Snowflake Syntax Error: Unexpected Token Troubleshooting Guide

“Unexpected EOF.” “Unexpected ON.” “Unexpected INTO.” If you’ve been working with Snowflake for more than a week, you’ve probably muttered some choice words at these error messages at some point. They’re vague, they point to locations that look perfectly fine, and worst of all – they pop up at the most inconvenient times (usually right before a demo or deployment). 

The frustrating part isn’t just that these errors exist. It’s that the same query can work in your worksheet but fails when deployed to production. It can run fine when you type it manually but break when your ETL tool generates it. 

But what looks like inconsistent behavior is actually Snowflake being extremely consistent – it’s just following rules that aren’t obvious. Once you know what to look for, these errors become way less mysterious, and way more fixable. 

Understanding Snowflake “Unexpected” Syntax Errors

When Snowflake throws a syntax error with the word “unexpected” followed by a token like EOF, ON, or INTO, it’s telling you that its SQL parser encountered something it didn’t anticipate at that specific location in your code. Think of it like reading a sentence where someone switches languages mid-thought – your brain stops because the pattern broke. 

These errors appear like this: 

SQL compilation error: syntax error line X at position Y unexpected 'TOKEN'

The key components are: 

• Line X: Where in your SQL statement the problem occurs
• Position Y: The character position on the line
• TOKEN: What Snowflake found that it didn’t expect

Here’s what makes these errors so tricky: the problem isn’t always with the token mentioned in the error. 

Often, the issue is what came before. Snowflake’s parser was expecting something specific based on your previous syntax, and when it encountered TOKEN instead, it raised the error. 

Why Snowflake Throws Unexpected Token Errors

The root cause almost always falls into one of these categories: 

• Incomplete SQL statements
• Invisible character issues
• Context-specific syntax rules
• Reserved keywords in wrong places

Incomplete SQL Statements

The most common culprit is incomplete syntax. You started a SQL construct but didn’t finish it, so when Snowflake encounters the next element, it’s unexpected because the previous statement wasn’t closed properly. 

This includes: 

• Missing closing parentheses in subqueries or function calls
• Unclosed quote marks around strings
• Incomplete CREATE, ALTER, or DROP statements 
• Functions missing required parameters

Invisible Character Issues

This one drives developers up the wall because you literally can’t see the problem. Non-ASCII characters, byte order marks (BOM), or hidden formatting characters from copying code between different editors can cause Snowflake’s parser to fail. 

Context-Specific Syntax Rules

Snowflake has different syntax rules depending on context, especially when it comes to stored procedures, tasks, and Snowflake Scripting blocks. What works in a standard SQL worksheet might fail inside a stored procedure, and what’s valid in a CREATE TABLE statement might not work in a MERGE operation. 

Reserved Keywords in Wrong Places

Snowflake reserves certain keywords like ON, INTO, FROM, and START. When these appear where Snowflake’s parser doesn’t expect them – likely because of a previous syntax error – you’ll get the unexpected token error. 

How These Errors Impact Your Bottom Line

Before we dive into fixes, let’s talk about why these errors matter beyond just being annoying. 

Development Velocity Killer

Every time your team gets hit with one of these errors, someone has to stop building features and start debugging. For engineering teams, that means context switching costs, deployment delays when the errors appear in CI/CD pipelines, and emergency fixes that disrupt sprint planning. 

Product Reliability Risks

If these errors make it into production workflows, the impact doubles, leaving you with failed stored procedures, broken scheduled tasks, and stalled data pipelines. 

Indication of Bigger Problems

Frequent unexpected syntax errors often mean your team needs better deployment practices in regards to Snowflake. This might mean that you need to improve testing environments, establish code review processes for SQL, or provide better training on Snowflake-specific syntax quirks. 

Thankfully, once you understand the patterns, these errors become much easier to prevent. 

How to Fix Unexpected Token Errors in Snowflake

You should understand exactly what this error entails and why it happens. Now let’s take a closer look at exactly how you can fix it. 

Step 1: Locate the Exact Problem

Start by finding the precise location Snowflake identified. The error message gives you line and position numbers, so make sure to use them. 

-- Error: syntax error line 3 at position 45 unexpected 'ON'
SELECT
    c.customer_id,
    c.customer_name
FROM customers c
INNER JOIN orders o ON c.customer_id = o.customer_id;

Snowflake counts characters including white spaces and tabs, so manual counting is often unreliable. Use your editor’s column indicator instead of counting manually. But remember: the actual issues might be on the line before. Snowflake reports where it gave up parsing, not necessarily where the actual mistake is. 

