DataFusion Tracing

DataFusion Tracing is an extension for Apache DataFusion that helps you monitor and debug queries. It uses tracing and OpenTelemetry to gather DataFusion metrics, trace execution steps, and preview partial query results.

Note: This is not an official Apache Software Foundation release.

Overview

When you run queries with DataFusion Tracing enabled, it automatically adds tracing around execution steps, records all native DataFusion metrics such as execution time and output row count, lets you preview partial results for easier debugging, and integrates with OpenTelemetry for distributed tracing. This makes it simpler to understand and improve query performance.

See it in action

Here's what DataFusion Tracing can look like in practice:

Getting Started

Installation

Include DataFusion Tracing in your project's Cargo.toml:

[dependencies]
datafusion = "50.0.0"
datafusion-tracing = "50.0.2"

Compatibility note

The ellipsis truncation indicator in pretty_format_compact_batch is disabled in this version because it requires comfy-table >= 7.1.4, while Apache Arrow currently pins comfy-table to 7.1.2 to preserve its MSRV. Context: comfy-table 7.2.0 bumped MSRV to Rust 1.85 while Arrow remains at 1.84. See arrow-rs issue #8243 and PR #8244. Arrow used an exact pin rather than ~7.1, which would also preserve MSRV while allowing 7.1.x (including 7.1.4). We will re-enable it once Arrow relaxes the pin to allow >= 7.1.4.

Quick Start Example

use datafusion::{
    arrow::{array::RecordBatch, util::pretty::pretty_format_batches},
    error::Result,
    execution::SessionStateBuilder,
    prelude::*,
};
use datafusion_tracing::{
    instrument_with_info_spans, pretty_format_compact_batch, InstrumentationOptions,
};
use std::sync::Arc;
use tracing::field;

#[tokio::main]
async fn main() -> Result<()> {
    // Initialize tracing subscriber as usual
    // (See examples/otlp.rs for a complete example).

    // Set up tracing options (you can customize these).
    let options = InstrumentationOptions::builder()
        .record_metrics(true)
        .preview_limit(5)
        .preview_fn(Arc::new(|batch: &RecordBatch| {
            pretty_format_compact_batch(batch, 64, 3, 10).map(|fmt| fmt.to_string())
        }))
        .add_custom_field("env", "production")
        .add_custom_field("region", "us-west")
        .build();

    let instrument_rule = instrument_with_info_spans!(
        options: options,
        env = field::Empty,
        region = field::Empty,
    );

    let session_state = SessionStateBuilder::new()
        .with_default_features()
        .with_physical_optimizer_rule(instrument_rule)
        .build();

    let ctx = SessionContext::new_with_state(session_state);

    let results = ctx.sql("SELECT 1").await?.collect().await?;
    println!(
        "Query Results:\n{}",
        pretty_format_batches(results.as_slice())?
    );

    Ok(())
}

A more complete example can be found in the examples directory.

Optimizer rule ordering (put instrumentation last)

Always register the instrumentation rule last in your physical optimizer chain.

• Many optimizer rules identify nodes using as_any().downcast_ref::<ConcreteExec>(). Since instrumentation wraps each node in a private InstrumentedExec, those downcasts won’t match if instrumentation runs first, causing rules to be skipped or, in code that assumes success, to panic.
• Some rules may rewrite parts of the plan after instrumentation. While InstrumentedExec re-wraps many common mutations, placing the rule last guarantees full, consistent coverage regardless of other rules’ behaviors.

Why is InstrumentedExec private?

• To prevent downstream code from downcasting to or unwrapping the wrapper, which would be brittle and force long-term compatibility constraints on its internals. The public contract is the optimizer rule, not the concrete node.

How to ensure it is last:

• When chaining: builder.with_physical_optimizer_rule(rule_a) .with_physical_optimizer_rule(rule_b) .with_physical_optimizer_rule(instrument_rule)
• Or collect: builder.with_physical_optimizer_rules(vec![..., instrument_rule])

Setting Up a Collector

Before diving into DataFusion Tracing, you'll need to set up an OpenTelemetry collector to receive and process the tracing data. There are several options available:

Jaeger (Local Development)

For local development and testing, Jaeger is a great choice. It's an open-source distributed tracing system that's easy to set up. You can run it with Docker using:

docker run --rm --name jaeger \
  -p 16686:16686 \
  -p 4317:4317 \
  -p 4318:4318 \
  -p 5778:5778 \
  -p 9411:9411 \
  jaegertracing/jaeger:2.7.0

Once running, you can access the Jaeger UI at http://localhost:16686. For more details, check out their getting started guide.

DataDog (Cloud-Native)

For a cloud-native approach, DataDog offers a hosted solution for OpenTelemetry data. You can send your traces directly to their platform by configuring your DataDog API key and endpoint - their OpenTelemetry integration guide has all the details.

Other Collectors

Of course, you can use any OpenTelemetry-compatible collector. The official OpenTelemetry Collector is a good starting point if you want to build a custom setup.

Repository Structure

The repository is organized as follows:

• datafusion-tracing/: Core tracing functionality for DataFusion
• instrumented-object-store/: Object store instrumentation
• integration-utils/: Integration utilities and helpers for examples and tests (not for production use)
• examples/: Example applications demonstrating the library usage
• tests/: Integration tests
• docs/: Documentation, including logos and screenshots

Building and Testing

Use these commands to build and test:

cargo build --workspace
cargo test --workspace

Test data: generate TPCH Parquet files

Integration tests and examples expect TPCH tables in Parquet format to be present in integration-utils/data (not checked in). Generate them locally with:

cargo install tpchgen-cli
./dev/generate_tpch_parquet.sh

This produces all TPCH tables at scale factor 0.1 as single Parquet files in integration-utils/data. CI installs tpchgen-cli and runs the same script automatically before tests. If a required file is missing, the helper library will return a clear error instructing you to run the script.

Contributing

Contributions are welcome. Make sure your code passes all tests, follow existing formatting and coding styles, and include tests and documentation. See CONTRIBUTING.md for detailed guidelines.

License

Licensed under the Apache License, Version 2.0. See LICENSE.

Acknowledgments

This project includes software developed at Datadog (info@datadoghq.com).