Work with storage and file systems - Amazon EMR
Access file systems
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Work with storage and file systems

Amazon EMR and Hadoop provide a variety of file systems that you can use when processing cluster steps. You specify which file system to use by the prefix of the URI used to access the data. For example, s3://DOC-EXAMPLE-BUCKET1/path references an Amazon S3 bucket using EMRFS. The following table lists the available file systems, with recommendations about when it's best to use each one.

Amazon EMR and Hadoop typically use two or more of the following file systems when processing a cluster. HDFS and EMRFS are the two main file systems used with Amazon EMR.

Important

Beginning with Amazon EMR release 5.22.0, Amazon EMR uses Amazon Signature Version 4 exclusively to authenticate requests to Amazon S3. Earlier Amazon EMR releases use Amazon Signature Version 2 in some cases, unless the release notes indicate that Signature Version 4 is used exclusively. For more information, see Authenticating Requests (Amazon Signature Version 4) and Authenticating Requests (Amazon Signature Version 2) in the Amazon Simple Storage Service Developer Guide.

File system Prefix Description
HDFS hdfs:// (or no prefix)

HDFS is a distributed, scalable, and portable file system for Hadoop. An advantage of HDFS is data awareness between the Hadoop cluster nodes managing the clusters and the Hadoop cluster nodes managing the individual steps. For more information, see Hadoop documentation.

HDFS is used by the master and core nodes. One advantage is that it's fast; a disadvantage is that it's ephemeral storage which is reclaimed when the cluster ends. It's best used for caching the results produced by intermediate job-flow steps.

EMRFS s3://

EMRFS is an implementation of the Hadoop file system used for reading and writing regular files from Amazon EMR directly to Amazon S3. EMRFS provides the convenience of storing persistent data in Amazon S3 for use with Hadoop while also providing features like Amazon S3 server-side encryption, read-after-write consistency, and list consistency.

Note

Previously, Amazon EMR used the s3n and s3a file systems. While both still work, we recommend that you use the s3 URI scheme for the best performance, security, and reliability.
local file system

The local file system refers to a locally connected disk. When a Hadoop cluster is created, each node is created from an EC2 instance that comes with a preconfigured block of preattached disk storage called an instance store. Data on instance store volumes persists only during the life of its EC2 instance. Instance store volumes are ideal for storing temporary data that is continually changing, such as buffers, caches, scratch data, and other temporary content. For more information, see Amazon EC2 instance storage.

The local file system is used by HDFS, but Python also runs from the local file system and you can choose to store additional application files on instance store volumes.
(Legacy) Amazon S3 block file system s3bfs://

The Amazon S3 block file system is a legacy file storage system. We strongly discourage the use of this system.

Important

We recommend that you do not use this file system because it can trigger a race condition that might cause your cluster to fail. However, it might be required by legacy applications.

Access file systems

You specify which file system to use by the prefix of the uniform resource identifier (URI) used to access the data. The following procedures illustrate how to reference several different types of file systems.

To access a local HDFS

  • Specify the hdfs:/// prefix in the URI. Amazon EMR resolves paths that do not specify a prefix in the URI to the local HDFS. For example, both of the following URIs would resolve to the same location in HDFS. 

    
hdfs:///path-to-data
							
/path-to-data
To access a remote HDFS

  • Include the IP address of the master node in the URI, as shown in the following examples. 

    
hdfs://master-ip-address/path-to-data
						
master-ip-address/path-to-data
To access Amazon S3

  • Use the s3:// prefix.

    						
s3://bucket-name/path-to-file-in-bucket
To access the Amazon S3 block file system

  • Use only for legacy applications that require the Amazon S3 block file system. To access or store data with this file system, use the s3bfs:// prefix in the URI.

    The Amazon S3 block file system is a legacy file system that was used to support uploads to Amazon S3 that were larger than 5 GB in size. With the multipart upload functionality Amazon EMR provides through the Amazon Java SDK, you can upload files of up to 5 TB in size to the Amazon S3 native file system, and the Amazon S3 block file system is deprecated.

    Warning

    Because this legacy file system can create race conditions that can corrupt the file system, you should avoid this format and use EMRFS instead. 

    
s3bfs://bucket-name/path-to-file-in-bucket