HDFS is a filesystem designed for storing very large files with streaming data access patterns, running on clusters of commodity hardware. Let’s examine this statement in more detail:
Very large files
“Very large” in this context means files that are hundreds of megabytes, gigabytes, or terabytes in size. There are Hadoop clusters running today that store petabytes of data.
Streaming data access
HDFS is built around the idea that the most efficient data processing pattern is a write-once, read-many-times pattern. A dataset is typically generated or copied analysis will involve a large proportion, if not all, of the dataset, so the time to read the whole dataset is more important than the latency in reading the first record.
Hadoop doesn’t require expensive, highly reliable hardware to run on. It’s designed to run on clusters of commodity hardware (commonly available hardware available from multiple vendors) for which the chance of node failure across the cluster is high, at least for large clusters. HDFS is designed to carry on working without a noticeable interruption to the user in the face of such failure.
It is also worth examining the applications for which using HDFS does not work so well. While this may change in the future, these are areas where HDFS is not a good fit today:
Low-latency data access
Applications that require low-latency access to data, in the tens of milli seconds range, will not work well with HDFS. Remember, HDFS is optimized for delivering a high throughput of data, and this may be at the expense of latency. HBase (Chapter Zookeeper) is currently a better choice for low-latency access.
Lots of small files
Since the namenode holds file system meta data in memory, the limit to the number of files in a file system is governed by the amount of memory on the name node. As a rule of thumb, each file, directory, and block takes about 150 bytes. So, for example, if you had one million files, each taking one block, you would need at least 300 MB of memory. While storing millions of files is feasible, billions is beyond the capability of current hardware. Multiple writers, arbitrary file modifications Files in HDFS may be written to by a single writer. Writes are always made at the end of the file. There is no support for multiple writers, or for modifications atarbitrary offsets in the file. (These might be supported in the future, but they are likely to be relatively inefficient.)
See Setting Up A Hadoop Cluster Chapter for a typical machine specification.
For an in-depth exposition of the scalability limits of HDFS, see Konstantin V. Shvachko’s “Scalability of the Hadoop Distributed File System,” and the companion paper “HDFS Scalability: The limits to growth,”.
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The Hadoop Distributed Filesystem
Developing A Mapreduce Application
How Mapreduce Works
Mapreduce Types And Formats
Setting Up A Hadoop Cluster
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