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SQL001 – SQL Query Syntax Error in Databricks

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Introduction

The SQL001 – SQL query syntax error in Databricks occurs when a SQL statement contains incorrect syntax, unsupported commands, or incompatible references. This error typically appears when running SQL queries in notebooks, Databricks SQL, or Apache Spark SQL.

🚨 Common symptoms of SQL001 errors:

  • Error Message: “SQL001: Syntax error in SQL statement.”
  • Query fails to execute in Databricks SQL or a notebook cell.
  • Unexpected keyword or missing clause in the SQL statement.

This guide covers common causes of SQL001 errors, troubleshooting steps, and best practices to resolve SQL syntax issues in Databricks.


1. Check for Missing or Incorrect Keywords

Symptoms:

  • Error: “SQL001: Syntax error near SELECT.”
  • Error: “Expected keyword not found in SQL statement.”

Causes:

  • Incorrect SQL command structure.
  • Missing mandatory clauses (e.g., FROM in SELECT, WHERE in DELETE).
  • Using Spark SQL functions that differ from ANSI SQL.

Fix:

Ensure the SQL query follows correct syntax:
Bad Query (Missing FROM Clause)

SELECT name, age;

Correct Query

SELECT name, age FROM employees;

Check that all required clauses are present in the query.


2. Verify Table and Column Names

Symptoms:

  • Error: “Table not found: my_table.”
  • Error: “Column ‘customer_id’ does not exist.”

Causes:

  • Table does not exist or is misspelled.
  • Column name is incorrect or missing from the table schema.
  • Using case-sensitive table names incorrectly.

Fix:

Check available tables using:

SHOW TABLES;

Check table schema to verify column names:

DESCRIBE TABLE employees;

Ensure table exists before querying:

SELECT * FROM my_catalog.my_schema.my_table;

3. Use Correct Quotation for Identifiers and Strings

Symptoms:

  • Error: “Invalid syntax near ‘customer’.”
  • Error: “String literal not properly terminated.”

Causes:

  • Using double quotes (") for string literals instead of single quotes (').
  • Using single quotes (') instead of backticks ( ) for column names.

Fix:

Use single quotes for string literals:

SELECT * FROM users WHERE name = 'John Doe';

Use backticks for column/table names with spaces or special characters:

SELECT `customer name` FROM customers;

4. Check for Incorrect Joins and Aliases

Symptoms:

  • Error: “Invalid alias reference.”
  • Error: “Table alias not recognized in JOIN condition.”

Causes:

  • Alias used in WHERE without defining it in FROM.
  • Incorrect JOIN condition referencing alias improperly.

Fix:

Use correct alias definitions in JOINs:

SELECT e.name, d.department
FROM employees e
JOIN departments d ON e.dept_id = d.id;

5. Ensure Compatible Data Types in Conditions

Symptoms:

  • Error: “Cannot compare INTEGER to STRING.”
  • Error: “Function expected numeric argument but received STRING.”

Causes:

  • Mismatched data types in WHERE or JOIN conditions.
  • Using non-numeric columns in mathematical functions.

Fix:

Use explicit type casting to match types:

SELECT * FROM orders WHERE CAST(order_id AS STRING) = '1001';

Ensure numeric functions receive numeric inputs:

SELECT ROUND(price, 2) FROM products;

6. Check for Missing Parentheses in Functions

Symptoms:

  • Error: “Function call syntax error.”
  • Error: “Aggregation function missing parentheses.”

Causes:

  • Forgetting to add parentheses in functions.
  • Incorrect function usage in SQL.

Fix:

Ensure functions include parentheses:

SELECT COUNT(*) FROM employees;

Use correct syntax for aggregate functions:

SELECT SUM(salary) FROM employees WHERE department = 'HR';

7. Validate Reserved Keywords and Aliases

Symptoms:

  • Error: “Keyword cannot be used as column or table name.”
  • Error: “Unexpected keyword at position X.”

Causes:

  • Using SQL reserved keywords as column names.
  • Using a reserved word as a table alias without escaping it.

Fix:

Avoid reserved keywords in table/column names:

SELECT `order`, `group` FROM sales;

Rename columns that conflict with SQL keywords:

ALTER TABLE employees RENAME COLUMN order TO order_number;

8. Check Databricks-Specific SQL Syntax

Symptoms:

  • Error: “Syntax error in Spark SQL.”
  • Error: “Databricks-specific function not recognized.”

Causes:

  • Using Databricks SQL syntax in Spark SQL or vice versa.
  • Using unsupported SQL functions or missing parameters.

Fix:

Ensure syntax is compatible with Databricks SQL:

SELECT approx_count_distinct(user_id) FROM events;

For Spark SQL, use built-in functions like:

SELECT percentile_approx(salary, 0.5) FROM employees;

Refer to Databricks SQL documentation for supported functions.


9. Debugging SQL001 Errors in Databricks

Step 1: Check Error Message for Clues

  • Databricks usually highlights the position of the syntax error.
  • Look for missing keywords, extra commas, or incorrect function usage.

Step 2: Validate SQL Using EXPLAIN Statement

EXPLAIN SELECT name, age FROM employees;
  • This shows the execution plan and helps find errors.

Step 3: Test Query in Smaller Parts

  • Run SELECT statements separately before adding WHERE, JOIN, or GROUP BY.
  • Check for column name mismatches or missing tables.

Best Practices to Avoid SQL Syntax Errors

Always Check Query Structure Before Execution

  • Use correct SELECT, FROM, WHERE, GROUP BY, and ORDER BY clauses.

Use Aliases and Joins Correctly

  • Always define table aliases before using them in conditions.

Ensure Proper Data Type Usage

  • Convert data types when comparing strings and numbers.

Use Backticks for Reserved Keywords

  • Escape column names that match SQL keywords.

Refer to Databricks SQL Documentation for Function Syntax

  • Use Databricks-supported SQL syntax when running queries.

Conclusion

The SQL001 syntax error in Databricks is often caused by missing clauses, incorrect joins, invalid identifiers, or incompatible data types. By following troubleshooting steps, checking query structure, and validating syntax, you can quickly resolve SQL errors and ensure smooth execution of your queries.

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