BRIN index for large sequential data in PostgreSQL - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a BRIN index on the column 'event_time' in the 'logs' table.

PostgreSQL

CREATE INDEX brin_event_time_idx ON logs USING [1] (event_time);

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

Abtree

Bbrin

Chash

Dgin

Attempts:

3 left

💡 Hint

Common Mistakes

Using btree instead of brin for large sequential data.

Using gin or hash which are for different use cases.

The BRIN index type is specified by the keyword brin. It is suitable for large tables with sequential data.

2fill in blank

medium

Complete the code to create a BRIN index with a pages_per_range of 128 on the 'event_time' column.

PostgreSQL

CREATE INDEX brin_event_time_idx ON logs USING brin (event_time) WITH (pages_per_range = [1]);

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

A128

B64

C256

D32

Attempts:

3 left

💡 Hint

Common Mistakes

Using too small or too large values for pages_per_range.

Confusing pages_per_range with other parameters.

The pages_per_range parameter controls how many pages each BRIN range covers. Setting it to 128 is common for large tables.

3fill in blank

hard

Fix the error in the code to create a BRIN index on 'event_time' with the correct syntax for options.

PostgreSQL

CREATE INDEX brin_event_time_idx ON logs USING brin (event_time) WITH ([1] = 128);

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

Apages_per_ranges

Bpage_per_range

Cpages_per_range

Dpage_per_ranges

Attempts:

3 left

💡 Hint

Common Mistakes

Using singular 'page_per_range' instead of plural.

Adding extra 's' or misspelling the option name.

The correct option name is pages_per_range (plural 'pages').

4fill in blank

hard

Fill both blanks to create a BRIN index on 'event_time' with autosummarize disabled.

PostgreSQL

CREATE INDEX brin_event_time_idx ON logs USING [1] (event_time) WITH (autosummarize = [2]);

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

Abrin

Btrue

Cfalse

Dbtree

Attempts:

3 left

💡 Hint

Common Mistakes

Using btree instead of brin for the index type.

Setting autosummarize to true when the task asks to disable it.

The index type must be brin. Setting autosummarize to false disables automatic summary updates.

5fill in blank

hard

Fill all three blanks to create a BRIN index on 'event_time' with pages_per_range 64 and autosummarize enabled.

PostgreSQL

CREATE INDEX brin_event_time_idx ON logs USING [1] (event_time) WITH (pages_per_range = [2], autosummarize = [3]);

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

Abtree

B64

Ctrue

Dbrin

Attempts:

3 left

💡 Hint

Common Mistakes

Using btree instead of brin.

Confusing true and false for autosummarize.

Using wrong values for pages_per_range.

The index type is brin. The pages_per_range is set to 64, and autosummarize is enabled with true.

Practice

1. What is the main advantage of using a BRIN index on a very large, sequentially ordered table in PostgreSQL?

easy

A. It automatically sorts the table data for you.

B. It uses very little disk space by summarizing data in block ranges.

C. It creates a full copy of the table for faster access.

D. It replaces the need for any other indexes on the table.

Solution

1. Step 1: Understand BRIN index purpose

   BRIN indexes summarize data in block ranges instead of indexing every row, saving space.

2. Step 2: Compare options

   Options A, B, and D describe behaviors not true for BRIN indexes.

3. Final Answer:

   It uses very little disk space by summarizing data in block ranges. -> Option B

4. Quick Check:

   BRIN = space-efficient summary index [OK]

Hint: BRIN saves space by indexing block summaries, not rows [OK]

Common Mistakes:

• Thinking BRIN sorts data automatically
• Confusing BRIN with full table copies
• Assuming BRIN replaces all other indexes

2. Which of the following is the correct syntax to create a BRIN index on the column timestamp of a table named events?

easy

A. CREATE INDEX events_brin ON events USING BRIN (timestamp);

B. CREATE BRIN INDEX ON events (timestamp);

C. CREATE INDEX ON events USING BRIN timestamp;

D. CREATE INDEX brin_events ON events USING BRIN timestamp;

Solution

1. Step 1: Recall correct CREATE INDEX syntax

   The syntax is: CREATE INDEX index_name ON table_name USING BRIN (column);

2. Step 2: Check each option

   CREATE INDEX events_brin ON events USING BRIN (timestamp); matches the correct syntax with index name, table, method, and column in parentheses.

