Error 47: UNKNOWN_IDENTIFIER

Tip

This error occurs when a query references a column name, alias, or identifier that does not exist in the specified scope or context. It typically indicates a column name that doesn't exist in the table, a missing alias, or incorrect identifier resolution in complex queries.

Most common causes

1. Column does not exist in table

   • Referencing a column name that is not present in the table schema
   • Typo in column name
   • Column was dropped or never created

2. Incorrect identifier scope in joins

   • Referencing columns without proper table aliases
   • Ambiguous column references in multi-table queries
   • Column from wrong side of join
   • Using columns that exist in one table but not in the joined result

3. Missing columns in subqueries or CTEs

   • Column not selected in inner query but referenced in outer query
   • Column not available in the scope where it's being referenced
   • Incorrect nesting of subqueries

4. Alias issues

   • Using an alias before it's defined
   • Referencing column by original name after aliasing
   • Alias not properly propagated through query stages

5. Materialized view or integration issues

   • Column missing from source table in materialized view
   • Schema mismatch between source and target
   • Replication or CDC tools referencing non-existent columns

6. Aggregation context problems

   • Using non-aggregated columns not in GROUP BY clause
   • Referencing columns that are only available after aggregation
   • Incorrect use of columns in HAVING vs WHERE clauses

Common solutions

1. Verify column exists in table

-- Check table structure
DESCRIBE TABLE your_table;

-- Or check system tables
SELECT name, type 
FROM system.columns 
WHERE database = 'your_database' 
AND table = 'your_table';

2. Use proper table aliases in joins

-- WRONG: Ambiguous reference
SELECT column1, unique_column
FROM table1
INNER JOIN table2 ON table1.id = table2.id;

-- CORRECT: Explicit table references
SELECT t1.column1, t2.unique_column
FROM table1 AS t1
INNER JOIN table2 AS t2 ON t1.id = t2.id;

3. Check column availability in scope

-- WRONG: Column not in subquery SELECT
SELECT outer_column
FROM (
    SELECT inner_column 
    FROM table1
) 
WHERE outer_column > 10;

-- CORRECT: Include needed columns in subquery
SELECT outer_column
FROM (
    SELECT inner_column, outer_column 
    FROM table1
)
WHERE outer_column > 10;

4. Fix aggregation issues

-- WRONG: Non-aggregated column not in GROUP BY
SELECT user_id, count(*), email
FROM users
GROUP BY user_id;

-- CORRECT: Include all non-aggregated columns in GROUP BY
SELECT user_id, count(*), email
FROM users
GROUP BY user_id, email;

-- OR: Use any() aggregate function
SELECT user_id, count(*), any(email) AS email
FROM users
GROUP BY user_id;

5. Use EXPLAIN to debug

EXPLAIN SYNTAX
SELECT column_name FROM your_table;

This shows how ClickHouse interprets your query and may reveal the actual column names being used.

6. Handle case sensitivity

-- Column names are case-sensitive in ClickHouse
SELECT UserID FROM users;  -- May fail

-- Use exact case from schema
SELECT userId FROM users;  -- Correct

Common scenarios

Scenario 1: Missing column in materialized view

Error: Missing columns: 'email_id' while processing query

Solution: Ensure the column exists in the source table or add it to the materialized view definition.

Scenario 2: Column ambiguity in joins

Error: Unknown column: customtag1, there are only columns sum(viewercount), sumMap(eventcount_map)

Solution: The column exists in the joined table but isn't in the aggregation scope. Use proper aliases and ensure the column is accessible in the aggregation context.

Scenario 3: Alias before definition

-- WRONG
SELECT count(*) FROM table WHERE count > 10;

-- CORRECT
SELECT count(*) AS count FROM table HAVING count > 10;

Prevention tips

1. Use explicit table aliases: Always use table.column or alias.column syntax in joins
2. Verify schema before querying: Use DESCRIBE TABLE to confirm column names and types
3. Check column case: Column names are case-sensitive
4. Review aggregation logic: Ensure all non-aggregated columns are in GROUP BY
5. Use IDE or query validator: Many tools can catch column reference errors before execution
6. Test subqueries independently: Verify inner queries work before nesting them
7. Monitor schema changes: Track ALTER TABLE operations that might remove columns

Debugging steps

If you're experiencing this error:

1. Check the error message carefully - it often suggests similar column names with "maybe you meant: ['column_name']"

2. Verify table schema:

   DESCRIBE TABLE your_table;
   

3. Check if column is in the right scope for joins and subqueries

4. Use EXPLAIN SYNTAX to see how ClickHouse interprets your query

5. Test with simpler query - remove joins and subqueries to isolate the issue

6. Check for typos in column names (including case sensitivity)

7. Review recent schema changes - was the column recently dropped or renamed?

8. For integrations/materialized views - verify source and target schemas match

Special considerations

For CDC and replication tools:

• This error often occurs when schema changes aren't synchronized
• The source table may have different columns than expected
• Check both source and target schemas

For complex queries with aggregations:

• Remember that aggregation changes the available columns
• Use proper aggregate functions or add columns to GROUP BY
• HAVING clause has different column availability than WHERE

For materialized views:

• The source table must have all columns referenced in the view query
• Schema changes to source tables can break materialized views
• Consider using SELECT * cautiously as it can cause issues with schema evolution