Installation (running your own crawlers)

We recommend using Docker Compose to run your crawlers in production, and we have an example project to help you get started.

  • Make a copy of the memorious/example directory.
  • Add your own crawler YAML configurations into the config directory.
  • Add your Python extensions into the src directory (if applicable).
  • Update with the name of your project and any additional dependencies.
  • If you need to (eg. if your database connection or directory structure is different), update any environment variables in the Dockerfile or docker-compose.yml, although the defaults should work fine.
  • Run docker-compose up -d. This might take a while when it’s building for the first time.

Run a crawler

  • You can access the Memorious CLI through the shell container:
docker-compose run --rm shell

To see the crawlers available to you:

memorious list

And to run a crawler:

memorious run my_crawler

See Usage (or run memorious --help) for the complete list of Memorious commands.

Note: you can use any directory structure you like, src and config are not required, and nor is separation of YAML and Python files. So long as the MEMORIOUS_CONFIG_PATH environment variable points to a directory containing, within any level of directory nesting, your YAML files, Memorious will find them.

Environment variables

Your Memorious instance is configured by a set of environment variables that control database connectivity and general principles of how the system operates. You can set all of these in the Dockerfile.

  • MEMORIOUS_CONFIG_PATH: a path to crawler pipeline YAML configurations.
  • MEMORIOUS_DEBUG: whether to go into a simple mode with task threading disabled. Defaults to False.
  • MEMORIOUS_INCREMENTAL: executing part of a crawler only once per an interval. Defaults to True.
  • MEMORIOUS_EXPIRE: how many days until cached crawled data expires. Defaults to 1 day.
  • MEMORIOUS_DB_RATE_LIMIT: maximum number of database inserts per minute. Defaults to 6000.
  • MEMORIOUS_HTTP_RATE_LIMIT: maximum number of http calls to a host per minute. Defaults to 120.
  • MEMORIOUS_HTTP_CACHE: HTTP request configuration.
  • MEMORIOUS_USER_AGENT: Custom User-Agent string for Memorious.
  • MEMORIOUS_DATASTORE_URI: connection path for an operational database (which crawlers can send data to using the db method). Defaults to a local datastore.sqllite3.
  • REDIS_URL: address of Redis instance to use for crawler logs (uses a temporary FakeRedis if missing).
  • ARCHIVE_TYPE: either file(local file system is used for storage) or s3(Amazon S3 is used) or gs(Google Cloud Storage is used).
  • ARCHIVE_PATH: local directory to use for storage if ARCHIVE_TYPE is file
  • ARCHIVE_BUCKET: bucket name if ARCHIVE_TYPE is s3 or gs
  • AWS_ACCESS_KEY_ID: AWS Access Key ID. (Only needed if ARCHIVE_TYPE is s3)
  • AWS_SECRET_ACCESS_KEY: AWS Secret Access Key. (Only needed if ARCHIVE_TYPE is s3)
  • AWS_REGION: a regional AWS endpoint. (Only needed if ARCHIVE_TYPE is s3)
  • ALEPH_HOST, default is, but any instance of Aleph 2.0 or greater should work.
  • ALEPH_API_KEY, a valid API key for use by the upload operation.

Shut it down

To gracefully exit, run docker-compose down.

Files which were downloaded by crawlers you ran, Memorious progress data from the Redis database, and the Redis task queue, are all persisted in the build directory, and will be reused next time you start it up. (If you need a completely fresh start, you can delete this directory).

Building a crawler

To understand what goes into your config and src directories, check out the examples and reference documentation.

Crawler Development mode

When you’re working on your crawlers, it’s not convenient to rebuild your Docker containers all the time. To run without Docker:

  • Copy the environment variables from the to Make sure MEMORIOUS_CONFIG_PATH points to your crawler YAML files, wherever they may be.
  • Run source
  • Run pip install memorious. If your crawlers use Python extensions, you’ll need to run pip install in your crawlers directory as well
  • Run memorious list to list your crawlers and memorious run your-crawler to run a crawler.

Note: In development mode Memorious uses a single threaded worker (because FakeRedis is single threaded). So task execution concurrency is limited and the worker executes stages in a crawler’s pipeline linearly one after another.