Introducing Flexibility and Control in Metrics Data

Learn about a metrics data pipeline that enhances metrics collection and processing flexibility in a complex environment, employing various components for improved uptime, cost control, and data management, working towards a unified observability stack.

By Abhishek Kothari

Metrics are key telemetry data that provides insight into the health of our system and applications running on them. At Segment, we rely heavily on our Observability tooling to monitor, detect, and resolve issues. Even a momentary downtime in the metrics platform could pose a massive risk to the overall system. This necessitates our metrics platform be reliable and resilient to temporary downtimes and are up and running at any time. 

Conventionally, we have been running a Datadog dependent metrics stack that emits DogStatsD metrics and these metrics get sent directly to Datadog. Since StatsD is a UDP based protocol, there are times we’d end up losing data if an agent takes too long to start. This happened frequently for us during the upgrades. Additionally, if any issues arose within Datadog, it would render us completely lost. We had to solve this. With this vision in mind, we set on a journey to re-architect our metrics stack to make it more flexible, resilient, and ready for any third-party downtime. 

Metrics data - the scale at which we work

As of today, our metrics platform deals with over 50M+ active series at any time and has had peaks of up to 90M+ active series. We have a major footprint on AWS. Majority of workloads today run either on EKS or Lambda. Within EKS, at any point in time we run about 100k+ pods and about 5k+ nodes. This exemplifies the scale at which we work. 

Problems with our current metrics stack 


At this scale, you have very little control over the observability data that gets sent out to the end destination and how much of it is really used. This led to random spikes in our platform costs and also high MTTD for the cause of that spike. To resolve this in the short term, we built an automation that gathers the data around metrics and its metadata. This data helped us identify the metrics & tags that were truly used! It worked well, but was unable to help us control a spike from occurring in the first place. 

Additionally, with the standard agents like Datadog agent and Veneur that we use to push out metrics, we do not have the luxury to filter out specific tags or metrics in order to control the costs. A high cardinality tag could easily pass through without us getting notified at all, eventually resulting in a spike in the cost. This makes our actions, against the rising cost, mostly reactive. 


Since DogstatsD uses a push based mechanism for posting the metric data, any downtime for the metric agents would lead to failing requests and corresponding data loss for metrics. In a scenario, where the agent takes longer to start or just fails to start due to lack of required memory/CPU, the data loss increases. There is no buffering at agent level implemented as of today. 

In addition to this, a massive outage like the one that happened in March, could leave us completely unaware if we do not have the facility to quickly route the data to a different backend. 

Lack of control over quality and quantity of data 

We were unable to query or analyze the metrics data once sent to Datadog on any platform. As soon as the data was shipped out, we no longer owned it. Our agents (Veneur and Datadog agent) then did not have any possibility of sending these metrics to a different destination. If a situation arose where we wished to analyze high cardinality data in a different backend, we did not have a solution to do so. 

Flexibility to route data to a different backend

Since both Datadog and Veneur supported Datadog as the primary sink, we had no option to route the data to a different backend. If, for instance, we wished to route some high cardinality metrics to different destinations and prevent them from spiking Datadog costs, it was not possible with our existing agents. 

These problems formed the base of our Metrics platform - Vision 2.0. 

Metrics platform - Vision 2.0

With the new vision in mind, we set the below functional requirements for our new platform to be built out: 

  • Add buffering capabilities to tolerate downtime of agents 

  • Reduce the restart of cluster level agents 

  • Flexibility to tag data with standard tags at each ingestion point 

  • Ability to easily route metrics to a different destination as and when needed

  • Allow users to choose where to route the metrics - by just adding a simple tag in the metrics 

  • Own the metrics data and ship it where you want 

  • Reduce/eliminate vendor-locking into custom protocols/features

Based on the goals above, we set out to research the available tooling in the market for building what we call a metrics data pipeline. A metrics data pipeline essentially is a set of tools that come together to programmatically route metrics to the desired destinations. In the process, we also get control over the data getting sent to the sink. Thus, we could manage the tags, frequency of data and the actual metrics too. This helps us greatly in controlling the costs as well as refining and cleaning out data. 

