clair VS thanos

Besides pointing pentester tools like metasploit at yourself, there are some nice scanners out there.

https://github.com/quay/clair

https://github.com/anchore/grype/

Clair. Vulnerability Static Analysis for Containers.

https://github.com/quay/clair 9.4k stars, updated 17 hours ago

It scaled well compared to a naive graph abstraction implemented outside the database, but when performance wasn't great, it REALLY wasn't great. We ended up throwing it out in later versions to try and get more consistent performance.

I've since worked on SpiceDB[1] which takes the traditional design approach for graph databases and simply treating Postgres as triple-store and that scales far better. IME, if you need a graph, you probably want to use a database optimized for graph access patterns. Most general-purpose graph databases are just bags of optimizations for common traversals.

[0]: https://github.com/quay/clair

[1]: https://github.com/authzed/spicedb

Clair GitHub

Open source: Trivy, Gryp and Clair are widely used open source tools for container scanning.

Testing the image with github.com/fullhunt/log4j-scan and https://github.com/quay/clair shows no vulnerabilities

Amazon Elastic Container Registry is a fully-managed Docker container registry. It makes it easy for developers to store and manage Docker images inside their AWS environment. ECR supports two types of image scanning. Enhanced image scanning requires an integration with Amazon Inspector. It will scan your repositories continuously. Basic image scanning will use the Common Vulnerabilities and Exposures (CVEs) database (open-source Clair) to find vulnerabilities in your images. You can trigger scans on image push or manually.

Monitoring = netdata on each RPi https://www.netdata.cloud/ binded to the vpn interface being scraped into a prometeus thaons https://thanos.io/ setup with grafana to give management the Green all is good screens (very important).

Sounds like you want something like Thanos

Yes, but also no. The Prometheus ecosystem already has two FOSS time-series databases that are complementary to Prometheus itself. Thanos and Mimir. Not to mention M3db, developed at Uber, and Cortex, then ancestor of Mimir. There's a bunch of others I won't mention as it would take too long.

Long term storage all depends on your needs and sophistication. I use Thanos for our system since it has an extremely flexible scaling system. But there is also Grafana Mimir. They're both similar in that they use Prometheus TSDB format as part of the underlying storage. One nice Thanos advantage is that it does do downsampling in addition to being able to store raw metric data for a long time. It will auto-select downsampled data to make requests faster.

You can aggregate all your clusters Prometheus metrics together with a wonderful tool called Thanos. This will allow you to use just a single Grafana instance against Thanos and using a label select which cluster you wish to see metrics from. The downside of this, is that none of the Grafana dashboards from the internet will work as-is. You'll need to customize all of them for Thanos support. The other downside is, you have a single point of failure, and (see next item) you can't customize who can access what in regards to your dev vs production data/metrics/access.

Depending on how you want to set things up, you can use Thanos or Mimir to create the single-pane-of-glass view of your data.

You could look at something like Thanos and store your data in S3: https://thanos.io/