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+++ title="Garage v0.7: Kubernetes and OpenTelemetry" date=2022-04-04 +++
We just published Garage v0.7, our second public beta release. In this post, we do a quick tour of its 2 new features: Kubernetes integration and OpenTelemetry support.
Two months ago, we were impressed by the success of our open beta launch at FOSDEM and on Hacker News: our initial post lead to more than 40k views in 10 days, going up to 100 views/minute, and all requests were served by Garage without cache! Since this event, we continued to improve Garage, and - 2 months after the initial release - we are happy to announce version 0.7.0.
But first, we would like to thank the contributors that made this new release possible: Alex, Jill, Max Audron, Maximilien, Quentin, Rune Henrisken, Steam, and trinity-1686a. This is also our first time welcoming contributors external to the core team, and as we wish for Garage to be a community-driven project, we encourage it!
You can get this release using our binaries or the package provided by your distribution. We ship statically compiled binaries for most Linux architectures (amd64, i386, aarch64 and armv6) and associated Docker containers. Garage now is also packaged by third parties on some OS/distributions. We are currently aware of FreeBSD and AUR for Arch Linux. Feel free to reach out to us if you are packaging (or planning to package) Garage, we welcome maintainers and will upstream specific patches if that can help. If you already did package Garage, tell us and we'll add it to the documentation.
Speaking about the changes of this new version, it obviously includes many bug fixes. We listed them in our changelogs, take a look, we might have fixed something that annoyed you! Besides bugfixes, there is two new features: a better Kubernetes integration and support for OpenTelemetry.
Kubernetes integration
Before Garage v0.7.0, you had to deploy a Consul cluster or spawn a "coordinating" pod to deploy Garage on Kubernetes (K8S). In this new version, Garage integrates a method to discover other peers by using Kubernetes Custom Resources (CR) to simplify cluster discovery.
CR discovery can be quickly enabled by configuring the name of the desired service (kubernetes_namespace) and which namespace to look for (kubernetes_service_name) in your Garage config:
kubernetes_namespace = "default"
kubernetes_service_name = "garage-daemon"
Custom Resources must be defined a priori with Custom Resource Definition (CRD). If the CRD does not exist, Garage can create it for you. It is enabled by default but it requires some additional permissions. If you prefer limiting accesses to your K8S cluster, you can create the resource manually and prevent Garage from automatically creating it:
kubernetes_skip_crd = true
If you want to try Garage on K8S, we currently only provide some basic example files. These files register a ConfigMap, a ClusterRoleBinding, and a StatefulSet with a Persistent Volumes.
Once these files deployed, you will be able to interact with Garage as follow:
kubectl exec -it garage-0 --container garage -- /garage status
# ==== HEALTHY NODES ====
# ID Hostname Address Tags Zone Capacity
# e628.. garage-0 172.17.0.5:3901 NO ROLE ASSIGNED
# 570f.. garage-2 172.17.0.7:3901 NO ROLE ASSIGNED
# e199.. garage-1 172.17.0.6:3901 NO ROLE ASSIGNED
You can then follow the regular documentation to complete the configuration of your cluster.
If you target a production deployment, you should avoid binding admin rights to your cluster to create Garage's CRD. You will also need to expose some Services to make your cluster reachable. Keep also in mind that Garage is a stateful service, so you must be very careful how you handle your data in Kubernetes to not lose them. In the near future, we plan to release a proper Helm chart and write "best practises" on our documentation.
If Kubernetes is not your thing, know that we are running Garage on a Nomad+Consul cluster. We have not documented it yet but you can get a look at our Nomad service.
OpenTelemetry support
OpenTelemetry standardizes how software generate and collect system telemetry, namely metrics, logs and traces. By implementing this standard in Garage, we hope that it will help you to better monitor, manage and tune your cluster. Note that to fully leverage this feature, you must be already familiar with monitoring stacks like Prometheus+Grafana or ElasticSearch+Kibana.
To activate OpenTelemetry on Garage, you must add to your configuration file the following entries (supposing that your collector is also on localhost):
[admin]
api_bind_addr = "127.0.0.1:3903"
trace_sink = "http://localhost:4317"
We provide some files to help you quickly bootstrap a testing monitoring stack. It includes a docker-compose file and a pre-configured Grafana dashboard. You can use them if you want to reproduce the following examples.
Now that your telemetry data is collected and stored, you can visualize it.
Grafana is particularly adapted to understand how your cluster is performing from a "bird's eye view". For example, the following graph shows S3 API calls sent to your node per time-unit, you can use it to better understand how your users are interacting with your cluster.
Thanks to this graph, we know that starting at 14:55, an important upload has been started. This upload is made of many small files, as we see many PutObject calls that are often used for small files. It also has some large objects, as we observe some Multipart Uploads requests. Conversely, at this time, no read are done as the corresponding read enpoints (ListBuckets, ListObjectsv2, etc.) receive 0 request per time unit.
Garage also collects metrics from lower level parts of the system. You can use them to better understand how Garage is interacting with your OS and your hardware.
This plot has been captured at the same moment than the previous one. We do not see a correlation between the writes and the API requests for the full upload but only for its beginning. More precisely, it maps well to Multipart Uploads requests, and this is expected. Large files (of the Multipart Uploads) will saturate the writes of your disk but the uploading of small files (via the PutObject endpoint) will be throttled by other parts of the system.
This simple example covers only 2 metrics over the 20+ ones that we already defined, but we were still able to precisely describe our cluster usage and identifies where bottlenecks could be. We are confident that cleverly using these metrics on a production cluster will give you many more valuable insights on your cluster.
While metrics are good to have a large, general overview of your system, they are however not adapted to dig and pinpoint a specific performance issue on a specific code path. Thankfully, we also have a solution for this problem: tracing.
Using Application Performance Monitoring (APM) in conjunction with Kibana, we get the following visualization:
On the top of the screenshot, we see the latency distribution of all PutObject requests. We learn that the selected request took ~1ms to execute, while 95% of all requests took less than 80ms to run. Having some dispersion between requests is expected as Garage does not run on a strong real-time system, but in this case, you must also consider that a request duration is impacted by the size of the object that is sent (a 10B object will be quicker to process than a 10MB one). Consequently, this request corresponds probably to a very tiny file.
Below this first histogram, you can select the request you want to inspect, and then see its stacktrace on the bottom part.
You can break down this trace in 4 parts: fetching the API key to check authentication (key get), fetching the bucket identifier from its name (bucket_alias get), fetching the bucket configuration to check authorizations (bucket_v2 get), and finally inserting the object in the storage (object insert).
With this example, we demonstrated that we can inspect Garage internals to find slow requests, then see which codepath has been taken by a request, to finally identify which part of the code took time.
Keep in mind that this is our first iteration on telemetry for Garage, so things are a bit rough around the edges (step by step documentation is missing, our Grafana dashboard is a work in a progress, etc.). In all cases, your feedback is welcome on our Matrix channel.
Conclusion
This is only the first iteration of the Kubernetes and OpenTelemetry into Garage, so things are still a bit rough. We plan to polish their integration in the coming months based on our experience and your feedback.
You may also ask yourself what will be the other works we plan to conduct: stay tuned, we will release a roadmap soon! In the mean time, we hope you will enjoy Garave v0.7!