Technological requirements 

In our old setup of the metrics tooling, we had the majority of our footprint in Datadog while only some of it was on Amazon Managed Prometheus (Metrics) & Grafana (Visualization). Our technical requirements are summarized below. We required the ability to: 

  • Ingest StatsD metrics 

  • Ingest metrics via Datadog REST API 

  • Ingest metrics via Prometheus Remote Write 

  • Send out metrics to Datadog

  • Send out metrics to AMP (Amazon Managed Prometheus) 

  • Support custom tag processing - Add, Remove and Update tags for every metrics

  • Support for filtering out (skipping) metrics selectively from the target destination

Metrics pipeline - Tooling options

As we set out to research which tools could make this possible, there were two clear options - Open Telemetry Collector and Vector. Upon evaluating the presented options against the above technical requirements, Vector became a clear tool of choice. Open Telemetry collector unfortunately did not support Datadog agent API nor did it support the Prometheus Remote Write receiver. These key gaps led us to the decision of using Vector for the purpose of Pipelining our metrics data. 

That said, we did use Open Telemetry collector in agent mode to overcome the horizontal scalability limitation of Prometheus during the time we were architecting the pipeline. We will discuss more about this in our final architecture diagram displaying all the components that we use. 

Metrics data pipeline - High Level architecture


The metrics data pipeline provides end users the flexibility to easily pass their metrics to desired destinations without the need to make any major changes in the instrumentation within the code. Let us dive deeper into the components that make up the pipeline. 

Kube State Metrics & Node Exporter

These applications are responsible for gathering metrics in and around Kubernetes infrastructure. The metrics from Kube State Metrics pods are collected by Prometheus Operator while Datadog agent has an embedded Kube state metrics library for faster performance. For node exporters, both Datadog agent and Prometheus operator continuously scrape the agents on each node. 

Prometheus Operator 

Prometheus Operator is used specifically for collecting the Kubernetes system metrics and sending them to Amazon Managed Prometheus. Prometheus operator supports recording rules which helps us pre-process certain complex metric equations in order to speed up the queries in Grafana. Additionally, we are also able to control the volume of metrics and cost thereby as we completely block the actual metrics from being sent to Amazon Managed Prometheus. This adds some overhead of modifying rules when in need of new metrics. However, over time it has gotten quite stable and we have all we need. 

Open telemetry agent (Otel Agent)

In order to overcome the scalability problems of Prometheus and prevent application metrics from having downtime due to the same, we modernized our approach for scraping Prometheus metrics from the applications. While considering a 20-30% increase in the metrics being published, we shifted the recording rules for some of our applications onto Amazon Managed Prometheus (AMP) thereby sending all the application metrics data to AMP via Otel agent.


Veneur is a metric aggregation agent that ingests metrics from multiple pods on the node, aggregates them over a period of 10 seconds (configurable) and publishes them eventually to Datadog. With the new metrics pipeline, a vector agent runs alongside veneur to consume data from veneur that eventually reaches Datadog.

Vector agent

Vector running as an agent is responsible to inject a few common cluster level tags and buffer the metrics data when the central unit is overloaded or restarting. The buffering ability helps ensure decrease in data loss when the central unit restarts. As a general rule, we attempt to restart this agent only in an event of standard version upgrades to ensure minimal data loss. 

Datadog agent 

Datadog agent is the system metrics consumer for Datadog. It scrapes metrics from Node Exporter, uses Kube State metrics as a library to gather K8s and nodes related metrics. Additionally, Datadog agent is also responsible to scrape Prometheus metrics that need to be sent to Datadog and consume distribution metrics from certain applications. There is a Datadog cluster agent that does the job of running standard checks and commanding the agents to scrape the metrics. This is primarily to reduce the load on K8s API as well as Datadog agent by reducing the number of agents querying Kubernetes resources. 

Vector Deployment

As our central unit, our choice was fairly clear - we had to go with Vector. It is the only application that supports Datadog agent, Prometheus Remote write, as well as StatsD as a source. Using it as an agent as well as a central unit helps reduce the learning curve too. We did not really use the StatsD source for the central unit but intend to replace Veneur in the long run by consuming StatsD metrics directly. The central unit runs as a deployment that’s autoscaled using an HPA.

