DataFusion: Observing Intermediate Aggregation Results

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DataFusion: Observing Intermediate Aggregation Results

Introduction

Hey guys! Today, let's dive into an exciting feature request for DataFusion: support for observing intermediate aggregation results. For those of you who've dealt with long-running aggregation queries, you know the pain of waiting until the very end to see any results. This proposal aims to change that by allowing users to peek at intermediate results as the query chugs along.

The Problem: Long-Running Aggregation Queries

Imagine running a complex aggregation query on a massive dataset. Currently, you have to sit tight and wait for the entire process to complete before getting any output. This can be a real bummer for several reasons:

  • Progress Monitoring: It's hard to tell if the query is actually making progress or if it's stalled. Are things still moving, or did something break?
  • Early Insights: You might want to start analyzing partial results before the query finishes. Maybe the initial data already reveals some interesting trends.
  • User Experience (UX): In interactive applications like dashboards or Jupyter notebooks, users expect progressive updates. A blank screen until the very end isn't the best UX.
  • Approximate Query Processing: Sometimes, a "good enough" answer quickly is better than a perfect answer late. Being able to refine results as more data is processed is a huge win.

Other mature query engines offer ways to observe query progress or return partial results, and it’s time DataFusion joined the party.

Proposed Solution: Peeking into Intermediate Aggregation State

The main idea is to find ways to observe the intermediate aggregation state for queries with unordered input, especially for grouped aggregations. Here are some key properties we're shooting for:

  • Non-Destructive: Peeking at intermediate state shouldn't mess with the final results. We want to observe without interfering.
  • Periodic: We need to be able to check the state at intervals, either based on time or the number of processed batches. Think of it like checking on a cake in the oven without opening the door too often.
  • Efficient: The overhead should be proportional to the amount of data observed, not require re-processing everything. We don't want peeking to slow things down significantly.
  • Opt-In: This feature should be optional. If you don't need it, it shouldn't impact regular queries. Think of it as an extra tool in the toolbox.

Preliminary Exploration: Callback-Based Peeking

We've already started kicking the tires and have some initial ideas. One approach involves callback-based peeking with a user-facing API. Here's a snippet of Rust code to illustrate:

let peek_config = IntermediatePeekConfig::new(1000, |context: PeekContext| {
    // Receive intermediate results as Arrow RecordBatch
    handle_intermediate_results(&context.intermediate_batch);
    Ok(())
});

let agg_exec = agg_exec.with_intermediate_peek_config(Some(peek_config));

In this example, IntermediatePeekConfig allows you to define a callback function that's triggered every 1000 rows. The callback receives the intermediate results as an Arrow RecordBatch, which you can then process as needed.

Extending Existing Traits with Peek Methods

Another idea is to extend existing traits with non-destructive peek methods. For instance:

pub trait GroupValues: Send {
    // ... existing methods ...
    
    fn peek(&self, num_groups: usize) -> Result<Vec<ArrayRef>> {
        not_impl_err!("peek not supported")  // Default implementation
    }
}

pub trait GroupsAccumulator: Send {
    // ... existing methods ...
    
    fn peek_evaluate(&self, num_groups: usize) -> Result<ArrayRef> {
        not_impl_err!("peek_evaluate not supported")  // Default implementation
    }
}

This approach adds a peek method to the GroupValues and GroupsAccumulator traits. The default implementation simply returns an error, indicating that peeking is not supported for that particular implementation. Implementations that support peeking can override this method to provide the intermediate results.

Community Input: Let's Brainstorm!

Now, here's where we need your help! We're super interested in hearing your thoughts on this. Specifically:

  • Does this use case resonate with you? Have you encountered similar needs in your projects?
  • Are there potential approaches we've missed? We're open to all ideas!
  • How might this fit with DataFusion's architecture and future direction? We want to make sure this aligns with the overall design.
  • Are there existing extension points we could leverage? Reusing existing mechanisms is always a good thing.

