Indexing materialized views in PostgreSQL - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a materialized view named sales_summary.

PostgreSQL

CREATE MATERIALIZED VIEW [1] AS SELECT product_id, SUM(quantity) AS total_qty FROM sales GROUP BY product_id;

Drag options to blanks, or click blank then click option'

Asales_summary

Bsummary_sales

Csales_view

Dsummary_view

Attempts:

3 left

💡 Hint

Common Mistakes

Using a regular view instead of a materialized view.

Choosing a name that does not reflect the content.

The materialized view is named sales_summary as specified.

2fill in blank

medium

Complete the code to create an index on the product_id column of the materialized view.

PostgreSQL

CREATE INDEX idx_[1] ON sales_summary (product_id);

Drag options to blanks, or click blank then click option'

Aproduct

Bproduct_id

Csummary

Dsales

Attempts:

3 left

💡 Hint

Common Mistakes

Using a generic index name that doesn't indicate the column.

Misspelling the column name.

The index is named idx_product_id to indicate it is on the product_id column.

3fill in blank

hard

Fix the error in the code to refresh the materialized view concurrently.

PostgreSQL

REFRESH MATERIALIZED VIEW [1] CONCURRENTLY;

Drag options to blanks, or click blank then click option'

Asummary_sales

Bsales_summary_view

Csales_summary

Dsales_view

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong or incomplete materialized view name.

Omitting the CONCURRENTLY keyword when needed.

The materialized view name must match exactly. sales_summary is correct.

4fill in blank

hard

Fill both blanks to create a unique index on the materialized view for product_id and date columns.

PostgreSQL

CREATE [1] INDEX idx_sales_summary_[2] ON sales_summary (product_id, sales_date);

Drag options to blanks, or click blank then click option'

AUNIQUE

BCONCURRENTLY

Cproduct_date

Dsales_date

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-unique index when uniqueness is required.

Naming the index unclearly.

The index is UNIQUE and named idx_sales_summary_product_date to reflect the columns.

5fill in blank

hard

Fill all three blanks to create a materialized view with data and an index on the customer_id column.

PostgreSQL

CREATE MATERIALIZED VIEW [1] AS SELECT customer_id, COUNT(order_id) AS order_count FROM orders GROUP BY [2] WITH DATA; CREATE INDEX [3] ON [1] (customer_id);

Drag options to blanks, or click blank then click option'

Acustomer_orders

Bcustomer_id

Cidx_customer_orders_customer_id

Dorders_summary

Attempts:

3 left

💡 Hint

Common Mistakes

Mismatching group by column and selected column.

Using inconsistent names for the view and index.

The materialized view is named customer_orders, grouped by customer_id, and indexed with idx_customer_orders_customer_id.

Practice

1. What is the main purpose of creating an index on a materialized view in PostgreSQL?

easy

A. To speed up query performance on the materialized view

B. To automatically refresh the materialized view

C. To store the materialized view data physically

D. To prevent the materialized view from being updated

Solution

1. Step 1: Understand what an index does

   An index helps the database find data faster by creating a quick lookup structure.

2. Step 2: Apply this to materialized views

   Since materialized views store query results physically, indexing them speeds up queries on that stored data.

3. Final Answer:

   To speed up query performance on the materialized view -> Option A

4. Quick Check:

   Indexing = Faster queries [OK]

Hint: Indexes speed up data retrieval on stored results [OK]

Common Mistakes:

• Thinking indexes refresh the view automatically
• Confusing storage with indexing
• Believing indexes prevent updates

2. Which of the following is the correct syntax to create an index on a materialized view named sales_summary on column region?

easy

A. CREATE INDEX idx_region ON TABLE sales_summary(region);

B. CREATE INDEX idx_region ON MATERIALIZED VIEW sales_summary(region);

C. CREATE INDEX idx_region ON sales_summary(region);

D. CREATE INDEX idx_region ON VIEW sales_summary(region);

Solution

1. Step 1: Recall index creation syntax

   Indexes are created with CREATE INDEX index_name ON table_name(column); syntax.

2. Step 2: Apply to materialized views

   Materialized views behave like tables for indexing, so no extra keywords like MATERIALIZED VIEW or VIEW are used.

3. Final Answer:

   CREATE INDEX idx_region ON sales_summary(region); -> Option C

4. Quick Check:

   Index syntax = CREATE INDEX name ON table(column) [OK]

Hint: Use table syntax for materialized views when creating indexes [OK]

Common Mistakes:

• Adding MATERIALIZED VIEW keyword in index creation
• Using VIEW keyword instead of table name
• Incorrect syntax with TABLE keyword

3. Given a materialized view mv_orders with an index on order_date, what will be the output of this query?

SELECT order_date FROM mv_orders WHERE order_date = '2023-01-01';

Assuming the materialized view is refreshed and contains data for that date.

medium

A. Returns all rows with order_date '2023-01-01' quickly using the index

B. Returns an error because indexes cannot be used on materialized views

C. Returns no rows because materialized views do not store data

D. Returns all rows but ignores the index, causing slow performance

Solution

1. Step 1: Understand index usage on materialized views

   Indexes on materialized views speed up queries by quickly locating matching rows.

2. Step 2: Analyze the query behavior

   The query filters by order_date, which is indexed, so it returns matching rows efficiently.

3. Final Answer:

   Returns all rows with order_date '2023-01-01' quickly using the index -> Option A

4. Quick Check:

   Indexed query = fast matching rows [OK]

Hint: Indexed columns speed up matching queries on materialized views [OK]

Common Mistakes:

• Thinking indexes don't work on materialized views
• Assuming materialized views are empty without refresh
• Believing query ignores indexes always

4. You created an index on a materialized view but notice queries are still slow. What is the most likely cause?

medium

A. Indexes do not work on materialized views in PostgreSQL

B. The materialized view has not been refreshed after data changes

C. The index was created on a non-existent column

D. Materialized views cannot have indexes on date columns

Solution

1. Step 1: Understand materialized view refresh

   Materialized views store data physically but need manual refresh to update data and indexes.

2. Step 2: Connect refresh to query speed

   If not refreshed, the view and its index are outdated, causing slow or incorrect query results.

3. Final Answer:

   The materialized view has not been refreshed after data changes -> Option B

4. Quick Check:

   Refresh needed for updated data and index [OK]

Hint: Always refresh materialized views after data changes [OK]

Common Mistakes:

• Believing indexes don't work on materialized views
• Creating index on wrong column silently
• Thinking date columns can't be indexed

5. You have a materialized view mv_sales refreshed daily. You want to speed up queries filtering by customer_id and sale_date. Which is the best indexing strategy?

hard

A. Do not create any index; rely on sequential scan

B. Create separate indexes on customer_id and sale_date individually

C. Create an index only on sale_date because it is a date column

D. Create a single composite index on (customer_id, sale_date)

Solution

1. Step 1: Understand composite vs single indexes

   A composite index on multiple columns speeds up queries filtering by both columns together.

2. Step 2: Apply to query filtering by customer_id and sale_date

   Since queries filter on both columns, a composite index is more efficient than separate indexes.

3. Final Answer:

   Create a single composite index on (customer_id, sale_date) -> Option D

4. Quick Check:

   Composite index = faster multi-column filters [OK]

Hint: Use composite index for multi-column filtering queries [OK]

Common Mistakes:

• Creating separate indexes causing less efficient queries
• Indexing only one column when filtering by two
• Skipping indexes and relying on slow scans