Comet: Implement Spark's Percentile_cont Function

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Hey guys! Today, we're diving into a feature request to support Spark's percentile_cont function within Comet. This is a pretty cool enhancement that will allow more Spark workloads to leverage Comet's native acceleration. Let's break down the problem, potential solutions, and how we can make this happen!

What's the Problem?

Currently, Comet doesn't natively support the Spark percentile_cont function. What does this mean? Well, any queries that use this function end up falling back to Spark's JVM execution instead of running natively on DataFusion. Essentially, we're missing out on potential performance gains.

The percentile_cont function is crucial because it calculates a percentile value based on a continuous distribution of numeric or ANSI interval columns at a given percentage. Think of it as finding the value below which a given percentage of observations in a group of observations falls. It's the SQL equivalent of PERCENTILE_CONT, using linear interpolation to estimate values between data points. Because it's a runtime-replaceable aggregate, it relies on an internal Percentile implementation. By adding support for this expression, we enable more Spark workloads to benefit from Comet's native acceleration capabilities. When this function isn't supported, queries that use it revert to Spark's JVM, which can be slower and less efficient than Comet's native execution. This is particularly important in scenarios where performance and efficiency are critical, such as large-scale data processing and real-time analytics. Supporting percentile_cont means more queries can run faster and more efficiently, improving overall system performance and reducing resource consumption. So, in essence, the lack of support for percentile_cont in Comet is a bottleneck that prevents users from fully leveraging Comet's acceleration capabilities for certain types of Spark workloads. Addressing this limitation would significantly enhance Comet's value proposition for Spark users. It's all about making things faster and more efficient!

Diving into the Potential Solution

So, how do we solve this? Let's look at the Spark specification and then map out an implementation approach.

Spark Specification

First, let's understand the syntax. The SQL syntax looks like this:

PERCENTILE_CONT(percentage) WITHIN GROUP (ORDER BY column [ASC|DESC])

In Scala, it's used internally by Catalyst but not directly exposed in the DataFrame API. The DataFrame API uses percentile_approx for similar functionality.

Arguments:

  • left: The column expression to calculate percentile over (ORDER BY clause).
  • right: The percentile value between 0.0 and 1.0.
  • reverse: A Boolean indicating whether to reverse the ordering (DESC vs ASC), defaulting to false.

Return Type:

It returns the same data type as the input column being aggregated (numeric types or ANSI interval types).

Supported Data Types:

It supports numeric data types and ANSI interval types, as determined by the underlying Percentile implementation's input type validation.

Edge Cases:

  • Null handling is delegated to the underlying Percentile implementation.
  • Empty input datasets return null.
  • Percentile values outside the 0.0-1.0 range cause validation errors.
  • It requires exactly one ordering column and throws QueryCompilationError for multiple orderings.
  • The DISTINCT clause is not supported and will be ignored.
  • An UnresolvedWithinGroup state indicates an incomplete ordering specification.

Examples:

-- Calculate median (50th percentile) of salary
SELECT PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY salary) as median_salary
FROM employees;

-- Calculate 95th percentile in descending order
SELECT PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY response_time DESC) as p95_response
FROM requests;

In Scala:

// This is an internal Catalyst expression
// DataFrame API equivalent would use approxQuantile or percentile_approx
df.stat.approxQuantile("salary", Array(0.5), 0.0)

Implementation Approach

To implement this, we'll follow the Comet guide on adding new expressions. Here's a breakdown:

  1. Scala Serde: Add an expression handler in spark/src/main/scala/org/apache/comet/serde/. This involves creating a serializer and deserializer for the PercentileCont expression. The serializer will convert the expression into a format that can be stored or transmitted, while the deserializer will convert it back into an executable expression. This step ensures that the expression can be properly handled within the Comet framework.
  2. Register: Add it to the appropriate map in QueryPlanSerde.scala. This step involves registering the newly created expression handler with Comet's query plan serialization and deserialization mechanism. By adding it to the appropriate map, Comet can recognize and correctly handle the PercentileCont expression during query processing. This ensures that the expression is properly integrated into Comet's query execution pipeline.
  3. Protobuf: Add a message type in native/proto/src/proto/expr.proto if needed. Protobuf (Protocol Buffers) is a language-neutral, platform-neutral extensible mechanism for serializing structured data. If the existing Protobuf definitions don't include a message type for PercentileCont, you'll need to add one. This message type will define the structure of the data that represents the expression, allowing it to be serialized and deserialized efficiently. This step is crucial for ensuring compatibility between different components of the Comet system.
  4. Rust: Implement it in native/spark-expr/src/. Check if DataFusion has built-in support first. The core logic of the percentile_cont function will be implemented in Rust. Before diving into the implementation, it's essential to check if DataFusion, the underlying query execution engine, already has built-in support for percentile calculations. If DataFusion provides a suitable implementation, you can leverage it within the Comet framework. Otherwise, you'll need to implement the percentile calculation logic from scratch in Rust. This step ensures that the percentile_cont function can be executed efficiently and natively within Comet.

