Simplify debts algorithm in LLD - Scalability & System Analysis

Scalability Analysis - Simplify debts algorithm

Growth Table: Simplify Debts Algorithm

Users	Debts Records	System Changes
100	~500 debts	Single server, in-memory processing, simple graph algorithm
10,000	~50,000 debts	Use database indexing, batch processing, caching intermediate results
1,000,000	~5,000,000 debts	Distributed processing, sharded database, asynchronous job queues
100,000,000	~500,000,000 debts	Microservices, graph partitioning, heavy caching, eventual consistency

First Bottleneck

The first bottleneck is the database when the number of debt records grows beyond tens of thousands. Querying and updating debts to simplify them involves complex joins and graph traversals that slow down the database.

Scaling Solutions

• Database Optimization: Add indexes on debtor and creditor fields to speed queries.
• Caching: Cache simplified debt results for frequent queries to reduce database load.
• Batch Processing: Run debt simplification as background jobs to avoid blocking user requests.
• Sharding: Partition debts by user groups or regions to distribute load across multiple databases.
• Horizontal Scaling: Add more application servers behind a load balancer to handle increased traffic.
• Graph Partitioning: Split the debt graph into smaller subgraphs to simplify computations in parallel.

Back-of-Envelope Cost Analysis

Assuming 1 million users with an average of 5 debts each, total debts = 5 million.

• Database QPS: If each user triggers 1 query per minute, total QPS = ~16,700. A single DB handles ~10,000 QPS, so need ~2 read replicas or sharding.
• Storage: Each debt record ~200 bytes, total storage ~1 GB.
• Network Bandwidth: If each query/response ~1 KB, total bandwidth ~17 MB/s, manageable with 1 Gbps network.

Interview Tip

Start by explaining the data size and how the debt graph grows. Identify the database as the first bottleneck due to complex queries. Then discuss caching and batch processing to reduce load. Finally, mention sharding and horizontal scaling for very large scale. Always justify each step with clear reasoning.

Self Check

Your database handles 1000 QPS. Traffic grows 10x to 10,000 QPS. What do you do first?

Answer: Add read replicas and implement caching to reduce direct database queries before considering more complex solutions.

Key Result

The database is the first bottleneck as debts grow; scaling requires caching, batch jobs, and sharding to handle large debt graphs efficiently.