Alternatives Considered

No response was provided in the original document.

Broader Benefits and Call for Collaboration

We believe this feature would benefit the entire DataFusion community, not just our specific use case. Imagine the possibilities for interactive analytics, approximate query processing, and even debugging complex queries!

We're looking for:

  • Design feedback: Which approach makes the most sense for DataFusion's architecture?
  • API suggestions: Is there a better way to expose this functionality to users?
  • Alternative approaches: Have we overlooked any existing mechanisms?
  • Collaboration: Would the DataFusion maintainers and community be interested in collaborating on this feature?
  • Scope guidance: Should we aim for comprehensive support from the get-go, or start with a minimal viable feature (MVF) and iterate?

If the community sees value in this and we can agree on an approach, we're more than willing to put in the engineering effort to implement it properly. We'd love to get guidance from the maintainers and are really looking forward to a fruitful discussion!

Use Cases in Detail

1. Interactive Analytics

Interactive analytics is all about exploring data in real-time. Users want to ask questions and get answers quickly, iterating on their queries as they discover new insights. Currently, with DataFusion, if an aggregation query takes a long time, the user is left staring at a blank screen. By providing intermediate results, we can create a much more engaging and responsive experience.

Imagine a data scientist using a notebook to analyze website traffic. They might start with a simple query to count the number of visits per day. If that query takes a few seconds, it's not a big deal. But what if they want to group the visits by region and device type? Now the query might take minutes, or even hours, to complete. With intermediate results, the data scientist can start seeing the trends emerge as the query runs, allowing them to refine their analysis and focus on the most interesting areas.

Benefits of Intermediate Results for Interactive Analytics:

  • Faster Iteration: Users can quickly see if their query is on the right track and adjust it accordingly.
  • Improved User Engagement: No more staring at a blank screen. Users see progress and are more likely to stay engaged.
  • Real-time Exploration: Users can explore the data as it's being processed, uncovering insights that might otherwise be missed.

2. Approximate Query Processing

Approximate query processing is a technique for getting answers quickly, even if they're not 100% accurate. This is particularly useful when dealing with massive datasets where a precise answer might take a very long time to compute. The idea is to provide an initial estimate and then refine it as more data is processed.

For example, consider a marketing team trying to determine the average age of their customers. They could run a query on their entire customer database, but that might take a long time. Instead, they could use approximate query processing to get an initial estimate based on a sample of the data. As more data is processed, the estimate becomes more accurate, eventually converging on the true average.

Benefits of Intermediate Results for Approximate Query Processing:

  • Faster Time-to-Insight: Get an initial answer quickly, even if it's not perfect.
  • Progressive Refinement: Improve the accuracy of the answer as more data is processed.
  • Resource Optimization: Stop the query when the answer is "good enough," saving valuable resources.

3. Debugging Complex Queries

Debugging complex queries can be a real headache. When a query returns an unexpected result, it can be difficult to pinpoint the source of the problem. Intermediate results can be a valuable tool for understanding what's happening at each stage of the query execution.

Imagine a data engineer trying to debug a complex ETL pipeline. The pipeline involves multiple aggregation steps, and the final result is incorrect. By examining the intermediate results at each stage, the data engineer can quickly identify where the error is occurring.

Benefits of Intermediate Results for Debugging:

  • Improved Visibility: See what's happening at each stage of the query execution.
  • Faster Root Cause Analysis: Quickly identify the source of the problem.
  • Reduced Debugging Time: Save time and effort by focusing on the areas where the error is most likely to occur.

Conclusion

Supporting the observation of intermediate aggregation results is a valuable feature for DataFusion. It addresses the challenges of long-running queries, enhances user experience, and opens up new possibilities for interactive analytics, approximate query processing, and debugging. By providing a mechanism to peek into the intermediate state of aggregations, DataFusion can become an even more powerful and versatile tool for data analysis. Let's work together to make this happen!