumer groups (partition assignment consistency)
Stateful stream processors (maintaining window state)
Database connection pools (session-based transaction management)

Impact:

Stateless services enable better load distribution and auto-scaling
Stateful services require careful consideration of state replication and recovery strategies

Performance Characteristics at Scale

Throughput Scaling:

Linear scaling expected for stateless services with proper load distribution
Non-linear degradation observed in sharded databases under high contention
Message broker throughput limited by disk I/O and network bandwidth

Latency Characteristics:

P50/P95 latencies degrade predictably with horizontal scaling
P99 latencies can spike during partition rebalancing or resource contention
Cache hit rate improvements provide linear latency benefits

Resource Utilization:

CPU utilization increases with concurrent event processing
Memory usage scales with event payload sizes and in-flight operations
Network bandwidth consumption grows linearly with message volume

Auto-scaling Strategy

Horizontal Pod Autoscaler (HPA):

Target CPU utilization: 70%
Target memory usage: 80% (per pod)
Minimum replica count: 3 for critical services
Maximum replica count: 50 for event processors

Vertical Pod Autoscaler (VPA):

For stateful services requiring memory tuning
Resource request adjustment based on historical usage patterns

KEDA Integration:

Event-driven scaling for Kafka consumers
Custom metrics from message queue depths and processing lag
Auto-scaling triggers based on queue size thresholds

Capacity Planning Considerations

Event Volume Projections:

10M events/day → 120K events/hour peak
500MB average event size → 60GB/day storage requirement
10x peak-to-average ratio for burst handling

Infrastructure Resource Allocation:

Compute: 2x CPU cores per service pod with 50% headroom
Memory: 4GB per service pod for stateless services, 8GB+ for stateful
Storage: 100GB per Kafka broker node with 2x redundancy

Network Bandwidth:

500 Mbps average bandwidth for event streaming
2Gbps peak bandwidth during ingestion bursts
Dedicated network paths for inter-service communication

Storage Scaling:

100GB per Kafka broker node with 2x redundancy
500GB per database node for metadata storage
Tiered storage strategy with hot/warm/cold access patterns

14. Infrastructure & Deployment Architecture

Cloud Provider and Region Selection

For a global event streaming platform, we'll select AWS as the primary cloud provider due to its mature ecosystem of messaging services (Kafka, SQS, SNS), extensive global infrastructure with 30+ regions, and strong support for multi-region deployments. The platform will be deployed across multiple AWS regions (us-east-1, eu-west-1, ap-southeast-1) to ensure low-latency access and high availability.

Kubernetes Cluster Design

We'll implement a multi-tenant Kubernetes cluster architecture with the following components:

Cluster Topology:

Control plane: Managed AWS EKS clusters with 3 control plane nodes across multiple AZs for high availability
Worker nodes: Auto-scaling groups with mixed instance types (on-demand and spot instances) for cost optimization
Namespace strategy:
- system namespace for core platform services (Kafka, Zookeeper, Schema Registry)
- eventing namespace for event processing services (Event Router, Processor)
- monitoring namespace for observability stack (Prometheus, Grafana, Jaeger)
- user-{team} namespaces for customer services (tenant isolation)

Resource Quota Model:

CPU and memory limits per namespace to prevent resource contention
Pod disruption budgets (PDBs) set at 100% for critical services, 90% for non-critical
Resource requests and limits aligned with service SLA requirements

Container Image Strategy

Base Images:

Ubuntu 22.04 LTS as base OS for stability and security
OpenJDK 17 or Python 3.10 as runtime environments for services

Build Pipeline:

Multi-stage Docker builds to minimize image size and attack surface
Build-time security scanning using Trivy or Snyk for vulnerable dependencies
Image registry: AWS ECR with lifecycle policies (retention of last 30 images, automatic cleanup of failed builds)

Deployment Strategy

Release Management:

Blue-green deployment strategy with traffic shifting via AWS Application Load Balancer
Canary deployments for new service versions with 10% traffic split to validate performance and stability
Rollback capability triggered by SLO violations or alerting thresholds

Traffic Management:

Kubernetes Ingress controller (NGINX or AWS Load Balancer Controller) for external traffic routing
Service mesh integration with Istio for internal service-to-service communication and observability

Auto-Scaling Design

Horizontal Pod Autoscaling (HPA):

CPU utilization-based scaling for event processing services (50% target CPU)
Memory-based scaling for stateful services (80% target memory)
Custom metrics scaling for Kafka consumer lag and event throughput

Vertical Pod Autoscaling (VPA):

