Introducing immudb v1.2.3 – instrumentation, performance and

Codenotary and the immudb team are proud to announce the release of immudb version 1.2.3 now available via github and docker hub. This release is chock full of goodies so let’s get right down do it.

Instrumentation and Statistics!

immudb Prometheus dashboard

One thing that community members constantly ask about is performance. How can we measure immudb’s performance and get a peek behind the curtain? Well we listened, and are glad to introduce a prometheus dashboard with a whole bunch of stats. You can now easily, at a glance, see what’s going in a few key areas, including the DB size and growth over time, indexing and transactions, B-tree (immudb’s internal data structure) and metrics related to the S3 storage backend (if you’re using one). The instrumentation is pretty detailed and comprehensive and we plan on covering it more in its own post, so please stay tuned in the coming days for even greater detail about what you can see, why it’s important and how you can tweak things by using it.

Performance and Administration Improvements

Lots of work has gone into improving immudb’s performance in a number of ways. We’ve continued to enhance B-tree functions, which is immudb’s internal data structure. As a result we’ve been able to speed up insertions by 22%. We’ve also reduced node sizes, effectively using less storage, we’ve also optimized the distribution of entries within b-tree nodes, which results in a nice speedup overall as well.

In terms of administration, it is now possible to dynamically load and unload a database without having to restart the server. This option is also configurable via the AutoLoad function as well. This now allows users to more easily work with multiple databases. We’ve also added a feature to allow the index to self-compact which reduces the number of blocks used for the index and therefore improved query performance.

There’s so much more!

So much great work has been on this release that we can ramble on for hours, but we think you got it! You can read the complete release notes on github https://github.com/codenotary/immudb/releases/tag/v1.2.3 to see the complete changelog and read more about all the great features, fixes and enhancements in this release. Make sure you check it out.

What are you building?

The feedback from the community has been tremendous and we love to hear what y’all are building. It’s what keeps us going day after day. Join us on Discord and Twitter to follow what we’re doing and let us know what you’re doing.

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Use Case - Tamper-resistant Clinical Trials

Goal:

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.).

Implementation:

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

Goal:

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.

Implementation:

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

Goal:

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, …)


Implementation:

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

Goal:

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.

Implementation:

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

Goal:

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.

Implementation:

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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