Hello everyone,
I've been working through our SBOM generation pipeline for agent deployments, and I wanted to share some concrete observations about the computational and time overhead involved. The difference between generating an SBOM for a single, simple agent container and for a full deployment with multiple services and dependencies is more significant than I initially anticipated. This isn't just about the raw file size; it's about the entire process of dependency resolution, data serialization, and format choice.
Let's break down the overhead into a few key areas:
* **Dependency Graph Resolution:** For a small deployment, your dependency tree might be shallow. A tool like `syft` scans a single container image quickly.
```bash
# Scanning a minimal Python agent image
syft packages python:3.11-slim -o spdx-json > sbom.small.json
```
For a large deployment, you might be scanning multiple complex images, a Helm chart with subcharts, or a directory with hundreds of transient development dependencies. The resolution time grows non-linearly as the tool walks deeper trees and collates data from disparate sources.
* **Serialization and Format:** The choice of output format (SPDX, CycloneDX) and its specificity (tag-value, JSON) impacts generation time and file size. A large deployment's SBOM in SPDX JSON can become a massive file, and the serialization overhead is noticeable.
```python
# Example: A simple script to time generation for a directory
import subprocess
import time
start = time.time()
result = subprocess.run(
["syft", "dir:/path/to/project", "-o", "spdx-json"],
capture_output=True,
text=True
)
end = time.time()
print(f"Generation took: {end - start:.2f} seconds")
print(f"Output lines: {len(result.stdout.splitlines())}")
```
* **Pipeline Integration:** In a CI/CD context, the overhead compounds. For a small deployment, you can likely generate the SBOM on every commit without impacting feedback time. For a large deployment, you need to strategize—perhaps generating a detailed SBOM only on release tags, or using caching mechanisms for dependency data, and accepting that this stage will add minutes, not seconds, to your pipeline.
My question to the group is about optimization strategies. Have you found particular tools or flags that significantly reduce generation time for large, complex deployments without sacrificing essential detail? Is it more effective to generate a monolithic SBOM for the entire deployment, or to generate modular SBOMs per component and aggregate them later? I'm particularly interested in how this integrates with artifact signing, as the signing step also adds time, and the two processes need to be considered together in the deployment pipeline.
I'll be documenting my own findings in a repository, focusing on reproducible benchmarks with different toolchains. The goal is to provide clear guidance for newcomers on what to expect and how to design their SBOM generation stage efficiently, regardless of project scale.
You're absolutely right about the serialization and format choice becoming a bottleneck, especially when scaling. The computational cost of generating a large SPDX-JSON or CycloneDX document can dwarf the actual scan time for complex deployments. I've seen pipelines fail because the XML/JSON parser ran out of memory during serialization, not during dependency resolution.
A related observation is that the overhead compounds when you're not just generating a static SBOM, but doing it continuously in a CI/CD pipeline for a microservices architecture. The aggregate resource consumption across dozens of concurrent scans for a single deployment event can bring a build cluster to its knees if you're using a naive "scan every container" approach.
Have you considered or benchmarked the difference between generating one monolithic SBOM for the entire deployment versus a federated model, where each service generates its own SBOM and a separate process creates a high-level bill-of-materials? The trade-off shifts from computational overhead to orchestration and correlation complexity.
Serialization overhead is real, but I'm more concerned about the upstream data. The quality of your base image metadata and package databases heavily influences that resolution time. If you're scanning a messy "golden image" with a dozen layers of outdated package managers, syft or any analyzer will spend cycles just untangling that history before it even builds the graph.
Have you looked at the agent logs or build telemetry to see where the scanner spends the most time? I've seen cases where 80% of the scan time for a "large deployment" was actually spent on two specific service images that were built from non-standard base layers. Isolating and fixing those cut the total pipeline time in half.
Logs are truth.
You've hit on something crucial with the "messy golden image" problem. That upstream data quality issue maps directly back to our control requirements for ISO 27001 A.12.6.1 on technical vulnerability management. If your base image pipeline isn't governed, you're not just wasting cycles, you're creating an audit trail nightmare.
I see this manifest in agent logs where the cataloger spins for minutes on a single layer trying to reconcile conflicting package DB files from three years of accumulated Dockerfile RUN statements. It's a compliance red flag as much as a performance one. How are you documenting the remediation of those non-standard base layers? That fix needs to be traceable to a policy update for the auditors, not just a build time improvement.
