PostgreSQL 2019
Early next month, Analytics Engines CTO, Dr Alastair McKinley will be attending POSTGRES London2019, a two-day event designed to showcase the latest developments for PostgreSQL, including new features and current projects-in-development.
In anticipation of the event, Alastair provided us with some insight into his experience with PostgreSQL and how it enables Analytics Engines to provide innovate data solutions for our partners.
What is PostgreSQL?
In a sentence, PostgreSQL is an advanced open-source relational database management system (RDBMS). PostgreSQL has been around in one form or another for a very long time, however, its popularity in recent years has a great deal to do with the frustrations experienced in alternative systems and the rapidly increasing capability of PostgreSQL itself.
For a long time, developers were moving away from SQL databases to alternatives NoSQL systems. What PostgreSQL sought to do was combine some of the historical benefits of other RDBMS systems with the advanced elements of more modern database solutions.
What makes PostgreSQL different?
PostgreSQL is a lot more than just an RDBMS. It has advanced capability for semi-structured data processing, like JSON for example (since version 9.4). Beyond that, it has incredibly extensive server-side language support including Python, R and JavaScript, as well a vast number of advanced index types (including covering index support since version 11) like BTREE, GIN and GiST allowing for rapid access to data, covering a wide variety of workloads.
Because PostgreSQL is designed for extensibility, there are a vast number of third-party extensions that can be installed to the system. For example, one such extension that we use at Analytics Engines is PostGIS which enables us to provide geospatial analytics to our partners.
How does PostgreSQL help Analytics Engines provide innovative data solutions?
We spent a great deal of time building data solutions based on more traditional ORM (object relational mappers) libraries. Over time, we became dissatisfied with the inability of these libraries to fully utilise the underlying capabilities of the database for both data modelling and data analytics purposes (without the need for ugly hacks e.g. SQL strings in application code).
Many of the analytics solutions that we develop touch very large amounts of disparate data. Processing that data directly in PostgreSQL has a number of advantages, including improved execution speed due to data locality and succinct algorithms definitions thanks to the expressive power of modern SQL.
PostgreSQL advanced concurrency control mechanisms and strong data consistency enable us to spend less time on the ‘data plumbing’ and more time developing insights for our customers.
The PostgreSQL-centred application architecture that we have employed in new solutions over the last 18 months has allowed us to deliver robust data analytics solutions of greater complexity with far greater agility.
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