This is an E-R diagram for our example social application: Display the posts by category and date (most recent first).Users can post URLs to articles by category (like news, sports.):.The E-R model can be used with your query and data access patterns to define the physical model so that the data that is read together are stored together.ДЄs a modeling example, we will use a social application similar to Reddit ( Note: I do not know how Reddit is really implemented). Relationships: Connections between entities, i.e.Attributes: Properties of the objects in your application.Entities: Main objects in your application.It is useful to start off with Entity Relationship modeling in order to define the entities, relationships, and attributes in your application: Grouping the data by key range provides for fast reads and writes by row key. With MapR-DB, you de-normalize your schema to store in one row or document what would be multiple tables with indexes in a relational world. The row document (JSON) or columns (HBase) should be designed to group data together that will be read together. MapR-DB has a query-first schema design in which queries should be identified first, then the row key should be designed to distribute the data evenly and also to give a meaningful primary index to query by. With MapR-DB, a table is automatically partitioned across a cluster by key range, and each server is the source for a subset of a table (called a tablet). However, joins cause bottlenecks on read, with data distributed across a cluster, and this model does not scale horizontally. Then, queries with joins bring the data back together again. With a relational database, you normalize your schema, which eliminates redundant data and makes storage efficient. In relational design, the focus and effort are around describing the entity and its relation to other entities - the queries and indexes are designed later. MapR-DB JSON is different than other document data stores in that the row key design is the same for both models, and both can store data (columns or documents) with different access patterns in a different column family with the same row key. MapR-DB as a document database with an Open JSON API.MapR-DB as a wide column database with an Apache HBase API.MapR-DB provides for data variety with two different data models: Simply put, the motivation behind NoSQL is data volume, velocity, and/or variety. We have an anecdote at MapR where one of our solution architects worked with a customer, and in a one-hour conversation about schema design, was able to improve access performance by a factor of 1,000x. A properly designed data model can make all the difference in how your application performs.

Typically, with a NoSQL data store, you want to aggregate your data so that the data can quickly be read together, instead of using joins. Document databases don't require the same predefined structure as a relational database, but you do have to define the facets of how you plan to organize your data. In this blog post, I'll discuss how NoSQL data modeling is different from traditional relational schema data modeling, and I'll also provide you with some guidelines for document database data modeling.Дocument databases, such as MapR-DB, are sometimes called 'schema-less' - but this is a misnomer.

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