1. Database Selection by QPS (Read vs Write)
| QPS Range | Type of Database | Examples | Scaling Techniques |
|---|---|---|---|
| Low (<1,000) | Relational / Document DB | MySQL, PostgreSQL, MongoDB | Vertical scaling, Caching |
| Medium (1k–10k) | Relational (tuned) / Document DB | MySQL + Replicas, DynamoDB | Read replicas, Caching, Indexing |
| High (10k–1M) | Distributed NoSQL | Cassandra, DynamoDB, Bigtable | Sharding, Horizontal scaling |
| Extreme (>1M) | Distributed In-memory / Advanced DB | Redis, CockroachDB, Spanner | Sharding, Partitioning, CDNs |
2. Mainstream Database QPS Capacity (Read vs Write)
| Database Type | Database | Read QPS (Per Node) | Write QPS (Per Node) |
|---|---|---|---|
| Relational DB | MySQL, PostgreSQL | 2k–10k QPS (optimized for reads) | 500–2k QPS (write-optimized) |
| Document DB | MongoDB | 5k–20k QPS (tuned for reads) | 1k–5k QPS (write-heavy) |
| Wide-Column Store | Cassandra | 10k–50k QPS (cluster optimized) | 5k–20k QPS (write-optimized) |
| Key-Value Store | Redis | 100k–1M QPS (in-memory optimized) | 100k–1M QPS (write-intensive) |
| Time-Series DB | InfluxDB | 50k–500k QPS | 10k–50k QPS |
| Distributed SQL | CockroachDB | 10k–50k QPS | 2k–10k QPS |
| Search Engines | Elasticsearch | 1k–20k QPS (query-dependent) | 500–5k QPS (write-intense) |
3. Sharding and Scaling: When and Why
Key Indicators for Sharding:
| Condition | Indicators | Action |
|---|---|---|
| Data Volume | Storage exceeds capacity of a single node or disk. | Use sharding when storage exceeds ~500GB–1TB per node. |
| High Write Throughput | Write latency increases due to bottlenecks. | Shard by write-heavy keys (>5k–10k writes/sec). |
| Read/Write Latency | Latency exceeds acceptable thresholds. | Partition data, shard for high loads. |
| Data Hotspotting | Some partitions/shards receive disproportionate traffic. | Implement sharding to balance load across nodes. |
| Query Performance | Queries are slow due to large tables. | Use partitioning to improve query performance. |
4. Other Scaling Concepts to Consider
1. Load Balancing
| Concept | Description | Techniques |
|---|---|---|
| Load Balancing | Distribute incoming traffic across multiple servers to ensure reliability and prevent overload. | Round-robin, Least Connections, Weighted balancing using NGINX, HAProxy, AWS ELB. |
2. Fault Tolerance, CAP Theorem
| Concept | Description | Techniques |
|---|---|---|
| Fault Tolerance | Ensuring that the system remains operational even if some components fail. | Replication, Consensus algorithms (e.g., Paxos, Raft), Failover systems. |
| CAP Theorem | A distributed system can only guarantee two of the following: Consistency, Availability, or Partition Tolerance. | Choose between eventual consistency or strong consistency depending on system requirements. |
| Eventual Consistency | Allowing data to propagate slowly across nodes, often used in NoSQL databases. | Use CRDTs, Event sourcing, CQRS, Tunable consistency. |
| Data Replication | Duplication of data across nodes to ensure high availability and fault tolerance. | Use Master-Slave replication, Multi-region replication. |
3. Queues and Asynchronous Processing
| Concept | Description | Techniques |
|---|---|---|
| Asynchronous Processing | Offloading long-running tasks to background jobs for better system responsiveness. | Message queues like RabbitMQ, Kafka, Amazon SQS, Job schedulers. |
| Event-driven Architectures | Architecting systems to react to events in real-time. | Use Event-driven systems with publish-subscribe models. |