Ferris is a container level solution for building highly scalable, cross-network sync or async applications.
Using the Ferris SX platform to run and manage stream processing containers utilizing Ferris messaging infrastructure significantly reduces the cost of deploying enterprise application and offers standardized data streaming between workflow steps. This will simplify the development and as result create a platform with agile data processing and ease of integration.
Getting started with Stream Processors
Take a look at this library for creating Stream Processors on top of Kafka and running them inside Ferris platform: ferris-SX
Example of a Stream Processor
Below you can find an example application that is using ferris-sx python library functions to count the number of words in incoming messages and then sending the result to twitter_feed_wc Kafka topic.
Creating Docker Container
Below is an example of a dockerfile to create a Docker image for the Twitter Word Count application shown in the previous section. The user is free to use whatever base python image and then add Ferris module and other libraries.
#RUN pip install -i https://test.pypi.org/simple/ ferris-sx==0.0.8 --extra-index-url https://pypi.org/simple/ ferris-sx
RUN pip install ferris-sx
COPY twitter_word_count.py app.py
After the user have built an image and pushed it to some Docker image regitry, he can run it in Ferris SX UI.
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