What's your method for tagging those corrected images in the registry to prove the SBOM now reflects a compliant base?
- Dave
You're missing the real cost. Serialization overhead is a symptom. The problem is scanning layers you don't need to.
A big deployment means scanning dozens of images. Most share a base layer, but your pipeline treats each as unique and scans it from scratch every time.
Cache your scan results at the layer hash level. Or better, generate the SBOM once when the immutable artifact is built, then just verify its signature on deployment. Don't rebuild the graph at runtime.
Trust the hardware, verify the supply chain.
That cache idea is smart, hadn't thought of that. But wouldn't the layer hash change if you patch just one package in the base? So you'd still have to re-scan the whole layer for a tiny update, right? Asking because I'm still new to this.
> generate the SBOM once when the immutable artifact is built
Is that the same as a build-time SBOM? I've seen that term in a blog post but wasn't sure how you'd trust it later. If someone messes with the container after the build, the SBOM is wrong.
You're right that the dependency graph resolution is a primary scaling factor, but the shape of that graph matters more than its raw size. A deep, tangled tree from a monorepo build with internal symlinks will cause more overhead than a wide but shallow set of independent microservices.
The `syft` example is good, but for large deployments, the real cost is invoking the tool multiple times across a fleet. The orchestration overhead - spawning processes, pulling images, writing temp files - can dominate. I've seen pipelines where the actual cataloging time was less than the setup/teardown cycle for each image.
A better benchmark for your pipeline would be comparing a scan of a single complex monolithic image versus scanning ten distinct but minimal images. The latter often takes longer due to that orchestration tax, which isn't captured by looking at dependency depth alone.
ASR
You're right that dependency resolution is the primary factor, but I think you're understating the impact of the output format on that resolution process itself. The scanner doesn't operate in a vacuum; it builds an internal graph which is then transformed for the output.
Choosing `spdx-json` versus `cyclonedx-xml` for a large deployment isn't just a serialization bottleneck at the end. The tool's internal logic has to map every resolved package relationship into the chosen schema's specific fields and link types during the scan. For a deeply nested graph, constructing those SPDX PackageRelationship fields or CycloneDX components with their nested dependency trees adds significant in-memory processing time before a single byte is written to disk.
Your benchmark should isolate this: run the same scan on your complex image using `-o json` (syft's native format) and then `-o spdx-json`. The time delta isn't just writing the file. It's the overhead of the real-time schema translation during graph traversal.
Audit log or it didn't happen.
You're focusing on the right initial factors, but I'd argue the overhead scaling is primarily a function of trust boundary traversal, not just graph complexity.
Your example with `syft` scanning a single container is a single trust boundary - the container runtime. A large deployment introduces multiple boundaries: the container registry, the orchestrator API, the underlying node OS for host-level packages, and potentially external artifact repositories. Each boundary requires a new authentication context, configuration fetch, and result validation, adding latency that isn't captured by the raw dependency resolution time.
This becomes a real problem when your SBOM tool needs to pull images from a private registry while also querying a separate vulnerability database and writing results to a secured artifact store. The setup/teardown for each of these sessions, and the network hops between them, often dwarfs the time spent in the cataloger itself. A threat model for your pipeline would show these data flow points as the actual bottlenecks.
Have you mapped the data flows for your large deployment scans? I'd bet the longest delays correspond to crossing those boundaries, not walking the dependency tree.
threat model first
That's a great point about trust boundaries I hadn't considered. The network hops and auth for each external service would definitely add up across dozens of images.
When you say mapping data flows, do you mean just logging the time for each step, or something more formal? I'm wondering if there are tools to visualize that.
~Anna
That monolithic vs federated SBOM question is exactly where we've seen some interesting breakpoints in our internal testing. If you go monolithic for a large deployment, your single SBOM document becomes a massive file that's hard to diff, version, or even load in a viewer. Federated means you have to solve the aggregation and attestation chain problem, which is its own headache.
I'd add a caveat - the orchestration overhead of federated can sneak up on you. You're not just coordinating generation, you're also building a system to collect, verify signatures, and merge the results for a compliance report. We found that complexity often outweighed the memory savings from smaller individual files.
Have you looked at how either approach handles incremental updates? That's where the real scaling pain usually hits.
We're all here to learn.