Blogs

4 Benefits of Integrating a Language With a Database

One of the hallmarks of a truly modern programming language is the coupling of a language—for creating applications, devising algorithms and solving business problems—with an underlying database. When there is practically no separation between a database and its programming language, developers immensely increase their productivity, maximize the effectiveness of the code they write and provide unparalleled speed and flexibility in supporting business needs.

Predicating a programming language on a database delivers these advantages by enabling developers to treat data as if it is in-memory, liberating them from the burden of manipulating how data is represented in the underlying database. This one simple advantage exponentially increases the flexibility for addressing business problems and, when used with advanced logic languages, enables developers to write applications in a fraction of the time and with less effort than it would otherwise require.

Specific benefits of tightly coupling a database with its programming language include eliminating the time-honored mismatch between data structures and database representations, smart caching with transactional technologies for committing or rolling back database changes and automatically changing object definitions.

These benefits remove the database as an otherwise complicating factor in supporting business use of data by freeing developers to flexibly innovate solutions—and speedily implement them.

In-Memory Manipulations

Developers focus on manipulating data structures to solve business problems. Traditionally, this concern was hamstrung by the onus of translating those structures into how data are represented in the database, a time-consuming necessity detracting from programmers’ attention to business problems. By fully integrating an object-oriented database or graph database with its programming environment, users can manipulate data objects in memory and they’ll automatically persist to storage. Developers can solely focus on working with data structures for business objectives without spending just as much, if not more, effort wrangling data’s representation in the database.

Programming languages create this effect when they are imbued with an intelligent caching layer that persists in-memory data manipulations into the database. This advantage requires basing the programming language on a specific database. It eliminates the mismatch between working on data structures and toiling to get the underlying data to and from disk.

Smart Caching

The caching layer also tames the inherent difficulties in data objects pointing to other objects, which is a hallmark of object-oriented systems and graph databases. These databases require intelligent caching because one object can conceivably include the majority of the database by pointing to objects that, in turn, point to additional objects. Tight coupling between that language and its database fixes this issue so developers cache what is needed to work on data in memory while automating its impact on disk. Such caching is significantly improved with transactional technologies so developers can specify when they want their modifications saved in the database.

This way, if they make a mistake they can simply go back in time before it was made and undo it. Conversely, after successfully completing their work, they can easily commit changes to the database. Like the other advantages, this benefit boosts developer productivity to match that of business requirements. It adds to the flexibility programmers need to innovate solutions for mission-critical objectives by writing or integrating applications.

Object and Class Definitions

The third boon of basing a smart programming language on a database is the capacity to dynamically change the definition of data objects. With conventional approaches, this task requires developers to stop writing or operating applications and manually write code to adjust the classification, which may involve either reading or writing the data in and out again. But with no separation between the database and its sufficiently smart programming language, such coupling can effectively keep track of classifications while automatically modifying them as needed.

Developers can seamlessly add or delete features without manual coding, which also furthers their productivity. For example, if they add an attribute to an object, the next time they see it it’ll magically have the new feature. Alternatively, if they create a new feature of an old classification and decide to delete it, they won’t see it anymore. Best of all, these changes occur without rewriting the classification.

Advanced Logic

There is one more capability of very smart programming languages: They can combine these advantages with advanced logic languages such as Prolog. Prolog, or a subset of Prolog called Datalog, can instantly be used as a query language. In addition, it becomes very easy to create rule-based applications with Prolog because one can write declarative, logic-based rules with minimal coding. For example, they can specify a series of event-based rules detecting customer churn, money laundering or any important business outcome without being impeded by unnecessary database concerns to deliver time-to-value that’s unmatched without this method.

Basing a programming language on a database improves developer efficiency and effectiveness by letting developers work on data in memory and persisting changes to storage, intelligently caching data and rolling back changes as required, and dynamically adjusting definitions.

Jans Aasman

Jans Aasman is a Ph.D. psychologist, expert in Cognitive Science and CEO of Franz Inc., an early innovator in Artificial Intelligence, creator of Allegro CL a Common Lisp platform, and leading provider of Semantic Database technology and Knowledge Graph solutions. As both a scientist and CEO, Dr. Aasman continues to break ground in the areas of Artificial Intelligence and Knowledge Graphs as he works hand-in-hand with numerous Fortune 500 organizations as well as U.S. and foreign governments.

Recent Posts

Building an Open Source Observability Platform

By investing in open source frameworks and LGTM tools, SRE teams can effectively monitor their apps and gain insights into…

10 hours ago

To Devin or Not to Devin?

Cognition Labs' Devin is creating a lot of buzz in the industry, but John Willis urges organizations to proceed with…

11 hours ago

Survey Surfaces Substantial Platform Engineering Gains

While most app developers work for organizations that have platform teams, there isn't much consistency regarding where that team reports.

1 day ago

EP 43: DevOps Building Blocks Part 6 – Day 2 DevOps, Operations and SRE

Day Two DevOps is a phase in the SDLC that focuses on enhancing, optimizing and continuously improving the software development…

1 day ago

Survey Surfaces Lack of Significant Observability Progress

A global survey of 500 IT professionals suggests organizations are not making a lot of progress in their ability to…

1 day ago

EP 42: DevOps Building Blocks Part 5: Flow, Bottlenecks and Continuous Improvement

In part five of this series, hosts Alan Shimel and Mitch Ashley are joined by Bryan Cole (Tricentis), Ixchel Ruiz…

1 day ago