Why Consistent Hashing is not suitable for Databases in Real World
Consistent hashing, while a powerful method for distributing data across nodes in distributed systems, is not always suitable for databases due to the following limitations:
1. Non-uniform Data Distribution
Issue: Consistent hashing assumes uniform key distribution. However, real-world database workloads often involve skewed distributions:
Certain keys (e.g., user IDs or popular tags) are accessed more frequently.
This can lead to hotspots where a few nodes handle a disproportionate load.
Impact: Hotspots degrade the overall performance of the database, leading to uneven utilization of resources.
2. Inefficient Range Queries
Issue: Databases often need to support range queries (e.g., "Get all users with IDs between 1000 and 2000").
Consistent hashing distributes keys arbitrarily across nodes, making it inefficient to process range queries.
Such queries may require fetching data from multiple nodes and performing expensive aggregations.
Impact: This increases query latency and reduces efficiency.
3. Rebalancing Overhead
Issue: When nodes are added or removed in a consistent hashing setup, only a portion of keys are remapped.
While this minimizes rebalancing, the remapped keys still need to be moved, and updating their metadata in a database can be costly.
If a database has strict consistency requirements, rebalancing can block operations until the system stabilizes.
Impact: This introduces downtime or degraded performance during scaling.
4. Lack of Global Indexing
Issue: Many databases rely on indexes to optimize query performance.
Consistent hashing fragments data across nodes without considering global indexing.
This makes operations like full-text search, secondary indexing, or transactional queries across multiple keys more complex.
Impact: Reduced query efficiency and increased complexity in maintaining indexes.
5. High Coordination Costs
Issue: Databases often require multi-key operations, like transactions or joins.
Consistent hashing scatters related keys across nodes, necessitating coordination among multiple nodes for such operations.
This leads to increased network latency and potential contention.
Impact: Decreased transactional throughput and higher operational costs.
6. Inefficient Data Locality
Issue: Many databases prioritize data locality for performance (e.g., colocating related data for faster access).
Consistent hashing ignores data locality since it prioritizes distribution, which can lead to fragmentation of related data.
Impact: Reduced query performance due to increased cross-node communication.
7. Schema Changes and Sharding Logic
Issue: Consistent hashing ties data placement to the hash of the key. If the sharding logic changes (e.g., introducing a composite key or modifying the hash function), it requires repartitioning the entire dataset.
Impact: This makes schema evolution or migrations more challenging in databases.
When is Consistent Hashing Suitable?
Caching Systems: Ideal for distributed caches like Redis or Memcached, where data retrieval doesn't require range queries or global indexing.
Decentralized Systems: Suitable for systems like distributed hash tables (e.g., Apache Cassandra) where write and read operations are primarily by key.
Alternatives for Databases
Range-based Sharding: Distributes data based on key ranges, supporting efficient range queries.
Hash-based Partitioning with Modulo: Ensures deterministic placement but requires full rebalancing during scaling.
Custom Partitioning Schemes: Designed to accommodate workload patterns, often combining hash-based and range-based strategies.