Realtime-use case examples
Stream Processing and Analytics
An application ingesting and analyzing data streams like tweets or stock prices in real-time To handle this efficiently, we require leverage multi-stage builds:
- Build stage: Installing libraries for message queuing, like RabbitMQ or Apache Kafka, which are just needed for testing and development, occurs at this step.
- Runtime stage: Only the application code and requirements required to process data streams are provided in this level. Removing superfluous libraries leads in a substantially smaller size and a quicker initialization.
Chatbots and Conversational AI
Have you thought about creating a real-time, Python-based chatbot that uses only natural languages processing (NLP) libraries? Multi-stage builds may improve responsiveness in the following ways:
- Build stage: This stage installs Natural Language Processing (NLP) libraries and training data used to train your chatbot model.
- Runtime stage: This stage includes the application code and the minimal NLP modules required for understanding user input and generating responses. By excluding unnecessary libraries, we ensure faster response times, leading to smooth and realistic conversations with your real-time chatbot.
What is an Multistage Dockerfile?
Docker has revolutionized the world of software development and software deployment by simplifying the process of creating, distributing, and running applications within containers. This feature of Docker is very helpful for developers, so Among Docker’s sea of features, multistage Dockerfile stands out as a very powerful tool for optimizing the size and efficiency of container images Let’s get familiar with multistage Dockerfiles and add another tool to our journey with DevOps.