def create_lambda_app(node_id: str) -> FlaskLambda:
"""Create flask application for hosting on AWS Lambda.
Args:
node_id: ID used to identify this node.
Returns:
app : FlaskLambda App instance.
"""
# Register dialects to talk to AWS RDS via Data API
import sqlalchemy_aurora_data_api
sqlalchemy_aurora_data_api.register_dialects()
database_name = os.environ.get("DB_NAME")
cluster_arn = os.environ.get("DB_CLUSTER_ARN")
secret_arn = os.environ.get("DB_SECRET_ARN")
app = FlaskLambda(__name__)
app.config.from_mapping(
DEBUG=False,
SQLALCHEMY_DATABASE_URI=f"mysql+auroradataapi://:@/{database_name}",
SQLALCHEMY_ENGINE_OPTIONS={
"connect_args":
dict(aurora_cluster_arn=cluster_arn, secret_arn=secret_arn)
},
SQLALCHEMY_TRACK_MODIFICATIONS=False,
)
# Register app blueprints
from .main import (
auth,
data_centric_routes,
hook,
local_worker,
main_routes,
model_centric_routes,
ws,
)
# Add a test route
@main_routes.route("/test-deployment/")
def test():
return Response(
json.dumps({"message": "Serverless deployment successful."}),
status=200,
mimetype="application/json",
)
# set_node_id(id)
local_worker.id = node_id
hook.local_worker._known_workers[node_id] = local_worker
local_worker.add_worker(hook.local_worker)
# Register app blueprints
app.register_blueprint(main_routes, url_prefix=r"/")
app.register_blueprint(model_centric_routes, url_prefix=r"/model-centric")
app.register_blueprint(data_centric_routes, url_prefix=r"/data-centric")
# Setup database
global db
db.init_app(app)
s = app.app_context().push() # Push the app into context
try:
db.Model.metadata.create_all(
db.engine, checkfirst=False) # Create database tables
seed_db() # Seed the database
except Exception as e:
print("Error", e)
db.session.commit()
# Set Authentication configs
app = auth.set_auth_configs(app)
CORS(app)
# Threads
executor.init_app(app)
app.config["EXECUTOR_PROPAGATE_EXCEPTIONS"] = True
app.config["EXECUTOR_TYPE"] = "thread"
return app
import sqlalchemy
from sqlalchemy import create_engine
from sqlalchemy_aurora_data_api import register_dialects
register_dialects()
cluster_arn = 'arn:aws:rds:us-east-1:797308887321:cluster:serratus-aurora'
secret_arn = 'arn:aws:secretsmanager:us-east-1:797308887321:secret:rds-db-credentials/cluster-KOFPN4Q2TKDBO5FHY6QO5M3S7Q/serratus-agdBn9'
table_name = 'srarun'
engine = create_engine('postgresql+auroradataapi://:@/summary',
connect_args=dict(aurora_cluster_arn=cluster_arn,
secret_arn=secret_arn))
dtype = {} # infer from pandas dataframe
def upload(df):
with engine.connect() as con:
df.to_sql(table_name,
con,
if_exists='append',
index=False,
dtype=dtype)
import os
from sqlalchemy import create_engine
import sqlalchemy_aurora_data_api
sqlalchemy_aurora_data_api.register_dialects()
def get_sqlalchemy_engine():
cluster_arn = os.environ.get("DB_CLUSTER_ARN")
secret_arn = os.environ.get("DB_SECRET_ARN")
return create_engine(
"postgresql+auroradataapi://:@/postgres",
connect_args=dict(aurora_cluster_arn=cluster_arn, secret_arn=secret_arn),
)
def get_test_sqlalchemy_engine():
return create_engine("postgresql://*****:*****@localhost:6543/postgres")
import os
import sys
from sqlalchemy import create_engine
import sqlalchemy_aurora_data_api as aurora
import logging
from alembic import context
aurora.register_dialects()
logging.basicConfig()
logging.getLogger("aurora_data_api").setLevel(logging.DEBUG)
log = logging.getLogger('db-migration')
log.setLevel(logging.DEBUG)
# this is the Alembic Config object, which provides
# access to the values within the .ini file in use.
config = context.config
# Interpret the config file for Python logging.
# This line sets up loggers basically.
# fileConfig(config.config_file_name)
# add your model's MetaData object here
# for 'autogenerate' support
# from myapp import mymodel
# target_metadata = mymodel.Base.metadata
target_metadata = None
# other values from the config, defined by the needs of env.py,
def setUpClass(cls):
register_dialects()
cls.db_name = os.environ.get("AURORA_DB_NAME", __name__)
dialect = "postgresql+auroradataapi://"
# dialect = "postgresql+psycopg2://" + getpass.getuser()
cls.engine = create_engine(dialect + ':@/' + cls.db_name)
def setUpClass(cls):
register_dialects()
cls.db_name = os.environ.get("AURORA_DB_NAME", __name__)
cls.engine = create_engine(cls.dialect + ':@/' + cls.db_name +
"?charset=utf8mb4")