Silicon Valley Code Camp : October 5th and 6th 2013
Chris Richardson is a developer and architect. He is a Java Champion, a JavaOne rock star and the author of POJOs in Action, which describes how to build enterprise Java applications with frameworks such as Spring and Hibernate. Chris was also the founder of the original CloudFoundry.com, an early Java PaaS for Amazon EC2.
Today, he is a recognized thought leader in microservices and speaks regularly at international conferences. Chris is the creator of Microservices.io, a pattern language for microservices, and is writing the book Microservice Patterns, which is available as a Manning MEAP. He provides microservices consulting and training to organizations that are adopting the microservice architecture and is working on his third startup Eventuate, an application platform for developing transactional microservices.
NoSQL databases such as Redis, MongoDB and Cassandra are emerging as a compelling choice for many applications. They can simplify the persistence of complex data models and offer significantly better scalability and performance. However, using a NoSQL database means giving up the benefits of the relational model such as SQL, constraints and ACID transactions. For some applications, the solution is polyglot persistence: using SQL and NoSQL databases together.
In this talk, you will learn about the benefits and drawbacks of polyglot persistence and how to design applications that use this approach. We will explore the architecture and implementation of an example application that uses MySQL as the system of record and Redis as a very high-performance database that handles queries from the front-end. You will learn about mechanisms for maintaining consistency across the various databases.
A modular, polyglot architecture has many advantages but it also adds complexity since each incoming request typically fans out to multiple distributed services. For example, in an online store application the information on a product details page - description, price, recommendations, etc - comes from numerous services. To minimize response time and improve scalability, these services must be invoked concurrently. However, traditional concurrency mechanisms are low-level, painful to use and error-prone.