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Scaling for throughput and latency are opposite tuning directions.\n\n\n## Scaling Data Volume\n\nThis becomes relevant when volume of the dataset exceeds the capacity of a single node.\nRead more about scaling for data volume in [Scaling Data Volume](scaling-data-volume/SKILL.md)\n\n\n## Scaling for Query Throughput\n\nIf your system needs to handle more parallel queries than a single node can handle,\n then you need to scale for query throughput.\n\nRead more about scaling for query throughput in [Scaling for Query Throughput](scaling-qps/SKILL.md)\n\n## Scaling for Query Latency\n\nLatency of a single query is determined by the slowest component in the query execution path.\nIt is in sometimes correlated with throughput, but not always. It might require different strategies for scaling.\n\nRead more about scaling for query latency in [Scaling for Query Latency](minimize-latency/SKILL.md)\n\n\n## Scaling for Query Volume\n\nBy query volume we understand the amount of results that a single query returns. \nIf the query volume is too high, it can cause performance issues and increase latency.\n\nTuning for query volume is opposite might require special strategies. \n\nRead more about scaling for query volume in [Scaling for Query 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