The Evolution of Distributed Databases from the 1970s to the Present

Distributed databases have evolved significantly since their inception in the 1970s. This evolution can be attributed to advances in technology, shifting demands, and the need to address emerging challenges. This article will discuss the key milestones and developments that have shaped the evolution of distributed databases from the 1970s to the present.

The concept of distributed databases emerged in the 1970s as a response to the limitations of centralized database systems. The advent of computer networking and the desire to improve database performance, reliability, and fault tolerance led to the development of the distributed database model. In 1978, Bruce G. Lindsay and his colleagues at IBM Research introduced the R* distributed relational database system, which extended the relational database model to a distributed environment.

During the 1980s, the two-phase commit (2PC) protocol was developed as a means of ensuring transactional consistency across distributed databases. The 2PC protocol works by coordinating multiple participating database nodes in a transaction, ensuring that either all changes are committed or none at all, thus maintaining the ACID properties. Additionally, replication techniques emerged as a way to improve the availability and fault tolerance of distributed databases. These advancements laid the foundation for the development of more sophisticated distributed database systems in the coming years.

The 1990s saw the rise of object-oriented databases, which store and manage complex data objects and their relationships. These databases provided more flexibility and adaptability than traditional relational databases, making them suitable for handling complex data structures in distributed environments. Concurrently, parallel processing techniques were developed to harness the power of multiple processors in executing database tasks, significantly improving performance and scalability.

The explosion of the internet and the increasing need for web-scale data processing led to the development of NoSQL (Not only SQL) databases in the 2000s. These databases were designed to handle massive amounts of unstructured, semi-structured, or schema-less data, providing greater flexibility, scalability, and performance in distributed environments. NoSQL databases use various data models, including key-value, document, column-family, and graph, and are often tailored for specific use cases.

The advent of big data and the growth of cloud computing in the 2010s led to a further evolution in distributed databases. The need to process, store, and analyze vast amounts of data generated from various sources led to the development of new distributed database systems that could scale horizontally, provide real-time analytics, and handle various data types. Technologies such as Hadoop, Cassandra, and HBase emerged as popular solutions for big data processing and storage.

In recent years, the adoption of microservices architecture and edge computing has further influenced the evolution of distributed databases. Microservices require databases that can scale independently and provide low-latency access to data, driving the development of new distributed database systems that can meet these requirements. Meanwhile, edge computing has created a demand for distributed databases that can operate at the edge of the network, closer to the data sources, to provide real-time analytics and decision-making capabilities.

If we start with the hardware framework, we see that the first-generation technology was characterized by vacuum-tube hardware. Languages that were developed during this period were designed to facilitate the generation of object code, the binary language that operates directly on the machine. These "code assembly" languages became very popular and added to the mystique of computing by requiring the programmer to learn a very cryptic and unreadable language. It is interesting to note that until very recently, assembly languages were still used to write operating-system and database software. Second-generation systems were characterized by transistors. This generation of systems gave rise to the first procedural languages that relied on high-level instructions that did not directly manipulate the computer's registers. This was the beginning of a trend toward "high-level" languages that could take care of all of the low-level machine operations, freeing the programmers to concentrate on the overall programming.

The third generation of computing hardware was characterized by the integrated circuit (IC), and heralded the introduction of computer languages that could be called user friendly.
Fourth-generation hardware has been characterized by Very Large Scale Integration (VLSI) of processors, and the languages have become even friendlier and easier to program. Fourth-generation languages such as Oracle PL/SQL have taken care of all the low-level programming, leaving it to the programmer to concentrate on high-level program implementations. This is the environment in which Oracle began implementation of distributed-database computing. The evolution of distributed databases from the 1970s to the present has been marked by significant technological advancements and shifting demands. As database systems continue to adapt to new challenges, the landscape of distributed databases is ongoing.