Synapse S3 Storage Provider

This module can be used by synapse as a storage provider, allowing it to fetch and store media in Amazon S3.

Usage

The s3_storage_provider.py should be on the PYTHONPATH when starting synapse.

Example of entry in synapse config:

media_storage_providers:
- module: s3_storage_provider.S3StorageProviderBackend
  store_local: True
  store_remote: True
  store_synchronous: True
  config:
    bucket: <S3_BUCKET_NAME>
    # All of the below options are optional, for use with non-AWS S3-like
    # services, or to specify access tokens here instead of some external method.
    region_name: <S3_REGION_NAME>
    endpoint_url: <S3_LIKE_SERVICE_ENDPOINT_URL>
    access_key_id: <S3_ACCESS_KEY_ID>
    secret_access_key: <S3_SECRET_ACCESS_KEY>
    session_token: <S3_SESSION_TOKEN>

    # Server Side Encryption for Customer-provided keys
    #sse_customer_key: <S3_SSEC_KEY>
    # Your SSE-C algorithm is very likely AES256
    # Default is AES256.
    #sse_customer_algo: <S3_SSEC_ALGO>

    # The object storage class used when uploading files to the bucket.
    # Default is STANDARD.
    #storage_class: "STANDARD_IA"

    # Prefix for all media in bucket, can't be changed once media has been uploaded
    # Useful if sharing the bucket between Synapses
    # Blank if not provided
    #prefix: "prefix/to/files/in/bucket"

    # The maximum number of concurrent threads which will be used to connect
    # to S3. Each thread manages a single connection. Default is 40.
    #
    #threadpool_size: 20

    # Set request_checksum_calculation or response_checksum_validation. 
    # Depending on your S3 provider you may need to set these values,
    # especially if you are using a self-hosted system that does not
    # support the functionality provided by AWS.
    # Default is 'when_required'
    # request_checksum_calculation: "when_supported" | "when_required"
    # response_checksum_validation: "when_supported" | "when_required"
    

This module uses boto3, and so the credentials should be specified as described here.

Regular cleanup job

There is additionally a script at scripts/s3_media_upload which can be used in a regular job to upload content to s3, then delete that from local disk. This script can be used in combination with configuration for the storage provider to pull media from s3, but upload it asynchronously.

Once the package is installed, the script should be run somewhat like the following. We suggest using tmux or screen as these can take a long time on larger servers.

database.yaml should contain the keys that would be passed to psycopg2 to connect to your database. They can be found in the contents of the database.args parameter in your homeserver.yaml.

More options are available in the command help.

> cd s3_media_upload
# cache.db will be created if absent. database.yaml is required to
# contain PG credentials
> ls
cache.db database.yaml
# Update cache from /path/to/media/store looking for files not used
# within 2 months
> s3_media_upload update /path/to/media/store 2m
Syncing files that haven't been accessed since: 2018-10-18 11:06:21.520602
Synced 0 new rows
100%|█████████████████████████████████████████████████████████████| 1074/1074 [00:33<00:00, 25.97files/s]
Updated 0 as deleted

> s3_media_upload upload /path/to/media/store matrix_s3_bucket_name --storage-class STANDARD_IA --delete
# prepare to wait a long time

Packaging and release

For maintainers:

1. Update the __version__ in setup.py. Then commit and push the changes:

   git add setup.py
   git commit -m "vX.Y.Z"
   git push
   

2. Create a signed tag and push that:

   git tag -s vX.Y.Z
   git push origin vX.Y.Z
   

3. Create a release on GitHub for this version.

4. When published, a GitHub action workflow will build the package and upload to PyPI.