One of the main challenges users face when adopting an on-premises solution is the ability to integrate it into their infrastructure. The days when EJBs and application servers ruled the world have gone, and organizations bet for virtualization. They offer convenient features like isolation and replication, but along with a critical drawback: performance. Docker has raised as a serious alternative to virtual machines, and dockerized applications are the new EJBs. It is not uncommon to find in a company’s infrastructure dockerized services and processes. In this sense, a question rises: what about dockerized text analytics?
The problem with virtual machines
By definition, a virtual machine runs a complete stack of virtualized hardware and operating system. It takes a powerful host machine to run a large amount of virtual machines seamlessly. Organizations often find themselves forced to invest in powerful servers to run solutions that are in fact not specially hardware-demanding.
In the last years, an alternative approach called containers has been widely adopted. In short, a container is an isolated file system, with its own processes, users, and network interfaces, but without any virtualized hardware.
Dockerized text analytics with MeaningCloud
MeaningCloud runs seamlessly in Docker containers, which makes it a convenient solution for deploying it in some infrastructures. It also takes advantage of some appealing aspects inherited from the Docker internal design.