Mastering Materialized Views in Hive: A Detailed Guide

Apache Hive is a powerful data warehousing solution built on top of Hadoop, which provides robust mechanisms to manage and query large data sets. Among its many features, one that stands out for its practicality and performance-enhancing potential is Materialized Views. In this blog, we'll delve deep into understanding what Materialized Views are, their benefits, and how to create and use them.

What are Materialized Views?

Materialized Views are a database object that contain the results of a query. Unlike standard views, which are just saved SQL queries, Materialized Views actually hold the result data of the query, acting like a physical table. They can be queried like a regular table, but their data is automatically updated when the underlying base table data changes, making them an excellent tool for optimizing complex and time-consuming queries.

Benefits of Materialized Views

The primary benefits of Materialized Views come from their ability to pre-compute, store, and optimize data retrieval for complex queries. Here are some advantages:

Query Performance : By storing the result of a query in advance, Materialized Views reduce the computation required when the query is run, leading to faster response times.
Data Consistency : As Materialized Views are updated when the base data changes, they ensure that the most accurate and up-to-date data is used for analysis.
Reduced Complexity : Materialized Views can abstract the complexity of underlying tables and queries. Users can query the Materialized View without needing to understand the details of the underlying tables.

Creating Materialized Views

Creating a Materialized View in Hive is similar to creating a regular view, but with the MATERIALIZED keyword. Here's the basic syntax:

Example in hive

CREATE MATERIALIZED VIEW view_name [IF NOT EXISTS] 
AS SELECT ...

Here's an example:

Example in hive

CREATE MATERIALIZED VIEW mv_employee_summary 
AS SELECT department, COUNT(*) as employee_count, AVG(salary) as avg_salary 
FROM employee 
GROUP BY department;

In this example, mv_employee_summary is a Materialized View that contains a summary of the number of employees and the average salary in each department. Once this Materialized View is created, you can query it just like a regular table:

Example in hive

SELECT * FROM mv_employee_summary WHERE avg_salary > 50000;

This query would return the data much faster than running the equivalent query on the original employee table, especially if that table contains a large amount of data.

Refreshing Materialized Views

Materialized Views are automatically updated in Hive when the underlying data changes. However, the update is not immediate and depends on the setting of the hive.materializedview.rewriting.time.window configuration property, which determines how often Hive checks for changes in the base tables of a Materialized View.

If you need to update a Materialized View immediately, you can manually refresh it using the REFRESH MATERIALIZED VIEW command:

Example in hive

REFRESH MATERIALIZED VIEW mv_employee_summary;

Conclusion

Materialized Views offer significant advantages when dealing with complex and time-consuming queries in Hive. By storing the results of a query in advance, they can dramatically improve query performance, simplify data analysis tasks, and ensure data consistency. However, they also require more storage space and need to be kept up-to-date as the base data changes. As such, they should be used strategically, in scenarios where the benefits in query performance outweigh the extra storage costs.