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Author: ealsur

articles/cosmos-db/performance-tips.md

@@ -125,7 +125,7 @@ So if you're asking "How can I improve my database performance?" consider the fo
      SQL .NET SDK version 1.9.0 and above support parallel queries, which enable you to query a partitioned collection in parallel. For more information, see [code samples](https://github.com/Azure/azure-documentdb-dotnet/blob/master/samples/code-samples/Queries/Program.cs) related to working with the SDKs. Parallel queries are designed to improve query latency and throughput over their serial counterpart. Parallel queries provide two parameters that users can tune to custom-fit their requirements, (a) MaxDegreeOfParallelism: to control the maximum number of partitions then can be queried in parallel, and (b) MaxBufferedItemCount: to control the number of pre-fetched results.
 
     (a) ***Tuning degree of parallelism\:***
-    Parallel query works by querying multiple partitions in parallel. However, data from an individual partitioned collect is fetched serially with respect to the query. So, setting the `MaxDegreeOfParallelism` in [SDK V2](sql-api-sdk-dotnet.md) or `MaxConcurrency` in [SDK V3](sql-api-sdk-dotnet-standard.md) to the number of partitions has the maximum chance of achieving the most performant query, provided all other system conditions remain the same. If you don't know the number of partitions, you can set the degree of parallelism to a high number, and the system chooses the minimum (number of partitions, user provided input) as the degree of parallelism.
+    Parallel query works by querying multiple partitions in parallel. However, data from an individual partition is fetched serially with respect to the query. Setting the `MaxDegreeOfParallelism` in [SDK V2](sql-api-sdk-dotnet.md) or `MaxConcurrency` in [SDK V3](sql-api-sdk-dotnet-standard.md) to the number of partitions has the maximum chance of achieving the most performant query, provided all other system conditions remain the same. If you don't know the number of partitions, you can set the degree of parallelism to a high number, and the system chooses the minimum (number of partitions, user provided input) as the degree of parallelism.
 
     It is important to note that parallel queries produce the best benefits if the data is evenly distributed across all partitions with respect to the query. If the partitioned collection is partitioned such a way that all or a majority of the data returned by a query is concentrated in a few partitions (one partition in worst case), then the performance of the query would be bottlenecked by those partitions.