qdrant-horizontal-scaling
About
This skill helps developers diagnose and implement Qdrant horizontal scaling strategies when data exceeds single-node capacity. It provides guidance on shard configuration, node counts, and replication for fault tolerance and performance. Use it when planning capacity expansion, adding nodes, or optimizing distributed Qdrant deployments.
Quick Install
Claude Code
Recommendednpx skills add qdrant/skills -a claude-code/plugin add https://github.com/qdrant/skillsgit clone https://github.com/qdrant/skills.git ~/.claude/skills/qdrant-horizontal-scalingCopy and paste this command in Claude Code to install this skill
Documentation
What to Do When Qdrant Needs More Capacity
Vertical first: simpler operations, no network overhead, good up to ~100M vectors per node depending on dimensions and quantization. Horizontal when: data exceeds single node capacity, need fault tolerance, need to isolate tenants, or IOPS-bound (more nodes = more independent IOPS).
Most basic distributed configuration
- 3 nodes, 3 shards with
replication_factor: 2for zero-downtime scaling
Minimum of 3 nodes is important for consensus and fault tolerance. With 3 nodes, you can lose 1 node without downtime. With 2 nodes, losing 1 node causes downtime for collection operations.
Replication factor of 2 means each shard has 1 replica, so you have 2 copies of data. This allows for zero-downtime scaling and maintenance. With replication_factor: 1, zero-downtime is not guaranteed even for point-level operations, and cluster maintenance requires downtime.
Choosing number of shards
Shards are the unit of data distribution. More shards allows more nodes and better distribution, but adds overhead. Fewer shards reduces overhead but limits horizontal scaling.
For cluster of 3-6 nodes the recommended shard count is 6-12. This allows for 2-4 shards per node, which balances distribution and overhead.
Changing number of shards
Use when: shard count isn't evenly divisible by node count, causing uneven distribution, or need to rebalance.
Resharding is expensive and time-consuming, it should be used as a last resort if regular data distribution is not possible. Resharding is designed to be transparent for user operations, updates and searches should still work during resharding with some small performance impact.
But resharding operation itself is time-consuming and requires to move large amounts of data between nodes.
- Available in Qdrant Cloud Resharding
- Resharding is not available for self-hosted deployments.
Better alternatives: over-provision shards initially, or spin up new cluster with correct config and migrate data.
What NOT to Do
- Do not jump to horizontal before exhausting vertical (adds complexity for no gain)
- Do not set
shard_numberthat isn't a multiple of node count (uneven distribution) - Do not use
replication_factor: 1in production if you need fault tolerance - Do not add nodes without rebalancing shards (use shard move API to redistribute)
- Do not scale down RAM without load testing (cache eviction causes days-long latency incidents)
- Do not hit the collection limit by using one collection per tenant (use payload partitioning)
GitHub Repository
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