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Update topnquery.md (#10944) 

minor edits of the English, no meanings changed (imo)
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@ -168,11 +168,11 @@ See [Multi-value dimensions](multi-value-dimensions.md) for more details.
The current TopN algorithm is an approximate algorithm. The top 1000 local results from each segment are returned for merging to determine the global topN. As such, the topN algorithm is approximate in both rank and results. Approximate results *ONLY APPLY WHEN THERE ARE MORE THAN 1000 DIM VALUES*. A topN over a dimension with fewer than 1000 unique dimension values can be considered accurate in rank and accurate in aggregates. The current TopN algorithm is an approximate algorithm. The top 1000 local results from each segment are returned for merging to determine the global topN. As such, the topN algorithm is approximate in both rank and results. Approximate results *ONLY APPLY WHEN THERE ARE MORE THAN 1000 DIM VALUES*. A topN over a dimension with fewer than 1000 unique dimension values can be considered accurate in rank and accurate in aggregates.
The threshold can be modified from it's default 1000 via the server parameter `druid.query.topN.minTopNThreshold` which need to restart servers to take effect or set `minTopNThreshold` in query context which take effect per query. The threshold can be modified from its default 1000 via the server parameter `druid.query.topN.minTopNThreshold`, which needs a restart of the servers to take effect, or via `minTopNThreshold` in the query context, which takes effect per query.
If you are wanting the top 100 of a high cardinality, uniformly distributed dimension ordered by some low-cardinality, uniformly distributed dimension, you are potentially going to get aggregates back that are missing data. If you are wanting the top 100 of a high cardinality, uniformly distributed dimension ordered by some low-cardinality, uniformly distributed dimension, you are potentially going to get aggregates back that are missing data.
To put it another way, the best use cases for topN are when you can have confidence that the overall results are uniformly in the top. For example, if a particular site ID is in the top 10 for some metric for every hour of every day, then it will probably be accurate in the topN over multiple days. But if a site barely in the top 1000 for any given hour, but over the whole query granularity is in the top 500 (example: a site which gets highly uniform traffic co-mingling in the dataset with sites with highly periodic data), then a top500 query may not have that particular site a the exact rank, and may not be accurate for that particular site's aggregates. To put it another way, the best use cases for topN are when you can have confidence that the overall results are uniformly in the top. For example, if a particular site ID is in the top 10 for some metric for every hour of every day, then it will probably be accurate in the topN over multiple days. But if a site is barely in the top 1000 for any given hour, but over the whole query granularity is in the top 500 (example: a site which gets highly uniform traffic co-mingling in the dataset with sites with highly periodic data), then a top500 query may not have that particular site at the exact rank, and may not be accurate for that particular site's aggregates.
Before continuing in this section, please consider if you really need exact results. Getting exact results is a very resource intensive process. For the vast majority of "useful" data results, an approximate topN algorithm supplies plenty of accuracy. Before continuing in this section, please consider if you really need exact results. Getting exact results is a very resource intensive process. For the vast majority of "useful" data results, an approximate topN algorithm supplies plenty of accuracy.