# Resurrecting gosumdb: Independently Auditing the Go Module Supply Chain
Wednesday, 5. November 2025


When you run `go get`, you're implicitly trusting Google's `sum.golang.org` checksum database (gosumdb) to serve the
correct cryptographic hash for your dependencies. This system is a massive leap forward for supply chain security, but
only if we can independently verify its trustworthiness.

That's why I built [gosumdb](https://monitor.raphting.dev/gosumdb/) (the project, not the service): an independent, third-party witness designed to audit
Google's `sum.golang.org`. It continuously fetches the database's Merkle tree, verifies its cryptographic integrity, and
checks for anomalies.

After a bit more than a month of planned downtime, I'm glad to announce that `gosumdb` is back online.
This post is about how I re-architected it to handle millions of hashes on a single, inexpensive VM.

*(For the complete background on the project's origins, see the original
posts: [Part 1](https://raphting.dev/posts/gosumdb-p1/), [Part 2](https://raphting.dev/posts/gosumdb-p2/), [Part 3](https://raphting.dev/posts/gosumdb-p3/))*

## The Challenge: Surviving the OOM Killer

The original `gosumdb` implementation was simple but resource-hungry. The Go checksum database is
a [Merkle tree](https://en.wikipedia.org/wiki/Merkle_tree) where module versions are hashed, and those hashes are hashed
again, eight levels deep. The raw data for this tree now amounts to **2.8 GB**.

My first implementation stored this entire tree in-memory for processing. This workes on machines with a lot of RAM, but
with less resources the Linux OOM (out-of-memory) killer would step in to save the machine, shutting down the `gosumdb`
process.

I was able to trade RAM for disk space and make gosumdb run on just a few hundred MB of RAM.

## The Solution

I broke the problem into two parts: handling the massive Merkle tree itself and efficiently checking for duplicates
among millions of entries.

### Taming the Merkle Tree: From In-Memory to On-Disk

The 2.8 GB of hash data doesn't need to live in RAM. A Merkle tree's hash coordinates can be mapped to a dense linear
ordering. This means I can treat a single large file as my data structure, using `WriteAt` and `ReadAt`
to store or retrieve the 32 bytes of any given hash at a specific, calculated index.

Every 32-byte chunk in the file represents a hash. As long as I stick to the dense linear ordering, I don't need any
other markers or complex database logic.

With this approach, I can happily write gigabytes of module hashes to a file. Linux handles the
complicated work of paging data between disk and RAM as needed. A few gigabytes of disk space are cheap,
and the OOM killer is no longer a threat.

### The Duplicate Problem: Finding a Needle in 46 Million records

The second major resource hog was the duplicate checker. The gosumdb's security model relies on a critical promise:
every module version (e.g., `my-module@v1.0.0`) only exists once in the entire database. If anyone could
inject a *second*, different hash for the same version, they could selectively serve a malicious version to certain
users.

This created a classic engineering problem: "How do you find duplicate strings in more than 46 million entries if you
can't fit them all in RAM?"

The solution I implemented is "hash partitions." Here’s how it works:

1. Take a module string, like `"my-module@v1.0.0"`, and hash it to a number (e.g., `39568375`).
2. Take that number modulo `1024` (an arbitrary number of partitions), which gives a result like `1015`.
3. Append the *original string* (`"my-module@v1.0.0"`) to a file named `partition-1015.txt`.
4. Repeat this for all 46 million+ entries.

Any identical strings will hash to the same number and end up in the same partition file.

Once all entries are processed, I can check for duplicates one file at a time. In practice, these partition files are
only about 6 MB large and trivially fit into RAM. I can then use standard in-memory sorting, sweep through the sorted
list, and check if any adjacent strings are equal.

## Why an Independent Witness Matters

Large parts of my implementation are unique. I didn't copy existing projects, and starting this in July 2023 also makes
me an early bird. I logged all reports since I started monitoring go's database.

I'm happy to report that the anomaly of a duplicate module version with a different hash has never happened yet. This
gives us high, independent confidence that all modules served via the module proxy are unaltered, which is exactly what
we expect. Needless to say, the root hash was always correct too.

I verify all hashes from the raw data up. I don't rely on intermediary tiles served by Google. This makes it not only
an integrity check, but a full check of all accessible data.

### A Personal Motivation

I've been working on `gosumdb` in my free time, driven by an interest that comes from 10 years of professional Go
development. I started when Go didn't even have modules and the big question was "What is your `GOPATH`?!" We used
third-party tools like `virtualgo` just to manage our workflows.

When Go 1.11 introduced modules and the `gosumdb`, I was excited to see a simpler and more secure way of managing
dependencies. Few languages secure their supply chain at this level. Having spent two years working professionally
in supply chain security, I was inspired to investigate `gosumdb` deeply and write the witness this article is about.

The [gosumdb](https://monitor.raphting.dev/gosumdb/) witness is back online as another layer in the swiss cheese model
to secure our infrastructure, available under https://monitor.raphting.dev/gosumdb/


By Raphael Sprenger
