Strong Consistency (CP System):
✅ Pros:
  - Guaranteed data consistency
  - No conflicting versions
  - Simpler application logic
  - Predictable behavior

❌ Cons:
  - Lower availability during partitions
  - Higher latency (wait for quorum)
  - Reduced throughput
  - Single point of failure risk

Example: Google Spanner, etcd, ZooKeeper

Eventual Consistency (AP System):
✅ Pros:
  - Higher availability
  - Lower latency
  - Better partition tolerance
  - Higher throughput

❌ Cons:
  - Temporary inconsistencies
  - Conflict resolution needed
  - Complex application logic
  - Stale reads possible

Example: DynamoDB, Cassandra, Riak

Consistency Level Trade-offs:

ONE (R=1, W=1):
- Latency: Lowest (~1ms)
- Availability: Highest
- Consistency: Weakest
- Use Case: Session data, caching

QUORUM (R=2, W=2, N=3):
- Latency: Medium (~5ms)
- Availability: Medium
- Consistency: Strong (R+W>N)
- Use Case: User profiles, configurations

ALL (R=3, W=3, N=3):
- Latency: Highest (~10ms)
- Availability: Lowest
- Consistency: Strongest
- Use Case: Financial transactions, critical data

LSM-Tree (Log-Structured Merge-Tree):
✅ Pros:
  - Excellent write performance (sequential writes)
  - Good compression ratios
  - Efficient for write-heavy workloads
  - Lower write amplification

❌ Cons:
  - Read amplification (check multiple levels)
  - Compaction overhead
  - Variable read performance
  - Higher space amplification

Used by: Cassandra, RocksDB, LevelDB

B-Tree:
✅ Pros:
  - Predictable read performance
  - Lower read amplification
  - In-place updates
  - Better for read-heavy workloads

❌ Cons:
  - Random writes (slower)
  - Write amplification
  - Fragmentation over time
  - Requires periodic rebalancing

Used by: MySQL, PostgreSQL, MongoDB

Synchronous WAL (fsync on every write):
✅ Pros:
  - Zero data loss on crash
  - Strong durability guarantees
  - Immediate persistence

❌ Cons:
  - High write latency (~5-10ms)
  - Lower throughput
  - Disk I/O bottleneck

Asynchronous WAL (fsync every 100ms):
✅ Pros:
  - Low write latency (~1ms)
  - Higher throughput
  - Better performance

❌ Cons:
  - Potential data loss (last 100ms)
  - Weaker durability
  - Recovery complexity

Master-Slave Replication:
✅ Pros:
  - Simple consistency model
  - Strong consistency possible
  - Easier to reason about
  - Centralized coordination

❌ Cons:
  - Single point of failure (master)
  - Write bottleneck at master
  - Failover complexity
  - Replication lag

Example: Redis, MySQL

Masterless (Peer-to-Peer):
✅ Pros:
  - No single point of failure
  - Better write scalability
  - Symmetric architecture
  - Easier horizontal scaling

❌ Cons:
  - Conflict resolution needed
  - Complex consistency model
  - Higher coordination overhead
  - Eventual consistency

Example: Cassandra, DynamoDB, Riak

Synchronous Replication:
✅ Pros:
  - Zero data loss
  - Strong consistency
  - Immediate failover
  - No replication lag

❌ Cons:
  - Higher write latency
  - Lower availability
  - Network dependency
  - Reduced throughput

Asynchronous Replication:
✅ Pros:
  - Low write latency
  - Higher availability
  - Better performance
  - Network resilience

❌ Cons:
  - Potential data loss
  - Replication lag
  - Eventual consistency
  - Complex failover

Hash Partitioning (Consistent Hashing):
✅ Pros:
  - Even data distribution
  - No hotspots
  - Simple implementation
  - Automatic load balancing

❌ Cons:
  - No range queries
  - Random data access
  - Difficult to scan
  - No locality

Example: Cassandra, DynamoDB

Range Partitioning:
✅ Pros:
  - Efficient range queries
  - Data locality
  - Sequential scans
  - Ordered data

❌ Cons:
  - Potential hotspots
  - Manual rebalancing
  - Uneven distribution
  - Complex management

Example: HBase, BigTable

Many Virtual Nodes (256+ per node):
✅ Pros:
  - Even data distribution
  - Fast rebalancing
  - Flexible scaling
  - Better fault tolerance

❌ Cons:
  - Higher metadata overhead
  - More complex routing
  - Increased memory usage
  - More network connections

Few Virtual Nodes (1-16 per node):
✅ Pros:
  - Lower overhead
  - Simpler routing
  - Less metadata
  - Fewer connections

❌ Cons:
  - Uneven distribution
  - Slower rebalancing
  - Less flexible
  - Hotspot risk

Cache-Aside (Lazy Loading):
✅ Pros:
  - Only cache what's needed
  - Lower memory usage
  - Resilient to cache failures
  - Simple implementation

❌ Cons:
  - Cache miss penalty
  - Stale data possible
  - Cache warming needed
  - Inconsistency window

Write-Through:
✅ Pros:
  - Always consistent
  - No cache misses
  - Predictable performance
  - Simpler consistency

❌ Cons:
  - Higher write latency
  - Wasted cache space
  - Write amplification
  - Cache dependency

