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Remote Data

Dask can read data from a variety of data stores including local file systems, network file systems, cloud object stores, and Hadoop. Typically this is done by prepending a protocol like "s3://" to paths used in common data access functions like dd.read_csv:

import dask.dataframe as dd
df = dd.read_csv('s3://bucket/path/to/data-*.csv')
df = dd.read_parquet('gcs://bucket/path/to/data-*.parq')

import dask.bag as db
b = db.read_text('hdfs://path/to/*.json').map(json.loads)

The following remote services are well supported and tested against the main codebase:

  • Local or Network File System: file:// - the local file system, default in the absence of any protocol
  • Hadoop File System: hdfs:// - Hadoop Distributed File System, for resilient, replicated files within a cluster. Can use either hdfs3 or pyarrow.
  • Amazon S3: s3:// - Amazon S3 remote binary store, often used with Amazon EC2, using the library s3fs
  • Google Cloud Storage: gcs:// or gs: - Google Cloud Storage, typically used with Google Compute resource using gcsfs (in development)
  • HTTP(s): http:// or https:// for reading data directly from HTTP web servers
  • Azure Datalake Storage: adl://, for use with the Microsoft Azure platform, using azure-data-lake-store-python. This is experimental, use at your own risk.

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://).

Lower-level details on how Dask handles remote data is described in Section :doc:`Internal Data Ingestion <bytes>`.

Optional Parameters

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://user@server: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 back-end 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 to which they refer.

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.

Hadoop File System

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.

HDFS support can be provided by either hdfs3 or pyarrow, defaulting to the first library installed in that order. To explicitly set which library to use, set hdfs_driver using dask.config.set:

# Use first available option in [hdfs3, pyarrow]
 dd.read_csv('hdfs:///path/to/*.csv')

# Use hdfs3 for HDFS I/O
with dask.config.set(hdfs_driver='hdfs3'):
    dd.read_csv('hdfs:///path/to/*.csv')

# Use pyarrow for HDFS I/O
with dask.config.set(hdfs_driver='pyarrow'):
    dd.read_csv('hdfs:///path/to/*.csv')

# Set pyarrow as the global hdfs driver
dask.config.set(hdfs_driver='pyarrow')

By default, both libraries attempt 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.

Extra Configuration for HDFS3

The following additional options may be passed to the hdfs3 driver via storage_options:

  • host, port, user: Basic authentication
  • ticket_cache, token: Kerberos authentication
  • pars: Dictionary of further parameters (e.g., for high availability)

The hdfs3 driver can also be affected by a few environment variables. For more information on these, see the hdfs3 documentation.

Extra Configuration for PyArrow

The following additional options may be passed to the pyarrow driver via storage_options:

  • host, port, user: Basic authentication
  • kerb_ticket: Path to kerberos ticket cache

PyArrow's libhdfs driver can also be affected by a few environment variables. For more information on these, see the pyarrow documentation.

Amazon S3

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

The S3 back-end available to Dask is s3fs, and 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 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 or 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

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

The GCS back-end is identified by the protocol identifiers gcs and gs, which are identical in their effect.

Multiple modes of authentication are supported. These options should be included in the storage_options dictionary as {'token': ..} submitted with your call to a storage-based Dask function/method. See the gcsfs documentation for further details.

General recommendations for distributed clusters, in order:

  • use 'anon' for public data
  • use 'cloud' if this is available
  • use gcloud to generate a JSON file, and distribute this to all workers, and supply the path to the file
  • use gcsfs directly with the 'browser' method to generate a token cache file (~/.gcs_tokens) and distribute this with method 'cache'

The final suggestion may be the fastest and simplest for authenticated access (as opposed to anonymous), since it will not require re-authentication. However, this method is not secure since credentials will be passed directly around the cluster. This is fine if you are certain that the cluster is itself secured. You need to create a GCSFileSystem object using any method that works for you and then pass its credentials directly:

gcs = GCSFileSystem(...)
dask_function(..., storage_options={'token': gcs.session.credentials})

Azure Datalake

Warning

Support for ADL is experimental - use at own risk.

Parameters tenant_id, client_id, and client_secret are required for authentication in storage_options=, and all other parameters will be passed on to the AzureDLFileSystem constructor (follow the link for further information). The auth parameters are passed directly to workers, so this should only be used within a secure cluster.

HTTP(s)

Direct file-like access to arbitrary URLs is available over HTTP and HTTPS. However, there is no such thing as glob functionality over HTTP, so only explicit lists of files can be used.

Server implementations differ in the information they provide - they may or may not specify the size of a file via a HEAD request or at the start of a download - and some servers may not respect byte range requests. The HTTPFileSystem therefore offers best-effort behaviour: the download is streamed but, if more data is seen than the configured block-size, an error will be raised. To be able to access such data you must read the whole file in one shot (and it must fit in memory).

Note that, currently, http:// and https:// are treated as separate protocols, and cannot be mixed.

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.

.. currentmodule:: dask.bytes.local

.. autoclass:: LocalFileSystem
   :members: