S3 Storage Features

Synapse can use a variety of storage mechanisms to store content, however the most common storage solution is AWS S3. This article illustrates some special features that can be used with S3 storage and how they interact with the Python client.

External storage locations

Synapse folders can be configured to use custom implementations for their underlying data storage. More information on this feature can be found here. The most common implementation of this is to configure a folder to store data in a user controlled AWS S3 bucket rather than Synapse’s default internal S3 storage.

The following illustrates creating a new folder backed by a user specified S3 bucket.

  1. Ensure that the bucket is properly configured.

  2. Create a folder and configure it to use external S3 storage:

# create a new folder to use with external S3 storage
folder = syn.store(Folder(name=folder_name, parent=parent))
folder, storage_location, project_setting = syn.create_s3_storage_location(

# if needed the unique storage location identifier can be obtained e.g.
storage_location_id = storage_location['storageLocationId']

Once an external S3 storage folder exists, you can interact with it as you would any other folder using Synapse tools. If you wish to add an object that is stored within the bucket to Synapse you can do that by adding a file handle for that object using the Python client and then storing the file to that handle.

parent_synapse_folder_id = 'syn123'
local_file_path = '/path/to/local/file'
bucket = 'my-external-synapse-bucket'
s3_key = 'path/within/bucket/file'

# in this example we use boto to create a file independently of Synapse
s3_client = boto3.client('s3')

# now we add a file handle for that file and store the file to that handle
file_handle = syn.create_external_s3_file_handle(
file = File(parentId=folder['id'], dataFileHandleId=file_handle['id'])
file_entity = syn.store(file)

STS Storage Locations

Create an STS enabled folder to use AWS Security Token Service credentials with S3 storage locations. These credentials can be scoped to access individual Synapse files or folders and can be used with external S3 tools such as the awscli and the boto3 library separately from Synapse to read and write files to and from Synapse storage. At this time read and write capabilities are supported for external storage locations, while default Synapse storage is limited to read only. Please read the linked documentation for a complete understanding of the capabilities and restrictions of STS enabled folders.

Creating an STS enabled folder

Creating an STS enabled folder is similar to creating an external storage folder as described above, but this time passing an additional sts_enabled=True keyword parameter. The bucket_name and base_key parameters apply to external storage locations and can be omitted to use Synapse internal storage. Note also that STS can only be enabled on an empty folder.

# create a new folder to use with STS and external S3 storage
folder = syn.store(Folder(name=folder_name, parent=parent))
folder, storage_location, project_setting = syn.create_s3_storage_location(

Using credentials with the awscli

This example illustrates obtaining STS credentials and using them with the awscli command line tool. The first command outputs the credentials as shell commands to execute which will then be picked up by subsequent aws cli commands.

$ synapse get-sts-token -o shell syn123 read_write

export SYNAPSE_STS_S3_LOCATION="s3://my-external-synapse-bucket/path/within/bucket"
export AWS_ACCESS_KEY_ID="<access_key_id>"
export AWS_SECRET_ACCESS_KEY="<secret_access_key>"
export AWS_SESSION_TOKEN="<session_token>

# if the above are executed in the shell, the awscli will automatically apply them

# e.g. copy a file directly to the bucket using the exported credentials
$ aws s3 cp /path/to/local/file $SYNAPSE_STS_S3_LOCATION

Using credentials with boto3 in python

This example illustrates retrieving STS credentials and using them with boto3 within python code, in this case to upload a file.

# the boto output_format is compatible with the boto3 session api.
credentials = syn.get_sts_storage_token('syn123', 'read_write', output_format='boto')

s3_client = boto3.client('s3', **credentials)

Automatic transfers to/from STS storage locations using boto3 with synapseclient

The Python Synapse client can be configured to automatically use STS tokens to perform uploads and downloads to enabled storage locations using an installed boto3 library rather than through the traditional Synapse client APIs. This can improve performance in certain situations, particularly uploads of large files, as the data transfer itself can be conducted purely against the AWS S3 APIs, only invoking the Synapse APIs to retrieve the necessary token and to update Synapse metadata in the case of an upload. Once configured to do so, retrieval of STS tokens for supported operations occurs automatically without any change in synapseclient usage.

