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Tracer/collect is the core data collection daemon that powers Tracer’s observability platform. It runs on Linux hosts, captures execution signals directly from the operating system, and streams structured telemetry to Tracer’s backend for analysis and visualization. Unlike tools that rely on framework integrations, logs, or application instrumentation, Tracer/collect observes execution where it actually happens, at the kernel boundary between user processes and the operating system.

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Overview

Tracer/collect provides low-overhead, high-fidelity visibility into scientific and cloud-based high-compute workflows.

Framework-agnostic

Works with any workflow engine, scheduler, language, or container runtime.

Safe and efficient

Uses kernel tracing without modifying system binaries or requiring code changes.

Actionable

Produces data that describes what ran, where it ran, and how resources were consumed.

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How Tracer/collect works

Tracer/collect is built as a static Rust daemon that orchestrates eBPF programs to observe the Linux kernel.

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Kernel-level observability with eBPF

Tracer/collect uses eBPF (extended Berkeley Packet Filter) to attach probes at key points in the operating system. eBPF is a verified, sandboxed execution environment in the Linux kernel that lets user-space programs monitor kernel events safely and efficiently. Using eBPF, Tracer/collect observes:
  • System calls and scheduling events: when processes start, stop, or yield CPU
  • Resource activity: CPU usage, memory usage, I/O operations, and network activity
  • Process lifecycles: which binary ran, with what arguments, and in which container context
These signals form the basis of Tracer’s visibility into pipelines. eBPF allows this without inserting code into user binaries or requiring any language-specific hooks.
Tool and binary detection showing all binaries, scripts, and containers active on the cluster
Tracer/collect identifies every binary, script, or container active on your cluster, including hidden dependencies or subprocesses. This includes native executables, Python-spawned processes, Java tools, shell commands, and short-lived child processes. Even tools that don’t produce logs appear with complete runtime information.
How Tracer/collect captures execution signals from the kernel level
Tracer/collect processes these kernel events and emits structured telemetry that maps to pipeline steps, tools, and tasks.

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Correlation through identifiers

Tracer/collect correlates kernel events with higher-level execution semantics by using identifiers that the Linux kernel exposes:
  • Process IDs (PIDs) and parent PIDs
  • Namespaces and cgroups that define container boundaries
This mapping lets Tracer distinguish between processes running concurrently on the same host and assign each observation to the correct tool, container, or pipeline step.

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Data collection stages

Tracer/collect’s internal workflow can be understood in four stages:
1

Attach

eBPF probes attach to kernel trace points such as system call entry/exit and scheduler events. No application changes or container restarts are required.
2

Capture

Kernel events are streamed into the daemon. Only relevant events, those associated with processes running scientific tools or workflow steps, are selected through filtering rules.
3

Correlate

Events are linked to processes, containers, and pipelines using kernel metadata and optional scheduler-provided trace identifiers.
4

Stream

Aggregated telemetry is batched and sent to Tracer’s backend for indexing, visualization, and analysis.

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Filtering and relevance

Kernel probes can generate a high volume of events. Tracer/collect minimizes overhead and focuses on meaningful signals by applying filter rules at the source.

Include rules

Events that correspond to known tools, binaries, or workflow steps

Exclude rules

Generic system activity that is not useful for pipeline observability
Clients can extend or refine these rules over time without redeploying the daemon.

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Deployment and lifecycle

Tracer/collect is designed for modern compute environments:
  • It runs as a daemon on each host.
  • It can be installed via launch scripts, imaging systems, or orchestration tools (cloud init, configuration management, container DaemonSets).
  • The daemon is self-updating and resilient to intermittent network issues.
Telemetry is buffered locally and retried until successfully delivered to the backend.

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Performance

Tracer/collect is optimized for minimal interference with workloads:

Low CPU overhead

Typically under 2% for most workloads

Minimal I/O impact

Event sampling and filtering reduce data volume early

Safe memory usage

Rust’s safety guarantees reduce crash risk on production systems
Typical overhead measurements come from controlled benchmarks on pipeline runs.

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When to use Tracer/collect

Use Tracer/collect when you want accurate, system-level insight into pipeline execution without modifying your workloads. You should deploy Tracer/collect when:
  • Your pipelines are stable but slow or expensive, and you need to understand resource and time usage.
  • You run heterogeneous workflows that span multiple tools, languages, and schedulers.
  • You need visibility inside containers and host processes at the execution boundary.
  • Application-level instrumentation is impractical or unavailable.
  • You want out-of-the-box insights that point you to the right optimization targets.
Tracer/collect is not a language-specific profiler. If you need function-level or code-line profiling, combine Tracer’s process-level signals with a dedicated profiling tool.

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Limits and boundaries

Tracer/collect does not:
  • Inspect or capture application payload data (input data or scientific output)
  • Provide source-code level traces (e.g., function call stacks) by default
  • Replace language-specific profilers (for deep sampling within process logic)
Instead, it surfaces resource and execution patterns that help you decide where deeper investigation belongs.

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Why Rust?

Rust enables Tracer/collect to be:

Memory-safe

Reducing the risk of crashes or undefined behavior

Efficient

Low runtime overhead

Portable

A single static binary that runs consistently across environments
These properties make it suitable for high-compute systems where stability and performance are essential.

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Summary

Tracer/collect is a system-level observability agent that delivers truthful execution data from the Linux kernel to Tracer’s analysis backend. It provides process-level visibility without requiring code changes, supports heterogeneous pipelines, and produces data that helps you understand and optimize complex workflows. This design makes it especially useful in bioinformatics, machine learning, and scientific computing environments where tooling diversity and performance transparency are critical.