Hadoop Hive is a runtime Hadoop support structure that allows anyone who is already fluent with SQL (which is commonplace for relational data-base developers) to leverage the Hadoop platform right out of the gate. Hive allows SQL developers to write Hive Query Language (HQL) statements that are similar to standard SQL statements. HQL is limited in the commands it understands, but it is still useful. HQL statements are broken down by the Hive service into MapReduce jobs and executed across a Hadoop cluster. Hive looks very much like traditional database code with SQL access. However, because Hive is based on Hadoop and MapReduce operations, there are several key differences. The first is that Hadoop is intended for long sequential scans, and because Hive is based on Hadoop, the queries have a very high latency (many minutes). This makes Hive not appropriate for applications that need very fast response times, as required by a database such as DB2. Finally, Hive is read-based and therefore not appropriate for transaction processing that typically involves a high percentage of write operations.
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Recommended reading list:
|Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale
Get ready to unlock the power of your data. With the fourth edition of this comprehensive guide, you’ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. This book is ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters.
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