Understanding Views and Materialized Views in SQL

What is a view in sql

A view in SQL is a saved query or virtual table that is based on the result of an SQL statement. It doesn't store data itself, but rather stores the query that, when executed, retrieves data from the underlying base tables. Whenever you trigger the view the it will always retrieve the update data from the data, that means the query is repetitive but data may not be repetitive as it dose not store the result of views it only saves the query which when triggered will give the updated result set from Database.

SYNTAX

CREATE VIEW view_name AS
SELECT col, col2
FROM table_name
WHERE condition;

What is a materialized view in SQL

A materialized view in SQL is a saved query or virtual table that is based on the result of an SQL statement. It stores data for the result set, materialized view when executed, retrieves data from the underlying base tables which is already saved which saves the time again fetching the data from Database/tables. that means the data is repetitive as it stores the result of views it also saves the query which when triaged will give the saved result set from Database.

SYNTAX

CREATE materialized VIEW view_name AS
SELECT col, col2, ...
FROM table_name
WHERE condition;

How to get updated data in a materialized view in SQL

We can Refresh a materialized view to get updated data , means updating its stored data to reflect the latest changes in its underlying base tables. As Materialized views stores pre-computed data in data base which need to be updated after certain period as per requirement.
Also to refresh a materialized view the command varies slightly depending on the database system you are using (e.g., PostgreSQL, Oracle, SQL Server, etc.).

The most common command to refresh a materialized view is typically:

Syntax

REFRESH MATERIALIZED VIEW view_name;

• However, there are different types of refresh options depending on the usability:

  Complete Refresh (Full Refresh): This is the default behavior in most systems if you don't specify otherwise. It completely re-executes the defining query of the materialized view, discards the old data, and populates it with the new results.

    - When to use: When the underlying data has changed significantly, or
    if you don't have a mechanism for incremental refreshes.
    - Pros: Ensures the materialized view is entirely up-to-date. 
    - Cons: Can be very slow and resource-intensive, especially for large materialized views,
    as it involves reprocessing all data. It often locks the materialized view, preventing concurrent reads during the refresh.
  

  Incremental Refresh (Fast Refresh): Some database systems (like Oracle and PostgreSQL with certain conditions) support incremental refreshes. This method only applies the changes (inserts, updates, deletes) that have occurred in the base tables since the last refresh. This is typically achieved by using "materialized view logs" or "change data capture" mechanisms on the base tables.

    - When to use: When you need more frequent updates and
      the changes to the base tables are relatively small.
    - Pros: Much faster and less resource-intensive than a complete refresh.
       Often allows concurrent reads during the refresh (e.g., CONCURRENTLY in PostgreSQL).
    - Cons: Requires additional setup (materialized view logs/CDC) on the base tables.
      May not be possible if the materialized view's query is too complex or involves functions that can't be incrementally tracked.
  

Implement Refreshing techniques:

1. Manual Refresh: The simplest way is to manually execute the REFRESH MATERIALIZED VIEW command in your SQL client.

SYNTAX

REFRESH MATERIALIZED VIEW my_employeetable_view;

2. Scheduled Refresh: For most production environments, you'll want to automate materialized view refreshes. This can be done using:

• Database Schedulers: Most database systems have built-in job schedulers (e.g., pg_cron in PostgreSQL, DBMS_SCHEDULER in Oracle, SQL Server Agent).
  You can create a job that runs the REFRESH MATERIALIZED VIEW command at specified intervals (e.g., every hour, daily, weekly).
• External Schedulers: Tools like cron (Linux/Unix), Windows Task Scheduler, or more advanced orchestration tools (e.g., Apache Airflow, dbt)
  can also be used to schedule SQL scripts that refresh your materialized views.
• ETL/ELT Processes: If you have an ETL/ELT pipeline, refreshing materialized views is often a step at the end of the data loading or
  transformation process to ensure downstream reports or applications have fresh data.

3. Trigger-based Refresh (Less common for full refreshes): While less common for a full refresh due to performance implications, you could technically set up a database trigger on the base tables that, upon a change, would initiate a refresh of the materialized view. However, this is generally discouraged for full refreshes as it would make every data modification a very slow operation. For incremental refreshes, this is implicitly handled by the database's internal mechanisms (like materialized view logs)

What is the drawback of materialized views?

There are several drawbacks that need to be carefully considered:

1. Lack of Real-time Data:
   ◦ This is the most significant drawback. Unlike regular views that always reflect the absolute latest data from the underlying tables, materialized views only contain data up to their last refresh.

2. Increased Storage Space:
   ◦ Materialized views physically store the result set of a query. This means they consume additional disk space, potentially a significant amount, especially for large datasets or complex queries that produce a lot of data.
   ◦ For numerous materialized views, this can add up to substantial storage costs and management overhead.

3. Refresh Overhead and Resource Consumption:
   ◦ Refreshing a materialized view, especially a full refresh, can be a resource-intensive operation. It involves re-executing the defining query, which can consume significant CPU, memory, and I/O resources on your database server.
   ◦ Even incremental (fast) refreshes, while more efficient, still incur some overhead.

4. Maintenance Complexity:
   ◦ You need to establish a refresh strategy (manual, scheduled, or triggered) and monitor its execution.

Notes: Views and materialized views are both database objects that represent the result of a query, but they differ significantly in how they store and manage data.
a view provides a dynamic, real-time look at your data without storing it, while a materialized view provides a static, pre-computed snapshot of your data for faster retrieval at the cost of potential staleness and increased storage/maintenance. The choice between them depends on your specific needs regarding data freshness, performance, and resource management.