Handling Big Data with HBase Part 1: Introduction
Handling Big Data with HBase Part 1: Introduction
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This is the first in a series of blogs that will introduce Apache HBase. This blog provides a brief introduction to HBase. In later blogs you will see how the the HBase shell can be used for quick and dirty data access via the command line, learn about the high-level architecture of HBase, learn the basics of the Java API, and learn how to live without SQL when designing HBase schemas.
In the past few years we have seen a veritable explosion in various ways to store and retrieve data. The so-called NoSQL databases have been leading the charge and creating all these new persistence choices. These alternatives have, in large part, become more popular due to the rise of Big Data led by companies such as Google, Amazon, Twitter, and Facebook as they have amassed vast amounts of data that must be stored, queried, and analyzed. But more and more companies are collecting massive amounts of data and they need to be able to effectively use all that data to fuel their business. For example, social networks all need to be able to analyze large social graphs of people and make recommendations for who to link to next, while almost every large website out there now has a recommendation engine that tries to suggest ever more things you might want to purchase. As these businesses collect more data, they need a way to be able to easily scale-up without needing to re-write entire systems.
Since the 1970s, relational database management systems (RDBMS) have dominated the data landscape. But as businesses collect, store and process more and more data, relational databases are harder and harder to scale. At first you might go from a single server to a master/slave setup, and add caching layers in front of the database to relieve load as more and more reads/writes hit the database. When performance of queries begins to degrade, usually the first thing to be dropped is indexes, followed quickly by denormalization to avoid joins as they become more costly. Later you might start to precompute (or materialize) the most costly queries so that queries then effectively become key lookups and perhaps distribute data in huge tables across multiple database shards. At this point if you step back, many of the key benefits of RDBMSs have been lost — referential integrity, ACID transactions, indexes, and so on. Of course, the scenario just described presumes you become very successful, very fast and need to handle more data with continually increasing data ingestion rates. In other words, you need to be the next Twitter.
Or do you? Maybe you are working on an environment monitoring project that will deploy a network of sensors around the world, and all these sensors will produce huge amounts of data. Or maybe you are working on DNA sequencing. If you know or think you are going to have massive data storage requirements where the number of rows run into the billions and number of columns potentially in the millions, you should consider alternative databases such as HBase. These new databases are designed from the ground-up to scale horizontally across clusters of commodity servers, as opposed to vertical scaling where you try to buy the next larger server (until there are no more bigger ones available anyway).
HBase is a database that provides real-time, random read and write access to tables meant to store billions of rows and millions of columns. It is designed to run on a cluster of commodity servers and to automatically scale as more servers are added, while retaining the same performance. In addition, it is fault tolerant precisely because data is divided across servers in the cluster and stored in a redundant file system such as the Hadoop Distributed File System (HDFS). When (not if) servers fail, your data is safe, and the data is automatically re-balanced over the remaining servers until replacements are online. HBase is a strongly consistent data store; changes you make are immediately visible to all other clients.
HBase is modeled after Google's Bigtable, which was described in a paper written by Google in 2006 as a "sparse, distributed, persistent multi-dimensional sorted map." So if you are used to relational databases, then HBase will at first seem foreign. While it has the concept of tables, they are not like relational tables, nor does HBase support the typical RDBMS concepts of joins, indexes, ACID transactions, etc. But even though you give those features up, you automatically and transparently gain scalability and fault-tolerance. HBase can be described as a key-value store with automatic data versioning.
You can CRUD (create, read, update, and delete) data just as you would expect. You can also perform scans of HBase table rows, which are always stored in HBase tables in ascending sort order. When you scan through HBase tables, rows are always returned in order by row key. Each row consists of a unique, sorted row key (think primary key in RDBMS terms) and an arbitrary number of columns, each column residing in a column family and having one or more versioned values. Values are simply byte arrays, and it's up to the application to transform these byte arrays as necessary to display and store them. HBase does not attempt to hide this column-oriented data model from developers, and the Java APIs are decidedly more lower-level than other persistence APIs you might have worked with. For example, JPA (Java Persistence API) and even JDBC are much more abstracted than what you find in the HBase APIs. You are working with bare metal when dealing with HBase.
Conclusion to Part 1
In this introductory blog we've learned that HBase is a non-relational, strongly consistent, distributed key-value store with automatic data versioning. It is horizontally scaleable via adding additional servers to a cluster, and provides fault-tolerance so data is not lost when (not if) servers fail. We've also discussed a bit about how data is organized within HBase tables; specifically each row has a unique row key, some number of column families, and an arbitrary number of columns within a family. In the next blog, we'll take first steps with HBase by showing interaction via the HBase shell.
- HBase web site, http://hbase.apache.org/
- HBase wiki, http://wiki.apache.org/hadoop/Hbase
- HBase Reference Guide http://hbase.apache.org/book/book.html
- HBase: The Definitive Guide, http://bit.ly/hbase-definitive-guide
- Google Bigtable Paper, http://labs.google.com/papers/bigtable.html
- Hadoop web site, http://hadoop.apache.org/
- Hadoop: The Definitive Guide, http://bit.ly/hadoop-definitive-guide
- Fallacies of Distributed Computing, http://en.wikipedia.org/wiki/Fallacies_of_Distributed_Computing
- HBase lightning talk slides, http://www.slideshare.net/scottleber/hbase-lightningtalk
- Sample code, https://github.com/sleberknight/basic-hbase-examples
Published at DZone with permission of Scott Leberknight , DZone MVB. See the original article here.
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