GIANT Stories at MongoDB

Building with Patterns: The Computed Pattern

We've looked at various ways of optimally storing data in the Building with Patterns series. Now, we're going to look at a different aspect of schema design. Just storing data and having it available isn't, typically, all that useful. The usefulness of data becomes much more apparent when we can compute values from it. What's the total sales revenue of the latest Amazon Alexa? How many viewers watched the latest blockbuster movie? These types of questions can be answered from data stored in a database but must be computed.

Building With Patterns: The Outlier Pattern

So far in this Building with Patterns series, we've looked at the Polymorphic, Attribute, and Bucket patterns. While the document schema in these patterns has slight variations, from an application and query standpoint, the document structures are fairly consistent. What happens, however, when this isn't the case? What happens when there is data that falls outside the "normal" pattern? What if there's an outlier?

Building with Patterns: The Bucket Pattern

In this edition of the Building with Patterns series, we're going to cover the Bucket Pattern. This pattern is particularly effective when working with Internet of Things (IoT), Real-Time Analytics, or Time-Series data in general. By bucketing data together we make it easier to organize specific groups of data, increasing the ability to discover historical trends or provide future forecasting and optimize our use of storage.

Building with Patterns: The Attribute Pattern

In this edition of the Building with Patterns series, we explore the Attribute Schema Design Pattern. We can use this pattern when we have queries that are targeting many similar fields in a document. The Attribute Pattern also provides easy document indexing options.

Building with Patterns: The Polymorphic Pattern

Over the course of this blog post series, we’ll take a look at twelve common Schema Design Patterns that work well in MongoDB. We hope this series will establish a common methodology and vocabulary you can use when designing schemas. Leveraging these patterns allows for the use of “building blocks” in schema planning, resulting in more methodology being used than art.