Memory and CPU recommendations for services based on historical usage patterns
VPA in recommendation mode to avoid over-provisioning while maintaining performance

KEDA Integration:

Event-driven scaling for Kafka consumers based on queue depth and message rate
Scaling triggers for Redis streams, SQS queues, and database change events

Infrastructure as Code Approach

IaC Tools:

Terraform for infrastructure provisioning and management across multiple AWS regions
HCL (HashiCorp Configuration Language) modules for reusable infrastructure components
GitOps approach using ArgoCD or FluxCD for declarative application deployment

State Management:

Terraform remote state with S3 backend and DynamoDB locking for multi-environment consistency
Git repository structure organized by environment (dev, staging, prod) and region

Configuration Management:

Secrets management using AWS Systems Manager Parameter Store or HashiCorp Vault
Environment-specific configuration overlays in separate Terraform variable files
CI/CD pipeline integration for automated infrastructure provisioning and updates

Monitoring Integration:

CloudWatch integration for infrastructure metrics and logs
Prometheus exporters for Kubernetes cluster monitoring
Alerting rules defined in Terraform to ensure infrastructure health visibility

This infrastructure design provides a robust, scalable, and cost-effective foundation for deploying and managing a global event streaming platform with multi-region resilience, automated scaling capabilities, and comprehensive observability.

15. Security Architecture

VPC Design and Network Segmentation

The platform operates within a multi-AZ VPC architecture with strict network segmentation:

Public Subnets: Host API gateways, load balancers, and edge services with internet-facing access
Private Subnets: Contain all backend services, databases, and internal components with no direct internet access
DMZ Subnets: Dedicated for integration services that need to communicate with external partners
Security Groups: Enforce strict ingress/egress rules at the instance level with least-privilege access
Network ACLs: Additional layer of security at subnet level for traffic filtering

Zero-Trust Network Model Implementation

The system follows a zero-trust security model where:

All network traffic is encrypted and authenticated regardless of source/destination
Services authenticate and authorize every request before processing
No implicit trust is given to any internal or external component
Micro-segmentation ensures that even if one service is compromised, others remain protected
Service mesh (Istio) enforces mTLS between services and provides fine-grained access control

TLS Termination Strategy

Edge Level: TLS termination at the API Gateway and Load Balancer level using AWS ACM or custom certificates
Service-to-Service: mTLS between services enforced via service mesh (Istio) with certificate management through Istio's built-in CA
Internal Services: All inter-service communication uses mutual TLS with certificate rotation every 90 days
Certificate Lifecycle: Automated renewal and rotation using HashiCorp Vault or AWS Certificate Manager

DDoS Protection Strategy

AWS Shield Advanced: Enabled for all public-facing endpoints to protect against volumetric DDoS attacks
Rate Limiting: Applied at API Gateway level with configurable thresholds per service and client
Traffic Filtering: Integration with AWS WAF for filtering malicious traffic patterns
Auto-scaling Protection: Services auto-scale to handle traffic spikes while maintaining security posture

Secrets Management Architecture

Centralized Secret Store: HashiCorp Vault or AWS Secrets Manager for all secrets, credentials, and configuration values
Rotation Policy: Secrets rotated every 90 days or upon compromise with automated secret rotation workflows
Access Control: Fine-grained RBAC policies ensuring only authorized services and users can access specific secrets
Encryption: All secrets encrypted at rest using AES-256 and in transit using TLS 1.3+
Audit Trail: Comprehensive logging of all secret access and modifications for compliance and forensic analysis

Vulnerability Scanning in CI/CD

Container Image Scanning: Integration with Trivy or Snyk to scan container images for known vulnerabilities before deployment
Infrastructure Scanning: Regular scanning of infrastructure configurations using tools like Checkov or tfsec
Dependency Analysis: Automated analysis of application dependencies for security vulnerabilities using OWASP Dependency-Check or similar tools
Security Policy Enforcement: CI/CD pipeline fails when security thresholds are exceeded with automated remediation workflows

WAF Rules for Critical Endpoints

OWASP Top 10 Protection: Custom rules to prevent common vulnerabilities including SQL injection, XSS, and CSRF attacks
Rate Limiting Rules: Configurable rate limits per endpoint to prevent abuse and DDoS attacks
IP Whitelisting: Critical endpoints restricted to known IP ranges with automated IP reputation monitoring
Custom Rule Sets: Service-specific rules for handling business logic security requirements and fraud detection patterns

16. Deployment & Release Strategy

Deployment Strategy and Traffic Management

The event streaming platform will utilize a canary deployment strategy with progressive traffic shifts to ensure safe rollouts and rapid rollback capabilities. The deployment pipeline will follow a blue-green approach for major version changes, with the new version running in parallel to the existing production environment.

