DataFusion: Fixing Expression Cache With Extension Functions

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Hey guys! Ever run into a quirky bug after upgrading your system? Today, we're diving deep into a tricky issue that surfaced after upgrading to DataFusion 49, specifically related to how it handles extension function caching. Let's break down the problem, how to reproduce it, and what the expected behavior should be.

Understanding the Bug

After upgrading to DataFusion 49 #1154, the expression cache (cached_exprs_evaluator) stumbled upon a problem: it failed to deduplicate identical extension functions. The heart of the matter lies in DataFusion's SimpleScalarUDF::equals function. Now, it strictly enforces pointer equality (Arc::ptr_eq) for function implementations, a change introduced in DataFusion PR #16781. The snag? Auron creates a brand-new Arc for each UDF instance. This means that even if two functions are logically the same, they end up with different memory addresses, causing the cache to miss the duplication.

Why is this a problem? Well, caching is all about efficiency. When the system can't recognize that two functions are identical, it ends up doing redundant work, which slows things down. Imagine asking two different people to solve the same math problem when one person already knows the answer – it's just a waste of resources! This bug impacts performance and negates the benefits of caching, which is designed to speed up query execution by reusing previously computed results. The expression cache's inability to deduplicate identical extension functions leads to redundant computations, increasing processing time and resource consumption. By addressing this issue, we can restore the efficiency of DataFusion's expression cache and ensure optimal performance for queries involving extension functions.

How to Reproduce the Issue

To see this bug in action, you can run a specific query. The key is to monitor the dups in cached_exprs_evaluator.rs. If the logging shows 0 duplicates found, you've successfully reproduced the issue.

Here’s the code snippet you can use:

test("my test") {
 withTable("my_table") {
 sql("""
 |create table my_cache_table using parquet as
 |select col1 from values (''{"a":"1", "b":"2"}'), ('{"a":"3", "b":"4"}'), ('{"a":"5", "b":"6"}')
 |""".stripMargin)
 sql("""
 |select 
 | get_json_object(col1, '$.a'),
 | get_json_object(col1, '$.b')
 |from my_cache_table
 |""".stripMargin).show()
 }
}

What does this code do? The code sets up a test case with a table named my_cache_table. The table is populated with JSON data. The query then selects and extracts specific JSON objects ($.a and $.b) from the col1 column using the get_json_object function. The goal is to trigger the caching mechanism and observe whether the get_json_object function calls are properly deduplicated.

When you run this test, you'll notice that the expression cache fails to recognize the duplicate calls to get_json_object, resulting in redundant computations. This is because each call to get_json_object creates a new Arc instance, leading to different memory addresses for logically identical functions. By reproducing this issue, you can confirm the bug and verify that the fix resolves the problem.

Expected Behavior

So, what should happen when things are working correctly? Ideally, the expression cache should recognize that the two calls to get_json_object are identical. It should then reuse the cached result for the first call instead of recomputing it for the second. This deduplication is crucial for optimizing query performance, especially when dealing with complex or frequently used functions. With the fix in place, running the test case should result in the expression cache correctly identifying and deduplicating the get_json_object function calls, leading to improved query execution time and resource utilization.

What does this mean in practice? The query should execute faster and consume fewer resources. The expression cache should effectively store and reuse the results of the get_json_object function, preventing redundant computations and optimizing overall performance. By ensuring the expected behavior, we can maintain the efficiency and scalability of DataFusion for data processing tasks.

Diving Deeper: Why Pointer Equality Matters

Let's explore why the shift to pointer equality (Arc::ptr_eq) in DataFusion's SimpleScalarUDF::equals caused this issue. Previously, the equality check for UDFs might have relied on comparing the function's properties or behavior. However, the move to pointer equality introduced a stricter comparison: two UDFs are only considered equal if they reside at the same memory address. This change was likely intended to enhance performance or simplify the equality check, but it inadvertently broke the deduplication of extension functions in Auron.

Why is pointer equality so strict? Pointer equality is a quick and efficient way to determine if two variables refer to the exact same object in memory. It avoids the need to compare the contents of the objects, which can be time-consuming for complex data structures. However, in the case of UDFs, this strict comparison can lead to unexpected behavior when logically equivalent functions are created as separate instances.

The Root Cause: Auron's UDF Instance Creation

The heart of the problem lies in how Auron creates UDF instances. Auron generates a new Arc for every UDF instance. Arc stands for Atomic Reference Counted smart pointer and it's used for shared ownership. Each time a UDF is invoked, a new Arc is created. Even if the UDFs are logically identical (i.e., they perform the same operation), they reside at different memory locations. Consequently, DataFusion's pointer equality check fails to recognize them as duplicates, leading to cache misses and redundant computations.

Why does Auron create new Arc instances? The reason behind Auron's UDF instance creation might stem from design choices related to memory management, concurrency, or other specific requirements of the Auron system. However, in the context of DataFusion's expression cache, this approach creates a challenge for deduplicating identical UDFs. By understanding the root cause, we can explore potential solutions that align with both Auron's design principles and DataFusion's caching mechanisms.

Potential Solutions and Workarounds

So, how can we tackle this issue? Here are a few potential solutions and workarounds:

  1. Modify Auron's UDF Instance Creation: The most direct solution is to modify how Auron creates UDF instances. Instead of creating a new Arc for every UDF invocation, Auron could reuse existing Arc instances for logically identical UDFs. This would ensure that the pointer equality check in DataFusion correctly identifies the duplicates.
  2. Adjust DataFusion's Equality Check: Another approach is to adjust DataFusion's equality check for UDFs. Instead of relying solely on pointer equality, DataFusion could incorporate a more sophisticated comparison that considers the function's properties or behavior. This would allow DataFusion to recognize logically identical UDFs even if they reside at different memory locations.
  3. Implement a Custom Caching Mechanism: As a workaround, you could implement a custom caching mechanism that sits in front of DataFusion's expression cache. This custom cache would be responsible for deduplicating UDFs based on their properties or behavior before passing them to DataFusion. This would require additional development effort but could provide a more immediate solution.

Conclusion

In summary, the expression cache issue with extension functions in DataFusion highlights the importance of understanding how different components interact within a complex system. By identifying the root cause and exploring potential solutions, we can ensure that DataFusion continues to deliver optimal performance for data processing tasks. Keep exploring, keep debugging, and happy coding, everyone!