OpenTelemetry End-User Discussions Summary for April 2023

For the month of April 2023, the OpenTelemetry end-user group meet took place for users in the Asia-Pacific (APAC) region. Due to KubeCon EU, the AMER and EMEA sessions did not take place; however, we will have meetings for all 3 regions again in May.

The discussions take place using a Lean Coffee format, whereby folks are invited to post their topics to the Agile Coffee board like this one, and everyone in attendance votes on what they want to talk about.

What we talked about

We talked about evangelizing the adoption of OpenTelemetry in a big organization and also discussed how to optimize observability data at scale.

Discussion Highlights

Below is the summary of this month’s discussion.

Note: The answers are provided by a mix of OTel community members and end-users to the best of their knowledge. The answers are not official recommendations by OpenTelemetry.

Evangelizing adoption of OpenTelemetry in a big organization

Q: How do you evangelize the adoption of OpenTelemetry in a big organization?

A: In a big organization, the first step would be to put out the current pain points of observability to leadership. There are benefits of having an open source standard for observability. If there is no standard in place, it gets very difficult to communicate across different teams. If you use OpenTelemetry, you do not have to depend on any vendor agents, and you have the flexibility to send data to multiple backends.

How to optimize observability data at scale?

Q: In a big organization, observability data can be in the range of TBs per day, which comes with associated costs. But there is always a feeling that 80% of captured data is unusable. None of the vendors help you understand what data is accessed and how to bring that visibility to the engineering teams sending the data.

A: One of the ways to optimize observability at scale is sampling. Here’s an article on tail sampling with OpenTelemetry. There are a number of options for you to reduce the data volumes at the SDKs level and the collector level.

Also, there is active work going on the OpenTelemetry Collector side to handle data at scale more efficiently. For example, there is work going around using Apache Arrow for serialization to optimize network costs.

One of the other ways to optimize observability data at scale is to decide how much of it you want to store for future use. You should optimize data storage so that you incur less cost.

Other Important discussion points

Maturity model for OpenTelemetry

Q: Is there some literature available around understanding steps to reach a certain maturity level in adopting OTel in your organization? For example, I should be able to go to a team and tell them to start with X, and then do Y to move ahead. In a big enterprise, you have to provide something for people to understand the maturity of OpenTelemetry.

A: For teams adopting OpenTelemetry, a good idea is to start with minimal changes. For example, teams can start with languages that have auto-instrumentation support. Seeing value from small changes can build more confidence in the team to go deeper into OpenTelemetry adoption.

There are also several OpenTelemetry receivers available. These receivers help to collect the telemetry end-users already have. For example, Prometheus receiver can help you receive metrics data in Prometheus format. Using these receivers, you can start sending telemetry data from different components of your application.

Meeting Notes & Recordings

For a deeper dive into the above topics, check out the following:

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