We are very early into our Spark days so the following may sound like a novice question :) I will try to keep this as short as possible.
We are trying to use Spark to introduce a recommendation engine that can be used to provide product recommendations and need help on some design decisions before moving forward. Ours is a web application running on Tomcat. So far, I have created a simple POC (standalone java program) that reads in a CSV file and feeds to FPGrowth and then fits the data and runs transformations. I would like to be able to do the following -
- Scheduler runs nightly in Tomcat (which it does currently) and reads everything from the DB to train/fit the system. This can grow into really some large data and everyday we will have new data. Should I just use SparkContext here, within my scheduler, to FIT the system? Is this correct way to go about this? I am also planning to save the model on S3 which should be okay. We also thought on using HDFS. The scheduler's job will be just to create model and save the same and be done with it.
- On the product page, we can then use the saved model to display the product recommendations for a particular product.
- My understanding is that I should be able to use SparkContext here in my web application to just load the saved model and use it to derive the recommendations. Is this a good design? The problem I see using this approach is that the SparkContext does take time to initialize and this may cost dearly. Or should we keep SparkContext per web application to use a single instance of the same? We can initialize a SparkContext during application context initializaion phase.
Since I am fairly new to using Spark properly, please help me take decision on whether the way I plan to use Spark is the recommended way? I have also seen use cases involving kafka tha does communication with Spark, but can we not do it directly using Spark Context? I am sure a lot of my understanding is wrong, so please feel free to correct me.
Thanks and Regards,