--- title: "How to use Tracer with Seqera" sidebarTitle: "Seqera" description: 'Execution behavior inside Nextflow pipelines' --- Seqera orchestrates scientific workflows built with Nextflow, defining pipeline structure, dependencies, and execution across compute environments. It determines what runs and when, but it does not observe how tasks behave at runtime inside containers or at the operating system level. Tracer complements Seqera by exposing execution behavior: CPU, memory, disk, and network usage, and more during pipeline runs. It collects this telemetry without modifying workflows or task definitions. ## What Seqera does well Seqera and Nextflow provide orchestration-level capabilities, including: - Pipeline structure and task dependencies - Scheduling across cloud, HPC, and hybrid environments - Retries, caching, and execution state tracking - Task logs and exit statuses These capabilities enable reproducible, scalable scientific workflows. They focus on defining what should run and when it should run. ## What Seqera does not see at runtime Workflow orchestration tools do not observe low-level execution behavior. In practice, they do not show: - CPU utilization versus requested allocation - Whether tasks are CPU-bound, memory-bound, or I/O-bound - Disk and network contention inside containers - Short-lived subprocesses and nested tools - Idle time during task execution This information exists below the workflow engine, inside the container and operating system. Some logs are available, but if a pipeline fails, logs are lost and your only option with Seqera is to add more compute. ## Why this gap matters in practice Pipeline resources are often over-allocated to reduce the risk of failure, especially when workloads vary by dataset. Without execution-level visibility, teams often struggle to answer: - Why a pipeline failed - Why a task runs slower than expected - Whether allocated CPUs are actively used - Whether performance is limited by storage or networking - Whether different instance types would perform better As a result, pipelines may succeed reliably but consume more time and cost than necessary. ## What Tracer adds Tracer observes execution directly from the host and container runtime and adds: - Observed CPU, memory, disk, and network usage per task - Visibility into subprocesses and nested execution - Detection of stalls, idle time, and contention - Attribution of resource usage by pipeline, run, task, and step This visibility is based on observed behavior, not metadata, configuration, or heuristics.