Step 2: Check for Missing Elements Before the Error

Work backwards from the error location. Look for incomplete constructs: 

-- Wrong - missing closing parenthesis before JOIN
SELECT
    customer_id,
    SUM(order_total
FROM orders
INNER JOIN customers ON orders.customer_id = customers.customer_id;

-- Right - close all parentheses
SELECT
    customer_id,
    SUM(order_total)
FROM orders
INNER JOIN customers ON orders.customer_id = customers.customer_id;

Step 3: Handle the EOF Error Specifically 

The “unexpected EOF” (End of File) error means Snowflake reached the end of your statement but was expecting more. This happens when you have: 

Incomplete CREATE statements: 

-- Wrong - missing closing elements
CREATE OR REPLACE TABLE my_table (
    id NUMBER,
    name VARCHAR,
    created_date DATE

-- Right - close parenthesis and add semicolon
CREATE OR REPLACE TABLE my_table (
    id NUMBER,
    name VARCHAR,
    created_date DATE
);

Unclosed CTEs: 

-- Wrong - CTE never finishes
WITH monthly_sales AS (
    SELECT
        DATE_TRUNC('month', order_date) AS month,
        SUM(total) AS monthly_total
    FROM orders

-- Right - complete the CTE
WITH monthly_sales AS (
    SELECT
        DATE_TRUNC('month', order_date) AS month,
        SUM(total) AS monthly_total
    FROM orders
)
SELECT * FROM monthly_sales;

Stored procedures missing END: 

-- Wrong - procedure body never closes
CREATE OR REPLACE PROCEDURE calculate_metrics()
RETURNS VARCHAR
LANGUAGE SQL
AS
$$
BEGIN
    INSERT INTO metrics_table
    SELECT * FROM staging_metrics;
    RETURN 'Success';
-- Missing END here

-- Right - properly closed procedure
CREATE OR REPLACE PROCEDURE calculate_metrics()
RETURNS VARCHAR
LANGUAGE SQL
AS
$$
BEGIN
    INSERT INTO metrics_table
    SELECT * FROM staging_metrics;
    RETURN 'Success';
END;
$$;

Step 4: Clean Up Hidden Characters

If your syntax looks correct but you’re still getting unexpected token errors, you might have invisible character issues. This often happens if you’re copying code from documentation websites, PDFs, or Slack. 

Try these techniques to address any hidden characters: 

Copy to plain text editor first: 

# Copy your SQL to a plain text editor like Notepad or vim
# Then copy from there to Snowflake
# This strips out hidden formatting

Pro tip: Retype problem areas manually. If a specific section keeps failing, just retype it from scratch rather than copying. 

Check for BOM markers: 

# If you're generating SQL from Python scripts
# Make sure you're not accidentally adding BOM
with open('query.sql', 'w', encoding='utf-8') as f:
    f.write(your_sql_query)

Step 5: Fix Context-Specific Syntax Issues

Different Snowflake contexts have different rules. Here’s how you should handle common scenarios: 

Stored Procedures

In SQL-based stored procedures, you must use Snowflake Scripting syntax:

-- Wrong - trying to use standard SQL variable assignment
CREATE OR REPLACE PROCEDURE update_status()
RETURNS VARCHAR
LANGUAGE SQL
AS
$$
DECLARE
    record_count NUMBER;
BEGIN
    record_count = (SELECT COUNT(*) FROM orders);  -- This fails
    RETURN record_count;
END;
$$;

-- Right - use proper variable assignment syntax
CREATE OR REPLACE PROCEDURE update_status()
RETURNS VARCHAR
LANGUAGE SQL
AS
$$
DECLARE
    record_count NUMBER;
BEGIN
    SELECT COUNT(*) INTO record_count FROM orders;
    RETURN record_count;
END;
$$;

Dynamic SQL

Be careful with quotes when building SQL strings dynamically. 

-- Wrong - quote handling breaks the syntax
CREATE OR REPLACE PROCEDURE dynamic_insert(table_name VARCHAR)
RETURNS VARCHAR
LANGUAGE SQL
AS
$$
BEGIN
    EXECUTE IMMEDIATE 'INSERT INTO ' || table_name || ' SELECT * FROM staging';
    RETURN 'Done';
END;
$$;
-- This fails if table_nameis use-supplied or requires quoting.

-- Right - handle identifiers properly
CREATE OR REPLACE PROCEDURE dynamic_insert(table_name VARCHAR)
RETURNS VARCHAR
LANGUAGE SQL
AS
$$
DECLARE
    query VARCHAR;
BEGIN
    query := 'INSERT INTO ' || IDENTIFIER(table_name) || ' SELECT * FROM staging';
    EXECUTE IMMEDIATE query;
    RETURN 'Done';
END;
$$;

Step 6: Validate Reserved Keywords

Some tokens that cause unexpected errors are reserved keywords used incorrectly: 

-- Wrong - using reserved keyword without quotes
CREATE TABLE user_data (
    user NUMBER,
    name VARCHAR,
    date DATE
);

-- Right - quote reserved keywords or rename
CREATE TABLE user_data (
    "user" NUMBER,
    name VARCHAR,
    "date" DATE
);

-- Better - use non-reserved names
CREATE TABLE user_data (
    user_id NUMBER,
    name VARCHAR,
    created_date DATE
);

Step 7 : Test in Isolation 

Complex queries can hide the root cause. Make sure to break them into smaller pieces: 

-- Start with the simplest version
SELECT * FROM customers;

-- Add the JOIN
SELECT *
FROM customers c
INNER JOIN orders o ON c.id = o.customer_id;

-- Add the WHERE clause
SELECT *
FROM customers c
INNER JOIN orders o ON c.id = o.customer_id
WHERE c.status = 'active';

-- Add aggregations
SELECT
    c.region,
    COUNT(*) AS customer_count,
    SUM(o.total) AS total_revenue
FROM customers c
INNER JOIN orders o ON c.id = o.customer_id
WHERE c.status = 'active'
GROUP BY c.region;

Pro tip: Try to build complexity gradually so you can pinpoint exactly where the syntax error appears. 

