Replication for High Availability
Replication keeps copies of (shards of) the vector index on multiple nodes so that if one fails, others can serve reads and optionally accept writes. This provides high availability (HA) and often load balancing for read-heavy workloads. Replication can be leader-based (primary-replica) or leaderless; consistency levels determine how quickly replicas see updates. For VDBs, replicating index builds and WAL/segments is more involved than simple key-value replication because of the size and structure of vector indexes.
Summary
- Replication keeps copies of (shards of) the vector index on multiple nodes for fault tolerance and high availability (HA); often enables load balancing for read-heavy workloads.
- Can be leader-based (primary-replica) or leaderless; consistency levels determine how quickly replicas see updates. Replicating index builds and WAL/segments is more involved than simple key-value replication.
- Sync vs. async replication; see disaster recovery and cross-region considerations. Pipeline: write to primary, replicate WAL/segments to replicas, replicas apply and serve reads. Practical tip: use async for lower write latency when eventual consistency is acceptable.
Replication and HA
Sync replication: the primary waits for replicas to acknowledge before acknowledging the write; strong consistency but higher write latency. Async replication: the primary acknowledges after local write; replicas receive updates asynchronously; lower latency but replicas may lag. For vector indexes, replicating often means streaming WAL or segment files; index builds may be replayed on replicas or copied.
Pipeline: write to primary, replicate WAL/segments to replicas, replicas apply and serve reads. Trade-off: sync gives strong consistency and higher write latency; async gives lower latency and eventual consistency. See disaster recovery and cross-region replication for multi-datacenter and failover. Practical tip: use async for lower write latency when eventual consistency is acceptable.
Frequently Asked Questions
Why replicate in a vector database?
To provide high availability (HA): if one node fails, others can serve reads and optionally accept writes. Replication also enables load balancing for read-heavy workloads. See sharding for how data is split; each shard can have replicas.
Leader-based vs. leaderless replication?
Leader-based: one primary for writes; replicas replicate from it. Leaderless: any node can accept writes; quorum or Raft/Paxos ensures agreement. Choice affects consistency, split-brain, and disaster recovery.
How does replication affect consistency?
Consistency levels determine when replicas see updates—strong (every read sees latest write) vs. eventual (replicas may lag). Affects when newly inserted or updated vectors appear in search results.
Is replicating vector indexes different from key-value?
Yes. Vector indexes are large and structured; replicating index builds and WAL/segments is more involved than simple key-value replication. Sync vs. async, cross-region, and disaster recovery add further considerations.