twitch-analytics-demo VS pymgclient

When we’re building demo applications to showcase Memgraph, we always use Docker Compose. This allows us to fire up the application on any system, which is useful when showing a demo at conferences or meetups. Also, applications created with Docker Compose are much easier to deploy. One such demo is the Twitch analytics demo. In the docker-compose.yml file, we defined a couple of services:

While building the whole web application, you save the necessary data to the database and load it whenever needed. Fetching the data can be error-prone since there may be that one minor typo that will bug you. In this tutorial, you'll learn how to build a small part of the Twitch analytics app with the help of GQLAlchemy, an object graph mapper (OGM) that makes building graph-based apps much easier.

To get started, read Part 1 and Part 2. If you want to skip that and hop right on the streaming part, you can find the backend and frontend implementations from the first two parts here.

If you still haven't, you can read already published Part 1 and then continue reading this part. Otherwise, use already implemented backend. In this part, we are going to create React application and visualize general statistics and some interesting insights from Twitch dataset. All implementation that will be mentioned in this part of the blog you can find in frontend folder of the project.

Sure, here is the scraper: https://github.com/memgraph/twitch-analytics-demo/tree/main/... and let me know if you have any questions regarding the Twitch API, since you have to make your own account :)

The data was collected using Twitch API. The data needed to be rearranged so that it could fit the idea of graph databases. Here you can find the script that creates .csv files which we'll load into Memgraph. The files which we'll use are: streamers.csv, teams.csv, vips.csv, moderators.csv and chatters.csv. In streamers.csv we can find important information about languages the user speaks and games the user streams. Those two will actually be nodes in our graph database.

FROM python:3.8 # Install CMake RUN apt-get update && \ apt-get --yes install cmake # Install mgclient RUN apt-get install -y git cmake make gcc g++ libssl-dev && \ git clone https://github.com/memgraph/mgclient.git /mgclient && \ cd mgclient && \ git checkout dd5dcaaed5d7c8b275fbfd5d2ecbfc5006fa5826 && \ mkdir build && \ cd build && \ cmake .. && \ make && \ make install # Install pymgclient RUN git clone https://github.com/memgraph/pymgclient /pymgclient && \ cd pymgclient && \ python3 setup.py build && \ python3 setup.py install # Install packages COPY requirements.txt ./ RUN pip3 install -r requirements.txt COPY card_fraud.py /app/card_fraud.py WORKDIR /app ENV FLASK_ENV=development ENV LC_ALL=C.UTF-8 ENV LANG=C.UTF-8 ENTRYPOINT ["python3", "card_fraud.py"]

As expected, Flask is there, so it will be installed in our container. Next, we have pymgclient, Memgraph database adapter for Python language on top of which gqlalchemy is built. We will connect to the database with gqlalchemy and it will assist us in writing and running queries on Memgraph.