To enable STS/boto3 transfers on all get and store operations, do the following:

  1. Ensure that boto3 is installed in the same Python installation as synapseclient.

pip install boto3
  1. To enable automatic transfers on all uploads and downloads, update your Synapse client configuration file (typically “.synapseConfig” in your $HOME directory, unless otherwise configured) with the [transfer] section, if it is not already present. To leverage STS/boto3 transfers on a per Synapse client object basis, set the use_boto_sts_transfers property.

# add to .synapseConfig to automatically apply as default for all synapse client instances

# alternatively set on a per instance basis within python code
syn.use_boto_sts_transfers = True

Note that if boto3 is not installed, then these settings will have no effect.

Storage location migration

There are circumstances where it can be useful to move the files underlying Synapse entities from one storage location to another without impacting the structure or identifiers of the Synapse entities themselves. An example scenario is needing to use STS features with an existing Synapse Project that was not initially configured with an STS enabled custom storage location.

The Synapse client has utilities for migrating entities to a new storage location without having to download the content locally and re-uploading it which can be slow, and may alter the meta data associated with the entities in undesirable ways.

Migrating programmatically

Migrating a Synapse project or folder programatically is a two step process.

First ensure that you know the id of the storage location you want to migrate to. More info on storage locations can be found above and here.

Once the storage location is known, the first step to migrate an entity is create a migratable index of its contents using the index_files_for_migration function, e.g.

import synapseutils

entity_id = 'syn123'  # a Synapse entity whose contents need to be migrated, e.g. a Project or Folder
storage_location_id = '12345'  # the id of the storage location being migrated to

# a path on disk where this utility can create a sqlite database to store its index.
# nothing needs to exist at this path, but it must be a valid path on a volume with sufficient
# disk space to store a meta data listing of all the contents in the indexed entity.
# a rough rule of thumb is 100kB per 1000 entities indexed.
db_path = '/tmp/foo/bar.db'

result = synapseutils.index_files_for_migration(

    # optional args, see function documentation linked above for a description of these parameters

If called on a container (e.g. a Project or Folder) the index_files_for_migration function will recursively index all of the children of that container (including its subfolders). Once the entity has been indexed you can optionally programmatically inspect the the contents of the index or output its contents to a csv file in order to manually inspect it using the available methods on the returned result object.

The next step to trigger the migration from the indexed files is using the migrate_indexed_files function, e.g.

result = synapseutils.migrate_indexed_files(

    # optional args, see function documentation linked above for a description of these parameters

The result can be again be inspected as above to see the results of the migration.

Note that above the force parameter is necessary if running from a non-interactive shell. Proceeding with a migration requires confirmation in the form of user prompt. If running programatically this parameter instead confirms your intention to proceed with the migration.

Migrating from the command line

Synapse entities can also be migrated from the command line. The options are similar to above. Whereas migrating programatically involves two separate function calls, from the command line there is a single migrate command with the dryRun argument providing the option to generate the index only without proceeding onto the migration.

Note that as above, confirmation is required before a migration starts. As above, this must either be in the form of confirming via a prompt if running the command from an interactive shell, or using the force command.

The optional csv_log_path argument will output the results to a csv file for record keeping, and is recommended.

synapse migrate syn123 54321 /tmp/migrate.db --csv_log_path /tmp/migrate.csv
Sample output:
Indexing Project syn123
Indexing file entity syn888
Indexing file entity syn999
Indexed 2 items, 2 needing migration, 0 already stored in destination storage location (54321). Encountered 0 errors.
21 items for migration to 54321. Proceed? (y/n)? y
Creating new version for file entity syn888
Creating new version for file entity syn999
Completed migration of syn123. 2 files migrated. 0 errors encountered
Writing csv log to /tmp/migrate.csv