mipasa-swarm-connector 1.0.1

MiPasa connector for Swarm (BZZ) distributed storage network

MiPasa Swarm Connector

This Python library implements connecting to Swarm (BZZ) distributed storage network.

Learn more about MiPasa: https://www.mipasa.com/

Learn more about Swarm: https://www.ethswarm.org/

Optional dependencies

• mipasa_swarm_connector[pandas] is required if you wish to read files as CSV or Parquet.
• mipasa_swarm_connector[parquet] is required if you wish to read files as Parquet. More specific versions of this dependency exist:
  • mipasa_swarm_connector[parquet-pyarrow]
  • mipasa_swarm_connector[parquet-fastparquet]

Specifying the Swarm node address

In order to work with Swarm, you will need to specify a Bee node address.

For production environments, we advise doing so via setting the environment variable BEE_GATEWAY_URL to the HTTP endpoint of the chosen node.

For development, you can also specify the node address directly:

from mipasa_swarm_connector import SwarmConnection

content = SwarmConnection('http://localhost:1633').read_file('36f0830b0ece6273a50cc0ff58c4597883e8e41ea9c8ddabb0d87d6b0ca95a1a')
print(repr(content))

Reading files

Simplest usage

from mipasa_swarm_connector import SwarmConnection, SwarmAPIError

try:
    content = SwarmConnection().read_file('<your_swarm_file_hash_here>')
    print(repr(content))
except SwarmAPIError as e:
    print('Failed to fetch, status: %d' % e.status_code)

This code will read the contents of the specified file on Swarm.

By default, the exact file type will be autodetected:

• If the file has a known file type (which is application/json, text/csv, application/vnd.apache.parquet and application/parquet) it will be reintepreted as a JSON object or a Pandas DataFrame.
• Otherwise, the file will be downloaded as bytes.

Any use of read_file can raise SwarmAPIError, which, generally, means that a file is not found (status code 404).

Read a file as CSV

from mipasa_swarm_connector import SwarmConnection, SwarmTypeError

try:
  dataframe = SwarmConnection().read_file('<your_swarm_file_hash_here>', as_type='csv', verify_type=True)
  print(repr(dataframe))
except SwarmTypeError as e:
  print('Expected type %s, got type %s' % (e.expected_type, e.actual_type))

This code will read the contents of the specified file on Swarm as a Pandas DataFrame, expecting it to be in CSV format.

verify_type=True can be passed (but not required) in order to check whether file's MIME type or filename matches CSV.

Read a file as Parquet

from mipasa_swarm_connector import SwarmConnection

dataframe = SwarmConnection().read_file('<your_swarm_file_hash_here>', as_type='parquet')
print(repr(dataframe))

When type-checking for Parquet, two MIME types are recognized:

• application/vnd.apache.parquet
• application/parquet

Read a file as JSON

from mipasa_swarm_connector import SwarmConnection

dataframe = SwarmConnection().read_file('<your_swarm_file_hash_here>', as_type='json')
print(repr(dataframe))

Writing files

from mipasa_swarm_connector import SwarmConnection, SwarmAPIError

try:
    swarm_hash = SwarmConnection().write_file(b'binary-content')
except SwarmAPIError as e:
    print('Failed to upload, status: %d' % e.status_code)

This code will attempt uploading the specified bytes to Swarm.

By default, the exact type of the uploaded file will be autodetected:

• If bytes or bytearray is passed, then the uploaded file type will be application/octet-stream.
• If a string is passed, it will be encoded as UTF-8 and the file type will be text/plain.
• If a Pandas DataFrame is passed, then the uploaded file type will be text/csv and the DataFrame will be formatted as CSV.
• If any other object is passed, JSON encoding will be attempted. If it succeeds, then the uploaded file type will be application/json.

Additionally, if your content is a Pandas DataFrame, you may specify as_type='parquet' in order to automatically format this file as Parquet (the file type will be application/vnd.apache.parquet).