What is hive, Is it a database?

Understanding Apache Hive: Is it a Database?

Explore Apache Hive, its architecture, how it functions within the Hadoop ecosystem, and clarify its role as a data warehousing solution, not a traditional relational database.

Apache Hive is a crucial component in the big data landscape, often used in conjunction with Hadoop. Many newcomers to big data, especially those familiar with traditional relational databases, frequently ask: "What is Hive, and is it a database?" This article aims to demystify Apache Hive, explain its architecture, and clarify its relationship with databases and the Hadoop ecosystem.

What is Apache Hive?

Apache Hive is an open-source data warehousing software project built on top of Apache Hadoop. It provides an SQL-like interface, called HiveQL (or HQL), to query and manage large datasets residing in distributed storage systems like HDFS (Hadoop Distributed File System). Hive translates HQL queries into MapReduce, Tez, or Spark jobs, which are then executed on the Hadoop cluster. Its primary purpose is to enable data analysts and data scientists to perform ad-hoc queries, summarization, and analysis on massive datasets without needing to write complex Java MapReduce programs.

flowchart TD
    A[User/Client] --> B[Hive CLI/JDBC/ODBC]
    B --> C[Hive Driver]
    C --> D[Compiler]
    D --> E[Optimizer]
    E --> F[Executor]
    F --> G[Hadoop (MapReduce/Tez/Spark)]
    G --> H[HDFS/S3/ADLS]
    C --> I[Metastore (RDBMS)]
    I -- Stores Metadata --> H
    H -- Stores Data --> G
    F -- Executes Jobs On --> G

Simplified Apache Hive Architecture Flow

Hive vs. Traditional Relational Databases

While Hive offers an SQL-like interface, it's fundamentally different from traditional relational databases (RDBMS) like MySQL, PostgreSQL, or Oracle. Here's a breakdown of key distinctions:

• Data Storage: RDBMS stores data in structured tables with fixed schemas, typically on local disk or SAN. Hive stores data in HDFS or other compatible file systems (like S3, ADLS) in various formats (text, ORC, Parquet), and imposes a schema on read.
• Schema Enforcement: RDBMS enforces schema on write, meaning data must conform to the table's schema before being written. Hive uses schema on read, allowing flexible data ingestion and defining schema at query time.
• Query Execution: RDBMS are optimized for low-latency, transactional queries (OLTP). Hive is designed for high-latency, batch-oriented analytical queries (OLAP) on massive datasets.
• Indexing: RDBMS heavily rely on indexing for fast data retrieval. Hive, while supporting some indexing features, primarily relies on full table scans or partition pruning due to its batch processing nature.
• Transactions: RDBMS support ACID (Atomicity, Consistency, Isolation, Durability) properties for transactions. Hive, especially in older versions, had limited transactional capabilities, though newer versions have improved support for ACID operations on managed tables.
• Update/Delete: RDBMS excel at row-level updates and deletes. Hive is less efficient for individual record modifications and is better suited for appending data or overwriting entire partitions.

Is Hive a Database? The Verdict

No, Apache Hive is not a database in the traditional sense. It's more accurately described as a data warehousing infrastructure or a query engine that sits on top of Hadoop. It provides a SQL abstraction layer for querying data stored in HDFS, but it does not store data itself (it relies on HDFS for that) nor does it offer the full transactional capabilities and low-latency query performance of an RDBMS.

The confusion often arises because Hive uses a 'Metastore' which is typically backed by a traditional relational database (like MySQL or PostgreSQL) to store metadata about the tables, their schemas, and their locations in HDFS. This Metastore is crucial for Hive's operation but does not store the actual user data.

In essence, Hive allows you to treat your Hadoop data as if it were in a relational database, enabling familiar SQL queries for big data analytics. It bridges the gap between the world of relational databases and the distributed processing power of Hadoop.

-- Example HiveQL (HQL) query
CREATE TABLE IF NOT EXISTS employees (
    id INT,
    name STRING,
    salary DOUBLE,
    department STRING
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE;

LOAD DATA INPATH '/user/hadoop/employee_data.csv' INTO TABLE employees;

SELECT department, AVG(salary) FROM employees GROUP BY department HAVING AVG(salary) > 50000;

Basic HiveQL commands for table creation, data loading, and querying.