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Project: Data Engineering

This is the monorepo containing all necessary components to run the project for the data engineering course.

Service Descriptions

  • Instagram API Mock Service: This services continuously pushes data into the Kafka topics, to simulate the process of scraping data from the Instagram API. Additionally the service creates mock pictures by calling thispersondoesnotexist.com and hosts them under the path /images.
  • Storage Service: This service subscribes to the Kafka Topic: instagram-profiles and upon new messages creates new entries in the postgresql data base. Any new media entries it finds (e.g. posts) triggers a download of the associated image, that is then uploaded to the minio instance.
  • Data Access Service: This REST-service can be called at /api/all to get a basic aggregation of the insight data of all currently tracked profiles.
  • Visualization Service: This service hosts a nodejs-based frontend that visualizes some basic statistics (number of likes, reach, saved, comments) over the lifetime of all media.

Configuration

All configuration should be done in the accompanying docker compose file using the environment variables.
All variables need to be set in the docker-compose file or the project won't start.

Variable Description Default
S3_EXTERNAL_ENDPOINT The external endpoint under which the S3-based storage can be accessed localhost:10002
S3_ENDPOINT The docker-network internal endpoint to call the S3-storage minio:10002
S3_ACCESS_KEY_ID Access key for accessing the minio storage/ui as a root user admin
S3_SECRET_ACCESS_KEY The password for accessing the minio storage/ui as a root user admin123
POSTGRES_USER Default postgres database user postgres
POSTGRES_PASSWORD Default password for the POSTGRES_USER test
POSTGRES_DB Default database to create/use in postgres postgres
POSTGRES_HOST Host under which postgres is reachable postgres
POSTGRES_PORT Port used to communicate with postgres 5432
CLICKHOUSE_ENDPOINT Endpoint used by the internal clients to communicate with the clickhouse instance clickhouse:9000
DATA_ACCESS_SERVICE_HOST Host called from within the visualization service to receive data data-access-service
DATA_ACCESS_SERVICE_PORT Port called from within the visualization service to receive data 3001
KAFKA_BOOTSTRAP_SERVERS Location of kafka servers kafka:9092

Additionally the instagram API mock service offers a few additional variables to configure the data creation.

Variable Description Default
NUM_PROFILES Number of profiles to simulate 10
NUM_PICTURES_PER_PROFILE Number of pictures per profile to simulate 10
INSIGHT_UPDATE_FREQ_MS Time in milliseconds between updates for insights of a media post 30
PROFILE_UPDATE_FREQ_MS Time in milliseconds between updates for a singular profile 50

Running the Project

There are currently two ways to start up the project:

  1. Use the included reset.sh-script. This script deletes any old leftovers of previous runs, rebuilds all containers and starts the environment.
./reset.sh
  1. Use docker compose directly to start the application
docker compose build 
docker compose up -d 

The project can be configured by adjusting the variables in the docker compose file.
Depending on the variables, the required start up time can vary in length. The Instagram API Mock Service in particular requires a longer start time, as there is a 500ms delay between fetching the individual images from thispersondoesnotexist.com to guarantee that unique images are fetched.
Therefore, this service usually needs (NUM_PROFILES*NUM_PICTURES_PER_PROFILE / 2) seconds to start.
After starting the project the data can be visualized by opening the url: http://localhost:5473 in a browser.

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A project for my study course

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