README.md

Ingestion server

Introduction

Ingestion Server is a small private API for copying data from an upstream source and loading it into the Openverse API. This is a two-step process:

1. The data is copied from the upstream catalog database and into the downstream API database.
2. Data from the downstream API database gets indexed in Elasticsearch.

Performance is dependent on the size of the target Elasticsearch cluster, database throughput, and bandwidth available to the ingestion server. The primary bottleneck is indexing to Elasticsearch.

How indexing works

Safety and security considerations

The server has been designed to fail gracefully in the event of network interruptions, full disks, etc. If a task fails to complete successfully, the whole process is rolled back with zero impact to production.

The server is designed to be run in a private network only. You must not expose the private Ingestion Server API to the public internet.

Notifications

If a SLACK_WEBHOOK variable is provided, the ingestion server will provide periodic updates on the progress of a data refresh, or relay any errors that may occur during the process.

Data refresh limit

The DATA_REFRESH_LIMIT variable can be used to define a limit to the number of rows pulled from the upstream catalog database. If the server is running in an ENVIRONMENT that is not prod or production, this is automatically set to 100k records.

Running on the host

1. Create environment variables from the template file.

   just env

2. Install Python dependencies.

   just install

3. Start the Gunicorn server.

   pipenv run gunicorn

Running the tests

Integration Tests

The integration tests can be run using just ingestion_server/test-local. Note that if an .env file exists in the folder you're running just from, it may interfere with the integration test variables and cause unexpected failures.

Making requests

To make cURL requests to the server

pipenv run \
  curl \
    --XPOST localhost:8001/task \
    -H "Content-Type: application/json" \
    -d '{"model": <model>, "action": <action>}'

Replace <model> and <action> with the correct values. For example, to download and index all new images, <model> will be "image" and <action> will be "INGEST_UPSTREAM".

Configuration

All configuration is performed through environment variables. See the env.template file for a comprehensive list of all environment variables. The ones with sane defaults have been commented out.

Pipenv will automatically load .env files when running commands with pipenv run.

Mapping database tables to Elasticsearch

In order to synchronize a given table to Elasticsearch, the following requirements must be met:

• The database table must have an autoincrementing integer primary key named id.
• A SyncableDoctype must be defined in es_syncer/elasticsearch_models. The SyncableDoctype must implement the function database_row_to_elasticsearch_model.
• The table name must be mapped to the corresponding Elasticsearch SyncableDoctype in database_table_to_elasticsearch_model map.

Example from es_syncer/elasticsearch_models.py:

class Image(SyncableDocType):
    title = Text(analyzer="english")
    identifier = Text(index="not_analyzed")
    creator = Text()
    creator_url = Text(index="not_analyzed")
    tags = Text(multi=True)
    created_on = Date()
    url = Text(index="not_analyzed")
    thumbnail = Text(index="not_analyzed")
    provider = Text(index="not_analyzed")
    source = Text(index="not_analyzed")
    license = Text(index="not_analyzed")
    license_version = Text("not_analyzed")
    foreign_landing_url = Text(index="not_analyzed")
    meta_data = Nested()

    class Meta:
        index = 'image'

    @staticmethod
    def database_row_to_elasticsearch_doc(row, schema):
        return Image(
            pg_id=row[schema['id']],
            title=row[schema['title']],
            identifier=row[schema['identifier']],
            creator=row[schema['creator']],
            creator_url=row[schema['creator_url']],
            created_on=row[schema['created_on']],
            url=row[schema['url']],
            thumbnail=row[schema['thumbnail']],
            provider=row[schema['provider']],
            source=row[schema['source']],
            license=row[schema['license']],
            license_version=row[schema['license_version']],
            foreign_landing_url=row[schema['foreign_landing_url']],
            meta_data=row[schema['meta_data']],
        )


# Table name -> Elasticsearch model
database_table_to_elasticsearch_model = {
    'image': Image
}

Deployment

This codebase is deployed as a Docker image to the GitHub Container Registry ghcr.io. The deployed image is then pulled in the production environment. See the ci_cd.yml workflow for deploying to GHCR.

The published image can be deployed using the minimal docker-compose.yml file defined in this folder (do not forget to update the .env file for production). The repository justfile can be used, but the environment variable IS_PROD must be set to true in order for it to reference the production docker-compose.yml file here. The version of the image to use can also be explicitly defined using the IMAGE_TAG environment variable (e.g. IMAGE_TAG=v2.1.1).

Old Docker Hub images

• openverse/ingestion_server
• creativecommons/ingestion_server