Consistency models (strong, eventual) in HLD - Scalability & System Analysis

Scalability Analysis - Consistency models (strong, eventual)

Growth Table: Consistency Models at Different Scales

Users	Strong Consistency	Eventual Consistency
100 users	Single primary DB node; synchronous replication; low latency acceptable	Simple replication; minor delays in data sync; easy to maintain
10,000 users	Primary with multiple synchronous replicas; increased write latency; network delays visible	Asynchronous replication; replicas may lag; better write throughput
1,000,000 users	High write latency; complex coordination; risk of bottlenecks on primary node	Multiple data centers; replicas updated asynchronously; stale reads possible but acceptable
100,000,000 users	Very high coordination overhead; write throughput limited; complex consensus protocols needed	Highly distributed; eventual convergence guaranteed; system favors availability and partition tolerance

First Bottleneck

For strong consistency, the first bottleneck is the write latency and coordination overhead on the primary database node. As user count grows, synchronous replication and consensus protocols slow down writes.

For eventual consistency, the bottleneck is data synchronization delays across replicas, which can cause stale reads but does not block writes.

Scaling Solutions

• Strong Consistency: Use distributed consensus algorithms (e.g., Paxos, Raft) optimized for latency; scale vertically with powerful nodes; limit write contention by sharding data.
• Eventual Consistency: Use asynchronous replication; deploy data centers closer to users; implement conflict resolution strategies; use caching to serve stale but fast data.
• Hybrid approaches: Use strong consistency for critical data and eventual consistency for less critical data to balance performance and correctness.

Back-of-Envelope Cost Analysis

Assuming 1 million users with 10 requests per second each:

• Total requests: 10 million QPS.
• Strong consistency DB can handle ~5,000 QPS per node; would need ~2,000 nodes or heavy sharding.
• Eventual consistency allows writes to be distributed; each node handles async replication; fewer nodes needed.
• Network bandwidth: 10 million QPS * 1 KB/request = ~10 GB/s; requires high bandwidth infrastructure.
• Storage: depends on data size and retention; replication increases storage needs by factor of replicas.

Interview Tip

Start by defining the consistency models clearly. Then discuss trade-offs between latency, availability, and correctness. Use real-world examples like banking (strong consistency) vs social media feeds (eventual consistency). Finally, explain how scaling affects each model and what solutions you would apply.

Self Check

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

Answer: Introduce read replicas to offload read traffic and implement sharding to distribute write load. Also, consider asynchronous replication if some delay is acceptable to improve write throughput.

Key Result

Strong consistency systems face write latency and coordination bottlenecks as users grow, requiring sharding and consensus optimization; eventual consistency scales better by allowing asynchronous replication and accepting stale reads.

Practice

1. Which statement best describes strong consistency in distributed systems?

easy

A. All users see the same data immediately after an update.

B. Data updates are delayed and may differ temporarily between users.

C. Data is never synchronized between nodes.

D. Users see different versions of data permanently.

Solution

1. Step 1: Understand strong consistency definition

   Strong consistency means every read returns the most recent write, so all users see the same data instantly.

2. Step 2: Compare options with definition

   All users see the same data immediately after an update. matches this definition exactly, while others describe delayed or inconsistent views.

3. Final Answer:

   All users see the same data immediately after an update. -> Option A

4. Quick Check:

   Strong consistency = immediate data sync [OK]

Hint: Strong consistency means instant data visibility everywhere [OK]

Common Mistakes:

• Confusing strong consistency with eventual consistency
• Thinking data can be permanently inconsistent
• Assuming delays are allowed in strong consistency

2. Which of the following is a correct characteristic of eventual consistency?

easy

A. No synchronization happens between nodes.

B. All nodes update simultaneously with no delay.

C. Data is always consistent after every write operation.

D. Data updates propagate over time and become consistent eventually.

Solution

1. Step 1: Recall eventual consistency meaning

   Eventual consistency means updates may take time to reach all nodes, but eventually all nodes will have the same data.

2. Step 2: Match options to definition

   Data updates propagate over time and become consistent eventually. correctly states that data updates propagate over time and become consistent eventually.

3. Final Answer:

   Data updates propagate over time and become consistent eventually. -> Option D

4. Quick Check:

   Eventual consistency = delayed but eventual sync [OK]

Hint: Eventual consistency means data sync happens over time [OK]

Common Mistakes:

• Assuming immediate synchronization in eventual consistency
• Confusing no synchronization with eventual consistency
• Thinking eventual consistency means permanent inconsistency

3. Consider a distributed database using eventual consistency. If a user updates a record on one node, what is the expected behavior immediately after the update?

medium

A. Other nodes may temporarily show the old record until synchronization completes.

B. All other nodes instantly reflect the updated record.

C. The update is rejected until all nodes confirm availability.

D. The record is deleted from all nodes except the updated one.

Solution

1. Step 1: Analyze eventual consistency update behavior

   In eventual consistency, updates propagate asynchronously, so other nodes may temporarily have stale data.

2. Step 2: Evaluate options against this behavior

   Other nodes may temporarily show the old record until synchronization completes. correctly states that other nodes may show old data until synchronization finishes.

3. Final Answer:

   Other nodes may temporarily show the old record until synchronization completes. -> Option A

4. Quick Check:

   Eventual consistency = temporary stale reads [OK]

Hint: Eventual consistency allows temporary stale data [OK]

Common Mistakes:

• Expecting instant update on all nodes
• Confusing update rejection with consistency model
• Assuming data deletion on other nodes

4. A system claims to provide strong consistency but users report seeing outdated data after updates. What is the most likely cause?

medium

A. The system uses synchronous replication correctly.

B. The system is actually using eventual consistency under the hood.

C. Users are refreshing their data too often.

D. The network is too fast causing data to update too quickly.

Solution

1. Step 1: Understand strong consistency guarantees

   Strong consistency guarantees all users see the latest data immediately after update.

2. Step 2: Analyze why outdated data appears

   If users see outdated data, the system likely does not enforce strong consistency, possibly using eventual consistency instead.

3. Final Answer:

   The system is actually using eventual consistency under the hood. -> Option B

4. Quick Check:

   Outdated data means no strong consistency [OK]

Hint: Outdated data means strong consistency is not enforced [OK]

Common Mistakes:

• Blaming network speed for outdated data
• Assuming user refresh frequency causes inconsistency
• Confusing synchronous replication with eventual consistency

5. You are designing a global e-commerce platform. Which consistency model should you choose for the inventory stock count to ensure customers never buy more items than available, and why?

hard

A. Eventual consistency, because it improves performance and availability.

B. No consistency model is needed for inventory data.

C. Strong consistency, because it guarantees all users see the latest stock count immediately.

D. Use eventual consistency with no synchronization.

Solution

1. Step 1: Identify critical data requirements

   Inventory stock count must be accurate to prevent overselling, requiring immediate data correctness.

2. Step 2: Choose consistency model based on requirements

   Strong consistency ensures all users see the latest stock count instantly, preventing overselling.

3. Final Answer:

   Strong consistency, because it guarantees all users see the latest stock count immediately. -> Option C

4. Quick Check:

   Critical data needs strong consistency [OK]

Hint: Critical data needs strong consistency for accuracy [OK]

Common Mistakes:

• Choosing eventual consistency for critical stock data
• Ignoring consistency needs for inventory
• Assuming no consistency is acceptable