System Design in Practice


1. Performance Optimization

Read & Write Optimization

    1. Read-Heavy Systems → Use Caching to reduce database load.
    • Implement Redis (In-Memory Cache) for frequently accessed data.
    • Use Memcached for lightweight key-value caching.
    • Bloom Filters to avoid unnecessary DB queries (e.g., checking if an element exists before querying).
    • CDN (Cloudflare, AWS CloudFront, Akamai) for caching static and dynamic content at the edge.
    2. Write-Heavy Systems → Use Message Queues & Write-Behind Caching for async processing.
    • Kafka, RabbitMQ, AWS SQS for queueing writes and processing asynchronously.
    • Write-Behind Caching in Redis ensures that writes are batched and written to DB periodically.
    • Event Sourcing for reconstructing past application states from logs.
    3. Low Latency Requirement → Optimize request-response time.
    • Use Cache (Redis, Memcached), CDN (Akamai, CloudFront, Fastly) for data delivery.
    • Implement Connection Pooling (HikariCP, PgBouncer) to minimize DB overhead.
    • Use gRPC with Protobuf to reduce payload size and serialization/deserialization overhead.
    4. High-Performing DB Queries → Reduce query execution time.
    • Implement B-Tree, Hash Indexes, and Covering Indexes for faster lookups.
    • Use Materialized Views for precomputed results.
    • Optimize queries using EXPLAIN ANALYZE in PostgreSQL or Query Execution Plans in MySQL.
    5. Scaling SQL Databases → Avoid bottlenecks.
    • Read Replicas for read scalability (e.g., AWS RDS Read Replicas).
    • Sharding (Range, Hash, List) for distributing load across multiple nodes.
    • ProxySQL for intelligent query routing.


2. Database & Storage Strategy

Database Selection

    6. ACID-Compliant Transactions → Use SQL Databases (PostgreSQL, MySQL, Oracle, SQL Server) for strict data integrity.
    • Implement Multi-Version Concurrency Control (MVCC) for high throughput.
    • Use 2-Phase Commit (2PC) in distributed transactions.

    7. Unstructured or Schema-Free Data → Use NoSQL (MongoDB, DynamoDB, Cassandra).

    • MongoDB for flexible document storage.
    • DynamoDB (Key-Value Store, Partitioned by Hash Keys) for predictable performance.
    • Cassandra (Column-Family Storage, Peer-to-Peer Architecture) for high availability.
    8. Graph Data (Nodes, Edges, Relationships) → Use Graph Databases (Neo4j, ArangoDB, JanusGraph) for recommendations and fraud detection.

Storage Solutions

    9. Handling Large Files, Videos, or Images → Use Object Storage.

    • Amazon S3, Azure Blob, Google Cloud Storage for scalability.
    • Use S3 Multipart Uploads for large files.
    • Implement CDN-backed caching (e.g., CloudFront + S3) for fast content delivery.
    10. Analytics & Historical Data → Store in Data Lakes & Columnar Storage.
    • Use AWS Lake Formation, Delta Lake, Apache Iceberg with Parquet, ORC formats for efficient queries.
    • BigQuery & Snowflake for large-scale analytical workloads.


3. High Availability, Scalability & Reliability

Load Balancing & Scalability

    11. Ensuring High Availability & Performance → Use Load Balancers (NGINX, AWS ALB/ELB, HAProxy).
    • Implement Health Checks for automatic failover.
    • Use Sticky Sessions for session-aware routing.
    12. Scaling System Components → Implement Horizontal Scaling.
    • Kubernetes, ECS, Nomad for auto-scaling workloads.
    • Event-Driven Architecture with Kafka Streams for real-time updates.
    13. Handling Traffic Spikes → Use Auto-Scaling (Kubernetes HPA, AWS Auto Scaling, GCP Managed Instance Groups).
    • Throttling & Load Shedding for managing high traffic volumes.

Redundancy & Fault Tolerance

    14. Avoiding Single Point of Failure (SPOF) → Implement Redundancy.
    • Multi-AZ Deployments in AWS RDS for failover.
    • Active-Passive Failover (e.g., Redis Sentinel, ZooKeeper for leader election).
    15. Ensuring Fault Tolerance & Durability → Use Data Replication.
    • Master-Slave Replication (PostgreSQL, MySQL).
    • Multi-Region Replication (MongoDB, Cassandra).
    16. Failure Detection in Distributed Systems → Implement Heartbeat Mechanisms (Consul, ZooKeeper, etcd).

    17. Ensuring Eventual Consistency → Use CRDTs, DynamoDB’s Eventual Consistency Model for distributed data.


4. Security & Access Control

    18. Preventing DoS Attacks & Server Overload → Implement Rate Limiting.
    • Guava RateLimiter, API Gateway Rate-Limiting Policies.
    • Web Application Firewall (AWS WAF, Cloudflare WAF) for request filtering.
    19. Ensuring Data Integrity → Use Checksum Algorithms (SHA-256, CRC32).
    • Implement Immutable Storage for audit logs.
    20. Protecting Sensitive Data →
    • AES-256 Encryption for data at rest.
    • TLS 1.3 for data in transit.
    • Role-Based Access Control (RBAC) using AWS IAM, Okta.

    21. Zero Trust Security Model

    • Identity & Access Management (IAM, OAuth, OpenID Connect, JWT)
    • Zero Trust Network (ZTNA, BeyondCorp by Google)


5. Event-Driven & Real-Time Communication

    22. Event-Driven Architecture → Use Event Streaming Platforms (Apache Kafka, AWS Kinesis, Pulsar).

    23. User-to-User Fast Communication → Use WebSockets (Socket.IO, SignalR).
    • Redis Pub/Sub, Kafka Streams for real-time messaging.


