Materialized views are a powerful database feature that allow for efficient computation and storage of complex query results. They come in handy when dealing with expensive queries that compute aggregations over large tables or involve multiple joins, providing improved performance by storing the results of these queries and refreshing them at your command. One of the databases that make excellent use of this feature is PostgreSQL. This blog post aims to unravel the mysteries surrounding materialized views in PostgreSQL and guide you through their practical implementation.

Understanding Materialized Views

Before we dive deep into the workings of Materialized Views, we first need to understand the concept of a view in PostgreSQL. Simply put, a view is a named query saved in the database. Whenever a view is referenced in a query, the database internally replaces the view with the query it represents and then executes it. However, a view does not store the query result, it simply reruns the query each time it's referenced.

Materialized views, on the other hand, take this concept a step further. They not only save the query but also store the query's results in a table-like form. Unlike views, the results for a materialized view are calculated at the time of materialized view creation, not at the time of query. This has the obvious advantage of speeding up query execution, but it also means that the results can become stale and need to be manually refreshed.

Creating a Materialized View

Creating a materialized view in PostgreSQL is straightforward. You can do this with the CREATE MATERIALIZED VIEW command, which follows the syntax:

CREATE MATERIALIZED VIEW view_name AS
SELECT column1, column2, ...
FROM table_name
WHERE condition;

For example, suppose we have a sales table with columns: product_id, sale_date, and sale_amount. If we frequently need to calculate the total sales for each product, we might create a materialized view like this:

CREATE MATERIALIZED VIEW total_sales_per_product AS
SELECT product_id, SUM(sale_amount) as total_sales
FROM sales
GROUP BY product_id;

Refreshing a Materialized View

As mentioned earlier, the results of a materialized view can become stale as the underlying data changes. PostgreSQL provides the REFRESH MATERIALIZED VIEW command to update the stored data.

REFRESH MATERIALIZED VIEW view_name;

You can use this command after making changes to your original data to ensure that your materialized view stays up to date. For example, to refresh our total_sales_per_product view, we would do:

REFRESH MATERIALIZED VIEW total_sales_per_product;

Querying a Materialized View

Once a materialized view is created, you can query it just like you would query a table. For instance, to retrieve all records from our total_sales_per_product view, we would use:

SELECT * FROM total_sales_per_product;

It is important to note that because a materialized view stores the results of a query, the time taken to execute this query is significantly reduced as compared to running the query on the actual table.

Deleting a Materialized View

If a materialized view is no longer needed, it can be removed from the database using the DROP MATERIALIZED VIEW command:

DROP MATERIALIZED VIEW view_name;

For instance, to drop the total_sales_per_product view, we would do:

DROP MATERIALIZED VIEW total_sales_per_product;

Materialized View vs Regular View

A question might arise: when should you use a materialized view instead of a regular view? The answer depends on your specific use case:

1. Data Volume: If the query is dealingwith a large volume of data, a materialized view might be a better choice. Materialized views store the result of the query, thus making data retrieval faster.
2. Data Change Frequency: If the underlying data changes infrequently and the cost of keeping the materialized view updated is less than the cost of executing the query, you should consider a materialized view.
3. Query Complexity: Materialized views can be beneficial for complex queries, especially those with multiple joins and aggregations, as the query results are pre-calculated and stored.

Indexing a Materialized View

Materialized views, just like tables, can be indexed to further speed up data retrieval. You can use the CREATE INDEX command to add an index to a materialized view. The syntax is:

CREATE INDEX index_name ON materialized_view_name (column_name);

For example, to create an index on the product_id column of the total_sales_per_product view, you would run:

CREATE INDEX total_sales_per_product_index ON total_sales_per_product (product_id);

This index would make queries that filter on product_id faster.

Concurrency Control

One issue with refreshing materialized views is that by default, the REFRESH MATERIALIZED VIEW command locks the entire table, and therefore blocks other accesses. PostgreSQL 9.4 introduced the CONCURRENTLY option, which allows the view to be refreshed without locking it for reads.

REFRESH MATERIALIZED VIEW CONCURRENTLY view_name;

There is a catch, though: in order to use the CONCURRENTLY option, a unique index must exist on every column of the materialized view's query.

FAQ

Q1: Can materialized views be updated incrementally in PostgreSQL?

As of my knowledge cutoff in September 2021, PostgreSQL does not natively support incremental updates to materialized views. Instead, the REFRESH MATERIALIZED VIEW command re-runs the entire query and recreates the materialized view from scratch.

Q2: Are materialized views automatically updated when the underlying data changes?

No, materialized views in PostgreSQL are not automatically updated when the underlying data changes. You need to manually refresh them using the REFRESH MATERIALIZED VIEW command.

Q3: Can I use TRUNCATE on a materialized view?

No, TRUNCATE can only be used with tables and not with views or materialized views. If you want to clear a materialized view, you can use REFRESH MATERIALIZED VIEW with no data.

Q4: Can I create a materialized view from another materialized view?

Yes, you can create a materialized view from another materialized view, just as you can create a view from another view or a table.

Q5: How does the performance of a materialized view compare to a table?

A materialized view behaves almost identically to a table in terms of data retrieval performance. However, it may lag in terms of data modification performance due to the overhead of maintaining the materialized view's data.

Materialized views in PostgreSQL are a flexible and powerful tool for optimizing expensive queries. By storing the results of these queries, they offer significant speed benefits at the cost of some complexity in keeping them up to date. As with any tool, they're most effective when used wisely and judiciously.

---