opentelemetry-lambda
jaeger
Our great sponsors
opentelemetry-lambda | jaeger | |
---|---|---|
8 | 94 | |
243 | 19,370 | |
4.5% | 1.3% | |
9.3 | 9.7 | |
5 days ago | 6 days ago | |
Go | Go | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
opentelemetry-lambda
-
Did OpenTelemetry deliver on its promise in 2023?
I mean, sure, you can improve performance a bit by increasing the RAM/compute capacity on the Lambda. But it always adds a pretty steep overhead right now, no matter how much capacity you throw at it.
https://github.com/open-telemetry/opentelemetry-lambda/issue...
https://github.com/aws-observability/aws-otel-lambda/issues/...
-
Instrumenting AWS Lambda functions with OpenTelemetry SDKs
OpenTelemetry AWS Lambda repository
- OpenTelemetry in 2023
-
Serverless Spy Vs. Spy Chapter 3: X-Ray vs Jaeger - Send Lambda traces with open telemetry
With the sample apps from the opentelemetry-lambda repository the Lambda part itself was easy to implement. What took me some time was to provide the jaeger Fargate service with IaC ouside of an k8s environment. But with ECS and ServiceDiscovery that was easy in the end. This should be even more simple in an EKS environment with the jaegertracing helm-charts.
-
AWS Lambda tracing with OpenTelemetry and OpenSearch
OpenTelemetry recently released https://github.com/open-telemetry/opentelemetry-lambda, but they also have this in the official docs https://opentelemetry.io/docs/instrumentation/js/serverless/. What do you consider to be the better option?
-
Serverless Spy Vs. Spy Chapter 2: AWS Distro for OpenTelemetry Lambda vs X-Ray SDK
opentelemetry-lambda
-
How to Instrument AWS Services with OpenTelemetry
You don’t have to create an opentelemetry configuration file such as this for each of your lambdas. In fact, you shouldn’t. In AWS, you can use Lambda Layers. You can define the OpenTelemetry tracing piece of code as a Lambda layer and use it in any Lambda you want. Furthermore, OpenTelemetry went ahead and implemented this opentelemetry-lambda layer for us. All we need to do is use it with our config.
-
Struggling to connect the dots - ADOT with Lambda using aws-otel-nodejs Lambda layer, not sure how to go from here to using custom instrumentation (e.g. instrumentation-pg, instrumentation-graphql, etc).
Sorry you're having trouble working with the ADOT Lambda Layers :(. Have you had a chance to open an issue on the GitHub repo for OTel Lambda or ADOT Lambda? You should add your expected vs your actual output!
jaeger
-
Observability with OpenTelemetry, Jaeger and Rails
Jaeger maps the flow of requests and data as they traverse a distributed system. These requests may make calls to multiple services, which may introduce their own delays or errors. https://www.jaegertracing.io/
-
Show HN: An open source performance monitoring tool
As engineers at past startups, we often had to debug slow queries, poor load times, inconsistent errors, etc... While tools like Jaegar [2] helped us inspect server-side performance, we had no way to tie user events to the traces we were inspecting. In other words, although we had an idea of what API route was slow, there wasn’t much visibility into the actual bottleneck.
This is where our performance product comes in: we’re rethinking a tracing/performance tool that focuses on bridging the gap between the client and server.
What’s unique about our approach is that we lean heavily into creating traces from the frontend. For example, if you’re using our Next.js SDK, we automatically connect browser HTTP requests with server-side code execution, all from the perspective of a user. We find this much more powerful because you can understand what part of your frontend codebase causes a given trace to occur. There’s an example here [3].
From an instrumentation perspective, we’ve built our SDKs on-top of OTel, so you can create custom spans to expand highlight-created traces in server routes that will transparently roll up into the flame graph you see in our UI. You can also send us raw OTel traces and manually set up the client-server connection if you want. [4] Here’s an example of what a trace looks like with a database integration using our Golang GORM SDK, triggered by a frontend GraphQL query [5] [6].
In terms of how it's built, we continue to rely heavily on ClickHouse as our time-series storage engine. Given that traces require that we also query based on an ID for specific groups of spans (more akin to an OLTP db), we’ve leveraged the power of CH materialized views to make these operations efficient (described here [7]).
To try it out, you can spin up the project with our self hosted docs [8] or use our cloud offering at app.highlight.io. The entire stack runs in docker via a compose file, including an OpenTelemetry collector for data ingestion. You’ll need to point your SDK to export data to it by setting the relevant OTLP endpoint configuration (ie. environment variable OTEL_EXPORTER_OTLP_LOGS_ENDPOINT [9]).
Overall, we’d really appreciate feedback on what we’re building here. We’re also all ears if anyone has opinions on what they’d like to see in a product like this!
