Hadoop and HBase

This may not be a surprise for a lot of people but it was for me. Even though I have been using lucene and nutch for some time, I didn’t really know enough about Hadoop and HBase until recently.

Hadoop

  • Scalable: Hadoop can reliably store and process petabytes.
  • Economical: It distributes the data and processing across clusters of commonly available computers. These clusters can number into the thousands of nodes.
  • Efficient: By distributing the data, Hadoop can process it in parallel on the nodes where the data is located. This makes it extremely rapid.
  • Reliable: Hadoop automatically maintains multiple copies of data and automatically redeploys computing tasks based on failures.


Hadoop implements MapReduce, using the Hadoop Distributed File System (HDFS) (see figure below.) MapReduce divides applications into many small blocks of work. HDFS creates multiple replicas of data blocks for reliability, placing them on compute nodes around the cluster. MapReduce can then process the data where it is located.

HBase

Google’s Bigtable, a distributed storage system for structured data, is a very effective mechanism for storing very large amounts of data in a distributed environment.

Just as Bigtable leverages the distributed data storage provided by the Google File System, Hbase will provide Bigtable-like capabilities on top of Hadoop.

Data is organized into tables, rows and columns, but a query language like SQL is not supported. Instead, an Iterator-like interface is available for scanning through a row range (and of course there is an ability to retrieve a column value for a specific key).

Any particular column may have multiple values for the same row key. A secondary key can be provided to select a particular value or an Iterator can be set up to scan through the key-value pairs for that column given a specific row key.

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