Introduction
In modern distributed systems, key-value stores are fundamental for high-performance applications requiring fast read/write operations. Designing a distributed key-value database involves balancing durability, availability, performance, and consistency while ensuring scalability.
This blog explores the core principles of a strongly consistent distributed key-value store, covering:
Architecture & Components
Consistency Models & Replication Strategies
Conflict Resolution & Fault Tolerance
Scalability & Performance Optimizations
1. Key Characteristics & Priorities
A well-designed distributed key-value store prioritizes:
Durability – Data must never be lost once written.
Availability – The system should remain operational despite failures.
Performance – Low-latency reads/writes are essential but secondary to durability and availability.
"If you lose customer data, you won’t be in business for long. It’s better to return data slowly than not at all."
Key Takeaways:
✔ Durability is non-negotiable—data loss is catastrophic.
✔ Availability trumps performance—slow responses are better than downtime.
✔ Security is assumed (e.g., client-side encryption) but not the focus here.
2. Strong Consistency Model
Unlike eventually consistent systems (e.g., Cassandra), this design enforces strong consistency:
A read after a successful write will always reflect the latest data.
ACID compliance is partial—Atomicity and Isolation are not guaranteed at row/table level.
Key Takeaways:
✔ Read-after-write consistency ensures predictable behavior.
✔ No full ACID support—trade-offs are made for scalability.
✔ Last-write-wins (LWW) conflict resolution via a sequencer.
3. Core Data Structure: Key-Value with Sequencer
Each record consists of:
Key (unique identifier)
Value (associated data)
16-byte Sequencer (monotonically increasing for conflict resolution)
"The last write wins, and the sequencer determines which write is the latest."
Key Takeaways:
✔ Sequencer resolves conflicts in concurrent writes.
✔ Simple schema enables fast lookups and horizontal scaling.
4. Distributed System Architecture
The system comprises several key components:
A. Load Balancer
Distributes incoming requests across Request Managers.
B. Request Manager
Routes requests to the correct Replication Group using metadata.
Maintains an in-memory metadata cache for efficiency.
C. Metadata Manager
Stores table-to-Replication-Group mappings.
Handles leader election (must be strongly consistent).
High read, low write workload.
D. Replication Group (RG)
Leader-Follower model (odd number of nodes for quorum).
All writes go through the leader.
Followers replicate data for redundancy.
E. Controller (Scheduler)
Monitors "hot" tables (high traffic/large size).
Splits tables across multiple RGs for scalability.
5. Replication & Consistency Mechanisms
Write Process (Strong Consistency Guarantee)
Client sends a PUT request.
Leader appends data to an append-only log.
Followers replicate the log entry.
Write succeeds only when a majority (quorum) acknowledge it.
Read Process
Reads can be served by leader or followers (configurable).
Followers may lag but strong consistency ensures latest data is returned.
"A PUT is acknowledged only when a majority of nodes (including leader) confirm it."
Key Takeaways:
✔ Quorum writes ensure durability and consistency.
✔ Append-only log enables efficient sequential writes.
✔ B+ Tree or LSM Tree indexes speed up reads.
6. Handling Failures & Edge Cases
| Failure Scenario | Resolution Mechanism |
|---|---|
| Split Brain (Two Leaders) | Quorum voting ensures only one leader is valid. |
| Leader Failure | New leader elected via majority consensus. |
| Network Partition | Outdated Request Managers refresh metadata. |
| Node Crash Before Indexing | Data recovered from append-only log. |
Key Takeaways:
✔ Consistency > Availability in conflict scenarios.
✔ Majority quorum prevents split-brain issues.
7. Scalability & Performance Optimizations
A. Handling "Hot" Tables
Controller detects large/high-traffic tables.
Splits them into smaller ranges across new RGs.
B. Data Storage & Indexing
Append-only log (fast sequential writes).
B+ Tree / LSM Tree (efficient indexing for reads).
C. Estimated Scalability
Petabyte-scale with sufficient RGs.
Key-value size limit: ~1MB (optimized for small records).
Key Takeaways:
✔ Automatic table splitting prevents bottlenecks.
✔ Efficient indexing balances read/write performance.
Conclusion
Designing a distributed key-value store requires careful trade-offs between consistency, availability, and performance. This system prioritizes:
Strong consistency via quorum writes and sequencers.
Fault tolerance through leader-follower replication.
Scalability via automatic table splitting.
By leveraging append-only logs, B+ trees, and a robust metadata layer, this architecture ensures durability, high availability, and efficient scaling.
No comments:
Post a Comment