[1] https://github.com/highlight/highlight/blob/main/LICENSE
[2] https://www.jaegertracing.io
[3] https://app.highlight.io/1383/sessions/COu90Th4Qc3PVYTXbx9Xe...
[4] https://www.highlight.io/docs/getting-started/native-opentel...
[5] https://static.highlight.io/assets/docs/gorm.png
[6] https://github.com/highlight/highlight/blob/1fc9487a676409f1...
[7] https://highlight.io/blog/clickhouse-materialized-views
[8] https://www.highlight.io/docs/getting-started/self-host/self...
[9] https://opentelemetry.io/docs/concepts/sdk-configuration/otl...
-
Kubernetes Ingress Visibility
For the request following, something like jeager https://www.jaegertracing.io/, because you are talking more about tracing than necessarily logging. For just monitoring, https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack would be the starting point, then it depends. Nginx gives metrics out of the box, then you can pull in the dashboard like https://grafana.com/grafana/dashboards/14314-kubernetes-nginx-ingress-controller-nextgen-devops-nirvana/ , or full metal with something like service mesh monitoring which would provably fulfil most of the requirements
-
Migrating to OpenTelemetry
Have you checked out Jaeger [1]? It is lightweight enough for a personal project, but featureful enough to really help "turn on the lightbulb" with other engineers to show them the difference between logging/monitoring and tracing.
[1] https://www.jaegertracing.io/
-
The Road to GraphQL At Enterprise Scale
From the perspective of the realization of GraphQL infrastructure, the interesting direction is "Finding". How to find the problem? How to find the bottleneck of the system? Distributed Tracing System (DTS) will help answer this question. Distributed tracing is a method of observing requests as they propagate through distributed environments. In our scenario, we have dozens of subgraphs, gateway, and transport layer through which the request goes. We have several tools that can be used to detect the whole lifecycle of the request through the system, e.g. Jaeger, Zipkin or solutions that provided DTS as a part of the solution NewRelic.
-
OpenTelemetry Exporters - Types and Configuration Steps
Jaeger is an open-source, distributed tracing system that monitors and troubleshoots the flow of requests through complex, microservices-based applications, providing a comprehensive view of system interactions.
-
Fault Tolerance in Distributed Systems: Strategies and Case Studies
However, ensuring fault tolerance in distributed systems is not at all easy. These systems are complex, with multiple nodes or components working together. A failure in one node can cascade across the system if not addressed timely. Moreover, the inherently distributed nature of these systems can make it challenging to pinpoint the exact location and cause of fault - that is why modern systems rely heavily on distributed tracing solutions pioneered by Google Dapper and widely available now in Jaeger and OpenTracing. But still, understanding and implementing fault tolerance becomes not just about addressing the failure but predicting and mitigating potential risks before they escalate.
-
Observability in Action Part 3: Enhancing Your Codebase with OpenTelemetry
In this article, we'll use HoneyComb.io as our tracing backend. While there are other tools in the market, some of which can be run on your local machine (e.g., Jaeger), I chose HoneyComb because of their complementary tools that offer improved monitoring of the service and insights into its behavior.
-
Building for Failure
The best way to do this, is with the help of tracing tools such as paid tools such as Honeycomb, or your own instance of the open source Jaeger offering, or perhaps Encore's built in tracing system.
-
Distributed Tracing and OpenTelemetry Guide
In this example, I will create 3 Node.js services (shipping, notification, and courier) using Amplication, add traces to all services, and show how to analyze trace data using Jaeger.
What are some alternatives?
terraform-aws-lambda - Terraform module, which takes care of a lot of AWS Lambda/serverless tasks (build dependencies, packages, updates, deployments) in countless combinations 🇺🇦
Sentry - Developer-first error tracking and performance monitoring
deploy-aws-lambda-to-vpc-with-terraform - Terraform module with all the cloud resources needed to run Lambda within a VPC
skywalking - APM, Application Performance Monitoring System
sqs-consumer - Build Amazon Simple Queue Service (SQS) based applications without the boilerplate
prometheus - The Prometheus monitoring system and time series database.
opentelemetry-examples
signoz - SigNoz is an open-source observability platform native to OpenTelemetry with logs, traces and metrics in a single application. An open-source alternative to DataDog, NewRelic, etc. 🔥 🖥. 👉 Open source Application Performance Monitoring (APM) & Observability tool
aws-otel-js - AWS Distro for OpenTelemetry JavaScript SDK
Pinpoint - APM, (Application Performance Management) tool for large-scale distributed systems.
helm-charts - Helm Charts for Jaeger backend
fluent-bit - Fast and Lightweight Logs and Metrics processor for Linux, BSD, OSX and Windows