6. Advanced Search & Query Optimization

    24. High-Volume Data Search → Use Search Engines.
    • Elasticsearch, Apache Solr, Algolia for text-based queries.
    • Implement Trie, Inverted Index for faster lookups.
    25. Location-Based Data Queries → Use Geospatial Indexing.
    • PostGIS, MongoDB Geospatial Queries, Google S2 Library for geo-based applications.


7. Network & Distributed System Design

    26. Efficient Data Transfer in a Decentralized System → Use Gossip Protocol.
    • Cassandra, Consul, Serf for distributed communication.
    27. Consistent Hashing for Load Distribution → Used in DynamoDB, Memcached, Cassandra.

    28. Domain Name Resolution & Traffic Routing → Use DNS (Route 53, Cloudflare DNS) with GeoDNS, Anycast Routing.


8. Workflow & Job Processing

    29. Bulk Job Processing → Use Batch Processing.
    • Apache Spark, Hadoop, AWS Glue for large-scale data jobs.
    30. Workflow Orchestration → Use Apache Airflow, Temporal, AWS Step Functions.


9. Observability & Monitoring

   31. Ensuring System Health & Performance → Implement Logging, Monitoring & Tracing
    • Centralized Logging → Use ELK Stack (Elasticsearch, Logstash, Kibana), AWS CloudWatch, Loki.
    • Distributed Tracing → OpenTelemetry, Jaeger, Zipkin for tracing microservices interactions.
    • Metrics Collection → Prometheus, Grafana for real-time system metrics.
    • Error Tracking & Alerting → Sentry, Datadog, PagerDuty for proactive issue detection.

   32. Data Pipeline & ETL Processing

    • Data Streaming Pipelines: Apache Flink, Kafka Streams, AWS Glue.
    • ETL vs ELT: Understanding when to extract, transform, and load vs. extracting and transforming later.
    • Real-Time Analytics: Druid, ClickHouse, Materialized Views for instant insights.


10. API Design & Best Practices

    33. Designing scalable, secure, and easy to maintain APIs

  • RESTful API

    • Nouns in URLs → /users/{id} instead of /getUser.
    • Versioning → /api/v1/users or Accept: version=1.0.
    • Follow Proper HTTP Methods → GET, POST, PUT, PATCH, DELETE.
    • Use Query Parameters for Filtering, Sorting & Pagination → 
      • /products?category=electronics&sort=price_desc&page=1&limit=10
    • Meaningful Status Codes → 200 OK, 201 Created, 400 Bad Request, 404 Not Found.
    • Consistent JSON Responses → { "status": "success", "data": {...} }.
    • Graceful Error Handling → { "error": "Invalid email format", "code": 400 }.
    • Caching → Use ETag, Redis, or a CDN.
    • Secure API → Use HTTPS, OAuth 2.0, JWT, rate limiting, and input validation.
    • Logging & Monitoring → Use structured logs and tools like Datadog, Prometheus.
    • Implement HATEOAS (Hypermedia as the Engine of Application State).
    • Pagination for large datasets (limit & offset).

  • GraphQL for Flexible Queries

    • Use GraphQL Federation for distributed microservices.
    • Avoid N+1 query problem using DataLoader.

  • gRPC for Low-Latency Communication

    • Use Protobuf for compact payloads.
    • Implement bidirectional streaming.


11. Data Consistency & Concurrency Handling

    34. Ensuring Data Consistency in Distributed Systems

  • CAP Theorem Considerations

    • Consistency (C) → Use strong consistency (Zookeeper, Spanner).
    • Availability (A) → Eventual consistency (Cassandra, DynamoDB).
    • Partition Tolerance (P) → Necessary for distributed systems.

  • Concurrency Control Techniques

    • Optimistic Locking (ETag-based versioning).
    • Pessimistic Locking (Row-Level Locks in SQL).
    • Compare-And-Swap (CAS) for atomic updates.

  • Distributed Transactions

    • SAGA Pattern for microservices.
    • Outbox Pattern to ensure consistency between services.


12. Cost Optimization Strategies

    35. Reducing Infrastructure Costs Without Compromising Performance

    • Serverless Computing → AWS Lambda, Google Cloud Functions for on-demand execution.
    • Spot & Reserved Instances → Use EC2 Spot Instances for batch processing, Reserved Instances for long-term cost savings.
    • Right-Sizing & Auto-Scaling → Optimize instance sizes and enable auto-scaling.
    • Data Storage Cost Optimization
      • Tiered Storage → Store cold data in S3 Glacier.
      • Deduplication & Compression → Use Zstandard, Snappy, LZ4 for data compression.


13. Edge Computing & IoT Architectures

    36. Handling Real-Time Processing at the Edge

    • Edge AI/ML → TensorFlow Lite, AWS Greengrass for on-device AI processing.
    • Data Processing at the Edge → AWS IoT Core, Azure IoT Edge for reducing cloud dependency.
    • Streaming Data from IoT Devices → MQTT, CoAP, Kafka for low-latency messaging.

   37. AI/ML Infrastructure

    • MLOps & Model Deployment: TensorFlow Serving, MLflow, Kubeflow
    • Feature Stores: Feast, AWS SageMaker Feature Store
    • AI-Powered Anomaly Detection for system logs & security


14. Multi-Tenancy & SaaS Architectures

   38. Building scalable, multi-tenant applications.

    • Database Strategies:
      • Shared DB, Shared Schema → Cost-efficient but requires strong tenant isolation.
      • Shared DB, Separate Schemas → Better isolation but more overhead.
      • Separate DBs per Tenant → Strongest isolation but complex management.
    • Tenant Isolation: Row-Level Security (RLS), API Gateway-based rate limiting.
    • Scaling Tenants: Kubernetes HPA, Auto-scaling groups, Load balancing.
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