Traffic management will be implemented using Kubernetes Ingress controllers with weighted routing to gradually shift traffic from the old version to the new version. The transition will be controlled through:

Gradual traffic percentage increases (5% → 25% → 50% → 75% → 100%)
Health check-based traffic routing
Manual approval gates for each traffic shift stage

Artifact Promotion and Release Process

The release process follows a trunk-based development model with continuous integration. Artifacts are promoted through environments in the following sequence:

Dev Environment: Continuous integration with automated unit and integration tests
Staging Environment: Full end-to-end testing including performance and load testing
Production: Canary deployment with automated rollback triggers

Release gates include:

Code review and approval (minimum 2 reviewers)
Automated test suite pass (unit, integration, E2E)
Security scanning and vulnerability assessment
Performance benchmark validation
Manual smoke testing in staging

Zero-Downtime Database Migrations

Database schema changes will be handled using online schema change tools (pt-online-schema-change for MySQL, or similar for other databases). For major schema migrations:

Schema versioning with explicit migration scripts
Feature flags to control new schema usage during transition
Rollback strategy with backup restoration capability
Read/write split to minimize impact on production traffic

Feature Flag Integration and Progressive Rollouts

Feature flags will be implemented using LaunchDarkly or Flagship for centralized feature management. The progressive rollout strategy includes:

Gradual rollouts with configurable percentage thresholds
Targeted rollouts based on user segments or geographic regions
A/B testing capabilities for new features
Real-time flag updates without service restarts

Canary Analysis and Auto-Rollback Triggers

Automated canary analysis will be implemented with:

Error rate thresholds (5% error rate triggers rollback)
Latency degradation detection (p99 latency > 2x baseline)
Throughput reduction monitoring (5% drop in QPS)
Metrics-based alerting with 30-second evaluation windows

Auto-rollback triggers will be configured to automatically revert to the previous stable version when:

Error rate exceeds 5% for more than 2 minutes
Latency increases by 100% or more
System throughput drops below 95% of baseline

Release Cadence and Branching Strategy

The platform will follow a trunk-based development model with the following cadence:

Minor releases: Every 2 weeks for feature enhancements
Patch releases: As needed for bug fixes and security patches
Major releases: Every 3 months with breaking changes

Branching strategy:

Main branch as the production-ready codebase
Feature branches for experimental work with short-lived lifecycles
Release branches created only for patch releases and hotfixes

Release Automation and Rollback Procedures

Release automation will be implemented using GitOps practices with:

Helm charts for service deployments
Argo CD or Flux for GitOps-based continuous delivery
Automated rollback on failure with rollback to last stable version

Rollback procedures include:

Immediate rollback to previous stable release within 5 minutes
Manual intervention for complex state changes
Rollback verification with health checks and metrics validation
Post-rollback analysis to identify root causes

Release Validation and Testing Strategy

Each release undergoes comprehensive validation:

Automated regression testing covering 95% of critical paths
Performance stress testing simulating peak load conditions
Security vulnerability scanning in CI/CD pipeline
Compliance validation for regulatory requirements
User acceptance testing in staging environment

The release process includes:

Pre-release validation (automated checks)
Manual smoke testing in staging
Production deployment with canary strategy
Post-deployment monitoring and validation
Rollback capability if any threshold is exceeded

Release Coordination and Communication

Release coordination will include:

Release windows scheduled during low-traffic periods (typically 2 AM - 6 AM UTC)
Communication plan for stakeholders including status updates and rollback notifications
Incident response procedures with escalation paths for release failures
Post-mortem process for any failed releases to prevent recurrence

17. Cost Architecture

Major Cost Drivers Analysis

Compute Costs (35-40% of total): Primarily driven by Kubernetes cluster nodes, message broker instances, and API gateway services. CPU-intensive operations like event processing, schema validation, and compression contribute most to compute usage.

Storage Costs (25-30% of total): Event data storage dominates costs, particularly with retention policies and backup strategies. Object storage for event archives and database storage for metadata and indexes.

Network & Data Transfer Costs (20-25% of total): Ingress/egress traffic between services, cross-region replication, and external API calls. High-volume event streaming creates significant data transfer costs.

Database & Caching Costs (10-15% of total): Relational databases for metadata, document stores for configuration, and distributed caching systems. Connection pooling and query optimization impact cost efficiency.

