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This blog post details an experience report for adopting OTel for metrics in Cloud Foundry. Co-authored-by: Carson Long <[email protected]>
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| title: 'Experience Report: Adopting OpenTelemetry for Metrics in Cloud Foundry' | ||
| linkTitle: OpenTelemetry in Cloud Foundry | ||
| date: 2023-10-31 | ||
| author: >- | ||
| [Matthew Kocher](https://github.com/mkocher) (VMware) and [Carson | ||
| Long](https://github.com/ctlong) (VMware) | ||
| cSpell:ignore: Kocher unscalable | ||
| --- | ||
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| [Cloud Foundry](https://www.cloudfoundry.org/) recently integrated the | ||
| OpenTelemetry Collector for metrics egress and we learned a lot along the way. | ||
| We're excited about what the integration offers today and all the possibilities | ||
| it opens up for us. | ||
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|
||
| ## What we were looking for | ||
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| Cloud Foundry is a large multi-tenant platform as a service that runs 12-factor | ||
| applications. Cloud Foundry platform engineering teams usually run 4 to 8 Cloud | ||
| Foundry deployments running thousands of applications and hundreds of thousands | ||
| of containers. There are many users of Cloud Foundry, and our goal when it comes | ||
| to metrics is to enable platform engineering teams to integrate with their | ||
| monitoring tool of choice, not to dictate a single solution. | ||
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| Historically adding support for a new monitoring tool was done via our own | ||
| Firehose API which required writing a new “Nozzle” for each tool, a high barrier | ||
| to entry. The Firehose API was also found to have inherent performance | ||
| limitations when being used for high volumes of logs and metrics. With an | ||
| unscalable API, and an unscalable approach to integrations, we started looking | ||
| to replace the way metrics egress from Cloud Foundry. | ||
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| We were looking for a scalable way to egress metrics from hundreds of VMs and | ||
| thousands of containers with no bottlenecks or single points of failure. We also | ||
| wanted a real community working to support the many monitoring tools that | ||
| platform engineering teams might want to use, so we could get out of the | ||
| business of writing and maintaining custom integrations. | ||
|
|
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| ## What we evaluated | ||
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| It was easy to align on adding an agent to each VM sending metrics directly to | ||
| monitoring tools. Within Cloud Foundry we had already seen that this approach | ||
| worked well for logs egress, eliminating bottlenecks and single points of | ||
| failure. | ||
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| We evaluated several popular metric egress solutions. Besides OpenTelemetry, we | ||
| also looked seriously at [Fluent Bit](https://fluentbit.io/) and | ||
| [Telegraf](https://www.influxdata.com/time-series-platform/telegraf/). | ||
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| For every solution we considered, we looked at them in following ways: | ||
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| - Flexibility: How many monitoring tools can we send to? | ||
| - Performance: How efficient is the CPU and memory usage? | ||
| - Community: How engaged is the community? What languages/tools do they use? | ||
| - Deployment: Can it be deployed as a BOSH job and work within the constraints | ||
| of a Cloud Foundry deployment? Is it possible to hot reload the configuration? | ||
|
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| ## Why we chose OpenTelemetry | ||
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| When we looked at Fluent Bit we found that it is being written in C, which may | ||
| offer some performance benefits, but we primarily write in Go. We discarded | ||
| Fluent Bit early because our ability to contribute to the codebase would be | ||
| minimal, which we worried would limit us in the future. | ||
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| We then looked more seriously at Telegraf and OpenTelemetry. Both are written in | ||
| Go, so we were good there. Telegraf has a more limited community, and does not | ||
| offer a customizable build process. We liked that OpenTelemetry allows us to | ||
| build a Collector with a curated selection of our own components and community | ||
| components. | ||
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| Additionally, when looking at OpenTelemetry we found that many tools and vendors | ||
| were adding native support for the OTLP protocol. This caused us to be confident | ||
| in adopting OpenTelemetry Collector as a widely adopted first-party | ||
| implementation of the OTLP protocol. | ||
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| We | ||
| [proposed adding the OpenTelemetry Collector to Cloud Foundry in an RFC](https://github.com/cloudfoundry/community/blob/0365df129e52ae7b784957a5569b16b7e133f97e/toc/rfc/rfc-0018-aggregate-metric-egress-with-opentelemetry-collector.md), | ||
| and solicited our community's feedback. It was accepted on 07-07-2023 and we got | ||
| to work. | ||
|
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| ## How we integrated OpenTelemetry with our current metric system | ||
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| In Cloud Foundry we have a suite of agents responsible for forwarding logs and | ||
| metrics. The front door is a “Forwarder Agent” which accepts logs and metrics in | ||
| our own custom format and multiplexes them to the other agents. | ||
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| We added support in our agent to translate metrics to OTLP and forward | ||
| them onto the OpenTelemetry Collector. This required | ||
| [just 200 lines of Go code](https://github.com/cloudfoundry/loggregator-agent-release/blob/1fd275fe85d6190bac73dc1195007cc8726c1871/src/pkg/otelcolclient/otelcolclient.go#L108-L153), | ||
| with many of those lines simply closing curly brackets. In writing this code we | ||
| had to think about how to take our existing data model and translate it into the | ||
| OpenTelemetry protobuf data model. We found OpenTelemetry to have considered | ||
| many edge cases we had encountered in the past, and plenty we had not yet | ||
| encountered. Someday we hope to understand how to use Scopes and Resources | ||
| effectively. | ||
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| Our implementation worked well, though we found the OpenTelemetry Collector to | ||
| be using large amounts of CPU. We had done the simplest thing to start with, and | ||
| were only sending one metric per gRPC request. Our Collector's Resource | ||
| utilization dropped drastically when we | ||
| [added batching](https://github.com/cloudfoundry/loggregator-agent-release/pull/396). | ||
|
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| ## How it works for Cloud Foundry platform engineering teams | ||
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| Platform Engineering teams can now | ||
| [optionally provide an OpenTelemetry Collector exporter configuration when deploying Cloud Foundry](https://github.com/cloudfoundry/cf-deployment/blob/fcde539a81a6b091a25d06992e16bb2fb641a329/operations/experimental/add-otel-collector.yml). | ||
| Every VM in the deployment will then run a Collector that uses that | ||
| configuration to forward metrics to the specified monitoring tools. | ||
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| We’ve started small and currently only support the OTLP and Prometheus | ||
| exporters. However, we’re looking forward to hearing what additional exporters | ||
| platform engineering teams want to use, and adding them to our OpenTelemetry | ||
| Collector distribution. | ||
|
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| ## What's next | ||
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| When we took on this track of work we focused exclusively on metrics. There’s a | ||
| clear path to add logs as another supported signal type for our OpenTelemetry | ||
| Collector integration. We do not yet support traces as a built-in signal in | ||
| Cloud Foundry, but we are excited about the possibilities that traces could | ||
| offer for both platform components and applications running on the platform. | ||
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| Our current OpenTelemetry Collector integration offers what we call “aggregate | ||
| drains”, which egress signals generated by the platform or by applications | ||
| running on the platform. We would also like to offer “application drains”, which | ||
| would only egress signals from individual applications to the monitoring tools | ||
| of the application teams’ choice. This involves complex routing and frequent | ||
| creation and removal of exporters, which may require new OpenTelemetry Collector | ||
| features. | ||
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| If we can accomplish those goals, we could replace our entire agent suite with a | ||
| single OpenTelemetry Collector running on each VM. Those Collectors would handle | ||
| logs, metrics and traces for our system components as well as every application | ||
| running on the platform. |
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