Ranking with ts_rank in PostgreSQL - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select the rank of the text search vector using ts_rank.

PostgreSQL

SELECT ts_rank([1], to_tsquery('english', 'database')) AS rank FROM documents;

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

Adocument_vector

Bto_tsvector('english', content)

Ccontent

Dto_tsquery('english', content)

Attempts:

3 left

💡 Hint

Common Mistakes

Passing plain text instead of a tsvector to ts_rank.

Using to_tsquery as the first argument instead of to_tsvector.

The ts_rank function requires a tsvector as its first argument. Using to_tsvector('english', content) converts the text content into a tsvector suitable for ranking.

2fill in blank

medium

Complete the code to filter documents matching the search query using @@ operator.

PostgreSQL

SELECT content FROM documents WHERE to_tsvector('english', content) [1] to_tsquery('english', 'database');

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

A=

BIN

CLIKE

D@@

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' instead of '@@' for text search matching.

Using LIKE which is for string pattern matching, not full-text search.

The @@ operator checks if the tsvector matches the tsquery, filtering relevant documents.

3fill in blank

hard

Fix the error in the code to correctly rank and order documents by relevance.

PostgreSQL

SELECT content, ts_rank(to_tsvector('english', content), to_tsquery('english', 'database')) AS rank FROM documents ORDER BY [1] DESC;

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

Arank

Bcontent

Cts_rank

Dto_tsvector

Attempts:

3 left

💡 Hint

Common Mistakes

Ordering by the content column instead of the rank.

Using the function name ts_rank directly in ORDER BY without alias.

The alias rank is used in the ORDER BY clause to sort results by their relevance score.

4fill in blank

hard

Fill both blanks to create a query that selects content and ranks it, filtering only relevant documents.

PostgreSQL

SELECT content, ts_rank([1], [2]) AS rank FROM documents WHERE [1] @@ [2] ORDER BY rank DESC;

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

Ato_tsvector('english', content)

Bto_tsquery('english', 'database')

Ccontent

Dts_rank

Attempts:

3 left

💡 Hint

Common Mistakes

Using plain content instead of to_tsvector for ranking and filtering.

Mixing up the order of arguments in ts_rank or @@ operator.

The to_tsvector converts content to a searchable vector, and to_tsquery creates the search query. Both are used for ranking and filtering.

5fill in blank

hard

Fill all three blanks to create a query that ranks documents with weighted ts_rank and filters by search query.

PostgreSQL

SELECT content, ts_rank([3], [1], [2]) AS rank FROM documents WHERE [1] @@ [2] ORDER BY rank DESC;

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

Ato_tsvector('english', content)

Bto_tsquery('english', 'database')

C'ABCD'

D'english'

Attempts:

3 left

💡 Hint

Common Mistakes

Passing plain text or language name as the third argument instead of weights string.

Using different expressions for ranking and filtering.

The weights string is the first argument to ts_rank, followed by the tsvector and the tsquery. This prioritizes matches based on position (A=0.1, B=0.2, C=0.4, D=1.0).

Practice

1. What does the ts_rank function do in PostgreSQL full-text search?

easy

A. It creates a search query from a string.

B. It converts text into a searchable vector format.

C. It calculates the relevance ranking of a document for a search query.

D. It sorts rows alphabetically by a text column.

Solution

1. Step 1: Understand the purpose of ts_rank

   The ts_rank function scores how well a document matches a search query in full-text search.

2. Step 2: Differentiate from related functions

   to_tsvector prepares text, to_tsquery creates queries, but ts_rank ranks results.

3. Final Answer:

   It calculates the relevance ranking of a document for a search query. -> Option C

4. Quick Check:

   ts_rank = relevance score [OK]

Hint: Remember ts_rank ranks relevance, not text conversion [OK]

Common Mistakes:

• Confusing ts_rank with to_tsvector
• Thinking ts_rank creates search queries
• Assuming ts_rank sorts alphabetically

2. Which of the following is the correct syntax to rank search results using ts_rank in PostgreSQL?

easy

A. SELECT *, ts_rank(to_tsvector(text_column), to_tsquery('search')) AS rank FROM table ORDER BY rank DESC;

B. SELECT *, ts_rank(text_column, 'search') AS rank FROM table ORDER BY rank ASC;

C. SELECT *, ts_rank(to_tsquery(text_column), to_tsvector('search')) AS rank FROM table;

D. SELECT *, ts_rank('search', text_column) AS rank FROM table ORDER BY rank DESC;

Solution

1. Step 1: Check correct function arguments

   ts_rank requires a tsvector and a tsquery, so to_tsvector(text_column) and to_tsquery('search') are correct.

2. Step 2: Verify ordering and syntax

   Ordering by rank descending shows best matches first, matching SELECT *, ts_rank(to_tsvector(text_column), to_tsquery('search')) AS rank FROM table ORDER BY rank DESC;.

