With many options available in the market (Presto, Spark SQL, etc.) for doing interactive SQL over data that is stored in Amazon S3 and HDFS, there are several reasons why using Hive and LLAP might be a good choice:
For those who are heavily invested in the Hive ecosystem and have external BI tools that connect to Hive over JDBC/ODBC connections, LLAP plugs in to their existing architecture without a steep learning curve.
It’s compatible with existing Hive SQL and other Hive tools, like HiveServer2, and JDBC drivers for Hive.
It has native support for security features with authentication and authorization (SQL standards-based authorization) using HiveServer2.
LLAP daemons are aware about of the columns and records that are being processed which enables you to enforce fine-grained access control.
It can use Hive’s vectorization capabilities to speed up queries, and Hive has better support for Parquet file format when vectorization is enabled.
It can take advantage of a number of Hive optimizations like merging multiple small files for query results, automatically determining the number of reducers for joins and groupbys, etc.
It’s optional and modular so it can be turned on or off depending on the compute and resource requirements of the cluster. This lets you to run other YARN applications concurrently without reserving a cluster specifically for LLAP.
Read on for more details, including the bootstrap action you need to take and how to use LLAP once you have it configured.