Cost Optimization Strategy

Infrastructure Optimization:

Use spot instances for non-critical workloads (e.g., development environments, batch processing)
Implement auto-scaling to reduce idle compute costs
Use reserved instances for predictable workloads (e.g., core message brokers)
Leverage multi-zone deployment to minimize cross-region data transfer costs

Storage Tiering Strategy:

Hot storage (SSD) for active event streams with high read/write frequency
Warm storage (HDD) for recent events with moderate access patterns
Cold storage (S3 Glacier) for archived events with infrequent access (90+ day retention)
Implement lifecycle policies to automatically move data between tiers

Event Compression & Serialization:

Use efficient serialization formats (Avro, Protocol Buffers) to reduce payload sizes
Implement automatic compression for event payloads (gzip, snappy)
Apply schema evolution strategies to minimize redundant metadata in streams

Reserved vs. Spot Instance Strategy

Reserved Instances:

Critical services (message brokers, API gateways) use reserved instances for predictable workloads
3-year reserved instances for long-term stability and cost savings (15-20% reduction)

Spot Instances:

Non-critical services (batch processors, dev/test environments) use spot instances
50-70% cost reduction with acceptable risk for non-production workloads

Data Egress Cost Minimization

Cross-Region Communication:

Implement regional event routing to minimize inter-region data transfer
Use local storage and replication within regions where possible
Apply content delivery networks for global event distribution

API Gateway Optimization:

Implement request/response compression to reduce bandwidth usage
Use efficient caching strategies to minimize repeated data transfers
Apply rate limiting to prevent unnecessary API calls

FinOps Dashboards and Budget Alerts

Cost Monitoring Stack:

Implement real-time cost dashboards using Grafana with Prometheus metrics
Set up automated alerts for cost anomalies (>10% deviation from budget)
Create per-service cost allocation dashboards with team-level visibility

Budget Allocation Strategy:

Allocate budgets per service team with automatic alerts at 80% threshold
Implement cost allocation tags for better resource tracking and accountability
Establish monthly cost review cycles with business stakeholders

Cost Optimization Automation:

Deploy auto-scaling policies based on cost-per-request metrics
Implement cost-aware scheduling for batch jobs during off-peak hours
Use cost optimization tools (e.g., AWS Cost Explorer, GCP Cost Management) for proactive recommendations

18. Open Questions, Risks & Trade-offs

Decisions requiring product / engineering alignment

Event ordering guarantees vs. throughput trade-off: Determining whether strict per-partition ordering is required for all use cases or if relaxed ordering can be acceptable for performance gains
Multi-tenancy model: Whether to support single-tenant or multi-tenant event streams with shared infrastructure, and how to enforce tenant isolation
Schema evolution strategy: How to handle schema changes in event streams without breaking consumers, including backward compatibility requirements
Cross-region replication strategy: Whether to support global event streams with cross-region replication or if regional isolation is acceptable for performance and compliance reasons

Key architectural risks

Risk	Impact	Likelihood	Mitigation
Data loss during partition failures	High	Medium	Implement strict durability guarantees with quorum-based writes and automatic failover
Event ordering inconsistency under high load	Medium	High	Use partitioning strategies that maintain order within partitions, implement proper consumer group management
Schema registry failure affecting all event processing	High	Low	Implement schema registry redundancy and fallback mechanisms
Cache invalidation storms causing DB overload	Medium	Medium	Implement cache warming strategies and proper cache invalidation coordination
Multi-region sync latency affecting real-time processing	High	Medium	Use eventual consistency patterns with proper conflict resolution strategies
Consumer lag accumulation causing system degradation	High	Medium	Implement automatic consumer rebalancing and alerting for lag thresholds

Trade-offs made with justification

Consistency vs. availability: Chose eventual consistency over strong consistency for better availability and horizontal scaling, with proper conflict resolution patterns for critical business events
Complexity vs. performance: Trade-off between having a single unified event streaming platform vs. separate systems for different use cases to optimize performance and reduce cross-service dependencies
Event ordering flexibility vs. strict ordering: Accepting relaxed ordering within partitions for better throughput and horizontal scaling instead of requiring global ordering across all events
Storage vs. compute trade-off: Using object storage for event retention and processing with compute-intensive event transformation engines to optimize cost and performance

Alternatives considered and rejected

Kafka vs. Pulsar: Rejected Kafka due to its more complex multi-tenancy model and less flexible schema support, despite its mature ecosystem
Monolithic vs. microservices: Rejected monolithic approach due to operational complexity and scalability limitations at large scale
Custom vs. managed services: Rejected fully custom implementation due to high operational overhead and maintenance burden, despite potential for optimization
Global vs. regional event streams: Rejected global event streams due to complexity in maintaining consistency and compliance across regions

