Handling load errors in Snowflake - Time & Space Complexity

Time Complexity: Handling load errors

O(n)

Understanding Time Complexity

When loading data into Snowflake, errors can happen and need handling.

We want to know how the time to handle these errors grows as data size grows.

Scenario Under Consideration

Analyze the time complexity of this error handling process during data load.

COPY INTO my_table
FROM @my_stage/file.csv
ON_ERROR = 'CONTINUE';

SELECT * FROM my_table_errors;

-- Process errors for correction or logging

This sequence loads data, continues on errors, then queries error details for handling.

Identify Repeating Operations

Look at what repeats during this load and error handling.

• Primary operation: Reading each data row and checking for errors during COPY INTO.
• How many times: Once per data row in the file.
• Error retrieval: Querying error table once after load.

How Execution Grows With Input

As the number of rows grows, the system checks each row once.

Input Size (n)	Approx. Api Calls/Operations
10	About 10 row checks + 1 error query
100	About 100 row checks + 1 error query
1000	About 1000 row checks + 1 error query

Pattern observation: The number of checks grows directly with rows; error query stays constant.

Final Time Complexity

Time Complexity: O(n)

This means the time to handle load errors grows linearly with the number of data rows.

Common Mistake

[X] Wrong: "Handling errors only takes constant time regardless of data size."

[OK] Correct: Each row must be checked for errors, so more rows mean more checks and longer time.

Interview Connect

Understanding how error handling scales helps you design reliable data pipelines and explain your choices clearly.

Self-Check

"What if we changed ON_ERROR from 'CONTINUE' to 'ABORT_STATEMENT'? How would the time complexity change when errors occur early?"

Practice

1. What does the ON_ERROR option do when loading data into Snowflake?

easy

A. It controls how Snowflake handles errors during data loading.

B. It encrypts the data before loading.

C. It speeds up the data loading process.

D. It automatically deletes duplicate rows.

Solution

1. Step 1: Understand the purpose of ON_ERROR

   The ON_ERROR option is used to specify how Snowflake should behave when it encounters errors during data loading.

2. Step 2: Identify the correct behavior

   It can skip bad rows, skip files, or stop the load depending on the setting, thus controlling error handling.

3. Final Answer:

   It controls how Snowflake handles errors during data loading. -> Option A

4. Quick Check:

   ON_ERROR controls error handling [OK]

Hint: ON_ERROR sets error handling behavior during load [OK]

Common Mistakes:

• Confusing ON_ERROR with encryption settings
• Thinking ON_ERROR speeds up loading
• Assuming ON_ERROR deletes duplicates

2. Which of the following is the correct syntax to skip bad rows during a Snowflake COPY INTO command?

easy

A. COPY INTO table_name FROM @stage FILE_FORMAT = (TYPE = 'CSV') ON_ERROR = 'ignore_error';

B. COPY INTO table_name FROM @stage FILE_FORMAT = (TYPE = 'CSV') ON_ERROR = 'skip_file';

C. COPY INTO table_name FROM @stage FILE_FORMAT = (TYPE = 'CSV') ON_ERROR = 'abort_load';

D. COPY INTO table_name FROM @stage FILE_FORMAT = (TYPE = 'CSV') ON_ERROR = 'skip_row';

Solution

1. Step 1: Recall valid ON_ERROR options

   Snowflake supports options like 'skip_file', 'skip_row', and 'abort_load' for ON_ERROR.

2. Step 2: Identify option to skip bad rows

   'skip_row' tells Snowflake to skip only the bad rows, not the entire file.

3. Final Answer:

   ON_ERROR = 'skip_row' -> Option D

4. Quick Check:

   Skip bad rows = skip_row [OK]

Hint: Use 'skip_row' to skip bad rows in ON_ERROR [OK]

Common Mistakes:

• Using 'skip_file' to skip rows instead of files
• Using invalid ON_ERROR values like 'ignore_error'
• Confusing 'abort_load' with skipping errors

3. Given this COPY command:

COPY INTO my_table FROM @my_stage FILE_FORMAT = (TYPE = 'CSV') ON_ERROR = 'skip_file';

If one file has 5 bad rows, what happens?

medium

A. The entire file with bad rows is skipped, other files load normally.

B. Only the 5 bad rows are skipped, rest of the file loads.

C. The load stops immediately with an error.

D. All files are skipped regardless of errors.

Solution

1. Step 1: Understand ON_ERROR = 'skip_file'

   This option skips the entire file if any error occurs in it.

2. Step 2: Apply to scenario

   Since one file has 5 bad rows, Snowflake skips that whole file but continues loading other files.

3. Final Answer:

   The entire file with bad rows is skipped, other files load normally. -> Option A

4. Quick Check:

   skip_file skips whole file on error [OK]

Hint: 'skip_file' skips whole file if any error found [OK]

Common Mistakes:

• Thinking only bad rows are skipped with 'skip_file'
• Assuming load stops on first error
• Believing all files skip on one bad file

4. You run a COPY INTO command with ON_ERROR = 'skip_row' but still see the load failing. What is a likely cause?

medium

A. ON_ERROR only works for JSON files, not CSV.

B. The file format is incorrect causing parsing errors.

C. You must set ON_ERROR to 'skip_file' to avoid failures.

D. The target table does not exist.

Solution

1. Step 1: Understand ON_ERROR limitations

   ON_ERROR skips bad rows but cannot fix fundamental file format or parsing errors.

2. Step 2: Identify cause of failure

   If file format is wrong, Snowflake cannot parse data, causing load failure despite ON_ERROR.

3. Final Answer:

   The file format is incorrect causing parsing errors. -> Option B

4. Quick Check:

   Wrong file format causes failure despite ON_ERROR [OK]

Hint: Check file format if ON_ERROR skip_row still fails [OK]

Common Mistakes:

• Thinking ON_ERROR fixes all errors
• Believing ON_ERROR only works for JSON
• Ignoring table existence errors

5. You want to load multiple CSV files but skip any file with more than 10 bad rows, while loading others fully. Which ON_ERROR setting should you use?

hard

A. ON_ERROR = 'continue' with MAX_ERROR = 10

B. ON_ERROR = 'skip_row' with MAX_ERROR = 10

C. ON_ERROR = 'skip_file' with MAX_ERROR = 10

D. ON_ERROR = 'abort_load' with MAX_ERROR = 10

Solution

1. Step 1: Understand requirement

   Skip entire files only if bad rows exceed 10, otherwise load fully.

2. Step 2: Match ON_ERROR and MAX_ERROR

   Using ON_ERROR = 'skip_file' with MAX_ERROR = 10 skips files exceeding 10 errors, loads others fully.

3. Final Answer:

   ON_ERROR = 'skip_file' with MAX_ERROR = 10 -> Option C

4. Quick Check:

   Skip files over 10 errors = skip_file + MAX_ERROR [OK]

Hint: Use skip_file with MAX_ERROR to limit bad rows per file [OK]

Common Mistakes:

• Using skip_row which skips rows, not files
• Assuming 'continue' skips files
• Thinking abort_load allows skipping