Pokemon Club, a demo app with Nebula Graph

About

In the database world, there is one type of NoSQL DB called graph database, which is good at the query of traversing relationships between small entities, like:

  • Find one person’s 5th-degree friend connections in LinkedIn
  • Detect fraud, starting from one bank account number, connected to its account address, card owner’s social identity number, and recent bank activities, the all linked relationship graph can be queried in realtime and then checked towards certain fraud pattern

Today, I will create a minimal PoC to build one tiny App leveraging a graph database, Nebula Graph.

Nebula Graph is an open-source new graph DB built natively for globally distributed deployment in hyper-scale, with the help of raft consensus algorithm and architecture of shared-nothing, it’s like the Google Spanner or CRDB/TiDB in the graph DB world.

Pokemon Club

Photo from Michael Rivera 🇵🇭

Today, let’s build a new SNS for the Pokemon Trainers, the Pokemon Clubhouse ;)

Pokemon Club is targeting to connect all Pokemon Trainers for their meetup or grouping to fight in certain Pokemon Go Gym.

Like FB or Linkedin, we for sure have the User System with relational DB backed and users metadata of age, avatar, location, and Pokemons she owned, etc. Users as a trainer can create meetups and share posts in Pokemon Club and her friends can get the news feed and comment, join or chat to her.

Apart from the main features, we will only focus on one feature to help trainers explore new friends recommended by the App, based on the Pokemons this trainer owns.

Now we assume either in streaming/ real-time or the batch way, we had put trainer and pokemon relationship in our Nebula Graph DB, and then to create a humble API to get the recommended trainer friends:

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/v1/recommended_friends/<user>
+
| +--------------+ +----------------+
| | | | |
+----------> API Server +---------> Nebula Graph |
| | | |
+--------------+ +----------------+

Then let’s start the hands-on part!

The Hands-on part!

Let’s do bottom-up from the Nebula Graph DB:

Deployment of Nebula Graph

Nebula Graph is released in shapes from source code tarball, [RPM/DEB package](RPM/DEB package), Docker containers, and docker-compose to helm chart or a k8s CRD/operator.

Here, we will start with the smallest footprint and the easiest way, the docker-compose fashion referring to https://github.com/vesoft-inc/nebula-docker-compose.

Here I spawned a preemptible(cheap, no SLA) VM from GCP for this ad-hoc purpose, with the spec of Intel CPU and 8GiB ram, in a Ubuntu 18.04 LTS image.

Also, I allowed TCP ports of 6996 and 7001 from the firewall setting associated with the VM.

Below is the deployment commands in this machine:

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sudo su -i
apt-get update
apt-get install docker.io docker-compose

# deploy a nebula cluster
git clone https://github.com/vesoft-inc/nebula-docker-compose.git
cd nebula-docker-compose/
docker-compose up -d

docker ps --filter health=healthy # wait and verify that 3x graphd, 3x metad, 3x storaged are healthy

# deploy a nebula Studio
cd
git clone https://github.com/vesoft-inc/nebula-web-docker.git
cd nebula-web-docker/v2
docker-compose pull
docker-compose up -d

Nebula Graph Schema and Mock Data Import

Graph Data Schema

Here we created the plainest schema with two types(tags) of vertices and two types of edges:

  • Vertices:

    • trainer
      • property:
        • name
    • pokemon
      • property:
        • name
  • Edges:

    • owns_pokemon
      • source: trainer
      • dest: pokemon
    • is_friend_with
      • source: trainer
      • dest: trainer

From the below image, it’s easier for us to understand the schema:

Data Import

For hyper-scale data migration into Nebula Graph, there is a Spark utility named Nebula Exchange to generate RocksDB SST files to enable extremely high import efficiency.

For a small scale of data, the utility nebula-importer is enough, with which, we could define mapping rules in a yaml file and import CSV files.

Here we leverage a GUI utility in Nebula Studio as it comes with a guided process to define the mapping rule in the web console itself and call the underlying nebula-importer for us w/o composing the yaml file ;).

We have mocked data for importing as below:

  • trainer–>pokemon
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Tom,pikachu
Tom,muk
Jerry,weedle
Jerry,pikachu
Sue,pikachu
Wey,pikachu
Joe,koffing
  • trainer –> trainer
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Tom,Jerry
Sue,Wey

Please refer to the video demo on how this mapping could be done in Nebula Studio :).

Query the data from Nebula Graph

Cool! Now we have a distributed graph DB with some data schema defined and some Pokemon Trainers data is also there!

