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Translation layer to convert to/from Mojaloop API to an internal format that is used in Central Services Stack.

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ml-api-adapter

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The ml api adapter is a series of services that facilitate clearing and settlement of transfers between DFSPs, including the following functions:

  • Brokering real-time messaging for funds clearing
  • Maintaining net positions for a deferred net settlement
  • Propagating scheme-level and off-transfer fees

The following documentation represents the services, APIs and endpoints responsible for various ledger functions.

Contents

Docker Image

Official Packaged Release

This package is available as a pre-built docker image on Docker Hub: https://hub.docker.com/r/mojaloop/ml-api-adapter

Build from Source

You can also build it directly from source: https://github.com/mojaloop/ml-api-adapter

However, take note of the default argument in the Dockerfile for NODE_VERSION:

ARG NODE_VERSION=lts-alpine

It is recommend that you set the NODE_VERSION argument against the version set in the local .nvmrc.

This can be done using the following command: npm run docker:build

Or via docker build directly:

docker build \
  --build-arg NODE_VERSION="$(cat .nvmrc)-alpine" \
  -t mojaloop/ml-api-adapter:local \
  .

Deployment

See the Onboarding guide for running the service locally.

Configuration

Environment variables

The ml api adapter has many options that can be configured through environment variables.

Environment variable Description Example values

API

For endpoint documentation, see the API documentation.

Logging

Logs are sent to standard output by default.

Tests

Take a look at the package.json file for a full list of the tests available.

Unit Tests

npm run test:unit

Integration Tests

# start docker-compose dependencies
docker-compose up -d

# wait for docker compoments to start
npm run wait-4-docker

# run the integration tests locally
export ENDPOINT_URL=http://localhost:4545/notification
npm run test:int

Running Functional Tests

If you want to run functional tests locally utilizing the ml-core-test-harness, you can run the following commands:

docker build -t mojaloop/ml-api-adapter:local .
npm run test:functional

By default this will clone the ml-core-test-harness into $ML_CORE_TEST_HARNESS_DIR.

See default values as specified in the test-functional.sh script.

Check test container logs for test results into $ML_CORE_TEST_HARNESS_DIR directory.

If you want to not have the ml-core-test-harness shutdown automatically by the script, make sure you set the following env var export ML_CORE_TEST_SKIP_SHUTDOWN=true.

By doing so, you are then able access TTK UI using the following URI: http://localhost:9660.

Or alternatively, you can monitor the ttk-func-ttk-tests-1 (See ML_CORE_TEST_HARNESS_TEST_FUNC_CONT_NAME in the test-functional.sh script) container for test results with the following command:

docker logs -f ttk-func-ttk-tests-1

TTK Test files:

  • Test Collection: $ML_CORE_TEST_HARNESS_DIR/docker/ml-testing-toolkit/test-cases/collections/tests/p2p.json
  • Env Config: $ML_CORE_TEST_HARNESS_DIR//docker/ml-testing-toolkit/test-cases/environments/default-env.json

Configuration modifiers:

Auditing Dependencies

We use audit-ci along with npm audit to check dependencies for node vulnerabilities, and keep track of resolved dependencies with an audit-ci.jsonc file.

To start a new resolution process, run:

npm run audit:fix

You can then check to see if the CI will pass based on the current dependencies with:

npm run audit:check

The audit-ci.jsonc contains any audit-exceptions that cannot be fixed to ensure that CircleCI will build correctly.

Container Scans

As part of our CI/CD process, we use anchore-cli to scan our built docker container for vulnerabilities upon release.

If you find your release builds are failing, refer to the container scanning in our shared Mojaloop CI config repo. There is a good chance you simply need to update the mojaloop-policy-generator.js file and re-run the circleci workflow.

For more information on anchore and anchore-cli, refer to:

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Translation layer to convert to/from Mojaloop API to an internal format that is used in Central Services Stack.

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