Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis.[2] Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop. Traditional SQL queries must be implemented in the MapReduce Java API to execute SQL applications and queries over distributed data. Hive provides the necessary SQL abstraction to integrate SQL-like queries (HiveQL) into the underlying Java without the need to implement queries in the low-level Java API. Since most data warehousing applications work with SQL-based querying languages, Hive aids portability of SQL-based applications to Hadoop.[3] While initially developed by Facebook, Apache Hive is used and developed by other companies such as Netflix and the Financial Industry Regulatory Authority (FINRA).[4][5] Amazon maintains a software fork of Apache Hive included in Amazon Elastic MapReduce on Amazon Web Services
Major components of the Hive architecture are:
Architecture
 | This section is in a list format that may be better presented using prose. (October 2016) |
Hive Architecture[19]
- Metastore: Stores metadata for each of the tables such as their schema and location. It also includes the partition metadata which helps the driver to track the progress of various data sets distributed over the cluster.[20] The data is stored in a traditional RDBMS format. The metadata helps the driver to keep a track of the data and it is highly crucial. Hence, a backup server regularly replicates the data which can be retrieved in case of data loss.
- Driver: Acts like a controller which receives the HiveQL statements. It starts the execution of statement by creating sessions and monitors the life cycle and progress of the execution. It stores the necessary metadata generated during the execution of an HiveQL statement. The driver also acts as a collection point of data or query result obtained after the Reduce operation.[14]
- Compiler: Performs compilation of the HiveQL query, which converts the query to an execution plan. This plan contains the tasks and steps needed to be performed by the Hadoop MapReduce to get the output as translated by the query. The compiler converts the query to an abstract syntax tree (AST). After checking for compatibility and compile time errors, it converts the AST to a directed acyclic graph (DAG).[21] The DAG divides operators to MapReduce stages and tasks based on the input query and data.[20]
- Optimizer: Performs various transformations on the execution plan to get an optimized DAG. Transformations can be aggregated together, such as converting a pipeline of joins to a single join, for better performance.[22] It can also split the tasks, such as applying a transformation on data before a reduce operation, to provide better performance and scalability. However, the logic of transformation used for optimization used can be modified or pipelined using another optimizer.[14]
- Executor: After compilation and optimization, the executor executes the tasks. It interacts with the job tracker of Hadoop to schedule tasks to be run. It takes care of pipelining the tasks by making sure that a task with dependency gets executed only if all other prerequisites are run.[22]
- CLI, UI, and Thrift Server: A command-line interface (CLI) provides a user interface for an external user to interact with Hive by submitting queries, instructions and monitoring the process status. Thrift server allows external clients to interact with Hive over a network, similar to the JDBC or ODBC protocols
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