Database Strategies
Diagram: Database Strategies · 75 elements
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"text": "CONCEPT: All data in single database server. No sharding, no\nreplication.\n\nPROS:\n• Simple to implement and manage\n• ACID transactions guaranteed\n• No consistency issues\n• Easy to query across all data\n\nCONS:\n• Single point of failure\n• Limited scalability (storage & throughput)\n• Cannot handle 100M+ users effectively\n• High latency if geographically distributed\n\nWHEN TO USE:\n• Startup/MVP phase (< 1M users)\n• Low write/read throughput needed\n• Strong consistency critical",
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"text": "CONCEPT: One master (write), multiple replicas (read-only).\nReplicas sync from master via binary logs.\n\nPROS:\n• Scales read capacity (replicas serve reads)\n• High availability (failover to replica if master fails)\n• Reduced latency (read from nearest replica)\n• Backup readily available\n\nCONS:\n• Replication lag (eventual consistency)\n• Write still bottlenecked to master\n• Master failover complexity (data loss risk)\n• Not suitable for extreme scales\n\nWHEN TO USE:\n• Read-heavy systems (80% reads, 20% writes)\n• Need geographic distribution for reads\n• Can tolerate eventual consistency\n• Scale up to 10M+ users",
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"text": "Master (Write)\n↓ Replicate ↓\nReplica1 Replica2\n(Read Only)",
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"text": "CONCEPT: Split data across multiple databases based on\nshard key (e.g., user_id). Each shard holds subset of data.\n\nPROS:\n• Scales both read AND write (by sharding)\n• Reduces data per server (faster queries)\n• Can handle 100M+ users\n• Inherently distributed\n\nCONS:\n• Complex to implement and manage\n• Cross-shard queries difficult (need aggregation)\n• Resharding needed when scale more (downtime/complex)\n• Data hotspots possible (bad shard key)\n• Transactions across shards not possible\n\nWHEN TO USE:\n• Large-scale systems (100M+ users)\n• Write-heavy workloads\n• Data naturally partitionable\n• Can design good shard key",
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"text": "User ID 1-10M\nShard 1\n\nUser ID 10M-20M\nShard 2",
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"text": "CONCEPT: Each shard replicated (master-replica per shard).\nBest of both worlds.\n\nPROS:\n• Scales both read and write (sharding + replication)\n• High availability (replica per shard acts as backup)\n• Geographic distribution (read locally, write to master)\n• Handles 100M+ users with HA\n• Industry standard for mega-scale systems\n\nCONS:\n• Most complex to operate\n• Highest infrastructure cost (many servers)\n• Difficult resharding process\n• Monitoring and maintenance complex\n\nWHEN TO USE:\n• Large-scale production systems (100M-1B users)\n• Need high availability AND scale\n• Can afford operational complexity\n• Geographic distribution required\n• Examples: Facebook, Twitter, Google",
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"text": "CONCEPT: Databases built for distributed (e.g., Cassandra,\nDynamoDB, MongoDB). Built-in replication/sharding.\n\nPROS:\n• Designed for distribution from ground up\n• Automatic sharding and replication\n• Horizontal scalability (add nodes, no resharding)\n• High availability (multi-node consensus)\n• Flexible schema (NoSQL)\n\nCONS:\n• No ACID across nodes (BASE instead of ACID)\n• Eventual consistency (read stale data)\n• Complex to operate and tune\n• Different query model (no SQL joins)\n• Data duplication/denormalization needed\n\nWHEN TO USE:\n• Massive scale with HA required\n• Can tolerate eventual consistency\n• Flexible/evolving data models\n• High throughput required\n• Examples: Cassandra (Netflix), DynamoDB (AWS), MongoDB\n(general)",
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