先看看示例
作者提供的示例，实际效果取决于你的输入和使用工具。
输入
数据已超出单节点容量且查询变慢，先判断是容量还是吞吐问题，再给出分片与加节点建议。
输出
预期交付：扩容目标判定结论与对应的扩容、分片或调优方向建议。（编辑说明，非实际运行结果）
方法说明
--- name: qdrant-scaling description: "Guides Qdrant scaling decisions. Use when someone asks 'how many nodes do I need', 'data doesn't fit on one node', 'need more throughput', 'cluster is slow', 'too many tenants', 'vertical or horizontal', 'how to shard', or 'need to add capacity'." allowed-tools: - Read - Grep - Glob --- # Qdrant Scaling First determine what you're scaling for: - data volume - query throughput (QPS) - query latency - query volume After determining the scaling goal, we can choose scaling strategy based on tradeoffs and assumptions. Each pulls toward different strategies. Scaling for throughput and latency are opposite tuning directions. ## Scaling Data Volume This becomes relevant when volume of the dataset exceeds the capacity of a single node. Read more about scaling for data volume in [Scaling Data Volume](scaling-data-volume/SKILL.md) ## Scaling for Query Throughput If your system needs to handle more parallel queries than a single node can handle, then you need to scale for query throughput. Read more about scaling for query throughput in [Scaling for Query Throughput](scaling-qps/SKILL.md) ## Scaling for Query Latency Latency of a single query is determined by the slowest component in the query execution path. It is in sometimes correlated with throughput, but not always. It might require different strategies for scaling. Read more about scaling for query latency in [Scaling for Query Latency](minimize-latency/SKILL.md) ## Scaling for Query Volume By query volume we understand the amount of results that a single query returns. If the query volume is too high, it can cause performance issues and increase latency. Tuning for query volume is opposite might require special strategies. Read more about scaling for query volume in [Scaling for Query Volume](scaling-query-volume/SKILL.md)