Chapter 6. NoSQL: Data Grids and Graph Databases

I’m more of an adventurous type than a relationship type.

Bob Dylan

Until relatively recently, the relational database (RDBMS) reigned over data in enterprise applications by a wide margin when contrasted with other approaches. RDBMSs follow a premise of storing heavily normalized data in relational structures such as tables, and are heavily based on rigorous mathematical set theory. Commercial offerings from Oracle and established open source projects like MySQL (reborn as MariaDB) and PostgreSQL became de facto choices when it came to storing, archiving, retrieving, and searching for data. In retrospect, it’s shocking that given the varying requirements from those operations, one solution was so widely lauded for so long.

In the late 2000s, a trend away from the strict ACID transactional properties could be clearly observed, given the emergence of data stores that organized information differently from the traditional row-based table model. In addition, many programmers were beginning to advocate for a release from strict transactions; in many use cases it appeared that this level of isolation wasn’t enough of a priority to warrant the computational expense necessary to provide ACID guarantees, and the often severe performance penalties it imposed on storage systems distributed across more than one server.

In 2009, Amazon’s Werner Vogels published Eventually Consistent, an article that advocated for the tolerance of inconsistent ...

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