Relational Versus NoSQL Databases: Which Is Right for Your Database Application?
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Relational databases have matured through decades of practical application and theoretical analysis. In contrast, NoSQL databases are relatively new alternatives that offer a range of features not found in relational databases. This article distinguishes between the various types of NoSQL databases and discusses the factors to consider when deciding between relational and NoSQL databases for a specific application.
Types of NoSQL Databases
NoSQL databases are particularly useful in situations that push the performance limits or flexibility of relational databases. For example, document databases such as MongoDB and Couchbase use data structures analogous to rows and tables, albeit without the need for a fixed schema. In these databases, documents, attributes, and collections correspond to the rows, columns, and tables of a relational database.
Document databases are sometimes called "schema-less" databases, but in actuality they possess a highly flexible schema. Unlike relational databases, document databases don't need an explicit schema definition. Instead, the schema is implied in the attributes used in the documents.
Graph databases such as Neo4j are most often used for modeling complex networks, such as those used in social media, IT infrastructures, transportation, and tracking the spread of infectious diseases. With graph databases, instead of querying rows and columns, you follow paths through a network model. Many business problems are a natural fit for network models.
In general, column family databases are a good fit for Big Data needs. Column family databases such as Cassandra and HBase are designed to manage millions of columns and billions of rows. This type of database usually requires a cluster of commodity hardware.
Key-value databases are often used as caches to improve relational database performance. Some, such as Redis and Riak, allow for persistent storage as well as memory caching.
Points to Consider
If you are looking for a single deciding factor between relational and NoSQL databases, choose the database type that best handles your application's specific needs.
Relational databases are a good option for applications with these aspects:
- The data requirements are well known and fixed.
- Database constraints are needed to maintain integrity rules.
- A normalized data structure is needed to help avoid data anomalies.
- The database must support transactions, such as in financial applications. More NoSQL databases are adding support for transactions, but that capability is not yet available in all NoSQL offerings.
NoSQL databases best support applications with these needs:
- The application deals with varying data sets. For instance, a catalog application typically needs multiple information types, with each type containing several possible values. A music product may include attributes such as performer, genre, release date, length, file format, and so on. Appliances listed in the same catalog may need attributes to describe size, weight, power consumption, and other attributes specific to such devices. Each type of item in the catalog needs its own set of attributes.
- Query performance is a top priority. The performance overhead of a NoSQL database is typically less than that of a relational database due to a number of design choices. These include not enforcing constraints, supporting limited forms of transactions, and implementing fast write operations.
- The application needs to handle a large volume of data. NoSQL databases are typically designed for use on commodity servers. Relational databases can scale up, but most likely will be more expensive due to their hardware requirements.
Summary
There has never been much use in arguing in the abstract whether NoSQL or relational databases are "better." The best database for the job depends on the job. The key to making the best decision is understanding which features offered by the various database structures would best suit your project.
Dan Sullivan, author of NoSQL for Mere Mortals, is an enterprise architect and consultant with over 20 years of IT experience, with engagements in advanced analytics, systems architecture, database design, enterprise security, and business intelligence.
James Sullivan is a business technology writer with concentrations in mobile, security, and database services. He is based in Portland, Oregon.