Designing a Scalable Distributed Cache System for 1 Billion Queries Per Minute

 

Designing a distributed cache system to handle 1 billion queries per minute for both read and write operations is a complex task. Here’s a high-level overview of how you might approach this:

1. Requirements Gathering

• Functional Requirements:
  • Read Data: Quickly retrieve data from the cache.
  • Write Data: Store data in the cache.
  • Eviction Policy: Automatically evict least recently/frequently used items.
  • Replication: Replicate data across multiple nodes for fault tolerance.
  • Consistency: Ensure data consistency across nodes.
  • Node Management: Add and remove cache nodes dynamically.
• Non-Functional Requirements:
  • Performance: Low latency for read and write operations.
  • Scalability: System should scale horizontally by adding more nodes.
  • Reliability: Ensure high availability and fault tolerance.
  • Durability: Persist data if required.
  • Security: Secure access to the cache system.

2. Capacity Estimation

• Traffic Estimate:
  • Read Traffic: Estimate the number of read requests per second.
  • Write Traffic: Estimate the number of write requests per second.
• Storage Estimate:
  • Data Size: Estimate the average size of each cache entry.
  • Total Data: Calculate the total amount of data to be stored in the cache.

3. High-Level Design

• Architecture:
  • Client Layer: Handles requests from users.
  • Load Balancer: Distributes incoming requests across cache nodes.
  • Cache Nodes: Multiple servers storing cached data.
  • Database: Persistent storage for data.
• Data Partitioning:
  • Use consistent hashing to distribute data across cache nodes.
• Replication:
  • Replicate data across multiple nodes to ensure fault tolerance.
• Eviction Policy:
  • Implement LRU (Least Recently Used) or LFU (Least Frequently Used) eviction policies.

4. Detailed Design

• Cache Write Strategy:
  • Write-Through: Data is written to the cache and the database simultaneously.
  • Write-Back: Data is written to the cache first and then to the database asynchronously.
• Cache Read Strategy:
  • Read-Through: Data is read from the cache, and if not found, it is fetched from the database and then cached.
• Consistency Models:
  • Strong Consistency: Ensures that all nodes have the same data at any given time.
  • Eventual Consistency: Ensures that all nodes will eventually have the same data, but not necessarily immediately.

5. Scalability and Fault Tolerance

• Horizontal Scaling: Add more cache nodes to handle increased load.
• Auto-Scaling: Automatically add or remove nodes based on traffic.
• Fault Tolerance: Use replication and data sharding to ensure data availability even if some nodes fail.

6. Monitoring and Maintenance

• Monitoring: Use tools to monitor cache performance, hit/miss ratios, and node health.
• Maintenance: Regularly update and maintain the cache system to ensure optimal performance.

Example Technologies

• Cache Solutions: Redis, Memcached.
• Load Balancers: NGINX, HAProxy.
• Monitoring Tools: Prometheus, Grafana.

This is a high-level overview, and each component can be further detailed based on specific requirements and constraints¹²³. 

Back of Envelops Calculations:

1. Traffic Estimate

• Total Queries: 1 billion queries per minute.
• Queries per Second (QPS):

  1,000,000,000 queries / 60 seconds ​= 16,666,667 QPS

• Read/Write Ratio: Assume 80% reads and 20% writes.
  • Read QPS:

    $$16,666,667\times 0.8=13,333,334\text{ reads per second}$$

  • Write QPS:

    $$16,666,667\times 0.2=3,333,333\text{ writes per second}$$

2. Storage Estimate

• Average Size of Each Cache Entry: Assume each entry is 1 KB.
• Total Data Stored: Assume the cache should store data for 1 hour.
  • Total Entries per Hour:

    $$16,666,667\text{ QPS }\times 3600\text{ seconds }=60,000,000,000\text{ entries}$$

  • Total Data Size:

    $$60,000,000,000\text{ entries}\times 1\text{ KB }=60,000,000,000\text{ KB }=60\text{ TB}$$

3. Node Estimation

• Cache Node Capacity: Assume each cache node can handle 100,000 QPS and store 1 TB of data.
• Number of Nodes for QPS:

  16,666,667 QPS ​/ 100,000 QPS/node = 167 nodes

• Number of Nodes for Storage:

  60 TB​ / 1 TB/node = 60 nodes

• Total Number of Nodes:

  $$\max (167,60)=167\text{ nodes}$$

4. Replication Factor

• Replication Factor: Assume a replication factor of 3 for fault tolerance.
• Total Nodes with Replication:

  $$167\text{ nodes }\times 3=501\text{ nodes}$$

Summary

• Total Queries per Second: 16,666,667 QPS.
• Read QPS: 13,333,334 reads per second.
• Write QPS: 3,333,333 writes per second.
• Total Data Stored: 60 TB.
• Total Cache Nodes Required: 501 nodes (with replication).

To estimate the RAM required for the distributed cache system, we need to consider the following factors:

1. Data Storage: The amount of data stored in the cache.
2. Overhead: Additional memory required for metadata, indexing, and other overheads.

Data Storage

From our previous calculation:

• Total Data Stored: 60 TB (60,000,000,000 KB).

Overhead

Assume an overhead of 10% for metadata and indexing.

Total Memory Requirement

• Total Memory for Data: 60 TB.
• Total Overhead:

  $$60\text{ TB}\times 0.10=6\text{ TB}$$

• Total RAM Required:

  $$60\text{ TB}+6\text{ TB}=66\text{ TB}$$

Per Node Memory Requirement

Assuming we have 501 nodes (with replication):

• RAM per Node:

  66 TB​ / 501 nodes ≈ 132 GB/node

Summary

• Total RAM Required: 66 TB.
• RAM per Node: Approximately 132 GB.

This is a simplified example, and actual capacity planning would need to consider additional factors like network latency, data consistency, and failover strategies.