Technical debt acknowledged

Schema evolution complexity: Current design assumes schema versioning and compatibility checking, but full backward compatibility validation is not yet implemented
Cross-service event consistency: Event processing across services still relies on eventual consistency patterns, which may introduce data synchronization delays
Multi-region coordination: Cross-region event replication and conflict resolution logic is not yet fully implemented and requires additional design work
Consumer lag monitoring: Consumer lag alerting and auto-scaling logic is not yet implemented, requiring future development for production readiness

19. Phased Implementation Roadmap

Phase 1: MVP (Minimum Viable Platform)

Timeline: 8-12 weeks
Critical Path: Core messaging infrastructure, basic topic management, and client SDKs

Core Messaging Infrastructure:
- Deploy Kafka cluster (3 brokers) with basic configuration
- Set up ZooKeeper for coordination (3 nodes)
- Implement basic topic creation and deletion APIs
- Build core producer/consumer client SDKs (Java, Python)
Basic Platform Features:
- Event schema registry with versioning support
- Basic consumer group management (no rebalancing yet)
- Simple event publishing and consumption workflows
- Basic metrics collection (latency, throughput, error rates)
Infrastructure Setup:
- Kubernetes cluster with 3 control plane nodes and 5 worker nodes
- Deploy Kafka Operator for managing topics and consumers
- Setup basic monitoring stack (Prometheus, Grafana)
- Implement basic logging with structured JSON format

Phase 2: Scale & Hardening

Timeline: 12-16 weeks
Critical Path: Multi-zone deployment, enhanced reliability, and performance optimization

Multi-Zone Deployment:
- Deploy Kafka clusters across 3 availability zones
- Implement cross-zone replication and failover mechanisms
- Set up multi-region disaster recovery capability (active-passive)
Enhanced Reliability:
- Add circuit breaker and bulkhead patterns to all services
- Implement comprehensive health checks and liveness probes
- Deploy Chaos Monkey for fault injection testing
- Add dead letter queue handling and poison pill detection
Performance & Scalability:
- Optimize Kafka partitioning strategy for different event types
- Implement connection pooling and efficient consumer batching
- Add caching layer (Redis) for frequently accessed metadata
- Enable auto-scaling for Kafka consumers based on queue depth
Advanced Features:
- Implement event replay and reprocessing capabilities
- Add support for exactly-once processing semantics
- Build event filtering and transformation pipeline

Phase 3: Excellence & Optimization

Timeline: 16-20 weeks
Critical Path: Advanced observability, security hardening, and cost optimization

Advanced Observability:
- Deploy distributed tracing (Jaeger) across all services
- Implement comprehensive alerting system with severity levels
- Set up error budget tracking dashboard and SLO monitoring
- Build capacity planning tools with load forecasting capabilities
Security & Compliance:
- Implement mTLS between all services
- Add comprehensive audit logging for all event operations
- Deploy WAF rules and DDoS protection mechanisms
- Implement secrets management with Vault integration
Cost Optimization:
- Analyze and optimize storage tiering strategy (hot/warm/cold)
- Implement reserved instance strategy for Kafka brokers
- Optimize network egress costs through CDN integration
- Deploy FinOps dashboards with budget alerts
Advanced Features & Extensibility:
- Add support for real-time search and analytics capabilities
- Implement multi-tenant isolation with resource quotas
- Build extensible plugin architecture for custom connectors
- Add support for different event formats (JSON, Avro, Protobuf)

Dependencies & Milestones:

Phase 1 Dependencies:
- Kubernetes cluster provisioning (Week 1)
- Kafka operator deployment (Week 2)
- Basic client SDKs (Week 3-4)
Phase 2 Dependencies:
- Multi-zone VPC setup (Week 1)
- Chaos engineering tooling (Week 2)
- Enhanced monitoring stack (Week 3)
Phase 3 Dependencies:
- Security infrastructure (Week 1)
- Cost optimization tools (Week 2)
- Advanced analytics platform (Week 3)

Risk Mitigation:

Technical Debt: Regular architecture review cycles every 4 weeks
Scaling Issues: Load testing at each phase with realistic traffic patterns
Security Gaps: Security audit and compliance check at Phase 2 completion
Cost Overruns: Weekly cost monitoring and optimization reviews

Success Metrics:

99.9% availability for core messaging services
<50ms p99 latency for event publishing and consumption
10,000+ concurrent consumers with sub-second latency
<2% error rate in event processing workflows
50% reduction in infrastructure costs by Phase 3