Duplicate Rows In Delta Lake Partitioned Tables A Whitespace Issue

Jul 29, 2025 by ADMIN 67 views

Repeated DELETE with Deletion Vectors Duplicate Rows in Partitioned Tables in Delta Lake with Partitions That Include Whitespace

Hey everyone! Today, let's dive deep into a tricky issue encountered in Delta Lake when performing repeated DELETE operations with deletion vectors on partitioned tables, specifically those with partitions that include whitespace. This is quite similar to a previous issue, but this time, whitespace adds a fun little twist to the mix. So, grab your favorite beverage, and let's get started!

Understanding the Problem

So, repeated DELETE operations in Delta Lake, especially when deletion vectors are enabled and we're dealing with partitioned tables that have partitions containing whitespace, can sometimes lead to duplicate rows. Yeah, that's not what we want, right? Imagine you have a table nicely organized by partitions, and some of those partitions have names like "I love whitespaces." You'd expect that deleting rows would just remove them, but sometimes, the rows seem to multiply instead! This issue is particularly noticeable when using Trino or Spark to query the Delta Lake table after these deletions. So, if you're finding yourself scratching your head over unexpected row counts, you're in the right place.

Why Does This Happen?

To really understand what's going on, we need to break down a few key concepts. First, partitioning is a way of dividing a table into smaller parts based on the values in one or more columns. This can make queries faster because the system only needs to look at the relevant partitions. Deletion vectors are a feature in Delta Lake that allows for faster DELETE operations by marking rows as deleted instead of rewriting the entire table. This is a big performance win, but it also adds some complexity. When you combine partitioning, deletion vectors, and whitespace in partition names, things can get a bit tricky. The issue often arises from how the system handles the interaction between the partition filtering and the deletion vector application. In essence, the system might incorrectly apply deletion vectors across partitions due to the way whitespace is handled in the partition paths, leading to rows being mistakenly marked as deleted multiple times. This is why you might see a count of 18 when you expect a count of 8, which is definitely not what we want.

Setting Up the Scenario

Let's walk through a practical example to illustrate the problem. Imagine we create a table named test_dv with two columns: an integer column a and a string column test_column. We'll enable deletion vectors and partition the table by test_column. The table's location is set to some storage path (which we'll represent as '...' for simplicity), and we'll use 'I love whitespaces' as the partition value. This is where the fun begins! We then insert 10 rows into the table, with values from 1 to 10 in the a column and 'I love whitespaces' in the test_column. So, now we have a table with 10 rows, all neatly tucked into the partition named 'I love whitespaces'. We can verify this by running a SELECT count(1) query, which should return 10. Now comes the interesting part: we start deleting rows. First, we delete the row where a = 5. A quick count should now give us 9 rows. Then, we delete the row where a = 9. At this point, you'd expect the count to be 8, but here's where the bug kicks in. Both Trino and Spark might return a count of 18, which is… unexpected, to say the least. This is because the deletion vectors are being applied incorrectly, leading to the duplicated deletion we talked about earlier.

Diving Deeper: The Technical Details

Okay, so we've seen the problem in action. But what's really happening under the hood? This issue boils down to how Delta Lake, in conjunction with query engines like Trino and Spark, handles partition discovery and deletion vector application when whitespace is involved. Let's break this down step by step.

Partition Discovery

When you create a partitioned table, Delta Lake organizes the data into directories corresponding to the partition values. In our case, with test_column as the partition column and the value 'I love whitespaces', Delta Lake will create a directory structure that reflects this. The exact path might look something like .../test_column=I love whitespaces. Notice the whitespace in the directory name? That's a key player in our mystery. When Trino or Spark queries the table, it first needs to discover these partitions. This involves listing the directories and extracting the partition values. The way this listing and extraction is done can vary slightly between engines, and this is where inconsistencies can creep in. If the whitespace is not handled consistently, the engine might misinterpret the partition structure, leading to problems later on.

Deletion Vector Application

Now, let's talk about deletion vectors. When you delete a row in Delta Lake with deletion vectors enabled, the row isn't immediately removed from the data files. Instead, Delta Lake creates a deletion vector, which is essentially a list of rows that should be considered deleted. This is a very efficient way to handle deletions, especially in large tables, because it avoids rewriting the entire table every time you delete something. However, it also means that the query engine needs to apply these deletion vectors when reading the table. This involves identifying the relevant deletion vectors and filtering out the deleted rows. The challenge here is ensuring that the deletion vectors are applied correctly, especially when dealing with partitions. The engine needs to match the deletion vectors to the correct partitions, and if the partition discovery process has been wonky due to whitespace, this matching can go wrong. In our example, the engine might be applying the same deletion vector multiple times, effectively deleting the same rows over and over again. This is why we end up with a row count of 18 when we expect 8.

