Key-Value Store - Problem Statement

Overview

Design a distributed key-value store similar to Redis, DynamoDB, or Cassandra that provides fast, scalable, and highly available data storage with configurable consistency levels. The system should support billions of operations per day while maintaining low latency and high reliability.

Functional Requirements

Core Storage Operations

PUT Operation: Store key-value pairs with optional TTL (time-to-live)
GET Operation: Retrieve values by key with sub-millisecond latency
DELETE Operation: Remove key-value pairs from the store
UPDATE Operation: Modify existing values atomically
BATCH Operations: Support batch reads and writes for efficiency
SCAN Operation: Iterate through keys with prefix matching

Data Types Support

Strings: Basic text and binary data storage
Numbers: Integer and floating-point values with atomic increment/decrement
Lists: Ordered collections with push/pop operations
Sets: Unordered unique collections with set operations
Hashes: Nested key-value structures (maps/dictionaries)
Sorted Sets: Ordered sets with score-based ranking

Advanced Features

TTL Management: Automatic expiration of keys after specified time
Conditional Operations: Compare-and-swap (CAS) for optimistic locking
Transactions: Multi-key atomic operations with ACID guarantees
Secondary Indexes: Query by non-primary key attributes
Range Queries: Retrieve keys within a specified range
Pub/Sub: Real-time message broadcasting to subscribers

Consistency Models

Strong Consistency: Linearizable reads and writes (CP system)
Eventual Consistency: High availability with eventual convergence (AP system)
Causal Consistency: Preserve cause-effect relationships
Read-Your-Writes: Guarantee users see their own updates
Monotonic Reads: Never see older data after reading newer data
Configurable Quorum: Tunable consistency levels per operation

Replication and Durability

Multi-Region Replication: Asynchronous cross-region data replication
Synchronous Replication: Within-region synchronous replication
Automatic Failover: Promote replicas on primary failure
Data Durability: Persist data to disk with configurable fsync
Backup and Restore: Point-in-time recovery and snapshots
Conflict Resolution: Last-write-wins, vector clocks, or custom resolvers

Non-Functional Requirements

Performance Requirements

Read Latency: <1ms for 99th percentile single-key reads
Write Latency: <5ms for 99th percentile single-key writes
Throughput: Support 1M+ operations per second per node
Batch Operations: 10x throughput improvement for batch requests
Connection Handling: Support 10,000+ concurrent client connections
Memory Efficiency: <10% overhead for metadata and indexing

Scalability Requirements

Horizontal Scaling: Add nodes without downtime or data loss
Data Partitioning: Distribute data across 1000+ nodes
Storage Capacity: Support petabytes of data across cluster
Hot Key Handling: Prevent performance degradation from popular keys
Auto-Scaling: Automatically add/remove nodes based on load
Geographic Distribution: Deploy across multiple regions and availability zones

Reliability Requirements

System Uptime: 99.99% availability (52 minutes downtime per year)
Data Durability: 99.999999999% (11 9's) durability
Replication Factor: Minimum 3 replicas for critical data
Disaster Recovery: <1 hour RTO, <5 minutes RPO
Graceful Degradation: Maintain read availability during network partitions
Zero-Downtime Operations: Rolling upgrades and maintenance

Consistency and Isolation

Configurable Consistency: Choose between strong and eventual per operation
Quorum Reads/Writes: R + W > N for strong consistency
Conflict Detection: Vector clocks or version vectors
Conflict Resolution: Automatic or application-defined strategies
Isolation Levels: Read committed, snapshot isolation
Linearizability: Optional strict ordering for critical operations

Real-time Constraints

Latency Requirements

Local Reads: <500μs for in-memory cached data
Disk Reads: <5ms for data on local SSD
Cross-AZ Reads: <10ms for data in same region
Cross-Region Reads: <100ms for global replication
Write Acknowledgment: <10ms for synchronous replication
Async Replication: <1 second for cross-region propagation

Throughput Requirements

Read Operations: 100K+ reads per second per node
Write Operations: 50K+ writes per second per node
Mixed Workload: 70% reads, 30% writes sustained
Peak Load: Handle 3x normal load during traffic spikes
Batch Processing: 1M+ operations per second in batch mode
Scan Operations: 10K+ keys per second for range scans

Resource Constraints

Memory Usage: 80% of available RAM for data caching
Disk I/O: Optimize for SSD with sequential writes
Network Bandwidth: <100MB/s per node for replication
CPU Utilization: <70% average CPU usage under normal load
Connection Overhead: <1KB memory per client connection
Compaction Impact: <10% performance degradation during compaction

Edge Cases and Constraints

Network Partitions

Split-Brain Prevention: Use quorum-based decisions
Partition Tolerance: Continue serving reads during partitions
Partition Recovery: Automatic reconciliation after healing
Conflict Resolution: Merge divergent replicas using vector clocks
Hinted Handoff: Store writes for unavailable nodes
Read Repair: Fix inconsistencies during read operations

Data Consistency Challenges

Concurrent Writes: Handle simultaneous updates to same key
Clock Skew: Use logical clocks instead of wall-clock time
Stale Reads: Detect and handle outdated replica reads
Write Conflicts: Resolve conflicts using timestamps or versions
Tombstone Management: Handle deleted keys in distributed system
Version Explosion: Limit version history to prevent bloat

Operational Edge Cases

Node Failures: Automatic detection and failover within seconds
Cascading Failures: Circuit breakers to prevent cascade
Data Corruption: Checksums and integrity verification
Disk Failures: Rebuild data from replicas automatically
Memory Pressure: Evict least-recently-used data gracefully
Slow Nodes: Detect and route around slow replicas

Client Behavior

Connection Storms: Handle sudden connection spikes
Large Values: Support values up to 1MB with warnings
Hot Keys: Replicate popular keys to multiple nodes
Key Expiration: Lazy deletion with background cleanup
Retry Storms: Exponential backoff and jitter
Client Failures: Timeout and cleanup abandoned connections

Success Metrics

Performance Metrics

P50 Read Latency: <500μs
P99 Read Latency: <2ms
P50 Write Latency: <2ms
P99 Write Latency: <10ms
Throughput: 1M+ ops/sec per node
Cache Hit Rate: >95% for read operations

Reliability Metrics

Availability: 99.99% uptime
Data Durability: Zero data loss events
Replication Lag: <100ms for cross-AZ replication
Failover Time: <30 seconds for automatic failover
Recovery Time: <5 minutes for node recovery
Backup Success Rate: 100% successful daily backups

Scalability Metrics

Horizontal Scaling: Add nodes in <5 minutes
Rebalancing Time: <1 hour for cluster rebalancing
Storage Efficiency: >90% disk space utilization
Network Efficiency: <10% overhead for replication
Connection Scalability: 10K+ concurrent connections per node
Query Performance: Consistent latency regardless of data size

Operational Metrics

Deployment Frequency: Zero-downtime rolling updates
Mean Time to Recovery: <15 minutes for incidents
False Positive Alerts: <5% of total alerts
Monitoring Coverage: 100% of critical metrics tracked
Incident Response: <5 minutes to acknowledge critical alerts
Cost Efficiency: <$0.10 per million operations

This problem statement establishes the foundation for designing a production-grade distributed key-value store that can serve as a critical infrastructure component for large-scale applications.