AIS POC

A poc pipeline demonstrating streaming information using akka streams to a postgres database.

Requirements

Docker + Docker-compose Gradle Java 8 Makefile

Instructions to run

make runapp

make startinfra (or look at Makefile to get the commands being issued)
Once the python app and the database are running then

gradle run

will compile and start the streaming application

You can access the database at localhost:5430 using psql

psql -d ais_poc -U postgres -h localhost -p 5430

password is 'goodsecurity' by default

Main Directories

database/: DDL/Views for SQL component
docs/: diagram/reporting sample output
python_geo_enrich/: Python service for enriching geo_points
src/: main code for streaming dispatcher.

Components

Main Stream scaffold (Scala + Akka): Extracts, Validates and pushes data to components, also capable of doing its own analytics and rendering them given more dev time.
Enrichment Application (Python): Small Enrichment App written using FastApi and uvicorn this performs a reverse lookup on point information and returns that information to the main stream pipeline. This is mainly for demonstrating the capabilities of how we can link together multiple data sources, as well as being a test of FastAPI and uvicorn as opposed to Flask. There are automatically generated documentations for the endpoint at

localhost:8000/docs

when the infrastructure is running within the framework of streaming as well as giving us a valuable insight into the spatial data.

Database (Postgis): Postgres equipped with more geospatial functions. Acts as main sink for data. The queries to create tables and views are mounted in docker-compose and then executed at startup time

Deployment

The original plan was to deploy all components in the same docker-compose template simulating a kubernetes deployment. There are some intermittent issue with FTP connections and docker that seems to happen on windows so for now the deployment is the enrichment application and the database are run as an infrastructure and the application is run directly using gradle. The stream_etl section can be uncommented, this will give an error without updating of the hosts and paths for the enrich application and the database

Output

The main output of this report is a minute by minute summary of different vessels and the countries waters they are associated with using the enriched data set.

Features

Cats functional programming framework used to validate data in parallel and push it to a "good path" and "bad path"
Enrichment application integrated with streaming data using batching to aim for thousands of events a second throughput
Dockerised deployments mean relative confidence in cross-platform functionality.
Using Akka Streams means we could have the exact same application hooked up to a Kafka broker or other databases instead of reading from FTP files and know the stream will operate the same way. Akka Streams DSL Also means it is relatively trivial to understand the flow of the main business logic sections.

e.g

    broadcast.out(0) ~> map_to_request ~> enrich_data ~> sink_enrichments_to_db ~> Sink.ignore
    broadcast.out(1) ~> sink_ais_messages_to_db ~> Sink.ignore

In addition to being able to stream off data to other message brokers without too much integration work, we could also hold data in collection (e.g a web interface in JSON listing all the cargo vessels latest locations) on the application locally, especially in a distributed context this can be very powerful without requiring the amount of infrastructure and work as spark or flink or other stream processors.

TRReeve/ais_poc