Can anyone tell me why there are temp tables in lakehouse after successfull run of notebook activity.

Question

i am creating temp tables using copy activity. then after successful run of copy activity, i am running notebook activity to process those temp table. if temp table exists then i rename them by remove underscore _temp and delete previous table. below is the code. do you think i am missing something ?

for tempTableName in table_names:
    try:
        TableName = tempTableName.replace('_temp', '')
        TableName_lower = TableName.lower()
        print(TableName)
        print(TableName_lower)
        Status = 0
        
        if spark.catalog.tableExists(f"{TableName}"):
            print(f"Table {TableName} exists in the Lakehouse!")
            
            # Get count of records from the temp table
            CurrentCount = spark.sql(
                "SELECT COUNT(1) FROM {0}".format(tempTableName)
            ).collect()
            
            # Get count of records from the original table
            PrevCount = spark.sql(
                "SELECT COUNT(1) FROM {0}".format(TableName)
            ).collect()

            Variance = 0

            # Calculate variance if the original table is not empty
            if PrevCount[0][0] != 0:
                Variance = ((CurrentCount[0][0] - PrevCount[0][0]) / PrevCount[0][0]) * 100

            # If variance is within threshold, proceed with table rename
            if abs(Variance) <= threshold:
                print(f"Variance is within the threshold for table {TableName}.")
                spark.sql(f"DROP TABLE IF EXISTS {TableName}")  # Drop the current table
                spark.sql(f"ALTER TABLE {tempTableName} RENAME TO {TableName}")  # Rename temp table to original table
                Status = 1

        else:
            print(f"Table {TableName} does not exist in the Lakehouse.")
            spark.sql(f"ALTER TABLE {tempTableName} RENAME TO {TableName}")  # Rename temp table to original table
            Status = 1

        # Drop the temp table if it exists
        if spark.catalog.tableExists(f"{tempTableName}"):
            spark.sql(f"DROP TABLE IF EXISTS {tempTableName}")
            Status = 0

        # Update status in Precheck table based on success or failure
        if Status == 1:
            spark.sql(f"""
                UPDATE Precheck
                SET IsPrecheckSucceeded = {Status}, LastSucceededDate = current_date()
                WHERE TableName = "{TableName_lower}"
            """)
        else:
            spark.sql(f"""
                UPDATE Precheck
                SET IsPrecheckSucceeded = {Status}
                WHERE TableName = "{TableName_lower}"
            """)

    except Exception as e:
        print(f"An error occurred while processing table {tempTableName}: {str(e)}")
        # Optional: Log the exception or perform additional error handling here
        spark.sql(f"""
            UPDATE Precheck
            SET IsPrecheckSucceeded = 0
            WHERE TableName = "{TableName_lower}"
        """)
        continue  # Continue with the next table in the loop

Answer

Potential Reasons for Persisting Temp Tables

Your DROP TABLE command may not execute successfully in some cases. This could happen due to table locks, transactional issues, or syntax problems.
Example: If the DROP TABLE command fails due to a concurrent process or temporary table locks, the table won't be deleted.
If there are multiple processes writing to or using the same temporary tables simultaneously, conflicts or overwrites can occur, leading to unexpected persistence of tables.
The Lakehouse might be case-sensitive. If the tempTableName and TableName are not consistently referenced with the same case, it could lead to issues where tables aren't properly identified for deletion or renaming.
While you have a try-except block, exceptions during DROP TABLE or ALTER TABLE operations might not be logged or handled completely. As a result, the process could silently fail and leave the temp tables behind.
If the Spark session doesn't explicitly commit the changes or if operations are part of an uncommitted transaction, tables might remain.
- If the Precheck table is being updated or queried concurrently, there may be a timing issue that affects the deletion process.

Here are steps you can take to address these issues:

Add explicit logging for each DROP TABLE operation to ensure it executes as expected:

  
  if spark.catalog.tableExists(f"{tempTableName}"):
  
    spark.sql(f"DROP TABLE {tempTableName}")
  
    print(f"Temp table {tempTableName} dropped successfully.")
  
  else:
  
    print(f"Temp table {tempTableName} does not exist, skipping drop.")

Ensure no other processes are accessing the temporary tables during this operation. Use tools like the Spark UI or Lakehouse monitoring logs to identify locks.

3. Use Consistent Case in Table Names

Normalize table names to lower case throughout your code:


     tempTableName = tempTableName.lower()

     TableName = TableName.lower()

4. Retry Logic for Table Operations

Add retry logic for DROP TABLE or ALTER TABLE commands in case of transient failures:


     import time

     def retry_sql_command(command, retries=3, delay=5):

         for attempt in range(retries):

             try:

                 spark.sql(command)

                 break

             except Exception as e:

                 print(f"Attempt {attempt + 1} failed: {e}")

                 time.sleep(delay)

                 if attempt == retries - 1:

                     raise

Use the function for table operations:


     retry_sql_command(f"DROP TABLE IF EXISTS {tempTableName}")

5. Ensure Transactions Commit

Ensure that changes are committed properly:


     spark.sql("SET spark.sql.legacy.allowCreatingManagedTableUsingNonemptyLocation = true")

6. Concurrency Check

Avoid concurrent operations on the same tables:
- Serialize dependent processes.
- Use locks or flags in your Precheck table to control execution order.

7. Log Failures Explicitly

Enhance your exception logging to capture detailed information:


     except Exception as e:

         print(f"Error while processing {tempTableName}: {e}")

         print(traceback.format_exc())

Verifying the Fix

After implementing these steps:

Check Lakehouse logs to confirm DROP TABLE execution.
Verify that all tables are renamed or dropped as intended.
Monitor for errors or concurrency issues in Spark UI and Precheck table logs.

These adjustments should help eliminate persistent temporary tables and improve the robustness of your pipeline.

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