Unleashing the Power of Transactional Tables in Hive: A Comprehensive Guide

Transactional tables in Apache Hive are a key feature that allows Hive to handle real-time data with ACID properties, making it possible to handle operational data in the big data world. In this blog post, we will explore transactional tables in Hive, their benefits, and how to create and manage them effectively.

What are Transactional Tables in Hive?

Transactional Tables in Hive are a type of table that supports ACID (Atomicity, Consistency, Isolation, Durability) properties. This means that operations on these tables are atomic (all-or-nothing), consistent (data remains in a consistent state), isolated (concurrent transactions don't affect each other), and durable (once a transaction is committed, it remains so).

Before Hive 0.14, Hive tables didn't support transactions, which made real-time data analysis challenging. With the introduction of transactional tables, Hive now supports insert, update, and delete operations at the row level.

Benefits of Transactional Tables

Transactional tables provide several benefits:

Real-Time Data Analysis: Transactional tables allow Hive to analyze real-time operational data.
Data Consistency: With ACID properties, transactional tables ensure data consistency, even in the case of system failures.
Concurrency Control: Transactional tables provide a higher degree of concurrency control, allowing multiple users to work on the same table without affecting each other's transactions.

Creating Transactional Tables

To create a transactional table in Hive, you need to set the hive.txn.manager to org.apache.hadoop.hive.ql.lockmgr.DbTxnManager and hive.support.concurrency to true . Also, the table must be stored in the ORC format, which is designed to work well with ACID transactions.

Here is an example of how to create a transactional table:

Example in hive

SET hive.txn.manager=org.apache.hadoop.hive.ql.lockmgr.DbTxnManager; 
SET hive.support.concurrency=true; 

CREATE TABLE orders ( 
    order_id int, 
    customer_id int, 
    order_date date, 
    order_amount double 
) CLUSTERED BY (order_id) INTO 4 BUCKETS 
STORED AS ORC 
TBLPROPERTIES('transactional'='true');

In this example, the orders table is a transactional table. The CLUSTERED BY clause is used to bucket the table, which is a requirement for transactional tables.

Working with Transactional Tables

Once the table is created, you can perform insert, update, and delete operations.

For example, to insert data:

Example in hive

INSERT INTO TABLE orders VALUES (1, 100, '2022-01-01', 100.0);

To update data:

Example in hive

UPDATE orders SET order_amount = 200.0 WHERE order_id = 1;

And to delete data:

Example in hive

DELETE FROM orders WHERE order_id = 1;

Conclusion

Transactional tables in Hive have bridged the gap between real-time data analysis and big data, making Hive a more powerful and versatile tool for data analysis. By understanding and effectively using transactional tables, you can ensure data consistency, handle real-time data, and perform concurrent transactions in Hive, thereby enhancing your data analysis capabilities.