To simplify the PoC, we assume that the recommendation API will query in this logic:

  • For a given trainer Tom, what are those trainers own the same Pokemons Tom owned?

I know it’s quite a silly logic though ;-), it’s just a minimal example here.

Nebula Graph comes with a SQL-like query language named nGQL, you can for sure refer to its doc here, and in the video, I showed you how to create a query with the help of the Explore GUI in Nebula Studio.

  • owns_pokemon is the edge
  • the query OVER it means to go FROM its source end, which is trainer tag
  • we YIELD its dest end, which is pokemon tag, as pokemon_id
  • then we used | (pipe operator) to pipe the query result in next query starting from the second GO
  • $- stands for the piped result namespace and wo start from $-.pokemon_id now
  • again we query OVER same edge with one extra REVERSELY key word, yes! it means the other direction this time: FROM its dest end!
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GO FROM "Tom" OVER owns_pokemon YIELD owns_pokemon._dst as pokemon_id \
| GO FROM $-.pokemon_id OVER owns_pokemon REVERSELY

The API code handles Web Request and Invokes Nebula Graph

There are different client libs there and now we are using the Python one: https://github.com/vesoft-inc/nebula-python.

I created a simple Flask App with nebula2-python to call our Nebula Graph cluster, the main code logic is as below:

  • it takes api query in format /v1/recommended_friends/<user> and returns result in JSON.
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@app.route("/v1/recommended_friends/<user>", methods=["GET"])
def recommended_friends(user):
"""
Get Recommended new Friends
"""
ng_credential = get_nebula_graph_crential()
session = connection_pool.get_session(*ng_credential)
try:
session.execute('USE pokemon_club')
query = (
f'GO FROM "{ user }" '
'OVER owns_pokemon '
'YIELD owns_pokemon._dst as pokemon_id '
'| GO FROM $-.pokemon_id '
'OVER owns_pokemon REVERSELY')
query_result = session.execute(query)
trainers = [str(trainer.values[0].get_sVal(), 'utf-8') for trainer in query_result.rows()]
trainers = list(set(trainers))
if user in trainers:
trainers.remove(user)
finally:
session.release()

return jsonify(trainers)

After deployed it to Cloud Run and configured the Nebula Graph’s graphd endpoint in env of the cloud run instance, we will have an external URL to enable anyone in the word to query it, and it looks like this:

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$curl https://pokemon-club-v1-****-uw.a.run.app/v1/recommended_friends/Tom | jq
[
"Sue",
"Wey",
"Jerry"
]

Below are how I deployed it into GCP Cloud Run and I put all code in GitHub(https://github.com/wey-gu/pokemon_club) as well.

The Dockerfile

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# Use Python37
FROM python:3.7

# Copy requirements.txt to the docker image and install packages
COPY requirements.txt /

RUN pip install -r requirements.txt

# Set the WORKDIR to be the folder
COPY . /app

# Expose port 5000
EXPOSE 5000
ENV PORT 5000
WORKDIR /app

# Nebula Graph
ENV NG_GCP_PROPJECT_ID=
ENV NG_GCP_PASSWORD_SECRET_ID=
ENV NG_GCP_USER_SECRET_ID=
ENV NG_GCP_CREDENTIAL_VERSION "latest"
ENV NG_ENDPOINTS "127.0.0.1:9669,"
ENV NG_MAX_CONN_POOL_SIZE 10

# Use gunicorn as the entrypoint
CMD exec gunicorn --bind :$PORT main:app --workers 1 --threads 1 --timeout 60

build and deploy container to Cloud Run

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export PROPJECT_ID=<GCP_PROJECT_ID>
docker build -t gcr.io/$PROPJECT_ID/pokemon_club:v1 . # I know I shouldnt build on my own machine but I dont have time to use GCP Cloud Build :P
docker push gcr.io/$PROPJECT_ID/pokemon_club:v1

gcloud run deploy pokemon-club-v1 \
--image gcr.io/$PROPJECT_ID/pokemon_club:v1 \
--region us-west1 \
--platform managed \
--memory 128Mi

More Working In Progress

To Do:

More on Nebula Graph

Nebula Graph is already used by some Gaint Companies and its community is quite active, with the help of that, it grows fast and comes with some great utilities like an extremely fast batch importing tool.

Apart from the k8s native deliverable, also it started to provide a managed service Graph DBaaS, in the test phase though.

Hopefully I could create more contents on Nebula Graph later :), stay tuned!