Hadoop’s Distributed File System is designed to reliably store very large files across machines in a large cluster. It is inspired by the Google File System. Hadoop DFS stores each file as a sequence of blocks, all blocks in a file except the last block are the same size. Blocks belonging to a file are replicated for fault tolerance. The block size and replication factor are configurable per file. Files in HDFS are “write once” and have strictly one writer at any time.

The Hadoop Distributed File system

When a dataset outgrows the storage capacity of a single physical machine, it becomes necessary to partition it across a number of separate machines. File systems that manage the storage across a network of machines are called distributed filesystems. Since they are network-based, all the complications of network programming kick in, thus making distributed filesystems more complex than regular disk filesystems. For example, one of the biggest challenges is making the filesystem tolerate node failure without suffering data loss.

Hadoop comes with a distributed filesystem called HDFS, which stands for Hadoop
Distributed Filesystem. (You may sometimes see references to “DFS”—informally or in older documentation or configurations—which is the same thing.) HDFS is Hadoop’s flagship filesystem and is the focus of this chapter, but Hadoop actually has a general purpose filesystem abstraction, so we’ll see along the way how Hadoop integrates with other storage systems (such as the local filesystem and Amazon S3).

The Design of HDFS

HDFS is a filesystem designed for storing very large files with streaming data access patterns, running on clusters of commodity hardware.

  • Very large files
  • Streaming data access
  • Commodity hardware
  • Low-latency data access
  • Lots of small files
  • Multiple writers, arbitrary file modifications

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