TTL (Time-To-Live):
✅ Pros:
  - Predictable expiration
  - Good for time-sensitive data
  - Automatic cleanup
  - Simple implementation

❌ Cons:
  - May evict hot data
  - Requires TTL management
  - Memory waste
  - Thundering herd risk

LRU (Least Recently Used):
✅ Pros:
  - Keeps hot data
  - Adaptive to access patterns
  - Better hit rate
  - No TTL management

❌ Cons:
  - Unpredictable eviction
  - Overhead for tracking
  - May keep stale data
  - Complex implementation

Last-Write-Wins (LWW):
✅ Pros:
  - Simple implementation
  - Low overhead
  - Fast resolution
  - No client complexity

❌ Cons:
  - Data loss possible
  - Clock skew issues
  - No causality tracking
  - Arbitrary winner

Vector Clocks:
✅ Pros:
  - Preserves causality
  - No data loss
  - Detects conflicts
  - Application control

❌ Cons:
  - Complex implementation
  - Higher overhead
  - Version explosion
  - Client complexity

Redis:
Architecture: Single-threaded, in-memory
Consistency: Strong (single master)
Availability: Master-slave replication
Partition Tolerance: Limited
Best For: Caching, sessions, real-time analytics
Trade-off: Limited by single-node memory

Cassandra:
Architecture: Masterless, distributed
Consistency: Tunable (eventual to strong)
Availability: High (peer-to-peer)
Partition Tolerance: Excellent
Best For: Time-series, IoT, high write throughput
Trade-off: Complex operations, eventual consistency

DynamoDB:
Architecture: Managed, distributed
Consistency: Tunable (eventual or strong)
Availability: High (multi-AZ)
Partition Tolerance: Excellent
Best For: Serverless, auto-scaling, managed service
Trade-off: Vendor lock-in, cost at scale

Embedded Key-Value Store (RocksDB, LevelDB):
✅ Pros:
  - Low latency (<100μs)
  - No network overhead
  - Simple deployment
  - Lower cost

❌ Cons:
  - Limited by single machine
  - No built-in replication
  - Manual sharding needed
  - Scaling complexity

Distributed Key-Value Store (Cassandra, DynamoDB):
✅ Pros:
  - Horizontal scaling
  - Built-in replication
  - High availability
  - Fault tolerance

❌ Cons:
  - Higher latency (network)
  - Complex operations
  - Higher cost
  - Operational overhead

Simple Key-Value:
✅ Pros:
  - Simple implementation
  - Predictable performance
  - Easy to scale
  - Lower overhead

❌ Cons:
  - Limited functionality
  - Application complexity
  - Multiple round trips
  - No atomic operations

Rich Data Types (Lists, Sets, Hashes):
✅ Pros:
  - Atomic operations
  - Fewer round trips
  - Server-side logic
  - Better performance

❌ Cons:
  - Complex implementation
  - Higher memory usage
  - Scaling challenges
  - Consistency complexity

Snappy:
- Compression Ratio: 2-3x
- Speed: Very fast (250MB/s)
- CPU: Low overhead
- Use Case: Hot data, real-time

LZ4:
- Compression Ratio: 2-3x
- Speed: Extremely fast (400MB/s)
- CPU: Very low overhead
- Use Case: High-throughput systems

Zstandard:
- Compression Ratio: 3-5x
- Speed: Fast (100MB/s)
- CPU: Medium overhead
- Use Case: Cold data, storage optimization

No Compression:
- Compression Ratio: 1x
- Speed: N/A
- CPU: Zero overhead
- Use Case: Small values, CPU-constrained

Detailed Monitoring:
✅ Pros:
  - Deep insights
  - Better debugging
  - Proactive alerts
  - Performance optimization

❌ Cons:
  - Higher overhead (1-5%)
  - More storage
  - Complex setup
  - Alert fatigue

Lightweight Monitoring:
✅ Pros:
  - Low overhead (<1%)
  - Simple setup
  - Lower cost
  - Essential metrics only

❌ Cons:
  - Limited insights
  - Reactive debugging
  - Missed issues
  - Harder optimization

High-Performance (NVMe SSD, 10Gbps network):
- Cost: $500/node/month
- Latency: <1ms
- Throughput: 100K ops/sec
- Use Case: Latency-sensitive applications

Balanced (SATA SSD, 1Gbps network):
- Cost: $200/node/month
- Latency: <5ms
- Throughput: 50K ops/sec
- Use Case: General-purpose applications

Cost-Optimized (HDD, 100Mbps network):
- Cost: $50/node/month
- Latency: <50ms
- Throughput: 10K ops/sec
- Use Case: Archival, cold storage

Use Strong Consistency When:
- Financial transactions
- Inventory management
- Critical user data
- Regulatory compliance

Use Eventual Consistency When:
- Social media feeds
- Analytics data
- Caching layers
- High availability required

Use LSM-Tree When:
- Write-heavy workloads
- Time-series data
- Log aggregation
- High compression needed

Use B-Tree When:
- Read-heavy workloads
- Range queries common
- Predictable performance needed
- In-place updates required

Use Masterless When:
- High availability critical
- Global distribution needed
- No single point of failure
- Write scalability important

Use Master-Slave When:
- Strong consistency required
- Simpler operations
- Read-heavy workloads
- Centralized control needed