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Explain the concept of metastore in Hive ?

In the context of Apache Hive, a metastore is a central component that manages metadata for Hive tables.

Hive is a data warehousing and SQL-like query language system built on top of the Hadoop Distributed File System (HDFS).

The metastore in Apache Hive works as a central repository for managing metadata related to Hive tables.

Overview of how the metastore in Hive works:

1. Table Creation

  • When a user creates a table in Hive using a HiveQL statement, the metastore is updated with metadata about the new table.
  • Metadata includes information about the table’s structure, such as column names, data types, and storage format.

2. Metadata Storage

  • The metastore stores this metadata persistently, often in a relational database (such as MySQL or Derby) or in a distributed storage system, depending on the Hive configuration.
  • Metadata may include details about databases, tables, columns, partitioning, and more.

3. Decoupling of Metadata and Data

  • The metastore keeps track of where the actual data is stored but doesn’t store the data itself.
  • This separation allows for flexibility in managing data stored in different locations and formats.

4. Query Execution

  • When a user issues a HiveQL query to analyze or retrieve data, the query planner in Hive consults the metastore to understand the structure and location of the data.
  • This information is crucial for optimizing query execution by determining how to access and process the underlying data efficiently.

5. Schema Evolution

  • The metastore supports schema evolution, enabling users to modify the structure of tables over time without disrupting existing data.
  • Changes to the table schema are tracked in the metastore, allowing for backward and forward compatibility.

6. Compatibility with Various Storage Systems

  • The metastore is designed to work with different storage systems, making it compatible with various distributed file systems beyond HDFS.

7. Concurrency Control

  • The metastore incorporates mechanisms for handling concurrent access and updates to metadata.
  • This ensures data consistency and integrity in a multi-user environment.

8. Security and Access Control

  • The metastore includes security features and access controls to manage permissions on metadata, restricting or allowing users to view or modify specific metadata elements.

Imagine the metastore as the librarian of a vast digital library.

The system manages and arranges information pertaining to books in tables, including details like titles, authors, genres (columns), and storage locations (data files in HDFS). This facilitates researchers (analysts) in locating specific information effortlessly, without being inundated by an excessive volume of data.

Metastore’s functions

1. Storing Metadata

  • Table definitions: This includes information like table names, column names, data types, storage format, and more.
  • Partition information: If tables are partitioned, the metastore stores details about partition keys and their corresponding data locations.
  • Data location: The metastore tracks where the actual data resides in the Hadoop Distributed File System (HDFS).
  • Security information: Access control lists (ACLs) and other security configurations are stored for managing data access.
  • Statistics: The metastore can store statistics about table data, such as the number of rows and column values, facilitating query optimization.

2. Providing Access

  • The metastore acts as a single point of access for all Hive components, including the Driver, compiler, and various services.
  • This allows these components to retrieve the necessary information about tables and data to perform their tasks.

3. Enabling Consistency

  • The metastore ensures the consistency and correctness of data across multiple Hive clients and applications.
  • It implements proper locking mechanisms to prevent concurrent modifications and data corruption.

4. Facilitating Administration

  • The metastore provides tools and interfaces for managing metadata, including creating, dropping, and modifying tables and partitions.
  • It also allows for backup and restoration of metadata for disaster recovery purposes.

5. Supporting Scalability

  • The metastore is designed to scale alongside the increasing data volume and user base of Hive.
  • It can be deployed on separate servers or clusters to handle large workloads.

6. Integrating with Other Tools

  • The metastore can integrate with other big data tools and platforms like HBase, Spark, and Impala.
  • This allows for seamless data sharing and analysis across different systems.

Benefits of Metastore

  • Centralized Data Management: Provides a single source of truth for all Hive data.
  • Efficient Data Access: Enables quick retrieval of metadata for faster query processing.
  • Scalability and Performance: Supports large datasets and concurrent user access.
  • Data Security and Consistency: Ensures data integrity and controlled access through ACLs.
  • Simplified Administration: Offers tools for managing metadata and ensuring system health.