Remote Data Services

As described in Section Internal Data Ingestion, various user-facing functions (such as dataframe.read_csv, dataframe.read_parquet, bag.read_text) and lower level byte-manipulating functions may point to data that lives not on the local storage of the workers, but on a remote system such as Amazon S3.

In this section we describe how to use the various known back-end storage systems. Below we give some details for interested developers on how further storage back-ends may be provided for dask’s use.

Known Storage Implementations

When specifying a storage location, a URL should be provided using the general form protocol://path/to/data. If no protocol is provided, the local file-system is assumed (same as file://). Two methods exist for passing parameters to the backend file-system driver: extending the URL to include username, password, server, port, etc.; and providing storage_options, a dictionary of parameters to pass on. Examples:

df = dd.read_csv('hdfs://[email protected]:port/path/*.csv')

df = dd.read_parquet('s3://bucket/path',
     storage_options={'anon': True, 'use_ssl': False})

Further details on how to provide configuration for each backend is listed next.

Each back-end has additional installation requirements and may not be available at runtime. The dictionary dask.bytes.core._filesystems contains the currently available file-systems. Some require appropriate imports before use.

The following list gives the protocol shorthands and the back-ends they refer to:

  • file: - the local file system, default in the absence of any protocol
  • hdfs: - Hadoop Distributed File System, a for resilient, replicated files within a cluster, using the library hdfs3
  • s3: - Amazon S3 remote binary store, often used with Amazon EC2, using the library s3fs
  • gcs: or gs: - Google Cloud Storage, typically used with Google Compute resource, using gcsfs (in development)

Local File System

Local files are always accessible, and all parameters passed as part of the URL (beyond the path itself) or with the storage_options dictionary will be ignored.

This is the default back-end, and the one used if no protocol is passed at all.

We assume here that each worker has access to the same file-system - either the workers are co-located on the same machine, or a network file system is mounted and referenced at the same path location for every worker node.

Locations specified relative to the current working directory will, in general, be respected (as they would be with the built-in python open), but this may fail in the case that the client and worker processes do not necessarily have the same working directory.


The Hadoop File System (HDFS) is a widely deployed, distributed, data-local file system written in Java. This file system backs many clusters running Hadoop and Spark.

Within dask, HDFS is only available when the module distributed.hdfs is explicitly imported, since the usage of HDFS usually only makes sense in a cluster setting. The distributed scheduler will prefer to allocate tasks which read from HDFS to machines which have local copies of the blocks required for their work, where possible.

By default, hdfs3 attempts to read the default server and port from local Hadoop configuration files on each node, so it may be that no configuration is required. However, the server, port and user can be passed as part of the url: hdfs://user:pass@server:port/path/to/data.

The following parameters may be passed to hdfs3 using storage_options:

  • host, port, user: basic authentication
  • ticket_cache, token: kerberos authentication
  • pars: dictionary of further parameters (e.g., for high availability)

Important environment variables:

  • HADOOP_CONF_DIR or HADOOP_INSTALL: directory containing core-site.xml and/or hdfs-site.xml, containing configuration information
  • LIBHDFS3_CONF: location of a specific xml file with configuration for the libhdfs3 client; this may be the same as one of the files above. Short circuit reads should be defined in this file (see here)


Amazon S3 (Simple Storage Service) is a web service offered by Amazon Web Services.

The S3 back-end will be available to dask is s3fs is importable when dask is imported.

Authentication for S3 is provided by the underlying library boto3. As described in the auth docs this could be achieved by placing credentials files in one of several locations on each node: ~/.aws/credentials, ~/.aws/config, /etc/boto.cfg and ~/.boto. Alternatively, for nodes located within Amazon EC2, IAM roles can be set up for each node, and then no further configuration is required. The final authentication option, is for user credentials can be passed directly in the URL (s3://keyID:keySecret/bucket/key/name) or using storage_options. In this case, however, the key/secret will be passed to all workers in-the-clear, so this method is only recommended on well-secured networks.

The following parameters may be passed to s3fs using storage_options:

  • anon: whether access should be anonymous (default False)
  • key, secret: for user authentication
  • token: if authentication has been done with some other S3 client
  • use_ssl: whether connections are encrypted and secure (default True)
  • client_kwargs: dict passed to the boto3 client, with keys such as region_name, endpoint_url
  • requester_pays: set True if the authenticated user will assume transfer costs, which is required by some providers of bulk data
  • default_block_size, default_fill_cache: these are not of particular interest to dask users, as they concern the behaviour of the buffer between successive reads
  • kwargs: other parameters are passed to the boto3 Session object, such as profile_name, to pick one of the authentication sections from the configuration files referred to above (see here)

Google Cloud Storage

(gcsfs is in early development, expect the details here to change)

Google Cloud Storage is a RESTful online file storage web service for storing and accessing data on Google’s infrastructure.

The GCS backend will be available only after importing gcsfs. The protocol identifiers gcs and gs are identical in their effect.

Authentication for GCS is based on OAuth2, and designed for user verification. Interactive authentication is available when token==None using the local browser, or by using gcloud to produce a JSON token file and passing that. In either case, gcsfs stores a cache of tokens in a local file, so subsequent authentication will not be necessary.

At the time of writing, gcsfs.GCSFileSystem instances pickle including the auth token, so sensitive information is passed between nodes of a dask distributed cluster. This will be changed to allow the use of either local JSON or pickle files for storing tokens and authenticating on each node automatically, instead of passing around an authentication token, similar to S3, above.

Every use of GCS requires the specification of a project to run within - if the project is left empty, the user will not be able to perform any bucket-level operations. The project can be defined using the variable GCSFS_DEFAULT_PROJECT in the environment of every worker, or by passing something like the following

dd.read_parquet('gs://bucket/path', storage_options={'project': 'myproject'}

Possible additional storage options:

  • access : ‘read_only’, ‘read_write’, ‘full_control’, access privilege level (note that the token cache uses a separate token for each level)
  • token: either an actual dictionary of a google token, or location of a JSON file created by gcloud.

Developer API

The prototype for any file-system back-end can be found in bytes.local.LocalFileSystem. Any new implementation should provide the same API, and make itself available as a protocol to dask. For example, the following would register the protocol “myproto”, described by the implementation class MyProtoFileSystem. URLs of the form myproto:// would thereafter be dispatched to the methods of this class.

dask.bytes.core._filesystems['myproto'] = MyProtoFileSystem

For a more complicated example, users may wish to also see dask.bytes.s3.DaskS3FileSystem.

class dask.bytes.local.LocalFileSystem(**storage_options)

API spec for the methods a filesystem

A filesystem must provide these methods, if it is to be registered as a backend for dask.

Implementation for local disc


For a template path, return matching files


Make any intermediate directories to make path writable

open(path, mode='rb', **kwargs)

Make a file-like object


mode: string

normally “rb”, “wb” or “ab” or other.

kwargs: key-value

Any other parameters, such as buffer size. May be better to set these on the filesystem instance, to apply to all files created by it. Not used for local.


Size in bytes of the file at path


Unique identifier, so we can tell if a file changed