Components overview
- Modified from cheat sheet from bhavul
Microservices
| Use Cases/Problems | Component | What it solves | Caveats/Issues | Mitigations | Examples of Tools |
|---|---|---|---|---|---|
| - Large applications: Enhances modularity and scalability. - Continuous delivery: Facilitates independent deployment. | Microservices | Improves modularity, independent deployment. | Increased communication complexity. | - Use service meshes. - Implement standardized APIs. - Use centralized logging and monitoring. | Docker, Kubernetes, Istio |
Scalability and Availability
| Use Cases/Problems | Component | What it solves | Caveats/Issues | Mitigations | Examples of Tools |
|---|---|---|---|---|---|
| - High traffic websites: Ensures uptime and balances load. - Scalable APIs: Distributes incoming requests. | Load Balancer | Distributes traffic, improves reliability and availability. | Single point of failure, adds complexity. | - Use multiple load balancers in different regions. - Implement health checks. - Use DNS-based load balancing. | Nginx, HAProxy, AWS ELB |
| - High availability: Ensures data is replicated and available. - Disaster recovery: Provides data redundancy. | Replication Service | Ensures data availability and redundancy. | Increases costs, consistency issues. | - Use asynchronous replication. - Implement conflict resolution. - Use multi-master replication. | AWS RDS Read Replicas, MongoDB Replica Sets |
| - Disaster recovery: Ensures data is safe and recoverable. - Data integrity: Maintains backups for compliance. | Backup and Recovery | Ensures data durability, protects against data loss. | Resource-intensive, regular testing needed. | - Use automated backup solutions. - Implement multi-region storage. - Regularly test backup and recovery processes. | AWS Backup, Google Cloud Backup, Veeam |
| - Content-heavy sites: Improves load times for users. - Global reach: Distributes content across regions. | CDN (Content Delivery Network) | Reduces latency, improves load times. | Cache invalidation complexity, cost. | - Implement cache purging strategies. - Use regional CDNs. - Monitor CDN performance and hit rates. | Cloudflare, Akamai, AWS CloudFront |
Monitoring and Management
| Use Cases/Problems | Component | What it solves | Caveats/Issues | Mitigations | Examples of Tools |
|---|---|---|---|---|---|
| - System reliability: Monitors uptime and performance. - Issue detection: Alerts for anomalies and failures. | Monitoring System | Tracks system health, enables alerting. | High overhead, potential noise. | - Use threshold tuning and anomaly detection. - Implement efficient data collection. - Use centralized monitoring dashboards. | Prometheus, Grafana, Datadog |
| - Debugging: Captures logs for issue diagnosis. - Compliance: Maintains audit trails. | Logging System | Aids in auditing and troubleshooting. | Large data volumes, storage and querying. | - Use log rotation and retention policies. - Implement centralized logging. - Optimize log storage and indexing. | ELK Stack, Splunk, Fluentd |
| - Microservices: Tracks requests across services. - Performance tuning: Identifies bottlenecks and delays. | Distributed Tracing | Aids in debugging and performance monitoring. | High overhead, integration required. | - Use sampling to reduce overhead. - Implement efficient trace storage. - Use correlation IDs for request tracking. | Jaeger, Zipkin, OpenTracing |
| - Dynamic applications: Centralizes config changes. - Large systems: Manages configurations across services. | Configuration Service | Centralizes configuration management. | Single point of failure, secure access needed. | - Use distributed configuration stores. - Implement encryption for sensitive data. - Use versioning and rollback mechanisms. | Consul, etcd, Spring Cloud Config |
| - Periodic tasks: Automates recurring jobs. - Batch processing: Manages large data processing tasks. | Scheduler | Manages background jobs and tasks. | Requires monitoring, can become bottleneck. | - Use distributed schedulers. - Implement job prioritization. - Use monitoring and retry mechanisms. | Apache Airflow, Celery, Kubernetes CronJobs |
| - API management: Protects against request floods. - Fair resource allocation: Ensures fair usage policies. | Rate Limiter | Controls request rate, prevents abuse. | Can impact user experience. | - Use dynamic rate limiting. - Implement user-based quotas. - Use monitoring to adjust limits. | Kong, Envoy, Nginx |
| - Secure applications: Manages user identity and access. - Single sign-on: Centralizes authentication across services. | Authentication Service | Enhances security, manages user authentication. | Single point of failure, security measures needed. | - Use multi-factor authentication. - Implement redundancy and failover. - Use secure token storage and management. | OAuth, Okta, Auth0 |
| - Fault tolerance: Prevents system overloads. - Resilient services: Isolates failures in microservices. | Circuit Breaker | Protects services from cascading failures. | Adds complexity, tuning needed. | - Use monitoring tools to detect failures. - Implement fallback strategies. - Use retries and exponential backoff. | Hystrix, Resilience4j, Istio |
Communication and Coordination
| Use Cases/Problems | Component | What it solves | Caveats/Issues | Mitigations | Examples of Tools |
|---|---|---|---|---|---|
| - Unified API access: Centralizes client requests. - Security: Manages authentication and authorization. | API Gateway | Single entry point, manages authentication and routing. | Can become a bottleneck, adds latency. | - Use multiple gateways with load balancing. - Implement rate limiting and caching. - Use circuit breakers and retries. | Kong, Apigee, AWS API Gateway |
| - Event streaming: Manages high-throughput data streams. - Real-time processing: Facilitates real-time data flows. | Message Broker | Facilitates message exchange, supports multiple patterns. | Bottleneck potential, delivery guarantees. | - Use scalable brokers with partitions. - Implement backpressure handling. - Monitor message broker performance. | Apache Kafka, RabbitMQ, ActiveMQ |
