System Design Scaling Cheat Sheet

[JiantaoFu]

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

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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)

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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.

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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.

4. Auto-Scaling and Elastic Infrastructure

5. Global Distribution & Multi-Region Deployment