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The "Hive/Tutorial" page has been changed by JoydeepSensarma. http://wiki.apache.org/hadoop/Hive/Tutorial?action=diff&rev1=27&rev2=28 -------------------------------------------------- = Concepts = == What is Hive == - Hive is the next generation infrastructure designed with the goals of providing data processing systems to enable easy data summarization, adhoc querying and analysis of large volumes of data. In addition it also provides a simple query language called QL, which is based on SQL and which enables users familiar with SQL to do ad-hoc querying, summarization and data analysis easily. At the same time, Hive QL also allows traditional map/reduce programmers to be able to plug in their custom mappers and reducers to do more sophisticated analysis that may not be supported by the built-in capabilities the language. + Hive is a data warehousing infrastructure based on the Hadoop. Hadoop provides massive scale out and fault tolerance capabilities for data storage and processing (using the map-reduce programming paradigm) on commodity hardware. + + Hive is designed to enable easy data summarization, ad-hoc querying and analysis of large volumes of data. It provides a simple query language called Hive QL, which is based on SQL and which enables users familiar with SQL to do ad-hoc querying, summarization and data analysis easily. At the same time, Hive QL also allows traditional map/reduce programmers to be able to plug in their custom mappers and reducers to do more sophisticated analysis that may not be supported by the built-in capabilities of the language. == What is NOT Hive == - Hive is based on Hadoop, which is a batch processing system. Accordingly, this system does not and cannot promise low latencies on queries. The paradigm here is strictly of submitting jobs and being notified when the jobs are completed as opposed to real time queries. As a result it should not be compared with systems such as Oracle where analyses are conducted on a significantly smaller amount of data but the analyses proceed much more iteratively with the response times between iterations being less than a few minutes. A typical Hive query's response time is usually greater than a couple of minutes. For large jobs they may even run into hours. What Hive provides is a fault-tolerant and an scale-out option, where more commodity boxes can be added to the Hadoop cluster as the data size and/or workload increases and Hive will automatically benefit from that. + Hadoop is a batch processing system and Hadoop jobs tend to have high latency and incur substantial overheads in job submission and scheduling. As a result - latency for Hive queries is generally very high (minutes) even when data sets involved are very small (say a few hundred megabytes). As a result it cannot be compared with systems such as Oracle where analyses are conducted on a significantly smaller amount of data but the analyses proceed much more iteratively with the response times between iterations being less than a few minutes. Hive aims to provide acceptable (but not optimal) latency for interactive data browsing, queries over small data sets or test queries. + + Hive is not designed for online transaction processing and does not offer real-time queries and row level updates. It is best used for batch jobs over large sets of immutable data (like web logs). In the following sections we provide a tutorial on the capabilities of the system. We start by describing the concepts of data types, tables and partitions (which are very similar to what you would find in a traditional relational DBMS) and then illustrate the capabilities of the QL language with the help of some examples.
