qdrant-scaling
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ChineseQdrant Scaling
Qdrant 扩容
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.
首先明确你的扩容目标：
- 数据容量
- 查询吞吐量（QPS）
- 查询延迟
- 查询返回数据量
确定扩容目标后，你可以基于权衡取舍和前提假设选择扩容策略。不同的目标对应不同的策略，吞吐量扩容和延迟优化的调优方向是相反的。
Scaling Data Volume
数据容量扩容
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
当数据集容量超出单节点承载上限时，就需要进行数据容量扩容。
阅读数据容量扩容了解更多关于数据容量扩容的内容。
Scaling for Query Throughput
查询吞吐量扩容
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
如果你的系统需要处理的并行查询数超出单节点承载上限，你就需要进行查询吞吐量扩容。
阅读查询吞吐量扩容了解更多关于查询吞吐量扩容的内容。
Scaling for Query Latency
查询延迟优化
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
单次查询的延迟由查询执行路径中最慢的组件决定。它有时与吞吐量相关，但并非总是如此，可能需要采用不同的扩容策略。
阅读查询延迟优化了解更多关于查询延迟优化的内容。
Scaling for Query Volume
查询返回数据量扩容
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
我们所说的查询返回数据量指的是单次查询返回的结果数量。如果查询返回数据量过大，可能会引发性能问题并升高延迟。针对查询返回数据量的调优可能需要采用特殊的策略。
阅读查询返回数据量扩容了解更多关于查询返回数据量扩容的内容。