Hive

Hive is altnerative way to program MapReduce . Hive write SQL-like queries which are then converted to MapReduce programs.

 

 

 

Here's a comparison between Pig and Hive:

Feature	Pig	Hive
Language	Pig Latin (procedural)	HiveQL (declarative, SQL-like)
Learning Curve	Moderate	Easy for SQL users
Data Model	Schema-optional	Schema-mandatory
Use Case	ETL and data pipeline processing	Data warehousing and SQL queries
Performance	Better for complex data transformations	Better for structured data analysis
Data Types	Rich set of data types and nested structures	Primitive and complex types, similar to SQL
Development By	Yahoo	Facebook
Extensibility	UDFs in Java, Python, etc.	UDFs primarily in Java

This comparison highlights the key differences in their approach to processing data in Hadoop ecosystems.

 

Hive Architecture

The Hive architecture consists of several key components that work together to process SQL-like queries into MapReduce jobs:

graph TD
    subgraph Query_Processing
        A["User Interface"] --> B["Driver"]
        B --> C["Query Processing Engine"]
        C -->|"Parsed & Optimized Query"| D["Executor"]
    end

    subgraph Query_Engine["Query Processing Engine"]
        P["Parser"]
        Q["Semantic Analyzer"]
        R["Query Optimizer"]
        P --> Q --> R
    end

    subgraph Hadoop_Environment
        M["NameNode"] 
        N["DataNodes"]
        O["MapReduce"]
        M --> N
        O --> M
        O --> N
    end

    D --> Hadoop_Environment
    D --> F["MetaStore"]
    F --> H["Database"]

    %% Mode of Interaction
    I["CLI"] --> A
    J["Web UI"] --> A
    K["JDBC/ODBC"] --> A
    L["API"] --> A

 

Key components:

• User Interface: Multiple modes of interaction including Command Line Interface (CLI), Web UI, JDBC/ODBC drivers, and API
• Query Processing Engine: Combines parser, semantic analyzer, and query optimizer to process and optimize HiveQL queries
• Executor: Executes the tasks using Hadoop MapReduce
• MetaStore: Stores metadata about tables, columns, partitions, and schema
• Hadoop Environment: Consists of NameNode for metadata management, DataNodes for storage, and MapReduce for distributed processing

This architecture allows Hive to efficiently process SQL-like queries by converting them into MapReduce jobs while maintaining metadata and providing multiple interaction options for users.