Step 8: Validate Statement Terminators

In some contexts, Snowflake needs proper statement terminators: 

-- Wrong - multiple statements without separators
CREATE TABLE temp1 (id NUMBER)
INSERT INTO temp1 VALUES (1)
SELECT * FROM temp1

-- Right - proper semicolons
CREATE TABLE temp1 (id NUMBER);
INSERT INTO temp1 VALUES (1);
SELECT * FROM temp1;

For stored procedures, you might need different terminators: 

CREATE OR REPLACE PROCEDURE multi_step()
RETURNS VARCHAR
LANGUAGE SQL
AS
$$
BEGIN
    -- Statements within BEGIN/END don't always need semicolons
    -- But it's good practice to include them
    CREATE TEMP TABLE staging (id NUMBER);
    INSERT INTO staging VALUES (1), (2), (3);
    RETURN 'Complete';
END;
$$;

Step 9: Review Your Query Builder or ORM

If you’re generating SQL programmatically with an ORM or query builders, the issue could be how the tool constructs queries: 

# Python example with Snowflake connector
# Wrong - string concatenation can introduce syntax errors
table = "orders"
query = f"SELECT * FROM {table} WHERE status = 'active'"

# Right - use parameterized queries or validate identifiers
from snowflake.connector import connect
conn = connect(...)
cursor = conn.cursor()

# Validate table name is safe
safe_table = table.replace('"', '""')
query = f'SELECT * FROM "{safe_table}" WHERE status = ?'
cursor.execute(query, ('active',))

Step 10: Implement Better Error Handling

Once you’ve fixed the immediate error, add comprehensive error handling to catch these issues sooner. For robust Snowflake error handling strategies, consider implementing automated testing and validation. 

def execute_snowflake_query(query, conn):
    """Execute query with detailed error reporting"""
    try:
        cursor = conn.cursor()
        cursor.execute(query)
        return cursor.fetchall()
    except Exception as e:
        error_msg = str(e)
        if "unexpected" in error_msg.lower():
            # Extract line and position from error
            import re
            match = re.search(r'line (\d+) at position (\d+)', error_msg)
            if match:
                line_num = int(match.group(1))
                position = int(match.group(2))
                query_lines = query.split('\n')
                if line_num <= len(query_lines):
                    problem_line = query_lines[line_num - 1]
                    print(f"Problem on line {line_num}:")
                    print(problem_line)
                    print(" " * (position - 1) + "^")
        raise

Unexpected Token Error Prevention Strategies

Now that you know how to fix these errors, here are the best ways to prevent them from happening in the future: 

• Establish SQL style guidelines: Create team standards for Snowflake SQL that reduce syntax errors.
• Implement pre-deployment validation: Add SQL linting to your CI/CD pipeline since tools can catch many syntax errors before they reach production.
• Use version control with diff reviews: Store all Snowflake SQL in version control and require code reviews since a second pair of eyes catches many syntax errors that can slip past the original developer. 
• Create testing environments that match production: Many unexpected token errors occur because development and production contexts differ, so make sure your testing environment uses the same Snowflake versions, privileges, and object structures. 
• Document common patterns: Build an internal knowledge base of Snowflake syntax patterns your team uses frequently, storing procedure templates, CTE structures, and proven dynamic SQL examples. 

When Prevention Isn’t Enough 

Even with the best prevention strategies, Snowflake syntax errors will still happen. The complexity of modern data pipelines, the variety of tools connecting to Snowflake, and the speed of development all contribute to these issues slipping through QA. 

That’s where having expertise in Snowflake optimization comes into play. Teams that excel with Snowflake don’t just fix errors faster – they build systems that minimize errors in the first place with better architecture and monitoring. 

Understanding Snowflake’s error codes comprehensively can also help your team diagnose issues more quickly and build more resilient data infrastructure.  

Let Yuki Handle the Complexity 

Debugging syntax errors is not a good way for your engineering team to spend their time. Every hour spent tracking down an unexpected token error is an hour not spent building features, optimizing queries, or improving your data architecture. 

Yuki specializes in taking the complexity of Snowflake off your plate. Our platform continuously monitors your environment, automatically optimizes warehouse configurations, and catches potential issues before they turn into production problems. On average, we’ve helped companies cut Snowflake costs by 37.6% while boosting performance and reliability. 

So, instead of your team getting stuck with troubleshooting, let Yuki handle the heavy lifting. We process over 500 million queries daily and have helped organizations reduce cluster counts by 30% – all without your developers having to lift a finger. 
Ready to see how much your team can save? Request a demo so you can see how much Yuki can optimize your Snowflake environment.