PySpark Streaming vs Batch Tutorial (wip)

The idea of this tutorial is to show how code can be shared between streaming and batch analysis in pyspark (see the functions in analysis.py).

The focus is maintenance of the code in the long term, i.e. you want to update your analysis functions, without affecting both streaming and batch pipelines.

Batch is currenty showing 2 use cases:

1. relaunch hashtag analysis -- think you want to have data on a specific temporal window
2. recompute keywords and relaunch analysis -- think you have an improved algorithm and need to update all historical data

This is a work in progress.

TODO:

• storage (relations, update)
• a consumer, like a web ui?
• refactoring
• better use of cluster

Running the Demo

Pre-req: a cluster with pyspark working.

In shell 1, feed some tweets:

$ nc -l -p 9999 -c "python3 tweets.py"

In shell 2, run the streaming app:

$ spark-submit app.py

When a bit of data is available, in shell 3 run the batch app:

$ spark-submit batch.py