3. Final Answer:

   CREATE INDEX events_brin ON events USING BRIN (timestamp); -> Option A

4. Quick Check:

   CREATE INDEX name ON table USING BRIN (column) [OK]

Hint: Always specify index name and use parentheses for columns [OK]

Common Mistakes:

• Omitting index name
• Missing parentheses around column
• Wrong order of keywords

3. Given a table logs with a BRIN index on log_time, what will the following query most likely do?

EXPLAIN ANALYZE SELECT * FROM logs WHERE log_time BETWEEN '2024-01-01' AND '2024-01-31';

medium

A. Use a bitmap index scan instead of BRIN.

B. Perform a full sequential scan ignoring the BRIN index.

C. Throw a syntax error because BRIN indexes cannot be used in WHERE clauses.

D. Use the BRIN index to quickly scan only relevant block ranges.

Solution

1. Step 1: Understand BRIN index behavior on range queries

   BRIN indexes speed up range queries by scanning only relevant block ranges.

2. Step 2: Analyze query and options

   The query filters by a range on log_time, so the BRIN index will be used to limit scanned blocks.

3. Final Answer:

   Use the BRIN index to quickly scan only relevant block ranges. -> Option D

4. Quick Check:

   BRIN helps range scans by block filtering [OK]

Hint: BRIN speeds up range scans by filtering block ranges [OK]

Common Mistakes:

• Assuming BRIN causes full table scan
• Thinking BRIN causes syntax errors
• Confusing BRIN with bitmap indexes

4. You created a BRIN index on a large table's created_at column, but queries using WHERE created_at > '2024-01-01' are still slow. What is a likely cause?

medium

A. The table data is not physically ordered by created_at.

B. BRIN indexes do not support range queries.

C. You forgot to VACUUM the table after creating the index.

D. The created_at column is of type TEXT instead of TIMESTAMP.

Solution

1. Step 1: Understand BRIN index efficiency depends on data order

   BRIN indexes work best when data is physically ordered or clustered by the indexed column.

2. Step 2: Analyze options

   The table data is not physically ordered by created_at. explains why the index is ineffective: unordered data causes BRIN to scan many blocks.

3. Final Answer:

   The table data is not physically ordered by created_at. -> Option A

4. Quick Check:

   BRIN needs ordered data for speed [OK]

Hint: BRIN needs physically ordered data for fast queries [OK]

Common Mistakes:

• Believing BRIN can't do range queries
• Ignoring physical data order importance
• Assuming VACUUM fixes index speed

5. You have a huge table sensor_data with a timestamp column mostly in ascending order. You want to speed up queries filtering by timestamp ranges but keep index size minimal. Which approach is best?

hard

A. Create a B-tree index on timestamp and vacuum the table daily.

B. Create a hash index on timestamp for faster equality searches.

C. Create a BRIN index on timestamp and periodically cluster the table by timestamp.

D. Create no index and rely on sequential scans for simplicity.

Solution

1. Step 1: Identify index type for large, mostly ordered data

   BRIN indexes are ideal for large tables with mostly sequential data to keep index size small.

2. Step 2: Consider table clustering

   Clustering the table by timestamp keeps data physically ordered, improving BRIN index effectiveness.

3. Step 3: Evaluate other options

   B-tree indexes use more space; hash indexes don't support range queries; no index is slow for large data.

4. Final Answer:

   Create a BRIN index on timestamp and periodically cluster the table by timestamp. -> Option C

5. Quick Check:

   BRIN + clustering = efficient, small index [OK]

Hint: Use BRIN plus clustering for big, ordered data [OK]

Common Mistakes:

• Choosing B-tree despite large size
• Using hash index for range queries
• Skipping indexing on large tables