HAR Format Data Model

The Hadoop Archive data format has the following layout:

foo.har/_masterindex //stores hashes and offsets
foo.har/_index //stores file statuses
foo.har/part-[1..n] //stores actual file data

The file data is stored in multipart files, which are indexed in order to retain the original separation of data. Moreover, the file parts can be accessed in parallel by MapReduce programs. The index files also record the original directory tree structures and file status.

HAR File System

Most archival systems, such as tar, are tools for archiving and de-archiving. Generally, they do not fit into the actual file system layer and hence are not transparent to the application writer in that the archives must be expanded before use.

The Hadoop Archive is integrated with the Hadoop file system interface. The HarFileSystem implements the FileSystem interface and provides access via the har:// scheme. This exposes the archived files and directory tree structures transparently to users. Files in a HAR can be accessed directly without expanding them.

For example, if we have the following command to copy an HDFS file to a local directory:

hdfs dfs –get hdfs://namenode/foo/file-1 localdir

Suppose a Hadoop Archive bar.har is created from the foo directory. With the HAR, the command to copy the original file becomes:

hdfs dfs –get har://namenode/bar.har/foo/file-1 localdir

Users only need to change the URI paths. Alternatively, users may choose to create a symbolic link (from hdfs://namenode/foo to har://namenode/bar.har/foo in the example above), and then even the URIs do not need to be changed. In either case, HarFileSystem will be invoked automatically to provide access to the files in the HAR. Because of this transparent layer, HAR is compatible with the Hadoop APIs, MapReduce, the FS shell command-line interface, and higher-level applications such as Pig, Zebra, Streaming, Pipes, and DistCp.

Hadoop Archiving Tool

Hadoop Archives can be created using the Hadoop archiving tool. The archiving tool uses MapReduce to efficiently create Hadoop Archives in parallel. The tool can be invoked using the command:

hadoop archive -archiveName name -p <parent> <src>* <dest>

A list of files is generated by traversing the source directories recursively, and then the list is split into map task inputs. Each map task creates a part file (about 2 GB, configurable) from a subset of the source files and outputs the metadata. Finally, a reduce task collects metadata and generates the index files.