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| <h2>Overview</h2> |
| <p>Impala raises the bar for SQL query performance on Apache Hadoop while retaining a familiar user experience. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time. Furthermore, Impala uses the same metadata, SQL syntax (Hive SQL), ODBC driver, and user interface (Hue Beeswax) as Apache Hive, providing a familiar and unified platform for batch-oriented or real-time queries. (For that reason, Hive users can utilize Impala with little setup overhead.) </p> |
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| <h3>Architecture</h3> |
| <p>To avoid latency, Impala circumvents MapReduce to directly access the data through a specialized distributed query engine that is very similar to those found in commercial parallel RDBMSs. The result is order-of-magnitude faster performance than Hive, depending on the type of query and configuration.</p> |
| <p align="center"><img src="img/impala.png"></p> |
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| <p>There are many advantages to this approach over alternative approaches for querying Hadoop data, including:: |
| <ul> |
| <li>Thanks to local processing on data nodes, network bottlenecks are avoided.</li> |
| <li>A single, open, and unified metadata store can be utilized. |
| </li> |
| <li>Costly data format conversion is unnecessary and thus no overhead is incurred.</li> |
| <li> All data is immediately query-able, with no delays for ETL. |
| </li> |
| <li>All hardware is utilized for Impala queries as well as for MapReduce. |
| </li> |
| <li>Only a single machine pool is needed to scale. |
| </li> |
| </ul> |
| We encourage you to |
| read <a href="http://cidrdb.org/cidr2015/Papers/CIDR15_Paper28.pdf">"Impala: A Modern, |
| Open-Source SQL Engine for Hadoop"</a> for details about Impala's architecture. |
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