Database design for Microservices

History of Microservices - 

A workshop of software architects held near Venice in May 2011 used the term "microservice" to describe what the participants saw as a common architectural style that many of them had been recently exploring.In May 2012, the same group decided on "microservices" as the most appropriate name. James Lewis presented some of those ideas as a case study in March 2012 at 33rd Degree in Kraków in Micro services - Java, the Unix Way as did Fred George about the same time. Adrian Cockcroft, former director for the Cloud Systems at Netflix, described this approach as "fine grained SOA", pioneered the style at web scale, as did many of the others mentioned in this article - Joe Walnes, Dan North, Evan Bottcher and Graham Tackley. <Source : Wikipedia>

Accepted Definition of Microservices - 

Microservices are a software development technique - a variant of the service-oriented architecture (SOA) structural style— that arranges an application as a collection of loosely coupled services. In a microservices architecture, services are fine-grained and the protocols are lightweight.

 Database design for Microservices

The main idea behind microservices architecture is that some types of applications become easier to build and maintain when they are broken down into smaller, composable pieces which work together. The main benefit of the microservices architecture is that it improves agility and reduced development time.When you  correctly decompose a system into microservices, you can develop and deploy each microservice independently and in parallel with the other services.

In order to be able to independently develop microservices , they must be loosely coupled. Each microservice’s persistent data must be private to that service and only accessible via it’s API . If two or more microservices were to share persistent data then you need to carefully coordinate changes to the data’s schema, which would slow down development.
There are a few different ways to keep a service’s persistent data private. You do not need to provision a database server for each service. For example,  if you are using a relational database then the options are:

• Private-tables-per-service – each service owns a set of tables that must only be accessed by that service
• Schema-per-service – each service has a database schema that’s private to that service
• Database-server-per-service – each service has it’s own database server.

Private-tables-per-service and schema-per-service have the lowest overhead.  Using a schema per service is ideal since it makes ownership clearer. For some applications, it might make sense for database intensive services to have their own database server.
It is a good idea to create barriers that enforce this modularity. You could, for example, assign a different database user id to each service and use a database access control mechanism. Without some kind of barrier to enforce encapsulation, developers will always be tempted to bypass a service’s API and access it’s data directly.It might also make sense to have a polyglot persistence architecture. For each service you choose the type of database that is best suited to that service’s requirements. For example, a service that does text searches could use ElasticSearch. A service that manipulates a social graph could use Neo4j. It might not make sense to use a relational database for every service.
There are some downsides to keeping a service’s persistent data private. Most notably, it can be challenging to implement business transactions that update data owned by multiple services. Rather than using distributed transaction, you typically must use an eventually consistent, event-driven approach to maintain database consistency.
Another problem, is that it is difficult to implement some queries because you can’t do database joins across the data owned by multiple services. Sometimes, you can join the data within a service. In other situations, you will need to use Command Query Responsibility Segregation (CQRS) and maintain denormalizes views.
Another challenge is that  services sometimes need to share data. For example, let’s imagine that several services need access to user profile data. One option is to encapsulate the user profile data with a service, that’s then called by other services. Another option is to use an event-driven mechanism to replicate data to each service that needs it.
In summary,  it is important that each service’s persistent data is private. There are, however,  a few different ways to accomplish this such as a schema-per-service. Some applications benefit from a polyglot persistence architecture that uses a mixture of database types.  A downside of not sharing databases is that maintaining data consistency and implementing queries is more challenging.