3. Final Answer:

   SELECT *, ts_rank(to_tsvector(text_column), to_tsquery('search')) AS rank FROM table ORDER BY rank DESC; -> Option A

4. Quick Check:

   Correct ts_rank syntax = SELECT *, ts_rank(to_tsvector(text_column), to_tsquery('search')) AS rank FROM table ORDER BY rank DESC; [OK]

Hint: ts_rank needs to_tsvector first, then to_tsquery second [OK]

Common Mistakes:

• Swapping to_tsvector and to_tsquery arguments
• Ordering results ascending instead of descending
• Passing plain text instead of tsvector or tsquery

3. Given the table documents with column content, what will this query return?

SELECT content, ts_rank(to_tsvector(content), to_tsquery('cat & dog')) AS rank FROM documents ORDER BY rank DESC LIMIT 2;

medium

A. The two documents containing either 'cat' or 'dog', sorted alphabetically.

B. The two documents with the highest relevance to both 'cat' and 'dog'.

C. All documents containing 'cat' or 'dog' without ranking.

D. An error because ts_rank cannot be used with LIMIT.

Solution

1. Step 1: Understand the query components

   The query uses to_tsquery('cat & dog') which searches for documents containing both 'cat' and 'dog'.

2. Step 2: Analyze ordering and limit

   Results are ordered by ts_rank descending, so highest relevance first, limited to 2 rows.

3. Final Answer:

   The two documents with the highest relevance to both 'cat' and 'dog'. -> Option B

4. Quick Check:

   ts_rank orders best matches first [OK]

Hint: AND in tsquery means both terms must appear [OK]

Common Mistakes:

• Thinking it returns documents with either word
• Assuming alphabetical order instead of rank
• Believing LIMIT causes an error with ts_rank

4. Identify the error in this query:

SELECT content, ts_rank(to_tsquery(content), to_tsvector('search')) AS rank FROM documents ORDER BY rank DESC;

medium

A. Arguments to ts_rank are reversed; tsvector should be first, tsquery second.

B. to_tsquery cannot be used on a column; it only accepts strings.

C. ORDER BY rank DESC is invalid syntax here.

D. The query is correct and will run without errors.

Solution

1. Step 1: Check ts_rank argument order

   ts_rank expects tsvector first, then tsquery. Here, they are reversed.

2. Step 2: Confirm argument types

   to_tsquery(content) is incorrect because content is text, not a query string; also to_tsvector('search') is a vector, but arguments are swapped.

3. Final Answer:

   Arguments to ts_rank are reversed; tsvector should be first, tsquery second. -> Option A

4. Quick Check:

   ts_rank(tsvector, tsquery) order matters [OK]

Hint: ts_rank always: tsvector first, tsquery second [OK]

Common Mistakes:

• Swapping ts_rank arguments
• Using to_tsquery on text column
• Ignoring argument types for ts_rank

5. You want to find the top 3 most relevant documents containing either 'apple' or 'orange' using high weights to boost relevance. Which query correctly uses ts_rank with weights to achieve this?

hard

A. SELECT content, ts_rank(setweight(to_tsvector(content), 'D'), to_tsquery('apple | orange')) AS rank FROM documents ORDER BY rank ASC LIMIT 3;

B. SELECT content, ts_rank(to_tsvector(content), to_tsquery('apple & orange')) AS rank FROM documents ORDER BY rank DESC LIMIT 3;

C. SELECT content, ts_rank(to_tsvector(content), to_tsquery('apple | orange')) AS rank FROM documents ORDER BY rank ASC LIMIT 3;

D. SELECT content, ts_rank(setweight(to_tsvector(content), 'A'), to_tsquery('apple | orange')) AS rank FROM documents ORDER BY rank DESC LIMIT 3;

Solution

1. Step 1: Understand weighting with setweight

   Using setweight with 'A' assigns highest weight to terms in to_tsvector, boosting the relevance of matching terms.

2. Step 2: Analyze the query logic

   to_tsquery('apple | orange') searches for either word, and ordering by rank descending with LIMIT 3 returns top 3 relevant documents.

3. Step 3: Compare options

   SELECT content, ts_rank(to_tsvector(content), to_tsquery('apple & orange')) AS rank FROM documents ORDER BY rank DESC LIMIT 3; uses AND (&) which requires both words, not either. SELECT content, ts_rank(to_tsvector(content), to_tsquery('apple | orange')) AS rank FROM documents ORDER BY rank ASC LIMIT 3; orders ascending, showing least relevant first. SELECT content, ts_rank(setweight(to_tsvector(content), 'D'), to_tsquery('apple | orange')) AS rank FROM documents ORDER BY rank ASC LIMIT 3; uses weight 'D' which is lowest, so it does not boost relevance as much.

4. Final Answer:

   SELECT content, ts_rank(setweight(to_tsvector(content), 'A'), to_tsquery('apple | orange')) AS rank FROM documents ORDER BY rank DESC LIMIT 3; -> Option D

5. Quick Check:

   Weighted ts_rank with OR query = SELECT content, ts_rank(setweight(to_tsvector(content), 'A'), to_tsquery('apple | orange')) AS rank FROM documents ORDER BY rank DESC LIMIT 3; [OK]

Hint: Use setweight with 'A' to boost preferred terms [OK]

Common Mistakes:

• Using AND instead of OR in tsquery
• Ordering by rank ascending instead of descending
• Assigning lowest weight to preferred terms