The Whitespace Conundrum

The core issue here is the whitespace. Whitespace in file paths and partition names can be a real headache for many systems. Different systems might handle whitespace differently, leading to inconsistencies. In the context of Delta Lake and Trino/Spark, the whitespace in the partition name 'I love whitespaces' can cause confusion during partition discovery and deletion vector application. The engine might encode or decode the whitespace in different ways at different stages of the process, leading to mismatches and incorrect filtering. For example, some systems might replace spaces with %20 (the URL-encoded representation of a space), while others might leave them as is. If these encodings aren't handled consistently, the engine might not be able to correctly match deletion vectors to partitions, and you guessed it, we're back to the duplicated deletion problem.

Reproducing the Issue

Want to see this in action for yourself? Here’s how you can reproduce the issue. First, you'll need access to a Delta Lake environment, preferably one where you can use Trino or Spark to query the data. You'll also need the ability to create tables and insert data. Once you have that set up, you can follow these steps:

Create the Table: Use the CREATE TABLE statement provided earlier to create the test_dv table. Make sure you enable deletion vectors and partition the table by test_column. Don't forget to set the location to a suitable storage path.
Insert Data: Insert the 10 rows into the table using the INSERT INTO statement. This will populate the table with the initial data.
Verify Initial Count: Run a SELECT count(1) query to verify that the table contains 10 rows. This is just a sanity check to make sure everything is set up correctly.
Delete Rows: Now, the fun begins! Delete the row where a = 5 and then the row where a = 9 using the DELETE FROM statements. These are the operations that will trigger the bug.
Check the Count: Finally, run another SELECT count(1) query. If you're encountering the issue, you'll likely see a count of 18 instead of the expected 8. This confirms that the deletion vectors are being applied incorrectly.

By following these steps, you can reproduce the issue and see firsthand how whitespace in partition names can lead to unexpected behavior. This is a great way to gain a deeper understanding of the problem and how it manifests in a real-world scenario.

Solutions and Workarounds

Okay, so we've thoroughly explored the problem and how to reproduce it. Now, let's talk solutions. What can you do if you encounter this issue? While a permanent fix might require changes in Delta Lake or the query engines themselves, there are a few workarounds you can use in the meantime.

1. Avoid Whitespace in Partition Names

This might seem obvious, but the simplest solution is often the best. If you can, avoid using whitespace in your partition names. This will prevent the issue from occurring in the first place. Instead of 'I love whitespaces', consider using 'I_love_whitespaces' or 'Ilovewhitespaces'. This will ensure that the partition paths are clean and consistent, reducing the risk of misinterpretation by the query engine. Of course, this might not always be possible, especially if you're dealing with existing data or have specific naming conventions you need to follow. But if you have the flexibility, this is the most straightforward solution.

2. Rewrite the Table

If you've already encountered the issue and have duplicate deletions, one workaround is to rewrite the table. This involves creating a new table, copying the data from the old table, and then dropping the old table. This effectively resets the deletion vectors and ensures that the data is consistent. Here's how you can do it:

Create a New Table: Create a new table with the same schema as the original table, but without deletion vectors enabled. You can use a CREATE TABLE AS SELECT statement to copy the data from the old table to the new table.
Drop the Old Table: Once the data has been copied, drop the old table. This will remove the table with the incorrect deletion vectors.
Rename the New Table: Finally, rename the new table to the original table name. This will effectively replace the old table with the corrected version.

This workaround can be time-consuming, especially for large tables, but it's a reliable way to resolve the issue. Just be sure to test the new table thoroughly to ensure that the data is correct.

3. Optimize Table with `REORGANIZE` and `VACUUM`

Delta Lake provides commands like REORGANIZE and VACUUM that can help optimize the table and clean up deleted data. While these commands might not directly fix the issue of duplicate deletions, they can help reduce the impact. REORGANIZE can consolidate small files into larger ones, which can improve query performance. VACUUM can remove data files that are no longer needed, including those marked for deletion by deletion vectors. Running these commands regularly can help keep your table in good shape and potentially mitigate the effects of the bug. However, be aware that VACUUM permanently removes the deleted data, so make sure you understand the implications before running it.

4. Apply Deletes in a Single Operation

Another workaround is to try to apply all your deletes in a single operation. Instead of running multiple DELETE statements, try to combine them into one. This can sometimes reduce the chances of the deletion vectors being applied incorrectly. For example, instead of running:

DELETE FROM test_dv WHERE a = 5;
DELETE FROM test_dv WHERE a = 9;

You can try running:

DELETE FROM test_dv WHERE a IN (5, 9);

This might not always be feasible, especially if your deletes are based on different conditions, but it's worth a try if you're encountering the issue.

Conclusion

So, we've journeyed through the world of Delta Lake, deletion vectors, partitioned tables, and the pesky problem of whitespace. We've seen how repeated DELETE operations can sometimes lead to unexpected results when whitespace is involved in partition names. While this issue can be tricky to deal with, understanding the underlying mechanisms and having a few workarounds in your toolkit can go a long way. Remember, avoiding whitespace in partition names is the best preventive measure, but if you do encounter the issue, rewriting the table or applying deletes in a single operation can help. Keep experimenting, keep learning, and happy data wrangling, folks!