12 down vote +100 paragon partition manager 11 free edition

1 kb documents are very efficient in Azure Cosmos DB. For more information, see Partitioning and horizontal scaling in Azure Cosmos DB | Microsoft Docs.įinally, we need to understand the document size. Item ID does a great job with evenly balancing partitioned throughput (RUs) and data storage since it’s a unique value.

When the access pattern is write-heavy you might want to choose item ID as the partition key. Queries can be efficiently routed to only the relevant physical partitions by including the partition key in the filter predicate. If the access pattern is read-heavy you want to choose a partition key that appears frequently as a filter in your queries.

Is the access pattern more read-heavy or write-heavy?.

To design an efficient data model it is important to understand how the client application will interact with Azure Cosmos DB. For more information, see Understanding the differences between Azure Cosmos DB NoSQL and relational databases | Microsoft Docs. As a result, this requires the database to be schema-agnostic and therefore we decided to use Azure Cosmos DB as our database. The schema of the data is different for various events and likely to change. The amount of data that will be stored is estimated at 100 GB. Users should be able to select an event and view a leaderboard.

Relational or NoSQLįor our scenario, we need to store data from sports events (e.g., marathon, triathlon, cycling, etc.). Part 2 looks at performance tuning and monitoring. This is the first of two articles in which we will walk you through the steps for realizing your first Azure Cosmos DB implementation. In this post, we cover:

12 down vote +100 paragon partition manager 11 free edition