Data Partitioning Rebalancing and Dynamic Scaling in Distributed Systems

Data Partitioning Rebalancing and Dynamic Scaling in Distributed Systems

1. Problem Background

In distributed storage systems, data is typically partitioned (or sharded) and distributed across multiple nodes. However, as the business grows, it may become necessary to scale out (e.g., add machines) or scale in (e.g., reduce resource costs). In such scenarios, the challenge of rebalancing the data distribution across the new set of nodes while ensuring service availability and data consistency is known as the Partition Rebalancing problem.

2. Core Challenges

The rebalancing process must meet the following requirements:

  1. Load Balancing: Data and requests should be evenly distributed across all nodes.
  2. Minimize Data Migration: Avoid unnecessary network bandwidth and I/O consumption.
  3. High Service Availability: Minimize disruption to read and write operations during rebalancing.
  4. Consistency Guarantee: Prevent data loss or duplication.

3. Common Solutions and Evolution Steps

3.1 Simple Approach: The Pitfall of Hash Modulo

  • Method: Hash the data key and then apply modulo with the number of nodes (hash(key) mod N) to determine data placement.
  • Problem: When the number of nodes N changes to N+1, the modulo result changes for the vast majority of data, leading to near-total data migration.
  • Example: Assume N=3, key key1 hashes to 1 mod 3, stored on node 1. When N=4, the modulo result may become 2, requiring migration to node 2.

3.2 Improved Approach: Consistent Hashing

  • Principle: Organize the hash space into a ring, mapping both nodes and keys onto the ring. Each key belongs to the nearest node in the clockwise direction.
  • Advantage: When nodes are added or removed, only data from adjacent nodes is affected, reducing migration volume from O(M) to O(M/N) (where M is the total data volume).
  • Drawbacks:
    • Distribution may be uneven with a small number of nodes (solved by virtual nodes).
    • Manual data migration is still required for scaling, lacking automatic rebalancing.

3.3 Dynamic Rebalancing Strategy: Automated Migration

Step 1: Pre-sharding
  • Divide data into a fixed number of shards (e.g., 1024), where the number of shards is much larger than the number of nodes.
  • Nodes are responsible for multiple shards; rebalancing occurs at the shard level to avoid fine-grained key-value operations.
Step 2: Routing Metadata Management
  • Introduce a metadata server (or use Gossip protocol for synchronization) to record the mapping between shards and nodes.
  • Clients query metadata before read/write operations to locate the target node.
Step 3: Migration Process Control
  1. New Node Joins: The controller (e.g., a coordinator) selects a subset of shards to migrate from existing nodes to the new node.
  2. Dual Writes and Synchronization:
    • During migration, the old node continues to serve read/write requests but forwards new writes to the new node (or logs them).
    • Asynchronously replicate shard data to the new node until synchronization is complete.
  3. Metadata Switch: Update the routing table and notify clients of the new shard locations.
  4. Cleanup Old Data: Delete the shard from the old node after confirming successful migration.
Step 4: Preventing Dirty Reads and Write Loss
  • Use version control or lease mechanisms to ensure that during migration, the same shard cannot accept write requests from multiple nodes simultaneously.
  • Example: Mark the shard as "migrating" in the metadata; write requests must be sent to both old and new nodes until the switch is complete.

4. Advanced Optimization Techniques

  1. Traffic-Aware Rebalancing: Consider not only data volume but also request frequency and node load (CPU/network) for dynamic adjustments.
  2. Batch Parallel Migration: Migrate multiple shards simultaneously while limiting bandwidth usage to avoid impacting normal service.
  3. Consistency Guarantee: Combine Raft/Paxos to achieve multi-replica consistency at the shard level, switching replica roles before migration.

5. Real-World System Examples

  • Kafka: Uses the partition reassignment tool (kafka-reassign-partitions.sh) to migrate topic partitions, relying on ZooKeeper for coordination.
  • Elasticsearch: Automatic shard rebalancing controlled by parameters like cluster.routing.allocation.
  • Cassandra: Based on consistent hashing, supports virtual nodes, and uses nodetool repair for data synchronization after scaling.

6. Summary

Partition rebalancing is a core aspect of scalability in distributed systems. The key points are:

  • Use sharding abstraction to reduce migration granularity.
  • Decouple data location from storage nodes through metadata.
  • Ensure data consistency and service continuity during migration.