YACE Exporter 

YACE Exporter is an excellent tool that allowed us to scrape Cloudwatch metrics and push them onto Prometheus backend. Although it was in alpha version and not actively being developed, we quickly gained knowledge into the tool and contributed a couple of PRs for enhancing its ability. It allows you to query Cloudwatch metrics in an optimal and highly configurable way along with resource tags. Tags is the key here - directly querying Cloudwatch metrics would not allow you to query metrics with resource tags. YACE made it possible. 

Amazon Managed Prometheus 

Amazon Managed Prometheus team from AWS has been a great partner for Twilio since we started using it. Over time, it has improved its performance and support massively. As of today, we leverage it as our backend for Prometheus metrics. Grafana supports Sigv4 authentication which makes it easy to integrate as well. AMP in its early days had a constraint of supporting hardly 5M active series from which it has scaled to a massive 500M active series support now. This made it possible for us to make it a single backend for all our Prometheus metrics. 

The only drawback with AMP is its support for Alerting. As of today, it only supports SNS as the Alertmanager sink and there is no user interface that can be used for creation of alerts. We did manage to simplify alert creation by building an abstract Terraform module for the same. 


Datadog has been the backend for about 80% of our metrics in use today. It serves well, has a great user interface and end-users find it to be great too.  We use it for Metrics dashboards, monitors, and SLO dashboards. We started using Datadog a while back and the main intent behind that was to have a simplified user experience while keeping the cost manageable. However, over the time, the costs have grown considerably leading to this entire Metrics pipeline initiative. 

The drawback we observe with Datadog is primarily the cost of using other telemetry signals on Datadog. They tend to be relatively costly and can spike the costs randomly without providing sufficient visibility into what caused it.

Building the above pipeline and maintaining it was certainly a complex affair. Let us dive deeper into the technical challenges we have today and how we plan to navigate it. 

Too many components

As evident from the large component list above, we have an elaborate list of components today that make this entire pipeline possible. In the long run, we certainly aim to get rid of some of these components by being opinionated about what we support and thereby move to a unified standard. One of the reasons for having these many components is our technical debt of every team trying to do what worked for them in the past. 

In order for us to standardize the stack, there is considerable effort involved in migrating/modifying dashboards and monitors to use common metrics. As an end goal, we aim to completely get rid of Veneur as well as the Prometheus operator.

Non-standard approaches

In a couple of special cases, we had to drift away from standard approaches like scraping Prometheus metrics using Open telemetry agent. We’d eventually overcome the restrictions and get rid of Prometheus Operator entirely. The primary reason for doing this is that Prometheus is a Single Point of Failure today. It consumes large amounts of memory and auto-scaling has not been easy. With Open telemetry agent, on the other hand, it is simple. As the nodes spin up, we’d have a dedicated agent for each node. The only downside of getting rid of the Prometheus operator is that we’d have to push all the metrics onto Amazon Managed Prometheus and apply the recording rules there. This has a minor cost impact - which is to some extent balanced by our current dedicated Prometheus infrastructure. 

Single Backend 

Our present approach of multiple backend serves the flexibility that the user requires. However, it’s definitely not the end state we’d expect. Multiple metric backends add to the responsibility of maintaining infrastructure, contracts, and thereby uptime of multiple components, too. We’re not presently opinionated about the backend we’d choose, however, the pipeline definitely makes it a lot simpler to switch to a single backend. We’d invest effort towards automating this entire transition by transforming the expressions based on the decided backend. 

Unified Observability UI 

One of the key gaps we’ve had in our Observability stack is the lack of a single user interface where users can view all the telemetry data and much more. Grafana Cloud plays a key role at alleviating this complexity. It enables our end-users to plot their metrics, logs, and traces in the future on a single UI, providing a uniform experience across the stack. This is indeed the end-state we’re looking to reach with the process of modernizing our observability stack. 


With the metrics data pipeline running successfully in Production for a while now, it has massively improved our metrics platform uptime. It has allowed us to control costs and data, as well as bring in tagging standards to the data sent out. 

The pipeline opens doors to imagination of what more we can do with our metrics data. Today, we can confidently serve a high cardinality, or high ingestion rate, for the metrics data without any massive change to developer code. Additionally, we could easily route metrics to totally different solutions for any specific use cases or long term storage for future analytics of the same too.

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