The immudb Coding Challenge – Winter 2020 Edition

More info at immuchallenge repository.

To all Gophers and data modeling wizards out there: do we have a challenge for you!!
Today we announce our first #immuchallenge coding competition – Winter 2020 Edition.

The challenge: Design and implement a document-like data model on immudb

Our Open Source immudb immutable datastore builds on the traditional key-value data model guaranteeing immutability in a quite unique manner. This simple yet generic data model provides the basis for the design of more complex and challenging immutable data models – like document-like data models.


A document data model is composed of identifiable elements which have properties and may be related to other elements in many different ways. For more information about document data models, we’ve listed some helpful resources.

We expect a minimal functional implementation written in Go (including unit testing) – either fully integrated into immudb or as a standalone component. The new immutable document data model needs to be built on top of the immudb immutable key-value model in order to leverage immutability properties.

Below are the commands for getting the immudb Docker image using a Linux shell. You’ll find more details about how to download and get started with immudb in our developer jumpstart.

  1. Pull immudb Docker Image from Docker Hub:
docker pull codenotary/immudb:latest
  1. Run immudb in a container:
docker run -it -d -p 3322:3322 -p 9497:9497 --name immudb codenotary/immudb:latest

In developing your solution, you’ll have to make decisions that are related to:

  1. API which stores and retrieves documents and provide proof of no tampering
  2. Data format which defines how objects are exchanged between final application and storage
  3. Atomicity at the level of document property, single, or multiple document
  4. Concurrency to allow concurrent read and update operations
  5. Granularity of evidence to prove whether a whole document or only the document property is unchanged and untampered

Please submit your solution via email to before December 20th, 2020, 0:00AM CST. All code submitted at a later time, will not be included in our decision process.

Competition Price Assignment Panel

Our panel is comprised of Jerónimo Irázabal, immudb maintainer and project manager, Dennis Zimmer, CTO of vChain, and Michele Meloni, immudb core developer.

The two winners of the competition will be announced on this page on December 22nd, 2020. We will select the best data models based on design decisions, implementation and performance as well as overall code quality.

Feel free to also share your approach, testing, and/or anything interesting you have learned in the #immuchallenge channel in slack.


  • 1st place – $750 ( Gift Card)
  • 2nd place – $350 ( Gift Card)

Questions & Clarifications

For further questions and modifications, contact

More Resources

immudb repository

immudb website

immudb documentation

immudb SDKs:

  1. Java: immudb4j
  2. Golang: immudb-go
  3. .NET: immudb4dotnet
  4. Python: immudb-py
  5. Node.js: immudb-node

Document data model references:


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Use Case - Tamper-resistant Clinical Trials


Blockchain PoCs were unsuccessful due to complexity and lack of developers.

Still the goal of data immutability as well as client verification is a crucial. Furthermore, the system needs to be easy to use and operate (allowing backup, maintenance windows aso.).


immudb is running in different datacenters across the globe. All clinical trial information is stored in immudb either as transactions or the pdf documents as a whole.

Having that single source of truth with versioned, timestamped, and cryptographically verifiable records, enables a whole new way of transparency and trust.

Use Case - Finance


Store the source data, the decision and the rule base for financial support from governments timestamped, verifiable.

A very important functionality is the ability to compare the historic decision (based on the past rulebase) with the rulebase at a different date. Fully cryptographic verifiable Time Travel queries are required to be able to achieve that comparison.


While the source data, rulebase and the documented decision are stored in verifiable Blobs in immudb, the transaction is stored using the relational layer of immudb.

That allows the use of immudb’s time travel capabilities to retrieve verified historic data and recalculate with the most recent rulebase.

Use Case - eCommerce and NFT marketplace


No matter if it’s an eCommerce platform or NFT marketplace, the goals are similar:

  • High amount of transactions (potentially millions a second)
  • Ability to read and write multiple records within one transaction
  • prevent overwrite or updates on transactions
  • comply with regulations (PCI, GDPR, …)


immudb is typically scaled out using Hyperscaler (i. e. AWS, Google Cloud, Microsoft Azure) distributed across the Globe. Auditors are also distributed to track the verification proof over time. Additionally, the shop or marketplace applications store immudb cryptographic state information. That high level of integrity and tamper-evidence while maintaining a very high transaction speed is key for companies to chose immudb.

Use Case - IoT Sensor Data


IoT sensor data received by devices collecting environment data needs to be stored locally in a cryptographically verifiable manner until the data is transferred to a central datacenter. The data integrity needs to be verifiable at any given point in time and while in transit.


immudb runs embedded on the IoT device itself and is consistently audited by external probes. The data transfer to audit is minimal and works even with minimum bandwidth and unreliable connections.

Whenever the IoT devices are connected to a high bandwidth, the data transfer happens to a data center (large immudb deployment) and the source and destination date integrity is fully verified.

Use Case - DevOps Evidence


CI/CD and application build logs need to be stored auditable and tamper-evident.
A very high Performance is required as the system should not slow down any build process.
Scalability is key as billions of artifacts are expected within the next years.
Next to a possibility of integrity validation, data needs to be retrievable by pipeline job id or digital asset checksum.


As part of the CI/CD audit functionality, data is stored within immudb using the Key/Value functionality. Key is either the CI/CD job id (i. e. Jenkins or GitLab) or the checksum of the resulting build or container image.

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