![]() Key Considerations and Resources for Data ModelingĪs you design your data model, one of the first decisions you need to make is how to model relationships between data. The optimal schema design will be determined by your application’s query patterns. But you can also model flat, tabular and columnar structures, simple key-value pairs, text, geospatial and time-series data, or the nodes and edges of connected graph data structures. The nesting of arrays and subdocuments makes documents very powerful at modeling complex relationships between data. Just because MongoDB has a flexible schema does not mean you can ignore schema design! While you can modify your schema at any time, applying schema design best practices at the outset of your project will save potential refactoring later on.Ī major advantage of JSON documents is that you have the flexibility to model your data any way your application needs. Tailoring your data model to the application’s query patterns produces more efficient queries, increases the throughput of insert and update operations, and more effectively distributes your workload across a sharded cluster. ![]() The first step in performance optimization is to understand your application’s query patterns so that you design your data model and select the appropriate indexes accordingly. This is something we call the “working set”. Firstly we’ll discuss schema design and the key resources to get you started, before then moving onto RAM sizing for your application’s most frequently accessed data and indexes. We’re going to start with two key considerations that will set up all of the performance best practices we discuss through the rest of this blog series.
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