emojirades 0.16.3

A Slack bot that understands the Emojirades game!

Emojirades

Slack bot that understands the emojirades game and handles score keeping.

Developing

Install the dependencies

pip3 install --upgrade pip wheel

pip3 install -r requirements.txt --upgrade
pip3 install -r test_requirements.txt --upgrade

Install the module

pip3 install -e .

Run the tests

# Linter
pylint emojirades

# Formatter
black --check .

# Tests
pytest

Creating new DB revisions

If you make changes to emojirades/persistence/models you'll need to generate new revisions. This tracks the changes and applies them to the DB at each bots startup

cd emojirades/persistence/models
alembic revision --autogenerate --message "<useful insightful few words>"

Running

Set Environment Variables

If you're using an auth file from AWS S3 you'll need to set the appropriate AWS_ environment variables!

Separate Database

Using a database like PostgreSQL, you'll need to have created a database with a username and password before starting this.

If you've just created a fresh DB, you'll need to load the initial database:

emojirades -vv init --db-uri "sqlite:///emojirades.db"

After initialising the DB you can load in any optional pre-existing state:

emojirades -vv populate --db-uri "sqlite:///emojirades.db" --table gamestate --data-file path/to/gamestate.json

The json files must be a list of objects, with each objects key: value representing a column in the associated model

If you are coming from the old style of state.json and scores.json you can run the following to produce json files that can be used in the above populate command

./bin/old_to_new_persistence.py --workspace-id TABC123 --state-file state.json --score-file scores.json

This will produce state.json.processed, scores.json.processed_scores and scores.json.processed_score_history

They can be populated by running:

emojirades -vv populate --db-uri "sqlite:///emojirades.db" --table gamestate --data-file state.json.processed
emojirades -vv populate --db-uri "sqlite:///emojirades.db" --table scoreboard --data-file scores.json.processed_scores
emojirades -vv populate --db-uri "sqlite:///emojirades.db" --table scoreboard_history --data-file scores.json.processed_score_history

Run the daemon for a single workspace

This command uses locally stored files to keep the game state:

emojirades single --db-uri sqlite:///emojirades.db --auth-uri auth.json

This command uses a separate PostgreSQL DB and an auth file from AWS S3:

`emojirades single --db-uri postgresql://user:pass@hostname/database --auth-uri s3://bucket/auth.json

Run the daemon for multiple workspaces

Here we provide a local folder of workspaces and an optional set of workspace ids (will load all in folder by default):

emojirades mulitple --workspaces-dir path/to/workspaces [--workspace-id A1B2C3D4E]

Here we provide an S3 path of workspaces and an optional set of workspace ids (will load all in folder by default):

emojirades multiple --workspaces-dir s3://bucket/path/to/workspaces [--workspace-id A1B2C3D4E]

Here we provide an S3 path of workspaces and an AWS SQS queue to listen to for new workspaces:

emojirades multiple --workspaces-dir s3://bucket/path/to/workspaces --onboarding-queue workspace-onboarding-queue

Here we provide an S3 path of workspaces and override the db_uri:

`emojirades multiple --workspaces-dir s3://bucket/path/to/workspaces --db-uri sqlite:///emojirades.db

The workspaces directory must be in the following format (local or s3):

./workspaces

./workspaces/shards
./workspaces/shards/0
./workspaces/shards/0/A1B2C3D4E.json
./workspaces/shards/0/Z9Y8X7W6V.json

./workspaces/directory
./workspaces/directory/A1B2C3D4E
./workspaces/directory/A1B2C3D4E/auth.json
./workspaces/directory/Z9Y8X7W6V
./workspaces/directory/Z9Y8X7W6V/auth.json

Each instance of the bot will listen to a specific shard (specified as the --workspaces-dir).

The contents of the shard config (eg. ./workspaces/shards/0/A1B2C3D4E.json) will be a file similar to:

{
  "workspace_id": "A1B2C3D4E",
  "db_uri": "sqlite:////data/emojirades.db",
  "auth_uri": "s3://bucket/workspaces/directory/A1B2C3D4E/auth.json",
}

The concept above with the two different directories is shards to allow for the bot to scale out horizontally. As the bot(s) get busier, the operator can increase the shard count (number of bot instances) and new onboarded workspaces are allocated to the next available shard with capacity.

The emojirades bot will take care of running multiple games across different channels in a single workspace. This is a limitation in the design currently where you need a bot-per-workspace.

Service configuration

cp emojirades.service /etc/systemd/system/
sudo chmod 0664 /etc/systemd/system/emojirades.service

# Edit the /etc/systemd/system/emojirades.service file and update the user and group

cp emojirades.config /etc/emojirades
sudo chmod 0400 /etc/emojirades

# Edit the /etc/emojirades config file with your configuration for the bot

sudo systemctl daemon-reload
sudo systemctl enable emojirades
sudo systemctl start emojirades

Release process (for master branch)

1. Update setup.py with the new version (vX.Y.Z)
2. Commit into the master branch
3. Tag the commit with vX.Y.Z
4. Github Actions will trigger the Release Job when a tagged commit to master is detected
   1. Changelog will be generated and a Github Release as well with the changelog
   2. New python wheel will be built and published to PyPI and attached to the Release
   3. New container image will be built and published to Docker Hub

Building the Container Image

docker build --pull --no-cache -t emojirades/emojirades:X.Y.Z -t emojirades/emojirades:latest .

Running the Container

In this example we run the game with S3 hosted configuration for a single workspace.

docker run -d \
  --name emojirades \
  --restart=always \
  -v "/path/to/your/.aws/:/root/.aws/:ro" \
  -v "emojirades-data:/data" \
  -e "AWS_PROFILE=emojirades" \
  emojirades/emojirades:X.Y.Z \
    --db-uri sqlite:////data/emojirades.db \
    --auth-uri s3://bucket/path/to/auth.json \
    -vv