Step-by-Step Implementation Guide

Alright, let's break down the implementation into manageable steps. Each of these steps will involve careful coding and testing to ensure everything works smoothly.

1. Scala Serde Implementation

First off, we need to handle the Scala serialization and deserialization. This makes sure Spark can properly convert PercentileCont expressions into a format Comet understands, and vice versa. This is crucial for seamless communication between Spark and Comet.

  • Create Handler Class: Create a new class, say PercentileContHandler.scala, in the spark/src/main/scala/org/apache/comet/serde/ directory. This class will manage the serialization and deserialization of PercentileCont expressions.
  • Serialization Logic: Implement the serialize method to convert a PercentileCont expression into a byte array. Include all necessary fields like the percentile value, ordering column, and sort order. Ensure all data types are correctly handled during serialization.
  • Deserialization Logic: Implement the deserialize method to reconstruct a PercentileCont expression from a byte array. Ensure proper error handling to manage corrupted or invalid data.
  • Testing: Write unit tests to verify the serialization and deserialization processes. Test with different percentile values, column types, and sort orders to ensure robustness.

2. Register in QueryPlanSerde.scala

Next, we need to register our new handler in QueryPlanSerde.scala. This lets Comet know how to handle PercentileCont expressions during query planning.

  • Locate Serde Mapping: Open QueryPlanSerde.scala and find the appropriate map for aggregate expressions.
  • Add Entry: Add a new entry to the map that associates the PercentileCont class with the PercentileContHandler class.
  • Testing: Run integration tests to ensure that the new expression handler is correctly invoked during query planning. Verify that queries using PercentileCont are properly processed without errors.

3. Protobuf Definition (If Necessary)

Check if a Protobuf message type for PercentileCont already exists. If not, we'll need to define one in native/proto/src/proto/expr.proto.

  • Check Existing Definitions: Open native/proto/src/proto/expr.proto and check for existing message types that could represent PercentileCont. If none exist, proceed to the next step.
  • Define New Message Type: Add a new message type for PercentileCont. Include fields for the percentile value, ordering column, and sort order. Ensure the field types match the corresponding Scala data types.
  • Compile Protobuf: Compile the Protobuf definition to generate the necessary Java and Rust code.
  • Update Serde: Update the Scala Serde to use the new Protobuf message type for serialization and deserialization.

4. Rust Implementation

Now, let's implement the core logic in Rust. We'll first check if DataFusion has built-in support for percentile calculations. If not, we'll implement it ourselves.

  • Check DataFusion Support: Explore DataFusion's documentation and code to determine if there is existing support for percentile calculations. If a suitable implementation exists, you can leverage it directly.
  • Implement in Rust: If DataFusion doesn't have built-in support, create a new Rust module in native/spark-expr/src/ to implement the percentile_cont function. Use appropriate algorithms for calculating percentiles with linear interpolation.
  • Integrate with Comet: Integrate the Rust implementation with the Comet framework. Ensure the Rust code can receive the necessary input data from Comet and return the calculated percentile value.
  • Testing: Write comprehensive unit tests to verify the Rust implementation. Test with various data types, percentile values, and edge cases. Use randomized testing to ensure robustness.

5. Comprehensive Testing

Testing is super important. We need to make sure our implementation works correctly across different scenarios.

  • Unit Tests: Write unit tests for each component, including the Scala Serde, Protobuf definition, and Rust implementation. Use JUnit for Scala and Rust's built-in testing framework for Rust.
  • Integration Tests: Create integration tests to verify the interaction between different components. Ensure that queries using PercentileCont are correctly processed from end to end.
  • Performance Tests: Conduct performance tests to measure the overhead of the new implementation. Compare the performance of queries using PercentileCont with and without Comet to quantify the acceleration benefits.

Additional Context

  • Difficulty: Medium
  • Spark Expression Class: org.apache.spark.sql.catalyst.expressions.PercentileCont

Related:

  • Percentile - The underlying implementation class
  • PercentileDisc - Discrete percentile calculation
  • RuntimeReplaceableAggregate - Base trait for replaceable aggregates
  • SupportsOrderingWithinGroup - Interface for WITHIN GROUP syntax

Conclusion

Adding support for Spark's percentile_cont function in Comet is a significant step towards enhancing its capabilities and providing better performance for Spark workloads. By following the outlined implementation approach and paying close attention to testing, we can ensure a robust and efficient solution. Let's get this done, team!