| - Event-driven systems: Manages asynchronous events. - Microservices: Decouples service communication. | Distributed Queue | Manages asynchronous communication, decouples components. | Message ordering and delivery guarantees. | - Use message brokers with strong ordering guarantees. - Implement idempotent message processing. - Use message deduplication techniques. | Apache Kafka, RabbitMQ, AWS SQS |
| - Microservices: Enables service discovery. - Dynamic environments: Tracks changing service instances. | Service Registry | Tracks services and their instances. | High availability required, consistency issues. | - Use distributed service registries. - Implement regular health checks. - Use consensus algorithms for consistency. | Consul, Eureka, Zookeeper |
| - Microservices: Handles inter-service communication. - Observability: Provides insights into service interactions. | Service Mesh | Manages microservices communication. | Adds operational complexity. | - Use managed service meshes. - Implement automation tools. - Use monitoring and observability tools. | Istio, Linkerd, Consul Connect |
| - Containerized apps: Automates container management. - Microservices: Coordinates service deployments. | Orchestration Tool | Automates deployment and management. | Adds complexity, learning curve. | - Use managed orchestration services. - Implement robust CI/CD pipelines. - Use monitoring and scaling tools. | Kubernetes, Docker Swarm, Mesos |
Data Management and Storage
| Use Cases/Problems | Component | What it solves | Caveats/Issues | Mitigations | Examples of Tools |
|---|---|---|---|---|---|
| - Financial transactions: Requires ACID compliance. - Complex queries: Needs structured and relational data. | SQL Database | Strong ACID properties, structured data, complex queries. | Limited scalability, schema management. | - Implement sharding. - Use read replicas. - Employ clustering and partitioning. | MySQL, PostgreSQL, MS SQL Server |
| - Large-scale data: Supports horizontal scaling. - Unstructured data: Flexible schema adapts to changes. | NoSQL Database | Flexible schema, horizontal scalability, high performance. | Eventual consistency, limited transaction support. | - Use consistency settings (e.g., quorum reads/writes). - Design for idempotent operations. - Implement conflict resolution strategies. | MongoDB, Cassandra, DynamoDB |
| - Social networks: Models complex relationships. - Recommendation engines: Analyzes connected data. | Graph Database | Efficiently handles graph-based data and relationships. | Steep learning curve, non-graph query inefficiency. | - Use graph-specific optimizations. - Implement hybrid models for different data types. - Use indexing and caching for performance. | Neo4j, Amazon Neptune, OrientDB |
| - Media storage: Handles large files like images and videos. - Backup solutions: Stores and retrieves backups. | File Storage | Scales with data growth, handles unstructured data. | Backup and redundancy required, retrieval latency. | - Use distributed file systems. - Implement multi-region replication. - Use lifecycle policies for data management. | AWS S3, Google Cloud Storage, HDFS |
| - Business intelligence: Centralizes analytics data. - Historical analysis: Supports complex querying over large datasets. | Data Warehouse | Centralizes data, supports complex queries. | High storage and maintenance costs. | - Use data compression and partitioning. - Implement data lifecycle management. - Use cloud-based, scalable data warehouses. | Amazon Redshift, Snowflake, Google BigQuery |
| - High read load: Reduces latency for frequent reads. - Session storage: Speeds up access to session data. | Cache | Reduces latency, decreases load on databases. | Cache consistency issues, potential for stale data. | - Implement cache invalidation strategies. - Use Time-to-Live (TTL) settings. - Employ write-through or write-back caching. | Redis, Memcached |
| - Real-time analytics: Requires fast data access. - Leaderboards: High-speed data retrieval is crucial. | In-Memory Database | Extremely fast data retrieval, reduces latency. | Volatile storage, high memory cost. | - Enable persistence options. - Use hybrid storage models (in-memory + disk). - Implement data backup strategies. | Redis, Memcached |
| - Big data analytics: Stores and processes vast data. - Data warehousing: Prepares raw data for analytics. | Data Lake | Supports diverse data types and analytics. | Governance required, risk of becoming data swamp. | - Use metadata management. - Implement data cataloging. - Use data lifecycle policies. | AWS Lake Formation, Azure Data Lake, Hadoop |
| - Event-driven architectures: Processes data streams in real-time. - Analytics: Real-time insights from continuous data flow. | Data Streaming Platform | Facilitates real-time data processing. | High operational complexity. | - Use managed streaming services. - Implement scaling strategies. - Monitor and optimize processing. | Apache Kafka, AWS Kinesis, Google Pub/Sub |
Analytics and Processing
| Use Cases/Problems | Component | What it solves | Caveats/Issues | Mitigations | Examples of Tools |
|---|---|---|---|---|---|
| - Data warehousing: Prepares data for analysis. - Data migration: Transforms data from multiple sources. | ETL (extract, transform, load) Pipeline | Facilitates data integration and analysis. | Complex to build and maintain. | - Use managed ETL services. - Implement monitoring and error handling. - Use data validation and transformation tools. | Apache Nifi, AWS Glue, Talend |
| - E-commerce sites: Provides fast product search. - Large datasets: Enables full-text search over extensive data. | Search Engine | Enables fast search over large datasets. | Indexing and maintenance required. | - Implement efficient indexing strategies. - Use distributed search architectures. - Optimize search queries and relevance. | Elasticsearch, Solr, Algolia |
| - Real-time dashboards: Aggregates live data feeds. - Monitoring: Provides instant insights from data streams. | Real-Time Data Aggregation | Enables real-time analytics and monitoring. | High complexity, data velocity issues. | - Use stream processing frameworks. - Implement windowing and aggregation techniques. - Monitor and scale processing infrastructure. | Apache Flink, Apache Storm, AWS Kinesis |
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