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In system-design interviews, demonstrating the ability to choose the right database scaling strategy is crucial. Below are six key strategies to scale databases effectively:
1. Vertical Scaling (Scale-Up)
Upgrade server hardware (CPU, RAM, SSD).
✅ Best for: Moderate datasets requiring fast setup.
⚠ Trade-off: Expensive hardware limitations.
Linux Command to Check Hardware:
lscpu Check CPU info free -h Check RAM df -h Check disk space
2. Horizontal Scaling (Scale-Out)
Add more commodity servers to distribute load.
✅ Best for: Unlimited scalability.
⚠ Trade-off: Network complexity.
Example with MySQL Replication:
On Master Node CHANGE MASTER TO MASTER_HOST='slave_ip', MASTER_USER='replica_user', MASTER_PASSWORD='password'; START SLAVE; On Slave Node SHOW SLAVE STATUS\G; Check replication status
3. Sharding (Data Partitioning)
Split data by key (e.g., user ID, region).
✅ Best for: Overcoming single-node limits.
⚠ Trade-off: Complex queries.
MongoDB Sharding Example:
mongos --configdb config_server1,config_server2,config_server3
sh.addShard("shard1_replica_set/shard1_node1:27017")
4. Replication
Maintain copies across master/replica nodes.
✅ Best for: High availability & read scaling.
⚠ Trade-off: Consistency lag.
PostgreSQL Replication Setup:
On Primary ALTER SYSTEM SET wal_level = 'replica'; CREATE ROLE replica_user WITH REPLICATION LOGIN PASSWORD 'password'; On Replica pg_basebackup -h primary_ip -D /var/lib/postgresql/ -U replica_user -P -v -R
5. Caching (Redis/Memcached)
Layer cache between app and DB.
✅ Best for: Read-heavy workloads.
⚠ Trade-off: Stale data risk.
Redis CLI Commands:
redis-cli SET key "value" redis-cli GET key redis-cli KEYS List all keys
6. CQRS (Command Query Responsibility Segregation)
Separate write/read models.
✅ Best for: Complex domains.
⚠ Trade-off: Synchronization challenges.
You Should Know:
Monitoring & Optimization
- Check Replication Lag (MySQL):
SHOW REPLICA STATUS\G;
- Redis Cache Hit Ratio:
redis-cli INFO stats | grep keyspace_hits
- PostgreSQL Query Optimization:
EXPLAIN ANALYZE SELECT FROM users WHERE id = 1;
Cloud Scaling (AWS Example)
aws rds modify-db-instance --db-instance-identifier mydb --db-instance-class db.m5.large Vertical scaling aws dynamodb update-table --table-name MyTable --provisioned-throughput ReadCapacityUnits=100,WriteCapacityUnits=100
What Undercode Say:
Database scaling is a balance between performance, cost, and complexity.
– For startups: Vertical scaling is quick but costly.
– For enterprises: Sharding + Caching ensures resilience.
– For real-time apps: Replication + CQRS minimizes latency.
Key Linux Commands for DB Admins:
dmesg | grep -i error Check system errors iotop -o Monitor disk I/O htop Real-time process monitoring netstat -tuln Check open ports
Expected Output:
A scalable, high-performance database architecture tailored to workload needs.
Prediction:
Future databases will integrate AI-driven auto-scaling, reducing manual tuning while optimizing costs dynamically.
Relevant URL:
Grokking the System Design Interview
References:
Reported By: Arslanahmad Systemdesign – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
